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# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch Depth Anything model. """
import unittest
from transformers import DepthAnythingConfig, Dinov2Config
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import DepthAnythingForDepthEstimation
from transformers.models.depth_anything.modeling_depth_anything import DEPTH_ANYTHING_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import DPTImageProcessor
class DepthAnythingModelTester:
# Copied from tests.models.dpt.test_modeling_dpt_auto_backbone.DPTModelTester.__init__
def __init__(
self,
parent,
batch_size=2,
num_channels=3,
image_size=32,
patch_size=16,
use_labels=True,
num_labels=3,
is_training=True,
hidden_size=4,
num_hidden_layers=2,
num_attention_heads=2,
intermediate_size=8,
out_features=["stage1", "stage2"],
apply_layernorm=False,
reshape_hidden_states=False,
neck_hidden_sizes=[2, 2],
fusion_hidden_size=6,
):
self.parent = parent
self.batch_size = batch_size
self.num_channels = num_channels
self.image_size = image_size
self.patch_size = patch_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.out_features = out_features
self.apply_layernorm = apply_layernorm
self.reshape_hidden_states = reshape_hidden_states
self.use_labels = use_labels
self.num_labels = num_labels
self.is_training = is_training
self.neck_hidden_sizes = neck_hidden_sizes
self.fusion_hidden_size = fusion_hidden_size
# DPT's sequence length
self.seq_length = (self.image_size // self.patch_size) ** 2 + 1
# Copied from tests.models.dpt.test_modeling_dpt_auto_backbone.DPTModelTester.prepare_config_and_inputs
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
labels = None
if self.use_labels:
labels = ids_tensor([self.batch_size, self.image_size, self.image_size], self.num_labels)
config = self.get_config()
return config, pixel_values, labels
def get_config(self):
return DepthAnythingConfig(
backbone_config=self.get_backbone_config(),
reassemble_hidden_size=self.hidden_size,
patch_size=self.patch_size,
neck_hidden_sizes=self.neck_hidden_sizes,
fusion_hidden_size=self.fusion_hidden_size,
)
# Copied from tests.models.dpt.test_modeling_dpt_auto_backbone.DPTModelTester.get_backbone_config
def get_backbone_config(self):
return Dinov2Config(
image_size=self.image_size,
patch_size=self.patch_size,
num_channels=self.num_channels,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
is_training=self.is_training,
out_features=self.out_features,
reshape_hidden_states=self.reshape_hidden_states,
)
# Copied from tests.models.dpt.test_modeling_dpt_auto_backbone.DPTModelTester.create_and_check_for_depth_estimation with DPT->DepthAnything
def create_and_check_for_depth_estimation(self, config, pixel_values, labels):
config.num_labels = self.num_labels
model = DepthAnythingForDepthEstimation(config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
self.parent.assertEqual(result.predicted_depth.shape, (self.batch_size, self.image_size, self.image_size))
# Copied from tests.models.dpt.test_modeling_dpt_auto_backbone.DPTModelTester.prepare_config_and_inputs_for_common
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, pixel_values, labels = config_and_inputs
inputs_dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class DepthAnythingModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
"""
Here we also overwrite some of the tests of test_modeling_common.py, as Depth Anything does not use input_ids, inputs_embeds,
attention_mask and seq_length.
"""
all_model_classes = (DepthAnythingForDepthEstimation,) if is_torch_available() else ()
pipeline_model_mapping = {"depth-estimation": DepthAnythingForDepthEstimation} if is_torch_available() else {}
test_pruning = False
test_resize_embeddings = False
test_head_masking = False
def setUp(self):
self.model_tester = DepthAnythingModelTester(self)
self.config_tester = ConfigTester(
self, config_class=DepthAnythingConfig, has_text_modality=False, hidden_size=37
)
def test_config(self):
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_from_and_save_pretrained_subfolder()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
@unittest.skip(reason="Depth Anything with AutoBackbone does not have a base model and hence no input_embeddings")
def test_inputs_embeds(self):
pass
def test_for_depth_estimation(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_depth_estimation(*config_and_inputs)
@unittest.skip(reason="Depth Anything does not support training yet")
def test_training(self):
pass
@unittest.skip(reason="Depth Anything does not support training yet")
def test_training_gradient_checkpointing(self):
pass
@unittest.skip(reason="Depth Anything with AutoBackbone does not have a base model and hence no input_embeddings")
def test_model_common_attributes(self):
pass
@unittest.skip(reason="Depth Anything with AutoBackbone does not have a base model")
def test_save_load_fast_init_from_base(self):
pass
@unittest.skip(reason="Depth Anything with AutoBackbone does not have a base model")
def test_save_load_fast_init_to_base(self):
pass
@unittest.skip(
reason="This architecture seems to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
)
def test_training_gradient_checkpointing_use_reentrant(self):
pass
@unittest.skip(
reason="This architecture seems to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
)
def test_training_gradient_checkpointing_use_reentrant_false(self):
pass
@slow
def test_model_from_pretrained(self):
for model_name in DEPTH_ANYTHING_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = DepthAnythingForDepthEstimation.from_pretrained(model_name)
self.assertIsNotNone(model)
# We will verify our results on an image of cute cats
def prepare_img():
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
return image
@require_torch
@require_vision
@slow
class DepthAnythingModelIntegrationTest(unittest.TestCase):
def test_inference(self):
image_processor = DPTImageProcessor.from_pretrained("LiheYoung/depth-anything-small-hf")
model = DepthAnythingForDepthEstimation.from_pretrained("LiheYoung/depth-anything-small-hf").to(torch_device)
image = prepare_img()
inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
# forward pass
with torch.no_grad():
outputs = model(**inputs)
predicted_depth = outputs.predicted_depth
# verify the predicted depth
expected_shape = torch.Size([1, 518, 686])
self.assertEqual(predicted_depth.shape, expected_shape)
expected_slice = torch.tensor(
[[8.8204, 8.6468, 8.6195], [8.3313, 8.6027, 8.7526], [8.6526, 8.6866, 8.7453]],
).to(torch_device)
self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3], expected_slice, atol=1e-6))
| transformers/tests/models/depth_anything/test_modeling_depth_anything.py/0 | {
"file_path": "transformers/tests/models/depth_anything/test_modeling_depth_anything.py",
"repo_id": "transformers",
"token_count": 3846
} | 156 |
# coding=utf-8
# Copyright 2021 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_vision_available
from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
if is_vision_available():
from transformers import ViTImageProcessor
class EfficientFormerImageProcessorTester(unittest.TestCase):
def __init__(
self,
parent,
batch_size=13,
num_channels=3,
image_size=224,
min_resolution=30,
max_resolution=400,
do_resize=True,
size=None,
do_normalize=True,
image_mean=[0.5, 0.5, 0.5],
image_std=[0.5, 0.5, 0.5],
):
size = size if size is not None else {"height": 18, "width": 18}
self.parent = parent
self.batch_size = batch_size
self.num_channels = num_channels
self.image_size = image_size
self.min_resolution = min_resolution
self.max_resolution = max_resolution
self.do_resize = do_resize
self.size = size
self.do_normalize = do_normalize
self.image_mean = image_mean
self.image_std = image_std
def prepare_image_processor_dict(self):
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
}
def expected_output_image_shape(self, images):
return self.num_channels, self.size["height"], self.size["width"]
def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
return prepare_image_inputs(
batch_size=self.batch_size,
num_channels=self.num_channels,
min_resolution=self.min_resolution,
max_resolution=self.max_resolution,
equal_resolution=equal_resolution,
numpify=numpify,
torchify=torchify,
)
@require_torch
@require_vision
class EfficientFormerImageProcessorTest(ImageProcessingTestMixin, unittest.TestCase):
image_processing_class = ViTImageProcessor if is_vision_available() else None
def setUp(self):
self.image_processor_tester = EfficientFormerImageProcessorTester(self)
@property
def image_processor_dict(self):
return self.image_processor_tester.prepare_image_processor_dict()
def test_image_proc_properties(self):
image_processor = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(image_processor, "image_mean"))
self.assertTrue(hasattr(image_processor, "image_std"))
self.assertTrue(hasattr(image_processor, "do_normalize"))
self.assertTrue(hasattr(image_processor, "do_resize"))
self.assertTrue(hasattr(image_processor, "size"))
| transformers/tests/models/efficientformer/test_image_processing_efficientformer.py/0 | {
"file_path": "transformers/tests/models/efficientformer/test_image_processing_efficientformer.py",
"repo_id": "transformers",
"token_count": 1379
} | 157 |
# coding=utf-8
# Copyright 2022 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import GLPNImageProcessor
class GLPNImageProcessingTester(unittest.TestCase):
def __init__(
self,
parent,
batch_size=7,
num_channels=3,
image_size=18,
min_resolution=30,
max_resolution=400,
do_resize=True,
size_divisor=32,
do_rescale=True,
):
self.parent = parent
self.batch_size = batch_size
self.num_channels = num_channels
self.image_size = image_size
self.min_resolution = min_resolution
self.max_resolution = max_resolution
self.do_resize = do_resize
self.size_divisor = size_divisor
self.do_rescale = do_rescale
def prepare_image_processor_dict(self):
return {
"do_resize": self.do_resize,
"size_divisor": self.size_divisor,
"do_rescale": self.do_rescale,
}
def expected_output_image_shape(self, images):
if isinstance(images[0], Image.Image):
width, height = images[0].size
else:
height, width = images[0].shape[1], images[0].shape[2]
height = height // self.size_divisor * self.size_divisor
width = width // self.size_divisor * self.size_divisor
return self.num_channels, height, width
def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
return prepare_image_inputs(
batch_size=self.batch_size,
num_channels=self.num_channels,
min_resolution=self.min_resolution,
max_resolution=self.max_resolution,
size_divisor=self.size_divisor,
equal_resolution=equal_resolution,
numpify=numpify,
torchify=torchify,
)
@require_torch
@require_vision
class GLPNImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
image_processing_class = GLPNImageProcessor if is_vision_available() else None
def setUp(self):
self.image_processor_tester = GLPNImageProcessingTester(self)
@property
def image_processor_dict(self):
return self.image_processor_tester.prepare_image_processor_dict()
def test_image_processor_properties(self):
image_processing = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(image_processing, "do_resize"))
self.assertTrue(hasattr(image_processing, "size_divisor"))
self.assertTrue(hasattr(image_processing, "resample"))
self.assertTrue(hasattr(image_processing, "do_rescale"))
def test_call_pil(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False)
for image in image_inputs:
self.assertIsInstance(image, Image.Image)
# Test not batched input (GLPNImageProcessor doesn't support batching)
encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
self.assertTrue(tuple(encoded_images.shape) == (1, *expected_output_image_shape))
def test_call_numpy(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True)
for image in image_inputs:
self.assertIsInstance(image, np.ndarray)
# Test not batched input (GLPNImageProcessor doesn't support batching)
encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
self.assertTrue(tuple(encoded_images.shape) == (1, *expected_output_image_shape))
def test_call_pytorch(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
for image in image_inputs:
self.assertIsInstance(image, torch.Tensor)
# Test not batched input (GLPNImageProcessor doesn't support batching)
encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
self.assertTrue(tuple(encoded_images.shape) == (1, *expected_output_image_shape))
def test_call_numpy_4_channels(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
self.image_processing_class.num_channels = 4
image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True)
for image in image_inputs:
self.assertIsInstance(image, np.ndarray)
# Test not batched input (GLPNImageProcessor doesn't support batching)
encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
self.assertTrue(tuple(encoded_images.shape) == (1, *expected_output_image_shape))
self.image_processing_class.num_channels = 3
| transformers/tests/models/glpn/test_image_processing_glpn.py/0 | {
"file_path": "transformers/tests/models/glpn/test_image_processing_glpn.py",
"repo_id": "transformers",
"token_count": 2610
} | 158 |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch GPTNeoXJapanese model. """
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseModel
class GPTNeoXJapaneseModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_multiple_size=4,
hidden_act="gelu",
hidden_dropout=0.0,
attention_dropout=0.1,
weight_tying=True,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_multiple_size = intermediate_multiple_size
self.hidden_act = hidden_act
self.hidden_dropout = hidden_dropout
self.attention_dropout = attention_dropout
self.weight_tying = weight_tying
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
token_labels = None
if self.use_labels:
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
config = self.get_config()
return config, input_ids, input_mask, token_labels
def get_config(self):
return GPTNeoXJapaneseConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_multiple_size=self.intermediate_multiple_size,
hidden_act=self.hidden_act,
hidden_dropout=self.hidden_dropout,
attention_dropout=self.attention_dropout,
weight_tying=self.weight_tying,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
is_decoder=False,
initializer_range=self.initializer_range,
)
def prepare_config_and_inputs_for_decoder(self):
config, input_ids, input_mask, token_labels = self.prepare_config_and_inputs()
config.is_decoder = True
return config, input_ids, input_mask, token_labels
def create_and_check_model(self, config, input_ids, input_mask):
model = GPTNeoXJapaneseModel(config=config)
model.to(torch_device)
model.eval()
_ = model(input_ids, attention_mask=input_mask)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_model_as_decoder(self, config, input_ids, input_mask):
config.add_cross_attention = True
model = GPTNeoXJapaneseModel(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_for_causal_lm(self, config, input_ids, input_mask, token_labels):
model = GPTNeoXJapaneseForCausalLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_decoder_model_past_large_inputs(self, config, input_ids, input_mask):
config.is_decoder = True
model = GPTNeoXJapaneseForCausalLM(config=config)
model.to(torch_device)
model.eval()
# first forward pass
outputs = model(input_ids, attention_mask=input_mask, use_cache=True)
past_key_values = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)
output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask, output_hidden_states=True)
output_from_no_past = output_from_no_past["hidden_states"][0]
output_from_past = model(
next_tokens,
attention_mask=next_attention_mask,
past_key_values=past_key_values,
output_hidden_states=True,
)["hidden_states"][0]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, input_ids, input_mask, token_labels = config_and_inputs
inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class GPTNeoXModelJapaneseTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
all_generative_model_classes = (GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
pipeline_model_mapping = (
{"feature-extraction": GPTNeoXJapaneseModel, "text-generation": GPTNeoXJapaneseForCausalLM}
if is_torch_available()
else {}
)
test_pruning = False
test_missing_keys = False
test_model_parallel = False
test_head_masking = False
def setUp(self):
self.model_tester = GPTNeoXJapaneseModelTester(self)
self.config_tester = ConfigTester(self, config_class=GPTNeoXJapaneseConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config, input_ids, input_mask, token_labels = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(config, input_ids, input_mask)
def test_model_as_decoder(self):
config, input_ids, input_mask, token_labels = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(config, input_ids, input_mask)
def test_model_as_decoder_with_default_input_mask(self):
# This regression test was failing with PyTorch < 1.3
config, input_ids, input_mask, token_labels = self.model_tester.prepare_config_and_inputs_for_decoder()
input_mask = None
self.model_tester.create_and_check_model_as_decoder(config, input_ids, input_mask)
def test_decoder_model_past_large_inputs(self):
config, input_ids, input_mask, token_labels = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(config, input_ids, input_mask)
def test_model_for_causal_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_causal_lm(*config_and_inputs)
@slow
def test_generation(self):
model_id = "abeja/gpt-neox-japanese-2.7b"
prompts = ["データサイエンティストとは、", "100年後に必要とされる会社は、", "フルリモートの環境で働くために必要なことは、", "国境の長いトンネルを抜けると", "美味しい日本食といえば、"] # fmt: skip
EXPECTED_OUTPUTS = [
"データサイエンティストとは、データを分析し、ビジネスに役立つ知見を導き出す専門家のことです。",
"100年後に必要とされる会社は、「人」が中心の会社です。",
"フルリモートの環境で働くために必要なことは、「自分の時間をコントロールする」ことです。",
"国境の長いトンネルを抜けると、そこは雪国だった。",
"美味しい日本食といえば、やっぱりお寿司ですよね。",
]
tokenizer = GPTNeoXJapaneseTokenizer.from_pretrained(model_id)
model = GPTNeoXJapaneseForCausalLM.from_pretrained(model_id)
predicted_outputs = []
for prompt in prompts:
input_ids = tokenizer(prompt, return_tensors="pt").input_ids
generated_ids = model.generate(input_ids, max_length=50)
generated_string = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
predicted_outputs += generated_string
self.assertListEqual(predicted_outputs, EXPECTED_OUTPUTS)
| transformers/tests/models/gpt_neox_japanese/test_modeling_gpt_neox_japanese.py/0 | {
"file_path": "transformers/tests/models/gpt_neox_japanese/test_modeling_gpt_neox_japanese.py",
"repo_id": "transformers",
"token_count": 4859
} | 159 |
# coding=utf-8
# Copyright 2022 Google LongT5 Authors and HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import tempfile
import unittest
from transformers import LongT5Config, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_QUESTION_ANSWERING_MAPPING,
AutoTokenizer,
LongT5EncoderModel,
LongT5ForConditionalGeneration,
LongT5Model,
)
from transformers.models.longt5.modeling_longt5 import LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST
class LongT5ModelTester:
def __init__(
self,
parent,
vocab_size=99,
batch_size=13,
encoder_seq_length=7,
decoder_seq_length=9,
local_radius=5,
encoder_attention_type="local",
global_block_size=3,
# For common tests
is_training=True,
use_attention_mask=True,
use_labels=True,
hidden_size=32,
num_hidden_layers=2,
num_attention_heads=4,
d_ff=37,
relative_attention_num_buckets=8,
dropout_rate=0.1,
initializer_factor=0.002,
eos_token_id=1,
pad_token_id=0,
decoder_start_token_id=0,
scope=None,
decoder_layers=None,
large_model_config_path="google/long-t5-local-large",
):
self.parent = parent
self.batch_size = batch_size
self.encoder_seq_length = encoder_seq_length
self.decoder_seq_length = decoder_seq_length
self.local_radius = local_radius
self.block_len = local_radius + 1
self.encoder_attention_type = encoder_attention_type
self.global_block_size = global_block_size
# For common tests
self.seq_length = self.decoder_seq_length
self.is_training = is_training
self.use_attention_mask = use_attention_mask
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.d_ff = d_ff
self.relative_attention_num_buckets = relative_attention_num_buckets
self.dropout_rate = dropout_rate
self.initializer_factor = initializer_factor
self.eos_token_id = eos_token_id
self.pad_token_id = pad_token_id
self.decoder_start_token_id = decoder_start_token_id
self.scope = None
self.decoder_layers = decoder_layers
self.large_model_config_path = large_model_config_path
def get_large_model_config(self):
return LongT5Config.from_pretrained(self.large_model_config_path)
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.encoder_seq_length], self.vocab_size)
decoder_input_ids = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)
attention_mask = None
decoder_attention_mask = None
if self.use_attention_mask:
attention_mask = ids_tensor([self.batch_size, self.encoder_seq_length], vocab_size=2)
decoder_attention_mask = ids_tensor([self.batch_size, self.decoder_seq_length], vocab_size=2)
lm_labels = None
if self.use_labels:
lm_labels = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)
config = self.get_config()
return (
config,
input_ids,
decoder_input_ids,
attention_mask,
decoder_attention_mask,
lm_labels,
)
def get_pipeline_config(self):
return LongT5Config(
vocab_size=166, # longt5 forces 100 extra tokens
d_model=self.hidden_size,
d_ff=self.d_ff,
d_kv=self.hidden_size // self.num_attention_heads,
num_layers=self.num_hidden_layers,
num_decoder_layers=self.decoder_layers,
num_heads=self.num_attention_heads,
relative_attention_num_buckets=self.relative_attention_num_buckets,
dropout_rate=self.dropout_rate,
initializer_factor=self.initializer_factor,
eos_token_id=self.eos_token_id,
bos_token_id=self.pad_token_id,
pad_token_id=self.pad_token_id,
decoder_start_token_id=self.decoder_start_token_id,
local_radius=self.local_radius,
encoder_attention_type=self.encoder_attention_type,
global_block_size=self.global_block_size,
)
def get_config(self):
return LongT5Config(
vocab_size=self.vocab_size,
d_model=self.hidden_size,
d_ff=self.d_ff,
d_kv=self.hidden_size // self.num_attention_heads,
num_layers=self.num_hidden_layers,
num_decoder_layers=self.decoder_layers,
num_heads=self.num_attention_heads,
relative_attention_num_buckets=self.relative_attention_num_buckets,
dropout_rate=self.dropout_rate,
initializer_factor=self.initializer_factor,
eos_token_id=self.eos_token_id,
bos_token_id=self.pad_token_id,
pad_token_id=self.pad_token_id,
decoder_start_token_id=self.decoder_start_token_id,
local_radius=self.local_radius,
encoder_attention_type=self.encoder_attention_type,
global_block_size=self.global_block_size,
)
def check_prepare_lm_labels_via_shift_left(
self,
config,
input_ids,
decoder_input_ids,
attention_mask,
decoder_attention_mask,
lm_labels,
):
model = LongT5Model(config=config)
model.to(torch_device)
model.eval()
# make sure that lm_labels are correctly padded from the right
lm_labels.masked_fill_((lm_labels == self.decoder_start_token_id), self.eos_token_id)
# add casaul pad token mask
triangular_mask = torch.tril(lm_labels.new_ones(lm_labels.shape)).logical_not()
lm_labels.masked_fill_(triangular_mask, self.pad_token_id)
decoder_input_ids = model._shift_right(lm_labels)
for i, (decoder_input_ids_slice, lm_labels_slice) in enumerate(zip(decoder_input_ids, lm_labels)):
# first item
self.parent.assertEqual(decoder_input_ids_slice[0].item(), self.decoder_start_token_id)
if i < decoder_input_ids_slice.shape[-1]:
if i < decoder_input_ids.shape[-1] - 1:
# items before diagonal
self.parent.assertListEqual(
decoder_input_ids_slice[1 : i + 1].tolist(), lm_labels_slice[:i].tolist()
)
# pad items after diagonal
if i < decoder_input_ids.shape[-1] - 2:
self.parent.assertListEqual(
decoder_input_ids_slice[i + 2 :].tolist(), lm_labels_slice[i + 1 : -1].tolist()
)
else:
# all items after square
self.parent.assertListEqual(decoder_input_ids_slice[1:].tolist(), lm_labels_slice[:-1].tolist())
def create_and_check_model(
self,
config,
input_ids,
decoder_input_ids,
attention_mask,
decoder_attention_mask,
lm_labels,
):
model = LongT5Model(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids=input_ids,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
)
result = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)
decoder_output = result.last_hidden_state
decoder_past = result.past_key_values
encoder_output = result.encoder_last_hidden_state
self.parent.assertEqual(encoder_output.size(), (self.batch_size, self.encoder_seq_length, self.hidden_size))
self.parent.assertEqual(decoder_output.size(), (self.batch_size, self.decoder_seq_length, self.hidden_size))
# There should be `num_layers` key value embeddings stored in decoder_past
self.parent.assertEqual(len(decoder_past), config.num_layers)
# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
self.parent.assertEqual(len(decoder_past[0]), 4)
def create_and_check_with_lm_head(
self,
config,
input_ids,
decoder_input_ids,
attention_mask,
decoder_attention_mask,
lm_labels,
):
model = LongT5ForConditionalGeneration(config=config).to(torch_device).eval()
outputs = model(
input_ids=input_ids,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
labels=lm_labels,
)
self.parent.assertEqual(len(outputs), 4)
self.parent.assertEqual(outputs["logits"].size(), (self.batch_size, self.decoder_seq_length, self.vocab_size))
self.parent.assertEqual(outputs["loss"].size(), ())
def create_and_check_decoder_model_past(
self,
config,
input_ids,
decoder_input_ids,
attention_mask,
decoder_attention_mask,
lm_labels,
):
model = LongT5Model(config=config).get_decoder().to(torch_device).eval()
# first forward pass
outputs = model(input_ids, use_cache=True)
outputs_use_cache_conf = model(input_ids)
outputs_no_past = model(input_ids, use_cache=False)
self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf))
self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)
output, past_key_values = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
output_from_no_past = model(next_input_ids)["last_hidden_state"]
output_from_past = model(next_tokens, past_key_values=past_key_values)["last_hidden_state"]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def create_and_check_decoder_model_attention_mask_past(
self,
config,
input_ids,
decoder_input_ids,
attention_mask,
decoder_attention_mask,
lm_labels,
):
model = LongT5Model(config=config).get_decoder()
model.to(torch_device)
model.eval()
# create attention mask
attn_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device)
half_seq_length = input_ids.shape[-1] // 2
attn_mask[:, half_seq_length:] = 0
# first forward pass
output, past_key_values = model(input_ids, attention_mask=attn_mask, use_cache=True).to_tuple()
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
# change a random masked slice from input_ids
random_seq_idx_to_change = ids_tensor((1,), half_seq_length).item() + 1
random_other_next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size).squeeze(-1)
input_ids[:, -random_seq_idx_to_change] = random_other_next_tokens
# append to next input_ids and attn_mask
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
attn_mask = torch.cat(
[attn_mask, torch.ones((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)],
dim=1,
)
# get two different outputs
output_from_no_past = model(next_input_ids, attention_mask=attn_mask)["last_hidden_state"]
output_from_past = model(next_tokens, past_key_values=past_key_values, attention_mask=attn_mask)[
"last_hidden_state"
]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def create_and_check_decoder_model_past_large_inputs(
self,
config,
input_ids,
decoder_input_ids,
attention_mask,
decoder_attention_mask,
lm_labels,
):
model = LongT5Model(config=config).get_decoder().to(torch_device).eval()
# first forward pass
outputs = model(input_ids, attention_mask=attention_mask, use_cache=True)
output, past_key_values = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
next_attention_mask = torch.cat([attention_mask, next_mask], dim=-1)
output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["last_hidden_state"]
output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[
"last_hidden_state"
]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def create_and_check_generate_with_past_key_values(
self,
config,
input_ids,
decoder_input_ids,
attention_mask,
decoder_attention_mask,
lm_labels,
):
model = LongT5ForConditionalGeneration(config=config).to(torch_device).eval()
torch.manual_seed(0)
output_without_past_cache = model.generate(
input_ids[:1], num_beams=2, max_length=5, do_sample=True, use_cache=False
)
torch.manual_seed(0)
output_with_past_cache = model.generate(input_ids[:1], num_beams=2, max_length=5, do_sample=True)
self.parent.assertTrue(torch.all(output_with_past_cache == output_without_past_cache))
def create_and_check_encoder_decoder_shared_weights(
self,
config,
input_ids,
decoder_input_ids,
attention_mask,
decoder_attention_mask,
lm_labels,
):
for model_class in [LongT5Model, LongT5ForConditionalGeneration]:
torch.manual_seed(0)
model = model_class(config=config).to(torch_device).eval()
# load state dict copies weights but does not tie them
model.encoder.load_state_dict(model.decoder.state_dict(), strict=False)
torch.manual_seed(0)
tied_config = copy.deepcopy(config)
tied_config.tie_encoder_decoder = True
tied_model = model_class(config=tied_config).to(torch_device).eval()
model_result = model(
input_ids=input_ids,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
)
tied_model_result = tied_model(
input_ids=input_ids,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
)
# check that models has less parameters
self.parent.assertLess(
sum(p.numel() for p in tied_model.parameters()), sum(p.numel() for p in model.parameters())
)
random_slice_idx = ids_tensor((1,), model_result[0].shape[-1]).item()
# check that outputs are equal
self.parent.assertTrue(
torch.allclose(
model_result[0][0, :, random_slice_idx], tied_model_result[0][0, :, random_slice_idx], atol=1e-4
)
)
# check that outputs after saving and loading are equal
with tempfile.TemporaryDirectory() as tmpdirname:
tied_model.save_pretrained(tmpdirname)
tied_model = model_class.from_pretrained(tmpdirname)
tied_model.to(torch_device)
tied_model.eval()
# check that models has less parameters
self.parent.assertLess(
sum(p.numel() for p in tied_model.parameters()), sum(p.numel() for p in model.parameters())
)
random_slice_idx = ids_tensor((1,), model_result[0].shape[-1]).item()
tied_model_result = tied_model(
input_ids=input_ids,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
)
# check that outputs are equal
self.parent.assertTrue(
torch.allclose(
model_result[0][0, :, random_slice_idx],
tied_model_result[0][0, :, random_slice_idx],
atol=1e-4,
)
)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
decoder_input_ids,
attention_mask,
decoder_attention_mask,
lm_labels,
) = config_and_inputs
inputs_dict = {
"input_ids": input_ids,
"attention_mask": attention_mask,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
"use_cache": False,
}
return config, inputs_dict
@require_torch
class LongT5ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (LongT5Model, LongT5ForConditionalGeneration) if is_torch_available() else ()
all_generative_model_classes = (LongT5ForConditionalGeneration,) if is_torch_available() else ()
pipeline_model_mapping = (
{
"conversational": LongT5ForConditionalGeneration,
"feature-extraction": LongT5Model,
"summarization": LongT5ForConditionalGeneration,
"text2text-generation": LongT5ForConditionalGeneration,
"translation": LongT5ForConditionalGeneration,
}
if is_torch_available()
else {}
)
fx_compatible = False
test_pruning = False
test_torchscript = True
test_resize_embeddings = True
test_model_parallel = False
is_encoder_decoder = True
def setUp(self):
self.model_tester = LongT5ModelTester(self)
self.config_tester = ConfigTester(self, config_class=LongT5Config, d_model=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_shift_right(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_prepare_lm_labels_via_shift_left(*config_and_inputs)
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_with_lm_head(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_with_lm_head(*config_and_inputs)
def test_decoder_model_past(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past(*config_and_inputs)
def test_decoder_model_past_with_attn_mask(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_attention_mask_past(*config_and_inputs)
def test_decoder_model_past_with_3d_attn_mask(self):
(
config,
input_ids,
decoder_input_ids,
attention_mask,
decoder_attention_mask,
lm_labels,
) = self.model_tester.prepare_config_and_inputs()
attention_mask = ids_tensor(
[self.model_tester.batch_size, self.model_tester.encoder_seq_length, self.model_tester.encoder_seq_length],
vocab_size=2,
)
decoder_attention_mask = ids_tensor(
[self.model_tester.batch_size, self.model_tester.decoder_seq_length, self.model_tester.decoder_seq_length],
vocab_size=2,
)
self.model_tester.create_and_check_decoder_model_attention_mask_past(
config,
input_ids,
decoder_input_ids,
attention_mask,
decoder_attention_mask,
lm_labels,
)
def test_decoder_model_past_with_large_inputs(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
def test_generate_with_past_key_values(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_generate_with_past_key_values(*config_and_inputs)
def test_encoder_decoder_shared_weights(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_encoder_decoder_shared_weights(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_name in LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = LongT5Model.from_pretrained(model_name)
self.assertIsNotNone(model)
@slow
def test_export_to_onnx(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
model = LongT5Model(config_and_inputs[0]).to(torch_device)
with tempfile.TemporaryDirectory() as tmpdirname:
torch.onnx.export(
model,
(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]),
f"{tmpdirname}/longt5_test.onnx",
export_params=True,
opset_version=13,
input_names=["input_ids", "decoder_input_ids"],
)
def test_generate_with_head_masking(self):
attention_names = ["encoder_attentions", "decoder_attentions", "cross_attentions"]
config_and_inputs = self.model_tester.prepare_config_and_inputs()
config = config_and_inputs[0]
max_length = config_and_inputs[1].shape[-1] + 3
model = LongT5ForConditionalGeneration(config).eval()
model.to(torch_device)
head_masking = {
"head_mask": torch.zeros(config.num_layers, config.num_heads, device=torch_device),
"decoder_head_mask": torch.zeros(config.num_decoder_layers, config.num_heads, device=torch_device),
"cross_attn_head_mask": torch.zeros(config.num_decoder_layers, config.num_heads, device=torch_device),
}
for attn_name, (name, mask) in zip(attention_names, head_masking.items()):
head_masks = {name: mask}
# Explicitly pass decoder_head_mask as it is required from LONGT5 model when head_mask specified
if name == "head_mask":
head_masks["decoder_head_mask"] = torch.ones(
config.num_decoder_layers, config.num_heads, device=torch_device
)
out = model.generate(
config_and_inputs[1],
num_beams=1,
max_length=max_length,
output_attentions=True,
return_dict_in_generate=True,
**head_masks,
)
# We check the state of decoder_attentions and cross_attentions just from the last step
attn_weights = out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
self.assertEqual(sum([w.sum().item() for w in attn_weights]), 0.0)
def test_attention_outputs(self):
if not self.has_attentions:
pass
else:
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
seq_len = getattr(self.model_tester, "seq_length", None)
decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len)
encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len)
decoder_key_length = getattr(self.model_tester, "decoder_key_length", decoder_seq_length)
encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
chunk_length = getattr(self.model_tester, "chunk_length", None)
block_len = getattr(self.model_tester, "block_len", None)
if chunk_length is not None and hasattr(self.model_tester, "num_hashes"):
encoder_seq_length = encoder_seq_length * self.model_tester.num_hashes
for model_class in self.all_model_classes:
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = False
config.return_dict = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
config.output_attentions = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, block_len, 3 * block_len],
)
out_len = len(outputs)
if self.is_encoder_decoder:
correct_outlen = 5
# loss is at first position
if "labels" in inputs_dict:
correct_outlen += 1 # loss is added to beginning
# Question Answering model returns start_logits and end_logits
if model_class in get_values(MODEL_FOR_QUESTION_ANSWERING_MAPPING):
correct_outlen += 1 # start_logits and end_logits instead of only 1 output
if "past_key_values" in outputs:
correct_outlen += 1 # past_key_values have been returned
self.assertEqual(out_len, correct_outlen)
# decoder attentions
decoder_attentions = outputs.decoder_attentions
self.assertIsInstance(decoder_attentions, (list, tuple))
self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(decoder_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length],
)
# cross attentions
cross_attentions = outputs.cross_attentions
self.assertIsInstance(cross_attentions, (list, tuple))
self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(cross_attentions[0].shape[-3:]),
[
self.model_tester.num_attention_heads,
decoder_seq_length,
encoder_key_length,
],
)
# Check attention is always last and order is fine
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
if hasattr(self.model_tester, "num_hidden_states_types"):
added_hidden_states = self.model_tester.num_hidden_states_types
elif self.is_encoder_decoder:
added_hidden_states = 2
else:
added_hidden_states = 1
self.assertEqual(out_len + added_hidden_states, len(outputs))
self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(self_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, block_len, 3 * block_len],
)
def _check_encoder_attention_for_generate(self, attentions, batch_size, config, seq_length):
block_len = getattr(self.model_tester, "block_len", None)
encoder_expected_shape = (batch_size, 1, config.num_attention_heads, block_len, 3 * block_len)
self.assertIsInstance(attentions, tuple)
self.assertListEqual(
[layer_attentions.shape for layer_attentions in attentions],
[encoder_expected_shape] * len(attentions),
)
@require_torch
class LongT5TGlobalModelTest(LongT5ModelTest):
def setUp(self):
self.model_tester = LongT5ModelTester(
self, encoder_attention_type="transient-global", large_model_config_path="google/long-t5-tglobal-large"
)
self.config_tester = ConfigTester(self, config_class=LongT5Config, d_model=37)
def test_attention_outputs(self):
if not self.has_attentions:
pass
else:
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
seq_len = getattr(self.model_tester, "seq_length", None)
decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len)
encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len)
decoder_key_length = getattr(self.model_tester, "decoder_key_length", decoder_seq_length)
encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
chunk_length = getattr(self.model_tester, "chunk_length", None)
block_len = getattr(self.model_tester, "block_len", None)
global_block_size = getattr(self.model_tester, "global_block_size", None)
global_seq_len = encoder_seq_length // global_block_size
if chunk_length is not None and hasattr(self.model_tester, "num_hashes"):
encoder_seq_length = encoder_seq_length * self.model_tester.num_hashes
for model_class in self.all_model_classes:
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = False
config.return_dict = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
config.output_attentions = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, block_len, 3 * block_len + global_seq_len],
)
out_len = len(outputs)
if self.is_encoder_decoder:
correct_outlen = 5
# loss is at first position
if "labels" in inputs_dict:
correct_outlen += 1 # loss is added to beginning
# Question Answering model returns start_logits and end_logits
if model_class in get_values(MODEL_FOR_QUESTION_ANSWERING_MAPPING):
correct_outlen += 1 # start_logits and end_logits instead of only 1 output
if "past_key_values" in outputs:
correct_outlen += 1 # past_key_values have been returned
self.assertEqual(out_len, correct_outlen)
# decoder attentions
decoder_attentions = outputs.decoder_attentions
self.assertIsInstance(decoder_attentions, (list, tuple))
self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(decoder_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length],
)
# cross attentions
cross_attentions = outputs.cross_attentions
self.assertIsInstance(cross_attentions, (list, tuple))
self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(cross_attentions[0].shape[-3:]),
[
self.model_tester.num_attention_heads,
decoder_seq_length,
encoder_key_length,
],
)
# Check attention is always last and order is fine
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
if hasattr(self.model_tester, "num_hidden_states_types"):
added_hidden_states = self.model_tester.num_hidden_states_types
elif self.is_encoder_decoder:
added_hidden_states = 2
else:
added_hidden_states = 1
self.assertEqual(out_len + added_hidden_states, len(outputs))
self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(self_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, block_len, 3 * block_len + global_seq_len],
)
def _check_encoder_attention_for_generate(self, attentions, batch_size, config, seq_length):
block_len = getattr(self.model_tester, "block_len", None)
global_block_size = getattr(self.model_tester, "global_block_size", None)
global_seq_length = seq_length // global_block_size
encoder_expected_shape = (
batch_size,
1,
config.num_attention_heads,
block_len,
3 * block_len + global_seq_length,
)
self.assertIsInstance(attentions, tuple)
self.assertListEqual(
[layer_attentions.shape for layer_attentions in attentions],
[encoder_expected_shape] * len(attentions),
)
class LongT5EncoderOnlyModelTester:
def __init__(
self,
parent,
vocab_size=99,
batch_size=13,
encoder_seq_length=7,
local_radius=5,
encoder_attention_type="local",
global_block_size=3,
# For common tests
use_attention_mask=True,
hidden_size=32,
num_hidden_layers=2,
num_attention_heads=4,
d_ff=37,
relative_attention_num_buckets=8,
is_training=False,
dropout_rate=0.1,
initializer_factor=0.002,
is_encoder_decoder=False,
eos_token_id=1,
pad_token_id=0,
scope=None,
large_model_config_path="google/long-t5-local-large",
):
self.parent = parent
self.batch_size = batch_size
self.encoder_seq_length = encoder_seq_length
self.local_radius = local_radius
self.block_len = local_radius + 1
self.encoder_attention_type = encoder_attention_type
self.global_block_size = global_block_size
# For common tests
self.seq_length = self.encoder_seq_length
self.use_attention_mask = use_attention_mask
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.d_ff = d_ff
self.relative_attention_num_buckets = relative_attention_num_buckets
self.dropout_rate = dropout_rate
self.initializer_factor = initializer_factor
self.eos_token_id = eos_token_id
self.pad_token_id = pad_token_id
self.is_encoder_decoder = is_encoder_decoder
self.scope = None
self.is_training = is_training
self.large_model_config_path = large_model_config_path
def get_large_model_config(self):
return LongT5Config.from_pretrained(self.large_model_config_path)
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.encoder_seq_length], self.vocab_size)
attention_mask = None
if self.use_attention_mask:
attention_mask = ids_tensor([self.batch_size, self.encoder_seq_length], vocab_size=2)
config = LongT5Config(
vocab_size=self.vocab_size,
d_model=self.hidden_size,
d_ff=self.d_ff,
d_kv=self.hidden_size // self.num_attention_heads,
num_layers=self.num_hidden_layers,
num_heads=self.num_attention_heads,
relative_attention_num_buckets=self.relative_attention_num_buckets,
dropout_rate=self.dropout_rate,
initializer_factor=self.initializer_factor,
eos_token_id=self.eos_token_id,
bos_token_id=self.pad_token_id,
pad_token_id=self.pad_token_id,
is_encoder_decoder=self.is_encoder_decoder,
local_radius=self.local_radius,
encoder_attention_type=self.encoder_attention_type,
global_block_size=self.global_block_size,
)
return (
config,
input_ids,
attention_mask,
)
def create_and_check_model(
self,
config,
input_ids,
attention_mask,
):
model = LongT5EncoderModel(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids=input_ids,
attention_mask=attention_mask,
)
result = model(input_ids=input_ids)
encoder_output = result.last_hidden_state
self.parent.assertEqual(encoder_output.size(), (self.batch_size, self.encoder_seq_length, self.hidden_size))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
attention_mask,
) = config_and_inputs
inputs_dict = {
"input_ids": input_ids,
"attention_mask": attention_mask,
}
return config, inputs_dict
class LongT5EncoderOnlyModelTest(ModelTesterMixin, unittest.TestCase):
all_model_classes = (LongT5EncoderModel,) if is_torch_available() else ()
test_pruning = False
test_torchscript = True
test_resize_embeddings = False
test_model_parallel = False
def setUp(self):
self.model_tester = LongT5EncoderOnlyModelTester(self)
self.config_tester = ConfigTester(self, config_class=LongT5Config, d_model=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_attention_outputs(self):
if not self.has_attentions:
pass
else:
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
block_len = getattr(self.model_tester, "block_len", 4)
for model_class in self.all_model_classes:
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = False
config.return_dict = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
config.output_attentions = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, block_len, 3 * block_len],
)
out_len = len(outputs)
# Check attention is always last and order is fine
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
if hasattr(self.model_tester, "num_hidden_states_types"):
added_hidden_states = self.model_tester.num_hidden_states_types
elif self.is_encoder_decoder:
added_hidden_states = 2
else:
added_hidden_states = 1
self.assertEqual(out_len + added_hidden_states, len(outputs))
self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(self_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, block_len, 3 * block_len],
)
class LongT5EncoderOnlyTGlobalModelTest(LongT5EncoderOnlyModelTest):
def setUp(self):
self.model_tester = LongT5EncoderOnlyModelTester(
self, encoder_attention_type="transient-global", large_model_config_path="google/long-t5-tglobal-large"
)
self.config_tester = ConfigTester(self, config_class=LongT5Config, d_model=37)
def test_attention_outputs(self):
if not self.has_attentions:
pass
else:
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
block_len = getattr(self.model_tester, "block_len", None)
seq_len = getattr(self.model_tester, "seq_length", None)
global_block_size = getattr(self.model_tester, "global_block_size", 4)
global_seq_len = seq_len // global_block_size
for model_class in self.all_model_classes:
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = False
config.return_dict = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
config.output_attentions = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, block_len, 3 * block_len + global_seq_len],
)
out_len = len(outputs)
# Check attention is always last and order is fine
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
if hasattr(self.model_tester, "num_hidden_states_types"):
added_hidden_states = self.model_tester.num_hidden_states_types
elif self.is_encoder_decoder:
added_hidden_states = 2
else:
added_hidden_states = 1
self.assertEqual(out_len + added_hidden_states, len(outputs))
self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(self_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, block_len, 3 * block_len + global_seq_len],
)
def use_task_specific_params(model, task):
model.config.update(model.config.task_specific_params[task])
@require_torch
@require_sentencepiece
@require_tokenizers
class LongT5ModelIntegrationTests(unittest.TestCase):
@cached_property
def model(self):
return LongT5ForConditionalGeneration.from_pretrained("Stancld/longt5-tglobal-large-16384-pubmed-3k_steps").to(
torch_device
)
@cached_property
def tokenizer(self):
return AutoTokenizer.from_pretrained("Stancld/longt5-tglobal-large-16384-pubmed-3k_steps")
def expected_summary(self):
return [
"background : coronary artery disease ( cad ) is the emerging cause of morbidity and mortality in"
" developing world . it provides an excellent resolution for visualization of the coronaryarteries for"
" catheter - based or operating interventions . although the association of this technique with major"
" complications such as mortality is highly uncommon , it is frequently associated with various cardiac"
" and noncardiac complications.materials and methods : in aortic stenosis , we aimed to report the"
" diagnostic performance of 128-slice computed tomography coronary angiogram in 50 patients undergoing for"
" major noncoron ary cardiac surgery referred"
]
@slow
def test_summarization(self):
model = self.model
tok = self.tokenizer
ARTICLE = """coronary artery disease ( cad ) is the emerging cause of morbidity and mortality in developing world . \n it provides an excellent resolution for visualization of the coronary arteries for catheter - based or operating interventions . \n
although the association of this technique with major complications such as mortality is highly uncommon , it is frequently associated with various cardiac and noncardiac complications . computed tomography ( ct ) coronary angiography is
a promising technique for the evaluation of cad noninvasively . \n it assesses disease within the coronary artery and provides qualitative and quantitative information about nonobstructive atherosclerotic plaque burden within the vessel
wall . \n thus , ct angiography - based disease evaluation may provide clinically more significant information than conventional angiography . the introduction of multi - slice computed tomography ( msct ) technology such as 64-slice , 12
8-slice , 256-slice , and now 320-slice msct has produced a high diagnostic accuracy of ct coronary angiography . \n it has consistently showed to have a very high negative predictive value ( well above 90% ) in ruling out patients with s
ignificant cad defined as coronary luminal stenosis of > 50% . \n the american college of cardiology / american heart association recommends that coronary angiography should be performed before valve surgery in men aged > 40 years , women
aged > 35 years with coronary risk factors and in postmenopausal women . \n the prevalence of cad in patients undergoing valve replacement is 2040% in developed countries . in the previous studies , \n the incidence of angiographically p
roven cad in acquired valvular diseases has been shown to vary widely from 9% to 41% . in aortic stenosis , \n we aimed to report the diagnostic performance of 128-slice ct coronary angiography in 50 patients undergoing for major noncoron
ary cardiac surgery referred for diagnostic invasive coronary angiography to assess the extent and severity of coronary stenosis . \n during january 2013 to december 2014 , we enrolled fifty major noncoronary cardiac surgery patients sche
duled for invasive coronary angiography who fulfilled the following inclusion criteria of age 40 years , having low or intermediate probability of cad , left ventricular ejection fraction ( lvef ) > 35% , and patient giving informed conse
nt for undergoing msct and conventional coronary angiography . \n those having any contraindication for contrast injection , lvef < 35% , high pretest probability of cad , and hemodynamic instability were excluded from the study . \n pati
ents with heart rates of > 70 bpm received ( unless they had known overt heart failure or electrocardiogram ( ecg ) atrioventricular conduction abnormalities ) a single oral dose of 100 mg metoprolol 45 min before the scan . \n patients w
ith heart rates of > 80 bpm received an additional oral dose of metoprolol if not contraindicated . \n all patients were scanned with a 128-slice ct scanner ( siemens , somatom definition as ) equipped with a new feature in msct technolog
y , so - called z - axis flying - focus technology . \n the central 32 detector rows acquire 0.6-mm slices , and the flying - focus spot switches back and forth between 2 z positions between each reading . \n two slices per detector row a
re acquired , which results in a higher oversampling rate in the z - axis , thereby reducing artifacts related to the spiral acquisition and improving spatial resolution down to 0.4 mm . \n a bolus of 6580 ml contrast material ( omnipaque
) was injected through an arm vein at a flow rate of 5 ml / s . \n a bolus tracking technique was used to synchronize the arrival of contrast in the coronary arteries with the initiation of the scan . to monitor the arrival of contrast m
aterial , \n axial scans were obtained at the level of the ascending aorta with a delay of 10 s after the start of the contrast injection . \n the scan was automatically started when a threshold of 150 hounsfield units was reached in a re
gion of interest positioned in the ascending aorta . \n images were reconstructed with ecg gating to obtain optimal , motion - free image quality . \n all scans were performed within 2 weeks of the msct coronary diagnostic angiogram . a s
ingle observer unaware of the multi - slice ct results identified coronary lesion as a single vessel , double vessel , or triple vessel disease . \n all lesion , regardless of size , were included for comparison with ct coronary angiograp
hy . \n lesions were classified as having nonsignificant disease ( luminal irregularities or < 50% stenosis ) or as having significant stenosis . \n stenosis was evaluated in two orthogonal views and classified as significant if the mean
lumen diameter reduction was 50% using a validated quantitative coronary angiography ( qca ) . \n all scans were analyzed independently by a radiologist and a cardiologist who were unaware of the results of conventional coronary angiograp
hy . \n total calcium scores of all patients were calculated with dedicated software and expressed as agatston scores . \n the agatston score is a commonly used scoring method that calculates the total amount of calcium on the basis of th
e number , areas , and peak hounsfield units of the detected calcified lesions . \n all available coronary segments were visually scored for the presence of > 50% considered as significant stenosis . \n maximum intensity projections were
used to identify coronary lesions and ( curved ) multiplanar reconstructions to classify lesions as significant or nonsignificant . \n data were analyzed using statistical system spss version 20 software ( chicago , il , usa ) . \n the di
agnostic performance of ct coronary angiography for the detection of significant lesions in coronary arteries with qca as the standard of reference is presented as sensitivity , specificity , positive and negative predictive values , and
positive and negative likelihood ratios with the corresponding exact 95% of confidence interval ( cis ) . \n comparison between ct and conventional coronary angiography was performed on the two level vessel by vessel ( no or any disease p
er vessel ) , and patient by patient ( no or any disease per patient ) . \n all scans were performed within 2 weeks of the msct coronary diagnostic angiogram . a single observer unaware of the multi - slice ct results identified coronary
lesion as a single vessel , double vessel , or triple vessel disease . \n all lesion , regardless of size , were included for comparison with ct coronary angiography . \n lesions were classified as having nonsignificant disease ( luminal
irregularities or < 50% stenosis ) or as having significant stenosis . \n stenosis was evaluated in two orthogonal views and classified as significant if the mean lumen diameter reduction was 50% using a validated quantitative coronary an
giography ( qca ) . \n all scans were analyzed independently by a radiologist and a cardiologist who were unaware of the results of conventional coronary angiography . \n total calcium scores of all patients were calculated with dedicated
software and expressed as agatston scores . \n the agatston score is a commonly used scoring method that calculates the total amount of calcium on the basis of the number , areas , and peak hounsfield units of the detected calcified lesi
ons . \n all available coronary segments were visually scored for the presence of > 50% considered as significant stenosis . \n maximum intensity projections were used to identify coronary lesions and ( curved ) multiplanar reconstruction
s to classify lesions as significant or nonsignificant . \n data were analyzed using statistical system spss version 20 software ( chicago , il , usa ) . \n the diagnostic performance of ct coronary angiography for the detection of signif
icant lesions in coronary arteries with qca as the standard of reference is presented as sensitivity , specificity , positive and negative predictive values , and positive and negative likelihood ratios with the corresponding exact 95% of
confidence interval ( cis ) . \n comparison between ct and conventional coronary angiography was performed on the two level vessel by vessel ( no or any disease per vessel ) , and patient by patient ( no or any disease per patient ) . \n
in this study , 29 ( 58% ) subjects were female , and 21 ( 42% ) were male showing an average age of 50.36 8.39 years . \n of fifty patients 24 ( 48% ) , 13 ( 26% ) , eight ( 16% ) , and five ( 10% ) underwent mitral valve replacement ,
double valve replacement ( dvr ) , aortic valve replacement , and other surgeries , respectively . \n high distribution of cad risk factors such as hypertension ( 24% ) , smoking ( 22% ) , and dyslipidemia ( 18% ) was observed in the stu
dy group . \n the mean creatinine level was 0.766 0.17 and average dye used in conventional angiography was 48.5 26.6 whereas for ct angiography it was 72.8 6.32 . \n average radiation dose in conventional coronary angiography and msct
coronary angiography was 5.2 msv and 9.2 msv , respectively . \n the majority of the patients had sinus rhythm ( 68% ) , whereas atrial fibrillation was found in 32% of the subjects . \n patients included in the study had low to intermed
iate probability of cad . in this study , three patients had complications after conventional angiography . \n complications were of local site hematoma , acute kidney injury managed conservatively , and acute heart failure . \n a patient
who developed hematoma was obese female patients with body mass index > 30 kg / m . \n the patient suffered from pseudoaneurysm , had hospitalized for 9 days , which leads to increased morbidity and cost of hospital stay . \n the diagnos
tic accuracy of ct coronary angiography was evaluated regarding true positive , true negative values and is presented in table 1 . the overall sensitivity and \n specificity of ct angiography technique was 100% ( 95% ci : 39.76%100% ) and
91.30% ( 95% ci : 79.21%97.58% ) , respectively [ table 2 ] . \n the positive predictive value ( 50% ; 95% ci : 15.70%84.30% ) and negative predictive value ( 100% ; 95% ci : 91.59%100% ) of ct angiography were also fairly high in these
patients . \n recent reports from multiple studies demonstrated that recent - generation msct scanners showed promise for noninvasive detection of coronary stenosis however , until now no studies were found regarding the clinical efficacy
or prognostic value of 128-slice ct coronary angiography versus conventional invasive coronary angiography in the diagnosis of patients planned for major noncoronary surgeries such as dvr , bentall , atrial septal defect closure , etc .
in our study , we reported 8% cad prevalence in patients planned for major noncoronary cardiac surgery . \n we performed conventional and msct coronary angiography in all patients and the results showed that ct coronary angiography with i
nvasive coronary angiography as the reference standard had a considerably high sensitivity ( 100% ) and specificity ( 95.65% ) . \n the health economic model using invasive coronary angiography as the reference standard showed that at a p
retest probability of cad of 70% or lower , ct coronary angiography resulted in lower cost per patient with a true positive diagnosis . at a pretest probability of cad of 70% or higher , invasive coronary angiography was associated with a
lower cost per patient with a true positive diagnosis . in our study population , \n two patients developed local site complications in the form of hematoma and pseudoaneurysm after conventional angiography . \n hence , msct coronary ang
iography will be more favorable in female obese patients with intermediate likelihood of cad . \n hence , msct coronary angiography will be cost - effective in patients of valvular heart diseases . \n however , ct angiography suffers from
a drawback that average amount of dye used in msct coronary angiography were 72.8 6.32 ml which is higher than average amount of dye required for conventional angiography ( 48.6 26.6 ml ) . \n hence , the use of ct coronary angiography
could not be used in patients with known renal dysfunction , where reduction of contrast dye load is highly advocated . \n our results show that 128-slice ct coronary angiography is a reliable technique to detect coronary stenosis in pat
ients planned for noncoronary cardiac surgery . \n although there has been important technological progress in the development of ct coronary angiography , its clinical application remains limited . \n a study wth large numbers of patient
s is required for the recommendation of only ct coronary angiography for the coronary evaluation in major non - cardiac surgeries . \n mehta institute of cardiology and research center ( affiliated to bj medical college , ahmedabad , guja
rat , india ) . \n u.n . mehta institute of cardiology and research center ( affiliated to bj medical college , ahmedabad , gujarat , india ) . \n """
dct = tok(
[ARTICLE],
max_length=1024,
padding="max_length",
truncation=True,
return_tensors="pt",
).to(torch_device)
hypotheses_batch = model.generate(
**dct,
num_beams=4,
length_penalty=2.0,
max_length=142,
min_length=56,
no_repeat_ngram_size=3,
do_sample=False,
early_stopping=True,
)
decoded = tok.batch_decode(hypotheses_batch, skip_special_tokens=True, clean_up_tokenization_spaces=False)
self.assertListEqual(
self.expected_summary(),
decoded,
)
@slow
def test_inference_hidden_states(self):
model = self.model
input_ids = torch.tensor(
[[100, 19, 3, 9, 7142, 1200, 145, 8, 1252, 14145, 2034, 812, 5, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
dtype=torch.long,
device=torch_device,
)
decoder_input_ids = torch.tensor(
[[100, 19, 3, 9, 7142, 1200, 145, 8, 1252, 14145, 2034, 812, 5, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
dtype=torch.long,
device=torch_device,
)
attention_mask = torch.tensor(
[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
dtype=torch.long,
device=torch_device,
)
output = model(
input_ids, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, output_hidden_states=True
)
# check if encoder_outputs match
expected_output_slice = torch.tensor([0.0629, -0.1294, -0.0089, 0.0772, 0.0663], device=torch_device)
self.assertTrue(torch.allclose(output.encoder_hidden_states[-1][0, 0, :5], expected_output_slice, atol=1e-4))
# check if logits match
expected_output_slice = torch.tensor([5.5231, 6.1058, 3.1766, 8.2391, -5.9453], device=torch_device)
self.assertTrue(torch.allclose(output.logits[0, 0, :5], expected_output_slice, atol=1e-4))
| transformers/tests/models/longt5/test_modeling_longt5.py/0 | {
"file_path": "transformers/tests/models/longt5/test_modeling_longt5.py",
"repo_id": "transformers",
"token_count": 29813
} | 160 |
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model")
if is_torch_available():
from transformers.models.mbart.modeling_mbart import shift_tokens_right
EN_CODE = 250004
RO_CODE = 250020
@require_sentencepiece
@require_tokenizers
class MBartTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
tokenizer_class = MBartTokenizer
rust_tokenizer_class = MBartTokenizerFast
test_rust_tokenizer = True
test_sentencepiece = True
def setUp(self):
super().setUp()
# We have a SentencePiece fixture for testing
tokenizer = MBartTokenizer(SAMPLE_VOCAB, keep_accents=True)
tokenizer.save_pretrained(self.tmpdirname)
def test_full_tokenizer(self):
tokenizer = MBartTokenizer(SAMPLE_VOCAB, keep_accents=True)
tokens = tokenizer.tokenize("This is a test")
self.assertListEqual(tokens, ["▁This", "▁is", "▁a", "▁t", "est"])
self.assertListEqual(
tokenizer.convert_tokens_to_ids(tokens),
[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]],
)
tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.")
self.assertListEqual(
tokens,
[
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"9",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"é",
".",
],
)
ids = tokenizer.convert_tokens_to_ids(tokens)
self.assertListEqual(
ids,
[
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
],
)
back_tokens = tokenizer.convert_ids_to_tokens(ids)
self.assertListEqual(
back_tokens,
[
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"<unk>",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"<unk>",
".",
],
)
# overwrite from test_tokenization_common to speed up test
def test_save_pretrained(self):
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
self.tokenizers_list[0] = (self.rust_tokenizer_class, "hf-internal-testing/tiny-random-mbart", {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
tmpdirname2 = tempfile.mkdtemp()
tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2)
tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2)
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files))
tokenizer_r_files = tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f)
self.assertSequenceEqual(tokenizer_r_files, tokenizer_p_files)
# Checks everything loads correctly in the same way
tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2)
tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2)
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(tokenizer_rp, key))
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(tmpdirname2)
# Save tokenizer rust, legacy_format=True
tmpdirname2 = tempfile.mkdtemp()
tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2, legacy_format=True)
tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2)
# Checks it save with the same files
self.assertSequenceEqual(tokenizer_r_files, tokenizer_p_files)
# Checks everything loads correctly in the same way
tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2)
tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2)
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(tokenizer_rp, key))
shutil.rmtree(tmpdirname2)
# Save tokenizer rust, legacy_format=False
tmpdirname2 = tempfile.mkdtemp()
tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2, legacy_format=False)
tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2)
# Checks it saved the tokenizer.json file
self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files))
# Checks everything loads correctly in the same way
tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2)
tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2)
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(tokenizer_rp, key))
shutil.rmtree(tmpdirname2)
@unittest.skip("Need to fix this after #26538")
def test_training_new_tokenizer(self):
pass
@require_torch
@require_sentencepiece
@require_tokenizers
class MBartEnroIntegrationTest(unittest.TestCase):
checkpoint_name = "facebook/mbart-large-en-ro"
src_text = [
" UN Chief Says There Is No Military Solution in Syria",
""" Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
]
tgt_text = [
"Şeful ONU declară că nu există o soluţie militară în Siria",
"Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"
' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
" face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
]
expected_src_tokens = [8274, 127873, 25916, 7, 8622, 2071, 438, 67485, 53, 187895, 23, 51712, 2, EN_CODE]
@classmethod
def setUpClass(cls):
cls.tokenizer: MBartTokenizer = MBartTokenizer.from_pretrained(
cls.checkpoint_name, src_lang="en_XX", tgt_lang="ro_RO"
)
cls.pad_token_id = 1
return cls
def check_language_codes(self):
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"], 250001)
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"], 250004)
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"], 250020)
def test_enro_tokenizer_batch_encode_plus(self):
ids = self.tokenizer.batch_encode_plus(self.src_text).input_ids[0]
self.assertListEqual(self.expected_src_tokens, ids)
def test_enro_tokenizer_decode_ignores_language_codes(self):
self.assertIn(RO_CODE, self.tokenizer.all_special_ids)
generated_ids = [RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2]
result = self.tokenizer.decode(generated_ids, skip_special_tokens=True)
expected_romanian = self.tokenizer.decode(generated_ids[1:], skip_special_tokens=True)
self.assertEqual(result, expected_romanian)
self.assertNotIn(self.tokenizer.eos_token, result)
def test_enro_tokenizer_truncation(self):
src_text = ["this is gunna be a long sentence " * 20]
assert isinstance(src_text[0], str)
desired_max_length = 10
ids = self.tokenizer(src_text, max_length=desired_max_length, truncation=True).input_ids[0]
self.assertEqual(ids[-2], 2)
self.assertEqual(ids[-1], EN_CODE)
self.assertEqual(len(ids), desired_max_length)
def test_mask_token(self):
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]), [250026, 250001])
def test_special_tokens_unaffacted_by_save_load(self):
tmpdirname = tempfile.mkdtemp()
original_special_tokens = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(tmpdirname)
new_tok = MBartTokenizer.from_pretrained(tmpdirname)
self.assertDictEqual(new_tok.fairseq_tokens_to_ids, original_special_tokens)
@require_torch
def test_batch_fairseq_parity(self):
batch = self.tokenizer(self.src_text, text_target=self.tgt_text, padding=True, return_tensors="pt")
batch["decoder_input_ids"] = shift_tokens_right(batch["labels"], self.tokenizer.pad_token_id)
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
assert batch.decoder_input_ids[1][-1] == 2
assert batch.labels[1][-2:].tolist() == [2, RO_CODE]
@require_torch
def test_enro_tokenizer_prepare_batch(self):
batch = self.tokenizer(
self.src_text,
text_target=self.tgt_text,
padding=True,
truncation=True,
max_length=len(self.expected_src_tokens),
return_tensors="pt",
)
batch["decoder_input_ids"] = shift_tokens_right(batch["labels"], self.tokenizer.pad_token_id)
self.assertIsInstance(batch, BatchEncoding)
self.assertEqual((2, 14), batch.input_ids.shape)
self.assertEqual((2, 14), batch.attention_mask.shape)
result = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens, result)
self.assertEqual(2, batch.decoder_input_ids[0, -1]) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens, [])
self.assertEqual(self.tokenizer.suffix_tokens, [self.tokenizer.eos_token_id, EN_CODE])
def test_seq2seq_max_length(self):
batch = self.tokenizer(self.src_text, padding=True, truncation=True, max_length=3, return_tensors="pt")
targets = self.tokenizer(
text_target=self.tgt_text, padding=True, truncation=True, max_length=10, return_tensors="pt"
)
labels = targets["input_ids"]
batch["decoder_input_ids"] = shift_tokens_right(labels, self.tokenizer.pad_token_id)
self.assertEqual(batch.input_ids.shape[1], 3)
self.assertEqual(batch.decoder_input_ids.shape[1], 10)
@require_torch
def test_tokenizer_translation(self):
inputs = self.tokenizer._build_translation_inputs(
"A test", return_tensors="pt", src_lang="en_XX", tgt_lang="ar_AR"
)
self.assertEqual(
nested_simplify(inputs),
{
# A, test, EOS, en_XX
"input_ids": [[62, 3034, 2, 250004]],
"attention_mask": [[1, 1, 1, 1]],
# ar_AR
"forced_bos_token_id": 250001,
},
)
| transformers/tests/models/mbart/test_tokenization_mbart.py/0 | {
"file_path": "transformers/tests/models/mbart/test_tokenization_mbart.py",
"repo_id": "transformers",
"token_count": 6595
} | 161 |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch MobileViT model. """
import unittest
from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel
from transformers.models.mobilevit.modeling_mobilevit import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import MobileViTImageProcessor
class MobileViTConfigTester(ConfigTester):
def create_and_test_config_common_properties(self):
config = self.config_class(**self.inputs_dict)
self.parent.assertTrue(hasattr(config, "hidden_sizes"))
self.parent.assertTrue(hasattr(config, "neck_hidden_sizes"))
self.parent.assertTrue(hasattr(config, "num_attention_heads"))
class MobileViTModelTester:
def __init__(
self,
parent,
batch_size=13,
image_size=32,
patch_size=2,
num_channels=3,
last_hidden_size=32,
num_attention_heads=4,
hidden_act="silu",
conv_kernel_size=3,
output_stride=32,
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
classifier_dropout_prob=0.1,
initializer_range=0.02,
is_training=True,
use_labels=True,
num_labels=10,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.image_size = image_size
self.patch_size = patch_size
self.num_channels = num_channels
self.last_hidden_size = last_hidden_size
self.num_attention_heads = num_attention_heads
self.hidden_act = hidden_act
self.conv_kernel_size = conv_kernel_size
self.output_stride = output_stride
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.classifier_dropout_prob = classifier_dropout_prob
self.use_labels = use_labels
self.is_training = is_training
self.num_labels = num_labels
self.initializer_range = initializer_range
self.scope = scope
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
labels = None
pixel_labels = None
if self.use_labels:
labels = ids_tensor([self.batch_size], self.num_labels)
pixel_labels = ids_tensor([self.batch_size, self.image_size, self.image_size], self.num_labels)
config = self.get_config()
return config, pixel_values, labels, pixel_labels
def get_config(self):
return MobileViTConfig(
image_size=self.image_size,
patch_size=self.patch_size,
num_channels=self.num_channels,
num_attention_heads=self.num_attention_heads,
hidden_act=self.hidden_act,
conv_kernel_size=self.conv_kernel_size,
output_stride=self.output_stride,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
classifier_dropout_prob=self.classifier_dropout_prob,
initializer_range=self.initializer_range,
hidden_sizes=[12, 16, 20],
neck_hidden_sizes=[8, 8, 16, 16, 32, 32, 32],
)
def create_and_check_model(self, config, pixel_values, labels, pixel_labels):
model = MobileViTModel(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
self.parent.assertEqual(
result.last_hidden_state.shape,
(
self.batch_size,
self.last_hidden_size,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
),
)
def create_and_check_for_image_classification(self, config, pixel_values, labels, pixel_labels):
config.num_labels = self.num_labels
model = MobileViTForImageClassification(config)
model.to(torch_device)
model.eval()
result = model(pixel_values, labels=labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_for_semantic_segmentation(self, config, pixel_values, labels, pixel_labels):
config.num_labels = self.num_labels
model = MobileViTForSemanticSegmentation(config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
self.parent.assertEqual(
result.logits.shape,
(
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
),
)
result = model(pixel_values, labels=pixel_labels)
self.parent.assertEqual(
result.logits.shape,
(
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
),
)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, pixel_values, labels, pixel_labels = config_and_inputs
inputs_dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class MobileViTModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
"""
Here we also overwrite some of the tests of test_modeling_common.py, as MobileViT does not use input_ids, inputs_embeds,
attention_mask and seq_length.
"""
all_model_classes = (
(MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
if is_torch_available()
else ()
)
pipeline_model_mapping = (
{
"image-feature-extraction": MobileViTModel,
"image-classification": MobileViTForImageClassification,
"image-segmentation": MobileViTForSemanticSegmentation,
}
if is_torch_available()
else {}
)
test_pruning = False
test_resize_embeddings = False
test_head_masking = False
has_attentions = False
def setUp(self):
self.model_tester = MobileViTModelTester(self)
self.config_tester = MobileViTConfigTester(self, config_class=MobileViTConfig, has_text_modality=False)
def test_config(self):
self.config_tester.run_common_tests()
@unittest.skip(reason="MobileViT does not use inputs_embeds")
def test_inputs_embeds(self):
pass
@unittest.skip(reason="MobileViT does not support input and output embeddings")
def test_model_common_attributes(self):
pass
@unittest.skip(reason="MobileViT does not output attentions")
def test_attention_outputs(self):
pass
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_hidden_states_output(self):
def check_hidden_states_output(inputs_dict, config, model_class):
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
hidden_states = outputs.hidden_states
expected_num_stages = 5
self.assertEqual(len(hidden_states), expected_num_stages)
# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
# with the width and height being successively divided by 2.
divisor = 2
for i in range(len(hidden_states)):
self.assertListEqual(
list(hidden_states[i].shape[-2:]),
[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor],
)
divisor *= 2
self.assertEqual(self.model_tester.output_stride, divisor // 2)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
check_hidden_states_output(inputs_dict, config, model_class)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
check_hidden_states_output(inputs_dict, config, model_class)
def test_for_image_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*config_and_inputs)
def test_for_semantic_segmentation(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = MobileViTModel.from_pretrained(model_name)
self.assertIsNotNone(model)
# We will verify our results on an image of cute cats
def prepare_img():
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
return image
@require_torch
@require_vision
class MobileViTModelIntegrationTest(unittest.TestCase):
@cached_property
def default_image_processor(self):
return MobileViTImageProcessor.from_pretrained("apple/mobilevit-xx-small") if is_vision_available() else None
@slow
def test_inference_image_classification_head(self):
model = MobileViTForImageClassification.from_pretrained("apple/mobilevit-xx-small").to(torch_device)
image_processor = self.default_image_processor
image = prepare_img()
inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
# forward pass
with torch.no_grad():
outputs = model(**inputs)
# verify the logits
expected_shape = torch.Size((1, 1000))
self.assertEqual(outputs.logits.shape, expected_shape)
expected_slice = torch.tensor([-1.9364, -1.2327, -0.4653]).to(torch_device)
self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))
@slow
def test_inference_semantic_segmentation(self):
model = MobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-xx-small")
model = model.to(torch_device)
image_processor = MobileViTImageProcessor.from_pretrained("apple/deeplabv3-mobilevit-xx-small")
image = prepare_img()
inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
# forward pass
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
# verify the logits
expected_shape = torch.Size((1, 21, 32, 32))
self.assertEqual(logits.shape, expected_shape)
expected_slice = torch.tensor(
[
[[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
[[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
[[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
],
device=torch_device,
)
self.assertTrue(torch.allclose(logits[0, :3, :3, :3], expected_slice, atol=1e-4))
@slow
def test_post_processing_semantic_segmentation(self):
model = MobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-xx-small")
model = model.to(torch_device)
image_processor = MobileViTImageProcessor.from_pretrained("apple/deeplabv3-mobilevit-xx-small")
image = prepare_img()
inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
# forward pass
with torch.no_grad():
outputs = model(**inputs)
outputs.logits = outputs.logits.detach().cpu()
segmentation = image_processor.post_process_semantic_segmentation(outputs=outputs, target_sizes=[(50, 60)])
expected_shape = torch.Size((50, 60))
self.assertEqual(segmentation[0].shape, expected_shape)
segmentation = image_processor.post_process_semantic_segmentation(outputs=outputs)
expected_shape = torch.Size((32, 32))
self.assertEqual(segmentation[0].shape, expected_shape)
| transformers/tests/models/mobilevit/test_modeling_mobilevit.py/0 | {
"file_path": "transformers/tests/models/mobilevit/test_modeling_mobilevit.py",
"repo_id": "transformers",
"token_count": 6035
} | 162 |
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_vision_available
from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
if is_vision_available():
from PIL import Image
from transformers import Owlv2ImageProcessor
class Owlv2ImageProcessingTester(unittest.TestCase):
def __init__(
self,
parent,
batch_size=7,
num_channels=3,
image_size=18,
min_resolution=30,
max_resolution=400,
do_resize=True,
size=None,
do_normalize=True,
image_mean=[0.48145466, 0.4578275, 0.40821073],
image_std=[0.26862954, 0.26130258, 0.27577711],
do_convert_rgb=True,
):
self.parent = parent
self.batch_size = batch_size
self.num_channels = num_channels
self.image_size = image_size
self.min_resolution = min_resolution
self.max_resolution = max_resolution
self.do_resize = do_resize
self.size = size if size is not None else {"height": 18, "width": 18}
self.do_normalize = do_normalize
self.image_mean = image_mean
self.image_std = image_std
self.do_convert_rgb = do_convert_rgb
def prepare_image_processor_dict(self):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
}
def expected_output_image_shape(self, images):
return self.num_channels, self.size["height"], self.size["width"]
def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
return prepare_image_inputs(
batch_size=self.batch_size,
num_channels=self.num_channels,
min_resolution=self.min_resolution,
max_resolution=self.max_resolution,
equal_resolution=equal_resolution,
numpify=numpify,
torchify=torchify,
)
@require_torch
@require_vision
class Owlv2ImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
image_processing_class = Owlv2ImageProcessor if is_vision_available() else None
def setUp(self):
self.image_processor_tester = Owlv2ImageProcessingTester(self)
@property
def image_processor_dict(self):
return self.image_processor_tester.prepare_image_processor_dict()
def test_image_processor_properties(self):
image_processing = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(image_processing, "do_resize"))
self.assertTrue(hasattr(image_processing, "size"))
self.assertTrue(hasattr(image_processing, "do_normalize"))
self.assertTrue(hasattr(image_processing, "image_mean"))
self.assertTrue(hasattr(image_processing, "image_std"))
def test_image_processor_from_dict_with_kwargs(self):
image_processor = self.image_processing_class.from_dict(self.image_processor_dict)
self.assertEqual(image_processor.size, {"height": 18, "width": 18})
image_processor = self.image_processing_class.from_dict(
self.image_processor_dict, size={"height": 42, "width": 42}
)
self.assertEqual(image_processor.size, {"height": 42, "width": 42})
@slow
def test_image_processor_integration_test(self):
processor = Owlv2ImageProcessor()
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
pixel_values = processor(image, return_tensors="pt").pixel_values
mean_value = round(pixel_values.mean().item(), 4)
self.assertEqual(mean_value, 0.2353)
@unittest.skip("OWLv2 doesn't treat 4 channel PIL and numpy consistently yet") # FIXME Amy
def test_call_numpy_4_channels(self):
pass
| transformers/tests/models/owlv2/test_image_processor_owlv2.py/0 | {
"file_path": "transformers/tests/models/owlv2/test_image_processor_owlv2.py",
"repo_id": "transformers",
"token_count": 1830
} | 163 |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch PEGASUS-X model. """
import copy
import math
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
require_torch_fp16,
slow,
torch_device,
)
from transformers.utils import cached_property
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import PegasusTokenizer, PegasusXConfig, PegasusXForConditionalGeneration, PegasusXModel
from transformers.models.pegasus_x.modeling_pegasus_x import PegasusXDecoder, PegasusXEncoder
def prepare_pegasus_x_inputs_dict(
config,
input_ids,
decoder_input_ids,
attention_mask=None,
decoder_attention_mask=None,
):
if attention_mask is None:
attention_mask = input_ids.ne(config.pad_token_id)
if decoder_attention_mask is None:
decoder_attention_mask = decoder_input_ids.ne(config.pad_token_id)
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
}
@require_torch
class PegasusXModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_labels=False,
vocab_size=99,
hidden_size=16,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=4,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=20,
eos_token_id=2,
pad_token_id=1,
bos_token_id=0,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.eos_token_id = eos_token_id
self.pad_token_id = pad_token_id
self.bos_token_id = bos_token_id
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size).clamp(
3,
)
input_ids[:, -1] = self.eos_token_id # Eos Token
decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
config = PegasusXConfig(
vocab_size=self.vocab_size,
d_model=self.hidden_size,
encoder_layers=self.num_hidden_layers,
decoder_layers=self.num_hidden_layers,
encoder_attention_heads=self.num_attention_heads,
decoder_attention_heads=self.num_attention_heads,
encoder_ffn_dim=self.intermediate_size,
decoder_ffn_dim=self.intermediate_size,
dropout=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
eos_token_id=self.eos_token_id,
bos_token_id=self.bos_token_id,
pad_token_id=self.pad_token_id,
stagger_local_blocks=False,
)
inputs_dict = prepare_pegasus_x_inputs_dict(config, input_ids, decoder_input_ids)
return config, inputs_dict
def prepare_config_and_inputs_for_common(self):
config, inputs_dict = self.prepare_config_and_inputs()
return config, inputs_dict
def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict):
model = PegasusXModel(config=config).get_decoder().to(torch_device).eval()
input_ids = inputs_dict["input_ids"]
attention_mask = inputs_dict["attention_mask"]
# first forward pass
outputs = model(input_ids, attention_mask=attention_mask, use_cache=True)
output, past_key_values = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
next_attn_mask = ids_tensor((self.batch_size, 3), 2)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
next_attention_mask = torch.cat([attention_mask, next_attn_mask], dim=-1)
output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["last_hidden_state"]
output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[
"last_hidden_state"
]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-2))
def check_encoder_decoder_model_standalone(self, config, inputs_dict):
model = PegasusXModel(config=config).to(torch_device).eval()
outputs = model(**inputs_dict)
encoder_last_hidden_state = outputs.encoder_last_hidden_state
last_hidden_state = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
encoder = model.get_encoder()
encoder.save_pretrained(tmpdirname)
encoder = PegasusXEncoder.from_pretrained(tmpdirname).to(torch_device)
encoder_last_hidden_state_2 = encoder(inputs_dict["input_ids"], attention_mask=inputs_dict["attention_mask"])[
0
]
self.parent.assertTrue((encoder_last_hidden_state_2 - encoder_last_hidden_state).abs().max().item() < 1e-3)
with tempfile.TemporaryDirectory() as tmpdirname:
decoder = model.get_decoder()
decoder.save_pretrained(tmpdirname)
decoder = PegasusXDecoder.from_pretrained(tmpdirname).to(torch_device)
last_hidden_state_2 = decoder(
input_ids=inputs_dict["decoder_input_ids"],
attention_mask=inputs_dict["decoder_attention_mask"],
encoder_hidden_states=encoder_last_hidden_state,
encoder_attention_mask=inputs_dict["attention_mask"],
)[0]
self.parent.assertTrue((last_hidden_state_2 - last_hidden_state).abs().max().item() < 1e-3)
@require_torch
class PegasusXModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (PegasusXModel, PegasusXForConditionalGeneration) if is_torch_available() else ()
all_generative_model_classes = (PegasusXForConditionalGeneration,) if is_torch_available() else ()
pipeline_model_mapping = (
{
"conversational": PegasusXForConditionalGeneration,
"feature-extraction": PegasusXModel,
"summarization": PegasusXForConditionalGeneration,
"text2text-generation": PegasusXForConditionalGeneration,
"translation": PegasusXForConditionalGeneration,
}
if is_torch_available()
else {}
)
is_encoder_decoder = True
test_pruning = False
test_head_masking = False
test_missing_keys = False
def setUp(self):
self.model_tester = PegasusXModelTester(self)
self.config_tester = ConfigTester(self, config_class=PegasusXConfig)
@unittest.skip(
"`PegasusXGlobalLocalAttention` returns attentions as dictionary - not compatible with torchscript "
)
def test_torchscript_output_attentions(self):
pass
def test_config(self):
self.config_tester.run_common_tests()
def test_save_load_strict(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
model = model_class(config)
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True)
self.assertEqual(info["missing_keys"], [])
def test_decoder_model_past_with_large_inputs(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
def test_encoder_decoder_model_standalone(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*config_and_inputs)
def test_inputs_embeds(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in (PegasusXModel, PegasusXForConditionalGeneration):
model = model_class(config)
model.to(torch_device)
model.eval()
inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class))
if not self.is_encoder_decoder:
input_ids = inputs["input_ids"]
del inputs["input_ids"]
else:
encoder_input_ids = inputs["input_ids"]
decoder_input_ids = inputs.get("decoder_input_ids", encoder_input_ids)
del inputs["input_ids"]
inputs.pop("decoder_input_ids", None)
wte = model.get_input_embeddings()
if not self.is_encoder_decoder:
inputs["inputs_embeds"] = wte(input_ids)
else:
inputs["inputs_embeds"] = wte(encoder_input_ids)
inputs["decoder_inputs_embeds"] = wte(decoder_input_ids)
with torch.no_grad():
model(**inputs)[0]
@require_torch_fp16
def test_generate_fp16(self):
config, input_dict = self.model_tester.prepare_config_and_inputs()
input_ids = input_dict["input_ids"]
attention_mask = input_ids.ne(1).to(torch_device)
model = PegasusXForConditionalGeneration(config).eval().to(torch_device)
model.half()
model.generate(input_ids, attention_mask=attention_mask)
model.generate(num_beams=4, do_sample=True, early_stopping=False, num_return_sequences=3)
def test_attention_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
seq_len = getattr(self.model_tester, "seq_length", None)
decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len)
encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len)
decoder_key_length = getattr(self.model_tester, "decoder_key_length", decoder_seq_length)
encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
chunk_length = getattr(self.model_tester, "chunk_length", None)
if chunk_length is not None and hasattr(self.model_tester, "num_hashes"):
encoder_seq_length = encoder_seq_length * self.model_tester.num_hashes
for model_class in self.all_model_classes:
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = False
config.return_dict = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
config.output_attentions = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(attentions[0]["local"].shape[-4:]),
[
self.model_tester.num_attention_heads,
math.ceil(encoder_seq_length / model.config.block_size),
model.config.block_size,
model.config.block_size + model.config.num_global_tokens,
],
)
out_len = len(outputs)
if self.is_encoder_decoder:
correct_outlen = 5
# loss is at first position
if "labels" in inputs_dict:
correct_outlen += 1 # loss is added to beginning
if "past_key_values" in outputs:
correct_outlen += 1 # past_key_values have been returned
self.assertEqual(out_len, correct_outlen)
# decoder attentions
decoder_attentions = outputs.decoder_attentions
self.assertIsInstance(decoder_attentions, (list, tuple))
self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(decoder_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length],
)
# cross attentions
cross_attentions = outputs.cross_attentions
self.assertIsInstance(cross_attentions, (list, tuple))
self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(cross_attentions[0].shape[-3:]),
[
self.model_tester.num_attention_heads,
decoder_seq_length,
encoder_key_length,
],
)
# Check attention is always last and order is fine
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
if hasattr(self.model_tester, "num_hidden_states_types"):
added_hidden_states = self.model_tester.num_hidden_states_types
elif self.is_encoder_decoder:
added_hidden_states = 2
else:
added_hidden_states = 1
self.assertEqual(out_len + added_hidden_states, len(outputs))
self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(self_attentions[0]["local"].shape[-4:]),
[
self.model_tester.num_attention_heads,
math.ceil(encoder_seq_length / model.config.block_size),
model.config.block_size,
model.config.block_size + model.config.num_global_tokens,
],
)
def _check_encoder_attention_for_generate(self, attentions, batch_size, config, seq_length):
encoder_expected_shape = (
batch_size,
config.num_attention_heads,
math.ceil(seq_length / config.block_size),
config.block_size,
config.block_size + config.num_global_tokens,
)
self.assertIsInstance(attentions, tuple)
self.assertListEqual(
[layer_attentions["local"].shape for layer_attentions in attentions],
[encoder_expected_shape] * len(attentions),
)
def _check_encoder_hidden_states_for_generate(self, hidden_states, batch_size, config, seq_length):
encoder_expected_shape = (batch_size, self.round_up(seq_length, config.block_size), config.hidden_size)
self.assertIsInstance(hidden_states, tuple)
# Only the last layer will have the hidden states truncated back to token level
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in hidden_states[:-1]],
[encoder_expected_shape] * (len(hidden_states) - 1),
)
# Only the last layer will have the hidden states truncated back to token level
self.assertEqual(
hidden_states[-1][0].shape,
(batch_size, seq_length, config.hidden_size),
)
def test_hidden_states_output(self):
def _check_hidden_states_output(inputs_dict, config, model_class):
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
expected_num_layers = getattr(
self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
)
self.assertEqual(len(hidden_states), expected_num_layers)
if hasattr(self.model_tester, "encoder_seq_length"):
seq_length = self.model_tester.encoder_seq_length
if hasattr(self.model_tester, "chunk_length") and self.model_tester.chunk_length > 1:
seq_length = seq_length * self.model_tester.chunk_length
else:
seq_length = self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[self.round_up(seq_length, config.block_size), self.model_tester.hidden_size],
)
if config.is_encoder_decoder:
hidden_states = outputs.decoder_hidden_states
self.assertIsInstance(hidden_states, (list, tuple))
self.assertEqual(len(hidden_states), expected_num_layers)
seq_len = getattr(self.model_tester, "seq_length", None)
decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len)
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[decoder_seq_length, self.model_tester.hidden_size],
)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
_check_hidden_states_output(inputs_dict, config, model_class)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
_check_hidden_states_output(inputs_dict, config, model_class)
def test_retain_grad_hidden_states_attentions(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.output_hidden_states = True
config.output_attentions = self.has_attentions
# no need to test all models as different heads yield the same functionality
model_class = self.all_model_classes[0]
model = model_class(config)
model.to(torch_device)
inputs = self._prepare_for_class(inputs_dict, model_class)
outputs = model(**inputs)
output = outputs[0]
if config.is_encoder_decoder:
# Seq2Seq models
encoder_hidden_states = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
decoder_hidden_states = outputs.decoder_hidden_states[0]
decoder_hidden_states.retain_grad()
if self.has_attentions:
encoder_attentions = outputs.encoder_attentions[0]
encoder_attentions["local"].retain_grad()
encoder_attentions["global"].retain_grad()
decoder_attentions = outputs.decoder_attentions[0]
decoder_attentions.retain_grad()
cross_attentions = outputs.cross_attentions[0]
cross_attentions.retain_grad()
output.flatten()[0].backward(retain_graph=True)
self.assertIsNotNone(encoder_hidden_states.grad)
self.assertIsNotNone(decoder_hidden_states.grad)
if self.has_attentions:
self.assertIsNotNone(encoder_attentions["local"].grad)
self.assertIsNotNone(encoder_attentions["global"].grad)
self.assertIsNotNone(decoder_attentions.grad)
self.assertIsNotNone(cross_attentions.grad)
else:
# Encoder-/Decoder-only models
hidden_states = outputs.hidden_states[0]
hidden_states.retain_grad()
if self.has_attentions:
attentions = outputs.attentions[0]
attentions.retain_grad()
output.flatten()[0].backward(retain_graph=True)
self.assertIsNotNone(hidden_states.grad)
if self.has_attentions:
self.assertIsNotNone(attentions.grad)
@classmethod
def round_up(cls, n, k):
return math.ceil(n / k) * k
def assert_tensors_close(a, b, atol=1e-12, prefix=""):
"""If tensors have different shapes, different values or a and b are not both tensors, raise a nice Assertion error."""
if a is None and b is None:
return True
try:
if torch.allclose(a, b, atol=atol):
return True
raise
except Exception:
pct_different = (torch.gt((a - b).abs(), atol)).float().mean().item()
if a.numel() > 100:
msg = f"tensor values are {pct_different:.1%} percent different."
else:
msg = f"{a} != {b}"
if prefix:
msg = prefix + ": " + msg
raise AssertionError(msg)
def _long_tensor(tok_lst):
return torch.tensor(tok_lst, dtype=torch.long, device=torch_device)
TOLERANCE = 1e-4
@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class PegasusXModelIntegrationTests(unittest.TestCase):
@cached_property
def default_tokenizer(self):
return PegasusTokenizer.from_pretrained("google/pegasus-x-base")
def test_inference_no_head(self):
model = PegasusXModel.from_pretrained("google/pegasus-x-base").to(torch_device)
input_ids = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
decoder_input_ids = _long_tensor([[2, 0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588]])
inputs_dict = prepare_pegasus_x_inputs_dict(model.config, input_ids, decoder_input_ids)
with torch.no_grad():
output = model(**inputs_dict)[0]
expected_shape = torch.Size((1, 11, 768))
self.assertEqual(output.shape, expected_shape)
# change to expected output here
expected_slice = torch.tensor(
[[0.0702, -0.1552, 0.1192], [0.0836, -0.1848, 0.1304], [0.0673, -0.1686, 0.1045]], device=torch_device
)
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=TOLERANCE))
def test_inference_head(self):
model = PegasusXForConditionalGeneration.from_pretrained("google/pegasus-x-base").to(torch_device)
# change to intended input
input_ids = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
decoder_input_ids = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
inputs_dict = prepare_pegasus_x_inputs_dict(model.config, input_ids, decoder_input_ids)
with torch.no_grad():
output = model(**inputs_dict)[0]
expected_shape = torch.Size((1, 11, model.config.vocab_size))
self.assertEqual(output.shape, expected_shape)
# change to expected output here
expected_slice = torch.tensor(
[[0.0, 9.5705185, 1.5897303], [0.0, 9.833374, 1.5828674], [0.0, 10.429961, 1.5643371]], device=torch_device
)
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=TOLERANCE))
def test_seq_to_seq_generation(self):
hf = PegasusXForConditionalGeneration.from_pretrained("google/pegasus-x-base-arxiv").to(torch_device)
tok = PegasusTokenizer.from_pretrained("google/pegasus-x-base")
batch_input = [
"While large pretrained Transformer models have proven highly capable at tackling natural language tasks,"
" handling long sequence inputs continues to be a significant challenge. One such task is long input"
" summarization, where inputs are longer than the maximum input context of most pretrained models. Through"
" an extensive set of experiments, we investigate what model architectural changes and pretraining"
" paradigms can most efficiently adapt a pretrained Transformer for long input summarization. We find that"
" a staggered, block-local Transformer with global encoder tokens strikes a good balance of performance"
" and efficiency, and that an additional pretraining phase on long sequences meaningfully improves"
" downstream summarization performance. Based on our findings, we introduce PEGASUS-X, an extension of the"
" PEGASUS model with additional long input pretraining to handle inputs of up to 16K tokens. PEGASUS-X"
" achieves strong performance on long input summarization tasks comparable with much larger models while"
" adding few additional parameters and not requiring model parallelism to train."
]
# The below article tests that we don't add any hypotheses outside of the top n_beams
dct = tok.batch_encode_plus(
batch_input,
max_length=512,
padding="max_length",
truncation_strategy="only_first",
truncation=True,
return_tensors="pt",
)
hypotheses_batch = hf.generate(
input_ids=dct["input_ids"].to(torch_device),
attention_mask=dct["attention_mask"].to(torch_device),
num_beams=2,
max_length=32,
)
EXPECTED = [
"we investigate the performance of a new pretrained model for long input summarization. <n> the model is a"
" superposition of two well -"
]
generated = tok.batch_decode(
hypotheses_batch.tolist(), clean_up_tokenization_spaces=True, skip_special_tokens=True
)
assert generated == EXPECTED
class PegasusXStandaloneDecoderModelTester:
def __init__(
self,
parent,
vocab_size=99,
batch_size=13,
d_model=16,
decoder_seq_length=7,
is_training=True,
is_decoder=True,
use_attention_mask=True,
use_cache=False,
use_labels=True,
decoder_start_token_id=2,
decoder_ffn_dim=32,
decoder_layers=2,
encoder_attention_heads=4,
decoder_attention_heads=4,
max_position_embeddings=30,
is_encoder_decoder=False,
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.decoder_seq_length = decoder_seq_length
# For common tests
self.seq_length = self.decoder_seq_length
self.is_training = is_training
self.use_attention_mask = use_attention_mask
self.use_labels = use_labels
self.vocab_size = vocab_size
self.d_model = d_model
self.hidden_size = d_model
self.num_hidden_layers = decoder_layers
self.decoder_layers = decoder_layers
self.decoder_ffn_dim = decoder_ffn_dim
self.encoder_attention_heads = encoder_attention_heads
self.decoder_attention_heads = decoder_attention_heads
self.num_attention_heads = decoder_attention_heads
self.eos_token_id = eos_token_id
self.bos_token_id = bos_token_id
self.pad_token_id = pad_token_id
self.decoder_start_token_id = decoder_start_token_id
self.use_cache = use_cache
self.max_position_embeddings = max_position_embeddings
self.is_encoder_decoder = is_encoder_decoder
self.scope = None
self.decoder_key_length = decoder_seq_length
self.base_model_out_len = 2
self.decoder_attention_idx = 1
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)
attention_mask = None
if self.use_attention_mask:
attention_mask = ids_tensor([self.batch_size, self.decoder_seq_length], vocab_size=2)
lm_labels = None
if self.use_labels:
lm_labels = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)
config = PegasusXConfig(
vocab_size=self.vocab_size,
d_model=self.d_model,
decoder_layers=self.decoder_layers,
decoder_ffn_dim=self.decoder_ffn_dim,
encoder_attention_heads=self.encoder_attention_heads,
decoder_attention_heads=self.decoder_attention_heads,
eos_token_id=self.eos_token_id,
bos_token_id=self.bos_token_id,
use_cache=self.use_cache,
pad_token_id=self.pad_token_id,
decoder_start_token_id=self.decoder_start_token_id,
max_position_embeddings=self.max_position_embeddings,
is_encoder_decoder=self.is_encoder_decoder,
)
return (
config,
input_ids,
attention_mask,
lm_labels,
)
def create_and_check_decoder_model_past(
self,
config,
input_ids,
attention_mask,
lm_labels,
):
config.use_cache = True
model = PegasusXDecoder(config=config).to(torch_device).eval()
# first forward pass
outputs = model(input_ids, use_cache=True)
outputs_use_cache_conf = model(input_ids)
outputs_no_past = model(input_ids, use_cache=False)
self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf))
self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)
past_key_values = outputs["past_key_values"]
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
output_from_no_past = model(next_input_ids)["last_hidden_state"]
output_from_past = model(next_tokens, past_key_values=past_key_values)["last_hidden_state"]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
assert torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)
def create_and_check_decoder_model_attention_mask_past(
self,
config,
input_ids,
attention_mask,
lm_labels,
):
model = PegasusXDecoder(config=config).to(torch_device).eval()
# create attention mask
attn_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device)
half_seq_length = input_ids.shape[-1] // 2
attn_mask[:, half_seq_length:] = 0
# first forward pass
past_key_values = model(input_ids, attention_mask=attn_mask, use_cache=True)["past_key_values"]
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
# change a random masked slice from input_ids
random_seq_idx_to_change = ids_tensor((1,), half_seq_length).item() + 1
random_other_next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size).squeeze(-1)
input_ids[:, -random_seq_idx_to_change] = random_other_next_tokens
# append to next input_ids and attn_mask
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
attn_mask = torch.cat(
[attn_mask, torch.ones((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)],
dim=1,
)
# get two different outputs
output_from_no_past = model(next_input_ids)["last_hidden_state"]
output_from_past = model(next_tokens, past_key_values=past_key_values)["last_hidden_state"]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
assert torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-2)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
attention_mask,
lm_labels,
) = config_and_inputs
inputs_dict = {
"input_ids": input_ids,
"attention_mask": attention_mask,
}
return config, inputs_dict
@require_torch
class PegasusXStandaloneDecoderModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
all_model_classes = (PegasusXDecoder,) if is_torch_available() else ()
all_generative_model_classes = ()
test_pruning = False
is_encoder_decoder = False
test_head_masking = False
def setUp(
self,
):
self.model_tester = PegasusXStandaloneDecoderModelTester(self, is_training=False)
self.config_tester = ConfigTester(self, config_class=PegasusXConfig)
def test_config(self):
self.config_tester.run_common_tests()
def test_decoder_model_past(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past(*config_and_inputs)
def test_decoder_model_attn_mask_past(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_attention_mask_past(*config_and_inputs)
def test_retain_grad_hidden_states_attentions(self):
# decoder cannot keep gradients
return
| transformers/tests/models/pegasus_x/test_modeling_pegasus_x.py/0 | {
"file_path": "transformers/tests/models/pegasus_x/test_modeling_pegasus_x.py",
"repo_id": "transformers",
"token_count": 16624
} | 164 |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model")
if is_torch_available():
from transformers.models.plbart.modeling_plbart import shift_tokens_right
EN_CODE = 50003
PYTHON_CODE = 50002
@require_sentencepiece
@require_tokenizers
class PLBartTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
tokenizer_class = PLBartTokenizer
rust_tokenizer_class = None
test_rust_tokenizer = False
def setUp(self):
super().setUp()
# We have a SentencePiece fixture for testing
tokenizer = PLBartTokenizer(SAMPLE_VOCAB, language_codes="base", keep_accents=True)
tokenizer.save_pretrained(self.tmpdirname)
def test_full_base_tokenizer(self):
tokenizer = PLBartTokenizer(SAMPLE_VOCAB, language_codes="base", keep_accents=True)
tokens = tokenizer.tokenize("This is a test")
self.assertListEqual(tokens, ["▁This", "▁is", "▁a", "▁t", "est"])
self.assertListEqual(
tokenizer.convert_tokens_to_ids(tokens),
[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]],
)
tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.")
self.assertListEqual(
tokens,
[
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"9",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"é",
".",
],
)
ids = tokenizer.convert_tokens_to_ids(tokens)
self.assertListEqual(
ids,
[
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
],
)
back_tokens = tokenizer.convert_ids_to_tokens(ids)
self.assertListEqual(
back_tokens,
[
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"<unk>",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"<unk>",
".",
],
)
end = tokenizer.vocab_size
language_tokens = [tokenizer.convert_ids_to_tokens(x) for x in range(end - 4, end)]
self.assertListEqual(language_tokens, ["__java__", "__python__", "__en_XX__", "<mask>"])
code = "java.lang.Exception, python.lang.Exception, javascript, php, ruby, go"
input_ids = tokenizer(code).input_ids
self.assertEqual(
tokenizer.decode(input_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False),
code,
)
def test_full_multi_tokenizer(self):
tokenizer = PLBartTokenizer(SAMPLE_VOCAB, language_codes="multi", keep_accents=True)
tokens = tokenizer.tokenize("This is a test")
self.assertListEqual(tokens, ["▁This", "▁is", "▁a", "▁t", "est"])
self.assertListEqual(
tokenizer.convert_tokens_to_ids(tokens),
[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]],
)
tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.")
self.assertListEqual(
tokens,
[
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"9",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"é",
".",
],
)
ids = tokenizer.convert_tokens_to_ids(tokens)
self.assertListEqual(
ids,
[
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
],
)
back_tokens = tokenizer.convert_ids_to_tokens(ids)
self.assertListEqual(
back_tokens,
[
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"<unk>",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"<unk>",
".",
],
)
end = tokenizer.vocab_size
language_tokens = [tokenizer.convert_ids_to_tokens(x) for x in range(end - 7, end)]
self.assertListEqual(
language_tokens, ["__java__", "__python__", "__en_XX__", "__javascript__", "__php__", "__ruby__", "__go__"]
)
code = "java.lang.Exception, python.lang.Exception, javascript, php, ruby, go"
input_ids = tokenizer(code).input_ids
self.assertEqual(
tokenizer.decode(input_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False),
code,
)
@require_torch
@require_sentencepiece
@require_tokenizers
class PLBartPythonEnIntegrationTest(unittest.TestCase):
checkpoint_name = "uclanlp/plbart-python-en_XX"
src_text = [
"def maximum(a,b,c):NEW_LINE_INDENTreturn max([a,b,c])",
"def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])",
]
tgt_text = [
"Returns the maximum value of a b c.",
"Sums the values of a b c.",
]
expected_src_tokens = [
134,
5452,
33460,
33441,
33463,
33465,
33463,
33449,
988,
20,
33456,
19,
33456,
771,
39,
4258,
889,
3318,
33441,
33463,
33465,
33463,
33449,
2471,
2,
PYTHON_CODE,
]
@classmethod
def setUpClass(cls):
cls.tokenizer: PLBartTokenizer = PLBartTokenizer.from_pretrained(
cls.checkpoint_name, language_codes="base", src_lang="python", tgt_lang="en_XX"
)
cls.pad_token_id = 1
return cls
def check_language_codes(self):
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__java__"], 50001)
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__python__"], 50002)
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__en_XX__"], 50003)
def test_python_en_tokenizer_batch_encode_plus(self):
ids = self.tokenizer.batch_encode_plus(self.src_text).input_ids[0]
self.assertListEqual(self.expected_src_tokens, ids)
def test_python_en_tokenizer_decode_ignores_language_codes(self):
self.assertIn(PYTHON_CODE, self.tokenizer.all_special_ids)
generated_ids = [EN_CODE, 9037, 33442, 57, 752, 153, 14, 56, 18, 9, 2]
result = self.tokenizer.decode(generated_ids, skip_special_tokens=True)
expected_english = self.tokenizer.decode(generated_ids[1:], skip_special_tokens=True)
self.assertEqual(result, expected_english)
self.assertNotIn(self.tokenizer.eos_token, result)
def test_python_en_tokenizer_truncation(self):
src_text = ["def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])" * 20]
self.assertIsInstance(src_text[0], str)
desired_max_length = 10
ids = self.tokenizer(src_text, max_length=desired_max_length, truncation=True).input_ids[0]
self.assertEqual(ids[-2], 2)
self.assertEqual(ids[-1], PYTHON_CODE)
self.assertEqual(len(ids), desired_max_length)
def test_mask_token(self):
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "__java__"]), [50004, 50001])
def test_special_tokens_unaffacted_by_save_load(self):
tmpdirname = tempfile.mkdtemp()
original_special_tokens = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(tmpdirname)
new_tok = PLBartTokenizer.from_pretrained(tmpdirname)
self.assertDictEqual(new_tok.fairseq_tokens_to_ids, original_special_tokens)
@require_torch
def test_batch_fairseq_parity(self):
batch = self.tokenizer(self.src_text, text_target=self.tgt_text, padding=True, return_tensors="pt")
batch["decoder_input_ids"] = shift_tokens_right(batch["labels"], self.tokenizer.pad_token_id)
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
self.assertEqual(batch.input_ids[1][-2:].tolist(), [2, PYTHON_CODE])
self.assertEqual(batch.decoder_input_ids[1][0], EN_CODE)
self.assertEqual(batch.decoder_input_ids[1][-1], 2)
self.assertEqual(batch.labels[1][-2:].tolist(), [2, EN_CODE])
@require_torch
def test_python_en_tokenizer_prepare_batch(self):
batch = self.tokenizer(
self.src_text,
text_target=self.tgt_text,
padding=True,
truncation=True,
max_length=len(self.expected_src_tokens),
return_tensors="pt",
)
batch["decoder_input_ids"] = shift_tokens_right(batch["labels"], self.tokenizer.pad_token_id)
self.assertIsInstance(batch, BatchEncoding)
self.assertEqual((2, 26), batch.input_ids.shape)
self.assertEqual((2, 26), batch.attention_mask.shape)
result = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens, result)
self.assertEqual(2, batch.decoder_input_ids[0, -1]) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens, [])
self.assertEqual(self.tokenizer.suffix_tokens, [self.tokenizer.eos_token_id, PYTHON_CODE])
def test_seq2seq_max_length(self):
batch = self.tokenizer(self.src_text, padding=True, truncation=True, max_length=3, return_tensors="pt")
targets = self.tokenizer(
text_target=self.tgt_text, padding=True, truncation=True, max_length=10, return_tensors="pt"
)
labels = targets["input_ids"]
batch["decoder_input_ids"] = shift_tokens_right(labels, self.tokenizer.pad_token_id)
self.assertEqual(batch.input_ids.shape[1], 3)
self.assertEqual(batch.decoder_input_ids.shape[1], 10)
@require_torch
def test_tokenizer_translation(self):
inputs = self.tokenizer._build_translation_inputs(
"A test", return_tensors="pt", src_lang="en_XX", tgt_lang="java"
)
self.assertEqual(
nested_simplify(inputs),
{
# A, test, EOS, en_XX
"input_ids": [[150, 242, 2, 50003]],
"attention_mask": [[1, 1, 1, 1]],
# java
"forced_bos_token_id": 50001,
},
)
| transformers/tests/models/plbart/test_tokenization_plbart.py/0 | {
"file_path": "transformers/tests/models/plbart/test_tokenization_plbart.py",
"repo_id": "transformers",
"token_count": 6876
} | 165 |
# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
# Copyright 2021 NVIDIA Corporation. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch QDQBERT model. """
import unittest
from transformers import QDQBertConfig, is_torch_available
from transformers.testing_utils import require_pytorch_quantization, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
QDQBertForMaskedLM,
QDQBertForMultipleChoice,
QDQBertForNextSentencePrediction,
QDQBertForQuestionAnswering,
QDQBertForSequenceClassification,
QDQBertForTokenClassification,
QDQBertLMHeadModel,
QDQBertModel,
)
from transformers.models.qdqbert.modeling_qdqbert import QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST
class QDQBertModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
def prepare_config_and_inputs(self):
# Set default quantizers before creating the model.
import pytorch_quantization.nn as quant_nn
from pytorch_quantization.tensor_quant import QuantDescriptor
# The default tensor quantizer is set to use Max calibration method
input_desc = QuantDescriptor(num_bits=8, calib_method="max")
# The default tensor quantizer is set to be per-channel quantization for weights
weight_desc = QuantDescriptor(num_bits=8, axis=((0,)))
quant_nn.QuantLinear.set_default_quant_desc_input(input_desc)
quant_nn.QuantLinear.set_default_quant_desc_weight(weight_desc)
# For the test cases, since QDQBert model is tested in one run without calibration, the quantized tensors are set as fake quantized tensors which give float type tensors in the end.
quant_nn.TensorQuantizer.use_fb_fake_quant = True
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def get_config(self):
return QDQBertConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
is_decoder=False,
initializer_range=self.initializer_range,
)
def prepare_config_and_inputs_for_decoder(self):
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = self.prepare_config_and_inputs()
config.is_decoder = True
encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def create_and_check_model(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = QDQBertModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
result = model(input_ids, token_type_ids=token_type_ids)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_model_as_decoder(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.add_cross_attention = True
model = QDQBertModel(config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
)
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
encoder_hidden_states=encoder_hidden_states,
)
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_for_causal_lm(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
model = QDQBertLMHeadModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_for_masked_lm(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = QDQBertForMaskedLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_model_for_causal_lm_as_decoder(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.add_cross_attention = True
model = QDQBertLMHeadModel(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
labels=token_labels,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
)
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
labels=token_labels,
encoder_hidden_states=encoder_hidden_states,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_decoder_model_past_large_inputs(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.is_decoder = True
config.add_cross_attention = True
model = QDQBertLMHeadModel(config=config)
model.to(torch_device)
model.eval()
# first forward pass
outputs = model(
input_ids,
attention_mask=input_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
use_cache=True,
)
past_key_values = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)
output_from_no_past = model(
next_input_ids,
attention_mask=next_attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_hidden_states=True,
)["hidden_states"][0]
output_from_past = model(
next_tokens,
attention_mask=next_attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_values=past_key_values,
output_hidden_states=True,
)["hidden_states"][0]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def create_and_check_for_next_sequence_prediction(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = QDQBertForNextSentencePrediction(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
labels=sequence_labels,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, 2))
def create_and_check_for_question_answering(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = QDQBertForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
start_positions=sequence_labels,
end_positions=sequence_labels,
)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def create_and_check_for_sequence_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = QDQBertForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_for_token_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = QDQBertForTokenClassification(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_for_multiple_choice(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_choices = self.num_choices
model = QDQBertForMultipleChoice(config=config)
model.to(torch_device)
model.eval()
multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
result = model(
multiple_choice_inputs_ids,
attention_mask=multiple_choice_input_mask,
token_type_ids=multiple_choice_token_type_ids,
labels=choice_labels,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
@require_pytorch_quantization
class QDQBertModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
QDQBertModel,
QDQBertForMaskedLM,
QDQBertForMultipleChoice,
QDQBertForNextSentencePrediction,
QDQBertForQuestionAnswering,
QDQBertForSequenceClassification,
QDQBertForTokenClassification,
QDQBertLMHeadModel,
)
if is_torch_available()
else ()
)
all_generative_model_classes = (QDQBertLMHeadModel,) if is_torch_available() else ()
pipeline_model_mapping = (
{
"feature-extraction": QDQBertModel,
"fill-mask": QDQBertForMaskedLM,
"question-answering": QDQBertForQuestionAnswering,
"text-classification": QDQBertForSequenceClassification,
"text-generation": QDQBertLMHeadModel,
"token-classification": QDQBertForTokenClassification,
"zero-shot": QDQBertForSequenceClassification,
}
if is_torch_available()
else {}
)
def setUp(self):
self.model_tester = QDQBertModelTester(self)
self.config_tester = ConfigTester(self, config_class=QDQBertConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_model_various_embeddings(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
config_and_inputs[0].position_embedding_type = type
self.model_tester.create_and_check_model(*config_and_inputs)
def test_model_as_decoder(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*config_and_inputs)
def test_model_as_decoder_with_default_input_mask(self):
# This regression test was failing with PyTorch < 1.3
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
) = self.model_tester.prepare_config_and_inputs_for_decoder()
input_mask = None
self.model_tester.create_and_check_model_as_decoder(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def test_for_causal_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_for_causal_lm(*config_and_inputs)
def test_for_masked_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
def test_for_causal_lm_decoder(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_for_causal_lm_as_decoder(*config_and_inputs)
def test_decoder_model_past_with_large_inputs(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
def test_for_multiple_choice(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
def test_for_next_sequence_prediction(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_next_sequence_prediction(*config_and_inputs)
def test_for_question_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
def test_for_sequence_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
def test_for_token_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_name in QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = QDQBertModel.from_pretrained(model_name)
self.assertIsNotNone(model)
# Override
def test_feed_forward_chunking(self):
# feed forward chunking is not supported in QDQBert
pass
@require_torch
@require_pytorch_quantization
class QDQBertModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_no_head_absolute_embedding(self):
# Set default quantizers before creating the model.
import pytorch_quantization.nn as quant_nn
from pytorch_quantization.tensor_quant import QuantDescriptor
# The default tensor quantizer is set to use Max calibration method
input_desc = QuantDescriptor(num_bits=8, calib_method="max")
# The default tensor quantizer is set to be per-channel quantization for weights
weight_desc = QuantDescriptor(num_bits=8, axis=((0,)))
quant_nn.QuantLinear.set_default_quant_desc_input(input_desc)
quant_nn.QuantLinear.set_default_quant_desc_weight(weight_desc)
model = QDQBertModel.from_pretrained("google-bert/bert-base-uncased")
input_ids = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]])
attention_mask = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
output = model(input_ids, attention_mask=attention_mask)[0]
expected_shape = torch.Size((1, 11, 768))
self.assertEqual(output.shape, expected_shape)
expected_slice = torch.tensor(
[[[0.4571, -0.0735, 0.8594], [0.2774, -0.0278, 0.8794], [0.3548, -0.0473, 0.7593]]]
)
self.assertTrue(torch.allclose(output[:, 1:4, 1:4], expected_slice, atol=1e-4))
| transformers/tests/models/qdqbert/test_modeling_qdqbert.py/0 | {
"file_path": "transformers/tests/models/qdqbert/test_modeling_qdqbert.py",
"repo_id": "transformers",
"token_count": 10441
} | 166 |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch SAM model. """
import gc
import unittest
import requests
from transformers import SamConfig, SamMaskDecoderConfig, SamPromptEncoderConfig, SamVisionConfig, pipeline
from transformers.testing_utils import backend_empty_cache, require_torch, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import SamModel, SamProcessor
from transformers.models.sam.modeling_sam import SAM_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
class SamPromptEncoderTester:
def __init__(
self,
hidden_size=32,
input_image_size=24,
patch_size=2,
mask_input_channels=4,
num_point_embeddings=4,
hidden_act="gelu",
):
self.hidden_size = hidden_size
self.input_image_size = input_image_size
self.patch_size = patch_size
self.mask_input_channels = mask_input_channels
self.num_point_embeddings = num_point_embeddings
self.hidden_act = hidden_act
def get_config(self):
return SamPromptEncoderConfig(
image_size=self.input_image_size,
patch_size=self.patch_size,
mask_input_channels=self.mask_input_channels,
hidden_size=self.hidden_size,
num_point_embeddings=self.num_point_embeddings,
hidden_act=self.hidden_act,
)
def prepare_config_and_inputs(self):
dummy_points = floats_tensor([self.batch_size, 3, 2])
config = self.get_config()
return config, dummy_points
class SamMaskDecoderTester:
def __init__(
self,
hidden_size=32,
hidden_act="relu",
mlp_dim=64,
num_hidden_layers=2,
num_attention_heads=4,
attention_downsample_rate=2,
num_multimask_outputs=3,
iou_head_depth=3,
iou_head_hidden_dim=32,
layer_norm_eps=1e-6,
):
self.hidden_size = hidden_size
self.hidden_act = hidden_act
self.mlp_dim = mlp_dim
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.attention_downsample_rate = attention_downsample_rate
self.num_multimask_outputs = num_multimask_outputs
self.iou_head_depth = iou_head_depth
self.iou_head_hidden_dim = iou_head_hidden_dim
self.layer_norm_eps = layer_norm_eps
def get_config(self):
return SamMaskDecoderConfig(
hidden_size=self.hidden_size,
hidden_act=self.hidden_act,
mlp_dim=self.mlp_dim,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
attention_downsample_rate=self.attention_downsample_rate,
num_multimask_outputs=self.num_multimask_outputs,
iou_head_depth=self.iou_head_depth,
iou_head_hidden_dim=self.iou_head_hidden_dim,
layer_norm_eps=self.layer_norm_eps,
)
def prepare_config_and_inputs(self):
config = self.get_config()
dummy_inputs = {
"image_embedding": floats_tensor([self.batch_size, self.hidden_size]),
}
return config, dummy_inputs
class SamModelTester:
def __init__(
self,
parent,
hidden_size=36,
intermediate_size=72,
projection_dim=62,
output_channels=32,
num_hidden_layers=2,
num_attention_heads=4,
num_channels=3,
image_size=24,
patch_size=2,
hidden_act="gelu",
layer_norm_eps=1e-06,
dropout=0.0,
attention_dropout=0.0,
initializer_range=0.02,
initializer_factor=1.0,
qkv_bias=True,
mlp_ratio=4.0,
use_abs_pos=True,
use_rel_pos=True,
rel_pos_zero_init=False,
window_size=14,
global_attn_indexes=[2, 5, 8, 11],
num_pos_feats=16,
mlp_dim=None,
batch_size=2,
):
self.parent = parent
self.image_size = image_size
self.patch_size = patch_size
self.output_channels = output_channels
self.num_channels = num_channels
self.hidden_size = hidden_size
self.projection_dim = projection_dim
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.dropout = dropout
self.attention_dropout = attention_dropout
self.initializer_range = initializer_range
self.initializer_factor = initializer_factor
self.hidden_act = hidden_act
self.layer_norm_eps = layer_norm_eps
self.qkv_bias = qkv_bias
self.mlp_ratio = mlp_ratio
self.use_abs_pos = use_abs_pos
self.use_rel_pos = use_rel_pos
self.rel_pos_zero_init = rel_pos_zero_init
self.window_size = window_size
self.global_attn_indexes = global_attn_indexes
self.num_pos_feats = num_pos_feats
self.mlp_dim = mlp_dim
self.batch_size = batch_size
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
num_patches = (image_size // patch_size) ** 2
self.seq_length = num_patches + 1
self.prompt_encoder_tester = SamPromptEncoderTester()
self.mask_decoder_tester = SamMaskDecoderTester()
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
config = self.get_config()
return config, pixel_values
def get_config(self):
vision_config = SamVisionConfig(
image_size=self.image_size,
patch_size=self.patch_size,
num_channels=self.num_channels,
hidden_size=self.hidden_size,
projection_dim=self.projection_dim,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
dropout=self.dropout,
attention_dropout=self.attention_dropout,
initializer_range=self.initializer_range,
initializer_factor=self.initializer_factor,
output_channels=self.output_channels,
qkv_bias=self.qkv_bias,
mlp_ratio=self.mlp_ratio,
use_abs_pos=self.use_abs_pos,
use_rel_pos=self.use_rel_pos,
rel_pos_zero_init=self.rel_pos_zero_init,
window_size=self.window_size,
global_attn_indexes=self.global_attn_indexes,
num_pos_feats=self.num_pos_feats,
mlp_dim=self.mlp_dim,
)
prompt_encoder_config = self.prompt_encoder_tester.get_config()
mask_decoder_config = self.mask_decoder_tester.get_config()
return SamConfig(
vision_config=vision_config,
prompt_encoder_config=prompt_encoder_config,
mask_decoder_config=mask_decoder_config,
)
def create_and_check_model(self, config, pixel_values):
model = SamModel(config=config)
model.to(torch_device)
model.eval()
with torch.no_grad():
result = model(pixel_values)
self.parent.assertEqual(result.iou_scores.shape, (self.batch_size, 1, 3))
self.parent.assertEqual(result.pred_masks.shape[:3], (self.batch_size, 1, 3))
def create_and_check_get_image_features(self, config, pixel_values):
model = SamModel(config=config)
model.to(torch_device)
model.eval()
with torch.no_grad():
result = model.get_image_embeddings(pixel_values)
self.parent.assertEqual(result[0].shape, (self.output_channels, 12, 12))
def create_and_check_get_image_hidden_states(self, config, pixel_values):
model = SamModel(config=config)
model.to(torch_device)
model.eval()
with torch.no_grad():
result = model.vision_encoder(
pixel_values,
output_hidden_states=True,
return_dict=True,
)
# after computing the convolutional features
expected_hidden_states_shape = (self.batch_size, 12, 12, 36)
self.parent.assertEqual(len(result[1]), self.num_hidden_layers + 1)
self.parent.assertEqual(result[1][0].shape, expected_hidden_states_shape)
with torch.no_grad():
result = model.vision_encoder(
pixel_values,
output_hidden_states=True,
return_dict=False,
)
# after computing the convolutional features
expected_hidden_states_shape = (self.batch_size, 12, 12, 36)
self.parent.assertEqual(len(result[1]), self.num_hidden_layers + 1)
self.parent.assertEqual(result[1][0].shape, expected_hidden_states_shape)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, pixel_values = config_and_inputs
inputs_dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class SamModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
"""
Here we also overwrite some of the tests of test_modeling_common.py, as SAM's vision encoder does not use input_ids, inputs_embeds,
attention_mask and seq_length.
"""
all_model_classes = (SamModel,) if is_torch_available() else ()
pipeline_model_mapping = (
{"feature-extraction": SamModel, "mask-generation": SamModel} if is_torch_available() else {}
)
fx_compatible = False
test_pruning = False
test_resize_embeddings = False
test_head_masking = False
test_torchscript = False
# TODO: Fix me @Arthur: `run_batch_test` in `tests/test_pipeline_mixin.py` not working
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
return True
def setUp(self):
self.model_tester = SamModelTester(self)
self.vision_config_tester = ConfigTester(self, config_class=SamVisionConfig, has_text_modality=False)
self.prompt_encoder_config_tester = ConfigTester(
self,
config_class=SamPromptEncoderConfig,
has_text_modality=False,
num_attention_heads=12,
num_hidden_layers=2,
)
self.mask_decoder_config_tester = ConfigTester(
self, config_class=SamMaskDecoderConfig, has_text_modality=False
)
def test_config(self):
self.vision_config_tester.run_common_tests()
self.prompt_encoder_config_tester.run_common_tests()
self.mask_decoder_config_tester.run_common_tests()
@unittest.skip(reason="SAM's vision encoder does not use inputs_embeds")
def test_inputs_embeds(self):
pass
def test_model_common_attributes(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
self.assertIsInstance(model.get_input_embeddings(), (nn.Module))
x = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(x, nn.Linear))
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_get_image_features(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_get_image_features(*config_and_inputs)
def test_image_hidden_states(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_get_image_hidden_states(*config_and_inputs)
def test_attention_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
expected_vision_attention_shape = (
self.model_tester.batch_size * self.model_tester.num_attention_heads,
196,
196,
)
expected_mask_decoder_attention_shape = (self.model_tester.batch_size, 1, 144, 32)
for model_class in self.all_model_classes:
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = False
config.return_dict = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
vision_attentions = outputs.vision_attentions
self.assertEqual(len(vision_attentions), self.model_tester.num_hidden_layers)
mask_decoder_attentions = outputs.mask_decoder_attentions
self.assertEqual(len(mask_decoder_attentions), self.model_tester.mask_decoder_tester.num_hidden_layers)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
config.output_attentions = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
vision_attentions = outputs.vision_attentions
self.assertEqual(len(vision_attentions), self.model_tester.num_hidden_layers)
mask_decoder_attentions = outputs.mask_decoder_attentions
self.assertEqual(len(mask_decoder_attentions), self.model_tester.mask_decoder_tester.num_hidden_layers)
self.assertListEqual(
list(vision_attentions[0].shape[-4:]),
list(expected_vision_attention_shape),
)
self.assertListEqual(
list(mask_decoder_attentions[0].shape[-4:]),
list(expected_mask_decoder_attention_shape),
)
@unittest.skip(reason="SamModel does not support training")
def test_training(self):
pass
@unittest.skip(reason="SamModel does not support training")
def test_training_gradient_checkpointing(self):
pass
@unittest.skip(
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
)
def test_training_gradient_checkpointing_use_reentrant(self):
pass
@unittest.skip(
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
)
def test_training_gradient_checkpointing_use_reentrant_false(self):
pass
@unittest.skip(reason="SamModel has no base class and is not available in MODEL_MAPPING")
def test_save_load_fast_init_from_base(self):
pass
@unittest.skip(reason="SamModel has no base class and is not available in MODEL_MAPPING")
def test_save_load_fast_init_to_base(self):
pass
@unittest.skip(reason="SamModel does not support training")
def test_retain_grad_hidden_states_attentions(self):
pass
@unittest.skip(reason="Hidden_states is tested in create_and_check_model tests")
def test_hidden_states_output(self):
pass
def check_pt_tf_outputs(self, tf_outputs, pt_outputs, model_class, tol=5e-5, name="outputs", attributes=None):
# Use a slightly higher default tol to make the tests non-flaky
super().check_pt_tf_outputs(tf_outputs, pt_outputs, model_class, tol=tol, name=name, attributes=attributes)
@slow
def test_model_from_pretrained(self):
for model_name in SAM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = SamModel.from_pretrained(model_name)
self.assertIsNotNone(model)
def prepare_image():
img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
return raw_image
def prepare_dog_img():
img_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/dog-sam.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
return raw_image
@slow
class SamModelIntegrationTest(unittest.TestCase):
def tearDown(self):
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
gc.collect()
backend_empty_cache(torch_device)
def test_inference_mask_generation_no_point(self):
model = SamModel.from_pretrained("facebook/sam-vit-base")
processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
model.to(torch_device)
model.eval()
raw_image = prepare_image()
inputs = processor(images=raw_image, return_tensors="pt").to(torch_device)
with torch.no_grad():
outputs = model(**inputs)
scores = outputs.iou_scores.squeeze()
masks = outputs.pred_masks[0, 0, 0, 0, :3]
self.assertTrue(torch.allclose(scores[-1], torch.tensor(0.4515), atol=2e-4))
self.assertTrue(torch.allclose(masks, torch.tensor([-4.1800, -3.4948, -3.4481]).to(torch_device), atol=2e-4))
def test_inference_mask_generation_one_point_one_bb(self):
model = SamModel.from_pretrained("facebook/sam-vit-base")
processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
model.to(torch_device)
model.eval()
raw_image = prepare_image()
input_boxes = [[[650, 900, 1000, 1250]]]
input_points = [[[820, 1080]]]
inputs = processor(
images=raw_image, input_boxes=input_boxes, input_points=input_points, return_tensors="pt"
).to(torch_device)
with torch.no_grad():
outputs = model(**inputs)
scores = outputs.iou_scores.squeeze()
masks = outputs.pred_masks[0, 0, 0, 0, :3]
self.assertTrue(torch.allclose(scores[-1], torch.tensor(0.9566), atol=2e-4))
self.assertTrue(
torch.allclose(masks, torch.tensor([-12.7729, -12.3665, -12.6061]).to(torch_device), atol=2e-4)
)
def test_inference_mask_generation_batched_points_batched_images(self):
model = SamModel.from_pretrained("facebook/sam-vit-base")
processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
model.to(torch_device)
model.eval()
raw_image = prepare_image()
input_points = [
[[[820, 1080]], [[820, 1080]], [[820, 1080]], [[820, 1080]]],
[[[510, 1080]], [[820, 1080]], [[820, 1080]], [[820, 1080]]],
]
inputs = processor(images=[raw_image, raw_image], input_points=input_points, return_tensors="pt").to(
torch_device
)
with torch.no_grad():
outputs = model(**inputs)
scores = outputs.iou_scores.squeeze().cpu()
masks = outputs.pred_masks[0, 0, 0, 0, :3].cpu()
EXPECTED_SCORES = torch.tensor(
[
[
[0.6765, 0.9379, 0.8803],
[0.6765, 0.9379, 0.8803],
[0.6765, 0.9379, 0.8803],
[0.6765, 0.9379, 0.8803],
],
[
[0.3317, 0.7264, 0.7646],
[0.6765, 0.9379, 0.8803],
[0.6765, 0.9379, 0.8803],
[0.6765, 0.9379, 0.8803],
],
]
)
EXPECTED_MASKS = torch.tensor([-2.8550, -2.7988, -2.9625])
self.assertTrue(torch.allclose(scores, EXPECTED_SCORES, atol=1e-3))
self.assertTrue(torch.allclose(masks, EXPECTED_MASKS, atol=1e-3))
def test_inference_mask_generation_one_point_one_bb_zero(self):
model = SamModel.from_pretrained("facebook/sam-vit-base")
processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
model.to(torch_device)
model.eval()
raw_image = prepare_image()
input_boxes = [[[620, 900, 1000, 1255]]]
input_points = [[[820, 1080]]]
labels = [[0]]
inputs = processor(
images=raw_image,
input_boxes=input_boxes,
input_points=input_points,
input_labels=labels,
return_tensors="pt",
).to(torch_device)
with torch.no_grad():
outputs = model(**inputs)
scores = outputs.iou_scores.squeeze()
self.assertTrue(torch.allclose(scores[-1], torch.tensor(0.7894), atol=1e-4))
def test_inference_mask_generation_one_point(self):
model = SamModel.from_pretrained("facebook/sam-vit-base")
processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
model.to(torch_device)
model.eval()
raw_image = prepare_image()
input_points = [[[400, 650]]]
input_labels = [[1]]
inputs = processor(
images=raw_image, input_points=input_points, input_labels=input_labels, return_tensors="pt"
).to(torch_device)
with torch.no_grad():
outputs = model(**inputs)
scores = outputs.iou_scores.squeeze()
self.assertTrue(torch.allclose(scores[-1], torch.tensor(0.9675), atol=1e-4))
# With no label
input_points = [[[400, 650]]]
inputs = processor(images=raw_image, input_points=input_points, return_tensors="pt").to(torch_device)
with torch.no_grad():
outputs = model(**inputs)
scores = outputs.iou_scores.squeeze()
self.assertTrue(torch.allclose(scores[-1], torch.tensor(0.9675), atol=1e-4))
def test_inference_mask_generation_two_points(self):
model = SamModel.from_pretrained("facebook/sam-vit-base")
processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
model.to(torch_device)
model.eval()
raw_image = prepare_image()
input_points = [[[400, 650], [800, 650]]]
input_labels = [[1, 1]]
inputs = processor(
images=raw_image, input_points=input_points, input_labels=input_labels, return_tensors="pt"
).to(torch_device)
with torch.no_grad():
outputs = model(**inputs)
scores = outputs.iou_scores.squeeze()
self.assertTrue(torch.allclose(scores[-1], torch.tensor(0.9762), atol=1e-4))
# no labels
inputs = processor(images=raw_image, input_points=input_points, return_tensors="pt").to(torch_device)
with torch.no_grad():
outputs = model(**inputs)
scores = outputs.iou_scores.squeeze()
self.assertTrue(torch.allclose(scores[-1], torch.tensor(0.9762), atol=1e-4))
def test_inference_mask_generation_two_points_batched(self):
model = SamModel.from_pretrained("facebook/sam-vit-base")
processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
model.to(torch_device)
model.eval()
raw_image = prepare_image()
input_points = [[[400, 650], [800, 650]], [[400, 650]]]
input_labels = [[1, 1], [1]]
inputs = processor(
images=[raw_image, raw_image], input_points=input_points, input_labels=input_labels, return_tensors="pt"
).to(torch_device)
with torch.no_grad():
outputs = model(**inputs)
scores = outputs.iou_scores.squeeze()
self.assertTrue(torch.allclose(scores[0][-1], torch.tensor(0.9762), atol=1e-4))
self.assertTrue(torch.allclose(scores[1][-1], torch.tensor(0.9637), atol=1e-4))
def test_inference_mask_generation_one_box(self):
model = SamModel.from_pretrained("facebook/sam-vit-base")
processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
model.to(torch_device)
model.eval()
raw_image = prepare_image()
input_boxes = [[[75, 275, 1725, 850]]]
inputs = processor(images=raw_image, input_boxes=input_boxes, return_tensors="pt").to(torch_device)
with torch.no_grad():
outputs = model(**inputs)
scores = outputs.iou_scores.squeeze()
self.assertTrue(torch.allclose(scores[-1], torch.tensor(0.7937), atol=1e-4))
def test_inference_mask_generation_batched_image_one_point(self):
model = SamModel.from_pretrained("facebook/sam-vit-base")
processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
model.to(torch_device)
model.eval()
raw_image = prepare_image()
raw_dog_image = prepare_dog_img()
input_points = [[[820, 1080]], [[220, 470]]]
inputs = processor(images=[raw_image, raw_dog_image], input_points=input_points, return_tensors="pt").to(
torch_device
)
with torch.no_grad():
outputs = model(**inputs)
scores_batched = outputs.iou_scores.squeeze()
input_points = [[[220, 470]]]
inputs = processor(images=raw_dog_image, input_points=input_points, return_tensors="pt").to(torch_device)
with torch.no_grad():
outputs = model(**inputs)
scores_single = outputs.iou_scores.squeeze()
self.assertTrue(torch.allclose(scores_batched[1, :], scores_single, atol=1e-4))
def test_inference_mask_generation_two_points_point_batch(self):
model = SamModel.from_pretrained("facebook/sam-vit-base")
processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
model.to(torch_device)
model.eval()
raw_image = prepare_image()
input_points = torch.Tensor([[[400, 650]], [[220, 470]]]).cpu() # fmt: skip
input_points = input_points.unsqueeze(0)
inputs = processor(raw_image, input_points=input_points, return_tensors="pt").to(torch_device)
with torch.no_grad():
outputs = model(**inputs)
iou_scores = outputs.iou_scores.cpu()
self.assertTrue(iou_scores.shape == (1, 2, 3))
torch.testing.assert_allclose(
iou_scores, torch.tensor([[[0.9105, 0.9825, 0.9675], [0.7646, 0.7943, 0.7774]]]), atol=1e-4, rtol=1e-4
)
def test_inference_mask_generation_three_boxes_point_batch(self):
model = SamModel.from_pretrained("facebook/sam-vit-base")
processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
model.to(torch_device)
model.eval()
raw_image = prepare_image()
# fmt: off
input_boxes = torch.Tensor([[[620, 900, 1000, 1255]], [[75, 275, 1725, 850]], [[75, 275, 1725, 850]]]).cpu()
EXPECTED_IOU = torch.tensor([[[0.9773, 0.9881, 0.9522],
[0.5996, 0.7661, 0.7937],
[0.5996, 0.7661, 0.7937]]])
# fmt: on
input_boxes = input_boxes.unsqueeze(0)
inputs = processor(raw_image, input_boxes=input_boxes, return_tensors="pt").to(torch_device)
with torch.no_grad():
outputs = model(**inputs)
iou_scores = outputs.iou_scores.cpu()
self.assertTrue(iou_scores.shape == (1, 3, 3))
torch.testing.assert_allclose(iou_scores, EXPECTED_IOU, atol=1e-4, rtol=1e-4)
def test_dummy_pipeline_generation(self):
generator = pipeline("mask-generation", model="facebook/sam-vit-base", device=torch_device)
raw_image = prepare_image()
_ = generator(raw_image, points_per_batch=64)
| transformers/tests/models/sam/test_modeling_sam.py/0 | {
"file_path": "transformers/tests/models/sam/test_modeling_sam.py",
"repo_id": "transformers",
"token_count": 13033
} | 167 |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch Table Transformer model. """
import inspect
import math
import unittest
from huggingface_hub import hf_hub_download
from transformers import ResNetConfig, TableTransformerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_timm, require_torch, require_vision, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import TableTransformerForObjectDetection, TableTransformerModel
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class TableTransformerModelTester:
def __init__(
self,
parent,
batch_size=8,
is_training=True,
use_labels=True,
hidden_size=32,
num_hidden_layers=2,
num_attention_heads=8,
intermediate_size=4,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
num_queries=12,
num_channels=3,
min_size=200,
max_size=200,
n_targets=8,
num_labels=3,
):
self.parent = parent
self.batch_size = batch_size
self.is_training = is_training
self.use_labels = use_labels
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.num_queries = num_queries
self.num_channels = num_channels
self.min_size = min_size
self.max_size = max_size
self.n_targets = n_targets
self.num_labels = num_labels
# we also set the expected seq length for both encoder and decoder
self.encoder_seq_length = math.ceil(self.min_size / 32) * math.ceil(self.max_size / 32)
self.decoder_seq_length = self.num_queries
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size])
pixel_mask = torch.ones([self.batch_size, self.min_size, self.max_size], device=torch_device)
labels = None
if self.use_labels:
# labels is a list of Dict (each Dict being the labels for a given example in the batch)
labels = []
for i in range(self.batch_size):
target = {}
target["class_labels"] = torch.randint(
high=self.num_labels, size=(self.n_targets,), device=torch_device
)
target["boxes"] = torch.rand(self.n_targets, 4, device=torch_device)
target["masks"] = torch.rand(self.n_targets, self.min_size, self.max_size, device=torch_device)
labels.append(target)
config = self.get_config()
return config, pixel_values, pixel_mask, labels
def get_config(self):
resnet_config = ResNetConfig(
num_channels=3,
embeddings_size=10,
hidden_sizes=[10, 20, 30, 40],
depths=[1, 1, 2, 1],
hidden_act="relu",
num_labels=3,
out_features=["stage2", "stage3", "stage4"],
out_indices=[2, 3, 4],
)
return TableTransformerConfig(
d_model=self.hidden_size,
encoder_layers=self.num_hidden_layers,
decoder_layers=self.num_hidden_layers,
encoder_attention_heads=self.num_attention_heads,
decoder_attention_heads=self.num_attention_heads,
encoder_ffn_dim=self.intermediate_size,
decoder_ffn_dim=self.intermediate_size,
dropout=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
num_queries=self.num_queries,
num_labels=self.num_labels,
use_timm_backbone=False,
backbone_config=resnet_config,
backbone=None,
use_pretrained_backbone=False,
)
def prepare_config_and_inputs_for_common(self):
config, pixel_values, pixel_mask, labels = self.prepare_config_and_inputs()
inputs_dict = {"pixel_values": pixel_values, "pixel_mask": pixel_mask}
return config, inputs_dict
def create_and_check_table_transformer_model(self, config, pixel_values, pixel_mask, labels):
model = TableTransformerModel(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values=pixel_values, pixel_mask=pixel_mask)
result = model(pixel_values)
self.parent.assertEqual(
result.last_hidden_state.shape, (self.batch_size, self.decoder_seq_length, self.hidden_size)
)
def create_and_check_table_transformer_object_detection_head_model(self, config, pixel_values, pixel_mask, labels):
model = TableTransformerForObjectDetection(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values=pixel_values, pixel_mask=pixel_mask)
result = model(pixel_values)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_queries, self.num_labels + 1))
self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_queries, 4))
result = model(pixel_values=pixel_values, pixel_mask=pixel_mask, labels=labels)
self.parent.assertEqual(result.loss.shape, ())
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_queries, self.num_labels + 1))
self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_queries, 4))
def create_and_check_table_transformer_no_timm_backbone(self, config, pixel_values, pixel_mask, labels):
config.use_timm_backbone = False
config.backbone_config = ResNetConfig()
model = TableTransformerForObjectDetection(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values=pixel_values, pixel_mask=pixel_mask)
result = model(pixel_values)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_queries, self.num_labels + 1))
self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_queries, 4))
result = model(pixel_values=pixel_values, pixel_mask=pixel_mask, labels=labels)
self.parent.assertEqual(result.loss.shape, ())
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_queries, self.num_labels + 1))
self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_queries, 4))
@require_torch
class TableTransformerModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
TableTransformerModel,
TableTransformerForObjectDetection,
)
if is_torch_available()
else ()
)
pipeline_model_mapping = (
{"image-feature-extraction": TableTransformerModel, "object-detection": TableTransformerForObjectDetection}
if is_torch_available()
else {}
)
is_encoder_decoder = True
test_torchscript = False
test_pruning = False
test_head_masking = False
test_missing_keys = False
# special case for head models
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
if return_labels:
if model_class.__name__ in ["TableTransformerForObjectDetection"]:
labels = []
for i in range(self.model_tester.batch_size):
target = {}
target["class_labels"] = torch.ones(
size=(self.model_tester.n_targets,), device=torch_device, dtype=torch.long
)
target["boxes"] = torch.ones(
self.model_tester.n_targets, 4, device=torch_device, dtype=torch.float
)
target["masks"] = torch.ones(
self.model_tester.n_targets,
self.model_tester.min_size,
self.model_tester.max_size,
device=torch_device,
dtype=torch.float,
)
labels.append(target)
inputs_dict["labels"] = labels
return inputs_dict
def setUp(self):
self.model_tester = TableTransformerModelTester(self)
self.config_tester = ConfigTester(self, config_class=TableTransformerConfig, has_text_modality=False)
def test_config(self):
self.config_tester.run_common_tests()
def test_table_transformer_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_table_transformer_model(*config_and_inputs)
def test_table_transformer_object_detection_head_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_table_transformer_object_detection_head_model(*config_and_inputs)
def test_table_transformer_no_timm_backbone(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_table_transformer_no_timm_backbone(*config_and_inputs)
@unittest.skip(reason="Table Transformer does not use inputs_embeds")
def test_inputs_embeds(self):
pass
@unittest.skip(reason="Table Transformer does not have a get_input_embeddings method")
def test_model_common_attributes(self):
pass
@unittest.skip(reason="Table Transformer is not a generative model")
def test_generate_without_input_ids(self):
pass
@unittest.skip(reason="Table Transformer does not use token embeddings")
def test_resize_tokens_embeddings(self):
pass
@slow
def test_model_outputs_equivalence(self):
# TODO Niels: fix me!
pass
def test_attention_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
decoder_seq_length = self.model_tester.decoder_seq_length
encoder_seq_length = self.model_tester.encoder_seq_length
decoder_key_length = self.model_tester.decoder_seq_length
encoder_key_length = self.model_tester.encoder_seq_length
for model_class in self.all_model_classes:
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = False
config.return_dict = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
config.output_attentions = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
)
out_len = len(outputs)
if self.is_encoder_decoder:
correct_outlen = 5
# loss is at first position
if "labels" in inputs_dict:
correct_outlen += 1 # loss is added to beginning
# Object Detection model returns pred_logits and pred_boxes
if model_class.__name__ == "TableTransformerForObjectDetection":
correct_outlen += 2
if "past_key_values" in outputs:
correct_outlen += 1 # past_key_values have been returned
self.assertEqual(out_len, correct_outlen)
# decoder attentions
decoder_attentions = outputs.decoder_attentions
self.assertIsInstance(decoder_attentions, (list, tuple))
self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(decoder_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length],
)
# cross attentions
cross_attentions = outputs.cross_attentions
self.assertIsInstance(cross_attentions, (list, tuple))
self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(cross_attentions[0].shape[-3:]),
[
self.model_tester.num_attention_heads,
decoder_seq_length,
encoder_key_length,
],
)
# Check attention is always last and order is fine
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
if hasattr(self.model_tester, "num_hidden_states_types"):
added_hidden_states = self.model_tester.num_hidden_states_types
elif self.is_encoder_decoder:
added_hidden_states = 2
else:
added_hidden_states = 1
self.assertEqual(out_len + added_hidden_states, len(outputs))
self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(self_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
)
def test_retain_grad_hidden_states_attentions(self):
# removed retain_grad and grad on decoder_hidden_states, as queries don't require grad
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.output_hidden_states = True
config.output_attentions = True
# no need to test all models as different heads yield the same functionality
model_class = self.all_model_classes[0]
model = model_class(config)
model.to(torch_device)
inputs = self._prepare_for_class(inputs_dict, model_class)
outputs = model(**inputs)
output = outputs[0]
encoder_hidden_states = outputs.encoder_hidden_states[0]
encoder_attentions = outputs.encoder_attentions[0]
encoder_hidden_states.retain_grad()
encoder_attentions.retain_grad()
decoder_attentions = outputs.decoder_attentions[0]
decoder_attentions.retain_grad()
cross_attentions = outputs.cross_attentions[0]
cross_attentions.retain_grad()
output.flatten()[0].backward(retain_graph=True)
self.assertIsNotNone(encoder_hidden_states.grad)
self.assertIsNotNone(encoder_attentions.grad)
self.assertIsNotNone(decoder_attentions.grad)
self.assertIsNotNone(cross_attentions.grad)
def test_forward_auxiliary_loss(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.auxiliary_loss = True
# only test for object detection and segmentation model
for model_class in self.all_model_classes[1:]:
model = model_class(config)
model.to(torch_device)
inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
outputs = model(**inputs)
self.assertIsNotNone(outputs.auxiliary_outputs)
self.assertEqual(len(outputs.auxiliary_outputs), self.model_tester.num_hidden_layers - 1)
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
if model.config.is_encoder_decoder:
expected_arg_names = ["pixel_values", "pixel_mask"]
expected_arg_names.extend(
["head_mask", "decoder_head_mask", "encoder_outputs"]
if "head_mask" and "decoder_head_mask" in arg_names
else []
)
self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)
else:
expected_arg_names = ["pixel_values", "pixel_mask"]
self.assertListEqual(arg_names[:1], expected_arg_names)
def test_different_timm_backbone(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
# let's pick a random timm backbone
config.backbone = "tf_mobilenetv3_small_075"
for model_class in self.all_model_classes:
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
if model_class.__name__ == "TableTransformerForObjectDetection":
expected_shape = (
self.model_tester.batch_size,
self.model_tester.num_queries,
self.model_tester.num_labels + 1,
)
self.assertEqual(outputs.logits.shape, expected_shape)
self.assertTrue(outputs)
def test_greyscale_images(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
# use greyscale pixel values
inputs_dict["pixel_values"] = floats_tensor(
[self.model_tester.batch_size, 1, self.model_tester.min_size, self.model_tester.max_size]
)
# let's set num_channels to 1
config.num_channels = 1
config.backbone_config.num_channels = 1
for model_class in self.all_model_classes:
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
self.assertTrue(outputs)
def test_initialization(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
configs_no_init = _config_zero_init(config)
configs_no_init.init_xavier_std = 1e9
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
for name, param in model.named_parameters():
if param.requires_grad:
if "bbox_attention" in name and "bias" not in name:
self.assertLess(
100000,
abs(param.data.max().item()),
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
else:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item(),
[0.0, 1.0],
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
TOLERANCE = 1e-4
# We will verify our results on an image of cute cats
def prepare_img():
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
return image
@require_timm
@require_vision
@slow
class TableTransformerModelIntegrationTests(unittest.TestCase):
def test_table_detection(self):
image_processor = AutoImageProcessor.from_pretrained("microsoft/table-transformer-detection")
model = TableTransformerForObjectDetection.from_pretrained("microsoft/table-transformer-detection")
model.to(torch_device)
file_path = hf_hub_download(repo_id="nielsr/example-pdf", repo_type="dataset", filename="example_pdf.png")
image = Image.open(file_path).convert("RGB")
inputs = image_processor(image, return_tensors="pt").to(torch_device)
# forward pass
with torch.no_grad():
outputs = model(**inputs)
expected_shape = (1, 15, 3)
self.assertEqual(outputs.logits.shape, expected_shape)
expected_logits = torch.tensor(
[[-6.7329, -16.9590, 6.7447], [-8.0038, -22.3071, 6.9288], [-7.2445, -20.9855, 7.3465]],
device=torch_device,
)
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3], expected_logits, atol=1e-4))
expected_boxes = torch.tensor(
[[0.4868, 0.1764, 0.6729], [0.6674, 0.4621, 0.3864], [0.4720, 0.1757, 0.6362]], device=torch_device
)
self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3], expected_boxes, atol=1e-3))
| transformers/tests/models/table_transformer/test_modeling_table_transformer.py/0 | {
"file_path": "transformers/tests/models/table_transformer/test_modeling_table_transformer.py",
"repo_id": "transformers",
"token_count": 10595
} | 168 |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch TVLT model. """
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import (
TvltConfig,
is_datasets_available,
is_speech_available,
is_torch_available,
is_vision_available,
)
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
import torch.nn as nn
from transformers import TvltForAudioVisualClassification, TvltForPreTraining, TvltModel
from transformers.models.tvlt.modeling_tvlt import TVLT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_datasets_available():
from datasets import load_dataset
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_available():
from transformers import TvltFeatureExtractor
class TvltModelTester:
def __init__(
self,
parent,
batch_size=2,
image_size=32,
spectrogram_length=32,
frequency_length=16,
image_patch_size=[2, 2],
audio_patch_size=[2, 2],
num_image_channels=3,
num_audio_channels=1,
num_frames=2,
hidden_size=32,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=128,
hidden_act="gelu",
hidden_dropout_prob=0.0,
attention_probs_dropout_prob=0.0,
initializer_range=0.02,
layer_norm_eps=1e-12,
qkv_bias=True,
use_mean_pooling=True,
decoder_num_attention_heads=4,
decoder_hidden_size=32,
decoder_num_hidden_layers=2,
decoder_intermediate_size=128,
image_mask_ratio=0.75,
audio_mask_ratio=0.15,
audio_mask_type="frame-level",
task_matching=True,
task_mae=True,
num_labels=1,
is_training=True,
):
self.parent = parent
self.batch_size = batch_size
self.image_size = image_size
self.spectrogram_length = spectrogram_length
self.frequency_length = frequency_length
self.image_patch_size = image_patch_size
self.audio_patch_size = audio_patch_size
self.num_image_channels = num_image_channels
self.num_audio_channels = num_audio_channels
self.num_frames = num_frames
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.qkv_bias = qkv_bias
self.use_mean_pooling = use_mean_pooling
self.decoder_num_attention_heads = decoder_num_attention_heads
self.decoder_hidden_size = decoder_hidden_size
self.decoder_num_hidden_layers = decoder_num_hidden_layers
self.decoder_intermediate_size = decoder_intermediate_size
self.image_mask_ratio = image_mask_ratio
self.audio_mask_ratio = audio_mask_ratio
self.task_matching = task_matching
self.task_mae = task_mae
self.num_labels = num_labels
self.expected_pixel_seq_len = (self.image_size // self.image_patch_size[0]) ** 2 * self.num_frames
self.expected_audio_seq_len = (self.spectrogram_length // self.audio_patch_size[0]) * (
self.frequency_length // self.audio_patch_size[1]
)
# we set the expected sequence length (which is used in several tests)
# this is equal to the seq length of number of image/video patches + number of audio patches
self.expected_seq_len = self.expected_pixel_seq_len + self.expected_audio_seq_len + 1
self.image_mae_output_dim = image_patch_size[0] ** 2 * num_image_channels
self.audio_mae_output_dim = audio_patch_size[0] * audio_patch_size[1] * num_audio_channels
self.is_training = is_training
def prepare_config_and_inputs(self):
pixel_values = floats_tensor(
[self.batch_size, self.num_frames, self.num_image_channels, self.image_size, self.image_size]
)
audio_values = floats_tensor(
[self.batch_size, self.num_audio_channels, self.spectrogram_length, self.frequency_length]
)
pixel_mask = floats_tensor([self.batch_size, self.expected_pixel_seq_len])
audio_mask = floats_tensor([self.batch_size, self.expected_audio_seq_len])
config = self.get_config()
return (config, pixel_values, audio_values, pixel_mask, audio_mask)
def prepare_config_and_inputs_for_pretraining(self):
pixel_values = floats_tensor(
[self.batch_size, self.num_frames, self.num_image_channels, self.image_size, self.image_size]
)
audio_values = floats_tensor(
[self.batch_size, self.num_audio_channels, self.spectrogram_length, self.frequency_length]
)
pixel_mask = floats_tensor([self.batch_size, self.expected_pixel_seq_len])
audio_mask = floats_tensor([self.batch_size, self.expected_audio_seq_len])
pixel_values_mixed = floats_tensor(
[self.batch_size, self.num_frames, self.num_image_channels, self.image_size, self.image_size]
)
pixel_mask_mixed = floats_tensor([self.batch_size, self.expected_pixel_seq_len])
labels = floats_tensor([self.batch_size])
config = self.get_config()
return (
config,
pixel_values,
audio_values,
pixel_mask,
audio_mask,
pixel_values_mixed,
pixel_mask_mixed,
labels,
)
def get_config(self):
return TvltConfig(
image_size=self.image_size,
spectrogram_length=self.spectrogram_length,
frequency_length=self.frequency_length,
image_patch_size=self.image_patch_size,
audio_patch_size=self.audio_patch_size,
num_image_channels=self.num_image_channels,
num_audio_channels=self.num_audio_channels,
num_frames=self.num_frames,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
initializer_range=self.initializer_range,
layer_norm_eps=self.layer_norm_eps,
qkv_bias=self.qkv_bias,
use_mean_pooling=self.use_mean_pooling,
decoder_num_attention_heads=self.decoder_num_attention_heads,
decoder_hidden_size=self.decoder_hidden_size,
decoder_num_hidden_layers=self.decoder_num_hidden_layers,
decoder_intermediate_size=self.decoder_intermediate_size,
image_mask_ratio=self.image_mask_ratio,
audio_mask_ratio=self.audio_mask_ratio,
task_matching=self.task_matching,
task_mae=self.task_mae,
num_labels=self.num_labels,
)
def create_and_check_model(self, config, pixel_values, audio_values, pixel_mask, audio_mask):
model = TvltModel(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values, audio_values, pixel_mask=pixel_mask, audio_mask=audio_mask)
result = model(pixel_values, audio_values)
self.parent.assertEqual(
result.last_hidden_state.shape, (self.batch_size, self.expected_seq_len, self.hidden_size)
)
def create_and_check_for_audiovisual_classification(
self, config, pixel_values, audio_values, pixel_mask, audio_mask
):
model = TvltForAudioVisualClassification(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values, audio_values, pixel_mask=pixel_mask, audio_mask=audio_mask)
result = model(pixel_values, audio_values)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_for_pretraining(
self,
config,
pixel_values,
audio_values,
pixel_mask,
audio_mask,
pixel_values_mixed,
pixel_mask_mixed,
labels,
):
model = TvltForPreTraining(config=config)
model.to(torch_device)
model.train()
result = model(
pixel_values,
audio_values,
pixel_mask,
audio_mask,
pixel_values_mixed=pixel_values_mixed,
pixel_mask_mixed=pixel_mask_mixed,
labels=labels,
)
self.parent.assertEqual(
result.pixel_logits.shape, (self.batch_size, self.expected_pixel_seq_len, self.image_mae_output_dim)
)
self.parent.assertEqual(
result.audio_logits.shape, (self.batch_size, self.expected_audio_seq_len, self.audio_mae_output_dim)
)
self.parent.assertEqual(result.matching_logits.shape, (self.batch_size, self.num_labels))
def create_and_check_for_pretraining_inference(
self,
config,
pixel_values,
audio_values,
pixel_mask,
audio_mask,
pixel_values_mixed,
pixel_mask_mixed,
labels,
):
model = TvltForPreTraining(config=config)
model.to(torch_device)
model.eval()
result = model(
pixel_values,
audio_values,
pixel_mask,
audio_mask,
pixel_values_mixed=pixel_values_mixed,
pixel_mask_mixed=pixel_mask_mixed,
labels=labels,
)
if result.pixel_logits is not None:
self.parent.assertEqual(
result.pixel_logits.shape, (self.batch_size, self.expected_pixel_seq_len, self.image_mae_output_dim)
)
if result.audio_logits is not None:
self.parent.assertEqual(
result.audio_logits.shape, (self.batch_size, self.expected_audio_seq_len, self.audio_mae_output_dim)
)
self.parent.assertEqual(result.matching_logits.shape, (self.batch_size, self.num_labels))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(config, pixel_values, audio_values, pixel_mask, audio_mask) = config_and_inputs
inputs_dict = {
"pixel_values": pixel_values,
"audio_values": audio_values,
"pixel_mask": pixel_mask,
"audio_mask": audio_mask,
}
return config, inputs_dict
def prepare_pixel_values(self):
return floats_tensor(
[self.batch_size, self.num_frames, self.num_image_channels, self.image_size, self.image_size]
)
def prepare_audio_values(self):
return floats_tensor(
[self.batch_size, self.num_audio_channels, self.spectrogram_length, self.frequency_length]
)
@require_torch
class TvltModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(TvltModel, TvltForPreTraining, TvltForAudioVisualClassification) if is_torch_available() else ()
)
pipeline_model_mapping = {"feature-extraction": TvltModel} if is_torch_available() else {}
fx_compatible = False
test_pruning = False
test_headmasking = False
test_torchscript = False
test_resize_embeddings = False
main_input_name = "pixel_values"
# TvltForAudioVisualClassification and TvltForPreTraining require special treatment
def _prepare_for_class(self, inputs_dict, model_class, return_labels=True):
inputs_dict = copy.deepcopy(inputs_dict)
if return_labels:
if model_class.__name__ == "TvltForAudioVisualClassification":
inputs_dict["labels"] = torch.zeros(
(self.model_tester.batch_size,), dtype=torch.long, device=torch_device
)
elif model_class.__name__ == "TvltForPreTraining":
inputs_dict["labels"] = torch.zeros(
(self.model_tester.batch_size,), dtype=torch.float, device=torch_device
)
inputs_dict["pixel_values_mixed"] = torch.zeros(
(
self.model_tester.batch_size,
self.model_tester.num_frames,
self.model_tester.num_image_channels,
self.model_tester.image_size,
self.model_tester.image_size,
),
dtype=torch.float,
device=torch_device,
)
inputs_dict["pixel_mask_mixed"] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.expected_pixel_seq_len),
dtype=torch.float,
device=torch_device,
)
return inputs_dict
def setUp(self):
self.model_tester = TvltModelTester(self)
self.config_tester = ConfigTester(self, config_class=TvltConfig, has_text_modality=False, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
@unittest.skip(reason="TVLT does not use inputs_embeds")
def test_inputs_embeds(self):
pass
def test_model_common_attributes(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
input_embeddings = model.get_input_embeddings()
self.assertIsInstance(input_embeddings, (tuple))
for embedding in input_embeddings:
self.assertIsInstance(embedding, (nn.Module))
x = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(x, nn.Linear))
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["pixel_values", "audio_values"]
self.assertListEqual(arg_names[:2], expected_arg_names)
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_for_audiovisual_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_audiovisual_classification(*config_and_inputs)
def test_for_pretraining(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_pretraining()
self.model_tester.create_and_check_for_pretraining(*config_and_inputs)
self.model_tester.create_and_check_for_pretraining_inference(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_name in TVLT_PRETRAINED_MODEL_ARCHIVE_LIST:
model = TvltModel.from_pretrained(model_name)
self.assertIsNotNone(model)
def test_training(self):
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes[1:]:
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
model = model_class(config)
model.to(torch_device)
model.train()
inputs = self._prepare_for_class(inputs_dict, model_class)
for k, v in inputs.items():
print(k, v.shape)
loss = model(**inputs).loss
loss.backward()
def test_training_gradient_checkpointing(self):
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes[1:]:
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.use_cache = False
config.return_dict = True
model = model_class(config)
model.to(torch_device)
model.gradient_checkpointing_enable()
model.train()
inputs = self._prepare_for_class(inputs_dict, model_class)
loss = model(**inputs).loss
loss.backward()
def test_attention_outputs(self):
if not self.has_attentions:
pass
else:
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
for model_class in self.all_model_classes[2:]:
seq_len = self.model_tester.expected_seq_len
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = False
config.return_dict = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
config.output_attentions = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, seq_len, seq_len],
)
out_len = len(outputs)
# Check attention is always last and order is fine
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
self.assertEqual(out_len + 1, len(outputs))
self_attentions = outputs.attentions
self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(self_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, seq_len, seq_len],
)
def test_hidden_states_output(self):
def check_hidden_states_output(inputs_dict, config, model_class):
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
hidden_states = outputs.hidden_states
expected_num_layers = self.model_tester.num_hidden_layers + 1
self.assertEqual(len(hidden_states), expected_num_layers)
seq_length = self.model_tester.expected_seq_len
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[seq_length, self.model_tester.hidden_size],
)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes[2:]:
inputs_dict["output_hidden_states"] = True
check_hidden_states_output(inputs_dict, config, model_class)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
check_hidden_states_output(inputs_dict, config, model_class)
# We will verify our results on a video of eating spaghetti
# Frame indices used: [164 168 172 176 181 185 189 193 198 202 206 210 215 219 223 227]
def prepare_video(num_frames=8):
file = hf_hub_download(
repo_id="hf-internal-testing/spaghetti-video", filename="eating_spaghetti.npy", repo_type="dataset"
)
video = np.load(file)[:num_frames]
return list(video)
def prepare_audio(num_samples=1):
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
# automatic decoding with librispeech
speech_samples = ds.sort("id").select(range(num_samples))[:num_samples]["audio"]
return [x["array"] for x in speech_samples]
@require_torch
@require_vision
class TvltModelIntegrationTest(unittest.TestCase):
@cached_property
def default_processors(self):
# logits were tested with a different mean and std, so we use the same here
return (
TvltImageProcessor() if is_vision_available() else None,
TvltFeatureExtractor(),
)
def test_inference_for_base_model(self):
model = TvltModel.from_pretrained("ZinengTang/tvlt-base").to(torch_device)
image_processor, audio_feature_extractor = self.default_processors
video = prepare_video()
audio = prepare_audio()
video_inputs = image_processor(video, return_tensors="pt").to(torch_device)
audio_inputs = audio_feature_extractor(audio, return_tensors="pt").to(torch_device)
inputs = {}
inputs.update(video_inputs)
inputs.update(audio_inputs)
# forward pass
with torch.no_grad():
outputs = model(**inputs)
# verify the logits
expected_last_hidden_state_slice = torch.tensor([[-0.0186, -0.0691], [0.0242, -0.0398]], device=torch_device)
self.assertTrue(
torch.allclose(outputs.last_hidden_state[:, :2, :2], expected_last_hidden_state_slice, atol=1e-4)
)
def test_inference_for_pretraining(self):
model = TvltForPreTraining.from_pretrained("ZinengTang/tvlt-base").to(torch_device)
image_processor, audio_feature_extractor = self.default_processors
video = prepare_video()
video_mixed = prepare_video()
audio = prepare_audio()
video_inputs = image_processor(video, return_tensors="pt", mask_pixel=True).to(torch_device)
video_mixed_inputs = image_processor(video_mixed, is_mixed=True, return_tensors="pt").to(torch_device)
audio_inputs = audio_feature_extractor(audio, return_tensors="pt", mask_audio=True).to(torch_device)
labels = torch.tensor([[0.0]], device=torch_device)
inputs = {}
inputs.update(video_inputs)
inputs.update(video_mixed_inputs)
inputs.update(audio_inputs)
inputs.update({"labels": labels})
# forward pass
with torch.no_grad():
outputs = model(**inputs)
# verify the logits
expected_pixel_logits_shape = torch.Size([1, 1568, 768])
expected_audio_logits_shape = torch.Size([1, 96, 256])
expected_matching_logits_shape = torch.Size([1, 1])
if outputs.pixel_logits is not None:
self.assertEqual(outputs.pixel_logits.shape, expected_pixel_logits_shape)
if outputs.audio_logits is not None:
self.assertEqual(outputs.audio_logits.shape, expected_audio_logits_shape)
self.assertTrue(outputs.matching_logits.shape, expected_matching_logits_shape)
| transformers/tests/models/tvlt/test_modeling_tvlt.py/0 | {
"file_path": "transformers/tests/models/tvlt/test_modeling_tvlt.py",
"repo_id": "transformers",
"token_count": 11461
} | 169 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor
@require_tokenizers
@require_vision
class VisionTextDualEncoderProcessorTest(unittest.TestCase):
def setUp(self):
self.tmpdirname = tempfile.mkdtemp()
vocab_tokens = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"] # fmt: skip
self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
image_processor_map = {
"do_resize": True,
"size": {"height": 18, "width": 18},
"do_normalize": True,
"image_mean": [0.5, 0.5, 0.5],
"image_std": [0.5, 0.5, 0.5],
}
self.image_processor_file = os.path.join(self.tmpdirname, IMAGE_PROCESSOR_NAME)
with open(self.image_processor_file, "w", encoding="utf-8") as fp:
json.dump(image_processor_map, fp)
def get_tokenizer(self, **kwargs):
return BertTokenizer.from_pretrained(self.tmpdirname, **kwargs)
def get_image_processor(self, **kwargs):
return ViTImageProcessor.from_pretrained(self.tmpdirname, **kwargs)
def tearDown(self):
shutil.rmtree(self.tmpdirname)
def prepare_image_inputs(self):
"""This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True,
or a list of PyTorch tensors if one specifies torchify=True.
"""
image_inputs = [np.random.randint(255, size=(3, 30, 400), dtype=np.uint8)]
image_inputs = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in image_inputs]
return image_inputs
def test_save_load_pretrained_default(self):
tokenizer = self.get_tokenizer()
image_processor = self.get_image_processor()
processor = VisionTextDualEncoderProcessor(tokenizer=tokenizer, image_processor=image_processor)
processor.save_pretrained(self.tmpdirname)
processor = VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname)
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer.get_vocab())
self.assertIsInstance(processor.tokenizer, (BertTokenizer, BertTokenizerFast))
self.assertEqual(processor.image_processor.to_json_string(), image_processor.to_json_string())
self.assertIsInstance(processor.image_processor, ViTImageProcessor)
def test_save_load_pretrained_additional_features(self):
processor = VisionTextDualEncoderProcessor(
tokenizer=self.get_tokenizer(), image_processor=self.get_image_processor()
)
processor.save_pretrained(self.tmpdirname)
tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)")
image_processor_add_kwargs = self.get_image_processor(do_normalize=False, padding_value=1.0)
processor = VisionTextDualEncoderProcessor.from_pretrained(
self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False, padding_value=1.0
)
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
self.assertIsInstance(processor.tokenizer, (BertTokenizer, BertTokenizerFast))
self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string())
self.assertIsInstance(processor.image_processor, ViTImageProcessor)
def test_image_processor(self):
image_processor = self.get_image_processor()
tokenizer = self.get_tokenizer()
processor = VisionTextDualEncoderProcessor(tokenizer=tokenizer, image_processor=image_processor)
image_input = self.prepare_image_inputs()
input_feat_extract = image_processor(image_input, return_tensors="np")
input_processor = processor(images=image_input, return_tensors="np")
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2)
def test_tokenizer(self):
image_processor = self.get_image_processor()
tokenizer = self.get_tokenizer()
processor = VisionTextDualEncoderProcessor(tokenizer=tokenizer, image_processor=image_processor)
input_str = "lower newer"
encoded_processor = processor(text=input_str)
encoded_tok = tokenizer(input_str)
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key], encoded_processor[key])
def test_processor(self):
image_processor = self.get_image_processor()
tokenizer = self.get_tokenizer()
processor = VisionTextDualEncoderProcessor(tokenizer=tokenizer, image_processor=image_processor)
input_str = "lower newer"
image_input = self.prepare_image_inputs()
inputs = processor(text=input_str, images=image_input)
self.assertListEqual(list(inputs.keys()), ["input_ids", "token_type_ids", "attention_mask", "pixel_values"])
# test if it raises when no input is passed
with self.assertRaises(ValueError):
processor()
def test_tokenizer_decode(self):
image_processor = self.get_image_processor()
tokenizer = self.get_tokenizer()
processor = VisionTextDualEncoderProcessor(tokenizer=tokenizer, image_processor=image_processor)
predicted_ids = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
decoded_processor = processor.batch_decode(predicted_ids)
decoded_tok = tokenizer.batch_decode(predicted_ids)
self.assertListEqual(decoded_tok, decoded_processor)
def test_model_input_names(self):
image_processor = self.get_image_processor()
tokenizer = self.get_tokenizer()
processor = VisionTextDualEncoderProcessor(tokenizer=tokenizer, image_processor=image_processor)
input_str = "lower newer"
image_input = self.prepare_image_inputs()
inputs = processor(text=input_str, images=image_input)
self.assertListEqual(list(inputs.keys()), processor.model_input_names)
| transformers/tests/models/vision_text_dual_encoder/test_processor_vision_text_dual_encoder.py/0 | {
"file_path": "transformers/tests/models/vision_text_dual_encoder/test_processor_vision_text_dual_encoder.py",
"repo_id": "transformers",
"token_count": 2796
} | 170 |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch ViTDet model. """
import unittest
from transformers import VitDetConfig
from transformers.testing_utils import is_flaky, require_torch, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import VitDetBackbone, VitDetModel
class VitDetModelTester:
def __init__(
self,
parent,
batch_size=13,
image_size=30,
patch_size=2,
num_channels=3,
is_training=True,
use_labels=True,
hidden_size=32,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
type_sequence_label_size=10,
initializer_range=0.02,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.image_size = image_size
self.patch_size = patch_size
self.num_channels = num_channels
self.is_training = is_training
self.use_labels = use_labels
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.scope = scope
self.num_patches_one_direction = self.image_size // self.patch_size
self.seq_length = (self.image_size // self.patch_size) ** 2
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
labels = None
if self.use_labels:
labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
config = self.get_config()
return config, pixel_values, labels
def get_config(self):
return VitDetConfig(
image_size=self.image_size,
pretrain_image_size=self.image_size,
patch_size=self.patch_size,
num_channels=self.num_channels,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
is_decoder=False,
initializer_range=self.initializer_range,
)
def create_and_check_model(self, config, pixel_values, labels):
model = VitDetModel(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
self.parent.assertEqual(
result.last_hidden_state.shape,
(self.batch_size, self.hidden_size, self.num_patches_one_direction, self.num_patches_one_direction),
)
def create_and_check_backbone(self, config, pixel_values, labels):
model = VitDetBackbone(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
# verify hidden states
self.parent.assertEqual(len(result.feature_maps), len(config.out_features))
self.parent.assertListEqual(
list(result.feature_maps[0].shape),
[self.batch_size, self.hidden_size, self.num_patches_one_direction, self.num_patches_one_direction],
)
# verify channels
self.parent.assertEqual(len(model.channels), len(config.out_features))
self.parent.assertListEqual(model.channels, [config.hidden_size])
# verify backbone works with out_features=None
config.out_features = None
model = VitDetBackbone(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
# verify feature maps
self.parent.assertEqual(len(result.feature_maps), 1)
self.parent.assertListEqual(
list(result.feature_maps[0].shape),
[self.batch_size, self.hidden_size, self.num_patches_one_direction, self.num_patches_one_direction],
)
# verify channels
self.parent.assertEqual(len(model.channels), 1)
self.parent.assertListEqual(model.channels, [config.hidden_size])
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, pixel_values, labels = config_and_inputs
inputs_dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class VitDetModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
"""
Here we also overwrite some of the tests of test_modeling_common.py, as VitDet does not use input_ids, inputs_embeds,
attention_mask and seq_length.
"""
all_model_classes = (VitDetModel, VitDetBackbone) if is_torch_available() else ()
pipeline_model_mapping = {"feature-extraction": VitDetModel} if is_torch_available() else {}
fx_compatible = False
test_pruning = False
test_resize_embeddings = False
test_head_masking = False
def setUp(self):
self.model_tester = VitDetModelTester(self)
self.config_tester = ConfigTester(self, config_class=VitDetConfig, has_text_modality=False, hidden_size=37)
@is_flaky(max_attempts=3, description="`torch.nn.init.trunc_normal_` is flaky.")
def test_initialization(self):
super().test_initialization()
# TODO: Fix me (once this model gets more usage)
@unittest.skip("Does not work on the tiny model as we keep hitting edge cases.")
def test_cpu_offload(self):
super().test_cpu_offload()
# TODO: Fix me (once this model gets more usage)
@unittest.skip("Does not work on the tiny model as we keep hitting edge cases.")
def test_disk_offload_bin(self):
super().test_disk_offload()
@unittest.skip("Does not work on the tiny model as we keep hitting edge cases.")
def test_disk_offload_safetensors(self):
super().test_disk_offload()
# TODO: Fix me (once this model gets more usage)
@unittest.skip("Does not work on the tiny model as we keep hitting edge cases.")
def test_model_parallelism(self):
super().test_model_parallelism()
def test_config(self):
self.config_tester.run_common_tests()
@unittest.skip(reason="VitDet does not use inputs_embeds")
def test_inputs_embeds(self):
pass
def test_model_common_attributes(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
self.assertIsInstance(model.get_input_embeddings(), (nn.Module))
x = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(x, nn.Linear))
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_backbone(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*config_and_inputs)
def test_hidden_states_output(self):
def check_hidden_states_output(inputs_dict, config, model_class):
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
hidden_states = outputs.hidden_states
expected_num_stages = self.model_tester.num_hidden_layers
self.assertEqual(len(hidden_states), expected_num_stages + 1)
# VitDet's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[
self.model_tester.num_patches_one_direction,
self.model_tester.num_patches_one_direction,
],
)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
check_hidden_states_output(inputs_dict, config, model_class)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
check_hidden_states_output(inputs_dict, config, model_class)
# overwrite since VitDet only supports retraining gradients of hidden states
def test_retain_grad_hidden_states_attentions(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.output_hidden_states = True
config.output_attentions = self.has_attentions
# no need to test all models as different heads yield the same functionality
model_class = self.all_model_classes[0]
model = model_class(config)
model.to(torch_device)
inputs = self._prepare_for_class(inputs_dict, model_class)
outputs = model(**inputs)
output = outputs[0]
# Encoder-/Decoder-only models
hidden_states = outputs.hidden_states[0]
hidden_states.retain_grad()
output.flatten()[0].backward(retain_graph=True)
self.assertIsNotNone(hidden_states.grad)
@unittest.skip(reason="VitDet does not support feedforward chunking")
def test_feed_forward_chunking(self):
pass
@unittest.skip(reason="VitDet does not have standalone checkpoints since it used as backbone in other models")
def test_model_from_pretrained(self):
pass
@require_torch
class VitDetBackboneTest(unittest.TestCase, BackboneTesterMixin):
all_model_classes = (VitDetBackbone,) if is_torch_available() else ()
config_class = VitDetConfig
has_attentions = False
def setUp(self):
self.model_tester = VitDetModelTester(self)
| transformers/tests/models/vitdet/test_modeling_vitdet.py/0 | {
"file_path": "transformers/tests/models/vitdet/test_modeling_vitdet.py",
"repo_id": "transformers",
"token_count": 4686
} | 171 |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch YOLOS model. """
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import YolosForObjectDetection, YolosModel
from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class YolosModelTester:
def __init__(
self,
parent,
batch_size=13,
image_size=[30, 30],
patch_size=2,
num_channels=3,
is_training=True,
use_labels=True,
hidden_size=32,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
type_sequence_label_size=10,
initializer_range=0.02,
num_labels=3,
scope=None,
n_targets=8,
num_detection_tokens=10,
):
self.parent = parent
self.batch_size = batch_size
self.image_size = image_size
self.patch_size = patch_size
self.num_channels = num_channels
self.is_training = is_training
self.use_labels = use_labels
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.scope = scope
self.n_targets = n_targets
self.num_detection_tokens = num_detection_tokens
# we set the expected sequence length (which is used in several tests)
# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
num_patches = (image_size[1] // patch_size) * (image_size[0] // patch_size)
self.expected_seq_len = num_patches + 1 + self.num_detection_tokens
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]])
labels = None
if self.use_labels:
# labels is a list of Dict (each Dict being the labels for a given example in the batch)
labels = []
for i in range(self.batch_size):
target = {}
target["class_labels"] = torch.randint(
high=self.num_labels, size=(self.n_targets,), device=torch_device
)
target["boxes"] = torch.rand(self.n_targets, 4, device=torch_device)
labels.append(target)
config = self.get_config()
return config, pixel_values, labels
def get_config(self):
return YolosConfig(
image_size=self.image_size,
patch_size=self.patch_size,
num_channels=self.num_channels,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
is_decoder=False,
initializer_range=self.initializer_range,
num_detection_tokens=self.num_detection_tokens,
num_labels=self.num_labels,
)
def create_and_check_model(self, config, pixel_values, labels):
model = YolosModel(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
self.parent.assertEqual(
result.last_hidden_state.shape, (self.batch_size, self.expected_seq_len, self.hidden_size)
)
def create_and_check_for_object_detection(self, config, pixel_values, labels):
model = YolosForObjectDetection(config)
model.to(torch_device)
model.eval()
result = model(pixel_values=pixel_values)
result = model(pixel_values)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_detection_tokens, self.num_labels + 1))
self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_detection_tokens, 4))
result = model(pixel_values=pixel_values, labels=labels)
self.parent.assertEqual(result.loss.shape, ())
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_detection_tokens, self.num_labels + 1))
self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_detection_tokens, 4))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, pixel_values, labels = config_and_inputs
inputs_dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class YolosModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
"""
Here we also overwrite some of the tests of test_modeling_common.py, as YOLOS does not use input_ids, inputs_embeds,
attention_mask and seq_length.
"""
all_model_classes = (YolosModel, YolosForObjectDetection) if is_torch_available() else ()
pipeline_model_mapping = (
{"image-feature-extraction": YolosModel, "object-detection": YolosForObjectDetection}
if is_torch_available()
else {}
)
test_pruning = False
test_resize_embeddings = False
test_head_masking = False
test_torchscript = False
# special case for head model
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
if return_labels:
if model_class.__name__ == "YolosForObjectDetection":
labels = []
for i in range(self.model_tester.batch_size):
target = {}
target["class_labels"] = torch.ones(
size=(self.model_tester.n_targets,), device=torch_device, dtype=torch.long
)
target["boxes"] = torch.ones(
self.model_tester.n_targets, 4, device=torch_device, dtype=torch.float
)
labels.append(target)
inputs_dict["labels"] = labels
return inputs_dict
def setUp(self):
self.model_tester = YolosModelTester(self)
self.config_tester = ConfigTester(self, config_class=YolosConfig, has_text_modality=False, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_inputs_embeds(self):
# YOLOS does not use inputs_embeds
pass
def test_model_common_attributes(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
self.assertIsInstance(model.get_input_embeddings(), (nn.Module))
x = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(x, nn.Linear))
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_attention_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
# in YOLOS, the seq_len is different
seq_len = self.model_tester.expected_seq_len
for model_class in self.all_model_classes:
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = False
config.return_dict = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
config.output_attentions = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, seq_len, seq_len],
)
out_len = len(outputs)
# Check attention is always last and order is fine
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
added_hidden_states = 1
self.assertEqual(out_len + added_hidden_states, len(outputs))
self_attentions = outputs.attentions
self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(self_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, seq_len, seq_len],
)
def test_hidden_states_output(self):
def check_hidden_states_output(inputs_dict, config, model_class):
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
hidden_states = outputs.hidden_states
expected_num_layers = getattr(
self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
)
self.assertEqual(len(hidden_states), expected_num_layers)
# YOLOS has a different seq_length
seq_length = self.model_tester.expected_seq_len
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[seq_length, self.model_tester.hidden_size],
)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
check_hidden_states_output(inputs_dict, config, model_class)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
check_hidden_states_output(inputs_dict, config, model_class)
def test_for_object_detection(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_object_detection(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = YolosModel.from_pretrained(model_name)
self.assertIsNotNone(model)
# We will verify our results on an image of cute cats
def prepare_img():
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
return image
@require_torch
@require_vision
class YolosModelIntegrationTest(unittest.TestCase):
@cached_property
def default_image_processor(self):
return AutoImageProcessor.from_pretrained("hustvl/yolos-small") if is_vision_available() else None
@slow
def test_inference_object_detection_head(self):
model = YolosForObjectDetection.from_pretrained("hustvl/yolos-small").to(torch_device)
image_processor = self.default_image_processor
image = prepare_img()
inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
# forward pass
with torch.no_grad():
outputs = model(inputs.pixel_values)
# verify outputs
expected_shape = torch.Size((1, 100, 92))
self.assertEqual(outputs.logits.shape, expected_shape)
expected_slice_logits = torch.tensor(
[[-23.7219, -10.3165, -14.9083], [-41.5429, -15.2403, -24.1478], [-29.3909, -12.7173, -19.4650]],
device=torch_device,
)
expected_slice_boxes = torch.tensor(
[[0.2536, 0.5449, 0.4643], [0.2037, 0.7735, 0.3672], [0.7692, 0.4056, 0.4549]], device=torch_device
)
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3], expected_slice_logits, atol=1e-4))
self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3], expected_slice_boxes, atol=1e-4))
# verify postprocessing
results = image_processor.post_process_object_detection(
outputs, threshold=0.3, target_sizes=[image.size[::-1]]
)[0]
expected_scores = torch.tensor([0.9991, 0.9801, 0.9978, 0.9875, 0.9848]).to(torch_device)
expected_labels = [75, 75, 17, 63, 17]
expected_slice_boxes = torch.tensor([331.8438, 80.5440, 369.9546, 188.0579]).to(torch_device)
self.assertEqual(len(results["scores"]), 5)
self.assertTrue(torch.allclose(results["scores"], expected_scores, atol=1e-4))
self.assertSequenceEqual(results["labels"].tolist(), expected_labels)
self.assertTrue(torch.allclose(results["boxes"][0, :], expected_slice_boxes))
| transformers/tests/models/yolos/test_modeling_yolos.py/0 | {
"file_path": "transformers/tests/models/yolos/test_modeling_yolos.py",
"repo_id": "transformers",
"token_count": 6777
} | 172 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from transformers import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
PreTrainedTokenizerBase,
is_vision_available,
)
from transformers.pipelines import ImageClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_torch_or_tf,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class Image:
@staticmethod
def open(*args, **kwargs):
pass
@is_pipeline_test
@require_torch_or_tf
@require_vision
class ImageClassificationPipelineTests(unittest.TestCase):
model_mapping = MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
tf_model_mapping = TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
def get_test_pipeline(self, model, tokenizer, processor):
image_classifier = ImageClassificationPipeline(model=model, image_processor=processor, top_k=2)
examples = [
Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"),
"http://images.cocodataset.org/val2017/000000039769.jpg",
]
return image_classifier, examples
def run_pipeline_test(self, image_classifier, examples):
outputs = image_classifier("./tests/fixtures/tests_samples/COCO/000000039769.png")
self.assertEqual(
outputs,
[
{"score": ANY(float), "label": ANY(str)},
{"score": ANY(float), "label": ANY(str)},
],
)
import datasets
# we use revision="refs/pr/1" until the PR is merged
# https://hf.co/datasets/hf-internal-testing/fixtures_image_utils/discussions/1
dataset = datasets.load_dataset("hf-internal-testing/fixtures_image_utils", split="test", revision="refs/pr/1")
# Accepts URL + PIL.Image + lists
outputs = image_classifier(
[
Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"),
"http://images.cocodataset.org/val2017/000000039769.jpg",
# RGBA
dataset[0]["image"],
# LA
dataset[1]["image"],
# L
dataset[2]["image"],
]
)
self.assertEqual(
outputs,
[
[
{"score": ANY(float), "label": ANY(str)},
{"score": ANY(float), "label": ANY(str)},
],
[
{"score": ANY(float), "label": ANY(str)},
{"score": ANY(float), "label": ANY(str)},
],
[
{"score": ANY(float), "label": ANY(str)},
{"score": ANY(float), "label": ANY(str)},
],
[
{"score": ANY(float), "label": ANY(str)},
{"score": ANY(float), "label": ANY(str)},
],
[
{"score": ANY(float), "label": ANY(str)},
{"score": ANY(float), "label": ANY(str)},
],
],
)
@require_torch
def test_small_model_pt(self):
small_model = "hf-internal-testing/tiny-random-vit"
image_classifier = pipeline("image-classification", model=small_model)
outputs = image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
self.assertEqual(
nested_simplify(outputs, decimals=4),
[{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
)
outputs = image_classifier(
[
"http://images.cocodataset.org/val2017/000000039769.jpg",
"http://images.cocodataset.org/val2017/000000039769.jpg",
],
top_k=2,
)
self.assertEqual(
nested_simplify(outputs, decimals=4),
[
[{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
[{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
],
)
@require_tf
def test_small_model_tf(self):
small_model = "hf-internal-testing/tiny-random-vit"
image_classifier = pipeline("image-classification", model=small_model, framework="tf")
outputs = image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
self.assertEqual(
nested_simplify(outputs, decimals=4),
[{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
)
outputs = image_classifier(
[
"http://images.cocodataset.org/val2017/000000039769.jpg",
"http://images.cocodataset.org/val2017/000000039769.jpg",
],
top_k=2,
)
self.assertEqual(
nested_simplify(outputs, decimals=4),
[
[{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
[{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
],
)
def test_custom_tokenizer(self):
tokenizer = PreTrainedTokenizerBase()
# Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
image_classifier = pipeline(
"image-classification", model="hf-internal-testing/tiny-random-vit", tokenizer=tokenizer
)
self.assertIs(image_classifier.tokenizer, tokenizer)
@slow
@require_torch
def test_perceiver(self):
# Perceiver is not tested by `run_pipeline_test` properly.
# That is because the type of feature_extractor and model preprocessor need to be kept
# in sync, which is not the case in the current design
image_classifier = pipeline("image-classification", model="deepmind/vision-perceiver-conv")
outputs = image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
self.assertEqual(
nested_simplify(outputs, decimals=4),
[
{"score": 0.4385, "label": "tabby, tabby cat"},
{"score": 0.321, "label": "tiger cat"},
{"score": 0.0502, "label": "Egyptian cat"},
{"score": 0.0137, "label": "crib, cot"},
{"score": 0.007, "label": "radiator"},
],
)
image_classifier = pipeline("image-classification", model="deepmind/vision-perceiver-fourier")
outputs = image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
self.assertEqual(
nested_simplify(outputs, decimals=4),
[
{"score": 0.5658, "label": "tabby, tabby cat"},
{"score": 0.1309, "label": "tiger cat"},
{"score": 0.0722, "label": "Egyptian cat"},
{"score": 0.0707, "label": "remote control, remote"},
{"score": 0.0082, "label": "computer keyboard, keypad"},
],
)
image_classifier = pipeline("image-classification", model="deepmind/vision-perceiver-learned")
outputs = image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
self.assertEqual(
nested_simplify(outputs, decimals=4),
[
{"score": 0.3022, "label": "tabby, tabby cat"},
{"score": 0.2362, "label": "Egyptian cat"},
{"score": 0.1856, "label": "tiger cat"},
{"score": 0.0324, "label": "remote control, remote"},
{"score": 0.0096, "label": "quilt, comforter, comfort, puff"},
],
)
@slow
@require_torch
def test_multilabel_classification(self):
small_model = "hf-internal-testing/tiny-random-vit"
# Sigmoid is applied for multi-label classification
image_classifier = pipeline("image-classification", model=small_model)
image_classifier.model.config.problem_type = "multi_label_classification"
outputs = image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
self.assertEqual(
nested_simplify(outputs, decimals=4),
[{"label": "LABEL_1", "score": 0.5356}, {"label": "LABEL_0", "score": 0.4612}],
)
outputs = image_classifier(
[
"http://images.cocodataset.org/val2017/000000039769.jpg",
"http://images.cocodataset.org/val2017/000000039769.jpg",
]
)
self.assertEqual(
nested_simplify(outputs, decimals=4),
[
[{"label": "LABEL_1", "score": 0.5356}, {"label": "LABEL_0", "score": 0.4612}],
[{"label": "LABEL_1", "score": 0.5356}, {"label": "LABEL_0", "score": 0.4612}],
],
)
@slow
@require_torch
def test_function_to_apply(self):
small_model = "hf-internal-testing/tiny-random-vit"
# Sigmoid is applied for multi-label classification
image_classifier = pipeline("image-classification", model=small_model)
outputs = image_classifier(
"http://images.cocodataset.org/val2017/000000039769.jpg",
function_to_apply="sigmoid",
)
self.assertEqual(
nested_simplify(outputs, decimals=4),
[{"label": "LABEL_1", "score": 0.5356}, {"label": "LABEL_0", "score": 0.4612}],
)
| transformers/tests/pipelines/test_pipelines_image_classification.py/0 | {
"file_path": "transformers/tests/pipelines/test_pipelines_image_classification.py",
"repo_id": "transformers",
"token_count": 4939
} | 173 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_decord,
require_tf,
require_torch,
require_torch_or_tf,
require_vision,
)
from .test_pipelines_common import ANY
@is_pipeline_test
@require_torch_or_tf
@require_vision
@require_decord
class VideoClassificationPipelineTests(unittest.TestCase):
model_mapping = MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING
def get_test_pipeline(self, model, tokenizer, processor):
example_video_filepath = hf_hub_download(
repo_id="nateraw/video-demo", filename="archery.mp4", repo_type="dataset"
)
video_classifier = VideoClassificationPipeline(model=model, image_processor=processor, top_k=2)
examples = [
example_video_filepath,
"https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4",
]
return video_classifier, examples
def run_pipeline_test(self, video_classifier, examples):
for example in examples:
outputs = video_classifier(example)
self.assertEqual(
outputs,
[
{"score": ANY(float), "label": ANY(str)},
{"score": ANY(float), "label": ANY(str)},
],
)
@require_torch
def test_small_model_pt(self):
small_model = "hf-internal-testing/tiny-random-VideoMAEForVideoClassification"
small_feature_extractor = VideoMAEFeatureExtractor(
size={"shortest_edge": 10}, crop_size={"height": 10, "width": 10}
)
video_classifier = pipeline(
"video-classification", model=small_model, feature_extractor=small_feature_extractor, frame_sampling_rate=4
)
video_file_path = hf_hub_download(repo_id="nateraw/video-demo", filename="archery.mp4", repo_type="dataset")
outputs = video_classifier(video_file_path, top_k=2)
self.assertEqual(
nested_simplify(outputs, decimals=4),
[{"score": 0.5199, "label": "LABEL_0"}, {"score": 0.4801, "label": "LABEL_1"}],
)
outputs = video_classifier(
[
video_file_path,
video_file_path,
],
top_k=2,
)
self.assertEqual(
nested_simplify(outputs, decimals=4),
[
[{"score": 0.5199, "label": "LABEL_0"}, {"score": 0.4801, "label": "LABEL_1"}],
[{"score": 0.5199, "label": "LABEL_0"}, {"score": 0.4801, "label": "LABEL_1"}],
],
)
@require_tf
def test_small_model_tf(self):
pass
| transformers/tests/pipelines/test_pipelines_video_classification.py/0 | {
"file_path": "transformers/tests/pipelines/test_pipelines_video_classification.py",
"repo_id": "transformers",
"token_count": 1516
} | 174 |
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import shutil
import sys
import tempfile
import unittest
from contextlib import contextmanager
from pathlib import Path
git_repo_path = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_copies # noqa: E402
from check_copies import convert_to_localized_md, find_code_in_transformers, is_copy_consistent # noqa: E402
# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
REFERENCE_CODE = """ def __init__(self, config):
super().__init__()
self.transform = BertPredictionHeadTransform(config)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.bias = nn.Parameter(torch.zeros(config.vocab_size))
# Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
self.decoder.bias = self.bias
def forward(self, hidden_states):
hidden_states = self.transform(hidden_states)
hidden_states = self.decoder(hidden_states)
return hidden_states
"""
MOCK_BERT_CODE = """from ...modeling_utils import PreTrainedModel
def bert_function(x):
return x
class BertAttention(nn.Module):
def __init__(self, config):
super().__init__()
class BertModel(BertPreTrainedModel):
def __init__(self, config):
super().__init__()
self.bert = BertEncoder(config)
@add_docstring(BERT_DOCSTRING)
def forward(self, x):
return self.bert(x)
"""
MOCK_BERT_COPY_CODE = """from ...modeling_utils import PreTrainedModel
# Copied from transformers.models.bert.modeling_bert.bert_function
def bert_copy_function(x):
return x
# Copied from transformers.models.bert.modeling_bert.BertAttention
class BertCopyAttention(nn.Module):
def __init__(self, config):
super().__init__()
# Copied from transformers.models.bert.modeling_bert.BertModel with Bert->BertCopy all-casing
class BertCopyModel(BertCopyPreTrainedModel):
def __init__(self, config):
super().__init__()
self.bertcopy = BertCopyEncoder(config)
@add_docstring(BERTCOPY_DOCSTRING)
def forward(self, x):
return self.bertcopy(x)
"""
MOCK_DUMMY_BERT_CODE_MATCH = """
class BertDummyModel:
attr_1 = 1
attr_2 = 2
def __init__(self, a=1, b=2):
self.a = a
self.b = b
# Copied from transformers.models.dummy_gpt2.modeling_dummy_gpt2.GPT2DummyModel.forward
def forward(self, c):
return 1
def existing_common(self, c):
return 4
def existing_diff_to_be_ignored(self, c):
return 9
"""
MOCK_DUMMY_ROBERTA_CODE_MATCH = """
# Copied from transformers.models.dummy_bert_match.modeling_dummy_bert_match.BertDummyModel with BertDummy->RobertaBertDummy
class RobertaBertDummyModel:
attr_1 = 1
attr_2 = 2
def __init__(self, a=1, b=2):
self.a = a
self.b = b
# Ignore copy
def only_in_roberta_to_be_ignored(self, c):
return 3
# Copied from transformers.models.dummy_gpt2.modeling_dummy_gpt2.GPT2DummyModel.forward
def forward(self, c):
return 1
def existing_common(self, c):
return 4
# Ignore copy
def existing_diff_to_be_ignored(self, c):
return 6
"""
MOCK_DUMMY_BERT_CODE_NO_MATCH = """
class BertDummyModel:
attr_1 = 1
attr_2 = 2
def __init__(self, a=1, b=2):
self.a = a
self.b = b
# Copied from transformers.models.dummy_gpt2.modeling_dummy_gpt2.GPT2DummyModel.forward
def forward(self, c):
return 1
def only_in_bert(self, c):
return 7
def existing_common(self, c):
return 4
def existing_diff_not_ignored(self, c):
return 8
def existing_diff_to_be_ignored(self, c):
return 9
"""
MOCK_DUMMY_ROBERTA_CODE_NO_MATCH = """
# Copied from transformers.models.dummy_bert_no_match.modeling_dummy_bert_no_match.BertDummyModel with BertDummy->RobertaBertDummy
class RobertaBertDummyModel:
attr_1 = 1
attr_2 = 3
def __init__(self, a=1, b=2):
self.a = a
self.b = b
# Ignore copy
def only_in_roberta_to_be_ignored(self, c):
return 3
# Copied from transformers.models.dummy_gpt2.modeling_dummy_gpt2.GPT2DummyModel.forward
def forward(self, c):
return 1
def only_in_roberta_not_ignored(self, c):
return 2
def existing_common(self, c):
return 4
def existing_diff_not_ignored(self, c):
return 5
# Ignore copy
def existing_diff_to_be_ignored(self, c):
return 6
"""
EXPECTED_REPLACED_CODE = """
# Copied from transformers.models.dummy_bert_no_match.modeling_dummy_bert_no_match.BertDummyModel with BertDummy->RobertaBertDummy
class RobertaBertDummyModel:
attr_1 = 1
attr_2 = 2
def __init__(self, a=1, b=2):
self.a = a
self.b = b
# Copied from transformers.models.dummy_gpt2.modeling_dummy_gpt2.GPT2DummyModel.forward
def forward(self, c):
return 1
def only_in_bert(self, c):
return 7
def existing_common(self, c):
return 4
def existing_diff_not_ignored(self, c):
return 8
# Ignore copy
def existing_diff_to_be_ignored(self, c):
return 6
# Ignore copy
def only_in_roberta_to_be_ignored(self, c):
return 3
"""
def replace_in_file(filename, old, new):
with open(filename, "r", encoding="utf-8") as f:
content = f.read()
content = content.replace(old, new)
with open(filename, "w", encoding="utf-8", newline="\n") as f:
f.write(content)
def create_tmp_repo(tmp_dir):
"""
Creates a mock repository in a temporary folder for testing.
"""
tmp_dir = Path(tmp_dir)
if tmp_dir.exists():
shutil.rmtree(tmp_dir)
tmp_dir.mkdir(exist_ok=True)
model_dir = tmp_dir / "src" / "transformers" / "models"
model_dir.mkdir(parents=True, exist_ok=True)
models = {
"bert": MOCK_BERT_CODE,
"bertcopy": MOCK_BERT_COPY_CODE,
"dummy_bert_match": MOCK_DUMMY_BERT_CODE_MATCH,
"dummy_roberta_match": MOCK_DUMMY_ROBERTA_CODE_MATCH,
"dummy_bert_no_match": MOCK_DUMMY_BERT_CODE_NO_MATCH,
"dummy_roberta_no_match": MOCK_DUMMY_ROBERTA_CODE_NO_MATCH,
}
for model, code in models.items():
model_subdir = model_dir / model
model_subdir.mkdir(exist_ok=True)
with open(model_subdir / f"modeling_{model}.py", "w", encoding="utf-8", newline="\n") as f:
f.write(code)
@contextmanager
def patch_transformer_repo_path(new_folder):
"""
Temporarily patches the variables defines in `check_copies` to use a different location for the repo.
"""
old_repo_path = check_copies.REPO_PATH
old_doc_path = check_copies.PATH_TO_DOCS
old_transformer_path = check_copies.TRANSFORMERS_PATH
repo_path = Path(new_folder).resolve()
check_copies.REPO_PATH = str(repo_path)
check_copies.PATH_TO_DOCS = str(repo_path / "docs" / "source" / "en")
check_copies.TRANSFORMERS_PATH = str(repo_path / "src" / "transformers")
try:
yield
finally:
check_copies.REPO_PATH = old_repo_path
check_copies.PATH_TO_DOCS = old_doc_path
check_copies.TRANSFORMERS_PATH = old_transformer_path
class CopyCheckTester(unittest.TestCase):
def test_find_code_in_transformers(self):
with tempfile.TemporaryDirectory() as tmp_folder:
create_tmp_repo(tmp_folder)
with patch_transformer_repo_path(tmp_folder):
code = find_code_in_transformers("models.bert.modeling_bert.BertAttention")
reference_code = (
"class BertAttention(nn.Module):\n def __init__(self, config):\n super().__init__()\n"
)
self.assertEqual(code, reference_code)
def test_is_copy_consistent(self):
path_to_check = ["src", "transformers", "models", "bertcopy", "modeling_bertcopy.py"]
with tempfile.TemporaryDirectory() as tmp_folder:
# Base check
create_tmp_repo(tmp_folder)
with patch_transformer_repo_path(tmp_folder):
file_to_check = os.path.join(tmp_folder, *path_to_check)
diffs = is_copy_consistent(file_to_check)
self.assertEqual(diffs, [])
# Base check with an inconsistency
create_tmp_repo(tmp_folder)
with patch_transformer_repo_path(tmp_folder):
file_to_check = os.path.join(tmp_folder, *path_to_check)
replace_in_file(file_to_check, "self.bertcopy(x)", "self.bert(x)")
diffs = is_copy_consistent(file_to_check)
self.assertEqual(diffs, [["models.bert.modeling_bert.BertModel", 22]])
_ = is_copy_consistent(file_to_check, overwrite=True)
with open(file_to_check, "r", encoding="utf-8") as f:
self.assertEqual(f.read(), MOCK_BERT_COPY_CODE)
def test_is_copy_consistent_with_ignored_match(self):
path_to_check = ["src", "transformers", "models", "dummy_roberta_match", "modeling_dummy_roberta_match.py"]
with tempfile.TemporaryDirectory() as tmp_folder:
# Base check
create_tmp_repo(tmp_folder)
with patch_transformer_repo_path(tmp_folder):
file_to_check = os.path.join(tmp_folder, *path_to_check)
diffs = is_copy_consistent(file_to_check)
self.assertEqual(diffs, [])
def test_is_copy_consistent_with_ignored_no_match(self):
path_to_check = [
"src",
"transformers",
"models",
"dummy_roberta_no_match",
"modeling_dummy_roberta_no_match.py",
]
with tempfile.TemporaryDirectory() as tmp_folder:
# Base check with an inconsistency
create_tmp_repo(tmp_folder)
with patch_transformer_repo_path(tmp_folder):
file_to_check = os.path.join(tmp_folder, *path_to_check)
diffs = is_copy_consistent(file_to_check)
# line 6: `attr_2 = 3` in `MOCK_DUMMY_ROBERTA_CODE_NO_MATCH`.
# (which has a leading `\n`.)
self.assertEqual(
diffs, [["models.dummy_bert_no_match.modeling_dummy_bert_no_match.BertDummyModel", 6]]
)
_ = is_copy_consistent(file_to_check, overwrite=True)
with open(file_to_check, "r", encoding="utf-8") as f:
self.assertEqual(f.read(), EXPECTED_REPLACED_CODE)
def test_convert_to_localized_md(self):
localized_readme = check_copies.LOCALIZED_READMES["README_zh-hans.md"]
md_list = (
"1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
" Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
" Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
" Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1."
" **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),"
" released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
" lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same"
" method has been applied to compress GPT2 into"
" [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
" [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
" Multilingual BERT into"
" [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
" version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**"
" (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders"
" as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang"
" Luong, Quoc V. Le, Christopher D. Manning."
)
localized_md_list = (
"1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
" Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
" Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
" Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
)
converted_md_list_sample = (
"1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
" Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
" Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
" Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1."
" **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文"
" [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
" lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same"
" method has been applied to compress GPT2 into"
" [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
" [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
" Multilingual BERT into"
" [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
" version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自"
" Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather"
" than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,"
" Christopher D. Manning 发布。\n"
)
num_models_equal, converted_md_list = convert_to_localized_md(
md_list, localized_md_list, localized_readme["format_model_list"]
)
self.assertFalse(num_models_equal)
self.assertEqual(converted_md_list, converted_md_list_sample)
num_models_equal, converted_md_list = convert_to_localized_md(
md_list, converted_md_list, localized_readme["format_model_list"]
)
# Check whether the number of models is equal to README.md after conversion.
self.assertTrue(num_models_equal)
link_changed_md_list = (
"1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
" Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
" Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
" Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut."
)
link_unchanged_md_list = (
"1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and"
" the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
" Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
" Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
)
converted_md_list_sample = (
"1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
" Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
" Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
" Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
)
num_models_equal, converted_md_list = convert_to_localized_md(
link_changed_md_list, link_unchanged_md_list, localized_readme["format_model_list"]
)
# Check if the model link is synchronized.
self.assertEqual(converted_md_list, converted_md_list_sample)
| transformers/tests/repo_utils/test_check_copies.py/0 | {
"file_path": "transformers/tests/repo_utils/test_check_copies.py",
"repo_id": "transformers",
"token_count": 7781
} | 175 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
literal_eval(os.getenv("TEST_SAGEMAKER", "False")) is not True,
reason="Skipping test because should only be run when releasing minor transformers version",
)
@pytest.mark.usefixtures("sm_env")
@parameterized_class(
[
{
"framework": "pytorch",
"script": "run_glue.py",
"model_name_or_path": "distilbert/distilbert-base-cased",
"instance_type": "ml.g4dn.xlarge",
"results": {"train_runtime": 650, "eval_accuracy": 0.6, "eval_loss": 0.9},
},
{
"framework": "tensorflow",
"script": "run_tf.py",
"model_name_or_path": "distilbert/distilbert-base-cased",
"instance_type": "ml.g4dn.xlarge",
"results": {"train_runtime": 600, "eval_accuracy": 0.3, "eval_loss": 0.9},
},
]
)
class SingleNodeTest(unittest.TestCase):
def setUp(self):
if self.framework == "pytorch":
subprocess.run(
f"cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py".split(),
encoding="utf-8",
check=True,
)
assert hasattr(self, "env")
def create_estimator(self, instance_count=1):
# creates estimator
return HuggingFace(
entry_point=self.script,
source_dir=self.env.test_path,
role=self.env.role,
image_uri=self.env.image_uri,
base_job_name=f"{self.env.base_job_name}-single",
instance_count=instance_count,
instance_type=self.instance_type,
debugger_hook_config=False,
hyperparameters={**self.env.hyperparameters, "model_name_or_path": self.model_name_or_path},
metric_definitions=self.env.metric_definitions,
py_version="py36",
)
def save_results_as_csv(self, job_name):
TrainingJobAnalytics(job_name).export_csv(f"{self.env.test_path}/{job_name}_metrics.csv")
def test_glue(self):
# create estimator
estimator = self.create_estimator()
# run training
estimator.fit()
# result dataframe
result_metrics_df = TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()
# extract kpis
eval_accuracy = list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"])
eval_loss = list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"])
# get train time from SageMaker job, this includes starting, preprocessing, stopping
train_runtime = (
Session().describe_training_job(estimator.latest_training_job.name).get("TrainingTimeInSeconds", 999999)
)
# assert kpis
assert train_runtime <= self.results["train_runtime"]
assert all(t >= self.results["eval_accuracy"] for t in eval_accuracy)
assert all(t <= self.results["eval_loss"] for t in eval_loss)
# dump tests result into json file to share in PR
with open(f"{estimator.latest_training_job.name}.json", "w") as outfile:
json.dump({"train_time": train_runtime, "eval_accuracy": eval_accuracy, "eval_loss": eval_loss}, outfile)
| transformers/tests/sagemaker/test_single_node_gpu.py/0 | {
"file_path": "transformers/tests/sagemaker/test_single_node_gpu.py",
"repo_id": "transformers",
"token_count": 1592
} | 176 |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import json
import os
import random
import unittest
from pathlib import Path
from transformers.testing_utils import (
is_pipeline_test,
require_decord,
require_pytesseract,
require_timm,
require_torch,
require_torch_or_tf,
require_vision,
)
from transformers.utils import direct_transformers_import, logging
from .pipelines.test_pipelines_audio_classification import AudioClassificationPipelineTests
from .pipelines.test_pipelines_automatic_speech_recognition import AutomaticSpeechRecognitionPipelineTests
from .pipelines.test_pipelines_conversational import ConversationalPipelineTests
from .pipelines.test_pipelines_depth_estimation import DepthEstimationPipelineTests
from .pipelines.test_pipelines_document_question_answering import DocumentQuestionAnsweringPipelineTests
from .pipelines.test_pipelines_feature_extraction import FeatureExtractionPipelineTests
from .pipelines.test_pipelines_fill_mask import FillMaskPipelineTests
from .pipelines.test_pipelines_image_classification import ImageClassificationPipelineTests
from .pipelines.test_pipelines_image_feature_extraction import ImageFeatureExtractionPipelineTests
from .pipelines.test_pipelines_image_segmentation import ImageSegmentationPipelineTests
from .pipelines.test_pipelines_image_to_image import ImageToImagePipelineTests
from .pipelines.test_pipelines_image_to_text import ImageToTextPipelineTests
from .pipelines.test_pipelines_mask_generation import MaskGenerationPipelineTests
from .pipelines.test_pipelines_object_detection import ObjectDetectionPipelineTests
from .pipelines.test_pipelines_question_answering import QAPipelineTests
from .pipelines.test_pipelines_summarization import SummarizationPipelineTests
from .pipelines.test_pipelines_table_question_answering import TQAPipelineTests
from .pipelines.test_pipelines_text2text_generation import Text2TextGenerationPipelineTests
from .pipelines.test_pipelines_text_classification import TextClassificationPipelineTests
from .pipelines.test_pipelines_text_generation import TextGenerationPipelineTests
from .pipelines.test_pipelines_text_to_audio import TextToAudioPipelineTests
from .pipelines.test_pipelines_token_classification import TokenClassificationPipelineTests
from .pipelines.test_pipelines_translation import TranslationPipelineTests
from .pipelines.test_pipelines_video_classification import VideoClassificationPipelineTests
from .pipelines.test_pipelines_visual_question_answering import VisualQuestionAnsweringPipelineTests
from .pipelines.test_pipelines_zero_shot import ZeroShotClassificationPipelineTests
from .pipelines.test_pipelines_zero_shot_audio_classification import ZeroShotAudioClassificationPipelineTests
from .pipelines.test_pipelines_zero_shot_image_classification import ZeroShotImageClassificationPipelineTests
from .pipelines.test_pipelines_zero_shot_object_detection import ZeroShotObjectDetectionPipelineTests
pipeline_test_mapping = {
"audio-classification": {"test": AudioClassificationPipelineTests},
"automatic-speech-recognition": {"test": AutomaticSpeechRecognitionPipelineTests},
"conversational": {"test": ConversationalPipelineTests},
"depth-estimation": {"test": DepthEstimationPipelineTests},
"document-question-answering": {"test": DocumentQuestionAnsweringPipelineTests},
"feature-extraction": {"test": FeatureExtractionPipelineTests},
"fill-mask": {"test": FillMaskPipelineTests},
"image-classification": {"test": ImageClassificationPipelineTests},
"image-feature-extraction": {"test": ImageFeatureExtractionPipelineTests},
"image-segmentation": {"test": ImageSegmentationPipelineTests},
"image-to-image": {"test": ImageToImagePipelineTests},
"image-to-text": {"test": ImageToTextPipelineTests},
"mask-generation": {"test": MaskGenerationPipelineTests},
"object-detection": {"test": ObjectDetectionPipelineTests},
"question-answering": {"test": QAPipelineTests},
"summarization": {"test": SummarizationPipelineTests},
"table-question-answering": {"test": TQAPipelineTests},
"text2text-generation": {"test": Text2TextGenerationPipelineTests},
"text-classification": {"test": TextClassificationPipelineTests},
"text-generation": {"test": TextGenerationPipelineTests},
"text-to-audio": {"test": TextToAudioPipelineTests},
"token-classification": {"test": TokenClassificationPipelineTests},
"translation": {"test": TranslationPipelineTests},
"video-classification": {"test": VideoClassificationPipelineTests},
"visual-question-answering": {"test": VisualQuestionAnsweringPipelineTests},
"zero-shot": {"test": ZeroShotClassificationPipelineTests},
"zero-shot-audio-classification": {"test": ZeroShotAudioClassificationPipelineTests},
"zero-shot-image-classification": {"test": ZeroShotImageClassificationPipelineTests},
"zero-shot-object-detection": {"test": ZeroShotObjectDetectionPipelineTests},
}
for task, task_info in pipeline_test_mapping.items():
test = task_info["test"]
task_info["mapping"] = {
"pt": getattr(test, "model_mapping", None),
"tf": getattr(test, "tf_model_mapping", None),
}
# The default value `hf-internal-testing` is for running the pipeline testing against the tiny models on the Hub.
# For debugging purpose, we can specify a local path which is the `output_path` argument of a previous run of
# `utils/create_dummy_models.py`.
TRANSFORMERS_TINY_MODEL_PATH = os.environ.get("TRANSFORMERS_TINY_MODEL_PATH", "hf-internal-testing")
if TRANSFORMERS_TINY_MODEL_PATH == "hf-internal-testing":
TINY_MODEL_SUMMARY_FILE_PATH = os.path.join(Path(__file__).parent.parent, "tests/utils/tiny_model_summary.json")
else:
TINY_MODEL_SUMMARY_FILE_PATH = os.path.join(TRANSFORMERS_TINY_MODEL_PATH, "reports", "tiny_model_summary.json")
with open(TINY_MODEL_SUMMARY_FILE_PATH) as fp:
tiny_model_summary = json.load(fp)
PATH_TO_TRANSFORMERS = os.path.join(Path(__file__).parent.parent, "src/transformers")
# Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
transformers_module = direct_transformers_import(PATH_TO_TRANSFORMERS)
logger = logging.get_logger(__name__)
class PipelineTesterMixin:
model_tester = None
pipeline_model_mapping = None
supported_frameworks = ["pt", "tf"]
def run_task_tests(self, task):
"""Run pipeline tests for a specific `task`
Args:
task (`str`):
A task name. This should be a key in the mapping `pipeline_test_mapping`.
"""
if task not in self.pipeline_model_mapping:
self.skipTest(
f"{self.__class__.__name__}::test_pipeline_{task.replace('-', '_')} is skipped: `{task}` is not in "
f"`self.pipeline_model_mapping` for `{self.__class__.__name__}`."
)
model_architectures = self.pipeline_model_mapping[task]
if not isinstance(model_architectures, tuple):
model_architectures = (model_architectures,)
if not isinstance(model_architectures, tuple):
raise ValueError(f"`model_architectures` must be a tuple. Got {type(model_architectures)} instead.")
for model_architecture in model_architectures:
model_arch_name = model_architecture.__name__
# Get the canonical name
for _prefix in ["Flax", "TF"]:
if model_arch_name.startswith(_prefix):
model_arch_name = model_arch_name[len(_prefix) :]
break
tokenizer_names = []
processor_names = []
commit = None
if model_arch_name in tiny_model_summary:
tokenizer_names = tiny_model_summary[model_arch_name]["tokenizer_classes"]
processor_names = tiny_model_summary[model_arch_name]["processor_classes"]
if "sha" in tiny_model_summary[model_arch_name]:
commit = tiny_model_summary[model_arch_name]["sha"]
# Adding `None` (if empty) so we can generate tests
tokenizer_names = [None] if len(tokenizer_names) == 0 else tokenizer_names
processor_names = [None] if len(processor_names) == 0 else processor_names
repo_name = f"tiny-random-{model_arch_name}"
if TRANSFORMERS_TINY_MODEL_PATH != "hf-internal-testing":
repo_name = model_arch_name
self.run_model_pipeline_tests(
task, repo_name, model_architecture, tokenizer_names, processor_names, commit
)
def run_model_pipeline_tests(self, task, repo_name, model_architecture, tokenizer_names, processor_names, commit):
"""Run pipeline tests for a specific `task` with the give model class and tokenizer/processor class names
Args:
task (`str`):
A task name. This should be a key in the mapping `pipeline_test_mapping`.
repo_name (`str`):
A model repository id on the Hub.
model_architecture (`type`):
A subclass of `PretrainedModel` or `PretrainedModel`.
tokenizer_names (`List[str]`):
A list of names of a subclasses of `PreTrainedTokenizerFast` or `PreTrainedTokenizer`.
processor_names (`List[str]`):
A list of names of subclasses of `BaseImageProcessor` or `FeatureExtractionMixin`.
"""
# Get an instance of the corresponding class `XXXPipelineTests` in order to use `get_test_pipeline` and
# `run_pipeline_test`.
pipeline_test_class_name = pipeline_test_mapping[task]["test"].__name__
for tokenizer_name in tokenizer_names:
for processor_name in processor_names:
if self.is_pipeline_test_to_skip(
pipeline_test_class_name,
model_architecture.config_class,
model_architecture,
tokenizer_name,
processor_name,
):
logger.warning(
f"{self.__class__.__name__}::test_pipeline_{task.replace('-', '_')} is skipped: test is "
f"currently known to fail for: model `{model_architecture.__name__}` | tokenizer "
f"`{tokenizer_name}` | processor `{processor_name}`."
)
continue
self.run_pipeline_test(task, repo_name, model_architecture, tokenizer_name, processor_name, commit)
def run_pipeline_test(self, task, repo_name, model_architecture, tokenizer_name, processor_name, commit):
"""Run pipeline tests for a specific `task` with the give model class and tokenizer/processor class name
The model will be loaded from a model repository on the Hub.
Args:
task (`str`):
A task name. This should be a key in the mapping `pipeline_test_mapping`.
repo_name (`str`):
A model repository id on the Hub.
model_architecture (`type`):
A subclass of `PretrainedModel` or `PretrainedModel`.
tokenizer_name (`str`):
The name of a subclass of `PreTrainedTokenizerFast` or `PreTrainedTokenizer`.
processor_name (`str`):
The name of a subclass of `BaseImageProcessor` or `FeatureExtractionMixin`.
"""
repo_id = f"{TRANSFORMERS_TINY_MODEL_PATH}/{repo_name}"
if TRANSFORMERS_TINY_MODEL_PATH != "hf-internal-testing":
model_type = model_architecture.config_class.model_type
repo_id = os.path.join(TRANSFORMERS_TINY_MODEL_PATH, model_type, repo_name)
tokenizer = None
if tokenizer_name is not None:
tokenizer_class = getattr(transformers_module, tokenizer_name)
tokenizer = tokenizer_class.from_pretrained(repo_id, revision=commit)
processor = None
if processor_name is not None:
processor_class = getattr(transformers_module, processor_name)
# If the required packages (like `Pillow` or `torchaudio`) are not installed, this will fail.
try:
processor = processor_class.from_pretrained(repo_id, revision=commit)
except Exception:
logger.warning(
f"{self.__class__.__name__}::test_pipeline_{task.replace('-', '_')} is skipped: Could not load the "
f"processor from `{repo_id}` with `{processor_name}`."
)
return
# TODO: Maybe not upload such problematic tiny models to Hub.
if tokenizer is None and processor is None:
logger.warning(
f"{self.__class__.__name__}::test_pipeline_{task.replace('-', '_')} is skipped: Could not find or load "
f"any tokenizer / processor from `{repo_id}`."
)
return
# TODO: We should check if a model file is on the Hub repo. instead.
try:
model = model_architecture.from_pretrained(repo_id, revision=commit)
except Exception:
logger.warning(
f"{self.__class__.__name__}::test_pipeline_{task.replace('-', '_')} is skipped: Could not find or load "
f"the model from `{repo_id}` with `{model_architecture}`."
)
return
pipeline_test_class_name = pipeline_test_mapping[task]["test"].__name__
if self.is_pipeline_test_to_skip_more(pipeline_test_class_name, model.config, model, tokenizer, processor):
logger.warning(
f"{self.__class__.__name__}::test_pipeline_{task.replace('-', '_')} is skipped: test is "
f"currently known to fail for: model `{model_architecture.__name__}` | tokenizer "
f"`{tokenizer_name}` | processor `{processor_name}`."
)
return
# validate
validate_test_components(self, task, model, tokenizer, processor)
if hasattr(model, "eval"):
model = model.eval()
# Get an instance of the corresponding class `XXXPipelineTests` in order to use `get_test_pipeline` and
# `run_pipeline_test`.
task_test = pipeline_test_mapping[task]["test"]()
pipeline, examples = task_test.get_test_pipeline(model, tokenizer, processor)
if pipeline is None:
# The test can disable itself, but it should be very marginal
# Concerns: Wav2Vec2ForCTC without tokenizer test (FastTokenizer don't exist)
logger.warning(
f"{self.__class__.__name__}::test_pipeline_{task.replace('-', '_')} is skipped: Could not get the "
"pipeline for testing."
)
return
task_test.run_pipeline_test(pipeline, examples)
def run_batch_test(pipeline, examples):
# Need to copy because `Conversation` are stateful
if pipeline.tokenizer is not None and pipeline.tokenizer.pad_token_id is None:
return # No batching for this and it's OK
# 10 examples with batch size 4 means there needs to be a unfinished batch
# which is important for the unbatcher
def data(n):
for _ in range(n):
# Need to copy because Conversation object is mutated
yield copy.deepcopy(random.choice(examples))
out = []
if task == "conversational":
for item in pipeline(data(10), batch_size=4, max_new_tokens=5):
out.append(item)
else:
for item in pipeline(data(10), batch_size=4):
out.append(item)
self.assertEqual(len(out), 10)
run_batch_test(pipeline, examples)
@is_pipeline_test
def test_pipeline_audio_classification(self):
self.run_task_tests(task="audio-classification")
@is_pipeline_test
def test_pipeline_automatic_speech_recognition(self):
self.run_task_tests(task="automatic-speech-recognition")
@is_pipeline_test
def test_pipeline_conversational(self):
self.run_task_tests(task="conversational")
@is_pipeline_test
@require_vision
@require_timm
@require_torch
def test_pipeline_depth_estimation(self):
self.run_task_tests(task="depth-estimation")
@is_pipeline_test
@require_pytesseract
@require_torch
@require_vision
def test_pipeline_document_question_answering(self):
self.run_task_tests(task="document-question-answering")
@is_pipeline_test
def test_pipeline_feature_extraction(self):
self.run_task_tests(task="feature-extraction")
@is_pipeline_test
def test_pipeline_fill_mask(self):
self.run_task_tests(task="fill-mask")
@is_pipeline_test
@require_torch_or_tf
@require_vision
def test_pipeline_image_classification(self):
self.run_task_tests(task="image-classification")
@is_pipeline_test
@require_vision
@require_timm
@require_torch
def test_pipeline_image_segmentation(self):
self.run_task_tests(task="image-segmentation")
@is_pipeline_test
@require_vision
def test_pipeline_image_to_text(self):
self.run_task_tests(task="image-to-text")
@is_pipeline_test
@require_timm
@require_vision
@require_torch
def test_pipeline_image_feature_extraction(self):
self.run_task_tests(task="image-feature-extraction")
@unittest.skip(reason="`run_pipeline_test` is currently not implemented.")
@is_pipeline_test
@require_vision
@require_torch
def test_pipeline_mask_generation(self):
self.run_task_tests(task="mask-generation")
@is_pipeline_test
@require_vision
@require_timm
@require_torch
def test_pipeline_object_detection(self):
self.run_task_tests(task="object-detection")
@is_pipeline_test
def test_pipeline_question_answering(self):
self.run_task_tests(task="question-answering")
@is_pipeline_test
def test_pipeline_summarization(self):
self.run_task_tests(task="summarization")
@is_pipeline_test
def test_pipeline_table_question_answering(self):
self.run_task_tests(task="table-question-answering")
@is_pipeline_test
def test_pipeline_text2text_generation(self):
self.run_task_tests(task="text2text-generation")
@is_pipeline_test
def test_pipeline_text_classification(self):
self.run_task_tests(task="text-classification")
@is_pipeline_test
@require_torch_or_tf
def test_pipeline_text_generation(self):
self.run_task_tests(task="text-generation")
@is_pipeline_test
@require_torch
def test_pipeline_text_to_audio(self):
self.run_task_tests(task="text-to-audio")
@is_pipeline_test
def test_pipeline_token_classification(self):
self.run_task_tests(task="token-classification")
@is_pipeline_test
def test_pipeline_translation(self):
self.run_task_tests(task="translation")
@is_pipeline_test
@require_torch_or_tf
@require_vision
@require_decord
def test_pipeline_video_classification(self):
self.run_task_tests(task="video-classification")
@is_pipeline_test
@require_torch
@require_vision
def test_pipeline_visual_question_answering(self):
self.run_task_tests(task="visual-question-answering")
@is_pipeline_test
def test_pipeline_zero_shot(self):
self.run_task_tests(task="zero-shot")
@is_pipeline_test
@require_torch
def test_pipeline_zero_shot_audio_classification(self):
self.run_task_tests(task="zero-shot-audio-classification")
@is_pipeline_test
@require_vision
def test_pipeline_zero_shot_image_classification(self):
self.run_task_tests(task="zero-shot-image-classification")
@is_pipeline_test
@require_vision
@require_torch
def test_pipeline_zero_shot_object_detection(self):
self.run_task_tests(task="zero-shot-object-detection")
# This contains the test cases to be skipped without model architecture being involved.
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
"""Skip some tests based on the classes or their names without the instantiated objects.
This is to avoid calling `from_pretrained` (so reducing the runtime) if we already know the tests will fail.
"""
# No fix is required for this case.
if (
pipeline_test_casse_name == "DocumentQuestionAnsweringPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith("Fast")
):
# `DocumentQuestionAnsweringPipelineTests` requires a fast tokenizer.
return True
return False
def is_pipeline_test_to_skip_more(self, pipeline_test_casse_name, config, model, tokenizer, processor): # noqa
"""Skip some more tests based on the information from the instantiated objects."""
# No fix is required for this case.
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer is not None
and getattr(tokenizer, "pad_token", None) is None
and not tokenizer.__class__.__name__.endswith("Fast")
):
# `QAPipelineTests` doesn't work with a slow tokenizer that has no pad token.
return True
return False
def validate_test_components(test_case, task, model, tokenizer, processor):
# TODO: Move this to tiny model creation script
# head-specific (within a model type) necessary changes to the config
# 1. for `BlenderbotForCausalLM`
if model.__class__.__name__ == "BlenderbotForCausalLM":
model.config.encoder_no_repeat_ngram_size = 0
# TODO: Change the tiny model creation script: don't create models with problematic tokenizers
# Avoid `IndexError` in embedding layers
CONFIG_WITHOUT_VOCAB_SIZE = ["CanineConfig"]
if tokenizer is not None:
config_vocab_size = getattr(model.config, "vocab_size", None)
# For CLIP-like models
if config_vocab_size is None:
if hasattr(model.config, "text_config"):
config_vocab_size = getattr(model.config.text_config, "vocab_size", None)
elif hasattr(model.config, "text_encoder"):
config_vocab_size = getattr(model.config.text_encoder, "vocab_size", None)
if config_vocab_size is None and model.config.__class__.__name__ not in CONFIG_WITHOUT_VOCAB_SIZE:
raise ValueError(
"Could not determine `vocab_size` from model configuration while `tokenizer` is not `None`."
)
| transformers/tests/test_pipeline_mixin.py/0 | {
"file_path": "transformers/tests/test_pipeline_mixin.py",
"repo_id": "transformers",
"token_count": 9643
} | 177 |
# coding=utf-8
# Copyright 2020 The Hugging Face Team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import io
import unittest
from dataclasses import dataclass
from typing import Optional
from transformers import AlbertForMaskedLM
from transformers.testing_utils import require_torch
from transformers.utils import ModelOutput, is_torch_available
if is_torch_available():
import torch
from transformers.pytorch_utils import is_torch_greater_or_equal_than_2_2
@dataclass
class ModelOutputTest(ModelOutput):
a: float
b: Optional[float] = None
c: Optional[float] = None
class ModelOutputTester(unittest.TestCase):
def test_get_attributes(self):
x = ModelOutputTest(a=30)
self.assertEqual(x.a, 30)
self.assertIsNone(x.b)
self.assertIsNone(x.c)
with self.assertRaises(AttributeError):
_ = x.d
def test_index_with_ints_and_slices(self):
x = ModelOutputTest(a=30, b=10)
self.assertEqual(x[0], 30)
self.assertEqual(x[1], 10)
self.assertEqual(x[:2], (30, 10))
self.assertEqual(x[:], (30, 10))
x = ModelOutputTest(a=30, c=10)
self.assertEqual(x[0], 30)
self.assertEqual(x[1], 10)
self.assertEqual(x[:2], (30, 10))
self.assertEqual(x[:], (30, 10))
def test_index_with_strings(self):
x = ModelOutputTest(a=30, b=10)
self.assertEqual(x["a"], 30)
self.assertEqual(x["b"], 10)
with self.assertRaises(KeyError):
_ = x["c"]
x = ModelOutputTest(a=30, c=10)
self.assertEqual(x["a"], 30)
self.assertEqual(x["c"], 10)
with self.assertRaises(KeyError):
_ = x["b"]
def test_dict_like_properties(self):
x = ModelOutputTest(a=30)
self.assertEqual(list(x.keys()), ["a"])
self.assertEqual(list(x.values()), [30])
self.assertEqual(list(x.items()), [("a", 30)])
self.assertEqual(list(x), ["a"])
x = ModelOutputTest(a=30, b=10)
self.assertEqual(list(x.keys()), ["a", "b"])
self.assertEqual(list(x.values()), [30, 10])
self.assertEqual(list(x.items()), [("a", 30), ("b", 10)])
self.assertEqual(list(x), ["a", "b"])
x = ModelOutputTest(a=30, c=10)
self.assertEqual(list(x.keys()), ["a", "c"])
self.assertEqual(list(x.values()), [30, 10])
self.assertEqual(list(x.items()), [("a", 30), ("c", 10)])
self.assertEqual(list(x), ["a", "c"])
with self.assertRaises(Exception):
x = x.update({"d": 20})
with self.assertRaises(Exception):
del x["a"]
with self.assertRaises(Exception):
_ = x.pop("a")
with self.assertRaises(Exception):
_ = x.setdefault("d", 32)
def test_set_attributes(self):
x = ModelOutputTest(a=30)
x.a = 10
self.assertEqual(x.a, 10)
self.assertEqual(x["a"], 10)
def test_set_keys(self):
x = ModelOutputTest(a=30)
x["a"] = 10
self.assertEqual(x.a, 10)
self.assertEqual(x["a"], 10)
def test_instantiate_from_dict(self):
x = ModelOutputTest({"a": 30, "b": 10})
self.assertEqual(list(x.keys()), ["a", "b"])
self.assertEqual(x.a, 30)
self.assertEqual(x.b, 10)
def test_instantiate_from_iterator(self):
x = ModelOutputTest([("a", 30), ("b", 10)])
self.assertEqual(list(x.keys()), ["a", "b"])
self.assertEqual(x.a, 30)
self.assertEqual(x.b, 10)
with self.assertRaises(ValueError):
_ = ModelOutputTest([("a", 30), (10, 10)])
x = ModelOutputTest(a=(30, 30))
self.assertEqual(list(x.keys()), ["a"])
self.assertEqual(x.a, (30, 30))
@require_torch
def test_torch_pytree(self):
# ensure torch.utils._pytree treats ModelOutput subclasses as nodes (and not leaves)
# this is important for DistributedDataParallel gradient synchronization with static_graph=True
import torch.utils._pytree as pytree
x = ModelOutput({"a": 1.0, "c": 2.0})
self.assertFalse(pytree._is_leaf(x))
x = ModelOutputTest(a=1.0, c=2.0)
self.assertFalse(pytree._is_leaf(x))
expected_flat_outs = [1.0, 2.0]
expected_tree_spec = pytree.TreeSpec(ModelOutputTest, ["a", "c"], [pytree.LeafSpec(), pytree.LeafSpec()])
actual_flat_outs, actual_tree_spec = pytree.tree_flatten(x)
self.assertEqual(expected_flat_outs, actual_flat_outs)
self.assertEqual(expected_tree_spec, actual_tree_spec)
unflattened_x = pytree.tree_unflatten(actual_flat_outs, actual_tree_spec)
self.assertEqual(x, unflattened_x)
if is_torch_greater_or_equal_than_2_2:
self.assertEqual(
pytree.treespec_dumps(actual_tree_spec),
'[1, {"type": "tests.utils.test_model_output.ModelOutputTest", "context": "[\\"a\\", \\"c\\"]", "children_spec": [{"type": null, "context": null, "children_spec": []}, {"type": null, "context": null, "children_spec": []}]}]',
)
# TODO: @ydshieh
@unittest.skip("CPU OOM")
@require_torch
def test_export_serialization(self):
if not is_torch_greater_or_equal_than_2_2:
return
model_cls = AlbertForMaskedLM
model_config = model_cls.config_class()
model = model_cls(model_config)
input_dict = {"input_ids": torch.randint(0, 30000, (1, 512), dtype=torch.int64, requires_grad=False)}
ep = torch.export.export(model, (), input_dict)
buffer = io.BytesIO()
torch.export.save(ep, buffer)
buffer.seek(0)
loaded_ep = torch.export.load(buffer)
input_dict = {"input_ids": torch.randint(0, 30000, (1, 512), dtype=torch.int64, requires_grad=False)}
assert torch.allclose(model(**input_dict).logits, loaded_ep(**input_dict).logits)
class ModelOutputTestNoDataclass(ModelOutput):
"""Invalid test subclass of ModelOutput where @dataclass decorator is not used"""
a: float
b: Optional[float] = None
c: Optional[float] = None
class ModelOutputSubclassTester(unittest.TestCase):
def test_direct_model_output(self):
# Check that direct usage of ModelOutput instantiates without errors
ModelOutput({"a": 1.1})
def test_subclass_no_dataclass(self):
# Check that a subclass of ModelOutput without @dataclass is invalid
# A valid subclass is inherently tested other unit tests above.
with self.assertRaises(TypeError):
ModelOutputTestNoDataclass(a=1.1, b=2.2, c=3.3)
| transformers/tests/utils/test_model_output.py/0 | {
"file_path": "transformers/tests/utils/test_model_output.py",
"repo_id": "transformers",
"token_count": 3168
} | 178 |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import glob
import os
from get_test_info import get_tester_classes
if __name__ == "__main__":
failures = []
pattern = os.path.join("tests", "models", "**", "test_modeling_*.py")
test_files = glob.glob(pattern)
# TODO: deal with TF/Flax too
test_files = [
x for x in test_files if not (x.startswith("test_modeling_tf_") or x.startswith("test_modeling_flax_"))
]
for test_file in test_files:
tester_classes = get_tester_classes(test_file)
for tester_class in tester_classes:
# A few tester classes don't have `parent` parameter in `__init__`.
# TODO: deal this better
try:
tester = tester_class(parent=None)
except Exception:
continue
if hasattr(tester, "get_config"):
config = tester.get_config()
for k, v in config.to_dict().items():
if isinstance(v, int):
target = None
if k in ["vocab_size"]:
target = 100
elif k in ["max_position_embeddings"]:
target = 128
elif k in ["hidden_size", "d_model"]:
target = 40
elif k == ["num_layers", "num_hidden_layers", "num_encoder_layers", "num_decoder_layers"]:
target = 5
if target is not None and v > target:
failures.append(
f"{tester_class.__name__} will produce a `config` of type `{config.__class__.__name__}`"
f' with config["{k}"] = {v} which is too large for testing! Set its value to be smaller'
f" than {target}."
)
if len(failures) > 0:
raise Exception(f"There were {len(failures)} failures:\n" + "\n".join(failures))
| transformers/utils/check_model_tester.py/0 | {
"file_path": "transformers/utils/check_model_tester.py",
"repo_id": "transformers",
"token_count": 1240
} | 179 |
from transformers import BertTokenizer
class CustomTokenizer(BertTokenizer):
pass
| transformers/utils/test_module/custom_tokenization.py/0 | {
"file_path": "transformers/utils/test_module/custom_tokenization.py",
"repo_id": "transformers",
"token_count": 25
} | 180 |
version: 2.1
setup: true
orbs:
continuation: circleci/[email protected]
parameters:
nightly:
type: boolean
default: false
jobs:
# Ensure running with CircleCI/huggingface
check_circleci_user:
docker:
- image: cimg/python:3.8.12
parallelism: 1
steps:
- run: echo $CIRCLE_PROJECT_USERNAME
- run: |
if [ "$CIRCLE_PROJECT_USERNAME" = "huggingface" ]; then
exit 0
else
echo "The CI is running under $CIRCLE_PROJECT_USERNAME personal account. Please follow https://support.circleci.com/hc/en-us/articles/360008097173-Troubleshooting-why-pull-requests-are-not-triggering-jobs-on-my-organization- to fix it."; exit -1
fi
# Fetch the tests to run
fetch_tests:
working_directory: ~/transformers
docker:
- image: cimg/python:3.8.12
parallelism: 1
steps:
- checkout
- run: pip install --upgrade --upgrade-strategy eager pip
- run: pip install -U --upgrade-strategy eager GitPython
- run: pip install -U --upgrade-strategy eager .
- run: mkdir -p test_preparation
- run: python utils/tests_fetcher.py | tee tests_fetched_summary.txt
- store_artifacts:
path: ~/transformers/tests_fetched_summary.txt
- run: |
if [ -f test_list.txt ]; then
cp test_list.txt test_preparation/test_list.txt
else
touch test_preparation/test_list.txt
fi
- run: |
if [ -f examples_test_list.txt ]; then
mv examples_test_list.txt test_preparation/examples_test_list.txt
else
touch test_preparation/examples_test_list.txt
fi
- run: |
if [ -f filtered_test_list_cross_tests.txt ]; then
mv filtered_test_list_cross_tests.txt test_preparation/filtered_test_list_cross_tests.txt
else
touch test_preparation/filtered_test_list_cross_tests.txt
fi
- run: |
if [ -f doctest_list.txt ]; then
cp doctest_list.txt test_preparation/doctest_list.txt
else
touch test_preparation/doctest_list.txt
fi
- run: |
if [ -f test_repo_utils.txt ]; then
mv test_repo_utils.txt test_preparation/test_repo_utils.txt
else
touch test_preparation/test_repo_utils.txt
fi
- run: python utils/tests_fetcher.py --filter_tests
- run: |
if [ -f test_list.txt ]; then
mv test_list.txt test_preparation/filtered_test_list.txt
else
touch test_preparation/filtered_test_list.txt
fi
- store_artifacts:
path: test_preparation/test_list.txt
- store_artifacts:
path: test_preparation/doctest_list.txt
- store_artifacts:
path: ~/transformers/test_preparation/filtered_test_list.txt
- store_artifacts:
path: test_preparation/examples_test_list.txt
- run: python .circleci/create_circleci_config.py --fetcher_folder test_preparation
- run: |
if [ ! -s test_preparation/generated_config.yml ]; then
echo "No tests to run, exiting early!"
circleci-agent step halt
fi
- run: cp test_preparation/generated_config.yml test_preparation/generated_config.txt
- store_artifacts:
path: test_preparation/generated_config.txt
- store_artifacts:
path: test_preparation/filtered_test_list_cross_tests.txt
- continuation/continue:
configuration_path: test_preparation/generated_config.yml
# To run all tests for the nightly build
fetch_all_tests:
working_directory: ~/transformers
docker:
- image: cimg/python:3.8.12
parallelism: 1
steps:
- checkout
- run: pip install --upgrade --upgrade-strategy eager pip
- run: pip install -U --upgrade-strategy eager GitPython
- run: pip install -U --upgrade-strategy eager .
- run: |
mkdir test_preparation
echo -n "tests" > test_preparation/test_list.txt
echo -n "all" > test_preparation/examples_test_list.txt
echo -n "tests/repo_utils" > test_preparation/test_repo_utils.txt
- run: |
echo -n "tests" > test_list.txt
python utils/tests_fetcher.py --filter_tests
mv test_list.txt test_preparation/filtered_test_list.txt
- run: python .circleci/create_circleci_config.py --fetcher_folder test_preparation
- run: cp test_preparation/generated_config.yml test_preparation/generated_config.txt
- store_artifacts:
path: test_preparation/generated_config.txt
- continuation/continue:
configuration_path: test_preparation/generated_config.yml
check_code_quality:
working_directory: ~/transformers
docker:
- image: cimg/python:3.8.12
resource_class: large
environment:
TRANSFORMERS_IS_CI: yes
PYTEST_TIMEOUT: 120
parallelism: 1
steps:
- checkout
- restore_cache:
keys:
- v0.7-code_quality-pip-{{ checksum "setup.py" }}
- v0.7-code-quality-pip
- restore_cache:
keys:
- v0.7-code_quality-site-packages-{{ checksum "setup.py" }}
- v0.7-code-quality-site-packages
- run: pip install --upgrade --upgrade-strategy eager pip
- run: pip install -U --upgrade-strategy eager .[all,quality]
- save_cache:
key: v0.7-code_quality-pip-{{ checksum "setup.py" }}
paths:
- '~/.cache/pip'
- save_cache:
key: v0.7-code_quality-site-packages-{{ checksum "setup.py" }}
paths:
- '~/.pyenv/versions/'
- run:
name: Show installed libraries and their versions
command: pip freeze | tee installed.txt
- store_artifacts:
path: ~/transformers/installed.txt
- run: ruff check examples tests src utils
- run: ruff format tests src utils --check
- run: python utils/custom_init_isort.py --check_only
- run: python utils/sort_auto_mappings.py --check_only
- run: python utils/check_doc_toc.py
check_repository_consistency:
working_directory: ~/transformers
docker:
- image: cimg/python:3.8.12
resource_class: large
environment:
TRANSFORMERS_IS_CI: yes
PYTEST_TIMEOUT: 120
parallelism: 1
steps:
- checkout
- restore_cache:
keys:
- v0.7-repository_consistency-pip-{{ checksum "setup.py" }}
- v0.7-repository_consistency-pip
- restore_cache:
keys:
- v0.7-repository_consistency-site-packages-{{ checksum "setup.py" }}
- v0.7-repository_consistency-site-packages
- run: pip install --upgrade --upgrade-strategy eager pip
- run: pip install -U --upgrade-strategy eager .[all,quality]
- save_cache:
key: v0.7-repository_consistency-pip-{{ checksum "setup.py" }}
paths:
- '~/.cache/pip'
- save_cache:
key: v0.7-repository_consistency-site-packages-{{ checksum "setup.py" }}
paths:
- '~/.pyenv/versions/'
- run:
name: Show installed libraries and their versions
command: pip freeze | tee installed.txt
- store_artifacts:
path: ~/transformers/installed.txt
- run: python utils/check_copies.py
- run: python utils/check_table.py
- run: python utils/check_dummies.py
- run: python utils/check_repo.py
- run: python utils/check_inits.py
- run: python utils/check_config_docstrings.py
- run: python utils/check_config_attributes.py
- run: python utils/check_doctest_list.py
- run: make deps_table_check_updated
- run: python utils/update_metadata.py --check-only
- run: python utils/check_task_guides.py
- run: python utils/check_docstrings.py
- run: python utils/check_support_list.py
workflows:
version: 2
setup_and_quality:
when:
not: <<pipeline.parameters.nightly>>
jobs:
- check_circleci_user
- check_code_quality
- check_repository_consistency
- fetch_tests
nightly:
when: <<pipeline.parameters.nightly>>
jobs:
- check_circleci_user
- check_code_quality
- check_repository_consistency
- fetch_all_tests | transformers/.circleci/config.yml/0 | {
"file_path": "transformers/.circleci/config.yml",
"repo_id": "transformers",
"token_count": 5200
} | 0 |
FROM rocm/dev-ubuntu-22.04:5.6
LABEL maintainer="Hugging Face"
ARG DEBIAN_FRONTEND=noninteractive
ARG PYTORCH='2.1.1'
ARG TORCH_VISION='0.16.1'
ARG TORCH_AUDIO='2.1.1'
ARG ROCM='5.6'
RUN apt update && \
apt install -y --no-install-recommends \
libaio-dev \
git \
# These are required to build deepspeed.
python3-dev \
python-is-python3 \
rocrand-dev \
rocthrust-dev \
hipsparse-dev \
hipblas-dev \
rocblas-dev && \
apt clean && \
rm -rf /var/lib/apt/lists/*
RUN python3 -m pip install --no-cache-dir --upgrade pip ninja "pydantic<2"
RUN python3 -m pip uninstall -y apex torch torchvision torchaudio
RUN python3 -m pip install torch==$PYTORCH torchvision==$TORCH_VISION torchaudio==$TORCH_AUDIO --index-url https://download.pytorch.org/whl/rocm$ROCM --no-cache-dir
# Pre-build DeepSpeed, so it's be ready for testing (to avoid timeout)
RUN DS_BUILD_CPU_ADAM=1 DS_BUILD_FUSED_ADAM=1 python3 -m pip install deepspeed --global-option="build_ext" --global-option="-j8" --no-cache-dir -v --disable-pip-version-check 2>&1
ARG REF=main
WORKDIR /
# Invalidate docker cache from here if new commit is available.
ADD https://api.github.com/repos/huggingface/transformers/git/refs/heads/main version.json
RUN git clone https://github.com/huggingface/transformers && cd transformers && git checkout $REF
RUN python3 -m pip install --no-cache-dir ./transformers[accelerate,testing,sentencepiece,sklearn]
# When installing in editable mode, `transformers` is not recognized as a package.
# this line must be added in order for python to be aware of transformers.
RUN cd transformers && python3 setup.py develop
RUN python3 -c "from deepspeed.launcher.runner import main" | transformers/docker/transformers-pytorch-deepspeed-amd-gpu/Dockerfile/0 | {
"file_path": "transformers/docker/transformers-pytorch-deepspeed-amd-gpu/Dockerfile",
"repo_id": "transformers",
"token_count": 639
} | 1 |
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# Wie erstellt man eine benutzerdefinierte Pipeline?
In dieser Anleitung sehen wir uns an, wie Sie eine benutzerdefinierte Pipeline erstellen und sie auf dem [Hub](https://hf.co/models) freigeben oder sie der
🤗 Transformers-Bibliothek hinzufügen.
Zuallererst müssen Sie entscheiden, welche Roheingaben die Pipeline verarbeiten kann. Es kann sich um Strings, rohe Bytes,
Dictionaries oder was auch immer die wahrscheinlichste gewünschte Eingabe ist. Versuchen Sie, diese Eingaben so rein wie möglich in Python zu halten
denn das macht die Kompatibilität einfacher (auch mit anderen Sprachen über JSON). Dies werden die Eingaben der
Pipeline (`Vorverarbeitung`).
Definieren Sie dann die `Outputs`. Dieselbe Richtlinie wie für die Eingänge. Je einfacher, desto besser. Dies werden die Ausgaben der
Methode `Postprocess`.
Beginnen Sie damit, die Basisklasse `Pipeline` mit den 4 Methoden zu erben, die für die Implementierung von `preprocess` benötigt werden,
Weiterleiten", "Nachbearbeitung" und "Parameter säubern".
```python
from transformers import Pipeline
class MyPipeline(Pipeline):
def _sanitize_parameters(self, **kwargs):
preprocess_kwargs = {}
if "maybe_arg" in kwargs:
preprocess_kwargs["maybe_arg"] = kwargs["maybe_arg"]
return preprocess_kwargs, {}, {}
def preprocess(self, inputs, maybe_arg=2):
model_input = Tensor(inputs["input_ids"])
return {"model_input": model_input}
def _forward(self, model_inputs):
# model_inputs == {"model_input": model_input}
outputs = self.model(**model_inputs)
# Maybe {"logits": Tensor(...)}
return outputs
def postprocess(self, model_outputs):
best_class = model_outputs["logits"].softmax(-1)
return best_class
```
Die Struktur dieser Aufteilung soll eine relativ nahtlose Unterstützung für CPU/GPU ermöglichen und gleichzeitig die Durchführung von
Vor-/Nachbearbeitung auf der CPU in verschiedenen Threads
Preprocess" nimmt die ursprünglich definierten Eingaben und wandelt sie in etwas um, das in das Modell eingespeist werden kann. Es kann
mehr Informationen enthalten und ist normalerweise ein `Dict`.
`_forward` ist das Implementierungsdetail und ist nicht dafür gedacht, direkt aufgerufen zu werden. Weiterleiten" ist die bevorzugte
aufgerufene Methode, da sie Sicherheitsvorkehrungen enthält, die sicherstellen, dass alles auf dem erwarteten Gerät funktioniert. Wenn etwas
mit einem realen Modell verknüpft ist, gehört es in die Methode `_forward`, alles andere gehört in die Methoden preprocess/postprocess.
Die Methode `Postprocess` nimmt die Ausgabe von `_forward` und verwandelt sie in die endgültige Ausgabe, die zuvor festgelegt wurde.
zuvor entschieden wurde.
Die Methode `_sanitize_parameters` ermöglicht es dem Benutzer, beliebige Parameter zu übergeben, wann immer er möchte, sei es bei der Initialisierung
Zeit `pipeline(...., maybe_arg=4)` oder zur Aufrufzeit `pipe = pipeline(...); output = pipe(...., maybe_arg=4)`.
Die Rückgabe von `_sanitize_parameters` sind die 3 Dicts von kwargs, die direkt an `preprocess` übergeben werden,
`_forward` und `postprocess` übergeben werden. Füllen Sie nichts aus, wenn der Aufrufer keinen zusätzlichen Parameter angegeben hat. Das
erlaubt es, die Standardargumente in der Funktionsdefinition beizubehalten, was immer "natürlicher" ist.
Ein klassisches Beispiel wäre das Argument `top_k` in der Nachbearbeitung bei Klassifizierungsaufgaben.
```python
>>> pipe = pipeline("my-new-task")
>>> pipe("This is a test")
[{"label": "1-star", "score": 0.8}, {"label": "2-star", "score": 0.1}, {"label": "3-star", "score": 0.05}
{"label": "4-star", "score": 0.025}, {"label": "5-star", "score": 0.025}]
>>> pipe("This is a test", top_k=2)
[{"label": "1-star", "score": 0.8}, {"label": "2-star", "score": 0.1}]
```
In order to achieve that, we'll update our `postprocess` method with a default parameter to `5`. and edit
`_sanitize_parameters` to allow this new parameter.
```python
def postprocess(self, model_outputs, top_k=5):
best_class = model_outputs["logits"].softmax(-1)
# Add logic to handle top_k
return best_class
def _sanitize_parameters(self, **kwargs):
preprocess_kwargs = {}
if "maybe_arg" in kwargs:
preprocess_kwargs["maybe_arg"] = kwargs["maybe_arg"]
postprocess_kwargs = {}
if "top_k" in kwargs:
postprocess_kwargs["top_k"] = kwargs["top_k"]
return preprocess_kwargs, {}, postprocess_kwargs
```
Versuchen Sie, die Eingaben/Ausgaben sehr einfach und idealerweise JSON-serialisierbar zu halten, da dies die Verwendung der Pipeline sehr einfach macht
ohne dass die Benutzer neue Arten von Objekten verstehen müssen. Es ist auch relativ üblich, viele verschiedene Arten von Argumenten zu unterstützen
von Argumenten zu unterstützen (Audiodateien, die Dateinamen, URLs oder reine Bytes sein können).
## Hinzufügen zur Liste der unterstützten Aufgaben
Um Ihre `neue Aufgabe` in die Liste der unterstützten Aufgaben aufzunehmen, müssen Sie sie zur `PIPELINE_REGISTRY` hinzufügen:
```python
from transformers.pipelines import PIPELINE_REGISTRY
PIPELINE_REGISTRY.register_pipeline(
"new-task",
pipeline_class=MyPipeline,
pt_model=AutoModelForSequenceClassification,
)
```
Wenn Sie möchten, können Sie ein Standardmodell angeben. In diesem Fall sollte es mit einer bestimmten Revision (die der Name einer Verzweigung oder ein Commit-Hash sein kann, hier haben wir `"abcdef"` genommen) sowie mit dem Typ versehen sein:
```python
PIPELINE_REGISTRY.register_pipeline(
"new-task",
pipeline_class=MyPipeline,
pt_model=AutoModelForSequenceClassification,
default={"pt": ("user/awesome_model", "abcdef")},
type="text", # current support type: text, audio, image, multimodal
)
```
## Teilen Sie Ihre Pipeline auf dem Hub
Um Ihre benutzerdefinierte Pipeline auf dem Hub freizugeben, müssen Sie lediglich den benutzerdefinierten Code Ihrer `Pipeline`-Unterklasse in einer
Python-Datei speichern. Nehmen wir zum Beispiel an, Sie möchten eine benutzerdefinierte Pipeline für die Klassifizierung von Satzpaaren wie folgt verwenden:
```py
import numpy as np
from transformers import Pipeline
def softmax(outputs):
maxes = np.max(outputs, axis=-1, keepdims=True)
shifted_exp = np.exp(outputs - maxes)
return shifted_exp / shifted_exp.sum(axis=-1, keepdims=True)
class PairClassificationPipeline(Pipeline):
def _sanitize_parameters(self, **kwargs):
preprocess_kwargs = {}
if "second_text" in kwargs:
preprocess_kwargs["second_text"] = kwargs["second_text"]
return preprocess_kwargs, {}, {}
def preprocess(self, text, second_text=None):
return self.tokenizer(text, text_pair=second_text, return_tensors=self.framework)
def _forward(self, model_inputs):
return self.model(**model_inputs)
def postprocess(self, model_outputs):
logits = model_outputs.logits[0].numpy()
probabilities = softmax(logits)
best_class = np.argmax(probabilities)
label = self.model.config.id2label[best_class]
score = probabilities[best_class].item()
logits = logits.tolist()
return {"label": label, "score": score, "logits": logits}
```
Die Implementierung ist Framework-unabhängig und funktioniert für PyTorch- und TensorFlow-Modelle. Wenn wir dies in einer Datei
einer Datei namens `pair_classification.py` gespeichert haben, können wir sie importieren und wie folgt registrieren:
```py
from pair_classification import PairClassificationPipeline
from transformers.pipelines import PIPELINE_REGISTRY
from transformers import AutoModelForSequenceClassification, TFAutoModelForSequenceClassification
PIPELINE_REGISTRY.register_pipeline(
"pair-classification",
pipeline_class=PairClassificationPipeline,
pt_model=AutoModelForSequenceClassification,
tf_model=TFAutoModelForSequenceClassification,
)
```
Sobald dies geschehen ist, können wir es mit einem vortrainierten Modell verwenden. Zum Beispiel wurde `sgugger/finetuned-bert-mrpc` auf den
auf den MRPC-Datensatz abgestimmt, der Satzpaare als Paraphrasen oder nicht klassifiziert.
```py
from transformers import pipeline
classifier = pipeline("pair-classification", model="sgugger/finetuned-bert-mrpc")
```
Dann können wir sie auf dem Hub mit der Methode `save_pretrained` in einem `Repository` freigeben:
```py
from huggingface_hub import Repository
repo = Repository("test-dynamic-pipeline", clone_from="{your_username}/test-dynamic-pipeline")
classifier.save_pretrained("test-dynamic-pipeline")
repo.push_to_hub()
```
Dadurch wird die Datei, in der Sie `PairClassificationPipeline` definiert haben, in den Ordner `"test-dynamic-pipeline"` kopiert,
und speichert das Modell und den Tokenizer der Pipeline, bevor Sie alles in das Repository verschieben
`{Ihr_Benutzername}/test-dynamic-pipeline`. Danach kann jeder die Pipeline verwenden, solange er die Option
`trust_remote_code=True` angeben:
```py
from transformers import pipeline
classifier = pipeline(model="{your_username}/test-dynamic-pipeline", trust_remote_code=True)
```
## Hinzufügen der Pipeline zu 🤗 Transformers
Wenn Sie Ihre Pipeline zu 🤗 Transformers beitragen möchten, müssen Sie ein neues Modul im Untermodul `pipelines` hinzufügen
mit dem Code Ihrer Pipeline hinzufügen. Fügen Sie es dann der Liste der in `pipelines/__init__.py` definierten Aufgaben hinzu.
Dann müssen Sie noch Tests hinzufügen. Erstellen Sie eine neue Datei `tests/test_pipelines_MY_PIPELINE.py` mit Beispielen für die anderen Tests.
Die Funktion `run_pipeline_test` ist sehr allgemein gehalten und läuft auf kleinen Zufallsmodellen auf jeder möglichen
Architektur, wie durch `model_mapping` und `tf_model_mapping` definiert.
Dies ist sehr wichtig, um die zukünftige Kompatibilität zu testen, d.h. wenn jemand ein neues Modell für
`XXXForQuestionAnswering` hinzufügt, wird der Pipeline-Test versuchen, mit diesem Modell zu arbeiten. Da die Modelle zufällig sind, ist es
ist es unmöglich, die tatsächlichen Werte zu überprüfen. Deshalb gibt es eine Hilfsfunktion `ANY`, die einfach versucht, die
Ausgabe der Pipeline TYPE.
Außerdem *müssen* Sie 2 (idealerweise 4) Tests implementieren.
- `test_small_model_pt` : Definieren Sie 1 kleines Modell für diese Pipeline (es spielt keine Rolle, ob die Ergebnisse keinen Sinn ergeben)
und testen Sie die Ausgaben der Pipeline. Die Ergebnisse sollten die gleichen sein wie bei `test_small_model_tf`.
- `test_small_model_tf` : Definieren Sie 1 kleines Modell für diese Pipeline (es spielt keine Rolle, ob die Ergebnisse keinen Sinn ergeben)
und testen Sie die Ausgaben der Pipeline. Die Ergebnisse sollten die gleichen sein wie bei `test_small_model_pt`.
- `test_large_model_pt` (`optional`): Testet die Pipeline an einer echten Pipeline, bei der die Ergebnisse
Sinn machen. Diese Tests sind langsam und sollten als solche gekennzeichnet werden. Hier geht es darum, die Pipeline zu präsentieren und sicherzustellen
sicherzustellen, dass es in zukünftigen Versionen keine Abweichungen gibt.
- `test_large_model_tf` (`optional`): Testet die Pipeline an einer echten Pipeline, bei der die Ergebnisse
Sinn machen. Diese Tests sind langsam und sollten als solche gekennzeichnet werden. Hier geht es darum, die Pipeline zu präsentieren und sicherzustellen
sicherzustellen, dass es in zukünftigen Versionen keine Abweichungen gibt.
| transformers/docs/source/de/add_new_pipeline.md/0 | {
"file_path": "transformers/docs/source/de/add_new_pipeline.md",
"repo_id": "transformers",
"token_count": 4595
} | 2 |
<!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# Transformers Agents
<Tip warning={true}>
Transformers Agents ist eine experimentelle API, die jederzeit geändert werden kann. Die von den Agenten zurückgegebenen Ergebnisse
zurückgegeben werden, können variieren, da sich die APIs oder die zugrunde liegenden Modelle ändern können.
</Tip>
Transformers Version v4.29.0, die auf dem Konzept von *Tools* und *Agenten* aufbaut. Sie können damit spielen in
[dieses Colab](https://colab.research.google.com/drive/1c7MHD-T1forUPGcC_jlwsIptOzpG3hSj).
Kurz gesagt, es bietet eine API für natürliche Sprache auf der Grundlage von Transformers: Wir definieren eine Reihe von kuratierten Tools und entwerfen einen
Agenten, um natürliche Sprache zu interpretieren und diese Werkzeuge zu verwenden. Es ist von vornherein erweiterbar; wir haben einige relevante Tools kuratiert,
aber wir werden Ihnen zeigen, wie das System einfach erweitert werden kann, um jedes von der Community entwickelte Tool zu verwenden.
Beginnen wir mit einigen Beispielen dafür, was mit dieser neuen API erreicht werden kann. Sie ist besonders leistungsfähig, wenn es um
Sie ist besonders leistungsstark, wenn es um multimodale Aufgaben geht. Lassen Sie uns also eine Runde drehen, um Bilder zu erzeugen und Text vorzulesen.
```py
agent.run("Caption the following image", image=image)
```
| **Input** | **Output** |
|-----------------------------------------------------------------------------------------------------------------------------|-----------------------------------|
| <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/beaver.png" width=200> | A beaver is swimming in the water |
---
```py
agent.run("Read the following text out loud", text=text)
```
| **Input** | **Output** |
|-------------------------------------------------------------------------------------------------------------------------|----------------------------------------------|
| A beaver is swimming in the water | <audio controls><source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tts_example.wav" type="audio/wav"> your browser does not support the audio element. </audio>
---
```py
agent.run(
"In the following `document`, where will the TRRF Scientific Advisory Council Meeting take place?",
document=document,
)
```
| **Input** | **Output** |
|-----------------------------------------------------------------------------------------------------------------------------|----------------|
| <img src="https://datasets-server.huggingface.co/assets/hf-internal-testing/example-documents/--/hf-internal-testing--example-documents/test/0/image/image.jpg" width=200> | ballroom foyer |
## Schnellstart
Bevor Sie `agent.run` verwenden können, müssen Sie einen Agenten instanziieren, der ein großes Sprachmodell (LLM) ist.
Wir bieten Unterstützung für openAI-Modelle sowie für OpenSource-Alternativen von BigCode und OpenAssistant. Die openAI
Modelle sind leistungsfähiger (erfordern aber einen openAI-API-Schlüssel, können also nicht kostenlos verwendet werden); Hugging Face
bietet kostenlosen Zugang zu Endpunkten für BigCode- und OpenAssistant-Modelle.
To start with, please install the `agents` extras in order to install all default dependencies.
```bash
pip install transformers[agents]
```
Um openAI-Modelle zu verwenden, instanziieren Sie einen [`OpenAiAgent`], nachdem Sie die `openai`-Abhängigkeit installiert haben:
```bash
pip install openai
```
```py
from transformers import OpenAiAgent
agent = OpenAiAgent(model="text-davinci-003", api_key="<your_api_key>")
```
Um BigCode oder OpenAssistant zu verwenden, melden Sie sich zunächst an, um Zugriff auf die Inference API zu erhalten:
```py
from huggingface_hub import login
login("<YOUR_TOKEN>")
```
Dann instanziieren Sie den Agenten
```py
from transformers import HfAgent
# Starcoder
agent = HfAgent("https://api-inference.huggingface.co/models/bigcode/starcoder")
# StarcoderBase
# agent = HfAgent("https://api-inference.huggingface.co/models/bigcode/starcoderbase")
# OpenAssistant
# agent = HfAgent(url_endpoint="https://api-inference.huggingface.co/models/OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5")
```
Dies geschieht mit der Inferenz-API, die Hugging Face derzeit kostenlos zur Verfügung stellt. Wenn Sie Ihren eigenen Inferenz
Endpunkt für dieses Modell (oder einen anderen) haben, können Sie die obige URL durch Ihren URL-Endpunkt ersetzen.
<Tip>
StarCoder und OpenAssistant sind kostenlos und leisten bei einfachen Aufgaben bewundernswert gute Arbeit. Allerdings halten die Kontrollpunkte
nicht, wenn es um komplexere Aufforderungen geht. Wenn Sie mit einem solchen Problem konfrontiert sind, empfehlen wir Ihnen, das OpenAI
Modell auszuprobieren, das zwar leider nicht quelloffen ist, aber zur Zeit eine bessere Leistung erbringt.
</Tip>
Sie sind jetzt startklar! Lassen Sie uns in die beiden APIs eintauchen, die Ihnen jetzt zur Verfügung stehen.
### Einzelne Ausführung (run)
Die Methode der einmaligen Ausführung ist die Verwendung der [`~Agent.run`] Methode des Agenten:
```py
agent.run("Draw me a picture of rivers and lakes.")
```
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rivers_and_lakes.png" width=200>
Es wählt automatisch das (oder die) Werkzeug(e) aus, das (die) für die von Ihnen gewünschte Aufgabe geeignet ist (sind) und führt es (sie) entsprechend aus. Es
kann eine oder mehrere Aufgaben in der gleichen Anweisung ausführen (je komplexer Ihre Anweisung ist, desto wahrscheinlicher ist ein
der Agent scheitern).
```py
agent.run("Draw me a picture of the sea then transform the picture to add an island")
```
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/sea_and_island.png" width=200>
<br/>
Jede [`~Agent.run`] Operation ist unabhängig, so dass Sie sie mehrmals hintereinander mit unterschiedlichen Aufgaben ausführen können.
Beachten Sie, dass Ihr `Agent` nur ein großsprachiges Modell ist, so dass kleine Variationen in Ihrer Eingabeaufforderung völlig unterschiedliche Ergebnisse liefern können.
unterschiedliche Ergebnisse liefern. Es ist wichtig, dass Sie die Aufgabe, die Sie ausführen möchten, so genau wie möglich erklären. Wir gehen noch weiter ins Detail
wie man gute Prompts schreibt [hier](custom_tools#writing-good-user-inputs).
Wenn Sie einen Status über Ausführungszeiten hinweg beibehalten oder dem Agenten Nicht-Text-Objekte übergeben möchten, können Sie dies tun, indem Sie
Variablen, die der Agent verwenden soll. Sie könnten zum Beispiel das erste Bild von Flüssen und Seen erzeugen,
und das Modell bitten, dieses Bild zu aktualisieren und eine Insel hinzuzufügen, indem Sie Folgendes tun:
```python
picture = agent.run("Generate a picture of rivers and lakes.")
updated_picture = agent.run("Transform the image in `picture` to add an island to it.", picture=picture)
```
<Tip>
Dies kann hilfreich sein, wenn das Modell Ihre Anfrage nicht verstehen kann und die Werkzeuge verwechselt. Ein Beispiel wäre:
```py
agent.run("Draw me the picture of a capybara swimming in the sea")
```
Hier könnte das Modell auf zwei Arten interpretieren:
- Die Funktion `Text-zu-Bild` erzeugt ein Wasserschwein, das im Meer schwimmt.
- Oder Sie lassen das `Text-zu-Bild` ein Wasserschwein erzeugen und verwenden dann das Werkzeug `Bildtransformation`, um es im Meer schwimmen zu lassen.
Falls Sie das erste Szenario erzwingen möchten, können Sie dies tun, indem Sie die Eingabeaufforderung als Argument übergeben:
```py
agent.run("Draw me a picture of the `prompt`", prompt="a capybara swimming in the sea")
```
</Tip>
### Chat-basierte Ausführung (Chat)
Der Agent verfügt auch über einen Chat-basierten Ansatz, der die Methode [`~Agent.chat`] verwendet:
```py
agent.chat("Generate a picture of rivers and lakes")
```
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rivers_and_lakes.png" width=200>
```py
agent.chat("Transform the picture so that there is a rock in there")
```
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rivers_and_lakes_and_beaver.png" width=200>
<br/>
Dies ist ein interessanter Ansatz, wenn Sie den Zustand über Anweisungen hinweg beibehalten möchten. Er ist besser für Experimente geeignet,
eignet sich aber eher für einzelne Anweisungen als für komplexe Anweisungen (die die [`~Agent.run`]
Methode besser verarbeiten kann).
Diese Methode kann auch Argumente entgegennehmen, wenn Sie Nicht-Text-Typen oder bestimmte Aufforderungen übergeben möchten.
### ⚠️ Fernausführung
Zu Demonstrationszwecken und damit es mit allen Setups verwendet werden kann, haben wir Remote-Executors für mehrere
der Standard-Tools erstellt, auf die der Agent in dieser Version Zugriff hat. Diese werden erstellt mit
[inference endpoints](https://huggingface.co/inference-endpoints).
Wir haben diese vorerst deaktiviert, aber um zu sehen, wie Sie selbst Remote Executors Tools einrichten können,
empfehlen wir die Lektüre des [custom tool guide](./custom_tools).
### Was passiert hier? Was sind Tools und was sind Agenten?
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/diagram.png">
#### Agenten
Der "Agent" ist hier ein großes Sprachmodell, das wir auffordern, Zugang zu einem bestimmten Satz von Tools zu erhalten.
LLMs sind ziemlich gut darin, kleine Codeproben zu erzeugen. Diese API macht sich das zunutze, indem sie das
LLM ein kleines Codebeispiel gibt, das eine Aufgabe mit einer Reihe von Werkzeugen ausführt. Diese Aufforderung wird dann ergänzt durch die
Aufgabe, die Sie Ihrem Agenten geben, und die Beschreibung der Werkzeuge, die Sie ihm geben. Auf diese Weise erhält er Zugriff auf die Dokumentation der
Tools, insbesondere die erwarteten Eingaben und Ausgaben, und kann den entsprechenden Code generieren.
#### Tools
Tools sind sehr einfach: Sie bestehen aus einer einzigen Funktion mit einem Namen und einer Beschreibung. Wir verwenden dann die Beschreibungen dieser Tools
um den Agenten aufzufordern. Anhand der Eingabeaufforderung zeigen wir dem Agenten, wie er die Tools nutzen kann, um das zu tun, was in der
in der Abfrage angefordert wurde.
Dies geschieht mit brandneuen Tools und nicht mit Pipelines, denn der Agent schreibt besseren Code mit sehr atomaren Tools.
Pipelines sind stärker refaktorisiert und fassen oft mehrere Aufgaben in einer einzigen zusammen. Tools sind dafür gedacht, sich auf
eine einzige, sehr einfache Aufgabe konzentrieren.
#### Code-Ausführung?!
Dieser Code wird dann mit unserem kleinen Python-Interpreter auf den mit Ihren Tools übergebenen Eingaben ausgeführt.
Wir hören Sie schon schreien "Willkürliche Codeausführung!", aber lassen Sie uns erklären, warum das nicht der Fall ist.
Die einzigen Funktionen, die aufgerufen werden können, sind die von Ihnen zur Verfügung gestellten Tools und die Druckfunktion, so dass Sie bereits eingeschränkt sind
eingeschränkt, was ausgeführt werden kann. Sie sollten sicher sein, wenn es sich auf die Werkzeuge für das Umarmungsgesicht beschränkt.
Dann lassen wir keine Attributsuche oder Importe zu (die ohnehin nicht benötigt werden, um die
Inputs/Outputs an eine kleine Gruppe von Funktionen), so dass alle offensichtlichen Angriffe (und Sie müssten den LLM
dazu auffordern, sie auszugeben) kein Problem darstellen sollten. Wenn Sie auf Nummer sicher gehen wollen, können Sie die
run()-Methode mit dem zusätzlichen Argument return_code=True ausführen. In diesem Fall gibt der Agent nur den auszuführenden Code
zur Ausführung zurück und Sie können entscheiden, ob Sie ihn ausführen möchten oder nicht.
Die Ausführung bricht bei jeder Zeile ab, in der versucht wird, eine illegale Operation auszuführen, oder wenn ein regulärer Python-Fehler
mit dem vom Agenten generierten Code.
### Ein kuratierter Satz von Tools
Wir haben eine Reihe von Tools identifiziert, die solche Agenten unterstützen können. Hier ist eine aktualisierte Liste der Tools, die wir integriert haben
in `transformers` integriert haben:
- **Beantwortung von Fragen zu Dokumenten**: Beantworten Sie anhand eines Dokuments (z.B. PDF) im Bildformat eine Frage zu diesem Dokument ([Donut](./model_doc/donut))
- Beantworten von Textfragen**: Geben Sie einen langen Text und eine Frage an, beantworten Sie die Frage im Text ([Flan-T5](./model_doc/flan-t5))
- **Unbedingte Bildunterschriften**: Beschriften Sie das Bild! ([BLIP](./model_doc/blip))
- **Bildfragebeantwortung**: Beantworten Sie bei einem Bild eine Frage zu diesem Bild ([VILT](./model_doc/vilt))
- **Bildsegmentierung**: Geben Sie ein Bild und einen Prompt an und geben Sie die Segmentierungsmaske dieses Prompts aus ([CLIPSeg](./model_doc/clipseg))
- **Sprache in Text**: Geben Sie eine Audioaufnahme einer sprechenden Person an und transkribieren Sie die Sprache in Text ([Whisper](./model_doc/whisper))
- **Text in Sprache**: wandelt Text in Sprache um ([SpeechT5](./model_doc/speecht5))
- **Zero-Shot-Textklassifizierung**: Ermitteln Sie anhand eines Textes und einer Liste von Bezeichnungen, welcher Bezeichnung der Text am ehesten entspricht ([BART](./model_doc/bart))
- **Textzusammenfassung**: fassen Sie einen langen Text in einem oder wenigen Sätzen zusammen ([BART](./model_doc/bart))
- **Übersetzung**: Übersetzen des Textes in eine bestimmte Sprache ([NLLB](./model_doc/nllb))
Diese Tools sind in Transformatoren integriert und können auch manuell verwendet werden, zum Beispiel:
```py
from transformers import load_tool
tool = load_tool("text-to-speech")
audio = tool("This is a text to speech tool")
```
### Benutzerdefinierte Tools
Wir haben zwar eine Reihe von Tools identifiziert, sind aber der festen Überzeugung, dass der Hauptwert dieser Implementierung darin besteht
die Möglichkeit, benutzerdefinierte Tools schnell zu erstellen und weiterzugeben.
Indem Sie den Code eines Tools in einen Hugging Face Space oder ein Modell-Repository stellen, können Sie das Tool
direkt mit dem Agenten nutzen. Wir haben ein paar neue Funktionen hinzugefügt
**transformers-agnostic** Tools zur [`huggingface-tools` Organisation](https://huggingface.co/huggingface-tools) hinzugefügt:
- **Text-Downloader**: zum Herunterladen eines Textes von einer Web-URL
- **Text zu Bild**: erzeugt ein Bild nach einer Eingabeaufforderung und nutzt dabei stabile Diffusion
- **Bildtransformation**: verändert ein Bild anhand eines Ausgangsbildes und einer Eingabeaufforderung, unter Ausnutzung der stabilen pix2pix-Diffusion
- **Text zu Video**: Erzeugen eines kleinen Videos nach einer Eingabeaufforderung, unter Verwendung von damo-vilab
Das Text-zu-Bild-Tool, das wir von Anfang an verwendet haben, ist ein Remote-Tool, das sich in
[*huggingface-tools/text-to-image*](https://huggingface.co/spaces/huggingface-tools/text-to-image)! Wir werden
weiterhin solche Tools für diese und andere Organisationen veröffentlichen, um diese Implementierung weiter zu verbessern.
Die Agenten haben standardmäßig Zugriff auf die Tools, die sich auf [*huggingface-tools*](https://huggingface.co/huggingface-tools) befinden.
Wie Sie Ihre eigenen Tools schreiben und freigeben können und wie Sie jedes benutzerdefinierte Tool, das sich auf dem Hub befindet, nutzen können, erklären wir in [folgender Anleitung](custom_tools).
### Code-Erzeugung
Bisher haben wir gezeigt, wie Sie die Agenten nutzen können, um Aktionen für Sie durchzuführen. Der Agent generiert jedoch nur Code
den wir dann mit einem sehr eingeschränkten Python-Interpreter ausführen. Falls Sie den generierten Code in einer anderen Umgebung verwenden möchten
einer anderen Umgebung verwenden möchten, können Sie den Agenten auffordern, den Code zusammen mit einer Tooldefinition und genauen Importen zurückzugeben.
Zum Beispiel die folgende Anweisung
```python
agent.run("Draw me a picture of rivers and lakes", return_code=True)
```
gibt den folgenden Code zurück
```python
from transformers import load_tool
image_generator = load_tool("huggingface-tools/text-to-image")
image = image_generator(prompt="rivers and lakes")
```
die Sie dann selbst ändern und ausführen können.
| transformers/docs/source/de/transformers_agents.md/0 | {
"file_path": "transformers/docs/source/de/transformers_agents.md",
"repo_id": "transformers",
"token_count": 6629
} | 3 |
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# Create a custom architecture
An [`AutoClass`](model_doc/auto) automatically infers the model architecture and downloads pretrained configuration and weights. Generally, we recommend using an `AutoClass` to produce checkpoint-agnostic code. But users who want more control over specific model parameters can create a custom 🤗 Transformers model from just a few base classes. This could be particularly useful for anyone who is interested in studying, training or experimenting with a 🤗 Transformers model. In this guide, dive deeper into creating a custom model without an `AutoClass`. Learn how to:
- Load and customize a model configuration.
- Create a model architecture.
- Create a slow and fast tokenizer for text.
- Create an image processor for vision tasks.
- Create a feature extractor for audio tasks.
- Create a processor for multimodal tasks.
## Configuration
A [configuration](main_classes/configuration) refers to a model's specific attributes. Each model configuration has different attributes; for instance, all NLP models have the `hidden_size`, `num_attention_heads`, `num_hidden_layers` and `vocab_size` attributes in common. These attributes specify the number of attention heads or hidden layers to construct a model with.
Get a closer look at [DistilBERT](model_doc/distilbert) by accessing [`DistilBertConfig`] to inspect it's attributes:
```py
>>> from transformers import DistilBertConfig
>>> config = DistilBertConfig()
>>> print(config)
DistilBertConfig {
"activation": "gelu",
"attention_dropout": 0.1,
"dim": 768,
"dropout": 0.1,
"hidden_dim": 3072,
"initializer_range": 0.02,
"max_position_embeddings": 512,
"model_type": "distilbert",
"n_heads": 12,
"n_layers": 6,
"pad_token_id": 0,
"qa_dropout": 0.1,
"seq_classif_dropout": 0.2,
"sinusoidal_pos_embds": false,
"transformers_version": "4.16.2",
"vocab_size": 30522
}
```
[`DistilBertConfig`] displays all the default attributes used to build a base [`DistilBertModel`]. All attributes are customizable, creating space for experimentation. For example, you can customize a default model to:
- Try a different activation function with the `activation` parameter.
- Use a higher dropout ratio for the attention probabilities with the `attention_dropout` parameter.
```py
>>> my_config = DistilBertConfig(activation="relu", attention_dropout=0.4)
>>> print(my_config)
DistilBertConfig {
"activation": "relu",
"attention_dropout": 0.4,
"dim": 768,
"dropout": 0.1,
"hidden_dim": 3072,
"initializer_range": 0.02,
"max_position_embeddings": 512,
"model_type": "distilbert",
"n_heads": 12,
"n_layers": 6,
"pad_token_id": 0,
"qa_dropout": 0.1,
"seq_classif_dropout": 0.2,
"sinusoidal_pos_embds": false,
"transformers_version": "4.16.2",
"vocab_size": 30522
}
```
Pretrained model attributes can be modified in the [`~PretrainedConfig.from_pretrained`] function:
```py
>>> my_config = DistilBertConfig.from_pretrained("distilbert/distilbert-base-uncased", activation="relu", attention_dropout=0.4)
```
Once you are satisfied with your model configuration, you can save it with [`~PretrainedConfig.save_pretrained`]. Your configuration file is stored as a JSON file in the specified save directory:
```py
>>> my_config.save_pretrained(save_directory="./your_model_save_path")
```
To reuse the configuration file, load it with [`~PretrainedConfig.from_pretrained`]:
```py
>>> my_config = DistilBertConfig.from_pretrained("./your_model_save_path/config.json")
```
<Tip>
You can also save your configuration file as a dictionary or even just the difference between your custom configuration attributes and the default configuration attributes! See the [configuration](main_classes/configuration) documentation for more details.
</Tip>
## Model
The next step is to create a [model](main_classes/models). The model - also loosely referred to as the architecture - defines what each layer is doing and what operations are happening. Attributes like `num_hidden_layers` from the configuration are used to define the architecture. Every model shares the base class [`PreTrainedModel`] and a few common methods like resizing input embeddings and pruning self-attention heads. In addition, all models are also either a [`torch.nn.Module`](https://pytorch.org/docs/stable/generated/torch.nn.Module.html), [`tf.keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model) or [`flax.linen.Module`](https://flax.readthedocs.io/en/latest/api_reference/flax.linen/module.html) subclass. This means models are compatible with each of their respective framework's usage.
<frameworkcontent>
<pt>
Load your custom configuration attributes into the model:
```py
>>> from transformers import DistilBertModel
>>> my_config = DistilBertConfig.from_pretrained("./your_model_save_path/config.json")
>>> model = DistilBertModel(my_config)
```
This creates a model with random values instead of pretrained weights. You won't be able to use this model for anything useful yet until you train it. Training is a costly and time-consuming process. It is generally better to use a pretrained model to obtain better results faster, while using only a fraction of the resources required for training.
Create a pretrained model with [`~PreTrainedModel.from_pretrained`]:
```py
>>> model = DistilBertModel.from_pretrained("distilbert/distilbert-base-uncased")
```
When you load pretrained weights, the default model configuration is automatically loaded if the model is provided by 🤗 Transformers. However, you can still replace - some or all of - the default model configuration attributes with your own if you'd like:
```py
>>> model = DistilBertModel.from_pretrained("distilbert/distilbert-base-uncased", config=my_config)
```
</pt>
<tf>
Load your custom configuration attributes into the model:
```py
>>> from transformers import TFDistilBertModel
>>> my_config = DistilBertConfig.from_pretrained("./your_model_save_path/my_config.json")
>>> tf_model = TFDistilBertModel(my_config)
```
This creates a model with random values instead of pretrained weights. You won't be able to use this model for anything useful yet until you train it. Training is a costly and time-consuming process. It is generally better to use a pretrained model to obtain better results faster, while using only a fraction of the resources required for training.
Create a pretrained model with [`~TFPreTrainedModel.from_pretrained`]:
```py
>>> tf_model = TFDistilBertModel.from_pretrained("distilbert/distilbert-base-uncased")
```
When you load pretrained weights, the default model configuration is automatically loaded if the model is provided by 🤗 Transformers. However, you can still replace - some or all of - the default model configuration attributes with your own if you'd like:
```py
>>> tf_model = TFDistilBertModel.from_pretrained("distilbert/distilbert-base-uncased", config=my_config)
```
</tf>
</frameworkcontent>
### Model heads
At this point, you have a base DistilBERT model which outputs the *hidden states*. The hidden states are passed as inputs to a model head to produce the final output. 🤗 Transformers provides a different model head for each task as long as a model supports the task (i.e., you can't use DistilBERT for a sequence-to-sequence task like translation).
<frameworkcontent>
<pt>
For example, [`DistilBertForSequenceClassification`] is a base DistilBERT model with a sequence classification head. The sequence classification head is a linear layer on top of the pooled outputs.
```py
>>> from transformers import DistilBertForSequenceClassification
>>> model = DistilBertForSequenceClassification.from_pretrained("distilbert/distilbert-base-uncased")
```
Easily reuse this checkpoint for another task by switching to a different model head. For a question answering task, you would use the [`DistilBertForQuestionAnswering`] model head. The question answering head is similar to the sequence classification head except it is a linear layer on top of the hidden states output.
```py
>>> from transformers import DistilBertForQuestionAnswering
>>> model = DistilBertForQuestionAnswering.from_pretrained("distilbert/distilbert-base-uncased")
```
</pt>
<tf>
For example, [`TFDistilBertForSequenceClassification`] is a base DistilBERT model with a sequence classification head. The sequence classification head is a linear layer on top of the pooled outputs.
```py
>>> from transformers import TFDistilBertForSequenceClassification
>>> tf_model = TFDistilBertForSequenceClassification.from_pretrained("distilbert/distilbert-base-uncased")
```
Easily reuse this checkpoint for another task by switching to a different model head. For a question answering task, you would use the [`TFDistilBertForQuestionAnswering`] model head. The question answering head is similar to the sequence classification head except it is a linear layer on top of the hidden states output.
```py
>>> from transformers import TFDistilBertForQuestionAnswering
>>> tf_model = TFDistilBertForQuestionAnswering.from_pretrained("distilbert/distilbert-base-uncased")
```
</tf>
</frameworkcontent>
## Tokenizer
The last base class you need before using a model for textual data is a [tokenizer](main_classes/tokenizer) to convert raw text to tensors. There are two types of tokenizers you can use with 🤗 Transformers:
- [`PreTrainedTokenizer`]: a Python implementation of a tokenizer.
- [`PreTrainedTokenizerFast`]: a tokenizer from our Rust-based [🤗 Tokenizer](https://huggingface.co/docs/tokenizers/python/latest/) library. This tokenizer type is significantly faster - especially during batch tokenization - due to its Rust implementation. The fast tokenizer also offers additional methods like *offset mapping* which maps tokens to their original words or characters.
Both tokenizers support common methods such as encoding and decoding, adding new tokens, and managing special tokens.
<Tip warning={true}>
Not every model supports a fast tokenizer. Take a look at this [table](index#supported-frameworks) to check if a model has fast tokenizer support.
</Tip>
If you trained your own tokenizer, you can create one from your *vocabulary* file:
```py
>>> from transformers import DistilBertTokenizer
>>> my_tokenizer = DistilBertTokenizer(vocab_file="my_vocab_file.txt", do_lower_case=False, padding_side="left")
```
It is important to remember the vocabulary from a custom tokenizer will be different from the vocabulary generated by a pretrained model's tokenizer. You need to use a pretrained model's vocabulary if you are using a pretrained model, otherwise the inputs won't make sense. Create a tokenizer with a pretrained model's vocabulary with the [`DistilBertTokenizer`] class:
```py
>>> from transformers import DistilBertTokenizer
>>> slow_tokenizer = DistilBertTokenizer.from_pretrained("distilbert/distilbert-base-uncased")
```
Create a fast tokenizer with the [`DistilBertTokenizerFast`] class:
```py
>>> from transformers import DistilBertTokenizerFast
>>> fast_tokenizer = DistilBertTokenizerFast.from_pretrained("distilbert/distilbert-base-uncased")
```
<Tip>
By default, [`AutoTokenizer`] will try to load a fast tokenizer. You can disable this behavior by setting `use_fast=False` in `from_pretrained`.
</Tip>
## Image processor
An image processor processes vision inputs. It inherits from the base [`~image_processing_utils.ImageProcessingMixin`] class.
To use, create an image processor associated with the model you're using. For example, create a default [`ViTImageProcessor`] if you are using [ViT](model_doc/vit) for image classification:
```py
>>> from transformers import ViTImageProcessor
>>> vit_extractor = ViTImageProcessor()
>>> print(vit_extractor)
ViTImageProcessor {
"do_normalize": true,
"do_resize": true,
"image_processor_type": "ViTImageProcessor",
"image_mean": [
0.5,
0.5,
0.5
],
"image_std": [
0.5,
0.5,
0.5
],
"resample": 2,
"size": 224
}
```
<Tip>
If you aren't looking for any customization, just use the `from_pretrained` method to load a model's default image processor parameters.
</Tip>
Modify any of the [`ViTImageProcessor`] parameters to create your custom image processor:
```py
>>> from transformers import ViTImageProcessor
>>> my_vit_extractor = ViTImageProcessor(resample="PIL.Image.BOX", do_normalize=False, image_mean=[0.3, 0.3, 0.3])
>>> print(my_vit_extractor)
ViTImageProcessor {
"do_normalize": false,
"do_resize": true,
"image_processor_type": "ViTImageProcessor",
"image_mean": [
0.3,
0.3,
0.3
],
"image_std": [
0.5,
0.5,
0.5
],
"resample": "PIL.Image.BOX",
"size": 224
}
```
## Backbone
<div style="text-align: center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/Backbone.png">
</div>
Computer vision models consist of a backbone, neck, and head. The backbone extracts features from an input image, the neck combines and enhances the extracted features, and the head is used for the main task (e.g., object detection). Start by initializing a backbone in the model config and specify whether you want to load pretrained weights or load randomly initialized weights. Then you can pass the model config to the model head.
For example, to load a [ResNet](../model_doc/resnet) backbone into a [MaskFormer](../model_doc/maskformer) model with an instance segmentation head:
<hfoptions id="backbone">
<hfoption id="pretrained weights">
Set `use_pretrained_backbone=True` to load pretrained ResNet weights for the backbone.
```py
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, ResNetConfig
config = MaskFormerConfig(backbone="microsoft/resnet50", use_pretrained_backbone=True) # backbone and neck config
model = MaskFormerForInstanceSegmentation(config) # head
```
You could also load the backbone config separately and then pass it to the model config.
```py
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, ResNetConfig
backbone_config = ResNetConfig.from_pretrained("microsoft/resnet-50")
config = MaskFormerConfig(backbone_config=backbone_config)
model = MaskFormerForInstanceSegmentation(config)
```
</hfoption>
<hfoption id="random weights">
Set `use_pretrained_backbone=False` to randomly initialize a ResNet backbone.
```py
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, ResNetConfig
config = MaskFormerConfig(backbone="microsoft/resnet50", use_pretrained_backbone=False) # backbone and neck config
model = MaskFormerForInstanceSegmentation(config) # head
```
You could also load the backbone config separately and then pass it to the model config.
```py
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, ResNetConfig
backbone_config = ResNetConfig()
config = MaskFormerConfig(backbone_config=backbone_config)
model = MaskFormerForInstanceSegmentation(config)
```
</hfoption>
</hfoptions>
[timm](https://hf.co/docs/timm/index) models are loaded with [`TimmBackbone`] and [`TimmBackboneConfig`].
```python
from transformers import TimmBackboneConfig, TimmBackbone
backbone_config = TimmBackboneConfig("resnet50")
model = TimmBackbone(config=backbone_config)
```
## Feature extractor
A feature extractor processes audio inputs. It inherits from the base [`~feature_extraction_utils.FeatureExtractionMixin`] class, and may also inherit from the [`SequenceFeatureExtractor`] class for processing audio inputs.
To use, create a feature extractor associated with the model you're using. For example, create a default [`Wav2Vec2FeatureExtractor`] if you are using [Wav2Vec2](model_doc/wav2vec2) for audio classification:
```py
>>> from transformers import Wav2Vec2FeatureExtractor
>>> w2v2_extractor = Wav2Vec2FeatureExtractor()
>>> print(w2v2_extractor)
Wav2Vec2FeatureExtractor {
"do_normalize": true,
"feature_extractor_type": "Wav2Vec2FeatureExtractor",
"feature_size": 1,
"padding_side": "right",
"padding_value": 0.0,
"return_attention_mask": false,
"sampling_rate": 16000
}
```
<Tip>
If you aren't looking for any customization, just use the `from_pretrained` method to load a model's default feature extractor parameters.
</Tip>
Modify any of the [`Wav2Vec2FeatureExtractor`] parameters to create your custom feature extractor:
```py
>>> from transformers import Wav2Vec2FeatureExtractor
>>> w2v2_extractor = Wav2Vec2FeatureExtractor(sampling_rate=8000, do_normalize=False)
>>> print(w2v2_extractor)
Wav2Vec2FeatureExtractor {
"do_normalize": false,
"feature_extractor_type": "Wav2Vec2FeatureExtractor",
"feature_size": 1,
"padding_side": "right",
"padding_value": 0.0,
"return_attention_mask": false,
"sampling_rate": 8000
}
```
## Processor
For models that support multimodal tasks, 🤗 Transformers offers a processor class that conveniently wraps processing classes such as a feature extractor and a tokenizer into a single object. For example, let's use the [`Wav2Vec2Processor`] for an automatic speech recognition task (ASR). ASR transcribes audio to text, so you will need a feature extractor and a tokenizer.
Create a feature extractor to handle the audio inputs:
```py
>>> from transformers import Wav2Vec2FeatureExtractor
>>> feature_extractor = Wav2Vec2FeatureExtractor(padding_value=1.0, do_normalize=True)
```
Create a tokenizer to handle the text inputs:
```py
>>> from transformers import Wav2Vec2CTCTokenizer
>>> tokenizer = Wav2Vec2CTCTokenizer(vocab_file="my_vocab_file.txt")
```
Combine the feature extractor and tokenizer in [`Wav2Vec2Processor`]:
```py
>>> from transformers import Wav2Vec2Processor
>>> processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)
```
With two basic classes - configuration and model - and an additional preprocessing class (tokenizer, image processor, feature extractor, or processor), you can create any of the models supported by 🤗 Transformers. Each of these base classes are configurable, allowing you to use the specific attributes you want. You can easily setup a model for training or modify an existing pretrained model to fine-tune.
| transformers/docs/source/en/create_a_model.md/0 | {
"file_path": "transformers/docs/source/en/create_a_model.md",
"repo_id": "transformers",
"token_count": 5528
} | 4 |
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# Auto Classes
In many cases, the architecture you want to use can be guessed from the name or the path of the pretrained model you
are supplying to the `from_pretrained()` method. AutoClasses are here to do this job for you so that you
automatically retrieve the relevant model given the name/path to the pretrained weights/config/vocabulary.
Instantiating one of [`AutoConfig`], [`AutoModel`], and
[`AutoTokenizer`] will directly create a class of the relevant architecture. For instance
```python
model = AutoModel.from_pretrained("google-bert/bert-base-cased")
```
will create a model that is an instance of [`BertModel`].
There is one class of `AutoModel` for each task, and for each backend (PyTorch, TensorFlow, or Flax).
## Extending the Auto Classes
Each of the auto classes has a method to be extended with your custom classes. For instance, if you have defined a
custom class of model `NewModel`, make sure you have a `NewModelConfig` then you can add those to the auto
classes like this:
```python
from transformers import AutoConfig, AutoModel
AutoConfig.register("new-model", NewModelConfig)
AutoModel.register(NewModelConfig, NewModel)
```
You will then be able to use the auto classes like you would usually do!
<Tip warning={true}>
If your `NewModelConfig` is a subclass of [`~transformers.PretrainedConfig`], make sure its
`model_type` attribute is set to the same key you use when registering the config (here `"new-model"`).
Likewise, if your `NewModel` is a subclass of [`PreTrainedModel`], make sure its
`config_class` attribute is set to the same class you use when registering the model (here
`NewModelConfig`).
</Tip>
## AutoConfig
[[autodoc]] AutoConfig
## AutoTokenizer
[[autodoc]] AutoTokenizer
## AutoFeatureExtractor
[[autodoc]] AutoFeatureExtractor
## AutoImageProcessor
[[autodoc]] AutoImageProcessor
## AutoProcessor
[[autodoc]] AutoProcessor
## Generic model classes
The following auto classes are available for instantiating a base model class without a specific head.
### AutoModel
[[autodoc]] AutoModel
### TFAutoModel
[[autodoc]] TFAutoModel
### FlaxAutoModel
[[autodoc]] FlaxAutoModel
## Generic pretraining classes
The following auto classes are available for instantiating a model with a pretraining head.
### AutoModelForPreTraining
[[autodoc]] AutoModelForPreTraining
### TFAutoModelForPreTraining
[[autodoc]] TFAutoModelForPreTraining
### FlaxAutoModelForPreTraining
[[autodoc]] FlaxAutoModelForPreTraining
## Natural Language Processing
The following auto classes are available for the following natural language processing tasks.
### AutoModelForCausalLM
[[autodoc]] AutoModelForCausalLM
### TFAutoModelForCausalLM
[[autodoc]] TFAutoModelForCausalLM
### FlaxAutoModelForCausalLM
[[autodoc]] FlaxAutoModelForCausalLM
### AutoModelForMaskedLM
[[autodoc]] AutoModelForMaskedLM
### TFAutoModelForMaskedLM
[[autodoc]] TFAutoModelForMaskedLM
### FlaxAutoModelForMaskedLM
[[autodoc]] FlaxAutoModelForMaskedLM
### AutoModelForMaskGeneration
[[autodoc]] AutoModelForMaskGeneration
### TFAutoModelForMaskGeneration
[[autodoc]] TFAutoModelForMaskGeneration
### AutoModelForSeq2SeqLM
[[autodoc]] AutoModelForSeq2SeqLM
### TFAutoModelForSeq2SeqLM
[[autodoc]] TFAutoModelForSeq2SeqLM
### FlaxAutoModelForSeq2SeqLM
[[autodoc]] FlaxAutoModelForSeq2SeqLM
### AutoModelForSequenceClassification
[[autodoc]] AutoModelForSequenceClassification
### TFAutoModelForSequenceClassification
[[autodoc]] TFAutoModelForSequenceClassification
### FlaxAutoModelForSequenceClassification
[[autodoc]] FlaxAutoModelForSequenceClassification
### AutoModelForMultipleChoice
[[autodoc]] AutoModelForMultipleChoice
### TFAutoModelForMultipleChoice
[[autodoc]] TFAutoModelForMultipleChoice
### FlaxAutoModelForMultipleChoice
[[autodoc]] FlaxAutoModelForMultipleChoice
### AutoModelForNextSentencePrediction
[[autodoc]] AutoModelForNextSentencePrediction
### TFAutoModelForNextSentencePrediction
[[autodoc]] TFAutoModelForNextSentencePrediction
### FlaxAutoModelForNextSentencePrediction
[[autodoc]] FlaxAutoModelForNextSentencePrediction
### AutoModelForTokenClassification
[[autodoc]] AutoModelForTokenClassification
### TFAutoModelForTokenClassification
[[autodoc]] TFAutoModelForTokenClassification
### FlaxAutoModelForTokenClassification
[[autodoc]] FlaxAutoModelForTokenClassification
### AutoModelForQuestionAnswering
[[autodoc]] AutoModelForQuestionAnswering
### TFAutoModelForQuestionAnswering
[[autodoc]] TFAutoModelForQuestionAnswering
### FlaxAutoModelForQuestionAnswering
[[autodoc]] FlaxAutoModelForQuestionAnswering
### AutoModelForTextEncoding
[[autodoc]] AutoModelForTextEncoding
### TFAutoModelForTextEncoding
[[autodoc]] TFAutoModelForTextEncoding
## Computer vision
The following auto classes are available for the following computer vision tasks.
### AutoModelForDepthEstimation
[[autodoc]] AutoModelForDepthEstimation
### AutoModelForImageClassification
[[autodoc]] AutoModelForImageClassification
### TFAutoModelForImageClassification
[[autodoc]] TFAutoModelForImageClassification
### FlaxAutoModelForImageClassification
[[autodoc]] FlaxAutoModelForImageClassification
### AutoModelForVideoClassification
[[autodoc]] AutoModelForVideoClassification
### AutoModelForMaskedImageModeling
[[autodoc]] AutoModelForMaskedImageModeling
### TFAutoModelForMaskedImageModeling
[[autodoc]] TFAutoModelForMaskedImageModeling
### AutoModelForObjectDetection
[[autodoc]] AutoModelForObjectDetection
### AutoModelForImageSegmentation
[[autodoc]] AutoModelForImageSegmentation
### AutoModelForImageToImage
[[autodoc]] AutoModelForImageToImage
### AutoModelForSemanticSegmentation
[[autodoc]] AutoModelForSemanticSegmentation
### TFAutoModelForSemanticSegmentation
[[autodoc]] TFAutoModelForSemanticSegmentation
### AutoModelForInstanceSegmentation
[[autodoc]] AutoModelForInstanceSegmentation
### AutoModelForUniversalSegmentation
[[autodoc]] AutoModelForUniversalSegmentation
### AutoModelForZeroShotImageClassification
[[autodoc]] AutoModelForZeroShotImageClassification
### TFAutoModelForZeroShotImageClassification
[[autodoc]] TFAutoModelForZeroShotImageClassification
### AutoModelForZeroShotObjectDetection
[[autodoc]] AutoModelForZeroShotObjectDetection
## Audio
The following auto classes are available for the following audio tasks.
### AutoModelForAudioClassification
[[autodoc]] AutoModelForAudioClassification
### AutoModelForAudioFrameClassification
[[autodoc]] TFAutoModelForAudioClassification
### TFAutoModelForAudioFrameClassification
[[autodoc]] AutoModelForAudioFrameClassification
### AutoModelForCTC
[[autodoc]] AutoModelForCTC
### AutoModelForSpeechSeq2Seq
[[autodoc]] AutoModelForSpeechSeq2Seq
### TFAutoModelForSpeechSeq2Seq
[[autodoc]] TFAutoModelForSpeechSeq2Seq
### FlaxAutoModelForSpeechSeq2Seq
[[autodoc]] FlaxAutoModelForSpeechSeq2Seq
### AutoModelForAudioXVector
[[autodoc]] AutoModelForAudioXVector
### AutoModelForTextToSpectrogram
[[autodoc]] AutoModelForTextToSpectrogram
### AutoModelForTextToWaveform
[[autodoc]] AutoModelForTextToWaveform
## Multimodal
The following auto classes are available for the following multimodal tasks.
### AutoModelForTableQuestionAnswering
[[autodoc]] AutoModelForTableQuestionAnswering
### TFAutoModelForTableQuestionAnswering
[[autodoc]] TFAutoModelForTableQuestionAnswering
### AutoModelForDocumentQuestionAnswering
[[autodoc]] AutoModelForDocumentQuestionAnswering
### TFAutoModelForDocumentQuestionAnswering
[[autodoc]] TFAutoModelForDocumentQuestionAnswering
### AutoModelForVisualQuestionAnswering
[[autodoc]] AutoModelForVisualQuestionAnswering
### AutoModelForVision2Seq
[[autodoc]] AutoModelForVision2Seq
### TFAutoModelForVision2Seq
[[autodoc]] TFAutoModelForVision2Seq
### FlaxAutoModelForVision2Seq
[[autodoc]] FlaxAutoModelForVision2Seq
| transformers/docs/source/en/model_doc/auto.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/auto.md",
"repo_id": "transformers",
"token_count": 2598
} | 5 |
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the License. You may obtain a copy of the License at
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
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# Blenderbot
## Overview
The Blender chatbot model was proposed in [Recipes for building an open-domain chatbot](https://arxiv.org/pdf/2004.13637.pdf) Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu,
Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston on 30 Apr 2020.
The abstract of the paper is the following:
*Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that
scaling neural models in the number of parameters and the size of the data they are trained on gives improved results,
we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of
skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to
their partners, and displaying knowledge, empathy and personality appropriately, while maintaining a consistent
persona. We show that large scale models can learn these skills when given appropriate training data and choice of
generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter models, and make our models
and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn
dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing
failure cases of our models.*
This model was contributed by [sshleifer](https://huggingface.co/sshleifer). The authors' code can be found [here](https://github.com/facebookresearch/ParlAI) .
## Usage tips and example
Blenderbot is a model with absolute position embeddings so it's usually advised to pad the inputs on the right
rather than the left.
An example:
```python
>>> from transformers import BlenderbotTokenizer, BlenderbotForConditionalGeneration
>>> mname = "facebook/blenderbot-400M-distill"
>>> model = BlenderbotForConditionalGeneration.from_pretrained(mname)
>>> tokenizer = BlenderbotTokenizer.from_pretrained(mname)
>>> UTTERANCE = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer([UTTERANCE], return_tensors="pt")
>>> reply_ids = model.generate(**inputs)
>>> print(tokenizer.batch_decode(reply_ids))
["<s> That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?</s>"]
```
## Implementation Notes
- Blenderbot uses a standard [seq2seq model transformer](https://arxiv.org/pdf/1706.03762.pdf) based architecture.
- Available checkpoints can be found in the [model hub](https://huggingface.co/models?search=blenderbot).
- This is the *default* Blenderbot model class. However, some smaller checkpoints, such as
`facebook/blenderbot_small_90M`, have a different architecture and consequently should be used with
[BlenderbotSmall](blenderbot-small).
## Resources
- [Causal language modeling task guide](../tasks/language_modeling)
- [Translation task guide](../tasks/translation)
- [Summarization task guide](../tasks/summarization)
## BlenderbotConfig
[[autodoc]] BlenderbotConfig
## BlenderbotTokenizer
[[autodoc]] BlenderbotTokenizer
- build_inputs_with_special_tokens
## BlenderbotTokenizerFast
[[autodoc]] BlenderbotTokenizerFast
- build_inputs_with_special_tokens
<frameworkcontent>
<pt>
## BlenderbotModel
See [`~transformers.BartModel`] for arguments to *forward* and *generate*
[[autodoc]] BlenderbotModel
- forward
## BlenderbotForConditionalGeneration
See [`~transformers.BartForConditionalGeneration`] for arguments to *forward* and *generate*
[[autodoc]] BlenderbotForConditionalGeneration
- forward
## BlenderbotForCausalLM
[[autodoc]] BlenderbotForCausalLM
- forward
</pt>
<tf>
## TFBlenderbotModel
[[autodoc]] TFBlenderbotModel
- call
## TFBlenderbotForConditionalGeneration
[[autodoc]] TFBlenderbotForConditionalGeneration
- call
</tf>
<jax>
## FlaxBlenderbotModel
[[autodoc]] FlaxBlenderbotModel
- __call__
- encode
- decode
## FlaxBlenderbotForConditionalGeneration
[[autodoc]] FlaxBlenderbotForConditionalGeneration
- __call__
- encode
- decode
</jax>
</frameworkcontent>
| transformers/docs/source/en/model_doc/blenderbot.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/blenderbot.md",
"repo_id": "transformers",
"token_count": 1405
} | 6 |
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the License. You may obtain a copy of the License at
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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# ERNIE
## Overview
ERNIE is a series of powerful models proposed by baidu, especially in Chinese tasks,
including [ERNIE1.0](https://arxiv.org/abs/1904.09223), [ERNIE2.0](https://ojs.aaai.org/index.php/AAAI/article/view/6428),
[ERNIE3.0](https://arxiv.org/abs/2107.02137), [ERNIE-Gram](https://arxiv.org/abs/2010.12148), [ERNIE-health](https://arxiv.org/abs/2110.07244), etc.
These models are contributed by [nghuyong](https://huggingface.co/nghuyong) and the official code can be found in [PaddleNLP](https://github.com/PaddlePaddle/PaddleNLP) (in PaddlePaddle).
### Usage example
Take `ernie-1.0-base-zh` as an example:
```Python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("nghuyong/ernie-1.0-base-zh")
model = AutoModel.from_pretrained("nghuyong/ernie-1.0-base-zh")
```
### Model checkpoints
| Model Name | Language | Description |
|:-------------------:|:--------:|:-------------------------------:|
| ernie-1.0-base-zh | Chinese | Layer:12, Heads:12, Hidden:768 |
| ernie-2.0-base-en | English | Layer:12, Heads:12, Hidden:768 |
| ernie-2.0-large-en | English | Layer:24, Heads:16, Hidden:1024 |
| ernie-3.0-base-zh | Chinese | Layer:12, Heads:12, Hidden:768 |
| ernie-3.0-medium-zh | Chinese | Layer:6, Heads:12, Hidden:768 |
| ernie-3.0-mini-zh | Chinese | Layer:6, Heads:12, Hidden:384 |
| ernie-3.0-micro-zh | Chinese | Layer:4, Heads:12, Hidden:384 |
| ernie-3.0-nano-zh | Chinese | Layer:4, Heads:12, Hidden:312 |
| ernie-health-zh | Chinese | Layer:12, Heads:12, Hidden:768 |
| ernie-gram-zh | Chinese | Layer:12, Heads:12, Hidden:768 |
You can find all the supported models from huggingface's model hub: [huggingface.co/nghuyong](https://huggingface.co/nghuyong), and model details from paddle's official
repo: [PaddleNLP](https://paddlenlp.readthedocs.io/zh/latest/model_zoo/transformers/ERNIE/contents.html)
and [ERNIE](https://github.com/PaddlePaddle/ERNIE/blob/repro).
## Resources
- [Text classification task guide](../tasks/sequence_classification)
- [Token classification task guide](../tasks/token_classification)
- [Question answering task guide](../tasks/question_answering)
- [Causal language modeling task guide](../tasks/language_modeling)
- [Masked language modeling task guide](../tasks/masked_language_modeling)
- [Multiple choice task guide](../tasks/multiple_choice)
## ErnieConfig
[[autodoc]] ErnieConfig
- all
## Ernie specific outputs
[[autodoc]] models.ernie.modeling_ernie.ErnieForPreTrainingOutput
## ErnieModel
[[autodoc]] ErnieModel
- forward
## ErnieForPreTraining
[[autodoc]] ErnieForPreTraining
- forward
## ErnieForCausalLM
[[autodoc]] ErnieForCausalLM
- forward
## ErnieForMaskedLM
[[autodoc]] ErnieForMaskedLM
- forward
## ErnieForNextSentencePrediction
[[autodoc]] ErnieForNextSentencePrediction
- forward
## ErnieForSequenceClassification
[[autodoc]] ErnieForSequenceClassification
- forward
## ErnieForMultipleChoice
[[autodoc]] ErnieForMultipleChoice
- forward
## ErnieForTokenClassification
[[autodoc]] ErnieForTokenClassification
- forward
## ErnieForQuestionAnswering
[[autodoc]] ErnieForQuestionAnswering
- forward | transformers/docs/source/en/model_doc/ernie.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/ernie.md",
"repo_id": "transformers",
"token_count": 1417
} | 7 |
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# LUKE
## Overview
The LUKE model was proposed in [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://arxiv.org/abs/2010.01057) by Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda and Yuji Matsumoto.
It is based on RoBERTa and adds entity embeddings as well as an entity-aware self-attention mechanism, which helps
improve performance on various downstream tasks involving reasoning about entities such as named entity recognition,
extractive and cloze-style question answering, entity typing, and relation classification.
The abstract from the paper is the following:
*Entity representations are useful in natural language tasks involving entities. In this paper, we propose new
pretrained contextualized representations of words and entities based on the bidirectional transformer. The proposed
model treats words and entities in a given text as independent tokens, and outputs contextualized representations of
them. Our model is trained using a new pretraining task based on the masked language model of BERT. The task involves
predicting randomly masked words and entities in a large entity-annotated corpus retrieved from Wikipedia. We also
propose an entity-aware self-attention mechanism that is an extension of the self-attention mechanism of the
transformer, and considers the types of tokens (words or entities) when computing attention scores. The proposed model
achieves impressive empirical performance on a wide range of entity-related tasks. In particular, it obtains
state-of-the-art results on five well-known datasets: Open Entity (entity typing), TACRED (relation classification),
CoNLL-2003 (named entity recognition), ReCoRD (cloze-style question answering), and SQuAD 1.1 (extractive question
answering).*
This model was contributed by [ikuyamada](https://huggingface.co/ikuyamada) and [nielsr](https://huggingface.co/nielsr). The original code can be found [here](https://github.com/studio-ousia/luke).
## Usage tips
- This implementation is the same as [`RobertaModel`] with the addition of entity embeddings as well
as an entity-aware self-attention mechanism, which improves performance on tasks involving reasoning about entities.
- LUKE treats entities as input tokens; therefore, it takes `entity_ids`, `entity_attention_mask`,
`entity_token_type_ids` and `entity_position_ids` as extra input. You can obtain those using
[`LukeTokenizer`].
- [`LukeTokenizer`] takes `entities` and `entity_spans` (character-based start and end
positions of the entities in the input text) as extra input. `entities` typically consist of [MASK] entities or
Wikipedia entities. The brief description when inputting these entities are as follows:
- *Inputting [MASK] entities to compute entity representations*: The [MASK] entity is used to mask entities to be
predicted during pretraining. When LUKE receives the [MASK] entity, it tries to predict the original entity by
gathering the information about the entity from the input text. Therefore, the [MASK] entity can be used to address
downstream tasks requiring the information of entities in text such as entity typing, relation classification, and
named entity recognition.
- *Inputting Wikipedia entities to compute knowledge-enhanced token representations*: LUKE learns rich information
(or knowledge) about Wikipedia entities during pretraining and stores the information in its entity embedding. By
using Wikipedia entities as input tokens, LUKE outputs token representations enriched by the information stored in
the embeddings of these entities. This is particularly effective for tasks requiring real-world knowledge, such as
question answering.
- There are three head models for the former use case:
- [`LukeForEntityClassification`], for tasks to classify a single entity in an input text such as
entity typing, e.g. the [Open Entity dataset](https://www.cs.utexas.edu/~eunsol/html_pages/open_entity.html).
This model places a linear head on top of the output entity representation.
- [`LukeForEntityPairClassification`], for tasks to classify the relationship between two entities
such as relation classification, e.g. the [TACRED dataset](https://nlp.stanford.edu/projects/tacred/). This
model places a linear head on top of the concatenated output representation of the pair of given entities.
- [`LukeForEntitySpanClassification`], for tasks to classify the sequence of entity spans, such as
named entity recognition (NER). This model places a linear head on top of the output entity representations. You
can address NER using this model by inputting all possible entity spans in the text to the model.
[`LukeTokenizer`] has a `task` argument, which enables you to easily create an input to these
head models by specifying `task="entity_classification"`, `task="entity_pair_classification"`, or
`task="entity_span_classification"`. Please refer to the example code of each head models.
Usage example:
```python
>>> from transformers import LukeTokenizer, LukeModel, LukeForEntityPairClassification
>>> model = LukeModel.from_pretrained("studio-ousia/luke-base")
>>> tokenizer = LukeTokenizer.from_pretrained("studio-ousia/luke-base")
# Example 1: Computing the contextualized entity representation corresponding to the entity mention "Beyoncé"
>>> text = "Beyoncé lives in Los Angeles."
>>> entity_spans = [(0, 7)] # character-based entity span corresponding to "Beyoncé"
>>> inputs = tokenizer(text, entity_spans=entity_spans, add_prefix_space=True, return_tensors="pt")
>>> outputs = model(**inputs)
>>> word_last_hidden_state = outputs.last_hidden_state
>>> entity_last_hidden_state = outputs.entity_last_hidden_state
# Example 2: Inputting Wikipedia entities to obtain enriched contextualized representations
>>> entities = [
... "Beyoncé",
... "Los Angeles",
... ] # Wikipedia entity titles corresponding to the entity mentions "Beyoncé" and "Los Angeles"
>>> entity_spans = [(0, 7), (17, 28)] # character-based entity spans corresponding to "Beyoncé" and "Los Angeles"
>>> inputs = tokenizer(text, entities=entities, entity_spans=entity_spans, add_prefix_space=True, return_tensors="pt")
>>> outputs = model(**inputs)
>>> word_last_hidden_state = outputs.last_hidden_state
>>> entity_last_hidden_state = outputs.entity_last_hidden_state
# Example 3: Classifying the relationship between two entities using LukeForEntityPairClassification head model
>>> model = LukeForEntityPairClassification.from_pretrained("studio-ousia/luke-large-finetuned-tacred")
>>> tokenizer = LukeTokenizer.from_pretrained("studio-ousia/luke-large-finetuned-tacred")
>>> entity_spans = [(0, 7), (17, 28)] # character-based entity spans corresponding to "Beyoncé" and "Los Angeles"
>>> inputs = tokenizer(text, entity_spans=entity_spans, return_tensors="pt")
>>> outputs = model(**inputs)
>>> logits = outputs.logits
>>> predicted_class_idx = int(logits[0].argmax())
>>> print("Predicted class:", model.config.id2label[predicted_class_idx])
```
## Resources
- [A demo notebook on how to fine-tune [`LukeForEntityPairClassification`] for relation classification](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/LUKE)
- [Notebooks showcasing how you to reproduce the results as reported in the paper with the HuggingFace implementation of LUKE](https://github.com/studio-ousia/luke/tree/master/notebooks)
- [Text classification task guide](../tasks/sequence_classification)
- [Token classification task guide](../tasks/token_classification)
- [Question answering task guide](../tasks/question_answering)
- [Masked language modeling task guide](../tasks/masked_language_modeling)
- [Multiple choice task guide](../tasks/multiple_choice)
## LukeConfig
[[autodoc]] LukeConfig
## LukeTokenizer
[[autodoc]] LukeTokenizer
- __call__
- save_vocabulary
## LukeModel
[[autodoc]] LukeModel
- forward
## LukeForMaskedLM
[[autodoc]] LukeForMaskedLM
- forward
## LukeForEntityClassification
[[autodoc]] LukeForEntityClassification
- forward
## LukeForEntityPairClassification
[[autodoc]] LukeForEntityPairClassification
- forward
## LukeForEntitySpanClassification
[[autodoc]] LukeForEntitySpanClassification
- forward
## LukeForSequenceClassification
[[autodoc]] LukeForSequenceClassification
- forward
## LukeForMultipleChoice
[[autodoc]] LukeForMultipleChoice
- forward
## LukeForTokenClassification
[[autodoc]] LukeForTokenClassification
- forward
## LukeForQuestionAnswering
[[autodoc]] LukeForQuestionAnswering
- forward
| transformers/docs/source/en/model_doc/luke.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/luke.md",
"repo_id": "transformers",
"token_count": 2521
} | 8 |
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# Mixtral
## Overview
Mixtral-8x7B was introduced in the [Mixtral of Experts blogpost](https://mistral.ai/news/mixtral-of-experts/) by Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.
The introduction of the blog post says:
*Today, the team is proud to release Mixtral 8x7B, a high-quality sparse mixture of experts models (SMoE) with open weights. Licensed under Apache 2.0. Mixtral outperforms Llama 2 70B on most benchmarks with 6x faster inference. It is the strongest open-weight model with a permissive license and the best model overall regarding cost/performance trade-offs. In particular, it matches or outperforms GPT3.5 on most standard benchmarks.*
Mixtral-8x7B is the second large language model (LLM) released by [mistral.ai](https://mistral.ai/), after [Mistral-7B](mistral).
### Architectural details
Mixtral-8x7B is a decoder-only Transformer with the following architectural choices:
- Mixtral is a Mixture of Experts (MoE) model with 8 experts per MLP, with a total of 45 billion parameters. To learn more about mixture-of-experts, refer to the [blog post](https://huggingface.co/blog/moe).
- Despite the model having 45 billion parameters,, the compute required for a single forward pass is the same as that of a 14 billion parameter model. This is because even though each of the experts have to be loaded in RAM (70B like ram requirement) each token from the hidden states are dispatched twice (top 2 routing) and thus the compute (the operation required at each forward computation) is just 2 X sequence_length.
The following implementation details are shared with Mistral AI's first model [Mistral-7B](mistral):
- Sliding Window Attention - Trained with 8k context length and fixed cache size, with a theoretical attention span of 128K tokens
- GQA (Grouped Query Attention) - allowing faster inference and lower cache size.
- Byte-fallback BPE tokenizer - ensures that characters are never mapped to out of vocabulary tokens.
For more details refer to the [release blog post](https://mistral.ai/news/mixtral-of-experts/).
### License
`Mixtral-8x7B` is released under the Apache 2.0 license.
## Usage tips
The Mistral team has released 2 checkpoints:
- a base model, [Mixtral-8x7B-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1), which has been pre-trained to predict the next token on internet-scale data.
- an instruction tuned model, [Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1), which is the base model optimized for chat purposes using supervised fine-tuning (SFT) and direct preference optimization (DPO).
The base model can be used as follows:
```python
>>> from transformers import AutoModelForCausalLM, AutoTokenizer
>>> model = AutoModelForCausalLM.from_pretrained("mistralai/Mixtral-8x7B-v0.1", device_map="auto")
>>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mixtral-8x7B-v0.1")
>>> prompt = "My favourite condiment is"
>>> model_inputs = tokenizer([prompt], return_tensors="pt").to("cuda")
>>> model.to(device)
>>> generated_ids = model.generate(**model_inputs, max_new_tokens=100, do_sample=True)
>>> tokenizer.batch_decode(generated_ids)[0]
"My favourite condiment is to ..."
```
The instruction tuned model can be used as follows:
```python
>>> from transformers import AutoModelForCausalLM, AutoTokenizer
>>> model = AutoModelForCausalLM.from_pretrained("mistralai/Mixtral-8x7B-Instruct-v0.1", device_map="auto")
>>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mixtral-8x7B-Instruct-v0.1")
>>> messages = [
... {"role": "user", "content": "What is your favourite condiment?"},
... {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
... {"role": "user", "content": "Do you have mayonnaise recipes?"}
... ]
>>> model_inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda")
>>> generated_ids = model.generate(model_inputs, max_new_tokens=100, do_sample=True)
>>> tokenizer.batch_decode(generated_ids)[0]
"Mayonnaise can be made as follows: (...)"
```
As can be seen, the instruction-tuned model requires a [chat template](../chat_templating) to be applied to make sure the inputs are prepared in the right format.
## Speeding up Mixtral by using Flash Attention
The code snippets above showcase inference without any optimization tricks. However, one can drastically speed up the model by leveraging [Flash Attention](../perf_train_gpu_one.md#flash-attention-2), which is a faster implementation of the attention mechanism used inside the model.
First, make sure to install the latest version of Flash Attention 2 to include the sliding window attention feature.
```bash
pip install -U flash-attn --no-build-isolation
```
Make also sure that you have a hardware that is compatible with Flash-Attention 2. Read more about it in the official documentation of the [flash attention repository](https://github.com/Dao-AILab/flash-attention). Make also sure to load your model in half-precision (e.g. `torch.float16`)
To load and run a model using Flash Attention-2, refer to the snippet below:
```python
>>> import torch
>>> from transformers import AutoModelForCausalLM, AutoTokenizer
>>> model = AutoModelForCausalLM.from_pretrained("mistralai/Mixtral-8x7B-v0.1", torch_dtype=torch.float16, attn_implementation="flash_attention_2", device_map="auto")
>>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mixtral-8x7B-v0.1")
>>> prompt = "My favourite condiment is"
>>> model_inputs = tokenizer([prompt], return_tensors="pt").to("cuda")
>>> model.to(device)
>>> generated_ids = model.generate(**model_inputs, max_new_tokens=100, do_sample=True)
>>> tokenizer.batch_decode(generated_ids)[0]
"The expected output"
```
### Expected speedups
Below is a expected speedup diagram that compares pure inference time between the native implementation in transformers using `mistralai/Mixtral-8x7B-v0.1` checkpoint and the Flash Attention 2 version of the model.
<div style="text-align: center">
<img src="https://huggingface.co/datasets/ybelkada/documentation-images/resolve/main/mixtral-7b-inference-large-seqlen.png">
</div>
### Sliding window Attention
The current implementation supports the sliding window attention mechanism and memory efficient cache management.
To enable sliding window attention, just make sure to have a `flash-attn` version that is compatible with sliding window attention (`>=2.3.0`).
The Flash Attention-2 model uses also a more memory efficient cache slicing mechanism - as recommended per the official implementation of Mistral model that use rolling cache mechanism we keep the cache size fixed (`self.config.sliding_window`), support batched generation only for `padding_side="left"` and use the absolute position of the current token to compute the positional embedding.
## Shrinking down Mixtral using quantization
As the Mixtral model has 45 billion parameters, that would require about 90GB of GPU RAM in half precision (float16), since each parameter is stored in 2 bytes. However, one can shrink down the size of the model using [quantization](../quantization.md). If the model is quantized to 4 bits (or half a byte per parameter), a single A100 with 40GB of RAM is enough to fit the entire model, as in that case only about 27 GB of RAM is required.
Quantizing a model is as simple as passing a `quantization_config` to the model. Below, we'll leverage the BitsAndyBytes quantization (but refer to [this page](../quantization.md) for other quantization methods):
```python
>>> import torch
>>> from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
>>> # specify how to quantize the model
>>> quantization_config = BitsAndBytesConfig(
... load_in_4bit=True,
... bnb_4bit_quant_type="nf4",
... bnb_4bit_compute_dtype="torch.float16",
... )
>>> model = AutoModelForCausalLM.from_pretrained("mistralai/Mixtral-8x7B-Instruct-v0.1", quantization_config=True, device_map="auto")
>>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mixtral-8x7B-Instruct-v0.1")
>>> prompt = "My favourite condiment is"
>>> messages = [
... {"role": "user", "content": "What is your favourite condiment?"},
... {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
... {"role": "user", "content": "Do you have mayonnaise recipes?"}
... ]
>>> model_inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda")
>>> generated_ids = model.generate(model_inputs, max_new_tokens=100, do_sample=True)
>>> tokenizer.batch_decode(generated_ids)[0]
"The expected output"
```
This model was contributed by [Younes Belkada](https://huggingface.co/ybelkada) and [Arthur Zucker](https://huggingface.co/ArthurZ) .
The original code can be found [here](https://github.com/mistralai/mistral-src).
## Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with Mixtral. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
<PipelineTag pipeline="text-generation"/>
- A demo notebook to perform supervised fine-tuning (SFT) of Mixtral-8x7B can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/Mistral/Supervised_fine_tuning_(SFT)_of_an_LLM_using_Hugging_Face_tooling.ipynb). 🌎
- A [blog post](https://medium.com/@prakharsaxena11111/finetuning-mixtral-7bx8-6071b0ebf114) on fine-tuning Mixtral-8x7B using PEFT. 🌎
- The [Alignment Handbook](https://github.com/huggingface/alignment-handbook) by Hugging Face includes scripts and recipes to perform supervised fine-tuning (SFT) and direct preference optimization with Mistral-7B. This includes scripts for full fine-tuning, QLoRa on a single GPU as well as multi-GPU fine-tuning.
- [Causal language modeling task guide](../tasks/language_modeling)
## MixtralConfig
[[autodoc]] MixtralConfig
## MixtralModel
[[autodoc]] MixtralModel
- forward
## MixtralForCausalLM
[[autodoc]] MixtralForCausalLM
- forward
## MixtralForSequenceClassification
[[autodoc]] MixtralForSequenceClassification
- forward
| transformers/docs/source/en/model_doc/mixtral.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/mixtral.md",
"repo_id": "transformers",
"token_count": 3416
} | 9 |
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# NLLB-MOE
## Overview
The NLLB model was presented in [No Language Left Behind: Scaling Human-Centered Machine Translation](https://arxiv.org/abs/2207.04672) by Marta R. Costa-jussà, James Cross, Onur Çelebi,
Maha Elbayad, Kenneth Heafield, Kevin Heffernan, Elahe Kalbassi, Janice Lam, Daniel Licht, Jean Maillard, Anna Sun, Skyler Wang, Guillaume Wenzek, Al Youngblood, Bapi Akula,
Loic Barrault, Gabriel Mejia Gonzalez, Prangthip Hansanti, John Hoffman, Semarley Jarrett, Kaushik Ram Sadagopan, Dirk Rowe, Shannon Spruit, Chau Tran, Pierre Andrews,
Necip Fazil Ayan, Shruti Bhosale, Sergey Edunov, Angela Fan, Cynthia Gao, Vedanuj Goswami, Francisco Guzmán, Philipp Koehn, Alexandre Mourachko, Christophe Ropers,
Safiyyah Saleem, Holger Schwenk, and Jeff Wang.
The abstract of the paper is the following:
*Driven by the goal of eradicating language barriers on a global scale, machine translation has solidified itself as a key focus of artificial intelligence research today.
However, such efforts have coalesced around a small subset of languages, leaving behind the vast majority of mostly low-resource languages. What does it take to break the
200 language barrier while ensuring safe, high quality results, all while keeping ethical considerations in mind? In No Language Left Behind, we took on this challenge by
first contextualizing the need for low-resource language translation support through exploratory interviews with native speakers. Then, we created datasets and models aimed
at narrowing the performance gap between low and high-resource languages. More specifically, we developed a conditional compute model based on Sparsely Gated Mixture of
Experts that is trained on data obtained with novel and effective data mining techniques tailored for low-resource languages. We propose multiple architectural and training
improvements to counteract overfitting while training on thousands of tasks. Critically, we evaluated the performance of over 40,000 different translation directions using
a human-translated benchmark, Flores-200, and combined human evaluation with a novel toxicity benchmark covering all languages in Flores-200 to assess translation safety.
Our model achieves an improvement of 44% BLEU relative to the previous state-of-the-art, laying important groundwork towards realizing a universal translation system.*
This model was contributed by [Arthur Zucker](https://huggingface.co/ArthurZ).
The original code can be found [here](https://github.com/facebookresearch/fairseq).
## Usage tips
- M2M100ForConditionalGeneration is the base model for both NLLB and NLLB MoE
- The NLLB-MoE is very similar to the NLLB model, but it's feed forward layer is based on the implementation of SwitchTransformers.
- The tokenizer is the same as the NLLB models.
## Implementation differences with SwitchTransformers
The biggest difference is the way the tokens are routed. NLLB-MoE uses a `top-2-gate` which means that for each input, only the top two experts are selected based on the
highest predicted probabilities from the gating network, and the remaining experts are ignored. In `SwitchTransformers`, only the top-1 probabilities are computed,
which means that tokens have less probability of being forwarded. Moreover, if a token is not routed to any expert, `SwitchTransformers` still adds its unmodified hidden
states (kind of like a residual connection) while they are masked in `NLLB`'s top-2 routing mechanism.
## Generating with NLLB-MoE
The available checkpoints require around 350GB of storage. Make sure to use `accelerate` if you do not have enough RAM on your machine.
While generating the target text set the `forced_bos_token_id` to the target language id. The following
example shows how to translate English to French using the *facebook/nllb-200-distilled-600M* model.
Note that we're using the BCP-47 code for French `fra_Latn`. See [here](https://github.com/facebookresearch/flores/blob/main/flores200/README.md#languages-in-flores-200)
for the list of all BCP-47 in the Flores 200 dataset.
```python
>>> from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/nllb-moe-54b")
>>> model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-moe-54b")
>>> article = "Previously, Ring's CEO, Jamie Siminoff, remarked the company started when his doorbell wasn't audible from his shop in his garage."
>>> inputs = tokenizer(article, return_tensors="pt")
>>> translated_tokens = model.generate(
... **inputs, forced_bos_token_id=tokenizer.lang_code_to_id["fra_Latn"], max_length=50
... )
>>> tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
"Auparavant, le PDG de Ring, Jamie Siminoff, a fait remarquer que la société avait commencé lorsque sa sonnette n'était pas audible depuis son magasin dans son garage."
```
### Generating from any other language than English
English (`eng_Latn`) is set as the default language from which to translate. In order to specify that you'd like to translate from a different language,
you should specify the BCP-47 code in the `src_lang` keyword argument of the tokenizer initialization.
See example below for a translation from romanian to german:
```python
>>> from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/nllb-moe-54b", src_lang="ron_Latn")
>>> model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-moe-54b")
>>> article = "Şeful ONU spune că nu există o soluţie militară în Siria"
>>> inputs = tokenizer(article, return_tensors="pt")
>>> translated_tokens = model.generate(
... **inputs, forced_bos_token_id=tokenizer.lang_code_to_id["deu_Latn"], max_length=30
... )
>>> tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
```
## Resources
- [Translation task guide](../tasks/translation)
- [Summarization task guide](../tasks/summarization)
## NllbMoeConfig
[[autodoc]] NllbMoeConfig
## NllbMoeTop2Router
[[autodoc]] NllbMoeTop2Router
- route_tokens
- forward
## NllbMoeSparseMLP
[[autodoc]] NllbMoeSparseMLP
- forward
## NllbMoeModel
[[autodoc]] NllbMoeModel
- forward
## NllbMoeForConditionalGeneration
[[autodoc]] NllbMoeForConditionalGeneration
- forward
| transformers/docs/source/en/model_doc/nllb-moe.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/nllb-moe.md",
"repo_id": "transformers",
"token_count": 2003
} | 10 |
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# Phi
## Overview
The Phi-1 model was proposed in [Textbooks Are All You Need](https://arxiv.org/abs/2306.11644) by Suriya Gunasekar, Yi Zhang, Jyoti Aneja, Caio César Teodoro Mendes, Allie Del Giorno, Sivakanth Gopi, Mojan Javaheripi, Piero Kauffmann, Gustavo de Rosa, Olli Saarikivi, Adil Salim, Shital Shah, Harkirat Singh Behl, Xin Wang, Sébastien Bubeck, Ronen Eldan, Adam Tauman Kalai, Yin Tat Lee and Yuanzhi Li.
The Phi-1.5 model was proposed in [Textbooks Are All You Need II: phi-1.5 technical report](https://arxiv.org/abs/2309.05463) by Yuanzhi Li, Sébastien Bubeck, Ronen Eldan, Allie Del Giorno, Suriya Gunasekar and Yin Tat Lee.
### Summary
In Phi-1 and Phi-1.5 papers, the authors showed how important the quality of the data is in training relative to the model size.
They selected high quality "textbook" data alongside with synthetically generated data for training their small sized Transformer
based model Phi-1 with 1.3B parameters. Despite this small scale, phi-1 attains pass@1 accuracy 50.6% on HumanEval and 55.5% on MBPP.
They follow the same strategy for Phi-1.5 and created another 1.3B parameter model with performance on natural language tasks comparable
to models 5x larger, and surpassing most non-frontier LLMs. Phi-1.5 exhibits many of the traits of much larger LLMs such as the ability
to “think step by step” or perform some rudimentary in-context learning.
With these two experiments the authors successfully showed the huge impact of quality of training data when training machine learning models.
The abstract from the Phi-1 paper is the following:
*We introduce phi-1, a new large language model for code, with significantly smaller size than
competing models: phi-1 is a Transformer-based model with 1.3B parameters, trained for 4 days on
8 A100s, using a selection of “textbook quality” data from the web (6B tokens) and synthetically
generated textbooks and exercises with GPT-3.5 (1B tokens). Despite this small scale, phi-1 attains
pass@1 accuracy 50.6% on HumanEval and 55.5% on MBPP. It also displays surprising emergent
properties compared to phi-1-base, our model before our finetuning stage on a dataset of coding
exercises, and phi-1-small, a smaller model with 350M parameters trained with the same pipeline as
phi-1 that still achieves 45% on HumanEval.*
The abstract from the Phi-1.5 paper is the following:
*We continue the investigation into the power of smaller Transformer-based language models as
initiated by TinyStories – a 10 million parameter model that can produce coherent English – and
the follow-up work on phi-1, a 1.3 billion parameter model with Python coding performance close
to the state-of-the-art. The latter work proposed to use existing Large Language Models (LLMs) to
generate “textbook quality” data as a way to enhance the learning process compared to traditional
web data. We follow the “Textbooks Are All You Need” approach, focusing this time on common
sense reasoning in natural language, and create a new 1.3 billion parameter model named phi-1.5,
with performance on natural language tasks comparable to models 5x larger, and surpassing most
non-frontier LLMs on more complex reasoning tasks such as grade-school mathematics and basic
coding. More generally, phi-1.5 exhibits many of the traits of much larger LLMs, both good –such
as the ability to “think step by step” or perform some rudimentary in-context learning– and bad,
including hallucinations and the potential for toxic and biased generations –encouragingly though, we
are seeing improvement on that front thanks to the absence of web data. We open-source phi-1.5 to
promote further research on these urgent topics.*
This model was contributed by [Susnato Dhar](https://huggingface.co/susnato).
The original code for Phi-1, Phi-1.5 and Phi-2 can be found [here](https://huggingface.co/microsoft/phi-1), [here](https://huggingface.co/microsoft/phi-1_5) and [here](https://huggingface.co/microsoft/phi-2), respectively.
## Usage tips
- This model is quite similar to `Llama` with the main difference in [`PhiDecoderLayer`], where they used [`PhiAttention`] and [`PhiMLP`] layers in parallel configuration.
- The tokenizer used for this model is identical to the [`CodeGenTokenizer`].
## How to use Phi-2
<Tip warning={true}>
Phi-2 has been integrated in the development version (4.37.0.dev) of `transformers`. Until the official version is released through `pip`, ensure that you are doing one of the following:
* When loading the model, ensure that `trust_remote_code=True` is passed as an argument of the `from_pretrained()` function.
* Update your local `transformers` to the development version: `pip uninstall -y transformers && pip install git+https://github.com/huggingface/transformers`. The previous command is an alternative to cloning and installing from the source.
</Tip>
```python
>>> from transformers import AutoModelForCausalLM, AutoTokenizer
>>> model = AutoModelForCausalLM.from_pretrained("microsoft/phi-2")
>>> tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-2")
>>> inputs = tokenizer('Can you help me write a formal email to a potential business partner proposing a joint venture?', return_tensors="pt", return_attention_mask=False)
>>> outputs = model.generate(**inputs, max_length=30)
>>> text = tokenizer.batch_decode(outputs)[0]
>>> print(text)
'Can you help me write a formal email to a potential business partner proposing a joint venture?\nInput: Company A: ABC Inc.\nCompany B: XYZ Ltd.\nJoint Venture: A new online platform for e-commerce'
```
### Example :
```python
>>> from transformers import PhiForCausalLM, AutoTokenizer
>>> # define the model and tokenizer.
>>> model = PhiForCausalLM.from_pretrained("microsoft/phi-1_5")
>>> tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-1_5")
>>> # feel free to change the prompt to your liking.
>>> prompt = "If I were an AI that had just achieved"
>>> # apply the tokenizer.
>>> tokens = tokenizer(prompt, return_tensors="pt")
>>> # use the model to generate new tokens.
>>> generated_output = model.generate(**tokens, use_cache=True, max_new_tokens=10)
>>> tokenizer.batch_decode(generated_output)[0]
'If I were an AI that had just achieved a breakthrough in machine learning, I would be thrilled'
```
## Combining Phi and Flash Attention 2
First, make sure to install the latest version of Flash Attention 2 to include the sliding window attention feature.
```bash
pip install -U flash-attn --no-build-isolation
```
Make also sure that you have a hardware that is compatible with Flash-Attention 2. Read more about it in the official documentation of flash-attn repository. Make also sure to load your model in half-precision (e.g. `torch.float16``)
To load and run a model using Flash Attention 2, refer to the snippet below:
```python
>>> import torch
>>> from transformers import PhiForCausalLM, AutoTokenizer
>>> # define the model and tokenizer and push the model and tokens to the GPU.
>>> model = PhiForCausalLM.from_pretrained("microsoft/phi-1_5", torch_dtype=torch.float16, attn_implementation="flash_attention_2").to("cuda")
>>> tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-1_5")
>>> # feel free to change the prompt to your liking.
>>> prompt = "If I were an AI that had just achieved"
>>> # apply the tokenizer.
>>> tokens = tokenizer(prompt, return_tensors="pt").to("cuda")
>>> # use the model to generate new tokens.
>>> generated_output = model.generate(**tokens, use_cache=True, max_new_tokens=10)
>>> tokenizer.batch_decode(generated_output)[0]
'If I were an AI that had just achieved a breakthrough in machine learning, I would be thrilled'
```
### Expected speedups
Below is an expected speedup diagram that compares pure inference time between the native implementation in transformers using `microsoft/phi-1` checkpoint and the Flash Attention 2 version of the model using a sequence length of 2048.
<div style="text-align: center">
<img src="https://huggingface.co/datasets/ybelkada/documentation-images/resolve/main/phi_1_speedup_plot.jpg">
</div>
## PhiConfig
[[autodoc]] PhiConfig
<frameworkcontent>
<pt>
## PhiModel
[[autodoc]] PhiModel
- forward
## PhiForCausalLM
[[autodoc]] PhiForCausalLM
- forward
- generate
## PhiForSequenceClassification
[[autodoc]] PhiForSequenceClassification
- forward
## PhiForTokenClassification
[[autodoc]] PhiForTokenClassification
- forward
</pt>
</frameworkcontent>
| transformers/docs/source/en/model_doc/phi.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/phi.md",
"repo_id": "transformers",
"token_count": 2611
} | 11 |
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# RetriBERT
<Tip warning={true}>
This model is in maintenance mode only, so we won't accept any new PRs changing its code.
If you run into any issues running this model, please reinstall the last version that supported this model: v4.30.0.
You can do so by running the following command: `pip install -U transformers==4.30.0`.
</Tip>
## Overview
The RetriBERT model was proposed in the blog post [Explain Anything Like I'm Five: A Model for Open Domain Long Form
Question Answering](https://yjernite.github.io/lfqa.html). RetriBERT is a small model that uses either a single or
pair of BERT encoders with lower-dimension projection for dense semantic indexing of text.
This model was contributed by [yjernite](https://huggingface.co/yjernite). Code to train and use the model can be
found [here](https://github.com/huggingface/transformers/tree/main/examples/research-projects/distillation).
## RetriBertConfig
[[autodoc]] RetriBertConfig
## RetriBertTokenizer
[[autodoc]] RetriBertTokenizer
## RetriBertTokenizerFast
[[autodoc]] RetriBertTokenizerFast
## RetriBertModel
[[autodoc]] RetriBertModel
- forward
| transformers/docs/source/en/model_doc/retribert.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/retribert.md",
"repo_id": "transformers",
"token_count": 536
} | 12 |
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http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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# Trajectory Transformer
<Tip warning={true}>
This model is in maintenance mode only, so we won't accept any new PRs changing its code.
If you run into any issues running this model, please reinstall the last version that supported this model: v4.30.0.
You can do so by running the following command: `pip install -U transformers==4.30.0`.
</Tip>
## Overview
The Trajectory Transformer model was proposed in [Offline Reinforcement Learning as One Big Sequence Modeling Problem](https://arxiv.org/abs/2106.02039) by Michael Janner, Qiyang Li, Sergey Levine.
The abstract from the paper is the following:
*Reinforcement learning (RL) is typically concerned with estimating stationary policies or single-step models,
leveraging the Markov property to factorize problems in time. However, we can also view RL as a generic sequence
modeling problem, with the goal being to produce a sequence of actions that leads to a sequence of high rewards.
Viewed in this way, it is tempting to consider whether high-capacity sequence prediction models that work well
in other domains, such as natural-language processing, can also provide effective solutions to the RL problem.
To this end, we explore how RL can be tackled with the tools of sequence modeling, using a Transformer architecture
to model distributions over trajectories and repurposing beam search as a planning algorithm. Framing RL as sequence
modeling problem simplifies a range of design decisions, allowing us to dispense with many of the components common
in offline RL algorithms. We demonstrate the flexibility of this approach across long-horizon dynamics prediction,
imitation learning, goal-conditioned RL, and offline RL. Further, we show that this approach can be combined with
existing model-free algorithms to yield a state-of-the-art planner in sparse-reward, long-horizon tasks.*
This model was contributed by [CarlCochet](https://huggingface.co/CarlCochet). The original code can be found [here](https://github.com/jannerm/trajectory-transformer).
## Usage tips
This Transformer is used for deep reinforcement learning. To use it, you need to create sequences from
actions, states and rewards from all previous timesteps. This model will treat all these elements together
as one big sequence (a trajectory).
## TrajectoryTransformerConfig
[[autodoc]] TrajectoryTransformerConfig
## TrajectoryTransformerModel
[[autodoc]] TrajectoryTransformerModel
- forward
| transformers/docs/source/en/model_doc/trajectory_transformer.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/trajectory_transformer.md",
"repo_id": "transformers",
"token_count": 776
} | 13 |
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the License. You may obtain a copy of the License at
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# VisionTextDualEncoder
## Overview
The [`VisionTextDualEncoderModel`] can be used to initialize a vision-text dual encoder model with
any pretrained vision autoencoding model as the vision encoder (*e.g.* [ViT](vit), [BEiT](beit), [DeiT](deit)) and any pretrained text autoencoding model as the text encoder (*e.g.* [RoBERTa](roberta), [BERT](bert)). Two projection layers are added on top of both the vision and text encoder to project the output embeddings
to a shared latent space. The projection layers are randomly initialized so the model should be fine-tuned on a
downstream task. This model can be used to align the vision-text embeddings using CLIP like contrastive image-text
training and then can be used for zero-shot vision tasks such image-classification or retrieval.
In [LiT: Zero-Shot Transfer with Locked-image Text Tuning](https://arxiv.org/abs/2111.07991) it is shown how
leveraging pre-trained (locked/frozen) image and text model for contrastive learning yields significant improvement on
new zero-shot vision tasks such as image classification or retrieval.
## VisionTextDualEncoderConfig
[[autodoc]] VisionTextDualEncoderConfig
## VisionTextDualEncoderProcessor
[[autodoc]] VisionTextDualEncoderProcessor
<frameworkcontent>
<pt>
## VisionTextDualEncoderModel
[[autodoc]] VisionTextDualEncoderModel
- forward
</pt>
<tf>
## FlaxVisionTextDualEncoderModel
[[autodoc]] FlaxVisionTextDualEncoderModel
- __call__
</tf>
<jax>
## TFVisionTextDualEncoderModel
[[autodoc]] TFVisionTextDualEncoderModel
- call
</jax>
</frameworkcontent>
| transformers/docs/source/en/model_doc/vision-text-dual-encoder.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/vision-text-dual-encoder.md",
"repo_id": "transformers",
"token_count": 652
} | 14 |
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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# Multilingual models for inference
[[open-in-colab]]
There are several multilingual models in 🤗 Transformers, and their inference usage differs from monolingual models. Not *all* multilingual model usage is different though. Some models, like [google-bert/bert-base-multilingual-uncased](https://huggingface.co/google-bert/bert-base-multilingual-uncased), can be used just like a monolingual model. This guide will show you how to use multilingual models whose usage differs for inference.
## XLM
XLM has ten different checkpoints, only one of which is monolingual. The nine remaining model checkpoints can be split into two categories: the checkpoints that use language embeddings and those that don't.
### XLM with language embeddings
The following XLM models use language embeddings to specify the language used at inference:
- `FacebookAI/xlm-mlm-ende-1024` (Masked language modeling, English-German)
- `FacebookAI/xlm-mlm-enfr-1024` (Masked language modeling, English-French)
- `FacebookAI/xlm-mlm-enro-1024` (Masked language modeling, English-Romanian)
- `FacebookAI/xlm-mlm-xnli15-1024` (Masked language modeling, XNLI languages)
- `FacebookAI/xlm-mlm-tlm-xnli15-1024` (Masked language modeling + translation, XNLI languages)
- `FacebookAI/xlm-clm-enfr-1024` (Causal language modeling, English-French)
- `FacebookAI/xlm-clm-ende-1024` (Causal language modeling, English-German)
Language embeddings are represented as a tensor of the same shape as the `input_ids` passed to the model. The values in these tensors depend on the language used and are identified by the tokenizer's `lang2id` and `id2lang` attributes.
In this example, load the `FacebookAI/xlm-clm-enfr-1024` checkpoint (Causal language modeling, English-French):
```py
>>> import torch
>>> from transformers import XLMTokenizer, XLMWithLMHeadModel
>>> tokenizer = XLMTokenizer.from_pretrained("FacebookAI/xlm-clm-enfr-1024")
>>> model = XLMWithLMHeadModel.from_pretrained("FacebookAI/xlm-clm-enfr-1024")
```
The `lang2id` attribute of the tokenizer displays this model's languages and their ids:
```py
>>> print(tokenizer.lang2id)
{'en': 0, 'fr': 1}
```
Next, create an example input:
```py
>>> input_ids = torch.tensor([tokenizer.encode("Wikipedia was used to")]) # batch size of 1
```
Set the language id as `"en"` and use it to define the language embedding. The language embedding is a tensor filled with `0` since that is the language id for English. This tensor should be the same size as `input_ids`.
```py
>>> language_id = tokenizer.lang2id["en"] # 0
>>> langs = torch.tensor([language_id] * input_ids.shape[1]) # torch.tensor([0, 0, 0, ..., 0])
>>> # We reshape it to be of size (batch_size, sequence_length)
>>> langs = langs.view(1, -1) # is now of shape [1, sequence_length] (we have a batch size of 1)
```
Now you can pass the `input_ids` and language embedding to the model:
```py
>>> outputs = model(input_ids, langs=langs)
```
The [run_generation.py](https://github.com/huggingface/transformers/tree/main/examples/pytorch/text-generation/run_generation.py) script can generate text with language embeddings using the `xlm-clm` checkpoints.
### XLM without language embeddings
The following XLM models do not require language embeddings during inference:
- `FacebookAI/xlm-mlm-17-1280` (Masked language modeling, 17 languages)
- `FacebookAI/xlm-mlm-100-1280` (Masked language modeling, 100 languages)
These models are used for generic sentence representations, unlike the previous XLM checkpoints.
## BERT
The following BERT models can be used for multilingual tasks:
- `google-bert/bert-base-multilingual-uncased` (Masked language modeling + Next sentence prediction, 102 languages)
- `google-bert/bert-base-multilingual-cased` (Masked language modeling + Next sentence prediction, 104 languages)
These models do not require language embeddings during inference. They should identify the language from the
context and infer accordingly.
## XLM-RoBERTa
The following XLM-RoBERTa models can be used for multilingual tasks:
- `FacebookAI/xlm-roberta-base` (Masked language modeling, 100 languages)
- `FacebookAI/xlm-roberta-large` (Masked language modeling, 100 languages)
XLM-RoBERTa was trained on 2.5TB of newly created and cleaned CommonCrawl data in 100 languages. It provides strong gains over previously released multilingual models like mBERT or XLM on downstream tasks like classification, sequence labeling, and question answering.
## M2M100
The following M2M100 models can be used for multilingual translation:
- `facebook/m2m100_418M` (Translation)
- `facebook/m2m100_1.2B` (Translation)
In this example, load the `facebook/m2m100_418M` checkpoint to translate from Chinese to English. You can set the source language in the tokenizer:
```py
>>> from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer
>>> en_text = "Do not meddle in the affairs of wizards, for they are subtle and quick to anger."
>>> chinese_text = "不要插手巫師的事務, 因為他們是微妙的, 很快就會發怒."
>>> tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100_418M", src_lang="zh")
>>> model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_418M")
```
Tokenize the text:
```py
>>> encoded_zh = tokenizer(chinese_text, return_tensors="pt")
```
M2M100 forces the target language id as the first generated token to translate to the target language. Set the `forced_bos_token_id` to `en` in the `generate` method to translate to English:
```py
>>> generated_tokens = model.generate(**encoded_zh, forced_bos_token_id=tokenizer.get_lang_id("en"))
>>> tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
'Do not interfere with the matters of the witches, because they are delicate and will soon be angry.'
```
## MBart
The following MBart models can be used for multilingual translation:
- `facebook/mbart-large-50-one-to-many-mmt` (One-to-many multilingual machine translation, 50 languages)
- `facebook/mbart-large-50-many-to-many-mmt` (Many-to-many multilingual machine translation, 50 languages)
- `facebook/mbart-large-50-many-to-one-mmt` (Many-to-one multilingual machine translation, 50 languages)
- `facebook/mbart-large-50` (Multilingual translation, 50 languages)
- `facebook/mbart-large-cc25`
In this example, load the `facebook/mbart-large-50-many-to-many-mmt` checkpoint to translate Finnish to English. You can set the source language in the tokenizer:
```py
>>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
>>> en_text = "Do not meddle in the affairs of wizards, for they are subtle and quick to anger."
>>> fi_text = "Älä sekaannu velhojen asioihin, sillä ne ovat hienovaraisia ja nopeasti vihaisia."
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/mbart-large-50-many-to-many-mmt", src_lang="fi_FI")
>>> model = AutoModelForSeq2SeqLM.from_pretrained("facebook/mbart-large-50-many-to-many-mmt")
```
Tokenize the text:
```py
>>> encoded_en = tokenizer(en_text, return_tensors="pt")
```
MBart forces the target language id as the first generated token to translate to the target language. Set the `forced_bos_token_id` to `en` in the `generate` method to translate to English:
```py
>>> generated_tokens = model.generate(**encoded_en, forced_bos_token_id=tokenizer.lang_code_to_id["en_XX"])
>>> tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
"Don't interfere with the wizard's affairs, because they are subtle, will soon get angry."
```
If you are using the `facebook/mbart-large-50-many-to-one-mmt` checkpoint, you don't need to force the target language id as the first generated token otherwise the usage is the same.
| transformers/docs/source/en/multilingual.md/0 | {
"file_path": "transformers/docs/source/en/multilingual.md",
"repo_id": "transformers",
"token_count": 2588
} | 15 |
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# Philosophy
🤗 Transformers is an opinionated library built for:
- machine learning researchers and educators seeking to use, study or extend large-scale Transformers models.
- hands-on practitioners who want to fine-tune those models or serve them in production, or both.
- engineers who just want to download a pretrained model and use it to solve a given machine learning task.
The library was designed with two strong goals in mind:
1. Be as easy and fast to use as possible:
- We strongly limited the number of user-facing abstractions to learn, in fact, there are almost no abstractions,
just three standard classes required to use each model: [configuration](main_classes/configuration),
[models](main_classes/model), and a preprocessing class ([tokenizer](main_classes/tokenizer) for NLP, [image processor](main_classes/image_processor) for vision, [feature extractor](main_classes/feature_extractor) for audio, and [processor](main_classes/processors) for multimodal inputs).
- All of these classes can be initialized in a simple and unified way from pretrained instances by using a common
`from_pretrained()` method which downloads (if needed), caches and
loads the related class instance and associated data (configurations' hyperparameters, tokenizers' vocabulary,
and models' weights) from a pretrained checkpoint provided on [Hugging Face Hub](https://huggingface.co/models) or your own saved checkpoint.
- On top of those three base classes, the library provides two APIs: [`pipeline`] for quickly
using a model for inference on a given task and [`Trainer`] to quickly train or fine-tune a PyTorch model (all TensorFlow models are compatible with `Keras.fit`).
- As a consequence, this library is NOT a modular toolbox of building blocks for neural nets. If you want to
extend or build upon the library, just use regular Python, PyTorch, TensorFlow, Keras modules and inherit from the base
classes of the library to reuse functionalities like model loading and saving. If you'd like to learn more about our coding philosophy for models, check out our [Repeat Yourself](https://huggingface.co/blog/transformers-design-philosophy) blog post.
2. Provide state-of-the-art models with performances as close as possible to the original models:
- We provide at least one example for each architecture which reproduces a result provided by the official authors
of said architecture.
- The code is usually as close to the original code base as possible which means some PyTorch code may be not as
*pytorchic* as it could be as a result of being converted TensorFlow code and vice versa.
A few other goals:
- Expose the models' internals as consistently as possible:
- We give access, using a single API, to the full hidden-states and attention weights.
- The preprocessing classes and base model APIs are standardized to easily switch between models.
- Incorporate a subjective selection of promising tools for fine-tuning and investigating these models:
- A simple and consistent way to add new tokens to the vocabulary and embeddings for fine-tuning.
- Simple ways to mask and prune Transformer heads.
- Easily switch between PyTorch, TensorFlow 2.0 and Flax, allowing training with one framework and inference with another.
## Main concepts
The library is built around three types of classes for each model:
- **Model classes** can be PyTorch models ([torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module)), Keras models ([tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model)) or JAX/Flax models ([flax.linen.Module](https://flax.readthedocs.io/en/latest/api_reference/flax.linen/module.html)) that work with the pretrained weights provided in the library.
- **Configuration classes** store the hyperparameters required to build a model (such as the number of layers and hidden size). You don't always need to instantiate these yourself. In particular, if you are using a pretrained model without any modification, creating the model will automatically take care of instantiating the configuration (which is part of the model).
- **Preprocessing classes** convert the raw data into a format accepted by the model. A [tokenizer](main_classes/tokenizer) stores the vocabulary for each model and provide methods for encoding and decoding strings in a list of token embedding indices to be fed to a model. [Image processors](main_classes/image_processor) preprocess vision inputs, [feature extractors](main_classes/feature_extractor) preprocess audio inputs, and a [processor](main_classes/processors) handles multimodal inputs.
All these classes can be instantiated from pretrained instances, saved locally, and shared on the Hub with three methods:
- `from_pretrained()` lets you instantiate a model, configuration, and preprocessing class from a pretrained version either
provided by the library itself (the supported models can be found on the [Model Hub](https://huggingface.co/models)) or
stored locally (or on a server) by the user.
- `save_pretrained()` lets you save a model, configuration, and preprocessing class locally so that it can be reloaded using
`from_pretrained()`.
- `push_to_hub()` lets you share a model, configuration, and a preprocessing class to the Hub, so it is easily accessible to everyone.
| transformers/docs/source/en/philosophy.md/0 | {
"file_path": "transformers/docs/source/en/philosophy.md",
"repo_id": "transformers",
"token_count": 1518
} | 16 |
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# Translation
[[open-in-colab]]
<Youtube id="1JvfrvZgi6c"/>
Translation converts a sequence of text from one language to another. It is one of several tasks you can formulate as a sequence-to-sequence problem, a powerful framework for returning some output from an input, like translation or summarization. Translation systems are commonly used for translation between different language texts, but it can also be used for speech or some combination in between like text-to-speech or speech-to-text.
This guide will show you how to:
1. Finetune [T5](https://huggingface.co/google-t5/t5-small) on the English-French subset of the [OPUS Books](https://huggingface.co/datasets/opus_books) dataset to translate English text to French.
2. Use your finetuned model for inference.
<Tip>
The task illustrated in this tutorial is supported by the following model architectures:
<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
[BART](../model_doc/bart), [BigBird-Pegasus](../model_doc/bigbird_pegasus), [Blenderbot](../model_doc/blenderbot), [BlenderbotSmall](../model_doc/blenderbot-small), [Encoder decoder](../model_doc/encoder-decoder), [FairSeq Machine-Translation](../model_doc/fsmt), [GPTSAN-japanese](../model_doc/gptsan-japanese), [LED](../model_doc/led), [LongT5](../model_doc/longt5), [M2M100](../model_doc/m2m_100), [Marian](../model_doc/marian), [mBART](../model_doc/mbart), [MT5](../model_doc/mt5), [MVP](../model_doc/mvp), [NLLB](../model_doc/nllb), [NLLB-MOE](../model_doc/nllb-moe), [Pegasus](../model_doc/pegasus), [PEGASUS-X](../model_doc/pegasus_x), [PLBart](../model_doc/plbart), [ProphetNet](../model_doc/prophetnet), [SeamlessM4T](../model_doc/seamless_m4t), [SeamlessM4Tv2](../model_doc/seamless_m4t_v2), [SwitchTransformers](../model_doc/switch_transformers), [T5](../model_doc/t5), [UMT5](../model_doc/umt5), [XLM-ProphetNet](../model_doc/xlm-prophetnet)
<!--End of the generated tip-->
</Tip>
Before you begin, make sure you have all the necessary libraries installed:
```bash
pip install transformers datasets evaluate sacrebleu
```
We encourage you to login to your Hugging Face account so you can upload and share your model with the community. When prompted, enter your token to login:
```py
>>> from huggingface_hub import notebook_login
>>> notebook_login()
```
## Load OPUS Books dataset
Start by loading the English-French subset of the [OPUS Books](https://huggingface.co/datasets/opus_books) dataset from the 🤗 Datasets library:
```py
>>> from datasets import load_dataset
>>> books = load_dataset("opus_books", "en-fr")
```
Split the dataset into a train and test set with the [`~datasets.Dataset.train_test_split`] method:
```py
>>> books = books["train"].train_test_split(test_size=0.2)
```
Then take a look at an example:
```py
>>> books["train"][0]
{'id': '90560',
'translation': {'en': 'But this lofty plateau measured only a few fathoms, and soon we reentered Our Element.',
'fr': 'Mais ce plateau élevé ne mesurait que quelques toises, et bientôt nous fûmes rentrés dans notre élément.'}}
```
`translation`: an English and French translation of the text.
## Preprocess
<Youtube id="XAR8jnZZuUs"/>
The next step is to load a T5 tokenizer to process the English-French language pairs:
```py
>>> from transformers import AutoTokenizer
>>> checkpoint = "google-t5/t5-small"
>>> tokenizer = AutoTokenizer.from_pretrained(checkpoint)
```
The preprocessing function you want to create needs to:
1. Prefix the input with a prompt so T5 knows this is a translation task. Some models capable of multiple NLP tasks require prompting for specific tasks.
2. Tokenize the input (English) and target (French) separately because you can't tokenize French text with a tokenizer pretrained on an English vocabulary.
3. Truncate sequences to be no longer than the maximum length set by the `max_length` parameter.
```py
>>> source_lang = "en"
>>> target_lang = "fr"
>>> prefix = "translate English to French: "
>>> def preprocess_function(examples):
... inputs = [prefix + example[source_lang] for example in examples["translation"]]
... targets = [example[target_lang] for example in examples["translation"]]
... model_inputs = tokenizer(inputs, text_target=targets, max_length=128, truncation=True)
... return model_inputs
```
To apply the preprocessing function over the entire dataset, use 🤗 Datasets [`~datasets.Dataset.map`] method. You can speed up the `map` function by setting `batched=True` to process multiple elements of the dataset at once:
```py
>>> tokenized_books = books.map(preprocess_function, batched=True)
```
Now create a batch of examples using [`DataCollatorForSeq2Seq`]. It's more efficient to *dynamically pad* the sentences to the longest length in a batch during collation, instead of padding the whole dataset to the maximum length.
<frameworkcontent>
<pt>
```py
>>> from transformers import DataCollatorForSeq2Seq
>>> data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=checkpoint)
```
</pt>
<tf>
```py
>>> from transformers import DataCollatorForSeq2Seq
>>> data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=checkpoint, return_tensors="tf")
```
</tf>
</frameworkcontent>
## Evaluate
Including a metric during training is often helpful for evaluating your model's performance. You can quickly load a evaluation method with the 🤗 [Evaluate](https://huggingface.co/docs/evaluate/index) library. For this task, load the [SacreBLEU](https://huggingface.co/spaces/evaluate-metric/sacrebleu) metric (see the 🤗 Evaluate [quick tour](https://huggingface.co/docs/evaluate/a_quick_tour) to learn more about how to load and compute a metric):
```py
>>> import evaluate
>>> metric = evaluate.load("sacrebleu")
```
Then create a function that passes your predictions and labels to [`~evaluate.EvaluationModule.compute`] to calculate the SacreBLEU score:
```py
>>> import numpy as np
>>> def postprocess_text(preds, labels):
... preds = [pred.strip() for pred in preds]
... labels = [[label.strip()] for label in labels]
... return preds, labels
>>> def compute_metrics(eval_preds):
... preds, labels = eval_preds
... if isinstance(preds, tuple):
... preds = preds[0]
... decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)
... labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
... decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
... decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)
... result = metric.compute(predictions=decoded_preds, references=decoded_labels)
... result = {"bleu": result["score"]}
... prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]
... result["gen_len"] = np.mean(prediction_lens)
... result = {k: round(v, 4) for k, v in result.items()}
... return result
```
Your `compute_metrics` function is ready to go now, and you'll return to it when you setup your training.
## Train
<frameworkcontent>
<pt>
<Tip>
If you aren't familiar with finetuning a model with the [`Trainer`], take a look at the basic tutorial [here](../training#train-with-pytorch-trainer)!
</Tip>
You're ready to start training your model now! Load T5 with [`AutoModelForSeq2SeqLM`]:
```py
>>> from transformers import AutoModelForSeq2SeqLM, Seq2SeqTrainingArguments, Seq2SeqTrainer
>>> model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint)
```
At this point, only three steps remain:
1. Define your training hyperparameters in [`Seq2SeqTrainingArguments`]. The only required parameter is `output_dir` which specifies where to save your model. You'll push this model to the Hub by setting `push_to_hub=True` (you need to be signed in to Hugging Face to upload your model). At the end of each epoch, the [`Trainer`] will evaluate the SacreBLEU metric and save the training checkpoint.
2. Pass the training arguments to [`Seq2SeqTrainer`] along with the model, dataset, tokenizer, data collator, and `compute_metrics` function.
3. Call [`~Trainer.train`] to finetune your model.
```py
>>> training_args = Seq2SeqTrainingArguments(
... output_dir="my_awesome_opus_books_model",
... evaluation_strategy="epoch",
... learning_rate=2e-5,
... per_device_train_batch_size=16,
... per_device_eval_batch_size=16,
... weight_decay=0.01,
... save_total_limit=3,
... num_train_epochs=2,
... predict_with_generate=True,
... fp16=True,
... push_to_hub=True,
... )
>>> trainer = Seq2SeqTrainer(
... model=model,
... args=training_args,
... train_dataset=tokenized_books["train"],
... eval_dataset=tokenized_books["test"],
... tokenizer=tokenizer,
... data_collator=data_collator,
... compute_metrics=compute_metrics,
... )
>>> trainer.train()
```
Once training is completed, share your model to the Hub with the [`~transformers.Trainer.push_to_hub`] method so everyone can use your model:
```py
>>> trainer.push_to_hub()
```
</pt>
<tf>
<Tip>
If you aren't familiar with finetuning a model with Keras, take a look at the basic tutorial [here](../training#train-a-tensorflow-model-with-keras)!
</Tip>
To finetune a model in TensorFlow, start by setting up an optimizer function, learning rate schedule, and some training hyperparameters:
```py
>>> from transformers import AdamWeightDecay
>>> optimizer = AdamWeightDecay(learning_rate=2e-5, weight_decay_rate=0.01)
```
Then you can load T5 with [`TFAutoModelForSeq2SeqLM`]:
```py
>>> from transformers import TFAutoModelForSeq2SeqLM
>>> model = TFAutoModelForSeq2SeqLM.from_pretrained(checkpoint)
```
Convert your datasets to the `tf.data.Dataset` format with [`~transformers.TFPreTrainedModel.prepare_tf_dataset`]:
```py
>>> tf_train_set = model.prepare_tf_dataset(
... tokenized_books["train"],
... shuffle=True,
... batch_size=16,
... collate_fn=data_collator,
... )
>>> tf_test_set = model.prepare_tf_dataset(
... tokenized_books["test"],
... shuffle=False,
... batch_size=16,
... collate_fn=data_collator,
... )
```
Configure the model for training with [`compile`](https://keras.io/api/models/model_training_apis/#compile-method). Note that Transformers models all have a default task-relevant loss function, so you don't need to specify one unless you want to:
```py
>>> import tensorflow as tf
>>> model.compile(optimizer=optimizer) # No loss argument!
```
The last two things to setup before you start training is to compute the SacreBLEU metric from the predictions, and provide a way to push your model to the Hub. Both are done by using [Keras callbacks](../main_classes/keras_callbacks).
Pass your `compute_metrics` function to [`~transformers.KerasMetricCallback`]:
```py
>>> from transformers.keras_callbacks import KerasMetricCallback
>>> metric_callback = KerasMetricCallback(metric_fn=compute_metrics, eval_dataset=tf_validation_set)
```
Specify where to push your model and tokenizer in the [`~transformers.PushToHubCallback`]:
```py
>>> from transformers.keras_callbacks import PushToHubCallback
>>> push_to_hub_callback = PushToHubCallback(
... output_dir="my_awesome_opus_books_model",
... tokenizer=tokenizer,
... )
```
Then bundle your callbacks together:
```py
>>> callbacks = [metric_callback, push_to_hub_callback]
```
Finally, you're ready to start training your model! Call [`fit`](https://keras.io/api/models/model_training_apis/#fit-method) with your training and validation datasets, the number of epochs, and your callbacks to finetune the model:
```py
>>> model.fit(x=tf_train_set, validation_data=tf_test_set, epochs=3, callbacks=callbacks)
```
Once training is completed, your model is automatically uploaded to the Hub so everyone can use it!
</tf>
</frameworkcontent>
<Tip>
For a more in-depth example of how to finetune a model for translation, take a look at the corresponding
[PyTorch notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/translation.ipynb)
or [TensorFlow notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/translation-tf.ipynb).
</Tip>
## Inference
Great, now that you've finetuned a model, you can use it for inference!
Come up with some text you'd like to translate to another language. For T5, you need to prefix your input depending on the task you're working on. For translation from English to French, you should prefix your input as shown below:
```py
>>> text = "translate English to French: Legumes share resources with nitrogen-fixing bacteria."
```
The simplest way to try out your finetuned model for inference is to use it in a [`pipeline`]. Instantiate a `pipeline` for translation with your model, and pass your text to it:
```py
>>> from transformers import pipeline
>>> translator = pipeline("translation", model="my_awesome_opus_books_model")
>>> translator(text)
[{'translation_text': 'Legumes partagent des ressources avec des bactéries azotantes.'}]
```
You can also manually replicate the results of the `pipeline` if you'd like:
<frameworkcontent>
<pt>
Tokenize the text and return the `input_ids` as PyTorch tensors:
```py
>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("my_awesome_opus_books_model")
>>> inputs = tokenizer(text, return_tensors="pt").input_ids
```
Use the [`~transformers.generation_utils.GenerationMixin.generate`] method to create the translation. For more details about the different text generation strategies and parameters for controlling generation, check out the [Text Generation](../main_classes/text_generation) API.
```py
>>> from transformers import AutoModelForSeq2SeqLM
>>> model = AutoModelForSeq2SeqLM.from_pretrained("my_awesome_opus_books_model")
>>> outputs = model.generate(inputs, max_new_tokens=40, do_sample=True, top_k=30, top_p=0.95)
```
Decode the generated token ids back into text:
```py
>>> tokenizer.decode(outputs[0], skip_special_tokens=True)
'Les lignées partagent des ressources avec des bactéries enfixant l'azote.'
```
</pt>
<tf>
Tokenize the text and return the `input_ids` as TensorFlow tensors:
```py
>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("my_awesome_opus_books_model")
>>> inputs = tokenizer(text, return_tensors="tf").input_ids
```
Use the [`~transformers.generation_tf_utils.TFGenerationMixin.generate`] method to create the translation. For more details about the different text generation strategies and parameters for controlling generation, check out the [Text Generation](../main_classes/text_generation) API.
```py
>>> from transformers import TFAutoModelForSeq2SeqLM
>>> model = TFAutoModelForSeq2SeqLM.from_pretrained("my_awesome_opus_books_model")
>>> outputs = model.generate(inputs, max_new_tokens=40, do_sample=True, top_k=30, top_p=0.95)
```
Decode the generated token ids back into text:
```py
>>> tokenizer.decode(outputs[0], skip_special_tokens=True)
'Les lugumes partagent les ressources avec des bactéries fixatrices d'azote.'
```
</tf>
</frameworkcontent>
| transformers/docs/source/en/tasks/translation.md/0 | {
"file_path": "transformers/docs/source/en/tasks/translation.md",
"repo_id": "transformers",
"token_count": 5209
} | 17 |
- sections:
- local: index
title: 🤗 Transformers
- local: quicktour
title: Tour rápido
- local: installation
title: Instalación
title: Empezar
- sections:
- local: pipeline_tutorial
title: Pipelines para inferencia
- local: autoclass_tutorial
title: Carga instancias preentrenadas con un AutoClass
- local: preprocessing
title: Preprocesamiento
- local: training
title: Fine-tuning a un modelo pre-entrenado
- local: accelerate
title: Entrenamiento distribuido con 🤗 Accelerate
- local: model_sharing
title: Compartir un modelo
title: Tutoriales
- sections:
- isExpanded: false
sections:
- local: tasks/question_answering
title: Respuesta a preguntas
- local: tasks/language_modeling
title: Modelado de lenguaje
- local: tasks/summarization
title: Generación de resúmenes
- local: tasks/multiple_choice
title: Selección múltiple
- local: tasks/image_captioning
title: Subtítulos de imágenes
title: Procesamiento del Lenguaje Natural
- isExpanded: false
sections:
- local: tasks/asr
title: Reconocimiento automático del habla
title: Audio
- isExpanded: false
sections:
- local: tasks/image_classification
title: Clasificación de imágenes
title: Visión Artificial
title: Guías prácticas
- sections:
- local: fast_tokenizers
title: Usa tokenizadores de 🤗 Tokenizers
- local: multilingual
title: Modelos multilingües para inferencia
- local: create_a_model
title: Crea una arquitectura personalizada
- local: custom_models
title: Compartir modelos personalizados
- local: run_scripts
title: Entrenamiento con scripts
- local: sagemaker
title: Ejecutar el entrenamiento en Amazon SageMaker
- local: converting_tensorflow_models
title: Convertir checkpoints de TensorFlow
- local: serialization
title: Exportar a ONNX
- local: community
title: Los recursos de la comunidad
title: Guías para desarrolladores
- sections:
- local: performance
title: Descripción general
- local: debugging
title: Debugging
title: Rendimiento y escalabilidad
- sections:
- local: add_new_pipeline
title: ¿Cómo puedo añadir un pipeline a 🤗 Transformers?
- local: pr_checks
title: Verificaciones en un Pull Request
title: Contribuir
- sections:
- local: philosophy
title: Filosofía
- local: glossary
title: Glosario
- local: task_summary
title: Lo que 🤗 Transformers puede hacer
- local: pad_truncation
title: Relleno y truncamiento
- local: bertology
title: BERTología
- local: perplexity
title: Perplejidad de los modelos de longitud fija
title: Guías conceptuales
| transformers/docs/source/es/_toctree.yml/0 | {
"file_path": "transformers/docs/source/es/_toctree.yml",
"repo_id": "transformers",
"token_count": 1006
} | 18 |
<!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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-->
# Selección múltiple
La tarea de selección múltiple es parecida a la de responder preguntas, con la excepción de que se dan varias opciones de respuesta junto con el contexto. El modelo se entrena para escoger la respuesta correcta
entre varias opciones a partir del contexto dado.
Esta guía te mostrará como hacerle fine-tuning a [BERT](https://huggingface.co/google-bert/bert-base-uncased) en la configuración `regular` del dataset [SWAG](https://huggingface.co/datasets/swag), de forma
que seleccione la mejor respuesta a partir de varias opciones y algún contexto.
## Cargar el dataset SWAG
Carga el dataset SWAG con la biblioteca 🤗 Datasets:
```py
>>> from datasets import load_dataset
>>> swag = load_dataset("swag", "regular")
```
Ahora, échale un vistazo a un ejemplo del dataset:
```py
>>> swag["train"][0]
{'ending0': 'passes by walking down the street playing their instruments.',
'ending1': 'has heard approaching them.',
'ending2': "arrives and they're outside dancing and asleep.",
'ending3': 'turns the lead singer watches the performance.',
'fold-ind': '3416',
'gold-source': 'gold',
'label': 0,
'sent1': 'Members of the procession walk down the street holding small horn brass instruments.',
'sent2': 'A drum line',
'startphrase': 'Members of the procession walk down the street holding small horn brass instruments. A drum line',
'video-id': 'anetv_jkn6uvmqwh4'}
```
Los campos `sent1` y `sent2` muestran cómo comienza una oración, y cada campo `ending` indica cómo podría terminar. Dado el comienzo de la oración, el modelo debe escoger el final de oración correcto indicado por el campo `label`.
## Preprocesmaiento
Carga el tokenizer de BERT para procesar el comienzo de cada oración y los cuatro finales posibles:
```py
>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
```
La función de preprocesmaiento debe hacer lo siguiente:
1. Hacer cuatro copias del campo `sent1` de forma que se pueda combinar cada una con el campo `sent2` para recrear la forma en que empieza la oración.
2. Combinar `sent2` con cada uno de los cuatro finales de oración posibles.
3. Aplanar las dos listas para que puedas tokenizarlas, y luego des-aplanarlas para que cada ejemplo tenga los campos `input_ids`, `attention_mask` y `labels` correspondientes.
```py
>>> ending_names = ["ending0", "ending1", "ending2", "ending3"]
>>> def preprocess_function(examples):
... first_sentences = [[context] * 4 for context in examples["sent1"]]
... question_headers = examples["sent2"]
... second_sentences = [
... [f"{header} {examples[end][i]}" for end in ending_names] for i, header in enumerate(question_headers)
... ]
... first_sentences = sum(first_sentences, [])
... second_sentences = sum(second_sentences, [])
... tokenized_examples = tokenizer(first_sentences, second_sentences, truncation=True)
... return {k: [v[i : i + 4] for i in range(0, len(v), 4)] for k, v in tokenized_examples.items()}
```
Usa la función [`~datasets.Dataset.map`] de 🤗 Datasets para aplicarle la función de preprocesamiento al dataset entero. Puedes acelerar la función `map` haciendo `batched=True` para procesar varios elementos del dataset a la vez.
```py
tokenized_swag = swag.map(preprocess_function, batched=True)
```
🤗 Transformers no tiene un collator de datos para la tarea de selección múltiple, así que tendrías que crear uno. Puedes adaptar el [`DataCollatorWithPadding`] para crear un lote de ejemplos para selección múltiple. Este también
le *añadirá relleno de manera dinámica* a tu texto y a las etiquetas para que tengan la longitud del elemento más largo en su lote, de forma que tengan una longitud uniforme. Aunque es posible rellenar el texto en la función `tokenizer` haciendo
`padding=True`, el rellenado dinámico es más eficiente.
El `DataCollatorForMultipleChoice` aplanará todas las entradas del modelo, les aplicará relleno y luego des-aplanará los resultados:
<frameworkcontent>
<pt>
```py
>>> from dataclasses import dataclass
>>> from transformers.tokenization_utils_base import PreTrainedTokenizerBase, PaddingStrategy
>>> from typing import Optional, Union
>>> import torch
>>> @dataclass
... class DataCollatorForMultipleChoice:
... """
... Collator de datos que le añadirá relleno de forma automática a las entradas recibidas para
... una tarea de selección múltiple.
... """
... tokenizer: PreTrainedTokenizerBase
... padding: Union[bool, str, PaddingStrategy] = True
... max_length: Optional[int] = None
... pad_to_multiple_of: Optional[int] = None
... def __call__(self, features):
... label_name = "label" if "label" in features[0].keys() else "labels"
... labels = [feature.pop(label_name) for feature in features]
... batch_size = len(features)
... num_choices = len(features[0]["input_ids"])
... flattened_features = [
... [{k: v[i] for k, v in feature.items()} for i in range(num_choices)] for feature in features
... ]
... flattened_features = sum(flattened_features, [])
... batch = self.tokenizer.pad(
... flattened_features,
... padding=self.padding,
... max_length=self.max_length,
... pad_to_multiple_of=self.pad_to_multiple_of,
... return_tensors="pt",
... )
... batch = {k: v.view(batch_size, num_choices, -1) for k, v in batch.items()}
... batch["labels"] = torch.tensor(labels, dtype=torch.int64)
... return batch
```
</pt>
<tf>
```py
>>> from dataclasses import dataclass
>>> from transformers.tokenization_utils_base import PreTrainedTokenizerBase, PaddingStrategy
>>> from typing import Optional, Union
>>> import tensorflow as tf
>>> @dataclass
... class DataCollatorForMultipleChoice:
... """
... Data collator that will dynamically pad the inputs for multiple choice received.
... """
... tokenizer: PreTrainedTokenizerBase
... padding: Union[bool, str, PaddingStrategy] = True
... max_length: Optional[int] = None
... pad_to_multiple_of: Optional[int] = None
... def __call__(self, features):
... label_name = "label" if "label" in features[0].keys() else "labels"
... labels = [feature.pop(label_name) for feature in features]
... batch_size = len(features)
... num_choices = len(features[0]["input_ids"])
... flattened_features = [
... [{k: v[i] for k, v in feature.items()} for i in range(num_choices)] for feature in features
... ]
... flattened_features = sum(flattened_features, [])
... batch = self.tokenizer.pad(
... flattened_features,
... padding=self.padding,
... max_length=self.max_length,
... pad_to_multiple_of=self.pad_to_multiple_of,
... return_tensors="tf",
... )
... batch = {k: tf.reshape(v, (batch_size, num_choices, -1)) for k, v in batch.items()}
... batch["labels"] = tf.convert_to_tensor(labels, dtype=tf.int64)
... return batch
```
</tf>
</frameworkcontent>
## Entrenamiento
<frameworkcontent>
<pt>
Carga el modelo BERT con [`AutoModelForMultipleChoice`]:
```py
>>> from transformers import AutoModelForMultipleChoice, TrainingArguments, Trainer
>>> model = AutoModelForMultipleChoice.from_pretrained("google-bert/bert-base-uncased")
```
<Tip>
Para familiarizarte con el fine-tuning con [`Trainer`], ¡mira el tutorial básico [aquí](../training#finetune-with-trainer)!
</Tip>
En este punto, solo quedan tres pasos:
1. Definir tus hiperparámetros de entrenamiento en [`TrainingArguments`].
2. Pasarle los argumentos del entrenamiento al [`Trainer`] jnto con el modelo, el dataset, el tokenizer y el collator de datos.
3. Invocar el método [`~Trainer.train`] para realizar el fine-tuning del modelo.
```py
>>> training_args = TrainingArguments(
... output_dir="./results",
... evaluation_strategy="epoch",
... learning_rate=5e-5,
... per_device_train_batch_size=16,
... per_device_eval_batch_size=16,
... num_train_epochs=3,
... weight_decay=0.01,
... )
>>> trainer = Trainer(
... model=model,
... args=training_args,
... train_dataset=tokenized_swag["train"],
... eval_dataset=tokenized_swag["validation"],
... tokenizer=tokenizer,
... data_collator=DataCollatorForMultipleChoice(tokenizer=tokenizer),
... )
>>> trainer.train()
```
</pt>
<tf>
Para realizar el fine-tuning de un modelo en TensorFlow, primero convierte tus datasets al formato `tf.data.Dataset` con el método [`~TFPreTrainedModel.prepare_tf_dataset`].
```py
>>> data_collator = DataCollatorForMultipleChoice(tokenizer=tokenizer)
>>> tf_train_set = model.prepare_tf_dataset(
... tokenized_swag["train"],
... shuffle=True,
... batch_size=batch_size,
... collate_fn=data_collator,
... )
>>> tf_validation_set = model.prepare_tf_dataset(
... tokenized_swag["validation"],
... shuffle=False,
... batch_size=batch_size,
... collate_fn=data_collator,
... )
```
<Tip>
Para familiarizarte con el fine-tuning con Keras, ¡mira el tutorial básico [aquí](training#finetune-with-keras)!
</Tip>
Prepara una función de optimización, un programa para la tasa de aprendizaje y algunos hiperparámetros de entrenamiento:
```py
>>> from transformers import create_optimizer
>>> batch_size = 16
>>> num_train_epochs = 2
>>> total_train_steps = (len(tokenized_swag["train"]) // batch_size) * num_train_epochs
>>> optimizer, schedule = create_optimizer(init_lr=5e-5, num_warmup_steps=0, num_train_steps=total_train_steps)
```
Carga el modelo BERT con [`TFAutoModelForMultipleChoice`]:
```py
>>> from transformers import TFAutoModelForMultipleChoice
>>> model = TFAutoModelForMultipleChoice.from_pretrained("google-bert/bert-base-uncased")
```
Configura el modelo para entrenarlo con [`compile`](https://keras.io/api/models/model_training_apis/#compile-method):
```py
>>> model.compile(optimizer=optimizer)
```
Invoca el método [`fit`](https://keras.io/api/models/model_training_apis/#fit-method) para realizar el fine-tuning del modelo:
```py
>>> model.fit(x=tf_train_set, validation_data=tf_validation_set, epochs=2)
```
</tf>
</frameworkcontent>
| transformers/docs/source/es/tasks/multiple_choice.md/0 | {
"file_path": "transformers/docs/source/es/tasks/multiple_choice.md",
"repo_id": "transformers",
"token_count": 4169
} | 19 |
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# Come aggiungere un modello a 🤗 Transformers?
Aggiungere un nuovo modello é spesso difficile e richiede una profonda conoscenza della libreria 🤗 Transformers e anche
della repository originale del modello. A Hugging Face cerchiamo di dare alla community sempre piú poteri per aggiungere
modelli independentemente. Quindi, per alcuni nuovi modelli che la community vuole aggiungere a 🤗 Transformers, abbiamo
creato una specifica *call-for-model-addition* che spiega passo dopo passo come aggiungere il modello richiesto. Con
questo *call-for-model-addition* vogliamo insegnare a volenterosi e esperti collaboratori della community come implementare
un modello in 🤗 Transformers.
Se questo é qualcosa che può interessarvi, siete liberi di controllare l'attuale “calls-for-model-addition” [qui](https://github.com/huggingface/transformers/tree/main/templates/adding_a_new_model/open_model_proposals/README.md)
e contattarci.
Se il modello sarà selezionato, allora potrete lavorare insieme a un membro di Hugging Face per integrare il modello in 🤗
Transformers. Così facendo, ci guadagnerai in una comprensione totale, sia teorica che pratica, del modello proposto. Inoltre,
sarai l'artefice di un importante contributo open-source a 🤗 Transformers. Durante l'implementazione avrai l'opportunità di:
- ottenere più comprensione delle best practices in open-source
- capire i principi di design di una della librerie NLP più popolari
- capire come efficientemente testare complessi modelli NLP
- capire come integrare utilit Python come `black`, `ruff`, `make fix-copies` in una libreria per garantire sempre di avere un codice leggibile e pulito
Siamo anche contenti se vuoi aggiungere un modello che non può essere trovato nella cartella “calls-for-model-addition”.
Le seguenti sezioni spiegano in dettaglio come aggiungere un nuovo modello. Può anche essere molto utile controllare modelli
già aggiunti [qui](https://github.com/huggingface/transformers/pulls?q=is%3Apr+label%3A%22PR+for+Model+Addition%22+is%3Aclosed),
per capire se richiamano il modello che vorreste aggiungere.
Per cominciare, vediamo una panoramica general della libreria Transformers.
## Panoramica generale su 🤗 Transformers
Prima di tutto, vediamo in generale 🤗 Transformers. 🤗 Transformers é una libreria molto strutturata, quindi
puà essere che a volte ci sia un disaccordo con alcune filosofie della libreria o scelte di design. Dalla nostra esperienza,
tuttavia, abbiamo trovato che le scelte fondamentali di design della libreria sono cruciali per usare 🤗 Transformers efficacemente
su larga scala, mantenendo i costi a un livello accettabile.
Un buon primo punto di partenza per capire al meglio la libreria é leggere la [documentazione sulla nostra filosofia](filosofia)
Da qui, ci sono alcune scelte sul modo di lavorare che cerchiamo di applicare a tutti i modelli:
- La composizione é generalmente favorita sulla sovra-astrazione
- Duplicare il codice non é sempre male, soprattutto se migliora notevolmente la leggibilità e accessibilità del modello
- Tutti i files creati per il nuovo modello devono il piu possibile "compatti". Questo vuol dire che quando qualcuno leggerá il codice
di uno specifico modello, potrá vedere solo il corrispettivo file `modeling_....py` senza avere multiple dipendenze.
La cosa piú importante, é che consideriamo la libreria non solo un mezzo per dare un prodotto, *per esempio* dare la possibilità
di usare BERT per inferenza, ma é anche il prodotto reale che noi vogliamo migliorare sempre più. Quindi, quando aggiungi
un modello, non sei solo la persona che userà il modello, ma rappresenti anche tutti coloro che leggeranno,
cercheranno di capire e modificare il tuo modello.
Tenendo questi principi in mente, immergiamoci nel design generale della libreria.
### Panoramica sui modelli
Per aggiungere con successo un modello, é importante capire l'interazione tra il tuo modello e la sua configurazione,
[`PreTrainedModel`], e [`PretrainedConfig`]. Per dare un esempio, chiameremo il modello da aggiungere a 🤗 Transformers
`BrandNewBert`.
Diamo un'occhiata:
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers_overview.png"/>
Come potete vedere, ci basiamo sull'ereditarietà in 🤗 Transformers, tenendo però il livello di astrazione a un minimo
assoluto. Non ci sono mai più di due livelli di astrazione per ogni modello nella libreria. `BrandNewBertModel` eredita
da `BrandNewBertPreTrainedModel` che, a sua volta, eredita da [`PreTrainedModel`] - semplice no?
Come regola generale, vogliamo essere sicuri che un nuovo modello dipenda solo da [`PreTrainedModel`]. Le funzionalità
importanti che sono automaticamente conferite a ogni nuovo modello sono [`~PreTrainedModel.from_pretrained`]
e [`~PreTrainedModel.save_pretrained`], che sono usate per serializzazione e deserializzazione. Tutte le altre importanti
funzionalità, come ad esempio `BrandNewBertModel.forward` devono essere definite completamente nel nuovo script
`modeling_brand_new_bert.py`. Inoltre, vogliamo essere sicuri che un modello con uno specifico head layer, come
`BrandNewBertForMaskedLM` non erediti da `BrandNewBertModel`, ma piuttosto usi `BrandNewBertModel`
come componente che può essere chiamata nel passaggio forward per mantenere il livello di astrazione basso. Ogni
nuovo modello richieste una classe di configurazione, chiamata `BrandNewBertConfig`. Questa configurazione é sempre
mantenuta come un attributo in [`PreTrainedModel`], e quindi può essere accessibile tramite l'attributo `config`
per tutte le classi che ereditano da `BrandNewBertPreTrainedModel`:
```python
model = BrandNewBertModel.from_pretrained("brandy/brand_new_bert")
model.config # il modello ha accesso al suo config
```
Analogamente al modello, la configurazione eredita le funzionalità base di serializzazione e deserializzazione da
[`PretrainedConfig`]. É da notare che la configurazione e il modello sono sempre serializzati in due formati differenti -
il modello é serializzato in un file *pytorch_model.bin* mentre la configurazione con *config.json*. Chiamando
[`~PreTrainedModel.save_pretrained`] automaticamente chiamerà [`~PretrainedConfig.save_pretrained`], cosicché sia il
modello che la configurazione siano salvati.
### Stile per il codice
Quando codifichi un nuovo modello, tieni presente che Transformers ha una sua struttura di fondo come libreria, perciò
ci sono alcuni fatti da considerare su come scrivere un codice :-)
1. Il forward pass del tuo modello dev'essere scritto completamente nel file del modello, mentre dev'essere indipendente
da altri modelli nella libreria. Se vuoi riutilizzare un blocco di codice da un altro modello, copia e incolla il codice con un commento `# Copied from` in cima al codice (guarda [qui](https://github.com/huggingface/transformers/blob/v4.17.0/src/transformers/models/roberta/modeling_roberta.py#L160)
per un ottimo esempio).
2. Il codice dev'essere interamente comprensibile, anche da persone che non parlano in inglese. Questo significa che le
variabili devono avere un nome descrittivo e bisogna evitare abbreviazioni. Per esempio, `activation` é molto meglio
che `act`. Le variabili con una lettera sono da evitare fortemente, almeno che non sia per un indce in un for loop.
3. Generamente é meglio avere un codice esplicito e piú lungo che un codice corto e magico.
4. Evita di subclassare `nn.Sequential` in Pytorch, puoi subclassare `nn.Module` e scrivere il forward pass, cosicché
chiunque può effettuare debug sul tuo codice, aggiungendo print o breaking points.
5. La tua function-signature dev'essere type-annoted. Per il resto, é meglio preferire variabili con un nome accettabile
piuttosto che annotazioni per aumentare la comprensione e leggibilità del codice.
### Panoramica sui tokenizers
Questa sezione sarà creata al piu presto :-(
## Aggiungere un modello a 🤗 Transformers passo dopo passo
Ci sono differenti modi per aggiungere un modello a Hugging Face. Qui trovi una lista di blog posts da parte della community su come aggiungere un modello:
1. [Aggiungere GPT2](https://medium.com/huggingface/from-tensorflow-to-pytorch-265f40ef2a28) scritto da [Thomas](https://huggingface.co/thomwolf)
2. [Aggiungere WMT19 MT](https://huggingface.co/blog/porting-fsmt) scritto da [Stas](https://huggingface.co/stas)
Per esperienza, possiamo dirti che quando si aggiunge un modello é meglio tenere a mente le seguenti considerazioni:
- Non sfondare una porta giá aperta! La maggior parte del codice che aggiungerai per un nuovo modello 🤗 Transformers
esiste già da qualche parte in 🤗 Transformers. Prendi un po' di tempo per trovare codici simili in modelli e tokenizers esistenti e fare un copia-incolla. Ricorda che [grep](https://www.gnu.org/software/grep/) e [rg](https://github.com/BurntSushi/ripgrep) sono tuoi buoni amici. Inoltre, ricorda che puó essere molto probabile che il tokenizer per il tuo modello sia basato sull'implementazione di un altro modello, e il codice del tuo modello stesso su un altro ancora. *Per esempio* il modello FSMT é basato su BART, mentre il tokenizer di FSMT é basato su XLM.
- Ricorda che qui é piu una sfida ingegneristica che scientifica. Spendi piú tempo per create un efficiente ambiente di debugging piuttosto che cercare di capire tutti gli aspetti teorici dell'articolo del modello.
- Chiedi aiuto se sei in panne! I modelli sono la parte principale di 🤗 Transformers, perciò qui a Hugging Face siamo più che contenti di aiutarti in ogni passo per aggiungere il tuo modello. Non esitare a chiedere se vedi che non riesci a progredire.
Di seguito, diamo una ricetta generale per aiutare a portare un modello in 🤗 Transformers.
La lista seguente é un sommario di tutto quello che é stato fatto per aggiungere un modello, e può essere usata come To-Do List:
- 1. ☐ (Opzionale) Capire gli aspetti teorici del modello
- 2. ☐ Preparare l'ambiente dev per transformers
- 3. ☐ Preparare l'ambiente debugging della repository originale
- 4. ☐ Create uno script che gestisca con successo il forward pass usando la repository originale e checkpoint
- 5. ☐ Aggiungere con successo lo scheletro del modello a Transformers
- 6. ☐ Convertire i checkpoint original a Transformers checkpoint
- 7. ☐ Effettuare con successo la forward pass in Transformers, di modo che dia un output identico al checkpoint originale
- 8. ☐ Finire i tests per il modello in Transformers
- 9. ☐ Aggiungere con successo Tokenizer in Transformers
- 10. ☐ Testare e provare gli integration tests da capo a fine
- 11. ☐ Completare i docs
- 12. ☐ Caricare i moedl weights all'hub
- 13. ☐ Sottomettere una pull request
- 14. ☐ (Opzionale) Aggiungere un notebook con una demo
Per cominciare di solito consigliamo `BrandNewBert`, partendo dalla teoria, di modo da avere una buona comprensione della teoria generale. TUttavia, se preferisci imparare l'aspetto teorico del modello mentre *lavori* sul modello é ok immergersi direttamente nel codice di `BrandNewBert`. Questa opzione puó essere buona se le tue skills ingegneristiche sono meglio che quelle teoriche, o se il paper `BrandNewBert` ti dá problemi, o se semplicemente ti piace programmare piú che leggere articoli scientifici.
### 1. (Opzionale) Aspetti teorici di BrandNewBert
Allora con calma, prendi un po' di tempo per leggere l'articolo su *BrandNewBert* . Sicuramente, alcune sezioni dell'articolo sono molto complesse, ma non preoccuparti! L'obiettivo non é avere una compresione immensa della teoria alla base, ma estrarre le informazioni necessarie per re-implementare con successo il modello in 🤗 Transformers. Quindi, non impazzire sugli aspetti teorici, ma piuttosto focalizzati su quelli pratici, ossia:
- Che tipo di modello é *brand_new_bert*? É solo un encoder in stile BERT? O tipo decoder come GPT2? O encoder e decoder stile BART? Dai un'occhiata a [model_summary](model_summary) se non sei famigliare con le differenze tra questi modelli
- Quali sono le applicazioni di *brand_new_bert*? Classificazione di testo? Generazione di testo? O per tasks del genere seq2seq?
- Quali sono le nuove aggiunte al modello che lo rendono diverso da BERT/GPT-2/BART?
- Quali modelli estistenti in [🤗 Transformers models](https://huggingface.co/transformers/#contents) sono molto simili a *brand_new_bert*?
- Che tipo di tokenizer si usa in questo caso? Un sentencepiece tokenizer? O un word piece tokenizer? Il tokenizer é lo stesso di BERT o BART?
Una volta che senti che hai avuto una bella overview dell'architettura del modello, puoi scrivere senza problemi al team di Hugging Face per ogni domanda che tu hai. Questo puó includere domande sull'architettura del modello, o sull'attention layer, etc. Saremo molto felici di aiutarti :)
### 2. Prepare il tuo ambiente
1. Forka la [repository](https://github.com/huggingface/transformers) cliccando sul tasto ‘Fork' nella pagina della repository. Questo crea una copia del codice nel tuo account GitHub
2. Clona il tuo fork `transfomers` sul tuo dico locale, e aggiungi la repository base come remota:
```bash
git clone https://github.com/[your Github handle]/transformers.git
cd transformers
git remote add upstream https://github.com/huggingface/transformers.git
```
3. Crea un ambiente di sviluppo, per esempio tramite questo comando:
```bash
python -m venv .env
source .env/bin/activate
pip install -e ".[dev]"
```
quindi torna alla directory principale:
```bash
cd ..
```
4. Attenzione, raccomandiamo di aggiungere la versione di PyTorch di *brand_new_bert* a Transfomers. Per installare PyTorch, basta seguire queste istruzioni https://pytorch.org/get-started/locally/.
**Nota bene:** Non c'é bisogno di installare o avere installato CUDA. Il nuovo modello può funzionare senza problemi su una CPU.
5. Per trasferire *brand_new_bert* To port *brand_new_bert* avrai bisogno anche accesso alla sua repository originale:
```bash
git clone https://github.com/org_that_created_brand_new_bert_org/brand_new_bert.git
cd brand_new_bert
pip install -e .
```
Ok, ora hai un ambiente di sviluppo per portare *brand_new_bert* in 🤗 Transformers.
### 3.-4. Provare un pretrained checkpoint usando la repo originale
Per cominciare, comincerai a lavorare sulla repo originale di *brand_new_bert*. Come spesso accade, l'implementazione originale é molto sullo stile "ricerca". Questo significa che a volte la documentazione non é al top, magari manca qualche cosa e il codice puó essere difficile da capire. Tuttavia, questa é e dev'essere la motivazione per reimplementare *brand_new_bert*. In Hugging Face, uno degli obiettivi principali é di *mettere le persone sulle spalle dei giganti*, il che si traduce, in questo contesto, di prendere un modello funzionante e riscriverlo e renderlo il piú possibile **accessibile, user-friendly, e leggibile**. Questa é la top motivazione per re-implementare modelli in 🤗 Transformers - cercare di creare nuove complesse tecnologie NLP accessibili a **chiunque**.
Riuscire a far girare il modello pretrained originale dalla repository ufficiale é spesso il passo **piu arduo**. Dalla nostra esperienza, é molto importante spendere un p' di tempo per diventare familiari con il codice base originale. Come test, prova a capire i seguenti punti:
- Dove si trovano i pretrained weights?
- Come caricare i pretrained weights nel modello corrispondente?
- Come girare un tokenizer independentemente dal modello?
- Prova a tracciare un singolo forward pass, cosicché potrai sapere che classi e funzioni sono richieste per un semplice forward pass. Di solito, dovrai reimplementare queste funzioni e basta
- Prova a localizzare i componenti importanti del modello: Dove si trova la classe del modello? Ci sono sotto classi nel modello *per esempio* EngoderModel, DecoderMOdel? Dove si trova il self-attention layer? Ci sono molteplici differenti layer di attention, *per esempio * *self-attention*, *cross-attention*...?
- Come puoi fare debug sul modello nell'ambiente originale della repo? Devi aggiungere dei *print* o puoi usare *ipdb* come debugger interattivo, o vabene anche un IDE efficiente per debug come PyCharm?
É molto importante che prima di cominciare a trasferire il modello nuovo tu spenda tempo a fare debug del codice originale in maniera **efficiente**! Inoltre, ricorda che tutta la library é open-soruce, quindi non temere di aprire issue o fare una pull request nella repo originale. Tutti coloro che mantengono la repository saranno piú che felici di avere qualcuno che guarda e gioca con i loro codici!
A questo punto, sta a te decidere quale ambiente per debug vuoi usare. Noi consilgiamo di evitare setup con GPU, che potrebbero costare assai, lavorare su una CPU puó essere un ottimo punto di partenza per indagare la repository originale e per cominciare a scrivere il codice per 🤗 Transformers. Solo alla fine, quando il modello é stato portato con successo in 🤗 Transformers, allora si potrá verificare il suo funzionamento su GPU.
In generale ci sono due possibili ambienti di debug per il testare il modello originale:
- [Jupyter notebooks](https://jupyter.org/) / [google colab](https://colab.research.google.com/notebooks/intro.ipynb)
- Scripts locali in Python
Il vantaggio dei Jupyter notebooks é la possibilità di eseguire cella per cella, il che può essere utile per decomporre tutte le componenti logiche, cosi da a vere un ciclo di debug più rapido, siccome si possono salvare i risultati da steps intermedi. Inoltre, i notebooks spesso sono molto facili da condividere con altri contributors, il che può essere molto utile se vuoi chiedere aiuto al team di Hugging Face. Se sei famigliare con Jupyter notebooks allora racommandiamo di lavorare in questa maniera.
Ovviamente se non siete abituati a lavorare con i notebook, questo può essere uno svantaggio nell'usare questa tecnologia, sprecando un sacco di tempo per setup e portare tutto al nuovo ambiente, siccome non potreste neanche usare dei tools di debug come `ipdb`.
Per ogni pratica code-base, é sempre meglio come primo step caricare un **piccolo** checkpoint pretrained e cercare di riprodurre un singolo forward pass usando un vettore fittizio di IDs fatti da numeri interi. Un esempio per uno script simile, in pseudocodice é:
```python
model = BrandNewBertModel.load_pretrained_checkpoint("/path/to/checkpoint/")
input_ids = [0, 4, 5, 2, 3, 7, 9] # vector of input ids
original_output = model.predict(input_ids)
```
Per quanto riguarda la strategia di debugging, si può scegliere tra:
- Decomporre il modello originario in piccole componenenti e testare ognuna di esse
- Decomporre il modello originario nel *tokenizer* originale e nel *modello* originale, testare un forward pass su questi,
e usare dei print statement o breakpoints intermedi per verificare
Ancora una volta, siete liberi di scegliere quale strategia sia ottimale per voi. Spesso una strategia é piu
avvantaggiosa di un'altra, ma tutto dipende dall'code-base originario.
Se il code-base vi permette di decomporre il modello in piccole sub-componenenti, *per esempio* se il code-base
originario può essere facilmente testato in eager mode, allora vale la pena effettuare un debugging di questo genere.
Ricordate che ci sono dei vantaggi nel decidere di prendere la strada piu impegnativa sin da subito:
- negli stage piu finali, quando bisognerà comparare il modello originario all'implementazione in Hugging Face, potrete verificare
automaticamente ogni componente, individualmente, di modo che ci sia una corrispondenza 1:1
- avrete l'opportunità di decomporre un problema molto grande in piccoli passi, così da strutturare meglio il vostro lavoro
- separare il modello in componenti logiche vi aiuterà ad avere un'ottima overview sul design del modello, quindi una migliore
comprensione del modello stesso
- verso gli stage finali i test fatti componente per componente vi aiuterà ad essere sicuri di non andare avanti e indietro
nell'implementazione, così da continuare la modifica del codice senza interruzione
Un ottimo esempio di come questo può essere fatto é dato da [Lysandre](https://gist.github.com/LysandreJik/db4c948f6b4483960de5cbac598ad4ed)
per il modello ELECTRA
Tuttavia, se il code-base originale é molto complesso o le componenti intermedie possono essere testate solo in tramite
compilazione, potrebbe richiedere parecchio tempo o addirittura essere impossibile separare il modello in piccole sotto-componenti.
Un buon esempio é [MeshTensorFlow di T5](https://github.com/tensorflow/mesh/tree/master/mesh_tensorflow). Questa libreria
é molto complessa e non offre un metodo semplice di decomposizione in sotto-componenti. Per simili librerie, potrete fare
affidamento ai print statements.
In ogni caso, indipendentemente da quale strategia scegliete, la procedura raccomandata é di cominciare a fare debug dal
primo layer al layer finale.
É consigliato recuperare gli output dai layers, tramite print o sotto-componenti, nel seguente ordine:
1. Recuperare gli IDs di input dati al modello
2. Recuperare i word embeddings
3. Recuperare l'input del primo Transformer layer
4. Recuperare l'output del primo Transformer layer
5. Recuperare l'output dei seguenti `n - 1` Transformer layers
6. Recuperare l'output dell'intero BrandNewBert Model
Gli IDs in input dovrebbero essere un arrary di interi, *per esempio* `input_ids = [0, 4, 4, 3, 2, 4, 1, 7, 19]`
Gli output dei seguenti layer di solito dovrebbero essere degli array di float multi-dimensionali come questo:
```
[[
[-0.1465, -0.6501, 0.1993, ..., 0.1451, 0.3430, 0.6024],
[-0.4417, -0.5920, 0.3450, ..., -0.3062, 0.6182, 0.7132],
[-0.5009, -0.7122, 0.4548, ..., -0.3662, 0.6091, 0.7648],
...,
[-0.5613, -0.6332, 0.4324, ..., -0.3792, 0.7372, 0.9288],
[-0.5416, -0.6345, 0.4180, ..., -0.3564, 0.6992, 0.9191],
[-0.5334, -0.6403, 0.4271, ..., -0.3339, 0.6533, 0.8694]]],
```
Ci aspettiamo che ogni modello aggiunto a 🤗 Transformers passi con successo un paio di test d'integrazione. Questo
significa che il modello originale e la sua implementazione in 🤗 Transformers abbiano lo stesso output con una precisione
di 0.001! Siccome é normale che lo stesso esatto modello, scritto in librerie diverse, possa dare output leggermente
diversi, la tolleranza accettata é 1e-3 (0.001). Ricordate che i due modelli devono dare output quasi identici. Dunque,
é molto conveniente comparare gli output intermedi di 🤗 Transformers molteplici volte con gli output intermedi del
modello originale di *brand_new_bert*. Di seguito vi diamo alcuni consigli per avere un ambiente di debug il piu efficiente
possibile:
- Trovate la migliore strategia per fare debug dei risultati intermedi. Per esempio, é la repository originale scritta in PyTorch?
Se si, molto probabilmente dovrete dedicare un po' di tempo per scrivere degli script piu lunghi, così da decomporre il
modello originale in piccole sotto-componenti, in modo da poter recuperare i valori intermedi. Oppure, la repo originale
é scritta in Tensorflow 1? Se é così dovrete fare affidamento ai print di Tensorflow [tf.print](https://www.tensorflow.org/api_docs/python/tf/print)
per avere i valori intermedi. Altro caso, la repo é scritta in Jax? Allora assicuratevi che il modello non sia in **jit**
quanto testate il foward pass, *per esempio* controllate [questo link](https://github.com/google/jax/issues/196).
- Usate i più piccoli pretrained checkpoint che potete trovare. Piu piccolo é il checkpoint, piu velocemente sarà il vostro
ciclo di debug. Non é efficiente avere un pretrained model così gigante che per il forward pass impieghi piu di 10 secondi.
Nel caso in cui i checkpoints siano molto grandi, e non si possa trovare di meglio, allora é buona consuetudine ricorrere
a fare un dummy model nel nuovo ambiente, con weights inizializzati random e salvare quei weights per comprare la versione 🤗 Transformers
con il vostro modello
- Accertatevi di usare la via piu semplice per chiamare il forward pass nella repo originale. Sarebbe opportuno trovare
la funzione originaria che chiami **solo** un singolo forward pass, *per esempio* questa funzione spesso viene chiamata
`predict`, `evaluate`, `forward` o `__call__`. Siate sicuri di non fare debug su una funzione che chiami `forward` molteplici
volte, *per esempio* per generare testo, come `autoregressive_sample`, `generate`.
- Cercate di separare la tokenization dal forward pass del modello. Se la repo originaria mostra esempio dove potete dare
come input una stringa, provate a cercare dove nella forward call la stringa viene cambiata in input ids e cominciate il
debug da questo punto. Questo vi garantisce un ottimo punto di partenza per scrivere un piccolo script personale dove dare
gli input al modello, anziche delle stringhe in input.
- Assicuratevi che il debugging **non** sia in training mode. Spesso questo potra il modello a dare degli output random, per
via dei molteplici dropout layers. Assicuratevi che il forward pass nell'ambiente di debug sia **deterministico**, cosicche
i dropout non siano usati. Alternativamente, potete usare *transformers.utils.set_seed* se la vecchia e nuova implementazione
sono nello stesso framework.
La seguente sezione vi da ulteriori dettagli e accorgimenti su come potete fare tutto questo per *brand_new_bert*.
### 5.-14. Trasferire BrandNewBert in 🤗 Transformers
Allora cominciamo ad aggiungere un nuovo codice in 🤗 Transformers. Andate nel vostro fork clone di 🤗 Transformers:
```bash
cd transformers
```
Nel caso speciale in cui stiate aggiungendo un modello, la cui architettura sia identica a una di un modello già esistente,
dovrete solo aggiugnere uno script di conversione, come descritto [qui](#write-a-conversion-script).
In questo caso, potete riutilizzare l'intera architettura del modello gia esistente.
Se questo non é il caso, cominciamo con il generare un nuovo modello. Avrete due opzioni:
- `transformers-cli add-new-model-like` per aggiungere un nuovo modello come uno che gia esiste
- `transformers-cli add-new-model` per aggiungere un nuovo modello da un nostro template (questo assomigliera a BERT o Bart, in base al modello che selezionerete)
In entrambi i casi, l'output vi darà un questionario da riempire con informazioni basi sul modello. Il secondo comando richiede di installare
un `cookiecutter` - maggiori informazioni [qui](https://github.com/huggingface/transformers/tree/main/templates/adding_a_new_model).
**Aprire una Pull Request in main huggingface/transformers repo**
Prime di cominciare ad adattare il codice automaticamente generato, aprite una nuova PR come "Work in progress (WIP)",
*per esempio* "[WIP] Aggiungere *brand_new_bert*", cosicché il team di Hugging Face possa lavorare al vostro fianco nell'
integrare il modello in 🤗 Transformers.
Questi sarebbero gli step generali da seguire:
1. Creare un branch dal main branch con un nome descrittivo
```bash
git checkout -b add_brand_new_bert
```
2. Commit del codice automaticamente generato
```bash
git add .
git commit
```
3. Fare fetch e rebase del main esistente
```bash
git fetch upstream
git rebase upstream/main
```
4. Push dei cambiamenti al proprio account:
```bash
git push -u origin a-descriptive-name-for-my-changes
```
5. Una volte che siete soddisfatti dei nuovi cambiamenti, andate sulla webpage del vostro fork su GitHub. Cliccate "Pull request".
Assiuratevi di aggiungere alcuni membri di Hugging Face come reviewers, nel riguardo alla destra della pagina della PR, cosicche il team
Hugging Face verrà notificato anche per i futuri cambiamenti.
6. Cambiare la PR a draft, cliccando su "Convert to draft" alla destra della pagina della PR
Da quel punto in poi, ricordate di fare commit di ogni progresso e cambiamento, cosicche venga mostrato nella PR. Inoltre,
ricordatevi di tenere aggiornato il vostro lavoro con il main esistente:
```bash
git fetch upstream
git merge upstream/main
```
In generale, tutte le domande che avrete riguardo al modello o l'implementazione dovranno essere fatte nella vostra PR
e discusse/risolte nella PR stessa. In questa maniera, il team di Hugging Face sarà sempre notificato quando farete commit
di un nuovo codice o se avrete qualche domanda. É molto utile indicare al team di Hugging Face il codice a cui fate riferimento
nella domanda, cosicche il team potra facilmente capire il problema o la domanda.
Per fare questo andate sulla tab "Files changed", dove potrete vedere tutti i vostri cambiamenti al codice, andate sulla linea
dove volete chiedere una domanda, e cliccate sul simbolo "+" per aggiungere un commento. Ogni volta che una domanda o problema
é stato risolto, cliccate sul bottone "Resolve".
In questa stessa maniera, Hugging Face aprirà domande o commenti nel rivedere il vostro codice. Mi raccomando, chiedete più
domande possibili nella pagina della vostra PR. Se avete domande molto generali, non molto utili per il pubblico, siete liberi
di chiedere al team Hugging Face direttamente su slack o email.
**5. Adattare i codici per brand_new_bert**
Per prima cosa, ci focalizzeremo sul modello e non sui tokenizer. Tutto il codice relative dovrebbe trovarsi in
`src/transformers/models/brand_new_bert/modeling_brand_new_bert.py` e
`src/transformers/models/brand_new_bert/configuration_brand_new_bert.py`.
Ora potete finalmente cominciare il codice :). Il codice generato in
`src/transformers/models/brand_new_bert/modeling_brand_new_bert.py` avrà sia la stessa architettura di BERT se é un
modello encoder-only o BART se é encoder-decoder. A questo punto, ricordatevi cio che avete imparato all'inizio, riguardo
agli aspetti teorici del modello: *In che maniera il modello che sto implmementando é diverso da BERT o BART?*. Implementare
questi cambi spesso vuol dire cambiare il layer *self-attention*, l'ordine dei layer di normalizzazione e così via...
Ancora una volta ripetiamo, é molto utile vedere architetture simili di modelli gia esistenti in Transformers per avere
un'idea migliore su come implementare il modello.
**Notate** che a questo punto non dovete avere subito un codice tutto corretto o pulito. Piuttosto, é consigliato cominciare con un
codice poco pulito, con copia-incolla del codice originale in `src/transformers/models/brand_new_bert/modeling_brand_new_bert.py`
fino a che non avrete tutto il codice necessario. In base alla nostra esperienza, é molto meglio aggiungere una prima bozza
del codice richiesto e poi correggere e migliorare iterativamente. L'unica cosa essenziale che deve funzionare qui é la seguente
instanza:
```python
from transformers import BrandNewBertModel, BrandNewBertConfig
model = BrandNewBertModel(BrandNewBertConfig())
```
Questo comando creerà un modello con i parametri di default definiti in `BrandNewBergConfig()` e weights random. Questo garantisce
che `init()` di tutte le componenti funzioni correttamente.
**6. Scrivere uno script di conversione**
Il prossimo step é scrivere uno script per convertire il checkpoint che avete usato per fare debug su *brand_new_berts* nella
repo originale in un checkpoint per la nuova implementazione di *brand_new_bert* in 🤗 Transformers. Non é consigliato scrivere
lo script di conversione da zero, ma piuttosto cercate e guardate script gia esistenti in 🤗 Transformers, così da trovarne
uno simile al vostro modello. Di solito basta fare una copia di uno script gia esistente e adattarlo al vostro caso.
Non esistate a chiedre al team di Hugging Face a riguardo.
- Se state convertendo un modello da TensorFlow a PyTorch, un ottimo inizio é vedere [questo script di conversione per BERT](https://github.com/huggingface/transformers/blob/7acfa95afb8194f8f9c1f4d2c6028224dbed35a2/src/transformers/models/bert/modeling_bert.py#L91)
- Se state convertendo un modello da PyTorch a PyTorch, [lo script di conversione di BART può esservi utile](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bart/convert_bart_original_pytorch_checkpoint_to_pytorch.py)
Qui di seguito spiegheremo come i modelli PyTorch salvano i weights per ogni layer e come i nomi dei layer sono definiti. In PyTorch,
il nomde del layer é definito dal nome della class attribute che date al layer. Definiamo un modello dummy in PyTorch,
chiamato `SimpleModel`:
```python
from torch import nn
class SimpleModel(nn.Module):
def __init__(self):
super().__init__()
self.dense = nn.Linear(10, 10)
self.intermediate = nn.Linear(10, 10)
self.layer_norm = nn.LayerNorm(10)
```
Ora possiamo creare un'instanza di questa definizione di modo da inizializzare a random weights: `dense`, `intermediate`, `layer_norm`.
Possiamo usare print per vedere l'architettura del modello:
```python
model = SimpleModel()
print(model)
```
Da cui si ottiene:
```
SimpleModel(
(dense): Linear(in_features=10, out_features=10, bias=True)
(intermediate): Linear(in_features=10, out_features=10, bias=True)
(layer_norm): LayerNorm((10,), eps=1e-05, elementwise_affine=True)
)
```
Si può vedere come i nomi dei layers siano definiti dal nome della class attribute in PyTorch. I valori dei weights di uno
specifico layer possono essere visualizzati:
```python
print(model.dense.weight.data)
```
ad esempio:
```
tensor([[-0.0818, 0.2207, -0.0749, -0.0030, 0.0045, -0.1569, -0.1598, 0.0212,
-0.2077, 0.2157],
[ 0.1044, 0.0201, 0.0990, 0.2482, 0.3116, 0.2509, 0.2866, -0.2190,
0.2166, -0.0212],
[-0.2000, 0.1107, -0.1999, -0.3119, 0.1559, 0.0993, 0.1776, -0.1950,
-0.1023, -0.0447],
[-0.0888, -0.1092, 0.2281, 0.0336, 0.1817, -0.0115, 0.2096, 0.1415,
-0.1876, -0.2467],
[ 0.2208, -0.2352, -0.1426, -0.2636, -0.2889, -0.2061, -0.2849, -0.0465,
0.2577, 0.0402],
[ 0.1502, 0.2465, 0.2566, 0.0693, 0.2352, -0.0530, 0.1859, -0.0604,
0.2132, 0.1680],
[ 0.1733, -0.2407, -0.1721, 0.1484, 0.0358, -0.0633, -0.0721, -0.0090,
0.2707, -0.2509],
[-0.1173, 0.1561, 0.2945, 0.0595, -0.1996, 0.2988, -0.0802, 0.0407,
0.1829, -0.1568],
[-0.1164, -0.2228, -0.0403, 0.0428, 0.1339, 0.0047, 0.1967, 0.2923,
0.0333, -0.0536],
[-0.1492, -0.1616, 0.1057, 0.1950, -0.2807, -0.2710, -0.1586, 0.0739,
0.2220, 0.2358]]).
```
Nello script di conversione, dovreste riempire quei valori di inizializzazione random con gli stessi weights del corrispondente
layer nel checkpoint. *Per esempio*
```python
# retrieve matching layer weights, e.g. by
# recursive algorithm
layer_name = "dense"
pretrained_weight = array_of_dense_layer
model_pointer = getattr(model, "dense")
model_pointer.weight.data = torch.from_numpy(pretrained_weight)
```
Così facendo, dovete verificare che ogni inizializzazione random di un peso del modello PyTorch e il suo corrispondente peso nel pretrained checkpoint
siano esattamente gli stessi e uguali in **dimensione/shape e nome**. Per fare questo, é **necessario** aggiungere un `assert`
per la dimensione/shape e nome:
```python
assert (
model_pointer.weight.shape == pretrained_weight.shape
), f"Pointer shape of random weight {model_pointer.shape} and array shape of checkpoint weight {pretrained_weight.shape} mismatched"
```
Inoltre, dovrete fare il print sia dei nomi che dei weights per essere sicuri che siano gli stessi:
```python
logger.info(f"Initialize PyTorch weight {layer_name} from {pretrained_weight.name}")
```
Se la dimensione o il nome non sono uguali, probabilmente avete sbagliato ad assegnare il peso nel checkpoint o nel layer costrutture di
🤗 Transformers.
Una dimensione sbagliata può essere dovuta ad un errore nei parameteri in `BrandNewBertConfig()`. Tuttavia, può essere anche
che l'implementazione del layer in PyTorch richieda di fare una transposizione della matrice dei weights.
Infine, controllate **tutti** che tutti i weights inizializzati e fate print di tutti i weights del checkpoint che non sono stati
usati per l'inizializzazione, di modo da essere sicuri che il modello sia correttamente convertito. É normale che ci siano
errori nel test di conversione, fai per un errore in `BrandNewBertConfig()`, o un errore nell'architettura in 🤗 Transformers,
o un bug in `init()`.
Questo step dev'essere fatto tramite iterazioni fino a che non si raggiungano gli stessi valori per i weights. Una volta che
il checkpoint é stato correttamente caricato in 🤗 Transformers, potete salvare il modello in una cartella di vostra scelta
`/path/to/converted/checkpoint/folder` che contenga sia
`pytorch_model.bin` che `config.json`:
```python
model.save_pretrained("/path/to/converted/checkpoint/folder")
```
**7. Implementare il forward pass**
Una volta che i weights pretrained sono stati correttamente caricati in 🤗 Transformers, dovrete assicurarvi che il forward pass
sia correttamente implementato. [Qui](#3-4-provare-un-pretrained-checkpoint-usando-la-repo-originale), avete give creato e provato
uno script che testi il forward pass del modello usando la repo originaria. Ora dovrete fare lo stesso con uno script analogo
usando l'implementazione in 🤗 Transformers anziché l'originale. Piu o meno lo script dovrebbe essere:
```python
model = BrandNewBertModel.from_pretrained("/path/to/converted/checkpoint/folder")
input_ids = [0, 4, 4, 3, 2, 4, 1, 7, 19]
output = model(input_ids).last_hidden_states
```
Di solito l'output da 🤗 Transformers non é uguale uguale all'output originario, sopratto la prima volta. Non vi abbattete -
é normale! Prima di tutto assicuratevi che non ci siano errori o che non vengano segnalati degli errori nella forward pass.
Spesso capita che ci siano dimensioni sbagliate o data type sbagliati, *ad esempio* `torch.long` anziche `torch.float32`.
Non esistate a chiedere al team Hugging Face!
Nella parte finale assicuratevi che l'implementazione 🤗 Transformers funzioni correttamente cosi da testare che gli output
siano equivalenti a una precisione di `1e-3`. Controllate che `outputs.shape` siano le stesse tra 🤗 Transformers e l'implementazione
originaria. Poi, controllate che i valori in output siano identici. Questa é sicuramente la parte più difficile, qui una serie
di errori comuni quando gli output non sono uguali:
- Alcuni layers non sono stati aggiunti, *ad esempio* un *activation* layer non é stato aggiunto, o ci si é scordati di una connessione
- La matrice del word embedding non é stata ripareggiata
- Ci sono degli embeddings posizionali sbagliati perché l'implementazione originaria ha un offset
- Il dropout é in azione durante il forward pass. Per sistemare questo errore controllate che *model.training = False* e che
il dropout non sia stato attivato nel forward pass, * per esempio * passate *self.training* a [PyTorch's functional dropout](https://pytorch.org/docs/stable/nn.functional.html?highlight=dropout#torch.nn.functional.dropout)
La miglior maniera per sistemare il problema é di vedere all'implementazione originaria del forward pass e in 🤗 Transformers
fianco a fianco e vedere se ci sono delle differenze. In teoria, con debug e print degli output intermedie di entrambe le
implementazioni nel forward pass nell'esatta posizione del network dovrebbe aiutarvi a vedere dove ci sono differenze tra
i due frameworks. Come prima mossa controllate che `input_ids` siano identici in entrambi gli scripts. Da lì andate fino
all'ultimo layer. Potrete notare una differenza tra le due implementazioni a quel punto.
Una volta che lo stesso output é stato ragguingi, verificate gli output con `torch.allclose(original_output, output, atol=1e-3)`.
A questo punto se é tutto a posto: complimenti! Le parti seguenti saranno una passeggiata 😊.
**8. Aggiungere i test necessari per il modello**
A questo punto avete aggiunto con successo il vostro nuovo modello. Tuttavia, é molto probabile che il modello non sia
del tutto ok con il design richiesto. Per essere sicuri che l'implementazione sia consona e compatibile con 🤗 Transformers é
necessario implementare dei tests. Il Cookiecutter dovrebbe fornire automaticamente dei file per test per il vostro modello,
di solito nella folder `tests/test_modeling_brand_new_bert.py`. Provate questo per verificare l'ok nei test piu comuni:
```bash
pytest tests/test_modeling_brand_new_bert.py
```
Una volta sistemati i test comuni, bisogna assicurarsi che il vostro lavoro sia correttamente testato cosicchè:
- a) La community puo capire in maniera semplice il vostro lavoro controllando tests specifici del modello *brand_new_bert*,
- b) Implementazioni future del vostro modello non rompano alcune feature importante del modello.
Per prima cosa agguingete dei test d'integrazione. Questi sono essenziali perche fanno la stessa funzione degli scripts di
debug usati precedentemente. Un template per questi tests esiste gia nel Cookiecutter ed é sotto il nome di `BrandNewBertModelIntegrationTests`,
voi dovrete solo completarlo. Una volta che questi tests sono OK, provate:
```bash
RUN_SLOW=1 pytest -sv tests/test_modeling_brand_new_bert.py::BrandNewBertModelIntegrationTests
```
<Tip>
Nel caso siate su Windows, sostituite `RUN_SLOW=1` con `SET RUN_SLOW=1`
</Tip>
Di seguito, tutte le features che sono utili e necessarire per *brand_new_bert* devono essere testate in test separati,
contenuti in `BrandNewBertModelTester`/ `BrandNewBertModelTest`. spesso la gente si scorda questi test, ma ricordate che sono utili per:
- Aiuta gli utenti a capire il vostro codice meglio, richiamando l'attenzione su queste nuove features
- Developers e contributors futuri potranno velocemente testare nuove implementazioni del modello testanto questi casi speciali.
**9. Implementare il tokenizer**
A questo punto avremo bisogno un tokenizer per *brand_new_bert*. Di solito il tokenizer é uguale ad altri modelli in 🤗 Transformers.
É importante che troviate il file con il tokenizer originale e che lo carichiate in 🤗 Transformers.
Per controllare che il tokenizer funzioni in modo corretto, create uno script nella repo originaria che riceva come input
una stringa e ritorni gli `input_ids`. Piu o meno questo potrebbe essere il codice:
```python
input_str = "This is a long example input string containing special characters .$?-, numbers 2872 234 12 and words."
model = BrandNewBertModel.load_pretrained_checkpoint("/path/to/checkpoint/")
input_ids = model.tokenize(input_str)
```
Potrebbe richiedere un po' di tempo, ma guardate ancora alla repo originaria per trovare la funzione corretta del tokenizer.
A volte capita di dover riscrivere il tokenizer nella repo originaria, di modo da avere come output gli `input_ids`.
A quel punto uno script analogo é necessario in 🤗 Transformers:
```python
from transformers import BrandNewBertTokenizer
input_str = "This is a long example input string containing special characters .$?-, numbers 2872 234 12 and words."
tokenizer = BrandNewBertTokenizer.from_pretrained("/path/to/tokenizer/folder/")
input_ids = tokenizer(input_str).input_ids
```
Una volta che `input_ids` sono uguali, bisogna aggiungere un test per il tokenizer.
Il file test per tokenizer di *brand_new_brand* dovrebbe avere un paio di hard-coded test d'integrazione.
**10. Test end-to-end**
Ora che avete il tokenizer, dovrete aggiungere dei test d'integrazione per l'intero workflow in `tests/test_modeling_brand_new_bert.py` in 🤗 Transformer.
Questi test devono mostrare che un significante campione text-to-text funzioni come ci si aspetta nell'implementazione di 🤗 Transformers.
*Per esempio* potreste usare dei source-to-target-translation, o un sommario di un articolo, o un domanda-risposta e cosi via.
Se nessuno dei checkpoints é stato ultra parametrizzato per task simili, allora i tests per il modello sono piu che sufficienti.
Nello step finale dovete assicurarvi che il modello sia totalmente funzionale, e consigliamo anche di provare a testare su GPU.
Puo succedere che ci si scordi un `.to(self.device)` ad esempio. Se non avete accesso a GPU, il team Hugging Face puo provvedere
a testare questo aspetto per voi.
**11. Aggiungere una Docstring**
Siete quasi alla fine! L'ultima cosa rimasta é avere una bella docstring e una pagina doc. Il Cookiecutter dovrebbe provvedere già
un template chiamato `docs/source/model_doc/brand_new_bert.rst`, che dovrete compilare. La prima cosa che un utente farà
per usare il vostro modello sarà dare una bella lettura al doc. Quindi proponete una documentazione chiara e concisa. É molto
utile per la community avere anche delle *Tips* per mostrare come il modello puo' essere usato. Non esitate a chiedere a Hugging Face
riguardo alle docstirng.
Quindi, assicuratevi che la docstring sia stata aggiunta a `src/transformers/models/brand_new_bert/modeling_brand_new_bert.py`.
Assicuratevi che la docstring sia corretta e che includa tutti i necessari input e output. Abbiamo una guida dettagliata per
scrivere la documentazione e docstring.
**Rifattorizzare il codice**
Perfetto! Ora che abbiamo tutto per *brand_new_bert* controllate che lo stile del codice sia ok:
```bash
make style
```
E che il codice passi i quality check:
```bash
make quality
```
A volte capita che manchino delle informazioninella docstring o alcuni nomi sbagliati, questo farà fallire i tests sopra.
Ripetiamo: chiedete pure a Hugging Face, saremo lieti di aiutarvi.
Per ultimo, fare del refactoring del codice una volta che é stato creato.
Avete finito con il codice, congratulazioni! 🎉 Siete fantasticiiiiiii! 😎
**12. Caricare il modello sul model hub**
In questa ultima parte dovrete convertire e caricare il modello, con tutti i checkpoints, nel model hub e aggiungere una
model card per ogni checkpoint caricato. Leggete la nostra guida [Model sharing and uploading Page](model_sharing) per
avere familiarità con l'hub. Di solito in questa parte lavorate a fianco di Hugging face per decidere un nome che sia ok
per ogni checkpoint, per ottenere i permessi necessari per caricare il modello nell'organizzazione dell'autore di *brand_new_bert*.
Il metodo `push_to_hub`, presente in tutti i modelli `transformers`, é una maniera rapida e indolore per caricare il vostro checkpoint sull'hub:
```python
brand_new_bert.push_to_hub(
repo_path_or_name="brand_new_bert",
# Uncomment the following line to push to an organization
# organization="<ORGANIZATION>",
commit_message="Add model",
use_temp_dir=True,
)
```
Vale la pena spendere un po' di tempo per creare una model card ad-hoc per ogni checkpoint. Le model cards dovrebbero
suggerire le caratteristiche specifiche del checkpoint, *per esempio* su che dataset il checkpoint é stato pretrained o fine-tuned.
O che su che genere di task il modello lavoro? E anche buona pratica includere del codice su come usare il modello correttamente.
**13. (Opzionale) Aggiungere un notebook**
É molto utile aggiungere un notebook, che dimostri in dettaglio come *brand_new_bert* si utilizzi per fare inferenza e/o
fine-tuned su specifiche task. Non é una cosa obbligatoria da avere nella vostra PR, ma é molto utile per la community.
**14. Sottomettere la PR**
L'ultimissimo step! Ovvero il merge della PR nel main. Di solito il team Hugging face a questo punto vi avrà gia aiutato,
ma é ok prendere un po' di tempo per pulire la descirzione e commenti nel codice.
### Condividete il vostro lavoro!!
É ora tempo di prendere un po' di credito dalla communità per il vostro lavoro! Caricare e implementare un nuovo modello
é un grandissimo contributo per Transformers e l'intera community NLP. Il codice e la conversione dei modelli pre-trained sara
sicuramente utilizzato da centinaia o migliaia di sviluppatori e ricercatori. Siate fieri e orgogliosi di condividere il vostro
traguardo con l'intera community :)
** Avete create un altro modello che é super facile da usare per tutti quanti nella community! 🤯**
| transformers/docs/source/it/add_new_model.md/0 | {
"file_path": "transformers/docs/source/it/add_new_model.md",
"repo_id": "transformers",
"token_count": 17333
} | 20 |
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# Inferenza Efficiente su GPU Multiple
Questo documento contiene informazioni su come fare inferenza in maniera efficiente su GPU multiple.
<Tip>
Nota: Un setup con GPU multiple può utilizzare la maggior parte delle strategie descritte nella [sezione con GPU singola](./perf_infer_gpu_one). Tuttavia, è necessario conoscere delle tecniche semplici che possono essere utilizzate per un risultato migliore.
</Tip>
## `BetterTransformer` per inferenza più rapida
Abbiamo recentemente integrato `BetterTransformer` per inferenza più rapida su multi-GPU per modelli su testo, immagini e audio. Controlla il documento con queste integrazioni [qui](https://huggingface.co/docs/optimum/bettertransformer/overview) per maggiori dettagli.
| transformers/docs/source/it/perf_infer_gpu_many.md/0 | {
"file_path": "transformers/docs/source/it/perf_infer_gpu_many.md",
"repo_id": "transformers",
"token_count": 420
} | 21 |
<!--
Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
⚠️ このファイルはMarkdown形式ですが、特定の文法が含まれており、通常のMarkdownビューアーでは正しく表示されない場合があります。
-->
# How to add a model to 🤗 Transformers?
🤗 Transformersライブラリは、コミュニティの貢献者のおかげで新しいモデルを提供できることがよくあります。
しかし、これは難しいプロジェクトであり、🤗 Transformersライブラリと実装するモデルについての深い知識が必要です。
Hugging Faceでは、コミュニティの多くの人々に積極的にモデルを追加する力を与えようと努力しており、
このガイドをまとめて、PyTorchモデルを追加するプロセスを説明します（[PyTorchがインストールされていることを確認してください](https://pytorch.org/get-started/locally/)）。
<Tip>
TensorFlowモデルを実装する興味がある場合は、[🤗 TransformersモデルをTensorFlowに変換する方法](add_tensorflow_model)ガイドを参照してみてください！
</Tip>
この過程で、以下のことを学びます：
- オープンソースのベストプラクティスに関する洞察
- 最も人気のある深層学習ライブラリの設計原則を理解する
- 大規模なモデルを効率的にテストする方法を学ぶ
- `black`、`ruff`、および`make fix-copies`などのPythonユーティリティを統合して、クリーンで読みやすいコードを確保する方法を学ぶ
Hugging Faceチームのメンバーがサポートを提供するので、一人ぼっちになることはありません。 🤗 ❤️
さあ、始めましょう！🤗 Transformersで見たいモデルについての[New model addition](https://github.com/huggingface/transformers/issues/new?assignees=&labels=New+model&template=new-model-addition.yml)のイシューを開いてください。
特定のモデルを提供することに特にこだわりがない場合、[New model label](https://github.com/huggingface/transformers/labels/New%20model)で未割り当てのモデルリクエストがあるかどうかを確認して、それに取り組むことができます。
新しいモデルリクエストを開いたら、最初のステップは🤗 Transformersをよく理解することです！
## General overview of 🤗 Transformers
まず、🤗 Transformersの一般的な概要を把握する必要があります。🤗 Transformersは非常に意見が分かれるライブラリですので、
ライブラリの哲学や設計選択について同意できない可能性があります。ただし、私たちの経験から、ライブラリの基本的な設計選択と哲学は、
🤗 Transformersを効率的にスケーリングし、適切なレベルで保守コストを抑えるために不可欠です。
ライブラリの理解を深めるための良い出発点は、[哲学のドキュメント](philosophy)を読むことです。
私たちの作業方法の結果、すべてのモデルに適用しようとするいくつかの選択肢があります：
- 一般的に、抽象化よりも構成が優先されます。
- コードの重複は、読みやすさやアクセス可能性を大幅に向上させる場合、必ずしも悪いわけではありません。
- モデルファイルはできるだけ自己完結的であるべきで、特定のモデルのコードを読む際には、理想的には該当する`modeling_....py`ファイルのみを見る必要があります。
私たちの意見では、このライブラリのコードは単なる製品を提供する手段だけでなく、*例えば、推論のためにBERTを使用する能力*などの製品そのもの.
### Overview of models
モデルを正常に追加するためには、モデルとその設定、[`PreTrainedModel`]、および[`PretrainedConfig`]の相互作用を理解することが重要です。
例示的な目的で、🤗 Transformersに追加するモデルを「BrandNewBert」と呼びます。
以下をご覧ください：
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers_overview.png"/>
ご覧のように、🤗 Transformersでは継承を使用していますが、抽象化のレベルを最小限に保っています。
ライブラリ内のどのモデルにも、抽象化のレベルが2つを超えることはありません。
`BrandNewBertModel` は `BrandNewBertPreTrainedModel` を継承し、さらに[`PreTrainedModel`]を継承しています。
これだけです。
一般的なルールとして、新しいモデルは[`PreTrainedModel`]にのみ依存するようにしたいと考えています。
すべての新しいモデルに自動的に提供される重要な機能は、[`~PreTrainedModel.from_pretrained`]および
[`~PreTrainedModel.save_pretrained`]です。
これらはシリアライゼーションとデシリアライゼーションに使用されます。
`BrandNewBertModel.forward`などの他の重要な機能は、新しい「modeling_brand_new_bert.py」スクリプトで完全に定義されるべきです。
次に、特定のヘッドレイヤーを持つモデル（たとえば `BrandNewBertForMaskedLM` ）が `BrandNewBertModel` を継承するのではなく、
抽象化のレベルを低く保つために、そのフォワードパスで `BrandNewBertModel` を呼び出すコンポーネントとして使用されるようにしたいと考えています。
新しいモデルには常に `BrandNewBertConfig` という設定クラスが必要です。この設定は常に[`PreTrainedModel`]の属性として保存され、
したがって、`BrandNewBertPreTrainedModel`から継承するすべてのクラスで`config`属性を介してアクセスできます。
```python
model = BrandNewBertModel.from_pretrained("brandy/brand_new_bert")
model.config # model has access to its config
```
モデルと同様に、設定は[`PretrainedConfig`]から基本的なシリアル化および逆シリアル化の機能を継承しています。注意すべきは、設定とモデルは常に2つの異なる形式にシリアル化されることです - モデルは*pytorch_model.bin*ファイルに、設定は*config.json*ファイルにシリアル化されます。[`~PreTrainedModel.save_pretrained`]を呼び出すと、自動的に[`~PretrainedConfig.save_pretrained`]も呼び出され、モデルと設定の両方が保存されます。
### Code style
新しいモデルをコーディングする際には、Transformersは意見があるライブラリであり、コードの書き方に関していくつかの独自の考え方があります :-)
1. モデルのフォワードパスはモデリングファイルに完全に記述され、ライブラリ内の他のモデルとは完全に独立している必要があります。他のモデルからブロックを再利用したい場合、コードをコピーしてトップに`# Copied from`コメントを付けて貼り付けます（良い例は[こちら](https://github.com/huggingface/transformers/blob/v4.17.0/src/transformers/models/roberta/modeling_roberta.py#L160)、コピーに関する詳細なドキュメンテーションは[ここ](pr_checks#check-copies)を参照してください）。
2. コードは完全に理解可能でなければなりません。これは記述的な変数名を選択し、省略形を避けるべきであることを意味します。例えば、`act`ではなく`activation`が好まれます。1文字の変数名は、forループ内のインデックスでない限り、強く非推奨です。
3. より一般的に、魔法のような短いコードよりも長くて明示的なコードを好みます。
4. PyTorchでは`nn.Sequential`をサブクラス化せずに、`nn.Module`をサブクラス化し、フォワードパスを記述し、コードを使用する他の人が簡単にデバッグできるようにします。プリントステートメントやブレークポイントを追加してデバッグできるようにします。
5. 関数のシグネチャは型アノテーションを付けるべきです。その他の部分に関しては、型アノテーションよりも良い変数名が読みやすく理解しやすいことがあります。
### Overview of tokenizers
まだ完了していません :-( このセクションは近日中に追加されます！
## Step-by-step recipe to add a model to 🤗 Transformers
モデルを追加する方法は人それぞれ異なるため、他のコントリビューターが🤗 Transformersにモデルを追加する際の要約を確認することが非常に役立つ場合があります。以下は、他のコントリビューターが🤗 Transformersにモデルをポートする際のコミュニティブログ投稿のリストです。
1. [GPT2モデルのポーティング](https://medium.com/huggingface/from-tensorflow-to-pytorch-265f40ef2a28) by [Thomas](https://huggingface.co/thomwolf)
2. [WMT19 MTモデルのポーティング](https://huggingface.co/blog/porting-fsmt) by [Stas](https://huggingface.co/stas)
経験から言えることは、モデルを追加する際に最も重要なことは次のようになります：
- 車輪の再発明をしないでください！新しい🤗 Transformersモデルのために追加するコードのほとんどはすでに🤗 Transformers内のどこかに存在しています。類似した既存のモデルやトークナイザを見つけるために、いくつかの時間をかけて探すことが重要です。[grep](https://www.gnu.org/software/grep/)と[rg](https://github.com/BurntSushi/ripgrep)はあなたの友達です。モデルのトークナイザは1つのモデル実装に基づいているかもしれませんが、モデルのモデリングコードは別の実装に基づいていることがあることに注意してください。例えば、FSMTのモデリングコードはBARTに基づいており、FSMTのトークナイザコードはXLMに基づいています。
- これは科学的な課題よりもエンジニアリングの課題です。モデルの論文の理論的な側面をすべて理解しようとするよりも、効率的なデバッグ環境を作成するために時間を費やすべきです。
- 行き詰まった場合は助けを求めてください！モデルは🤗 Transformersのコアコンポーネントであり、Hugging Faceではモデルを追加するための各ステップでお手伝いするのを喜んでいます。進行がないことに気付いた場合は、進展していないことを気にしないでください。
以下では、🤗 Transformersにモデルをポートする際に最も役立つと考えられる一般的なレシピを提供しようとしています。
次のリストは、モデルを追加するために行う必要があるすべてのことの要約であり、To-Doリストとして使用できます：
- ☐ （オプション）モデルの理論的な側面を理解しました
- ☐ 🤗 Transformersの開発環境を準備しました
- ☐ オリジナルのリポジトリのデバッグ環境をセットアップしました
- ☐ `forward()` パスをオリジナルのリポジトリとチェックポイントで正常に実行するスクリプトを作成しました
- ☐ モデルの骨格を🤗 Transformersに正常に追加しました
- ☐ オリジナルのチェックポイントを🤗 Transformersのチェックポイントに正常に変換しました
- ☐ 🤗 Transformersで実行される `forward()` パスを正常に実行し、オリジナルのチェックポイントと同一の出力を得ました
- ☐ 🤗 Transformersでのモデルテストを完了しました
- ☐ 🤗 Transformersにトークナイザを正常に追加しました
- ☐ エンドツーエンドの統合テストを実行しました
- ☐ ドキュメントを完成させました
- ☐ モデルのウェイトをHubにアップロードしました
- ☐ プルリクエストを提出しました
- ☐ （オプション）デモノートブックを追加しました
まず、通常、`BrandNewBert`の理論的な理解を深めることをお勧めします。
ただし、もしモデルの理論的な側面を「実務中に理解する」方が好ましい場合、`BrandNewBert`のコードベースに直接アクセスするのも問題ありません。
このオプションは、エンジニアリングのスキルが理論的なスキルよりも優れている場合、
`BrandNewBert`の論文を理解するのに苦労している場合、または科学的な論文を読むよりもプログラミングを楽しんでいる場合に適しています。
### 1. (Optional) Theoretical aspects of BrandNewBert
BrandNewBertの論文がある場合、その説明を読むための時間を取るべきです。論文の中には理解が難しい部分があるかもしれません。
その場合でも心配しないでください。目標は論文の深い理論的理解を得ることではなく、
🤗 Transformersでモデルを効果的に再実装するために必要な情報を抽出することです。
ただし、理論的な側面にあまり多くの時間をかける必要はありません。代わりに、実践的な側面に焦点を当てましょう。具体的には次の点です：
- *brand_new_bert*はどの種類のモデルですか？ BERTのようなエンコーダーのみのモデルですか？ GPT2のようなデコーダーのみのモデルですか？ BARTのようなエンコーダー-デコーダーモデルですか？
[model_summary](model_summary)を参照して、これらの違いについて詳しく知りたい場合があります。
- *brand_new_bert*の応用分野は何ですか？ テキスト分類ですか？ テキスト生成ですか？ Seq2Seqタスク、例えば要約ですか？
- モデルをBERT/GPT-2/BARTとは異なるものにする新しい機能は何ですか？
- 既存の[🤗 Transformersモデル](https://huggingface.co/transformers/#contents)の中で*brand_new_bert*に最も似ているモデルはどれですか？
- 使用されているトークナイザの種類は何ですか？ SentencePieceトークナイザですか？ WordPieceトークナイザですか？ BERTやBARTで使用されているトークナイザと同じですか？
モデルのアーキテクチャの良い概要を得たと感じたら、Hugging Faceチームに質問を送ることができます。
これにはモデルのアーキテクチャ、注意層などに関する質問が含まれるかもしれません。
私たちは喜んでお手伝いします。
### 2. Next prepare your environment
1. リポジトリのページで「Fork」ボタンをクリックして、[リポジトリ](https://github.com/huggingface/transformers)をフォークします。
これにより、コードのコピーがGitHubユーザーアカウントの下に作成されます。
2. ローカルディスクにある`transformers`フォークをクローンし、ベースリポジトリをリモートとして追加します：
```bash
git clone https://github.com/[your Github handle]/transformers.git
cd transformers
git remote add upstream https://github.com/huggingface/transformers.git
```
```bash
python -m venv .env
source .env/bin/activate
pip install -e ".[dev]"
```
3. 開発環境をセットアップするために、次のコマンドを実行してください：
```bash
python -m venv .env
source .env/bin/activate
pip install -e ".[dev]"
```
お使いのOSに応じて、およびTransformersのオプションの依存関係の数が増えているため、このコマンドでエラーが発生する可能性があります。
その場合は、作業しているDeep Learningフレームワーク（PyTorch、TensorFlow、および/またはFlax）をインストールし、次の手順を実行してください：
```bash
pip install -e ".[quality]"
```
これはほとんどのユースケースには十分であるはずです。その後、親ディレクトリに戻ることができます。
```bash
cd ..
```
4. Transformersに*brand_new_bert*のPyTorchバージョンを追加することをお勧めします。PyTorchをインストールするには、
https://pytorch.org/get-started/locally/ の指示に従ってください。
**注意:** CUDAをインストールする必要はありません。新しいモデルをCPUで動作させることで十分です。
5. *brand_new_bert*を移植するには、元のリポジトリへのアクセスも必要です。
```bash
git clone https://github.com/org_that_created_brand_new_bert_org/brand_new_bert.git
cd brand_new_bert
pip install -e .
```
*brand_new_bert*を🤗 Transformersにポートするための開発環境を設定しました。
### 3.-4. Run a pretrained checkpoint using the original repository
最初に、オリジナルの*brand_new_bert*リポジトリで作業します。通常、オリジナルの実装は非常に「研究的」であり、ドキュメンテーションが不足していたり、コードが理解しにくいことがあります。しかし、これが*brand_new_bert*を再実装する動機となるべきです。Hugging Faceでは、主要な目標の1つが、動作するモデルを取り、それをできるだけ**アクセス可能でユーザーフレンドリーで美しい**ものに書き直すことです。これは、🤗 Transformersにモデルを再実装する最も重要な動機です - 複雑な新しいNLP技術を**誰にでも**アクセス可能にしようとする試みです。
まず、オリジナルのリポジトリに入り込むことから始めるべきです。
公式の事前学習済みモデルをオリジナルのリポジトリで正常に実行することは、通常、**最も困難な**ステップです。
私たちの経験から、オリジナルのコードベースに慣れるのに時間をかけることが非常に重要です。以下のことを理解する必要があります：
- 事前学習済みの重みをどこで見つけるか？
- 対応するモデルに事前学習済みの重みをロードする方法は？
- モデルから独立してトークナイザを実行する方法は？
- 1つのフォワードパスを追跡して、単純なフォワードパスに必要なクラスと関数がわかるようにします。通常、これらの関数だけを再実装する必要があります。
- モデルの重要なコンポーネントを特定できること：モデルのクラスはどこにありますか？モデルのサブクラス、*例* EncoderModel、DecoderModelがありますか？自己注意レイヤーはどこにありますか？複数の異なる注意レイヤー、*例* *自己注意*、*クロスアテンション*などが存在しますか？
- オリジナルのリポジトリの環境でモデルをデバッグする方法は？*print*ステートメントを追加する必要があるか、*ipdb*のような対話型デバッガを使用できるか、PyCharmのような効率的なIDEを使用してモデルをデバッグする必要がありますか？
重要なのは、ポーティングプロセスを開始する前に、オリジナルのリポジトリでコードを**効率的に**デバッグできることです！また、これはオープンソースライブラリで作業していることを覚えておいてください。オリジナルのリポジトリでコードを調べる誰かを歓迎するために、問題をオープンにしたり、プルリクエストを送信したりすることをためらわないでください。このリポジトリのメンテナーは、彼らのコードを調べてくれる人に対して非常に喜んでいる可能性が高いです！
この段階では、オリジナルのモデルのデバッグにどのような環境と戦略を使用するかは、あなた次第です。最初にオリジナルのリポジトリに関するコードをデバッグできることが非常に重要です。また、GPU環境をセットアップすることはお勧めしません。まず、CPU上で作業し、モデルがすでに🤗 Transformersに正常にポートされていることを確認します。最後に、モデルがGPU上でも期待通りに動作するかどうかを検証する必要があります。
一般的に、オリジナルのモデルを実行するための2つのデバッグ環境があります：
- [Jupyter notebooks](https://jupyter.org/) / [google colab](https://colab.research.google.com/notebooks/intro.ipynb)
- ローカルなPythonスクリプト。
Jupyterノートブックは、セルごとに実行できるため、論理的なコンポーネントをより分割し、中間結果を保存できるため、デバッグサイクルが速くなるという利点があります。また、ノートブックは他の共同作業者と簡単に共有できることが多く、Hugging Faceチームに助けを求める場合に非常に役立つ場合があります。Jupyterノートブックに精通している場合、それ
```python
model = BrandNewBertModel.load_pretrained_checkpoint("/path/to/checkpoint/")
input_ids = [0, 4, 5, 2, 3, 7, 9] # vector of input ids
original_output = model.predict(input_ids)
```
デバッグ戦略については、通常、いくつかの選択肢があります：
- 元のモデルを多くの小さなテスト可能なコンポーネントに分解し、それぞれに対して前方パスを実行して検証します
- 元のモデルを元のトークナイザと元のモデルにのみ分解し、それらに対して前方パスを実行し、検証のために中間のプリントステートメントまたはブレークポイントを使用します
再度、どの戦略を選択するかはあなた次第です。元のコードベースに依存することが多く、元のコードベースに応じて一方または他方が有利なことがあります。
元のコードベースがモデルを小さなサブコンポーネントに分解できる場合、*例えば*元のコードベースが簡単にイーガーモードで実行できる場合、それを行う価値が通常あります。最初からより難しい方法を選択することにはいくつかの重要な利点があります：
- 後で元のモデルを🤗 Transformersの実装と比較する際に、各コンポーネントが対応する🤗 Transformers実装のコンポーネントと一致することを自動的に検証できるため、視覚的な比較に依存せずに済みます
- 大きな問題を小さな問題に分解する、つまり個々のコンポーネントのみをポーティングする問題に分割するのに役立ち、作業を構造化するのに役立ちます
- モデルを論理的な意味のあるコンポーネントに分割することで、モデルの設計をよりよく理解しやすくし、モデルをよりよく理解するのに役立ちます
- 後で、コンポーネントごとのテストを行うことで、コードを変更し続ける際にリグレッションが発生しないことを確認するのに役立ちます
[Lysandreの](https://gist.github.com/LysandreJik/db4c948f6b4483960de5cbac598ad4ed) ELECTRAの統合チェックは、これがどのように行われるかの良い例です。
ただし、元のコードベースが非常に複雑で、中間コンポーネントをコンパイルモードで実行することしか許可しない場合、モデルを小さなテスト可能なサブコンポーネントに分解することが時間がかかりすぎるか、不可能であることがあります。
良い例は[T5のMeshTensorFlow](https://github.com/tensorflow/mesh/tree/master/mesh_tensorflow)ライブラリであり、非常に複雑でモデルをサブコンポーネントに分解する簡単な方法を提供しないことがあります。このようなライブラリでは、通常、プリントステートメントを検証することに依存します。
どの戦略を選択しても、推奨される手順は通常同じで、最初のレイヤーからデバッグを開始し、最後のレイヤーからデバッグを行うべきです。
通常、以下の順序で次のレイヤーからの出力を取得することをお勧めします：
1. モデルに渡された入力IDを取得する
2. 単語の埋め込みを取得する
3. 最初のTransformerレイヤーの入力を取得する
4. 最初のTransformerレイヤーの出力を取得する
5. 次のn - 1つのTransformerレイヤーの出力を取得する
6. BrandNewBertモデル全体の出力を取得する
入力IDは整数の配列である必要があり、*例：* `input_ids = [0, 4, 4, 3, 2, 4, 1, 7, 19]` のようになります。
以下のレイヤーの出力は多次元の浮動小数点配列であることが多く、次のようになることがあります：
```
[[
[-0.1465, -0.6501, 0.1993, ..., 0.1451, 0.3430, 0.6024],
[-0.4417, -0.5920, 0.3450, ..., -0.3062, 0.6182, 0.7132],
[-0.5009, -0.7122, 0.4548, ..., -0.3662, 0.6091, 0.7648],
...,
[-0.5613, -0.6332, 0.4324, ..., -0.3792, 0.7372, 0.9288],
[-0.5416, -0.6345, 0.4180, ..., -0.3564, 0.6992, 0.9191],
[-0.5334, -0.6403, 0.4271, ..., -0.3339, 0.6533, 0.8694]]],
```
🤗 Transformersに追加されるすべてのモデルは、統合テストを数回合格することが期待されており、元のモデルと🤗 Transformersで再実装されたバージョンが、0.001の精度までまったく同じ出力を提供する必要があります。
異なるライブラリフレームワークで同じモデルを書いた場合、わずかに異なる出力を返すことが正常であるため、誤差許容値として1e-3（0.001）を受け入れています。モデルがほぼ同じ出力を返すだけでは不十分で、ほぼ同一である必要があります。そのため、🤗 Transformersバージョンの中間出力を元の*brand_new_bert*の実装の中間出力と複数回にわたって比較することになるでしょう。その際、元のリポジトリの**効率的な**デバッグ環境が非常に重要です。以下は、デバッグ環境をできるだけ効率的にするためのアドバイスです。
- 中間結果をデバッグする最適な方法を見つける。元のリポジトリはPyTorchで書かれていますか？その場合、元のモデルをより小さなサブコンポーネントに分解して中間値を取得する長いスクリプトを書くことがおそらく適切です。元のリポジトリがTensorflow 1で書かれている場合、[tf.print](https://www.tensorflow.org/api_docs/python/tf/print)などのTensorFlowのプリント操作を使用して中間値を出力する必要があるかもしれません。元のリポジトリがJaxで書かれている場合、フォワードパスの実行時にモデルが**jittedされていない**ことを確認してください。例：[このリンク](https://github.com/google/jax/issues/196)をチェック。
- 使用可能な最小の事前学習済みチェックポイントを使用します。チェックポイントが小さいほど、デバッグサイクルが速くなります。事前学習済みモデルがフォワードパスに10秒以上かかる場合、効率的ではありません。非常に大きなチェックポイントしか利用できない場合、新しい環境でランダムに初期化されたウェイトを持つダミーモデルを作成し、それらのウェイトを🤗 Transformersバージョンのモデルと比較する方が良いかもしれません。
- 元のリポジトリでフォワードパスを呼び出す最も簡単な方法を使用していることを確認してください。理想的には、元のリポジトリで**単一のフォワードパス**を呼び出す関数を見つけたいです。これは通常「predict」、「evaluate」、「forward」、「__call__」と呼ばれます。複数回「forward」を呼び出す関数をデバッグしたくありません。例：テキストを生成するために「autoregressive_sample」、「generate」と呼ばれる関数。
- トークナイゼーションとモデルの「フォワード」パスを分離しようとしてください。元のリポジトリが入力文字列を入力する必要がある例を示す場合、フォワードコール内で文字列入力が入力IDに変更される場所を特定し、このポイントから開始します。これは、スクリプトを自分で書くか、入力文字列ではなく入力IDを直接入力できるように元のコードを変更する必要があるかもしれません。
- デバッグセットアップ内のモデルがトレーニングモードではないことを確認してください。トレーニングモードでは、モデル内の複数のドロップアウトレイヤーのためにランダムな出力が生成されることがあります。デバッグ環境のフォワードパスが**決定論的**であることを確認し、ドロップアウトレイヤーが使用されないようにします。または、新しい実装が同じフレームワーク内にある場合、*transformers.utils.set_seed*を使用してください。
以下のセクションでは、*brand_new_bert*についてこれを具体的にどのように行うかについての詳細/ヒントを提供します。
### 5.-14. Port BrandNewBert to 🤗 Transformers
次に、ついに新しいコードを🤗 Transformersに追加できます。🤗 Transformersのフォークのクローンに移動してください：
```bash
cd transformers
```
特別なケースとして、既存のモデルと完全に一致するアーキテクチャのモデルを追加する場合、
[このセクション](#write-a-conversion-script)で説明されているように、変換スクリプトを追加するだけで済みます。
この場合、既存のモデルの完全なモデルアーキテクチャを再利用できます。
それ以外の場合、新しいモデルの生成を開始します。ここで2つの選択肢があります：
- `transformers-cli add-new-model-like`を使用して既存のモデルのような新しいモデルを追加します
- `transformers-cli add-new-model`を使用して、テンプレートから新しいモデルを追加します（モデルのタイプに応じてBERTまたはBartのように見えます）
どちらの場合でも、モデルの基本情報を入力するための質問事項が表示されます。
2番目のコマンドを実行するには、`cookiecutter`をインストールする必要があります。
詳細については[こちら](https://github.com/huggingface/transformers/tree/main/templates/adding_a_new_model)をご覧ください。
**主要な huggingface/transformers リポジトリでプルリクエストを開く**
自動生成されたコードを適応し始める前に、🤗 Transformers に「作業中（WIP）」プルリクエストを開くタイミングです。
例：「[WIP] *brand_new_bert* を追加」などです。
これにより、ユーザーと Hugging Face チームが🤗 Transformers にモデルを統合する作業を並行して行うことができます。
以下の手順を実行してください：
1. メインブランチから分かりやすい名前のブランチを作成します。
```bash
git checkout -b add_brand_new_bert
```
2. 自動生成されたコードをコミットしてください:
```bash
git add .
git commit
```
3. 現在の main ブランチにフェッチしてリベース
```bash
git fetch upstream
git rebase upstream/main
```
4. 変更をあなたのアカウントにプッシュするには、次のコマンドを使用します：
```bash
git push -u origin a-descriptive-name-for-my-changes
```
5. 満足したら、GitHub上のフォークのウェブページに移動します。[プルリクエスト]をクリックします。将来の変更に備えて、Hugging Face チームのメンバーのGitHubハンドルをレビュアーとして追加してください。
6. GitHubのプルリクエストウェブページの右側にある「ドラフトに変換」をクリックして、PRをドラフトに変更します。
以下では、進捗があった場合は常に作業をコミットし、プッシュしてプルリクエストに表示されるようにしてください。さらに、定期的にメインからの最新の変更を取り込むために、次のように行うことを忘れないでください：
```bash
git fetch upstream
git merge upstream/main
```
一般的に、モデルや実装に関する質問はPull Request (PR) で行い、PR内で議論し、解決します。
これにより、Hugging Face チームは新しいコードをコミットする際や質問がある場合に常に通知を受けることができます。
質問や問題が解決された際に、問題や質問が理解されやすいように、Hugging Face チームにコードを指摘することが非常に役立ちます。
このためには、「Files changed」タブに移動してすべての変更を表示し、質問したい行に移動して「+」シンボルをクリックしてコメントを追加します。
質問や問題が解決された場合は、作成されたコメントの「Resolve」ボタンをクリックできます。
同様に、Hugging Face チームはコードをレビューする際にコメントを開きます。
PR上でのほとんどの質問はGitHub上で行うことをお勧めします。
一般的な質問に関しては、公にはあまり役立たない質問については、SlackやメールでHugging Face チームに連絡することもできます。
**5. 生成されたモデルコードを"brand_new_bert"に適応させる**
最初に、モデル自体に焦点を当て、トークナイザには気にしないでください。
関連するコードは、生成されたファイル`src/transformers/models/brand_new_bert/modeling_brand_new_bert.py`および`src/transformers/models/brand_new_bert/configuration_brand_new_bert.py`で見つかるはずです。
さて、ついにコーディングを始めることができます :smile:。
`src/transformers/models/brand_new_bert/modeling_brand_new_bert.py`にある生成されたコードは、エンコーダーのみのモデルであればBERTと同じアーキテクチャを持っているか、エンコーダー-デコーダーモデルであればBARTと同じアーキテクチャを持っているはずです。
この段階では、モデルの理論的な側面について学んだことを思い出すべきです。つまり、「このモデルはBERTまたはBARTとどのように異なるのか？」ということです。
これらの変更を実装しますが、これは通常、セルフアテンションレイヤー、正規化レイヤーの順序などを変更することを意味します。
再び、あなたのモデルがどのように実装されるべきかをより良く理解するために、Transformers内に既存のモデルの類似アーキテクチャを見ることが役立つことがあります。
この時点では、コードが完全に正確またはクリーンである必要はありません。
むしろ、まずは必要なコードの最初の*クリーンでない*コピー＆ペーストバージョンを
`src/transformers/models/brand_new_bert/modeling_brand_new_bert.py`に追加し、必要なコードがすべて追加されていると感じるまで改善/修正を反復的に行うことがお勧めです。
私たちの経験から、必要なコードの最初のバージョンを迅速に追加し、次のセクションで説明する変換スクリプトを使用してコードを繰り返し改善/修正する方が効率的であることが多いです。
この時点で動作する必要があるのは、🤗 Transformersの"brand_new_bert"の実装をインスタンス化できることだけです。つまり、以下のコマンドが機能する必要があります：
```python
from transformers import BrandNewBertModel, BrandNewBertConfig
model = BrandNewBertModel(BrandNewBertConfig())
```
上記のコマンドは、`BrandNewBertConfig()` で定義されたデフォルトパラメータに従ってモデルを作成し、
すべてのコンポーネントの `init()` メソッドが正常に動作することを確認します。
すべてのランダムな初期化は、`BrandnewBertPreTrainedModel` クラスの `_init_weights` メソッドで行う必要があります。
このメソッドは、設定変数に依存するすべてのリーフモジュールを初期化する必要があります。以下は、BERT の `_init_weights` メソッドの例です：
```py
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
```
特定のモジュールに特別な初期化が必要な場合、カスタムスキームをさらに持つことができます。たとえば、
`Wav2Vec2ForPreTraining`では、最後の2つの線形層には通常のPyTorchの`nn.Linear`の初期化が必要ですが、
他のすべての層は上記のような初期化を使用する必要があります。これは以下のようにコーディングされています：
```py
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, Wav2Vec2ForPreTraining):
module.project_hid.reset_parameters()
module.project_q.reset_parameters()
module.project_hid._is_hf_initialized = True
module.project_q._is_hf_initialized = True
elif isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
```
`_is_hf_initialized`フラグは、サブモジュールを一度だけ初期化することを確実にするために内部で使用されます。
`module.project_q`と`module.project_hid`のためにそれを`True`に設定することで、
カスタム初期化が後で上書きされないようにし、`_init_weights`関数がそれらに適用されないようにします。
**6. 変換スクリプトを書く**
次に、*brand_new_bert* の元のリポジトリでデバッグに使用したチェックポイントを、新しく作成した 🤗 Transformers 実装の *brand_new_bert* と互換性のあるチェックポイントに変換できる変換スクリプトを書く必要があります。
変換スクリプトをゼロから書くことはお勧めされませんが、代わりに 🤗 Transformers で既に存在する類似のモデルを同じフレームワークで変換したスクリプトを調べることが良いでしょう。
通常、既存の変換スクリプトをコピーして、自分のユースケースにわずかに適応させることで十分です。
Hugging Face チームに既存のモデルに類似した変換スクリプトを教えてもらうことも躊躇しないでください。
- TensorFlowからPyTorchにモデルを移植している場合、良い出発点はBERTの変換スクリプトかもしれません [here](https://github.com/huggingface/transformers/blob/7acfa95afb8194f8f9c1f4d2c6028224dbed35a2/src/transformers/models/bert/modeling_bert.py#L91)
- PyTorchからPyTorchにモデルを移植している場合、良い出発点はBARTの変換スクリプトかもしれません [here](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bart/convert_bart_original_pytorch_checkpoint_to_pytorch.py)
以下では、PyTorchモデルが層の重みをどのように保存し、層の名前を定義するかについて簡単に説明します。
PyTorchでは、層の名前は層に与えるクラス属性の名前によって定義されます。
PyTorchで `SimpleModel` というダミーモデルを定義しましょう：
```python
from torch import nn
class SimpleModel(nn.Module):
def __init__(self):
super().__init__()
self.dense = nn.Linear(10, 10)
self.intermediate = nn.Linear(10, 10)
self.layer_norm = nn.LayerNorm(10)
```
これで、このモデル定義のインスタンスを作成し、`dense`、`intermediate`、`layer_norm`のすべての重みをランダムな重みで埋めたモデルを作成できます。モデルのアーキテクチャを確認するために、モデルを印刷してみましょう。
```python
model = SimpleModel()
print(model)
```
これは以下を出力します：
```
SimpleModel(
(dense): Linear(in_features=10, out_features=10, bias=True)
(intermediate): Linear(in_features=10, out_features=10, bias=True)
(layer_norm): LayerNorm((10,), eps=1e-05, elementwise_affine=True)
)
```
層の名前はPyTorchのクラス属性の名前によって定義されています。特定の層の重み値を出力することができます：
```python
print(model.dense.weight.data)
```
ランダムに初期化された重みを確認するために
```
tensor([[-0.0818, 0.2207, -0.0749, -0.0030, 0.0045, -0.1569, -0.1598, 0.0212,
-0.2077, 0.2157],
[ 0.1044, 0.0201, 0.0990, 0.2482, 0.3116, 0.2509, 0.2866, -0.2190,
0.2166, -0.0212],
[-0.2000, 0.1107, -0.1999, -0.3119, 0.1559, 0.0993, 0.1776, -0.1950,
-0.1023, -0.0447],
[-0.0888, -0.1092, 0.2281, 0.0336, 0.1817, -0.0115, 0.2096, 0.1415,
-0.1876, -0.2467],
[ 0.2208, -0.2352, -0.1426, -0.2636, -0.2889, -0.2061, -0.2849, -0.0465,
0.2577, 0.0402],
[ 0.1502, 0.2465, 0.2566, 0.0693, 0.2352, -0.0530, 0.1859, -0.0604,
0.2132, 0.1680],
[ 0.1733, -0.2407, -0.1721, 0.1484, 0.0358, -0.0633, -0.0721, -0.0090,
0.2707, -0.2509],
[-0.1173, 0.1561, 0.2945, 0.0595, -0.1996, 0.2988, -0.0802, 0.0407,
0.1829, -0.1568],
[-0.1164, -0.2228, -0.0403, 0.0428, 0.1339, 0.0047, 0.1967, 0.2923,
0.0333, -0.0536],
[-0.1492, -0.1616, 0.1057, 0.1950, -0.2807, -0.2710, -0.1586, 0.0739,
0.2220, 0.2358]]).
```
スクリプト内の変換スクリプトでは、ランダムに初期化された重みを、対応するチェックポイント内の正確な重みで埋める必要があります。例えば、以下のように翻訳します：
```python
# retrieve matching layer weights, e.g. by
# recursive algorithm
layer_name = "dense"
pretrained_weight = array_of_dense_layer
model_pointer = getattr(model, "dense")
model_pointer.weight.data = torch.from_numpy(pretrained_weight)
```
PyTorchモデルの各ランダム初期化された重みと対応する事前学習済みチェックポイントの重みが
**形状と名前の両方**で正確に一致することを確認する必要があります。
これを行うために、形状に対するassertステートメントを追加し、チェックポイントの重みの名前を出力することが
**必要不可欠**です。例えば、次のようなステートメントを追加する必要があります：
```python
assert (
model_pointer.weight.shape == pretrained_weight.shape
), f"Pointer shape of random weight {model_pointer.shape} and array shape of checkpoint weight {pretrained_weight.shape} mismatched"
```
また、両方の重みの名前を印刷して、一致していることを確認する必要があります。例えば、次のようにします：
```python
logger.info(f"Initialize PyTorch weight {layer_name} from {pretrained_weight.name}")
```
もし形状または名前のいずれかが一致しない場合、おそらく誤って🤗 Transformersの実装に初期化されたレイヤーに間違ったチェックポイントの重みを割り当ててしまった可能性があります。
誤った形状は、おそらく`BrandNewBertConfig()`での設定パラメーターが、変換したいチェックポイントで使用されたものと正確に一致しないためです。
ただし、PyTorchのレイヤーの実装によっては、重みを事前に転置する必要がある場合もあります。
最後に、**すべて**の必要な重みが初期化されていることを確認し、初期化に使用されなかったすべてのチェックポイントの重みを表示して、モデルが正しく変換されていることを確認してください。
変換トライアルが誤った形状ステートメントまたは誤った名前割り当てで失敗するのは完全に正常です。
これはおそらく、`BrandNewBertConfig()`で誤ったパラメーターを使用したか、🤗 Transformersの実装に誤ったアーキテクチャがあるか、🤗 Transformersの実装の1つのコンポーネントの`init()`関数にバグがあるか、チェックポイントの重みの1つを転置する必要があるためです。
このステップは、以前のステップと繰り返すべきです。すべてのチェックポイントの重みが正しく🤗 Transformersモデルに読み込まれるまで繰り返すべきです。
🤗 Transformers実装に正しくチェックポイントを読み込んだ後、選択したフォルダーにモデルを保存できます `/path/to/converted/checkpoint/folder`。このフォルダには`pytorch_model.bin`ファイルと`config.json`ファイルの両方が含まれるはずです。
```python
model.save_pretrained("/path/to/converted/checkpoint/folder")
```
**7. 順伝播（forward pass）の実装**
🤗 Transformers実装で事前学習済みの重みを正しく読み込んだ後、順伝播が正しく実装されていることを確認する必要があります。[元のリポジトリを理解する](#3-4-run-a-pretrained-checkpoint-using-the-original-repository)で、元のリポジトリを使用してモデルの順伝播を実行するスクリプトをすでに作成しました。今度は、元のリポジトリの代わりに🤗 Transformers実装を使用して類似のスクリプトを作成する必要があります。以下のようになります：
```python
model = BrandNewBertModel.from_pretrained("/path/to/converted/checkpoint/folder")
input_ids = [0, 4, 4, 3, 2, 4, 1, 7, 19]
output = model(input_ids).last_hidden_states
```
🤗 Transformersの実装と元のモデルの実装が最初の実行で完全に同じ出力を提供しないか、
フォワードパスでエラーが発生する可能性が非常に高いです。失望しないでください - これは予想されていることです！
まず、フォワードパスがエラーをスローしないことを確認する必要があります。
間違った次元が使用され、*次元の不一致*エラーや、誤ったデータ型オブジェクトが使用されることがよくあります。
例えば、`torch.long`ではなく`torch.float32`が使用されます。特定のエラーを解決できない場合は、
Hugging Faceチームに助けを求めることを躊躇しないでください。
🤗 Transformers実装が正しく機能することを確認する最終的な部分は、出力が`1e-3`の精度で同等であることを確認することです。
まず、出力の形状が同一であること、つまりスクリプトの🤗 Transformers実装と元の実装の両方で`outputs.shape`が同じ値を生成する必要があります。
次に、出力値が同一であることを確認する必要があります。
これは新しいモデルを追加する際の最も難しい部分の1つです。
出力が同一でない理由の一般的な間違いは以下の通りです。
- 一部のレイヤーが追加されていない、つまり*活性化*レイヤーが追加されていないか、リザバル接続が忘れられている
- 単語埋め込み行列が結ばれていない
- オリジナルの実装がオフセットを使用しているため、誤った位置埋め込みが使用されている
- フォワードパス中にドロップアウトが適用されています。これを修正するには、*model.trainingがFalse*であることを確認し、フォワードパス中に誤ってドロップアウトレイヤーがアクティブ化されないようにします。
*つまり* [PyTorchのfunctional dropout](https://pytorch.org/docs/stable/nn.functional.html?highlight=dropout#torch.nn.functional.dropout)に*model.training*を渡します。
問題を修正する最良の方法は、通常、元の実装と🤗 Transformers実装のフォワードパスを並べて表示し、違いがあるかどうかを確認することです。
理想的には、フォワードパスの両方の実装の中間出力をデバッグ/プリントアウトして、🤗 Transformers実装が元の実装と異なる出力を示すネットワーク内の正確な位置を見つけることができます。
最初に、両方のスクリプトのハードコーディングされた`input_ids`が同一であることを確認します。
次に、`input_ids`の最初の変換（通常、単語埋め込み）の出力が同一であることを確認します。
その後、ネットワークの最後のレイヤーまで作業を進めます。
いずれかの時点で、2つの実装間で違いがあることに気付くはずで、それにより🤗 Transformers実装のバグの場所が特定されます。
経験上、元の実装と🤗 Transformers実装のフォワードパスの同じ位置に多くのプリントステートメントを追加し、
中間プレゼンテーションで同じ値を示すプリントステートメントを段階的に削除するのがシンプルかつ効果的な方法です。
両方の実装が同じ出力を生成することに自信を持っている場合、`torch.allclose(original_output, output, atol=1e-3)`を使用して出力を確認すると、最も難しい部分が完了します！
おめでとうございます - 完了する作業は簡単なものになるはずです 😊。
**8. 必要なすべてのモデルテストを追加**
この時点で、新しいモデルが正常に追加されました。
ただし、モデルがまだ必要な設計に完全に準拠していない可能性が非常に高いです。
🤗 Transformersと完全に互換性があることを確認するために、すべての一般的なテストがパスする必要があります。
Cookiecutterはおそらくモデル用のテストファイルを自動的に追加しているはずで、おそらく同じディレクトリに`tests/models/brand_new_bert/test_modeling_brand_new_bert.py`として存在します。
このテストファイルを実行して、すべての一般的なテストがパスすることを確認してください：
```bash
pytest tests/models/brand_new_bert/test_modeling_brand_new_bert.py
```
すべての一般的なテストを修正したら、今度は実行したすべての素晴らしい作業が適切にテストされていることを確認することが非常に重要です。これにより、
- a) コミュニティは*brand_new_bert*の特定のテストを見ることで、あなたの作業を簡単に理解できます。
- b) モデルへの将来の変更がモデルの重要な機能を壊さないようにすることができます。
まず、統合テストを追加する必要があります。これらの統合テストは、基本的にはデバッグスクリプトと同じことを行います。これらのモデルテストのテンプレートはCookiecutterによって既に追加されており、「BrandNewBertModelIntegrationTests」と呼ばれています。このテストを記入するだけです。これらのテストが合格していることを確認するには、次のコマンドを実行します。
```bash
RUN_SLOW=1 pytest -sv tests/models/brand_new_bert/test_modeling_brand_new_bert.py::BrandNewBertModelIntegrationTests
```
<Tip>
Windowsを使用している場合、`RUN_SLOW=1`を`SET RUN_SLOW=1`に置き換えてください。
</Tip>
次に、*brand_new_bert*に特有のすべての特徴は、別個のテスト内で追加されるべきです。
`BrandNewBertModelTester`/`BrandNewBertModelTest`の下に。この部分はよく忘れられますが、2つの点で非常に役立ちます：
- モデルの追加中に獲得した知識をコミュニティに伝え、*brand_new_bert*の特別な機能がどのように動作するかを示すことによって、知識の共有を支援します。
- 将来の貢献者は、これらの特別なテストを実行することでモデルへの変更を迅速にテストできます。
**9. トークナイザの実装**
次に、*brand_new_bert*のトークナイザを追加する必要があります。通常、トークナイザは🤗 Transformersの既存のトークナイザと同等か非常に似ています。
トークナイザが正しく動作することを確認するためには、まず、元のリポジトリ内で文字列を入力し、`input_ids`を返すスクリプトを作成することをお勧めします。
このスクリプトは、次のように見えるかもしれません（疑似コードで示します）：
```python
input_str = "This is a long example input string containing special characters .$?-, numbers 2872 234 12 and words."
model = BrandNewBertModel.load_pretrained_checkpoint("/path/to/checkpoint/")
input_ids = model.tokenize(input_str)
```
オリジナルのリポジトリを詳しく調査し、正しいトークナイザの関数を見つける必要があるかもしれません。
または、オリジナルのリポジトリのクローンを変更して、`input_ids`だけを出力するようにする必要があるかもしれません。
オリジナルのリポジトリを使用した機能的なトークナイゼーションスクリプトを作成した後、
🤗 Transformers向けの類似したスクリプトを作成する必要があります。
以下のように見えるべきです：
```python
from transformers import BrandNewBertTokenizer
input_str = "This is a long example input string containing special characters .$?-, numbers 2872 234 12 and words."
tokenizer = BrandNewBertTokenizer.from_pretrained("/path/to/tokenizer/folder/")
input_ids = tokenizer(input_str).input_ids
```
`input_ids`が同じ値を生成した場合、最終ステップとしてトークナイザのテストファイルも追加するべきです。
*brand_new_bert*のモデルングテストファイルと同様に、*brand_new_bert*のトークナイズテストファイルには、いくつかのハードコードされた統合テストが含まれるべきです。
**10. エンドツーエンド統合テストの実行**
トークナイザを追加した後、`🤗 Transformers`内の`tests/models/brand_new_bert/test_modeling_brand_new_bert.py`に
モデルとトークナイザの両方を使用するいくつかのエンドツーエンド統合テストも追加する必要があります。
このようなテストは、🤗 Transformersの実装が期待どおりに機能することを示すべきです。
意味のあるテキスト対テキストのサンプルが含まれます。有用なテキスト対テキストのサンプルには、ソースからターゲットへの翻訳ペア、記事から要約へのペア、質問から回答へのペアなどが含まれます。
ポートされたチェックポイントがダウンストリームタスクでファインチューニングされていない場合、モデルのテストに依存するだけで十分です。
モデルが完全に機能していることを確認するために、すべてのテストをGPU上で実行することもお勧めします。
モデルの内部テンソルに`.to(self.device)`ステートメントを追加するのを忘れる可能性があるため、そのようなテストではエラーが表示されることがあります。
GPUにアクセスできない場合、Hugging Faceチームが代わりにこれらのテストを実行できます。
**11. ドキュメントの追加**
これで、*brand_new_bert*の必要なすべての機能が追加されました - ほぼ完了です！残りの追加すべきことは、良いドキュメントとドキュメントページです。
Cookiecutterが`docs/source/model_doc/brand_new_bert.md`というテンプレートファイルを追加しているはずで、これを記入する必要があります。
モデルのユーザーは通常、モデルを使用する前にまずこのページを見ます。したがって、ドキュメンテーションは理解しやすく簡潔である必要があります。
モデルの使用方法を示すためにいくつかの*Tips*を追加することはコミュニティにとって非常に役立ちます。ドキュメンテーションに関しては、Hugging Faceチームに問い合わせることをためらわないでください。
次に、`src/transformers/models/brand_new_bert/modeling_brand_new_bert.py`に追加されたドキュメンテーション文字列が正しいこと、およびすべての必要な入力および出力を含んでいることを確認してください。
ドキュメンテーションの書き方とドキュメンテーション文字列のフォーマットについて詳細なガイドが[こちら](writing-documentation)にあります。
ドキュメンテーションは通常、コミュニティとモデルの最初の接触点であるため、コードと同じくらい注意深く扱うべきであることを常に念頭に置いてください。
**コードのリファクタリング**
素晴らしい、これで*brand_new_bert*に必要なすべてのコードが追加されました。
この時点で、次のようなポテンシャルなコードスタイルの誤りを訂正するために以下を実行する必要があります：
```bash
make style
```
あなたのコーディングスタイルが品質チェックをパスすることを確認してください:
```bash
make quality
```
🤗 Transformersの非常に厳格なデザインテストには、まだ合格していない可能性があるいくつかの他のテストが存在するかもしれません。
これは、ドキュメント文字列に情報が不足しているか、名前が間違っていることが原因であることが多いです。Hugging Faceチームは、ここで詰まっている場合には必ず助けてくれるでしょう。
最後に、コードが正しく機能することを確認した後、コードをリファクタリングするのは常に良いアイデアです。
すべてのテストがパスした今、追加したコードを再度確認してリファクタリングを行うのは良いタイミングです。
これでコーディングの部分は完了しました、おめでとうございます！ 🎉 あなたは素晴らしいです！ 😎
**12. モデルをモデルハブにアップロード**
最後のパートでは、すべてのチェックポイントをモデルハブに変換してアップロードし、各アップロードしたモデルチェックポイントにモデルカードを追加する必要があります。
モデルハブの機能について詳しくは、[Model sharing and uploading Page](model_sharing)を読んで理解できます。
ここでは、*brand_new_bert*の著者組織の下にモデルをアップロードできるように必要なアクセス権を取得するために、Hugging Faceチームと協力する必要があります。
`transformers`のすべてのモデルに存在する`push_to_hub`メソッドは、チェックポイントをハブにプッシュする迅速かつ効率的な方法です。
以下に、少しのコードスニペットを示します：
```python
brand_new_bert.push_to_hub("brand_new_bert")
# Uncomment the following line to push to an organization.
# brand_new_bert.push_to_hub("<organization>/brand_new_bert")
```
各チェックポイントに適切なモデルカードを作成する価値があります。モデルカードは、この特定のチェックポイントの特性をハイライトするべきです。例えば、このチェックポイントはどのデータセットで事前学習/ファインチューニングされたか、どのような下流タスクでモデルを使用すべきかを示すべきです。また、モデルの正しい使用方法に関するコードも含めるべきです。
**13.（オプション）ノートブックの追加**
*brand_new_bert*を推論または下流タスクのファインチューニングにどのように詳細に使用できるかを示すノートブックを追加することは非常に役立ちます。これはあなたのPRをマージするために必須ではありませんが、コミュニティにとって非常に有用です。
**14. 完成したPRの提出**
プログラミングが完了したら、最後のステップに移動し、PRをメインブランチにマージしましょう。通常、Hugging Faceチームはこの時点で既にあなたをサポートしているはずですが、PRに良い説明を追加し、コードにコメントを追加して、レビュアーに特定の設計の選択肢を指摘したい場合はコメントを追加することも価値があります。
### Share your work!!
さあ、コミュニティからあなたの作業に対する評価を得る時が来ました！モデルの追加を完了することは、TransformersおよびNLPコミュニティにとって重要な貢献です。あなたのコードとポートされた事前学習済みモデルは、何百人、何千人という開発者や研究者によって確実に使用されるでしょう。あなたの仕事に誇りを持ち、コミュニティとあなたの成果を共有しましょう。
**あなたはコミュニティの誰でも簡単にアクセスできる別のモデルを作成しました！ 🤯**
| transformers/docs/source/ja/add_new_model.md/0 | {
"file_path": "transformers/docs/source/ja/add_new_model.md",
"repo_id": "transformers",
"token_count": 28026
} | 22 |
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# DeepSpeed Integration
[DeepSpeed](https://github.com/microsoft/DeepSpeed) は、[ZeRO 論文](https://arxiv.org/abs/1910.02054) で説明されているすべてを実装します。現在、次のものを完全にサポートしています。
1. オプティマイザーの状態分割 (ZeRO ステージ 1)
2. 勾配分割 (ZeRO ステージ 2)
3. パラメーターの分割 (ZeRO ステージ 3)
4. カスタム混合精度トレーニング処理
5. 一連の高速 CUDA 拡張ベースのオプティマイザー
6. CPU および NVMe への ZeRO オフロード
ZeRO-Offload には独自の専用ペーパーがあります: [ZeRO-Offload: Democratizing Billion-Scale Model Training](https://arxiv.org/abs/2101.06840)。 NVMe サポートについては、論文 [ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning](https://arxiv.org/abs/2104.07857)。
DeepSpeed ZeRO-2 は、その機能が推論には役に立たないため、主にトレーニングのみに使用されます。
DeepSpeed ZeRO-3 は、巨大なモデルを複数の GPU にロードできるため、推論にも使用できます。
単一の GPU では不可能です。
🤗 Transformers は、2 つのオプションを介して [DeepSpeed](https://github.com/microsoft/DeepSpeed) を統合します。
1. [`Trainer`] によるコア DeepSpeed 機能の統合。何でもやってくれるタイプです
統合の場合 - カスタム構成ファイルを指定するか、テンプレートを使用するだけで、他に何もする必要はありません。たいていの
このドキュメントではこの機能に焦点を当てています。
2. [`Trainer`] を使用せず、DeepSpeed を統合した独自のトレーナーを使用したい場合
`from_pretrained` や `from_config` などのコア機能には、重要な機能の統合が含まれています。
ZeRO ステージ 3 以降の `zero.Init`などの DeepSpeed の部分。この機能を活用するには、次のドキュメントをお読みください。
[非トレーナー DeepSpeed 統合](#nontrainer-deepspeed-integration)。
統合されているもの:
トレーニング：
1. DeepSpeed ZeRO トレーニングは、ZeRO-Infinity (CPU および NVME オフロード) を使用して完全な ZeRO ステージ 1、2、および 3 をサポートします。
推論：
1. DeepSpeed ZeRO Inference は、ZeRO-Infinity による ZeRO ステージ 3 をサポートします。トレーニングと同じ ZeRO プロトコルを使用しますが、
オプティマイザと lr スケジューラは使用せず、ステージ 3 のみが関連します。詳細については、以下を参照してください。
[ゼロ推論](#zero-inference)。
DeepSpeed Inference もあります。これは、Tensor Parallelism の代わりに Tensor Parallelism を使用するまったく異なるテクノロジーです。
ZeRO (近日公開)。
<a id='deepspeed-trainer-integration'></a>
## Trainer Deepspeed Integration
<a id='deepspeed-installation'></a>
### Installation
pypi 経由でライブラリをインストールします。
```bash
pip install deepspeed
```
または`tansformers`, `extras`経由:
```bash
pip install transformers[deepspeed]
```
または、[DeepSpeed の GitHub ページ](https://github.com/microsoft/deepspeed#installation) で詳細を確認してください。
[高度なインストール](https://www.deepspeed.ai/tutorials/advanced-install/)。
それでもビルドに苦労する場合は、まず [CUDA 拡張機能のインストール ノート](trainer#cuda-extension-installation-notes) を必ず読んでください。
拡張機能を事前ビルドせず、実行時に拡張機能がビルドされることに依存しており、上記の解決策をすべて試した場合
それが役に立たなかった場合、次に試すべきことは、モジュールをインストールする前にモジュールを事前にビルドすることです。
DeepSpeed のローカル ビルドを作成するには:
```bash
git clone https://github.com/microsoft/DeepSpeed/
cd DeepSpeed
rm -rf build
TORCH_CUDA_ARCH_LIST="8.6" DS_BUILD_CPU_ADAM=1 DS_BUILD_UTILS=1 pip install . \
--global-option="build_ext" --global-option="-j8" --no-cache -v \
--disable-pip-version-check 2>&1 | tee build.log
```
NVMe オフロードを使用する場合は、上記の手順に`DS_BUILD_AIO=1`を含める必要があります (また、
*libaio-dev* システム全体にインストールします)。
`TORCH_CUDA_ARCH_LIST` を編集して、使用する GPU カードのアーキテクチャのコードを挿入します。すべてを仮定すると
あなたのカードは同じで、次の方法でアーチを取得できます。
```bash
CUDA_VISIBLE_DEVICES=0 python -c "import torch; print(torch.cuda.get_device_capability())"
```
したがって、`8, 6`を取得した場合は、`TORCH_CUDA_ARCH_LIST="8.6"`を使用します。複数の異なるカードをお持ちの場合は、すべてをリストすることができます
それらのうち、`TORCH_CUDA_ARCH_LIST="6.1;8.6"`が好きです
複数のマシンで同じセットアップを使用する必要がある場合は、バイナリ ホイールを作成します。
```bash
git clone https://github.com/microsoft/DeepSpeed/
cd DeepSpeed
rm -rf build
TORCH_CUDA_ARCH_LIST="8.6" DS_BUILD_CPU_ADAM=1 DS_BUILD_UTILS=1 \
python setup.py build_ext -j8 bdist_wheel
```
`dist/deepspeed-0.3.13+8cd046f-cp38-cp38-linux_x86_64.whl`のようなものが生成されるので、これをインストールできます
`pip install deepspeed-0.3.13+8cd046f-cp38-cp38-linux_x86_64.whl`としてローカルまたは他のマシンにインストールします。
繰り返しますが、`TORCH_CUDA_ARCH_LIST`をターゲット アーキテクチャに合わせて調整することを忘れないでください。
NVIDIA GPU の完全なリストと、それに対応する **コンピューティング機能** (この記事の Arch と同じ) を見つけることができます。
コンテキスト) [ここ](https://developer.nvidia.com/cuda-gpus)。
以下を使用して、pytorch が構築されたアーチを確認できます。
```bash
python -c "import torch; print(torch.cuda.get_arch_list())"
```
ここでは、インストールされている GPU の 1 つのアーチを見つける方法を説明します。たとえば、GPU 0 の場合:
```bash
CUDA_VISIBLE_DEVICES=0 python -c "import torch; \
print(torch.cuda.get_device_properties(torch.device('cuda')))"
```
出力が次の場合:
```bash
_CudaDeviceProperties(name='GeForce RTX 3090', major=8, minor=6, total_memory=24268MB, multi_processor_count=82)
```
そうすれば、このカードのアーチが`8.6`であることがわかります。
`TORCH_CUDA_ARCH_LIST` を完全に省略することもできます。そうすれば、ビルド プログラムが自動的にクエリを実行します。
ビルドが行われる GPU のアーキテクチャ。これは、ターゲット マシンの GPU と一致する場合もあれば、一致しない場合もあります。
目的のアーチを明示的に指定することをお勧めします。
提案されたことをすべて試してもまだビルドの問題が発生する場合は、GitHub の問題に進んでください。
[ディープスピード](https://github.com/microsoft/DeepSpeed/issues)、
<a id='deepspeed-multi-gpu'></a>
### Deployment with multiple GPUs
DeepSpeed 統合をデプロイするには、[`Trainer`] コマンド ライン引数を調整して新しい引数 `--deepspeed ds_config.json` を含めます。ここで、`ds_config.json` は DeepSpeed 構成ファイルです。
[こちら](https://www.deepspeed.ai/docs/config-json/)に記載されています。ファイル名はあなた次第です。
DeepSpeed の`add_config_arguments`ユーティリティを使用して、必要なコマンド ライン引数をコードに追加することをお勧めします。
詳細については、[DeepSpeed の引数解析](https://deepspeed.readthedocs.io/en/latest/initialize.html#argument-parsing) ドキュメントを参照してください。
ここで選択したランチャーを使用できます。 pytorch ランチャーを引き続き使用できます。
```bash
torch.distributed.run --nproc_per_node=2 your_program.py <normal cl args> --deepspeed ds_config.json
```
または、`deepspeed`によって提供されるランチャーを使用します。
```bash
deepspeed --num_gpus=2 your_program.py <normal cl args> --deepspeed ds_config.json
```
ご覧のとおり、引数は同じではありませんが、ほとんどのニーズではどちらでも機能します。の
さまざまなノードと GPU を構成する方法の詳細については、[こちら](https://www.deepspeed.ai/getting-started/#resource-configuration-multi-node) を参照してください。
`deepspeed`ランチャーを使用し、利用可能なすべての GPU を使用したい場合は、`--num_gpus`フラグを省略するだけです。
以下は、利用可能なすべての GPU をデプロイする DeepSpeed で`run_translation.py`を実行する例です。
```bash
deepspeed examples/pytorch/translation/run_translation.py \
--deepspeed tests/deepspeed/ds_config_zero3.json \
--model_name_or_path google-t5/t5-small --per_device_train_batch_size 1 \
--output_dir output_dir --overwrite_output_dir --fp16 \
--do_train --max_train_samples 500 --num_train_epochs 1 \
--dataset_name wmt16 --dataset_config "ro-en" \
--source_lang en --target_lang ro
```
DeepSpeed のドキュメントには、`--deepspeed --deepspeed_config ds_config.json`が表示される可能性が高いことに注意してください。
DeepSpeed 関連の引数が 2 つありますが、簡単にするためであり、処理すべき引数がすでに非常に多いためです。
この 2 つを 1 つの引数に結合しました。
実際の使用例については、この [投稿](https://github.com/huggingface/transformers/issues/8771#issuecomment-759248400) を参照してください。
<a id='deepspeed-one-gpu'></a>
### Deployment with one GPU
1 つの GPU で DeepSpeed をデプロイするには、[`Trainer`] コマンド ライン引数を次のように調整します。
```bash
deepspeed --num_gpus=1 examples/pytorch/translation/run_translation.py \
--deepspeed tests/deepspeed/ds_config_zero2.json \
--model_name_or_path google-t5/t5-small --per_device_train_batch_size 1 \
--output_dir output_dir --overwrite_output_dir --fp16 \
--do_train --max_train_samples 500 --num_train_epochs 1 \
--dataset_name wmt16 --dataset_config "ro-en" \
--source_lang en --target_lang ro
```
これは複数の GPU の場合とほぼ同じですが、ここでは、DeepSpeed に 1 つの GPU だけを使用するように明示的に指示します。
`--num_gpus=1`。デフォルトでは、DeepSpeed は指定されたノード上で認識できるすべての GPU をデプロイします。起動する GPU が 1 つだけの場合
の場合、この引数は必要ありません。次の [ドキュメント](https://www.deepspeed.ai/getting-started/#resource-configuration-multi-node) では、ランチャー オプションについて説明しています。
1 つの GPU だけで DeepSpeed を使用したいのはなぜですか?
1. 一部の計算とメモリをホストの CPU と RAM に委任できる ZeRO オフロード機能を備えているため、
モデルのニーズに合わせてより多くの GPU リソースを残しておきます。より大きなバッチ サイズ、または非常に大きなモデルのフィッティングを可能にする
普通は合わないでしょう。
2. スマートな GPU メモリ管理システムを提供し、メモリの断片化を最小限に抑えます。
より大きなモデルとデータ バッチ。
次に構成について詳しく説明しますが、単一の GPU で大幅な改善を実現するための鍵は次のとおりです。
DeepSpeed を使用するには、構成ファイルに少なくとも次の構成が必要です。
```json
{
"zero_optimization": {
"stage": 2,
"offload_optimizer": {
"device": "cpu",
"pin_memory": true
},
"allgather_partitions": true,
"allgather_bucket_size": 2e8,
"reduce_scatter": true,
"reduce_bucket_size": 2e8,
"overlap_comm": true,
"contiguous_gradients": true
}
}
```
これにより、オプティマイザーのオフロードやその他の重要な機能が有効になります。バッファ サイズを試してみるとよいでしょう。
詳細については、以下のディスカッションを参照してください。
このタイプのデプロイメントの実際的な使用例については、この [投稿](https://github.com/huggingface/transformers/issues/8771#issuecomment-759176685) を参照してください。
このドキュメントで詳しく説明されているように、CPU および NVMe オフロードを備えた ZeRO-3 を試すこともできます。
ノート：
- GPU 0 とは異なる特定の GPU で実行する必要がある場合、`CUDA_VISIBLE_DEVICES` を使用して制限することはできません。
利用可能な GPU の表示範囲。代わりに、次の構文を使用する必要があります。
```bash
deepspeed --include localhost:1 examples/pytorch/translation/run_translation.py ...
```
この例では、DeepSpeed に GPU 1 (2 番目の GPU) を使用するように指示します。
<a id='deepspeed-multi-node'></a>
### 複数のノードを使用したデプロイメント
このセクションの情報は DeepSpeed 統合に固有のものではなく、あらゆるマルチノード プログラムに適用できます。ただし、DeepSpeed は、SLURM 環境でない限り、他のランチャーよりも使いやすい`deepspeed`ランチャーを提供します。
このセクションでは、それぞれ 8 GPU を備えた 2 つのノードがあると仮定します。また、最初のノードには `ssh hostname1` を使用して、2 番目のノードには `ssh hostname2` を使用して接続できます。両方ともパスワードなしでローカルの ssh 経由で相互に接続できる必要があります。もちろん、これらのホスト (ノード) 名を、作業している実際のホスト名に変更する必要があります。
#### The torch.distributed.run launcher
たとえば、`torch.distributed.run` を使用するには、次のようにします。
```bash
python -m torch.distributed.run --nproc_per_node=8 --nnode=2 --node_rank=0 --master_addr=hostname1 \
--master_port=9901 your_program.py <normal cl args> --deepspeed ds_config.json
```
各ノードに SSH で接続し、それぞれのノードで同じコマンドを実行する必要があります。急ぐ必要はありません。ランチャーは両方のノードが同期するまで待機します。
詳細については、[torchrun](https://pytorch.org/docs/stable/elastic/run.html) を参照してください。ちなみに、これは pytorch の数バージョン前の`torch.distributed.launch`を置き換えたランチャーでもあります。
#### ディープスピード ランチャー
代わりに`deepspeed`ランチャーを使用するには、まず`hostfile`ファイルを作成する必要があります。
```
hostname1 slots=8
hostname2 slots=8
```
そして、次のように起動できます。
```bash
deepspeed --num_gpus 8 --num_nodes 2 --hostfile hostfile --master_addr hostname1 --master_port=9901 \
your_program.py <normal cl args> --deepspeed ds_config.json
```
`torch.distributed.run`ランチャーとは異なり、`deepspeed`は両方のノードでこのコマンドを自動的に起動します。
詳細については、[リソース構成 (マルチノード)](https://www.deepspeed.ai/getting-started/#resource-configuration-multi-node) を参照してください。
#### Launching in a SLURM environment
SLURM 環境では、次のアプローチを使用できます。以下は、特定の SLURM 環境に適合させるために必要な slurm スクリプト `launch.slurm` です。
```bash
#SBATCH --job-name=test-nodes # name
#SBATCH --nodes=2 # nodes
#SBATCH --ntasks-per-node=1 # crucial - only 1 task per dist per node!
#SBATCH --cpus-per-task=10 # number of cores per tasks
#SBATCH --gres=gpu:8 # number of gpus
#SBATCH --time 20:00:00 # maximum execution time (HH:MM:SS)
#SBATCH --output=%x-%j.out # output file name
export GPUS_PER_NODE=8
export MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
export MASTER_PORT=9901
srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \
--nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \
--master_addr $MASTER_ADDR --master_port $MASTER_PORT \
your_program.py <normal cl args> --deepspeed ds_config.json'
```
あとは実行をスケジュールするだけです。
```bash
sbatch launch.slurm
```
#### Use of Non-shared filesystem
デフォルトでは、DeepSpeed はマルチノード環境が共有ストレージを使用することを想定しています。これが当てはまらず、各ノードがローカル ファイルシステムしか参照できない場合は、設定ファイルを調整して [`checkpoint`_section](https://www.deepspeed.ai/docs/config-json/#) を含める必要があります。チェックポイント オプション) を次の設定で指定します。
```json
{
"checkpoint": {
"use_node_local_storage": true
}
}
```
あるいは、[`Trainer`] の `--save_on_each_node` 引数を使用することもでき、上記の設定は自動的に追加されます。
<a id='deepspeed-notebook'></a>
### Deployment in Notebooks
ノートブックのセルをスクリプトとして実行する場合の問題は、依存する通常の`deepspeed`ランチャーがないことです。
特定の設定では、それをエミュレートする必要があります。
GPU を 1 つだけ使用している場合、DeepSpeed を使用するためにノートブック内のトレーニング コードを調整する必要がある方法は次のとおりです。
```python
# DeepSpeed requires a distributed environment even when only one process is used.
# This emulates a launcher in the notebook
import os
os.environ["MASTER_ADDR"] = "localhost"
os.environ["MASTER_PORT"] = "9994" # modify if RuntimeError: Address already in use
os.environ["RANK"] = "0"
os.environ["LOCAL_RANK"] = "0"
os.environ["WORLD_SIZE"] = "1"
# Now proceed as normal, plus pass the deepspeed config file
training_args = TrainingArguments(..., deepspeed="ds_config_zero3.json")
trainer = Trainer(...)
trainer.train()
```
注: `...` は、関数に渡す通常の引数を表します。
複数の GPU を使用する場合、DeepSpeed が動作するにはマルチプロセス環境を使用する必要があります。つまり、あなたは持っています
その目的でランチャーを使用することはできませんが、これは、提示された分散環境をエミュレートすることによっては実現できません。
このセクションの冒頭で。
現在のディレクトリのノートブックにその場で構成ファイルを作成したい場合は、専用の
セルの内容:
```python no-style
%%bash
cat <<'EOT' > ds_config_zero3.json
{
"fp16": {
"enabled": "auto",
"loss_scale": 0,
"loss_scale_window": 1000,
"initial_scale_power": 16,
"hysteresis": 2,
"min_loss_scale": 1
},
"optimizer": {
"type": "AdamW",
"params": {
"lr": "auto",
"betas": "auto",
"eps": "auto",
"weight_decay": "auto"
}
},
"scheduler": {
"type": "WarmupLR",
"params": {
"warmup_min_lr": "auto",
"warmup_max_lr": "auto",
"warmup_num_steps": "auto"
}
},
"zero_optimization": {
"stage": 3,
"offload_optimizer": {
"device": "cpu",
"pin_memory": true
},
"offload_param": {
"device": "cpu",
"pin_memory": true
},
"overlap_comm": true,
"contiguous_gradients": true,
"sub_group_size": 1e9,
"reduce_bucket_size": "auto",
"stage3_prefetch_bucket_size": "auto",
"stage3_param_persistence_threshold": "auto",
"stage3_max_live_parameters": 1e9,
"stage3_max_reuse_distance": 1e9,
"stage3_gather_16bit_weights_on_model_save": true
},
"gradient_accumulation_steps": "auto",
"gradient_clipping": "auto",
"steps_per_print": 2000,
"train_batch_size": "auto",
"train_micro_batch_size_per_gpu": "auto",
"wall_clock_breakdown": false
}
EOT
```
トレーニング スクリプトがノートブックのセルではなく通常のファイルにある場合は、次のようにして`deepspeed`を通常どおり起動できます。
細胞からのシェル。たとえば、`run_translation.py` を使用するには、次のように起動します。
```python no-style
!git clone https://github.com/huggingface/transformers
!cd transformers; deepspeed examples/pytorch/translation/run_translation.py ...
```
または、`%%bash` マジックを使用すると、シェル プログラムを実行するための複数行のコードを記述することができます。
```python no-style
%%bash
git clone https://github.com/huggingface/transformers
cd transformers
deepspeed examples/pytorch/translation/run_translation.py ...
```
そのような場合、このセクションの最初に示したコードは必要ありません。
注: `%%bash` マジックは優れていますが、現時点では出力をバッファリングするため、プロセスが終了するまでログは表示されません。
完了します。
<a id='deepspeed-config'></a>
### Configuration
設定ファイルで使用できる DeepSpeed 設定オプションの完全なガイドについては、次を参照してください。
[次のドキュメント](https://www.deepspeed.ai/docs/config-json/) にアクセスしてください。
さまざまな実際のニーズに対応する数十の DeepSpeed 構成例を [DeepSpeedExamples](https://github.com/microsoft/DeepSpeedExamples)で見つけることができます。
リポジトリ:
```bash
git clone https://github.com/microsoft/DeepSpeedExamples
cd DeepSpeedExamples
find . -name '*json'
```
上記のコードを続けて、Lamb オプティマイザーを構成しようとしているとします。したがって、次の中から検索できます
`.json` ファイルの例:
```bash
grep -i Lamb $(find . -name '*json')
```
さらにいくつかの例が [メイン リポジトリ](https://github.com/microsoft/DeepSpeed) にもあります。
DeepSpeed を使用する場合は、常に DeepSpeed 構成ファイルを指定する必要がありますが、一部の構成パラメータには
コマンドライン経由で設定します。微妙な違いについては、このガイドの残りの部分で説明します。
DeepSpeed 構成ファイルがどのようなものかを理解するために、ZeRO ステージ 2 機能を有効にする構成ファイルを次に示します。
オプティマイザー状態の CPU オフロードを含み、`AdamW`オプティマイザーと`WarmupLR`スケジューラーを使用し、混合を有効にします。
`--fp16` が渡された場合の精度トレーニング:
```json
{
"fp16": {
"enabled": "auto",
"loss_scale": 0,
"loss_scale_window": 1000,
"initial_scale_power": 16,
"hysteresis": 2,
"min_loss_scale": 1
},
"optimizer": {
"type": "AdamW",
"params": {
"lr": "auto",
"betas": "auto",
"eps": "auto",
"weight_decay": "auto"
}
},
"scheduler": {
"type": "WarmupLR",
"params": {
"warmup_min_lr": "auto",
"warmup_max_lr": "auto",
"warmup_num_steps": "auto"
}
},
"zero_optimization": {
"stage": 2,
"offload_optimizer": {
"device": "cpu",
"pin_memory": true
},
"allgather_partitions": true,
"allgather_bucket_size": 2e8,
"overlap_comm": true,
"reduce_scatter": true,
"reduce_bucket_size": 2e8,
"contiguous_gradients": true
},
"gradient_accumulation_steps": "auto",
"gradient_clipping": "auto",
"train_batch_size": "auto",
"train_micro_batch_size_per_gpu": "auto",
}
```
プログラムを実行すると、DeepSpeed は [`Trainer`] から受け取った設定をログに記録します。
コンソールに渡されるため、最終的にどのような設定が渡されたのかを正確に確認できます。
<a id='deepspeed-config-passing'></a>
### Passing Configuration
このドキュメントで説明したように、通常、DeepSpeed 設定は json ファイルへのパスとして渡されますが、
トレーニングの設定にコマンド ライン インターフェイスを使用せず、代わりにインスタンスを作成します。
[`Trainer`] via [`TrainingArguments`] その後、`deepspeed` 引数については次のことができます
ネストされた `dict` を渡します。これにより、その場で構成を作成でき、それを書き込む必要がありません。
[`TrainingArguments`] に渡す前にファイル システムを変更します。
要約すると、次のことができます。
```python
TrainingArguments(..., deepspeed="/path/to/ds_config.json")
```
または：
```python
ds_config_dict = dict(scheduler=scheduler_params, optimizer=optimizer_params)
TrainingArguments(..., deepspeed=ds_config_dict)
```
<a id='deepspeed-config-shared'></a>
### Shared Configuration
<Tip warning={true}>
このセクションは必読です
</Tip>
[`Trainer`] と DeepSpeed の両方が正しく機能するには、いくつかの設定値が必要です。
したがって、検出が困難なエラーにつながる可能性のある定義の競合を防ぐために、それらを構成することにしました。
[`Trainer`] コマンドライン引数経由。
さらに、一部の構成値はモデルの構成に基づいて自動的に導出されます。
複数の値を手動で調整することを忘れないでください。[`Trainer`] に大部分を任せるのが最善です
の設定を行います。
したがって、このガイドの残りの部分では、特別な設定値 `auto` が表示されます。これを設定すると、
正しい値または最も効率的な値に自動的に置き換えられます。これを無視することを自由に選択してください
推奨事項を参照し、値を明示的に設定します。この場合、次の点に十分注意してください。
[`Trainer`] 引数と DeepSpeed 設定は一致します。たとえば、同じものを使用していますか
学習率、バッチサイズ、または勾配累積設定?これらが一致しない場合、トレーニングは非常に失敗する可能性があります
方法を検出するのが難しい。あなたは警告を受けました。
DeepSpeed のみに固有の値や、それに合わせて手動で設定する必要がある値が他にも複数あります。
あなたの要望。
独自のプログラムで、DeepSpeed 構成をマスターとして変更したい場合は、次のアプローチを使用することもできます。
それに基づいて [`TrainingArguments`] を設定します。手順は次のとおりです。
1. マスター構成として使用する DeepSpeed 構成を作成またはロードします
2. これらの値に基づいて [`TrainingArguments`] オブジェクトを作成します
`scheduler.params.total_num_steps`などの一部の値は次のように計算されることに注意してください。
`train` 中に [`Trainer`] を実行しますが、もちろん自分で計算することもできます。
<a id='deepspeed-zero'></a>
### ZeRO
[Zero Redundancy Optimizer (ZeRO)](https://www.deepspeed.ai/tutorials/zero/) は、DeepSpeed の主力製品です。それ
3 つの異なるレベル (段階) の最適化をサポートします。最初のものは、スケーラビリティの観点からはあまり興味深いものではありません。
したがって、このドキュメントではステージ 2 と 3 に焦点を当てます。ステージ 3 は、最新の ZeRO-Infinity の追加によってさらに改善されています。
詳細については、DeepSpeed のドキュメントを参照してください。
構成ファイルの `zero_optimization` セクションは最も重要な部分です ([docs](https://www.deepspeed.ai/docs/config-json/#zero-optimizations-for-fp16-training))。ここで定義します
どの ZeRO ステージを有効にするか、そしてそれらをどのように構成するか。各パラメータの説明は、
DeepSpeed のドキュメント。
このセクションは、DeepSpeed 設定を介してのみ設定する必要があります - [`Trainer`] が提供します
同等のコマンドライン引数はありません。
注: 現在、DeepSpeed はパラメーター名を検証しないため、スペルを間違えると、デフォルト設定が使用されます。
スペルが間違っているパラメータ。 DeepSpeed エンジンの起動ログ メッセージを見て、その値を確認できます。
使用するつもりです。
<a id='deepspeed-zero2-config'></a>
#### ZeRO-2 Config
以下は、ZeRO ステージ 2 の構成例です。
```json
{
"zero_optimization": {
"stage": 2,
"offload_optimizer": {
"device": "cpu",
"pin_memory": true
},
"allgather_partitions": true,
"allgather_bucket_size": 5e8,
"overlap_comm": true,
"reduce_scatter": true,
"reduce_bucket_size": 5e8,
"contiguous_gradients": true
}
}
```
**性能調整：**
- `offload_optimizer` を有効にすると、GPU RAM の使用量が削減されます (`"stage": 2` が必要です)
- `"overlap_comm": true` は、GPU RAM 使用量の増加とトレードオフして、遅延をすべて削減します。 `overlap_comm`は 4.5x を使用します
`allgather_bucket_size`と`reduce_bucket_size`の値。したがって、5e8 に設定されている場合、9GB が必要になります。
フットプリント (`5e8 x 2Bytes x 2 x 4.5`)。したがって、8GB 以下の RAM を搭載した GPU を使用している場合、
OOM エラーが発生した場合は、これらのパラメータを`2e8`程度に減らす必要があり、それには 3.6GB が必要になります。やりたくなるでしょう
OOM に達し始めている場合は、より大容量の GPU でも同様です。
- これらのバッファを減らすと、より多くの GPU RAM を利用するために通信速度を犠牲にすることになります。バッファサイズが小さいほど、
通信が遅くなり、他のタスクで使用できる GPU RAM が増えます。したがって、バッチサイズが大きい場合は、
重要なのは、トレーニング時間を少し遅らせることは良いトレードになる可能性があります。
さらに、`deepspeed==0.4.4`には、次のコマンドで有効にできる新しいオプション`round_robin_gradients`が追加されました。
```json
{
"zero_optimization": {
"round_robin_gradients": true
}
}
```
これは、きめ細かい勾配パーティショニングによってランク間の CPU メモリへの勾配コピーを並列化する、CPU オフロードのステージ 2 最適化です。パフォーマンスの利点は、勾配累積ステップ (オプティマイザー ステップ間のコピーの増加) または GPU 数 (並列処理の増加) に応じて増加します。
<a id='deepspeed-zero3-config'></a>
#### ZeRO-3 Config
以下は、ZeRO ステージ 3 の構成例です。
```json
{
"zero_optimization": {
"stage": 3,
"offload_optimizer": {
"device": "cpu",
"pin_memory": true
},
"offload_param": {
"device": "cpu",
"pin_memory": true
},
"overlap_comm": true,
"contiguous_gradients": true,
"sub_group_size": 1e9,
"reduce_bucket_size": "auto",
"stage3_prefetch_bucket_size": "auto",
"stage3_param_persistence_threshold": "auto",
"stage3_max_live_parameters": 1e9,
"stage3_max_reuse_distance": 1e9,
"stage3_gather_16bit_weights_on_model_save": true
}
}
```
モデルまたはアクティベーションが GPU メモリに適合せず、CPU が未使用であるために OOM が発生している場合
`"device": "cpu"` を使用してオプティマイザの状態とパラメータを CPU メモリにメモリオフロードすると、この制限が解決される可能性があります。
CPU メモリにオフロードしたくない場合は、`device`エントリに`cpu`の代わりに`none`を使用します。オフロード先
NVMe については後ほど説明します。
固定メモリは、`pin_memory`を`true`に設定すると有効になります。この機能により、次のようなコストをかけてスループットを向上させることができます。
他のプロセスが使用できるメモリが少なくなります。ピン留めされたメモリは、それを要求した特定のプロセスのために確保されます。
通常、通常の CPU メモリよりもはるかに高速にアクセスされます。
**性能調整：**
- `stage3_max_live_parameters`: `1e9`
- `stage3_max_reuse_distance`: `1e9`
OOM に達した場合は、「stage3_max_live_parameters」と「stage3_max_reuse_ distance」を減らします。影響は最小限に抑えられるはずです
アクティブ化チェックポイントを実行しない限り、パフォーマンスに影響します。 `1e9`は約 2GB を消費します。記憶を共有しているのは、
`stage3_max_live_parameters` と `stage3_max_reuse_distance` なので、加算されるものではなく、合計で 2GB になります。
`stage3_max_live_parameters` は、特定の時点で GPU 上に保持する完全なパラメータの数の上限です。
時間。 「再利用距離」は、パラメータが将来いつ再び使用されるかを判断するために使用する指標です。
`stage3_max_reuse_ distance`を使用して、パラメータを破棄するか保持するかを決定します。パラメータが
近い将来に再び使用される予定 (`stage3_max_reuse_distance`未満) なので、通信を減らすために保持します。
オーバーヘッド。これは、アクティベーション チェックポイントを有効にしている場合に非常に役立ちます。フォワード再計算が行われ、
backward は単一レイヤー粒度を渡し、後方再計算までパラメータを前方再計算に保持したいと考えています。
次の構成値は、モデルの非表示サイズによって異なります。
- `reduce_bucket_size`: `hidden_size*hidden_size`
- `stage3_prefetch_bucket_size`: `0.9 * hidden_size * hidden_size`
- `stage3_param_persistence_threshold`: `10 * hidden_size`
したがって、これらの値を `auto` に設定すると、[`Trainer`] が推奨される値を自動的に割り当てます。
価値観。ただし、もちろん、これらを明示的に設定することもできます。
`stage3_gather_16bit_weights_on_model_save` は、モデルの保存時にモデル fp16 の重み統合を有効にします。大きい
モデルと複数の GPU の場合、これはメモリと速度の両方の点で高価な操作です。現在必須となっているのは、
トレーニングを再開する予定です。この制限を取り除き、より便利にする今後のアップデートに注目してください。
フレキシブル。
ZeRO-2 構成から移行している場合は、`allgather_partitions`、`allgather_bucket_size`、および
`reduce_scatter`設定パラメータは ZeRO-3 では使用されません。これらを設定ファイルに保存しておくと、
無視される。
- `sub_group_size`: `1e9`
`sub_group_size` は、オプティマイザーのステップ中にパラメーターが更新される粒度を制御します。パラメータは次のとおりです。
`sub_group_size` のバケットにグループ化され、各バケットは一度に 1 つずつ更新されます。 NVMeオフロードで使用する場合
したがって、ZeRO-Infinity の `sub_group_size`は、モデルの状態が CPU に出入りする粒度を制御します。
オプティマイザステップ中に NVMe からメモリを取得します。これにより、非常に大規模なモデルの CPU メモリ不足が防止されます。
NVMe オフロードを使用しない場合は、`sub_group_size`をデフォルト値の *1e9* のままにすることができます。変更することもできます
次の場合のデフォルト値:
1. オプティマイザー ステップ中に OOM が発生する: `sub_group_size` を減らして、一時バッファーのメモリ使用量を削減します。
2. オプティマイザー ステップに時間がかかります。`sub_group_size`を増やして、帯域幅の使用率を向上させます。
データバッファの増加。
#### ZeRO-0 Config
ステージ 0 と 1 はめったに使用されないため、最後にリストしていることに注意してください。
ステージ 0 では、すべてのタイプのシャーディングを無効にし、DDP として DeepSpeed のみを使用します。次のコマンドでオンにできます。
```json
{
"zero_optimization": {
"stage": 0
}
}
```
これにより、他に何も変更する必要がなく、基本的に ZeRO が無効になります。
#### ZeRO-1 Config
ステージ 1 は、ステージ 2 からグラデーション シャーディングを除いたものです。オプティマイザーの状態をシャード化するだけで、処理を少し高速化するためにいつでも試すことができます。
```json
{
"zero_optimization": {
"stage": 1
}
}
```
<a id='deepspeed-nvme'></a>
### NVMe Support
ZeRO-Infinity は、GPU と CPU メモリを NVMe メモリで拡張することで、非常に大規模なモデルのトレーニングを可能にします。おかげで
スマート パーティショニングおよびタイリング アルゴリズムでは、各 GPU が非常に少量のデータを送受信する必要があります。
オフロードにより、最新の NVMe がトレーニングに利用できる合計メモリ プールをさらに大きくするのに適していることが判明しました。
プロセス。 ZeRO-Infinity には、ZeRO-3 が有効になっている必要があります。
次の設定例では、NVMe がオプティマイザの状態とパラメータの両方をオフロードできるようにします。
```json
{
"zero_optimization": {
"stage": 3,
"offload_optimizer": {
"device": "nvme",
"nvme_path": "/local_nvme",
"pin_memory": true,
"buffer_count": 4,
"fast_init": false
},
"offload_param": {
"device": "nvme",
"nvme_path": "/local_nvme",
"pin_memory": true,
"buffer_count": 5,
"buffer_size": 1e8,
"max_in_cpu": 1e9
},
"aio": {
"block_size": 262144,
"queue_depth": 32,
"thread_count": 1,
"single_submit": false,
"overlap_events": true
},
"overlap_comm": true,
"contiguous_gradients": true,
"sub_group_size": 1e9,
"reduce_bucket_size": "auto",
"stage3_prefetch_bucket_size": "auto",
"stage3_param_persistence_threshold": "auto",
"stage3_max_live_parameters": 1e9,
"stage3_max_reuse_distance": 1e9,
"stage3_gather_16bit_weights_on_model_save": true
},
}
```
オプティマイザの状態とパラメータの両方を NVMe にオフロードするか、どちらか 1 つだけをオフロードするか、まったくオフロードしないかを選択できます。たとえば、次の場合
利用可能な CPU メモリが大量にある場合は、高速になるため、必ず CPU メモリのみにオフロードしてください (ヒント:
*"device": "CPU"*)。
[オプティマイザーの状態](https://www.deepspeed.ai/docs/config-json/#optimizer-offloading) と [パラメーター](https://www.deepspeed.ai/docs/config-json/#parameter-offloading)。
`nvme_path`が実際に NVMe であることを確認してください。NVMe は通常のハードドライブまたは SSD で動作しますが、
はるかに遅くなります。高速スケーラブルなトレーニングは、最新の NVMe 転送速度を念頭に置いて設計されました (この時点では
書き込みでは、読み取り最大 3.5 GB/秒、書き込み最大 3 GB/秒のピーク速度が得られます)。
最適な`aio`構成ブロックを見つけるには、ターゲット設定でベンチマークを実行する必要があります。
[ここで説明](https://github.com/microsoft/DeepSpeed/issues/998)。
<a id='deepspeed-zero2-zero3-performance'></a>
#### ZeRO-2 vs ZeRO-3 Performance
ZeRO-3 は、他のすべてが同じように構成されている場合、ZeRO-2 よりも遅くなる可能性があります。前者は収集する必要があるためです。
ZeRO-2 の機能に加えてモデルの重み付けを行います。 ZeRO-2 がニーズを満たし、数個の GPU を超えて拡張する必要がない場合
そうすれば、それに固執することを選択することもできます。 ZeRO-3 により、はるかに高いスケーラビリティ容量が可能になることを理解することが重要です
スピードを犠牲にして。
ZeRO-3 の構成を調整して、ZeRO-2 に近づけることができます。
- `stage3_param_persistence_threshold` を非常に大きな数値に設定します。たとえば、`6 * hidden_size * hidden_size` のように、最大パラメータよりも大きくなります。これにより、パラメータが GPU に保持されます。
- ZeRO-2 にはそのオプションがないため、`offload_params` をオフにします。
変更しなくても、`offload_params`をオフにするだけでパフォーマンスが大幅に向上する可能性があります。
`stage3_param_persistence_threshold`。もちろん、これらの変更はトレーニングできるモデルのサイズに影響します。それで
これらは、ニーズに応じて、スケーラビリティと引き換えに速度を向上させるのに役立ちます。
<a id='deepspeed-zero2-example'></a>
#### ZeRO-2 Example
以下は、完全な ZeRO-2 自動構成ファイル `ds_config_zero2.json` です。
```json
{
"fp16": {
"enabled": "auto",
"loss_scale": 0,
"loss_scale_window": 1000,
"initial_scale_power": 16,
"hysteresis": 2,
"min_loss_scale": 1
},
"optimizer": {
"type": "AdamW",
"params": {
"lr": "auto",
"betas": "auto",
"eps": "auto",
"weight_decay": "auto"
}
},
"scheduler": {
"type": "WarmupLR",
"params": {
"warmup_min_lr": "auto",
"warmup_max_lr": "auto",
"warmup_num_steps": "auto"
}
},
"zero_optimization": {
"stage": 2,
"offload_optimizer": {
"device": "cpu",
"pin_memory": true
},
"allgather_partitions": true,
"allgather_bucket_size": 2e8,
"overlap_comm": true,
"reduce_scatter": true,
"reduce_bucket_size": 2e8,
"contiguous_gradients": true
},
"gradient_accumulation_steps": "auto",
"gradient_clipping": "auto",
"steps_per_print": 2000,
"train_batch_size": "auto",
"train_micro_batch_size_per_gpu": "auto",
"wall_clock_breakdown": false
}
```
以下は、手動で設定された完全な ZeRO-2 のすべてが有効な構成ファイルです。ここでは主に、典型的なものを確認するためのものです。
値は次のようになりますが、複数の`auto`設定が含まれる値を使用することを強くお勧めします。
```json
{
"fp16": {
"enabled": true,
"loss_scale": 0,
"loss_scale_window": 1000,
"initial_scale_power": 16,
"hysteresis": 2,
"min_loss_scale": 1
},
"optimizer": {
"type": "AdamW",
"params": {
"lr": 3e-5,
"betas": [0.8, 0.999],
"eps": 1e-8,
"weight_decay": 3e-7
}
},
"scheduler": {
"type": "WarmupLR",
"params": {
"warmup_min_lr": 0,
"warmup_max_lr": 3e-5,
"warmup_num_steps": 500
}
},
"zero_optimization": {
"stage": 2,
"offload_optimizer": {
"device": "cpu",
"pin_memory": true
},
"allgather_partitions": true,
"allgather_bucket_size": 2e8,
"overlap_comm": true,
"reduce_scatter": true,
"reduce_bucket_size": 2e8,
"contiguous_gradients": true
},
"steps_per_print": 2000,
"wall_clock_breakdown": false
}
```
<a id='deepspeed-zero3-example'></a>
#### ZeRO-3 Example
以下は、完全な ZeRO-3 自動構成ファイル`ds_config_zero3.json`です。
```json
{
"fp16": {
"enabled": "auto",
"loss_scale": 0,
"loss_scale_window": 1000,
"initial_scale_power": 16,
"hysteresis": 2,
"min_loss_scale": 1
},
"optimizer": {
"type": "AdamW",
"params": {
"lr": "auto",
"betas": "auto",
"eps": "auto",
"weight_decay": "auto"
}
},
"scheduler": {
"type": "WarmupLR",
"params": {
"warmup_min_lr": "auto",
"warmup_max_lr": "auto",
"warmup_num_steps": "auto"
}
},
"zero_optimization": {
"stage": 3,
"offload_optimizer": {
"device": "cpu",
"pin_memory": true
},
"offload_param": {
"device": "cpu",
"pin_memory": true
},
"overlap_comm": true,
"contiguous_gradients": true,
"sub_group_size": 1e9,
"reduce_bucket_size": "auto",
"stage3_prefetch_bucket_size": "auto",
"stage3_param_persistence_threshold": "auto",
"stage3_max_live_parameters": 1e9,
"stage3_max_reuse_distance": 1e9,
"stage3_gather_16bit_weights_on_model_save": true
},
"gradient_accumulation_steps": "auto",
"gradient_clipping": "auto",
"steps_per_print": 2000,
"train_batch_size": "auto",
"train_micro_batch_size_per_gpu": "auto",
"wall_clock_breakdown": false
}
```
以下は、手動で設定された完全な ZeRO-3 のすべてが有効な構成ファイルです。ここでは主に、典型的なものを確認するためのものです。
値は次のようになりますが、複数の`auto`設定が含まれる値を使用することを強くお勧めします。
```json
{
"fp16": {
"enabled": true,
"loss_scale": 0,
"loss_scale_window": 1000,
"initial_scale_power": 16,
"hysteresis": 2,
"min_loss_scale": 1
},
"optimizer": {
"type": "AdamW",
"params": {
"lr": 3e-5,
"betas": [0.8, 0.999],
"eps": 1e-8,
"weight_decay": 3e-7
}
},
"scheduler": {
"type": "WarmupLR",
"params": {
"warmup_min_lr": 0,
"warmup_max_lr": 3e-5,
"warmup_num_steps": 500
}
},
"zero_optimization": {
"stage": 3,
"offload_optimizer": {
"device": "cpu",
"pin_memory": true
},
"offload_param": {
"device": "cpu",
"pin_memory": true
},
"overlap_comm": true,
"contiguous_gradients": true,
"sub_group_size": 1e9,
"reduce_bucket_size": 1e6,
"stage3_prefetch_bucket_size": 0.94e6,
"stage3_param_persistence_threshold": 1e4,
"stage3_max_live_parameters": 1e9,
"stage3_max_reuse_distance": 1e9,
"stage3_gather_16bit_weights_on_model_save": true
},
"steps_per_print": 2000,
"wall_clock_breakdown": false
}
```
#### How to Choose Which ZeRO Stage and Offloads To Use For Best Performance
これで、さまざまな段階があることがわかりました。どちらを使用するかをどのように決定すればよいでしょうか?このセクションでは、この質問に答えていきます。
一般に、次のことが当てはまります。
- 速度の点（左の方が右より速い）
ステージ 0 (DDP) > ステージ 1 > ステージ 2 > ステージ 2 + オフロード > ステージ 3 > ステージ 3 + オフロード
- GPU メモリの使用状況 (右は左よりも GPU メモリ効率が高い)
ステージ 0 (DDP) < ステージ 1 < ステージ 2 < ステージ 2 + オフロード < ステージ 3 < ステージ 3 + オフロード
したがって、最小限の数の GPU に収まりながら最速の実行を実現したい場合は、次のプロセスに従うことができます。最も速いアプローチから開始し、GPU OOM に陥った場合は、次に遅いアプローチに進みますが、これにより使用される GPU メモリが少なくなります。などなど。
まず、バッチ サイズを 1 に設定します (必要な有効バッチ サイズに対して、いつでも勾配累積を使用できます)。
1. `--gradient_checkpointing 1` (HF Trainer) または直接 `model.gradient_checkpointing_enable()` を有効にします - OOM の場合
2. 最初に ZeRO ステージ 2 を試してください。 OOMの場合
3. ZeRO ステージ 2 + `offload_optimizer` を試します - OOM の場合
4. ZeRO ステージ 3 に切り替える - OOM の場合
5. `cpu` に対して `offload_param` を有効にします - OOM の場合
6. OOM の場合は、`cpu`に対して`offload_optimizer`を有効にします。
7. それでもバッチ サイズ 1 に適合しない場合は、まずさまざまなデフォルト値を確認し、可能であれば値を下げます。たとえば、`generate`を使用し、広い検索ビームを使用しない場合は、大量のメモリを消費するため、検索ビームを狭くします。
8. fp32 では必ず混合半精度を使用します。つまり、Ampere 以上の GPU では bf16、古い GPU アーキテクチャでは fp16 を使用します。
9. それでも OOM を行う場合は、ハードウェアを追加するか、ZeRO-Infinity を有効にすることができます。つまり、オフロード `offload_param` と `offload_optimizer` を `nvme` に切り替えます。非常に高速な nvme であることを確認する必要があります。逸話として、ZeRO-Infinity を使用して小さな GPU で BLOOM-176B を推論することができましたが、非常に遅かったです。でも、うまくいきました！
もちろん、最も GPU メモリ効率の高い構成から始めて、後から逆に進むことで、これらの手順を逆に実行することもできます。あるいは二等分してみてください。
OOM を引き起こさないバッチ サイズ 1 を取得したら、実効スループットを測定します。
次に、バッチ サイズをできるだけ大きくしてみます。バッチ サイズが大きいほど、乗算する行列が巨大な場合に GPU のパフォーマンスが最高になるため、GPU の効率が向上します。
ここで、パフォーマンス最適化ゲームが始まります。一部のオフロード機能をオフにするか、ZeRO 段階でステップダウンしてバッチ サイズを増減して、実効スループットを再度測定することができます。満足するまで洗い流し、繰り返します。
永遠にこれに費やす必要はありませんが、3 か月のトレーニングを開始しようとしている場合は、スループットに関して最も効果的な設定を見つけるために数日かけてください。そのため、トレーニングのコストが最小限になり、トレーニングをより早く完了できます。現在の目まぐるしく変化する ML の世界では、何かをトレーニングするのにさらに 1 か月かかる場合、絶好の機会を逃す可能性があります。もちろん、これは私が意見を共有しているだけであり、決してあなたを急かそうとしているわけではありません。 BLOOM-176B のトレーニングを開始する前に、このプロセスに 2 日間費やし、スループットを 90 TFLOP から 150 TFLOP に向上させることができました。この取り組みにより、トレーニング時間を 1 か月以上節約できました。
これらのメモは主にトレーニング モード用に書かれたものですが、ほとんどの場合は推論にも適用されるはずです。たとえば、勾配チェックポイントはトレーニング中にのみ役立つため、推論中は何も行われません。さらに、マルチ GPU 推論を実行していて、[DeepSpeed-Inference](https://www.deepspeed.ai/tutorials/inference-tutorial/)、[Accelerate](https://ハグフェイス.co/blog/bloom-inference-pytorch-scripts) は優れたパフォーマンスを提供するはずです。
その他のパフォーマンス関連の簡単なメモ:
- 何かを最初からトレーニングしている場合は、常に 16 で割り切れる形状のテンソル (隠れたサイズなど) を使用するようにしてください。バッチ サイズについては、少なくとも 2 で割り切れるようにしてください。 GPU からさらに高いパフォーマンスを引き出したい場合は、ハードウェア固有の [波とタイルの量子化](https://developer.nvidia.com/blog/optimizing-gpu-performance-tensor-cores/) の可分性があります。
### Activation Checkpointing or Gradient Checkpointing
アクティベーション チェックポイントと勾配チェックポイントは、同じ方法論を指す 2 つの異なる用語です。とてもややこしいですが、こんな感じです。
勾配チェックポイントを使用すると、速度を GPU メモリと引き換えにできます。これにより、GPU OOM を克服したり、バッチ サイズを増やすことができ、多くの場合、パフォーマンスの向上につながります。
HF Transformers モデルは、DeepSpeed のアクティベーション チェックポイントについて何も知らないため、DeepSpeed 構成ファイルでその機能を有効にしようとしても、何も起こりません。
したがって、この非常に有益な機能を活用するには 2 つの方法があります。
1. HF Transformers モデルを使用したい場合は、`model.gradient_checkpointing_enable()` を実行するか、HF トレーナーで `--gradient_checkpointing` を使用します。これにより、これが自動的に有効になります。そこで使われるのが `torch.utils.checkpoint` です。
2. 独自のモデルを作成し、DeepSpeed のアクティベーション チェックポイントを使用したい場合は、[そこで規定されている API](https://deepspeed.readthedocs.io/en/latest/activation-checkpointing.html) を使用できます。 HF Transformers モデリング コードを使用して、`torch.utils.checkpoint` を DeepSpeed の API に置き換えることもできます。後者は、順方向アクティベーションを再計算する代わりに CPU メモリにオフロードできるため、より柔軟です。
### Optimizer and Scheduler
`offload_optimizer`を有効にしない限り、DeepSpeed スケジューラーと HuggingFace スケジューラーを組み合わせて使用できます。
オプティマイザー (HuggingFace スケジューラーと DeepSpeed オプティマイザーの組み合わせを除く):
| Combos | HF Scheduler | DS Scheduler |
|:-------------|:-------------|:-------------|
| HF Optimizer | Yes | Yes |
| DS Optimizer | No | Yes |
`offload_optimizer`が有効な場合、CPU と
GPU 実装 (LAMB を除く)。
<a id='deepspeed-optimizer'></a>
#### Optimizer
DeepSpeed の主なオプティマイザーは、Adam、AdamW、OneBitAdam、Lamb です。これらは ZeRO で徹底的にテストされており、
したがって、使用することをお勧めします。ただし、他のオプティマイザを「torch」からインポートすることはできます。完全なドキュメントは [こちら](https://www.deepspeed.ai/docs/config-json/#optimizer-parameters) にあります。
設定ファイルで `optimizer` エントリを設定しない場合、[`Trainer`] は
自動的に`AdamW`に設定され、指定された値または次のコマンドラインのデフォルトが使用されます。
引数: `--learning_rate`、`--adam_beta1`、`--adam_beta2`、`--adam_epsilon`、および `--weight_decay`。
以下は、`AdamW`の自動構成された`optimizer`エントリの例です。
```json
{
"optimizer": {
"type": "AdamW",
"params": {
"lr": "auto",
"betas": "auto",
"eps": "auto",
"weight_decay": "auto"
}
}
}
```
コマンドライン引数によって構成ファイル内の値が設定されることに注意してください。これは 1 つあるためです
値の決定的なソースを提供し、たとえば学習率が次のように設定されている場合に、見つけにくいエラーを回避します。
さまざまな場所でさまざまな価値観。コマンドラインのルール。オーバーライドされる値は次のとおりです。
- `lr` と `--learning_rate` の値
- `betas` と `--adam_beta1 --adam_beta2` の値
- `eps` と `--adam_epsilon` の値
- `weight_decay` と `--weight_decay` の値
したがって、コマンドラインで共有ハイパーパラメータを調整することを忘れないでください。
値を明示的に設定することもできます。
```json
{
"optimizer": {
"type": "AdamW",
"params": {
"lr": 0.001,
"betas": [0.8, 0.999],
"eps": 1e-8,
"weight_decay": 3e-7
}
}
}
```
ただし、[`Trainer`] コマンドライン引数と DeepSpeed を自分で同期することになります。
構成。
上記にリストされていない別のオプティマイザーを使用する場合は、トップレベルの構成に追加する必要があります。
```json
{
"zero_allow_untested_optimizer": true
}
```
`AdamW`と同様に、公式にサポートされている他のオプティマイザーを構成できます。これらは異なる設定値を持つ可能性があることに注意してください。例えばAdam の場合は、`weight_decay`を`0.01`付近にする必要があります。
さらに、オフロードは、Deepspeed の CPU Adam オプティマイザーと併用すると最も効果的に機能します。 `deepspeed==0.8.3` なので、オフロードで別のオプティマイザーを使用したい場合は、以下も追加する必要があります。
```json
{
"zero_force_ds_cpu_optimizer": false
}
```
最上位の構成に移行します。
<a id='deepspeed-scheduler'></a>
#### Scheduler
DeepSpeed は、`LRRangeTest`、`OneCycle`、`WarmupLR`、および`WarmupDecayLR`学習率スケジューラーをサポートしています。完全な
ドキュメントは[ここ](https://www.deepspeed.ai/docs/config-json/#scheduler-parameters)です。
ここでは、🤗 Transformers と DeepSpeed の間でスケジューラーが重複する場所を示します。
- `--lr_scheduler_type constant_with_warmup` 経由の `WarmupLR`
- `--lr_scheduler_type Linear` を介した `WarmupDecayLR`。これは `--lr_scheduler_type` のデフォルト値でもあります。
したがって、スケジューラを設定しない場合、これがデフォルトで設定されるスケジューラになります。
設定ファイルで `scheduler` エントリを設定しない場合、[`Trainer`] は
`--lr_scheduler_type`、`--learning_rate`、および `--warmup_steps` または `--warmup_ratio` の値を設定します。
🤗 それのトランスフォーマーバージョン。
以下は、`WarmupLR`の自動構成された`scheduler`エントリの例です。
```json
{
"scheduler": {
"type": "WarmupLR",
"params": {
"warmup_min_lr": "auto",
"warmup_max_lr": "auto",
"warmup_num_steps": "auto"
}
}
}
```
*"auto"* が使用されているため、[`Trainer`] 引数は設定に正しい値を設定します。
ファイル。これは、値の決定的なソースが 1 つあることと、たとえば次のような場合に見つけにくいエラーを避けるためです。
学習率は、場所ごとに異なる値に設定されます。コマンドラインのルール。設定される値は次のとおりです。
- `warmup_min_lr` の値は `0` です。
- `warmup_max_lr` と `--learning_rate` の値。
- `warmup_num_steps` と `--warmup_steps` の値 (指定されている場合)。それ以外の場合は `--warmup_ratio` を使用します
トレーニング ステップの数を乗算し、切り上げます。
- `total_num_steps` には `--max_steps` の値を指定するか、指定されていない場合は実行時に自動的に導出されます。
環境、データセットのサイズ、およびその他のコマンド ライン引数 (
`WarmupDecayLR`)。
もちろん、構成値の一部またはすべてを引き継いで、自分で設定することもできます。
```json
{
"scheduler": {
"type": "WarmupLR",
"params": {
"warmup_min_lr": 0,
"warmup_max_lr": 0.001,
"warmup_num_steps": 1000
}
}
}
```
ただし、[`Trainer`] コマンドライン引数と DeepSpeed を自分で同期することになります。
構成。
たとえば、`WarmupDecayLR`の場合は、次のエントリを使用できます。
```json
{
"scheduler": {
"type": "WarmupDecayLR",
"params": {
"last_batch_iteration": -1,
"total_num_steps": "auto",
"warmup_min_lr": "auto",
"warmup_max_lr": "auto",
"warmup_num_steps": "auto"
}
}
}
```
`total_num_steps`、`warmup_max_lr`、`warmup_num_steps`、および `total_num_steps` はロード時に設定されます。
<a id='deepspeed-fp32'></a>
### fp32 Precision
Deepspeed は、完全な fp32 と fp16 の混合精度をサポートします。
fp16 混合精度を使用すると、必要なメモリが大幅に削減され、速度が向上するため、
使用しているモデルがこのトレーニング モードで適切に動作しない場合は、使用しない方がよいでしょう。通常これ
モデルが fp16 混合精度で事前トレーニングされていない場合に発生します (たとえば、これは bf16 で事前トレーニングされた場合によく発生します)
モデル）。このようなモデルでは、オーバーフローまたはアンダーフローが発生し、`NaN`損失が発生する可能性があります。これがあなたの場合は、使用したいと思うでしょう
完全な fp32 モード。デフォルトの fp16 混合精度モードを次のように明示的に無効にします。
```json
{
"fp16": {
"enabled": false,
}
}
```
Ampere アーキテクチャ ベースの GPU を使用している場合、pytorch バージョン 1.7 以降は自動的に を使用するように切り替わります。
一部の操作でははるかに効率的な tf32 形式を使用しますが、結果は依然として fp32 になります。詳細と
ベンチマークについては、[Ampere デバイス上の TensorFloat-32(TF32)](https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices) を参照してください。文書には以下が含まれます
何らかの理由でこの自動変換を使用したくない場合は、この自動変換を無効にする方法について説明します。
🤗 トレーナーでは、`--tf32` を使用して有効にするか、`--tf32 0` または `--no_tf32` を使用して無効にすることができます。デフォルトでは、PyTorch のデフォルトが使用されます。
<a id='deepspeed-amp'></a>
### Automatic Mixed Precision
pytorch のような AMP の方法または apex のような方法で自動混合精度を使用できます。
### fp16
fp16 (float16) を設定して pytorch AMP のようなモードを設定するには:
```json
{
"fp16": {
"enabled": "auto",
"loss_scale": 0,
"loss_scale_window": 1000,
"initial_scale_power": 16,
"hysteresis": 2,
"min_loss_scale": 1
}
}
```
[`Trainer`] は、の値に基づいてそれを自動的に有効または無効にします。
`args.fp16_backend`。残りの設定値はあなた次第です。
このモードは、`--fp16 --fp16_backend amp`または`--fp16_full_eval`コマンドライン引数が渡されると有効になります。
このモードを明示的に有効/無効にすることもできます。
```json
{
"fp16": {
"enabled": true,
"loss_scale": 0,
"loss_scale_window": 1000,
"initial_scale_power": 16,
"hysteresis": 2,
"min_loss_scale": 1
}
}
```
ただし、[`Trainer`] コマンドライン引数と DeepSpeed を自分で同期することになります。
構成。
これが[ドキュメント](https://www.deepspeed.ai/docs/config-json/#fp16-training-options)です。
### BF16
fp16 の代わりに bf16 (bfloat16) が必要な場合は、次の構成セクションが使用されます。
```json
{
"bf16": {
"enabled": "auto"
}
}
```
bf16 は fp32 と同じダイナミック レンジを備えているため、損失スケーリングは必要ありません。
このモードは、`--bf16` または `--bf16_full_eval` コマンドライン引数が渡されると有効になります。
このモードを明示的に有効/無効にすることもできます。
```json
{
"bf16": {
"enabled": true
}
}
```
<Tip>
`deepspeed==0.6.0`の時点では、bf16 サポートは新しく実験的なものです。
bf16 が有効な状態で [勾配累積](#gradient-accumulation) を使用する場合は、bf16 で勾配が累積されることに注意する必要があります。この形式の精度が低いため、これは希望どおりではない可能性があります。損失のある蓄積につながります。
この問題を修正し、より高精度の `dtype` (fp16 または fp32) を使用するオプションを提供するための作業が行われています。
</Tip>
### NCCL Collectives
訓練体制の`dtype`があり、さまざまな削減や収集/分散操作などのコミュニケーション集合体に使用される別の`dtype`があります。
すべての収集/分散操作は、データが含まれているのと同じ `dtype` で実行されるため、bf16 トレーニング体制を使用している場合、データは bf16 で収集されます。収集は損失のない操作です。
さまざまなリデュース操作は非常に損失が大きい可能性があります。たとえば、複数の GPU 間で勾配が平均化される場合、通信が fp16 または bf16 で行われる場合、結果は損失が多くなる可能性があります。複数の数値を低精度でアドバタイズすると結果は正確ではないためです。 。 bf16 では fp16 よりも精度が低いため、さらにそうです。通常は非常に小さい grad を平均する際の損失が最小限に抑えられるため、fp16 で十分であることがよくあります。したがって、デフォルトでは、半精度トレーニングでは fp16 がリダクション演算のデフォルトとして使用されます。ただし、この機能を完全に制御でき、必要に応じて小さなオーバーヘッドを追加して、リダクションが累積 dtype として fp32 を使用し、結果の準備ができた場合にのみ半精度 `dtype` にダウンキャストするようにすることもできます。でトレーニング中です。
デフォルトをオーバーライドするには、新しい構成エントリを追加するだけです。
```json
{
"communication_data_type": "fp32"
}
```
この記事の執筆時点での有効な値は、"fp16"、"bfp16"、"fp32"です。
注: ステージ ゼロ 3 には、bf16 通信タイプに関するバグがあり、`deepspeed==0.8.1`で修正されました。
### apex
apex AMP のようなモード セットを設定するには:
```json
"amp": {
"enabled": "auto",
"opt_level": "auto"
}
```
[`Trainer`] は `args.fp16_backend` の値に基づいて自動的に設定します。
`args.fp16_opt_level`。
このモードは、`--fp16 --fp16_backend apex --fp16_opt_level 01`コマンド ライン引数が渡されると有効になります。
このモードを明示的に構成することもできます。
```json
{
"amp": {
"enabled": true,
"opt_level": "O1"
}
}
```
ただし、[`Trainer`] コマンドライン引数と DeepSpeed を自分で同期することになります。
構成。
これは[ドキュメント](https://www.deepspeed.ai/docs/config-json/#automatic-mixed-precision-amp-training-options)です。
<a id='deepspeed-bs'></a>
### Batch Size
バッチサイズを設定するには、次を使用します。
```json
{
"train_batch_size": "auto",
"train_micro_batch_size_per_gpu": "auto"
}
```
[`Trainer`] は自動的に `train_micro_batch_size_per_gpu` を次の値に設定します。
`args.per_device_train_batch_size`と`train_batch_size`を`args.world_size * args.per_device_train_batch_size * args.gradient_accumulation_steps`に変更します。
値を明示的に設定することもできます。
```json
{
"train_batch_size": 12,
"train_micro_batch_size_per_gpu": 4
}
```
ただし、[`Trainer`] コマンドライン引数と DeepSpeed を自分で同期することになります。
構成。
<a id='deepspeed-grad-acc'></a>
### Gradient Accumulation
勾配累積セットを構成するには:
```json
{
"gradient_accumulation_steps": "auto"
}
```
[`Trainer`] は自動的にそれを `args.gradient_accumulation_steps` の値に設定します。
値を明示的に設定することもできます。
```json
{
"gradient_accumulation_steps": 3
}
```
ただし、[`Trainer`] コマンドライン引数と DeepSpeed を自分で同期することになります。
構成。
<a id='deepspeed-grad-clip'></a>
### Gradient Clipping
グラデーション グラデーション クリッピング セットを構成するには:
```json
{
"gradient_clipping": "auto"
}
```
[`Trainer`] は自動的にそれを `args.max_grad_norm` の値に設定します。
値を明示的に設定することもできます。
```json
{
"gradient_clipping": 1.0
}
```
ただし、[`Trainer`] コマンドライン引数と DeepSpeed を自分で同期することになります。
構成。
<a id='deepspeed-weight-extraction'></a>
### Getting The Model Weights Out
トレーニングを継続し、DeepSpeed の使用を再開する限り、何も心配する必要はありません。 DeepSpeed ストア
fp32 のカスタム チェックポイント オプティマイザー ファイル内のマスターの重み。これは `global_step*/*optim_states.pt` (これは glob
パターン)、通常のチェックポイントの下に保存されます。
**FP16 ウェイト:**
モデルを ZeRO-2 で保存すると、モデルの重みを含む通常の `pytorch_model.bin` ファイルが作成されますが、
これらは重みの fp16 バージョンにすぎません。
ZeRO-3 では、モデルの重みが複数の GPU に分割されるため、状況はさらに複雑になります。
したがって、fp16 を保存するための `Trainer` を取得するには、`"stage3_gather_16bit_weights_on_model_save": true` が必要です。
重みのバージョン。この設定が`False`の場合、`pytorch_model.bin`は作成されません。これは、デフォルトで DeepSpeed の `state_dict` に実際の重みではなくプレースホルダーが含まれるためです。この `state_dict` を保存した場合、ロードし直すことはできません。
```json
{
"zero_optimization": {
"stage3_gather_16bit_weights_on_model_save": true
}
}
```
**FP32 重量:**
fp16 ウェイトはトレーニングを再開するのに適していますが、モデルの微調整が完了し、それを
[モデル ハブ](https://huggingface.co/models) にアクセスするか、fp32 を入手したいと思われる他の人に渡します。
重み。これは大量のメモリを必要とするプロセスであるため、トレーニング中に行うべきではないのが理想的です。
したがって、トレーニングの完了後にオフラインで実行するのが最適です。ただし、必要に応じて、空き CPU が十分にある場合は、
同じトレーニング スクリプトで実行できることを思い出してください。次のセクションでは、両方のアプローチについて説明します。
**ライブ FP32 ウェイト リカバリ:**
モデルが大きく、トレーニングの終了時に空き CPU メモリがほとんど残っていない場合、このアプローチは機能しない可能性があります。
少なくとも 1 つのチェックポイントを保存していて、最新のチェックポイントを使用したい場合は、次の手順を実行できます。
```python
from transformers.trainer_utils import get_last_checkpoint
from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint
checkpoint_dir = get_last_checkpoint(trainer.args.output_dir)
fp32_model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir)
```
`--load_best_model_at_end` class:*~transformers.TrainingArguments* 引数を使用している場合 (最適なモデルを追跡するため)
チェックポイント)、最初に最終モデルを明示的に保存してから、上記と同じことを行うことでトレーニングを終了できます。
```python
from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint
checkpoint_dir = os.path.join(trainer.args.output_dir, "checkpoint-final")
trainer.deepspeed.save_checkpoint(checkpoint_dir)
fp32_model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir)
```
<Tip>
`load_state_dict_from_zero_checkpoint` が実行されると、`model` はもはや使用できなくなることに注意してください。
同じアプリケーションの DeepSpeed コンテキスト。つまり、deepspeed エンジンを再初期化する必要があります。
`model.load_state_dict(state_dict)` はそこからすべての DeepSpeed マジックを削除します。したがって、これは最後にのみ実行してください
トレーニングの様子。
</Tip>
もちろん、class:*~transformers.Trainer* を使用する必要はなく、上記の例を独自のものに調整することができます。
トレーナー。
何らかの理由でさらに改良したい場合は、重みの fp32 `state_dict` を抽出して適用することもできます。
次の例に示すように、これらは自分で作成します。
```python
from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) # already on cpu
model = model.cpu()
model.load_state_dict(state_dict)
```
**オフライン FP32 ウェイト リカバリ:**
DeepSpeed は特別な変換スクリプト`zero_to_fp32.py`を作成し、チェックポイントの最上位に配置します。
フォルダ。このスクリプトを使用すると、いつでも重みを抽出できます。スクリプトはスタンドアロンなので、もう必要ありません。
抽出を行うための設定ファイルまたは `Trainer` が必要です。
チェックポイント フォルダーが次のようになっているとします。
```bash
$ ls -l output_dir/checkpoint-1/
-rw-rw-r-- 1 stas stas 1.4K Mar 27 20:42 config.json
drwxrwxr-x 2 stas stas 4.0K Mar 25 19:52 global_step1/
-rw-rw-r-- 1 stas stas 12 Mar 27 13:16 latest
-rw-rw-r-- 1 stas stas 827K Mar 27 20:42 optimizer.pt
-rw-rw-r-- 1 stas stas 231M Mar 27 20:42 pytorch_model.bin
-rw-rw-r-- 1 stas stas 623 Mar 27 20:42 scheduler.pt
-rw-rw-r-- 1 stas stas 1.8K Mar 27 20:42 special_tokens_map.json
-rw-rw-r-- 1 stas stas 774K Mar 27 20:42 spiece.model
-rw-rw-r-- 1 stas stas 1.9K Mar 27 20:42 tokenizer_config.json
-rw-rw-r-- 1 stas stas 339 Mar 27 20:42 trainer_state.json
-rw-rw-r-- 1 stas stas 2.3K Mar 27 20:42 training_args.bin
-rwxrw-r-- 1 stas stas 5.5K Mar 27 13:16 zero_to_fp32.py*
```
この例では、DeepSpeed チェックポイント サブフォルダー *global_step1* が 1 つだけあります。したがって、FP32を再構築するには
重みを実行するだけです:
```bash
python zero_to_fp32.py . pytorch_model.bin
```
これだよ。 `pytorch_model.bin`には、複数の GPU から統合された完全な fp32 モデルの重みが含まれるようになります。
スクリプトは、ZeRO-2 または ZeRO-3 チェックポイントを自動的に処理できるようになります。
`python zero_to_fp32.py -h` を実行すると、使用方法の詳細が表示されます。
スクリプトは、ファイル`latest`の内容を使用して deepspeed サブフォルダーを自動検出します。
例には`global_step1`が含まれます。
注: 現在、スクリプトには最終的な fp32 モデルの重みの 2 倍の一般 RAM が必要です。
### ZeRO-3 と Infinity Nuances
ZeRO-3 は、パラメータ シャーディング機能の点で ZeRO-2 とは大きく異なります。
ZeRO-Infinity は ZeRO-3 をさらに拡張し、NVMe メモリやその他の複数の速度とスケーラビリティの向上をサポートします。
モデルに特別な変更を加える必要がなくても正常に動作するようにあらゆる努力が払われてきましたが、特定の点では
状況によっては、次の情報が必要になる場合があります。
#### Constructing Massive Models
DeepSpeed/ZeRO-3 は、既存の RAM に収まらない可能性のある数兆のパラメータを持つモデルを処理できます。そのような場合、
また、初期化をより高速に実行したい場合は、*deepspeed.zero.Init()* を使用してモデルを初期化します。
コンテキスト マネージャー (関数デコレーターでもあります)。次のようになります。
```python
from transformers import T5ForConditionalGeneration, T5Config
import deepspeed
with deepspeed.zero.Init():
config = T5Config.from_pretrained("google-t5/t5-small")
model = T5ForConditionalGeneration(config)
```
ご覧のとおり、これによりランダムに初期化されたモデルが得られます。
事前トレーニングされたモデルを使用したい場合、`model_class.from_pretrained` は次の条件を満たす限りこの機能を有効にします。
`is_deepspeed_zero3_enabled()` は `True` を返します。これは現在、
[`TrainingArguments`] オブジェクト (渡された DeepSpeed 構成ファイルに ZeRO-3 構成が含まれている場合)
セクション。したがって、呼び出しの前に** [`TrainingArguments`] オブジェクトを作成する必要があります。
`from_pretrained`。考えられるシーケンスの例を次に示します。
```python
from transformers import AutoModel, Trainer, TrainingArguments
training_args = TrainingArguments(..., deepspeed=ds_config)
model = AutoModel.from_pretrained("google-t5/t5-small")
trainer = Trainer(model=model, args=training_args, ...)
```
公式のサンプル スクリプトを使用していて、コマンド ライン引数に `--deepspeed ds_config.json` が含まれている場合
ZeRO-3 設定を有効にすると、これがサンプル スクリプトの記述方法であるため、すべてがすでに完了しています。
注: モデルの fp16 重みが単一の GPU のメモリに収まらない場合は、この機能を使用する必要があります。
この方法とその他の関連機能の詳細については、[大規模モデルの構築](https://deepspeed.readthedocs.io/en/latest/zero3.html#constructing-massive-models) を参照してください。
また、fp16 で事前訓練されたモデルをロードするときは、`from_pretrained` に使用するように指示する必要があります。
`torch_dtype=torch.float16`。詳細については、[from_pretrained-torch-dtype](#from_pretrained-torch-dtype) を参照してください。
#### Gathering Parameters
複数の GPU 上の ZeRO-3 では、現在の GPU のパラメータでない限り、単一の GPU がすべてのパラメータを持つことはありません。
実行層。したがって、すべてのレイヤーのすべてのパラメーターに一度にアクセスする必要がある場合は、それを行うための特定の方法があります。
ほとんどの場合は必要ありませんが、必要な場合は、[パラメータの収集](https://deepspeed.readthedocs.io/en/latest/zero3.html#manual-parameter-coordination) を参照してください。
ただし、いくつかの場所で内部的に使用しています。その例の 1 つは、事前トレーニングされたモデルの重みをロードするときです。
`from_pretrained`。一度に 1 つのレイヤーをロードし、参加しているすべての GPU に即座に分割します。
大規模なモデルでは、メモリの関係で、1 つの GPU にロードしてから複数の GPU に分散することはできません。
制限。
また、ZeRO-3 では、独自のコードを作成し、次のようなモデル パラメーターの重みが発生するとします。
```python
tensor([1.0], device="cuda:0", dtype=torch.float16, requires_grad=True)
```
`tensor([1.])` にストレスを感じた場合、またはパラメータのサイズが `1` であるというエラーが発生した場合
より大きな多次元形状。これは、パラメーターが分割されており、表示されるのは ZeRO-3 プレースホルダーであることを意味します。
<a id='deepspeed-zero-inference'></a>
### ZeRO Inference
ZeRO Inference は、ZeRO-3 Training と同じ構成を使用します。オプティマイザーとスケジューラーのセクションは必要ありません。で
実際、同じものをトレーニングと共有したい場合は、これらを設定ファイルに残すことができます。彼らはただそうなるだろう
無視されました。
それ以外の場合は、通常の [`TrainingArguments`] 引数を渡すだけです。例えば：
```bash
deepspeed --num_gpus=2 your_program.py <normal cl args> --do_eval --deepspeed ds_config.json
```
唯一重要なことは、ZeRO-2 には何の利点もないため、ZeRO-3 構成を使用する必要があるということです。
ZeRO-3 のみがパラメーターのシャーディングを実行するのに対し、ZeRO-1 は勾配とオプティマイザーの状態をシャーディングするため、推論に役立ちます。
以下は、利用可能なすべての GPU をデプロイする DeepSpeed で`run_translation.py`を実行する例です。
```bash
deepspeed examples/pytorch/translation/run_translation.py \
--deepspeed tests/deepspeed/ds_config_zero3.json \
--model_name_or_path google-t5/t5-small --output_dir output_dir \
--do_eval --max_eval_samples 50 --warmup_steps 50 \
--max_source_length 128 --val_max_target_length 128 \
--overwrite_output_dir --per_device_eval_batch_size 4 \
--predict_with_generate --dataset_config "ro-en" --fp16 \
--source_lang en --target_lang ro --dataset_name wmt16 \
--source_prefix "translate English to Romanian: "
```
推論のために、オプティマイザーの状態と勾配によって使用される追加の大きなメモリは必要ないため、
はるかに大きなバッチやシーケンス長を同じハードウェアに適合できる必要があります。
さらに、DeepSpeed は現在、Deepspeed-Inference と呼ばれる関連製品を開発していますが、これとは何の関係もありません。
ZeRO テクノロジーに準拠していますが、代わりにテンソル並列処理を使用して、単一の GPU に収まらないモデルをスケーリングします。これは
現在開発中です。製品が完成したら統合を提供する予定です。
### Memory Requirements
Deepspeed ZeRO はメモリを CPU (および NVMe) にオフロードできるため、フレームワークは、使用されている GPU の数に応じて必要な CPU および GPU メモリの量を知ることができるユーティリティを提供します。
単一の GPU で `bigscience/T0_3B`を微調整するために必要なメモリの量を見積もってみましょう。
```bash
$ python -c 'from transformers import AutoModel; \
from deepspeed.runtime.zero.stage3 import estimate_zero3_model_states_mem_needs_all_live; \
model = AutoModel.from_pretrained("bigscience/T0_3B"); \
estimate_zero3_model_states_mem_needs_all_live(model, num_gpus_per_node=1, num_nodes=1)'
[...]
Estimated memory needed for params, optim states and gradients for a:
HW: Setup with 1 node, 1 GPU per node.
SW: Model with 2783M total params, 65M largest layer params.
per CPU | per GPU | Options
70.00GB | 0.25GB | offload_param=cpu , offload_optimizer=cpu , zero_init=1
70.00GB | 0.25GB | offload_param=cpu , offload_optimizer=cpu , zero_init=0
62.23GB | 5.43GB | offload_param=none, offload_optimizer=cpu , zero_init=1
62.23GB | 5.43GB | offload_param=none, offload_optimizer=cpu , zero_init=0
0.37GB | 46.91GB | offload_param=none, offload_optimizer=none, zero_init=1
15.56GB | 46.91GB | offload_param=none, offload_optimizer=none, zero_init=0
```
したがって、単一の 80 GB GPU で CPU オフロードなしで搭載することも、小さな 8 GB GPU でも最大 60 GB の CPU メモリが必要になることも可能です。 (これはパラメータ、オプティマイザの状態、および勾配のためのメモリであることに注意してください。cuda カーネル、アクティベーション、および一時メモリにはもう少し多くのメモリが必要です。)
次に、コストと速度のトレードオフになります。より小さい GPU を購入またはレンタルした方が安くなります (Deepspeed ZeRO では複数の GPU を使用できるため、GPU の数を減らすこともできます)。しかし、その場合は遅くなります。そのため、何かを実行する速度を気にしなくても、速度の低下は GPU の使用時間に直接影響し、コストが増大するため、どれが最も効果的かを実験して比較してください。
十分な GPU メモリがある場合は、すべてが高速になるため、CPU/NVMe オフロードを必ず無効にしてください。
たとえば、2 つの GPU に対して同じことを繰り返してみましょう。
```bash
$ python -c 'from transformers import AutoModel; \
from deepspeed.runtime.zero.stage3 import estimate_zero3_model_states_mem_needs_all_live; \
model = AutoModel.from_pretrained("bigscience/T0_3B"); \
estimate_zero3_model_states_mem_needs_all_live(model, num_gpus_per_node=2, num_nodes=1)'
[...]
Estimated memory needed for params, optim states and gradients for a:
HW: Setup with 1 node, 2 GPUs per node.
SW: Model with 2783M total params, 65M largest layer params.
per CPU | per GPU | Options
70.00GB | 0.25GB | offload_param=cpu , offload_optimizer=cpu , zero_init=1
70.00GB | 0.25GB | offload_param=cpu , offload_optimizer=cpu , zero_init=0
62.23GB | 2.84GB | offload_param=none, offload_optimizer=cpu , zero_init=1
62.23GB | 2.84GB | offload_param=none, offload_optimizer=cpu , zero_init=0
0.74GB | 23.58GB | offload_param=none, offload_optimizer=none, zero_init=1
31.11GB | 23.58GB | offload_param=none, offload_optimizer=none, zero_init=0
```
したがって、ここでは、CPU にオフロードせずに 2x 32GB 以上の GPU が必要になります。
詳細については、[メモリ推定ツール](https://deepspeed.readthedocs.io/en/latest/memory.html) を参照してください。
### Filing Issues
ここでは、問題の真相をすぐに解明し、作業のブロックを解除できるよう、問題を報告する方法を説明します。
レポートには必ず次の内容を含めてください。
1. レポート内の完全な Deepspeed 構成ファイル
2. [`Trainer`] を使用している場合はコマンドライン引数、または
トレーナーのセットアップを自分でスクリプト作成している場合は、[`TrainingArguments`] 引数。しないでください
[`TrainingArguments`] には無関係なエントリが多数含まれているため、ダンプします。
3. 次の出力:
```bash
python -c 'import torch; print(f"torch: {torch.__version__}")'
python -c 'import transformers; print(f"transformers: {transformers.__version__}")'
python -c 'import deepspeed; print(f"deepspeed: {deepspeed.__version__}")'
```
4. 可能であれば、問題を再現できる Google Colab ノートブックへのリンクを含めてください。これを使えます
[ノートブック](https://github.com/stas00/porting/blob/master/transformers/deepspeed/DeepSpeed_on_colab_CLI.ipynb) として
出発点。
5. 不可能でない限り、カスタムデータセットではなく、常に使用できる標準データセットを使用してください。
6. 可能であれば、既存の [サンプル](https://github.com/huggingface/transformers/tree/main/examples/pytorch) のいずれかを使用して問題を再現してみてください。
- Deepspeed が問題の原因ではないことがよくあります。
提出された問題の一部は、Deepspeed とは無関係であることが判明しました。それは、Deepspeed がセットアップから削除された後です。
問題はまだ残っていた。
したがって、完全に明白でない場合は、DeepSpeed 関連の問題です。
例外が発生し、DeepSpeed モジュールが関係していることがわかります。まず、DeepSpeed を含まないセットアップを再テストしてください。
問題が解決しない場合にのみ、Deepspeed について言及し、必要な詳細をすべて提供してください。
- 問題が統合部分ではなく DeepSpeed コアにあることが明らかな場合は、問題を提出してください。
[Deepspeed](https://github.com/microsoft/DeepSpeed/) を直接使用します。よくわからない場合でも、ご安心ください。
どちらの問題トラッカーでも問題ありません。投稿されたらそれを判断し、次の場合は別の問題トラッカーにリダイレクトします。
そうである必要がある。
### Troubleshooting
#### the `deepspeed` process gets killed at startup without a traceback
`deepspeed`プロセスが起動時にトレースバックなしで強制終了された場合、それは通常、プログラムが試行したことを意味します。
システムが持っているよりも多くの CPU メモリを割り当てるか、プロセスが割り当てを許可されているため、OS カーネルがそれを強制終了します。
プロセス。これは、設定ファイルに `offload_optimizer` または `offload_param` が含まれている可能性が高いためです。
どちらも`cpu`にオフロードするように設定されています。 NVMe を使用している場合は、次の環境で実行している場合は NVMe へのオフロードを試してください。
ゼロ-3。 [特定のモデルに必要なメモリ量を見積もる]方法は次のとおりです(https://deepspeed.readthedocs.io/en/latest/memory.html)。
#### training and/or eval/predict loss is `NaN`
これは、bf16 混合精度モードで事前トレーニングされたモデルを取得し、それを fp16 (混合精度の有無にかかわらず) で使用しようとした場合によく発生します。 TPU でトレーニングされたほとんどのモデル、および多くの場合、Google によってリリースされたモデルは、このカテゴリに分類されます (たとえば、ほぼすべての t5 ベースのモデル)。ここでの解決策は、ハードウェアがサポートしている場合 (TPU、Ampere GPU 以降)、fp32 または bf16 を使用することです。
```json
{
"fp16": {
"enabled": "auto",
"loss_scale": 0,
"loss_scale_window": 1000,
"initial_scale_power": 16,
"hysteresis": 2,
"min_loss_scale": 1
}
}
```
ログには、Deepspeed が次のように`OVERFLOW!`を報告していることがわかります。
```
0%| | 0/189 [00:00<?, ?it/s]
[deepscale] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 262144, reducing to 262144
1%|▌ | 1/189 [00:00<01:26, 2.17it/s]
[deepscale] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 262144, reducing to 131072.0
1%|█▏
[...]
[deepscale] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 1, reducing to 1
14%|████████████████▌ | 27/189 [00:14<01:13, 2.21it/s]
[deepscale] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 1, reducing to 1
15%|█████████████████▏ | 28/189 [00:14<01:13, 2.18it/s]
[deepscale] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 1, reducing to 1
15%|█████████████████▊ | 29/189 [00:15<01:13, 2.18it/s]
[deepscale] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 1, reducing to 1
[...]
```
これは、Deepspeed 損失スケーラーが損失オーバーフローを克服するスケーリング係数を見つけられないことを意味します。
(ログはここで読みやすくするためにマッサージされています。)
この場合、通常は `initial_scale_power` の値を上げる必要があります。通常、`initial_scale_power: 32` に設定すると問題が解決します。
### Notes
- DeepSpeed には pip でインストール可能な PyPI パッケージがありますが、ハードウェアに最も適合するように、また有効にする必要がある場合は、[ソース](https://github.com/microsoft/deepspeed#installation) からインストールすることを強くお勧めします。
1 ビット Adam などの特定の機能は、pypi ディストリビューションでは利用できません。
- 🤗 Transformers で DeepSpeed を使用するために [`Trainer`] を使用する必要はありません - 任意のモデルを使用できます
後者は [DeepSpeed 統合手順](https://www.deepspeed.ai/getting-started/#writing-deepspeed-models) に従って調整する必要があります。
## Non-Trainer Deepspeed Integration
[`~integrations.HfDeepSpeedConfig`] は、Deepspeed を 🤗 Transformers コアに統合するために使用されます
[`Trainer`] を使用しない場合の機能。実行する唯一のことは、Deepspeed ZeRO-3 パラメータ収集を処理し、`from_pretrained`呼び出し中にモデルを複数の GPU に自動的に分割することです。それ以外はすべて自分で行う必要があります。
[`Trainer`] を使用すると、すべてが自動的に処理されます。
[`Trainer`] を使用しない場合、DeepSpeed ZeRO-3 を効率的に導入するには、
モデルをインスタンス化する前に [`~integrations.HfDeepSpeedConfig`] オブジェクトを削除し、そのオブジェクトを生きたままにします。
Deepspeed ZeRO-1 または ZeRO-2 を使用している場合は、`HfDeepSpeedConfig`を使用する必要はまったくありません。
たとえば、事前トレーニングされたモデルの場合は次のようになります。
```python
from transformers.integrations import HfDeepSpeedConfig
from transformers import AutoModel
import deepspeed
ds_config = {...} # deepspeed config object or path to the file
# must run before instantiating the model to detect zero 3
dschf = HfDeepSpeedConfig(ds_config) # keep this object alive
model = AutoModel.from_pretrained("openai-community/gpt2")
engine = deepspeed.initialize(model=model, config_params=ds_config, ...)
```
または、事前トレーニングされていないモデルの場合:
```python
from transformers.integrations import HfDeepSpeedConfig
from transformers import AutoModel, AutoConfig
import deepspeed
ds_config = {...} # deepspeed config object or path to the file
# must run before instantiating the model to detect zero 3
dschf = HfDeepSpeedConfig(ds_config) # keep this object alive
config = AutoConfig.from_pretrained("openai-community/gpt2")
model = AutoModel.from_config(config)
engine = deepspeed.initialize(model=model, config_params=ds_config, ...)
```
[`Trainer`] 統合を使用していない場合は、完全に独力で行うことになることに注意してください。基本的には、[Deepspeed](https://www.deepspeed.ai/) Web サイトのドキュメントに従ってください。また、設定ファイルを明示的に設定する必要があります。`"auto"`値は使用できず、代わりに実際の値を入力する必要があります。
## HfDeepSpeedConfig
[[autodoc]] integrations.HfDeepSpeedConfig
- all
### Custom DeepSpeed ZeRO Inference
以下は、単一の GPU にモデルを適合できない場合に、[`Trainer`] を使用せずに DeepSpeed ZeRO 推論を実行する方法の例です。解決策には、追加の GPU の使用、または GPU メモリを CPU メモリにオフロードすることが含まれます。
ここで理解すべき重要なニュアンスは、ZeRO の設計方法により、異なる GPU で異なる入力を並行して処理できるということです。
この例には大量のメモがあり、自己文書化されています。
必ず次のことを行ってください。
1. 十分な GPU メモリがある場合は、CPU オフロードを無効にします (速度が低下するため)。
2. Ampere または新しい GPU を所有している場合は、処理を高速化するために bf16 を有効にします。そのハードウェアがない場合は、bf16 混合精度で事前トレーニングされたモデル (ほとんどの t5 モデルなど) を使用しない限り、fp16 を有効にすることができます。これらは通常、fp16 でオーバーフローし、出力としてガベージが表示されます。
```python
#!/usr/bin/env python
# This script demonstrates how to use Deepspeed ZeRO in an inference mode when one can't fit a model
# into a single GPU
#
# 1. Use 1 GPU with CPU offload
# 2. Or use multiple GPUs instead
#
# First you need to install deepspeed: pip install deepspeed
#
# Here we use a 3B "bigscience/T0_3B" model which needs about 15GB GPU RAM - so 1 largish or 2
# small GPUs can handle it. or 1 small GPU and a lot of CPU memory.
#
# To use a larger model like "bigscience/T0" which needs about 50GB, unless you have an 80GB GPU -
# you will need 2-4 gpus. And then you can adapt the script to handle more gpus if you want to
# process multiple inputs at once.
#
# The provided deepspeed config also activates CPU memory offloading, so chances are that if you
# have a lot of available CPU memory and you don't mind a slowdown you should be able to load a
# model that doesn't normally fit into a single GPU. If you have enough GPU memory the program will
# run faster if you don't want offload to CPU - so disable that section then.
#
# To deploy on 1 gpu:
#
# deepspeed --num_gpus 1 t0.py
# or:
# python -m torch.distributed.run --nproc_per_node=1 t0.py
#
# To deploy on 2 gpus:
#
# deepspeed --num_gpus 2 t0.py
# or:
# python -m torch.distributed.run --nproc_per_node=2 t0.py
from transformers import AutoTokenizer, AutoConfig, AutoModelForSeq2SeqLM
from transformers.integrations import HfDeepSpeedConfig
import deepspeed
import os
import torch
os.environ["TOKENIZERS_PARALLELISM"] = "false" # To avoid warnings about parallelism in tokenizers
# distributed setup
local_rank = int(os.getenv("LOCAL_RANK", "0"))
world_size = int(os.getenv("WORLD_SIZE", "1"))
torch.cuda.set_device(local_rank)
deepspeed.init_distributed()
model_name = "bigscience/T0_3B"
config = AutoConfig.from_pretrained(model_name)
model_hidden_size = config.d_model
# batch size has to be divisible by world_size, but can be bigger than world_size
train_batch_size = 1 * world_size
# ds_config notes
#
# - enable bf16 if you use Ampere or higher GPU - this will run in mixed precision and will be
# faster.
#
# - for older GPUs you can enable fp16, but it'll only work for non-bf16 pretrained models - e.g.
# all official t5 models are bf16-pretrained
#
# - set offload_param.device to "none" or completely remove the `offload_param` section if you don't
# - want CPU offload
#
# - if using `offload_param` you can manually finetune stage3_param_persistence_threshold to control
# - which params should remain on gpus - the larger the value the smaller the offload size
#
# For in-depth info on Deepspeed config see
# https://huggingface.co/docs/transformers/main/main_classes/deepspeed
# keeping the same format as json for consistency, except it uses lower case for true/false
# fmt: off
ds_config = {
"fp16": {
"enabled": False
},
"bf16": {
"enabled": False
},
"zero_optimization": {
"stage": 3,
"offload_param": {
"device": "cpu",
"pin_memory": True
},
"overlap_comm": True,
"contiguous_gradients": True,
"reduce_bucket_size": model_hidden_size * model_hidden_size,
"stage3_prefetch_bucket_size": 0.9 * model_hidden_size * model_hidden_size,
"stage3_param_persistence_threshold": 10 * model_hidden_size
},
"steps_per_print": 2000,
"train_batch_size": train_batch_size,
"train_micro_batch_size_per_gpu": 1,
"wall_clock_breakdown": False
}
# fmt: on
# next line instructs transformers to partition the model directly over multiple gpus using
# deepspeed.zero.Init when model's `from_pretrained` method is called.
#
# **it has to be run before loading the model AutoModelForSeq2SeqLM.from_pretrained(model_name)**
#
# otherwise the model will first be loaded normally and only partitioned at forward time which is
# less efficient and when there is little CPU RAM may fail
dschf = HfDeepSpeedConfig(ds_config) # keep this object alive
# now a model can be loaded.
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
# initialise Deepspeed ZeRO and store only the engine object
ds_engine = deepspeed.initialize(model=model, config_params=ds_config)[0]
ds_engine.module.eval() # inference
# Deepspeed ZeRO can process unrelated inputs on each GPU. So for 2 gpus you process 2 inputs at once.
# If you use more GPUs adjust for more.
# And of course if you have just one input to process you then need to pass the same string to both gpus
# If you use only one GPU, then you will have only rank 0.
rank = torch.distributed.get_rank()
if rank == 0:
text_in = "Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy"
elif rank == 1:
text_in = "Is this review positive or negative? Review: this is the worst restaurant ever"
tokenizer = AutoTokenizer.from_pretrained(model_name)
inputs = tokenizer.encode(text_in, return_tensors="pt").to(device=local_rank)
with torch.no_grad():
outputs = ds_engine.module.generate(inputs, synced_gpus=True)
text_out = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(f"rank{rank}:\n in={text_in}\n out={text_out}")
```
それを`t0.py`として保存して実行しましょう。
```bash
$ deepspeed --num_gpus 2 t0.py
rank0:
in=Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy
out=Positive
rank1:
in=Is this review positive or negative? Review: this is the worst restaurant ever
out=negative
```
これは非常に基本的な例であり、ニーズに合わせて調整してください。
### `generate` nuances
ZeRO Stage-3 で複数の GPU を使用する場合、`generate(..., synced_gpus=True)`を呼び出して GPU を同期する必要があります。これを行わないと、1 つの GPU が他の GPU より先に生成を終了した場合、残りの GPU が生成を停止した GPU からウェイトのシャードを受信できなくなるため、システム全体がハングします。
`transformers>=4.28` 以降、`synced_gpus` が明示的に指定されていない場合、これらの条件が検出されると自動的に `True` に設定されます。ただし、必要に応じて `synced_gpus` の値をオーバーライドすることもできます。
## Deepspeed 統合のテスト
DeepSpeed 統合を含む PR を送信する場合は、CircleCI PR CI セットアップには GPU がないことに注意してください。そのため、GPU を必要とするテストは別の CI で毎晩のみ実行されます。したがって、PR で緑色の CI レポートが表示されても、DeepSpeed テストが合格したことを意味するわけではありません。
DeepSpeed テストを実行するには、少なくとも以下を実行してください。
```bash
RUN_SLOW=1 pytest tests/deepspeed/test_deepspeed.py
```
モデリングまたは pytorch サンプル コードのいずれかを変更した場合は、Model Zoo テストも実行します。以下はすべての DeepSpeed テストを実行します。
```bash
RUN_SLOW=1 pytest tests/deepspeed
```
## Main DeepSpeed Resources
- [プロジェクトの github](https://github.com/microsoft/deepspeed)
- [使用方法ドキュメント](https://www.deepspeed.ai/getting-started/)
- [API ドキュメント](https://deepspeed.readthedocs.io/en/latest/index.html)
- [ブログ投稿](https://www.microsoft.com/en-us/research/search/?q=deepspeed)
論文:
- [ZeRO: 兆パラメータ モデルのトレーニングに向けたメモリの最適化](https://arxiv.org/abs/1910.02054)
- [ZeRO-Offload: 10 億規模のモデル トレーニングの民主化](https://arxiv.org/abs/2101.06840)
- [ZeRO-Infinity: 極限スケールの深層学習のための GPU メモリの壁を打ち破る](https://arxiv.org/abs/2104.07857)
最後に、HuggingFace [`Trainer`] は DeepSpeed のみを統合していることを覚えておいてください。
DeepSpeed の使用に関して問題や質問がある場合は、[DeepSpeed GitHub](https://github.com/microsoft/DeepSpeed/issues) に問題を提出してください。
| transformers/docs/source/ja/main_classes/deepspeed.md/0 | {
"file_path": "transformers/docs/source/ja/main_classes/deepspeed.md",
"repo_id": "transformers",
"token_count": 49427
} | 23 |
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# ALIGN
## 概要
ALIGNモデルは、「[Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://arxiv.org/abs/2102.05918)」という論文でChao Jia、Yinfei Yang、Ye Xia、Yi-Ting Chen、Zarana Parekh、Hieu Pham、Quoc V. Le、Yunhsuan Sung、Zhen Li、Tom Duerigによって提案されました。ALIGNはマルチモーダルな視覚言語モデルです。これは画像とテキストの類似度や、ゼロショット画像分類に使用できます。ALIGNは[EfficientNet](efficientnet)を視覚エンコーダーとして、[BERT](bert)をテキストエンコーダーとして搭載したデュアルエンコーダー構造を特徴とし、対照学習によって視覚とテキストの表現を整合させることを学びます。それまでの研究とは異なり、ALIGNは巨大でノイジーなデータセットを活用し、コーパスのスケールを利用して単純な方法ながら最先端の表現を達成できることを示しています。
論文の要旨は以下の通りです：
*事前学習された表現は、多くの自然言語処理（NLP）および知覚タスクにとって重要になっています。NLPにおける表現学習は、人間のアノテーションのない生のテキストでの学習へと移行していますが、視覚および視覚言語の表現は依然として精巧な学習データセットに大きく依存しており、これは高価であったり専門知識を必要としたりします。視覚アプリケーションの場合、ImageNetやOpenImagesのような明示的なクラスラベルを持つデータセットを使用して学習されることがほとんどです。視覚言語の場合、Conceptual Captions、MSCOCO、CLIPなどの人気のあるデータセットはすべて、それぞれ無視できないデータ収集（およびクリーニング）プロセスを含みます。このコストのかかるキュレーションプロセスはデータセットのサイズを制限し、訓練されたモデルのスケーリングを妨げます。本論文では、Conceptual Captionsデータセットの高価なフィルタリングや後処理ステップなしで得られた、10億を超える画像alt-textペアのノイズの多いデータセットを活用します。シンプルなデュアルエンコーダーアーキテクチャは、対照損失を使用して画像とテキストペアの視覚的および言語的表現を整合させることを学習します。我々は、コーパスの規模がそのノイズを補い、このような単純な学習スキームでも最先端の表現につながることを示します。我々の視覚表現は、ImageNetやVTABなどの分類タスクへの転移において強力な性能を発揮します。整合した視覚的および言語的表現は、ゼロショット画像分類を可能にし、また、より洗練されたクロスアテンションモデルと比較しても、Flickr30KおよびMSCOCO画像テキスト検索ベンチマークにおいて新たな最先端の結果を達成します。また、これらの表現は、複雑なテキストおよびテキスト+画像のクエリを用いたクロスモーダル検索を可能にします。*
このモデルは[Alara Dirik](https://huggingface.co/adirik)により提供されました。
オリジナルのコードは公開されておらず、この実装は元論文に基づいたKakao Brainの実装をベースにしています。
## 使用例
ALIGNはEfficientNetを使用して視覚的特徴を、BERTを使用してテキスト特徴を取得します。テキストと視覚の両方の特徴は、同一の次元を持つ潜在空間に射影されます。射影された画像とテキスト特徴間のドット積が類似度スコアとして使用されます。
[`AlignProcessor`]は、テキストのエンコードと画像の前処理を両方行うために、[`EfficientNetImageProcessor`]と[`BertTokenizer`]を単一のインスタンスにラップします。以下の例は、[`AlignProcessor`]と[`AlignModel`]を使用して画像-テキスト類似度スコアを取得する方法を示しています。
```python
import requests
import torch
from PIL import Image
from transformers import AlignProcessor, AlignModel
processor = AlignProcessor.from_pretrained("kakaobrain/align-base")
model = AlignModel.from_pretrained("kakaobrain/align-base")
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
candidate_labels = ["an image of a cat", "an image of a dog"]
inputs = processor(text=candidate_labels, images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
# this is the image-text similarity score
logits_per_image = outputs.logits_per_image
# we can take the softmax to get the label probabilities
probs = logits_per_image.softmax(dim=1)
print(probs)
```
## 参考資料
ALIGNの使用を開始するのに役立つ公式のHugging Faceとコミュニティ（🌎で示されている）の参考資料の一覧です。
- [ALIGNとCOYO-700Mデータセット](https://huggingface.co/blog/vit-align)に関するブログ投稿。
- ゼロショット画像分類[デモ](https://huggingface.co/spaces/adirik/ALIGN-zero-shot-image-classification)。
- `kakaobrain/align-base` モデルの[モデルカード](https://huggingface.co/kakaobrain/align-base)。
ここに参考資料を提出したい場合は、気兼ねなくPull Requestを開いてください。私たちはそれをレビューいたします！参考資料は、既存のものを複製するのではなく、何か新しいことを示すことが理想的です。
## AlignConfig
[[autodoc]] AlignConfig
- from_text_vision_configs
## AlignTextConfig
[[autodoc]] AlignTextConfig
## AlignVisionConfig
[[autodoc]] AlignVisionConfig
## AlignProcessor
[[autodoc]] AlignProcessor
## AlignModel
[[autodoc]] AlignModel
- forward
- get_text_features
- get_image_features
## AlignTextModel
[[autodoc]] AlignTextModel
- forward
## AlignVisionModel
[[autodoc]] AlignVisionModel
- forward
| transformers/docs/source/ja/model_doc/align.md/0 | {
"file_path": "transformers/docs/source/ja/model_doc/align.md",
"repo_id": "transformers",
"token_count": 2911
} | 24 |
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# BioGPT
## Overview
BioGPT モデルは、[BioGPT: generative pre-trained transformer for biomedical text generation and mining](https://academic.oup.com/bib/advance-article/doi/10.1093/bib/bbac409/6713511?guestAccessKey=a66d9b5d-4f83-4017-bb52-405815c907b9) by Renqian Luo、Liai Sun、Yingce Xia、 Tao Qin、Sheng Zhang、Hoifung Poon、Tie-Yan Liu。 BioGPT は、生物医学テキストの生成とマイニングのための、ドメイン固有の生成事前トレーニング済み Transformer 言語モデルです。 BioGPT は、Transformer 言語モデルのバックボーンに従い、1,500 万の PubMed 抄録で最初から事前トレーニングされています。
論文の要約は次のとおりです。
*事前トレーニング済み言語モデルは、一般的な自然言語領域での大きな成功に触発されて、生物医学領域でますます注目を集めています。一般言語ドメインの事前トレーニング済み言語モデルの 2 つの主なブランチ、つまり BERT (およびそのバリアント) と GPT (およびそのバリアント) のうち、1 つ目は BioBERT や PubMedBERT などの生物医学ドメインで広く研究されています。これらはさまざまな下流の生物医学的タスクで大きな成功を収めていますが、生成能力の欠如により応用範囲が制限されています。この論文では、大規模な生物医学文献で事前トレーニングされたドメイン固有の生成 Transformer 言語モデルである BioGPT を提案します。私たちは 6 つの生物医学的自然言語処理タスクで BioGPT を評価し、ほとんどのタスクで私たちのモデルが以前のモデルよりも優れていることを実証しました。特に、BC5CDR、KD-DTI、DDI のエンドツーエンド関係抽出タスクではそれぞれ 44.98%、38.42%、40.76% の F1 スコアを獲得し、PubMedQA では 78.2% の精度を獲得し、新記録を樹立しました。テキスト生成に関する私たちのケーススタディは、生物医学文献における BioGPT の利点をさらに実証し、生物医学用語の流暢な説明を生成します。*
## Usage tips
- BioGPT は絶対位置埋め込みを備えたモデルであるため、通常は入力を左側ではなく右側にパディングすることをお勧めします。
- BioGPT は因果言語モデリング (CLM) 目的でトレーニングされているため、シーケンス内の次のトークンを予測するのに強力です。 run_generation.py サンプル スクリプトで確認できるように、この機能を利用すると、BioGPT は構文的に一貫したテキストを生成できます。
- モデルは、以前に計算されたキーと値のアテンション ペアである`past_key_values`(PyTorch の場合) を入力として受け取ることができます。この (past_key_values または past) 値を使用すると、モデルがテキスト生成のコンテキストで事前に計算された値を再計算できなくなります。 PyTorch の使用法の詳細については、BioGptForCausalLM.forward() メソッドの past_key_values 引数を参照してください。
このモデルは、[kamalkraj](https://huggingface.co/kamalkraj) によって提供されました。元のコードは [ここ](https://github.com/microsoft/BioGPT) にあります。
## Documentation resources
- [因果言語モデリング タスク ガイド](../tasks/language_modeling)
## BioGptConfig
[[autodoc]] BioGptConfig
## BioGptTokenizer
[[autodoc]] BioGptTokenizer
- save_vocabulary
## BioGptModel
[[autodoc]] BioGptModel
- forward
## BioGptForCausalLM
[[autodoc]] BioGptForCausalLM
- forward
## BioGptForTokenClassification
[[autodoc]] BioGptForTokenClassification
- forward
## BioGptForSequenceClassification
[[autodoc]] BioGptForSequenceClassification
- forward
| transformers/docs/source/ja/model_doc/biogpt.md/0 | {
"file_path": "transformers/docs/source/ja/model_doc/biogpt.md",
"repo_id": "transformers",
"token_count": 1982
} | 25 |
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# CLIPSeg
## Overview
CLIPSeg モデルは、Timo Lüddecke, Alexander Ecker によって [Image Segmentation using Text and Image Prompts](https://arxiv.org/abs/2112.10003) で提案されました。
そしてアレクサンダー・エッカー。 CLIPSeg は、ゼロショットおよびワンショット画像セグメンテーションのために、凍結された [CLIP](clip) モデルの上に最小限のデコーダを追加します。
論文の要約は次のとおりです。
*画像のセグメンテーションは通常、トレーニングによって解決されます。
オブジェクト クラスの固定セットのモデル。後で追加のクラスやより複雑なクエリを組み込むとコストがかかります
これらの式を含むデータセットでモデルを再トレーニングする必要があるためです。ここでシステムを提案します
任意の情報に基づいて画像セグメンテーションを生成できます。
テスト時にプロンプトが表示されます。プロンプトはテキストまたは
画像。このアプローチにより、統一されたモデルを作成できます。
3 つの一般的なセグメンテーション タスクについて (1 回トレーニング済み)
参照式のセグメンテーション、ゼロショット セグメンテーション、ワンショット セグメンテーションという明確な課題が伴います。
CLIP モデルをバックボーンとして構築し、これをトランスベースのデコーダで拡張して、高密度なデータ通信を可能にします。
予測。の拡張バージョンでトレーニングした後、
PhraseCut データセット、私たちのシステムは、フリーテキスト プロンプトまたは
クエリを表す追加の画像。後者の画像ベースのプロンプトのさまざまなバリエーションを詳細に分析します。
この新しいハイブリッド入力により、動的適応が可能になります。
前述の 3 つのセグメンテーション タスクのみですが、
テキストまたは画像をクエリするバイナリ セグメンテーション タスクに
定式化することができる。最後に、システムがうまく適応していることがわかりました
アフォーダンスまたはプロパティを含む一般化されたクエリ*
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/clipseg_architecture.png"
alt="描画" width="600"/>
<small> CLIPSeg の概要。 <a href="https://arxiv.org/abs/2112.10003">元の論文から抜粋。</a> </small>
このモデルは、[nielsr](https://huggingface.co/nielsr) によって提供されました。
元のコードは [ここ](https://github.com/timojl/clipseg) にあります。
## Usage tips
- [`CLIPSegForImageSegmentation`] は、[`CLIPSegModel`] の上にデコーダを追加します。後者は [`CLIPModel`] と同じです。
- [`CLIPSegForImageSegmentation`] は、テスト時に任意のプロンプトに基づいて画像セグメンテーションを生成できます。プロンプトはテキストのいずれかです
(`input_ids` としてモデルに提供される) または画像 (`conditional_pixel_values` としてモデルに提供される)。カスタムを提供することもできます
条件付き埋め込み (`conditional_embeddings`としてモデルに提供されます)。
## Resources
CLIPSeg の使用を開始するのに役立つ、公式 Hugging Face およびコミュニティ (🌎 で示されている) リソースのリスト。ここに含めるリソースの送信に興味がある場合は、お気軽にプル リクエストを開いてください。審査させていただきます。リソースは、既存のリソースを複製するのではなく、何か新しいものを示すことが理想的です。
<PipelineTag pipeline="image-segmentation"/>
- [CLIPSeg を使用したゼロショット画像セグメンテーション](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/CLIPSeg/Zero_shot_image_segmentation_with_CLIPSeg.ipynb) を説明するノートブック。
## CLIPSegConfig
[[autodoc]] CLIPSegConfig
- from_text_vision_configs
## CLIPSegTextConfig
[[autodoc]] CLIPSegTextConfig
## CLIPSegVisionConfig
[[autodoc]] CLIPSegVisionConfig
## CLIPSegProcessor
[[autodoc]] CLIPSegProcessor
## CLIPSegModel
[[autodoc]] CLIPSegModel
- forward
- get_text_features
- get_image_features
## CLIPSegTextModel
[[autodoc]] CLIPSegTextModel
- forward
## CLIPSegVisionModel
[[autodoc]] CLIPSegVisionModel
- forward
## CLIPSegForImageSegmentation
[[autodoc]] CLIPSegForImageSegmentation
- forward | transformers/docs/source/ja/model_doc/clipseg.md/0 | {
"file_path": "transformers/docs/source/ja/model_doc/clipseg.md",
"repo_id": "transformers",
"token_count": 2201
} | 26 |
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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# Deformable DETR
## Overview
変形可能 DETR モデルは、Xizhou Zhu、Weijie Su、Lewei Lu、Bin Li、Xiaogang Wang, Jifeng Dai によって [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://arxiv.org/abs/2010.04159) で提案されました
変形可能な DETR は、参照周囲の少数の主要なサンプリング ポイントのみに注目する新しい変形可能なアテンション モジュールを利用することにより、収束の遅さの問題と元の [DETR](detr) の制限された特徴の空間解像度を軽減します。
論文の要約は次のとおりです。
*DETR は、優れたパフォーマンスを実証しながら、物体検出における多くの手作業で設計されたコンポーネントの必要性を排除するために最近提案されました。ただし、画像特徴マップの処理における Transformer アテンション モジュールの制限により、収束が遅く、特徴の空間解像度が制限されるという問題があります。これらの問題を軽減するために、私たちは Deformable DETR を提案しました。この DETR のアテンション モジュールは、参照周囲の少数の主要なサンプリング ポイントのみに注目します。変形可能な DETR は、10 分の 1 のトレーニング エポックで、DETR よりも優れたパフォーマンス (特に小さなオブジェクトの場合) を達成できます。 COCO ベンチマークに関する広範な実験により、私たちのアプローチの有効性が実証されました。*
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/deformable_detr_architecture.png"
alt="描画" width="600"/>
<small> 変形可能な DETR アーキテクチャ。 <a href="https://arxiv.org/abs/2010.04159">元の論文</a>から抜粋。</small>
このモデルは、[nielsr](https://huggingface.co/nielsr) によって提供されました。元のコードは [ここ](https://github.com/fundamentalvision/Deformable-DETR) にあります。
## Usage tips
- トレーニング Deformable DETR は、元の [DETR](detr) モデルをトレーニングすることと同等です。デモ ノートブックについては、以下の [resources](#resources) セクションを参照してください。
## Resources
Deformable DETR の使用を開始するのに役立つ公式 Hugging Face およびコミュニティ (🌎 で示される) リソースのリスト。
<PipelineTag pipeline="object-detection"/>
- [`DeformableDetrForObjectDetection`] のカスタム データセットでの推論と微調整に関するデモ ノートブックは、[こちら](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/Deformable-DETR) にあります。
- [物体検出タスクガイド](../tasks/object_detection) も参照してください。
ここに含めるリソースの送信に興味がある場合は、お気軽にプル リクエストを開いてください。審査させていただきます。リソースは、既存のリソースを複製するのではなく、何か新しいものを示すことが理想的です。
## DeformableDetrImageProcessor
[[autodoc]] DeformableDetrImageProcessor
- preprocess
- post_process_object_detection
## DeformableDetrFeatureExtractor
[[autodoc]] DeformableDetrFeatureExtractor
- __call__
- post_process_object_detection
## DeformableDetrConfig
[[autodoc]] DeformableDetrConfig
## DeformableDetrModel
[[autodoc]] DeformableDetrModel
- forward
## DeformableDetrForObjectDetection
[[autodoc]] DeformableDetrForObjectDetection
- forward
| transformers/docs/source/ja/model_doc/deformable_detr.md/0 | {
"file_path": "transformers/docs/source/ja/model_doc/deformable_detr.md",
"repo_id": "transformers",
"token_count": 1792
} | 27 |
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# Efficient Inference on a Single GPU
このガイドに加えて、[1つのGPUでのトレーニングガイド](perf_train_gpu_one)と[CPUでの推論ガイド](perf_infer_cpu)に関連する情報があります。
## Flash Attention 2
<Tip>
この機能は実験的であり、将来のバージョンで大幅に変更される可能性があります。たとえば、Flash Attention 2 APIは近い将来`BetterTransformer` APIに移行するかもしれません。
</Tip>
Flash Attention 2は、トランスフォーマーベースのモデルのトレーニングと推論速度を大幅に高速化できます。Flash Attention 2は、Tri Dao氏によって[公式のFlash Attentionリポジトリ](https://github.com/Dao-AILab/flash-attention)で導入されました。Flash Attentionに関する科学論文は[こちら](https://arxiv.org/abs/2205.14135)で見ることができます。
Flash Attention 2を正しくインストールするには、上記のリポジトリに記載されているインストールガイドに従ってください。
以下のモデルに対してFlash Attention 2をネイティブサポートしています：
- Llama
- Falcon
さらに多くのモデルにFlash Attention 2のサポートを追加することをGitHubで提案することもでき、変更を統合するためにプルリクエストを開くこともできます。サポートされているモデルは、パディングトークンを使用してトレーニングを含む、推論とトレーニングに使用できます（現在の`BetterTransformer` APIではサポートされていない）。
<Tip>
Flash Attention 2は、モデルのdtypeが`fp16`または`bf16`の場合にのみ使用でき、NVIDIA-GPUデバイスでのみ実行されます。この機能を使用する前に、モデルを適切なdtypeにキャストし、サポートされているデバイスにロードしてください。
</Tip>
### Quick usage
モデルでFlash Attention 2を有効にするには、`from_pretrained`の引数に`attn_implementation="flash_attention_2"`を追加します。
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, LlamaForCausalLM
model_id = "tiiuae/falcon-7b"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
attn_implementation="flash_attention_2",
)
```
こちらは、生成または微調整のために使用するテキストです。
### Expected speedups
特に長いシーケンスに対して、微調整と推論の際には、かなりの高速化が期待できます。ただし、Flash Attentionはパディングトークンを使用してアテンションスコアを計算しないため、シーケンスにパディングトークンが含まれる場合、バッチ推論においてアテンションスコアを手動でパッド/アンパッドする必要があり、パディングトークンを含むバッチ生成の大幅な遅延が発生します。
これを克服するために、トレーニング中にシーケンスにパディングトークンを使用せずにFlash Attentionを使用する必要があります（たとえば、データセットをパックすることにより、シーケンスを最大シーケンス長に達するまで連結することなど）。ここに[例](https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_clm.py#L516)が提供されています。
以下は、パディングトークンのない場合に、シーケンス長が4096の[tiiuae/falcon-7b](https://hf.co/tiiuae/falcon-7b)に対する単純なフォワードパスの予想される高速化です。さまざまなバッチサイズが示されています：
<div style="text-align: center">
<img src="https://huggingface.co/datasets/ybelkada/documentation-images/resolve/main/falcon-7b-inference-large-seqlen.png">
</div>
以下は、パディングトークンのない場合に、シーケンス長が4096の[`meta-llama/Llama-7b-hf`](https://hf.co/meta-llama/Llama-7b-hf)に対する単純なフォワードパスの予想される高速化です。さまざまなバッチサイズが示されています：
<div style="text-align: center">
<img src="https://huggingface.co/datasets/ybelkada/documentation-images/resolve/main/llama-7b-inference-large-seqlen.png">
</div>
パディングトークンを含むシーケンス（パディングトークンを使用してトレーニングまたは生成する）の場合、アテンションスコアを正しく計算するために入力シーケンスをアンパッド/パッドする必要があります。比較的小さいシーケンス長の場合、純粋なフォワードパスではパディングトークンが30%未満しか埋められていないため、これはわずかな高速化をもたらします。
<div style="text-align: center">
<img src="https://huggingface.co/datasets/ybelkada/documentation-images/resolve/main/llama-2-small-seqlen-padding.png">
</div>
しかし、大きなシーケンス長の場合、純粋な推論（トレーニングも含む）には興味深い高速化が得られます。
Flash Attentionは、アテンション計算をよりメモリ効率の良いものにし、大きなシーケンス長でのCUDA OOMの問題を回避できるようにします。大きなシーケンス長に対して最大20のメモリ削減をもたらすことがあります。詳細については、[公式のFlash Attentionリポジトリ](https://github.com/Dao-AILab/flash-attention)をご覧ください。
<div style="text-align: center">
<img src="https://huggingface.co/datasets/ybelkada/documentation-images/resolve/main/llama-2-large-seqlen-padding.png">
</div>
### Advanced usage
この機能をモデルの最適化に多くの既存の機能と組み合わせることができます。以下にいくつかの例を示します：
### Combining Flash Attention 2 and 8-bit models
この機能を8ビットの量子化と組み合わせることができます：
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, LlamaForCausalLM
model_id = "tiiuae/falcon-7b"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
load_in_8bit=True,
attn_implementation="flash_attention_2",
)
```
### Combining Flash Attention 2 and 4-bit models
この機能を 4 ビットの量子化と組み合わせることができます：
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, LlamaForCausalLM
model_id = "tiiuae/falcon-7b"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
load_in_4bit=True,
attn_implementation="flash_attention_2",
)
```
### Combining Flash Attention 2 and PEFT
この機能を使用して、Flash Attention 2をベースにアダプターをトレーニングする際にPEFTを組み合わせることができます。
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, LlamaForCausalLM
from peft import LoraConfig
model_id = "tiiuae/falcon-7b"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
load_in_4bit=True,
attn_implementation="flash_attention_2",
)
lora_config = LoraConfig(
r=8,
task_type="CAUSAL_LM"
)
model.add_adapter(lora_config)
... # train your model
```
## BetterTransformer
[BetterTransformer](https://huggingface.co/docs/optimum/bettertransformer/overview)は、🤗 TransformersモデルをPyTorchネイティブの高速パス実行に変換します。これにより、Flash Attentionなどの最適化されたカーネルが内部で呼び出されます。
BetterTransformerは、テキスト、画像、およびオーディオモデルの単一およびマルチGPUでの高速な推論をサポートしています。
<Tip>
Flash Attentionは、fp16またはbf16のdtypeを使用するモデルにのみ使用できます。BetterTransformerを使用する前に、モデルを適切なdtypeにキャストしてください。
</Tip>
### Encoder models
PyTorchネイティブの[`nn.MultiHeadAttention`](https://pytorch.org/blog/a-better-transformer-for-fast-transformer-encoder-inference/)アテンション高速パス、BetterTransformerと呼ばれるものは、[🤗 Optimumライブラリ](https://huggingface.co/docs/optimum/bettertransformer/overview)の統合を通じてTransformersと一緒に使用できます。
PyTorchのアテンション高速パスを使用すると、カーネルフュージョンと[ネストされたテンソル](https://pytorch.org/docs/stable/nested.html)の使用により、推論を高速化できます。詳細なベンチマーク情報は[このブログ記事](https://medium.com/pytorch/bettertransformer-out-of-the-box-performance-for-huggingface-transformers-3fbe27d50ab2)にあります。
[`optimum`](https://github.com/huggingface/optimum)パッケージをインストールした後、推論中にBetter Transformerを使用するには、関連する内部モジュールを呼び出すことで置き換える必要があります[`~PreTrainedModel.to_bettertransformer`]:
```python
model = model.to_bettertransformer()
```
メソッド [`~PreTrainedModel.reverse_bettertransformer`] は、モデルを保存する前に使用すべきで、標準のトランスフォーマーモデリングを使用するためのものです：
```python
model = model.reverse_bettertransformer()
model.save_pretrained("saved_model")
```
BetterTransformer APIを使ったエンコーダーモデルの可能性について詳しく知るには、[このブログポスト](https://medium.com/pytorch/bettertransformer-out-of-the-box-performance-for-huggingface-transformers-3fbe27d50ab2)をご覧ください。
### Decoder models
テキストモデル、特にデコーダーベースのモデル（GPT、T5、Llamaなど）にとって、BetterTransformer APIはすべての注意操作を[`torch.nn.functional.scaled_dot_product_attention`オペレーター](https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention)（SDPA）を使用するように変換します。このオペレーターはPyTorch 2.0以降でのみ利用可能です。
モデルをBetterTransformerに変換するには、以下の手順を実行してください：
```python
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("facebook/opt-350m")
# convert the model to BetterTransformer
model.to_bettertransformer()
# Use it for training or inference
```
SDPAは、ハードウェアや問題のサイズに応じて[Flash Attention](https://arxiv.org/abs/2205.14135)カーネルを使用することもできます。Flash Attentionを有効にするか、特定の設定（ハードウェア、問題サイズ）で使用可能かどうかを確認するには、[`torch.backends.cuda.sdp_kernel`](https://pytorch.org/docs/master/backends.html#torch.backends.cuda.sdp_kernel)をコンテキストマネージャとして使用します。
```diff
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")
model = AutoModelForCausalLM.from_pretrained("facebook/opt-350m", torch_dtype=torch.float16).to("cuda")
# convert the model to BetterTransformer
model.to_bettertransformer()
input_text = "Hello my dog is cute and"
inputs = tokenizer(input_text, return_tensors="pt").to("cuda")
+ with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=False, enable_mem_efficient=False):
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
もしトレースバックにバグが表示された場合
```bash
RuntimeError: No available kernel. Aborting execution.
```
Flash Attention の広範なカバレッジを持つかもしれない PyTorch のナイトリーバージョンを試してみることをお勧めします。
```bash
pip3 install -U --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu118
```
Or make sure your model is correctly casted in float16 or bfloat16
モデルが正しくfloat16またはbfloat16にキャストされていることを確認してください。
Have a look at [this detailed blogpost](https://pytorch.org/blog/out-of-the-box-acceleration/) to read more about what is possible to do with `BetterTransformer` + SDPA API.
`BetterTransformer` + SDPA APIを使用して何が可能かについて詳しく読むには、[この詳細なブログポスト](https://pytorch.org/blog/out-of-the-box-acceleration/)をご覧ください。
## `bitsandbytes` integration for FP4 mixed-precision inference
FP4混合精度推論のための`bitsandbytes`統合
You can install `bitsandbytes` and benefit from easy model compression on GPUs. Using FP4 quantization you can expect to reduce up to 8x the model size compared to its native full precision version. Check out below how to get started.
`bitsandbytes`をインストールし、GPUで簡単なモデルの圧縮を利用できます。FP4量子化を使用すると、ネイティブのフルプレシジョンバージョンと比較してモデルサイズを最大8倍削減できることが期待できます。以下を確認して、どのように始めるかをご覧ください。
<Tip>
Note that this feature can also be used in a multi GPU setup.
この機能は、マルチGPUセットアップでも使用できることに注意してください。
</Tip>
### Requirements [[requirements-for-fp4-mixedprecision-inference]]
- Latest `bitsandbytes` library
`pip install bitsandbytes>=0.39.0`
- Install latest `accelerate` from source
`pip install git+https://github.com/huggingface/accelerate.git`
- Install latest `transformers` from source
`pip install git+https://github.com/huggingface/transformers.git`
### Running FP4 models - single GPU setup - Quickstart
以下のコードを実行することで、簡単に単一のGPUでFP4モデルを実行できます:
```py
from transformers import AutoModelForCausalLM
model_name = "bigscience/bloom-2b5"
model_4bit = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", load_in_4bit=True)
```
注意: `device_map`はオプションですが、推論時に `device_map = 'auto'` を設定することが推奨されています。これにより、利用可能なリソースに効率的にモデルがディスパッチされます。
### Running FP4 models - multi GPU setup
混合4ビットモデルを複数のGPUにロードする方法は、単一GPUセットアップと同じです（単一GPUセットアップと同じコマンドです）：
```py
model_name = "bigscience/bloom-2b5"
model_4bit = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", load_in_4bit=True)
```
しかし、`accelerate`を使用して、各GPUに割り当てるGPU RAMを制御することができます。以下のように、`max_memory`引数を使用します：
```py
max_memory_mapping = {0: "600MB", 1: "1GB"}
model_name = "bigscience/bloom-3b"
model_4bit = AutoModelForCausalLM.from_pretrained(
model_name, device_map="auto", load_in_4bit=True, max_memory=max_memory_mapping
)
```
この例では、最初のGPUは600MBのメモリを使用し、2番目のGPUは1GBを使用します。
### Advanced usage
このメソッドのさらなる高度な使用法については、[量子化](main_classes/quantization)のドキュメンテーションページをご覧ください。
## `bitsandbytes` integration for Int8 mixed-precision matrix decomposition
<Tip>
この機能は、マルチGPU環境でも使用できます。
</Tip>
論文[`LLM.int8()：スケーラブルなTransformer向けの8ビット行列乗算`](https://arxiv.org/abs/2208.07339)によれば、Hugging Face統合がHub内のすべてのモデルでわずか数行のコードでサポートされています。このメソッドは、半精度（`float16`および`bfloat16`）の重みの場合に`nn.Linear`サイズを2倍、単精度（`float32`）の重みの場合は4倍に縮小し、外れ値に対してほとんど影響を与えません。
Int8混合精度行列分解は、行列乗算を2つのストリームに分割することによって動作します：(1) システマティックな特徴外れ値ストリームがfp16で行列乗算（0.01%）、(2) int8行列乗算の通常のストリーム（99.9%）。この方法を使用すると、非常に大きなモデルに対して予測の劣化なしにint8推論が可能です。
このメソッドの詳細については、[論文](https://arxiv.org/abs/2208.07339)または[この統合に関するブログ記事](https://huggingface.co/blog/hf-bitsandbytes-integration)をご確認ください。
なお、この機能を使用するにはGPUが必要であり、カーネルはGPU専用にコンパイルされている必要があります。この機能を使用する前に、モデルの1/4（またはハーフ精度の重みの場合は1/2）を保存するのに十分なGPUメモリがあることを確認してください。
このモジュールを使用する際のヘルプに関する詳細は、以下のノートをご覧いただくか、[Google Colabのデモ](#colab-demos)をご覧ください。
### Requirements [[requirements-for-int8-mixedprecision-matrix-decomposition]]
- `bitsandbytes<0.37.0`を使用する場合、NVIDIA GPUを使用していることを確認し、8ビットテンソルコアをサポートしていることを確認してください（Turing、Ampere、またはそれ以降のアーキテクチャー、例：T4、RTX20s RTX30s、A40-A100など）。`bitsandbytes>=0.37.0`の場合、すべてのGPUがサポートされるはずです。
- 正しいバージョンの`bitsandbytes`をインストールするには、次のコマンドを実行してください：
`pip install bitsandbytes>=0.31.5`
- `accelerate`をインストールします：
`pip install accelerate>=0.12.0`
### Running mixed-Int8 models - single GPU setup
必要なライブラリをインストールした後、ミックス 8 ビットモデルを読み込む方法は次の通りです：
```py
from transformers import AutoModelForCausalLM
model_name = "bigscience/bloom-2b5"
model_8bit = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", load_in_8bit=True)
```
以下はシンプルな例です：
* `pipeline()` 関数の代わりに、モデルの `generate()` メソッドを使用することをお勧めします。`pipeline()` 関数を使用して推論することは可能ですが、混合8ビットモデルに最適化されておらず、`generate()` メソッドを使用するよりも遅くなります。また、一部のサンプリング戦略（例：ヌクレウスサンプリング）は、`pipeline()` 関数では混合8ビットモデルではサポートされていません。
* すべての入力をモデルと同じデバイスに配置してください。
```py
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "bigscience/bloom-2b5"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model_8bit = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", load_in_8bit=True)
prompt = "Hello, my llama is cute"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
generated_ids = model.generate(**inputs)
outputs = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
```
### Running mixed-int8 models - multi GPU setup
複数のGPUに混合8ビットモデルをロードする方法は、次の通りです（シングルGPUセットアップと同じコマンドです）：
```py
model_name = "bigscience/bloom-2b5"
model_8bit = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", load_in_8bit=True)
```
`accelerate`を使用して各GPUに割り当てるGPU RAMを制御する際には、以下のように`max_memory`引数を使用します：
```py
max_memory_mapping = {0: "1GB", 1: "2GB"}
model_name = "bigscience/bloom-3b"
model_8bit = AutoModelForCausalLM.from_pretrained(
model_name, device_map="auto", load_in_8bit=True, max_memory=max_memory_mapping
)
```
In this example, the first GPU will use 1GB of memory and the second 2GB.
### Colab demos
この方法を使用すると、以前のGoogle Colabでは推論できなかったモデルに対して推論を行うことができます。以下は、Google Colabで8ビット量子化を使用してT5-11b（fp32で42GB）を実行するデモのリンクです：
[](https://colab.research.google.com/drive/1YORPWx4okIHXnjW7MSAidXN29mPVNT7F?usp=sharing)
また、BLOOM-3Bのデモもご覧いただけます：
[](https://colab.research.google.com/drive/1qOjXfQIAULfKvZqwCen8-MoWKGdSatZ4?usp=sharing)
## Advanced usage: mixing FP4 (or Int8) and BetterTransformer
異なる方法を組み合わせて、モデルの最適なパフォーマンスを得ることができます。例えば、BetterTransformerを使用してFP4ミックスプレシジョン推論とフラッシュアテンションを組み合わせることができます。
```py
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
quantization_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.float16
)
tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")
model = AutoModelForCausalLM.from_pretrained("facebook/opt-350m", quantization_config=quantization_config)
input_text = "Hello my dog is cute and"
inputs = tokenizer(input_text, return_tensors="pt").to("cuda")
with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=False, enable_mem_efficient=False):
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
``` | transformers/docs/source/ja/perf_infer_gpu_one.md/0 | {
"file_path": "transformers/docs/source/ja/perf_infer_gpu_one.md",
"repo_id": "transformers",
"token_count": 9331
} | 28 |
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# Preprocess
[[open-in-colab]]
データセットでモデルをトレーニングする前に、それをモデルの期待する入力形式に前処理する必要があります。
データがテキスト、画像、またはオーディオであるかどうかにかかわらず、それらはテンソルのバッチに変換して組み立てる必要があります。
🤗 Transformersは、データをモデル用に準備するのに役立つ前処理クラスのセットを提供しています。
このチュートリアルでは、次のことを学びます：
* テキストの場合、[Tokenizer](./main_classes/tokenizer)を使用してテキストをトークンのシーケンスに変換し、トークンの数値表現を作成し、それらをテンソルに組み立てる方法。
* 音声とオーディオの場合、[Feature extractor](./main_classes/feature_extractor)を使用してオーディオ波形から連続的な特徴を抽出し、それらをテンソルに変換する方法。
* 画像入力の場合、[ImageProcessor](./main_classes/image)を使用して画像をテンソルに変換する方法。
* マルチモーダル入力の場合、[Processor](./main_classes/processors)を使用してトークナイザと特徴抽出器または画像プロセッサを組み合わせる方法。
<Tip>
`AutoProcessor`は常に動作し、使用するモデルに適切なクラスを自動的に選択します。
トークナイザ、画像プロセッサ、特徴抽出器、またはプロセッサを使用しているかにかかわらず、動作します。
</Tip>
始める前に、🤗 Datasetsをインストールして、いくつかのデータセットを試すことができるようにしてください：
```bash
pip install datasets
```
## Natural Language Processing
<Youtube id="Yffk5aydLzg"/>
テキストデータの前処理に使用する主要なツールは、[トークナイザ](main_classes/tokenizer)です。トークナイザは、一連のルールに従ってテキストを*トークン*に分割します。トークンは数値に変換され、その後テンソルに変換され、モデルの入力となります。モデルが必要とする追加の入力は、トークナイザによって追加されます。
<Tip>
事前学習済みモデルを使用する予定の場合、関連する事前学習済みトークナイザを使用することが重要です。これにより、テキストが事前学習コーパスと同じ方法で分割され、事前学習中に通常*ボキャブ*として参照される対応するトークンインデックスを使用します。
</Tip>
[`AutoTokenizer.from_pretrained`]メソッドを使用して事前学習済みトークナイザをロードして、開始しましょう。これにより、モデルが事前学習された*ボキャブ*がダウンロードされます：
```python
>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-cased")
```
次に、テキストをトークナイザに渡します：
```py
>>> encoded_input = tokenizer("Do not meddle in the affairs of wizards, for they are subtle and quick to anger.")
>>> print(encoded_input)
{'input_ids': [101, 2079, 2025, 19960, 10362, 1999, 1996, 3821, 1997, 16657, 1010, 2005, 2027, 2024, 11259, 1998, 4248, 2000, 4963, 1012, 102],
'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
```
トークナイザは、重要な3つの項目を持つ辞書を返します：
* [input_ids](glossary#input-ids) は文中の各トークンに対応するインデックスです。
* [attention_mask](glossary#attention-mask) はトークンがアテンションを受ける必要があるかどうかを示します。
* [token_type_ids](glossary#token-type-ids) は複数のシーケンスがある場合、トークンがどのシーケンスに属しているかを識別します。
`input_ids` をデコードして入力を返します：
```python
>>> tokenizer.decode(encoded_input["input_ids"])
'[CLS] 魔法使いの事に干渉するな、彼らは微妙で怒りっぽい。 [SEP]'
```
如何にお分かりいただけるかと思いますが、トークナイザはこの文章に2つの特別なトークン、`CLS`（クラシファイア）と`SEP`（セパレータ）を追加しました。
すべてのモデルが特別なトークンを必要とするわけではありませんが、必要な場合、トークナイザは自動的にそれらを追加します。
複数の文章を前処理する場合、トークナイザにリストとして渡してください：
```py
>>> batch_sentences = [
... "But what about second breakfast?",
... "Don't think he knows about second breakfast, Pip.",
... "What about elevensies?",
... ]
>>> encoded_inputs = tokenizer(batch_sentences)
>>> print(encoded_inputs)
{'input_ids': [[101, 1252, 1184, 1164, 1248, 6462, 136, 102],
[101, 1790, 112, 189, 1341, 1119, 3520, 1164, 1248, 6462, 117, 21902, 1643, 119, 102],
[101, 1327, 1164, 5450, 23434, 136, 102]],
'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0]],
'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1]]}
```
### Pad
文章は常に同じ長さではないことがあり、これはテンソル（モデルの入力）が均一な形状を持つ必要があるため問題となります。
パディングは、短い文に特別な「パディングトークン」を追加して、テンソルを長いシーケンスに合わせるための戦略です。
バッチ内の短いシーケンスを最長のシーケンスに合わせるために、`padding`パラメータを`True`に設定します：
```py
>>> batch_sentences = [
... "But what about second breakfast?",
... "Don't think he knows about second breakfast, Pip.",
... "What about elevensies?",
... ]
>>> encoded_input = tokenizer(batch_sentences, padding=True)
>>> print(encoded_input)
{'input_ids': [[101, 1252, 1184, 1164, 1248, 6462, 136, 102, 0, 0, 0, 0, 0, 0, 0],
[101, 1790, 112, 189, 1341, 1119, 3520, 1164, 1248, 6462, 117, 21902, 1643, 119, 102],
[101, 1327, 1164, 5450, 23434, 136, 102, 0, 0, 0, 0, 0, 0, 0, 0]],
'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0]]}
```
1番目と3番目の文は、短いために`0`でパディングされています。
### Truncation
逆のスペクトルでは、時折、モデルが処理するのに長すぎるシーケンスがあるかもしれません。この場合、シーケンスを短縮する必要があります。
モデルが受け入れる最大の長さにシーケンスを切り詰めるには、`truncation`パラメータを`True`に設定します：
```py
>>> batch_sentences = [
... "But what about second breakfast?",
... "Don't think he knows about second breakfast, Pip.",
... "What about elevensies?",
... ]
>>> encoded_input = tokenizer(batch_sentences, padding=True, truncation=True)
>>> print(encoded_input)
{'input_ids': [[101, 1252, 1184, 1164, 1248, 6462, 136, 102, 0, 0, 0, 0, 0, 0, 0],
[101, 1790, 112, 189, 1341, 1119, 3520, 1164, 1248, 6462, 117, 21902, 1643, 119, 102],
[101, 1327, 1164, 5450, 23434, 136, 102, 0, 0, 0, 0, 0, 0, 0, 0]],
'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0]]}
```
<Tip>
異なるパディングと切り詰めの引数について詳しくは、[パディングと切り詰め](./pad_truncation)のコンセプトガイドをご覧ください。
</Tip>
### Build tensors
最後に、トークナイザがモデルに供給される実際のテンソルを返すように設定します。
`return_tensors`パラメータを`pt`（PyTorch用）または`tf`（TensorFlow用）に設定します：
<frameworkcontent>
<pt>
```py
>>> batch_sentences = [
... "But what about second breakfast?",
... "Don't think he knows about second breakfast, Pip.",
... "What about elevensies?",
... ]
>>> encoded_input = tokenizer(batch_sentences, padding=True, truncation=True, return_tensors="pt")
>>> print(encoded_input)
{'input_ids': tensor([[101, 1252, 1184, 1164, 1248, 6462, 136, 102, 0, 0, 0, 0, 0, 0, 0],
[101, 1790, 112, 189, 1341, 1119, 3520, 1164, 1248, 6462, 117, 21902, 1643, 119, 102],
[101, 1327, 1164, 5450, 23434, 136, 102, 0, 0, 0, 0, 0, 0, 0, 0]]),
'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]),
'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0]])}
```
</pt>
<tf>
```py
>>> batch_sentences = [
... "But what about second breakfast?",
... "Don't think he knows about second breakfast, Pip.",
... "What about elevensies?",
... ]
>>> encoded_input = tokenizer(batch_sentences, padding=True, truncation=True, return_tensors="tf")
>>> print(encoded_input)
{'input_ids': <tf.Tensor: shape=(2, 9), dtype=int32, numpy=
array([[101, 1252, 1184, 1164, 1248, 6462, 136, 102, 0, 0, 0, 0, 0, 0, 0],
[101, 1790, 112, 189, 1341, 1119, 3520, 1164, 1248, 6462, 117, 21902, 1643, 119, 102],
[101, 1327, 1164, 5450, 23434, 136, 102, 0, 0, 0, 0, 0, 0, 0, 0]],
dtype=int32)>,
'token_type_ids': <tf.Tensor: shape=(2, 9), dtype=int32, numpy=
array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=int32)>,
'attention_mask': <tf.Tensor: shape=(2, 9), dtype=int32, numpy=
array([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=int32)>}
```
</tf>
</frameworkcontent>
## Audio
オーディオタスクの場合、データセットをモデル用に準備するために[特徴抽出器](main_classes/feature_extractor)が必要です。
特徴抽出器は生のオーディオデータから特徴を抽出し、それらをテンソルに変換するために設計されています。
[PolyAI/minds14](https://huggingface.co/datasets/PolyAI/minds14)データセットをロードして（データセットのロード方法の詳細については🤗 [Datasetsチュートリアル](https://huggingface.co/docs/datasets/load_hub)を参照）、
オーディオデータセットで特徴抽出器をどのように使用できるかを確認してみましょう：
```python
>>> from datasets import load_dataset, Audio
>>> dataset = load_dataset("PolyAI/minds14", name="en-US", split="train")
```
アクセスして`audio`列の最初の要素を確認します。`audio`列を呼び出すと、自動的にオーディオファイルが読み込まれ、リサンプリングされます：
```py
>>> dataset[0]["audio"]
{'array': array([ 0. , 0.00024414, -0.00024414, ..., -0.00024414,
0. , 0. ], dtype=float32),
'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~JOINT_ACCOUNT/602ba55abb1e6d0fbce92065.wav',
'sampling_rate': 8000}
```
これにより、3つのアイテムが返されます：
* `array` は読み込まれた音声信号で、1Dの配列として読み込まれます。必要に応じてリサンプリングされることもあります。
* `path` は音声ファイルの場所を指します。
* `sampling_rate` は音声信号内のデータポイントが1秒間にいくつ測定されるかを示します。
このチュートリアルでは、[Wav2Vec2](https://huggingface.co/facebook/wav2vec2-base)モデルを使用します。
モデルカードを確認すると、Wav2Vec2が16kHzのサンプリングされた音声オーディオで事前学習されていることがわかります。
モデルの事前学習に使用されたデータセットのサンプリングレートと、あなたのオーディオデータのサンプリングレートが一致することが重要です。
データのサンプリングレートが異なる場合、データをリサンプリングする必要があります。
1. 🤗 Datasetsの [`~datasets.Dataset.cast_column`] メソッドを使用して、サンプリングレートを16kHzにアップサンプリングします：
```py
>>> dataset = dataset.cast_column("audio", Audio(sampling_rate=16_000))
```
2. 再び `audio` 列を呼び出してオーディオファイルをリサンプルします：
```py
>>> dataset[0]["audio"]
{'array': array([ 2.3443763e-05, 2.1729663e-04, 2.2145823e-04, ...,
3.8356509e-05, -7.3497440e-06, -2.1754686e-05], dtype=float32),
'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~JOINT_ACCOUNT/602ba55abb1e6d0fbce92065.wav',
'sampling_rate': 16000}
```
次に、入力を正規化しパディングするために特徴抽出器をロードします。テキストデータをパディングする場合、短いシーケンスには `0` が追加されます。同じ考え方がオーディオデータにも適用されます。特徴抽出器は `array` に `0` を追加します（これは無音として解釈されます）。
[`AutoFeatureExtractor.from_pretrained`]を使用して特徴抽出器をロードします：
```python
>>> from transformers import AutoFeatureExtractor
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base")
```
オーディオ `array` を特徴抽出器に渡します。特徴抽出器で発生する可能性のある無音エラーをより良くデバッグするために、特徴抽出器に `sampling_rate` 引数を追加することをお勧めします。
```python
>>> audio_input = [dataset[0]["audio"]["array"]]
>>> feature_extractor(audio_input, sampling_rate=16000)
{'input_values': [array([ 3.8106556e-04, 2.7506407e-03, 2.8015103e-03, ...,
5.6335266e-04, 4.6588284e-06, -1.7142107e-04], dtype=float32)]}
```
同様に、トークナイザと同様に、バッチ内の可変シーケンスを処理するためにパディングまたは切り詰めを適用できます。次に、これらの2つのオーディオサンプルのシーケンス長を確認してみましょう：
```python
>>> dataset[0]["audio"]["array"].shape
(173398,)
>>> dataset[1]["audio"]["array"].shape
(106496,)
```
この関数は、データセットを前処理してオーディオサンプルの長さを同じにするためのものです。最大サンプル長を指定し、特徴抽出器はシーケンスをそれに合わせてパディングまたは切り詰めます。
```py
>>> def preprocess_function(examples):
... audio_arrays = [x["array"] for x in examples["audio"]]
... inputs = feature_extractor(
... audio_arrays,
... sampling_rate=16000,
... padding=True,
... max_length=100000,
... truncation=True,
... )
... return inputs
```
`preprocess_function`をデータセットの最初の数例に適用します：
```python
>>> processed_dataset = preprocess_function(dataset[:5])
```
サンプルの長さは現在同じで、指定された最大長と一致しています。これで処理されたデータセットをモデルに渡すことができます！
```py
>>> processed_dataset["input_values"][0].shape
(100000,)
>>> processed_dataset["input_values"][1].shape
(100000,)
```
## Computer Vision
コンピュータビジョンタスクでは、モデル用にデータセットを準備するための[画像プロセッサ](main_classes/image_processor)が必要です。
画像の前処理には、画像をモデルが期待する入力形式に変換するためのいくつかのステップが含まれています。これらのステップには、リサイズ、正規化、カラーチャネルの補正、および画像をテンソルに変換するなどが含まれます。
<Tip>
画像の前処理は、通常、画像の増強の形式に従います。画像の前処理と画像の増強の両方は画像データを変換しますが、異なる目的があります：
* 画像の増強は、過学習を防ぎ、モデルの堅牢性を向上させるのに役立つ方法で画像を変更します。データを増強する方法は無限で、明るさや色の調整、クロップ、回転、リサイズ、ズームなど、様々な方法があります。ただし、増強操作によって画像の意味が変わらないように注意する必要があります。
* 画像の前処理は、画像がモデルの期待する入力形式と一致することを保証します。コンピュータビジョンモデルをファインチューニングする場合、画像はモデルが最初にトレーニングされたときとまったく同じ方法で前処理する必要があります。
画像の増強には任意のライブラリを使用できます。画像の前処理には、モデルに関連付けられた`ImageProcessor`を使用します。
</Tip>
コンピュータビジョンのデータセットで画像プロセッサを使用する方法を示すために、[food101](https://huggingface.co/datasets/food101)データセットをロードします（データセットのロード方法の詳細については🤗[Datasetsチュートリアル](https://huggingface.co/docs/datasets/load_hub)を参照）：
<Tip>
データセットがかなり大きいため、🤗 Datasetsの`split`パラメータを使用してトレーニングデータの小さなサンプルのみをロードします！
</Tip>
```python
>>> from datasets import load_dataset
>>> dataset = load_dataset("food101", split="train[:100]")
```
次に、🤗 Datasetsの [`Image`](https://huggingface.co/docs/datasets/package_reference/main_classes?highlight=image#datasets.Image) 機能で画像を見てみましょう：
```python
>>> dataset[0]["image"]
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/vision-preprocess-tutorial.png"/>
</div>
AutoImageProcessorを[`AutoImageProcessor.from_pretrained`]を使用してロードします：
```py
>>> from transformers import AutoImageProcessor
>>> image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224")
```
1. まず、画像の拡張を追加しましょう。好きなライブラリを使用できますが、このチュートリアルではtorchvisionの[`transforms`](https://pytorch.org/vision/stable/transforms.html)モジュールを使用します。別のデータ拡張ライブラリを使用したい場合は、[Albumentations](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification_albumentations.ipynb)または[Kornia notebooks](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification_kornia.ipynb)で詳細を学ぶことができます。
ここでは、[`Compose`](https://pytorch.org/vision/master/generated/torchvision.transforms.Compose.html)を使用していくつかの変換を連鎖させます - [`RandomResizedCrop`](https://pytorch.org/vision/main/generated/torchvision.transforms.RandomResizedCrop.html)と[`ColorJitter`](https://pytorch.org/vision/main/generated/torchvision.transforms.ColorJitter.html)。
サイズの変更に関しては、`image_processor`から画像サイズの要件を取得できます。
一部のモデルでは、正確な高さと幅が必要ですが、他のモデルでは`shortest_edge`のみが定義されています。
```py
>>> from torchvision.transforms import RandomResizedCrop, ColorJitter, Compose
>>> size = (
... image_processor.size["shortest_edge"]
... if "shortest_edge" in image_processor.size
... else (image_processor.size["height"], image_processor.size["width"])
... )
>>> _transforms = Compose([RandomResizedCrop(size), ColorJitter(brightness=0.5, hue=0.5)])
```
2. モデルは[`pixel_values`](model_doc/visionencoderdecoder#transformers.VisionEncoderDecoderModel.forward.pixel_values)を入力として受け取ります。
`ImageProcessor`は画像の正規化と適切なテンソルの生成を処理できます。
一連の画像に対する画像拡張と画像前処理を組み合わせ、`pixel_values`を生成する関数を作成します：
```python
>>> def transforms(examples):
... images = [_transforms(img.convert("RGB")) for img in examples["image"]]
... examples["pixel_values"] = image_processor(images, do_resize=False, return_tensors="pt")["pixel_values"]
... return examples
```
<Tip>
上記の例では、画像のサイズ変更を既に画像増強変換で行っているため、`do_resize=False`を設定しました。
適切な `image_processor` からの `size` 属性を活用しています。画像増強中に画像のサイズ変更を行わない場合は、このパラメータを省略してください。
デフォルトでは、`ImageProcessor` がサイズ変更を処理します。
画像を増強変換の一部として正規化したい場合は、`image_processor.image_mean` と `image_processor.image_std` の値を使用してください。
</Tip>
3. 次に、🤗 Datasetsの[`set_transform`](https://huggingface.co/docs/datasets/process#format-transform)を使用して、変換をリアルタイムで適用します：
```python
>>> dataset.set_transform(transforms)
```
4. 画像にアクセスすると、画像プロセッサが `pixel_values` を追加したことがわかります。これで処理済みのデータセットをモデルに渡すことができます！
```python
>>> dataset[0].keys()
```
以下は、変換が適用された後の画像の外観です。 画像はランダムに切り抜かれ、その色の特性も異なります。
```py
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> img = dataset[0]["pixel_values"]
>>> plt.imshow(img.permute(1, 2, 0))
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/preprocessed_image.png"/>
</div>
<Tip>
オブジェクト検出、意味セグメンテーション、インスタンスセグメンテーション、およびパノプティックセグメンテーションなどのタスクの場合、`ImageProcessor`は
ポスト処理メソッドを提供します。これらのメソッドは、モデルの生の出力を境界ボックスやセグメンテーションマップなどの意味のある予測に変換します。
</Tip>
### Pad
一部の場合、たとえば、[DETR](./model_doc/detr)をファインチューニングする場合、モデルはトレーニング時にスケールの変更を適用します。
これにより、バッチ内の画像のサイズが異なる場合があります。[`DetrImageProcessor`]から[`DetrImageProcessor.pad`]を使用し、
カスタムの`collate_fn`を定義して画像を一緒にバッチ処理できます。
```py
>>> def collate_fn(batch):
... pixel_values = [item["pixel_values"] for item in batch]
... encoding = image_processor.pad(pixel_values, return_tensors="pt")
... labels = [item["labels"] for item in batch]
... batch = {}
... batch["pixel_values"] = encoding["pixel_values"]
... batch["pixel_mask"] = encoding["pixel_mask"]
... batch["labels"] = labels
... return batch
```
## Multi Modal
マルチモーダル入力を使用するタスクの場合、モデル用にデータセットを準備するための[プロセッサ](main_classes/processors)が必要です。プロセッサは、トークナイザや特徴量抽出器などの2つの処理オブジェクトを結合します。
自動音声認識（ASR）のためのプロセッサの使用方法を示すために、[LJ Speech](https://huggingface.co/datasets/lj_speech)データセットをロードします（データセットのロード方法の詳細については🤗 [Datasets チュートリアル](https://huggingface.co/docs/datasets/load_hub)を参照）：
```python
>>> from datasets import load_dataset
>>> lj_speech = load_dataset("lj_speech", split="train")
```
ASR（自動音声認識）の場合、主に `audio` と `text` に焦点を当てているため、他の列を削除できます：
```python
>>> lj_speech = lj_speech.map(remove_columns=["file", "id", "normalized_text"])
```
次に、`audio`と`text`の列を見てみましょう：
```python
>>> lj_speech[0]["audio"]
{'array': array([-7.3242188e-04, -7.6293945e-04, -6.4086914e-04, ...,
7.3242188e-04, 2.1362305e-04, 6.1035156e-05], dtype=float32),
'path': '/root/.cache/huggingface/datasets/downloads/extracted/917ece08c95cf0c4115e45294e3cd0dee724a1165b7fc11798369308a465bd26/LJSpeech-1.1/wavs/LJ001-0001.wav',
'sampling_rate': 22050}
>>> lj_speech[0]["text"]
'Printing, in the only sense with which we are at present concerned, differs from most if not from all the arts and crafts represented in the Exhibition'
```
常に、オーディオデータセットのサンプリングレートを、モデルの事前学習に使用されたデータセットのサンプリングレートと一致させるように[リサンプル](preprocessing#audio)する必要があります！
```py
>>> lj_speech = lj_speech.cast_column("audio", Audio(sampling_rate=16_000))
```
プロセッサを [`AutoProcessor.from_pretrained`] を使用してロードします：
```py
>>> from transformers import AutoProcessor
>>> processor = AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h")
```
1. `array`内に含まれるオーディオデータを`input_values`に処理し、`text`を`labels`にトークン化する関数を作成します：
```py
>>> def prepare_dataset(example):
... audio = example["audio"]
... example.update(processor(audio=audio["array"], text=example["text"], sampling_rate=16000))
... return example
```
2. サンプルに`prepare_dataset`関数を適用します：
```py
>>> prepare_dataset(lj_speech[0])
```
| transformers/docs/source/ja/preprocessing.md/0 | {
"file_path": "transformers/docs/source/ja/preprocessing.md",
"repo_id": "transformers",
"token_count": 12720
} | 29 |
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# XLA Integration for TensorFlow Models
[[open-in-colab]]
加速線形代数（Accelerated Linear Algebra）、通称XLAは、TensorFlowモデルのランタイムを高速化するためのコンパイラです。[公式ドキュメント](https://www.tensorflow.org/xla)によれば、XLA（Accelerated Linear Algebra）は線形代数のためのドメイン固有のコンパイラで、TensorFlowモデルを潜在的にソースコードの変更なしで高速化できます。
TensorFlowでXLAを使用するのは簡単です。XLAは`tensorflow`ライブラリ内にパッケージ化されており、[`tf.function`](https://www.tensorflow.org/guide/intro_to_graphs)などのグラフを作成する関数内で`jit_compile`引数を使用してトリガーできます。`fit()`や`predict()`などのKerasメソッドを使用する場合、`model.compile()`に`jit_compile`引数を渡すだけでXLAを有効にできます。ただし、XLAはこれらのメソッドに限定されているわけではありません。任意の`tf.function`を高速化するためにも使用できます。
🤗 Transformers内のいくつかのTensorFlowメソッドは、XLAと互換性があるように書き直されています。これには、[GPT2](https://huggingface.co/docs/transformers/model_doc/gpt2)、[T5](https://huggingface.co/docs/transformers/model_doc/t5)、[OPT](https://huggingface.co/docs/transformers/model_doc/opt)などのテキスト生成モデルや、[Whisper](https://huggingface.co/docs/transformers/model_doc/whisper)などの音声処理モデルも含まれます。
速度向上の具体的な量はモデルに非常に依存しますが、🤗 Transformers内のTensorFlowテキスト生成モデルでは、約100倍の速度向上を確認しています。このドキュメントでは、これらのモデルにXLAを使用して最大のパフォーマンスを得る方法を説明します。また、ベンチマークとXLA統合のデザイン哲学について詳しく学びたい場合の追加リソースへのリンクも提供します。
## Running TF functions with XLA
以下のTensorFlowモデルを考えてみましょう：
```py
import tensorflow as tf
model = tf.keras.Sequential(
[tf.keras.layers.Dense(10, input_shape=(10,), activation="relu"), tf.keras.layers.Dense(5, activation="softmax")]
)
```
上記のモデルは、次元が`(10, )`の入力を受け入れます。このモデルをフォワードパスで実行するには、次のようにします：
```py
# Generate random inputs for the model.
batch_size = 16
input_vector_dim = 10
random_inputs = tf.random.normal((batch_size, input_vector_dim))
# Run a forward pass.
_ = model(random_inputs)
```
XLAでコンパイルされた関数を使用してフォワードパスを実行するには、以下のようにします：
```py
xla_fn = tf.function(model, jit_compile=True)
_ = xla_fn(random_inputs)
```
`model`のデフォルトの `call()` 関数はXLAグラフをコンパイルするために使用されます。ただし、XLAにコンパイルしたい他のモデル関数がある場合、それも可能です。以下はその方法です：
```py
my_xla_fn = tf.function(model.my_xla_fn, jit_compile=True)
```
## Running a TF text generation model with XLA from 🤗 Transformers
🤗 Transformers内でXLAでの高速化された生成を有効にするには、最新バージョンの`transformers`がインストールされている必要があります。次のコマンドを実行してインストールできます：
```bash
pip install transformers --upgrade
```
次に、次のコードを実行できます：
```py
import tensorflow as tf
from transformers import AutoTokenizer, TFAutoModelForCausalLM
# Will error if the minimal version of Transformers is not installed.
from transformers.utils import check_min_version
check_min_version("4.21.0")
tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2", padding_side="left", pad_token="</s>")
model = TFAutoModelForCausalLM.from_pretrained("openai-community/gpt2")
input_string = ["TensorFlow is"]
# One line to create an XLA generation function
xla_generate = tf.function(model.generate, jit_compile=True)
tokenized_input = tokenizer(input_string, return_tensors="tf")
generated_tokens = xla_generate(**tokenized_input, num_beams=2)
decoded_text = tokenizer.decode(generated_tokens[0], skip_special_tokens=True)
print(f"Generated -- {decoded_text}")
# Generated -- TensorFlow is an open-source, open-source, distributed-source application # framework for the
```
`generate()`でXLAを有効にするのは、たった一行のコードです。コードの残り部分は変更されていません。ただし、XLA固有のいくつかの注意点が上記のコードスニペットにあります。これらに注意する必要があり、XLAがもたらす速度向上を実現するためにそれらを把握することが重要です。次のセクションでこれらについて詳しく説明します。
## Gotchas to be aware of
XLAを有効にした関数（上記の`xla_generate()`など）を初めて実行すると、内部で計算グラフを推論しようとしますが、これは時間がかかります。このプロセスは["トレーシング"（tracing）](https://www.tensorflow.org/guide/intro_to_graphs#when_is_a_function_tracing)として知られています。
生成時間が高速ではないことに気付くかもしれません。`xla_generate()`（または他のXLA対応関数）の連続呼び出しでは、関数への入力が最初に計算グラフが構築されたときと同じ形状に従っている場合、計算グラフを推論する必要はありません。これは、入力形状が固定されているモダリティ（例：画像）には問題ありませんが、変数の入力形状モダリティ（例：テキスト）を扱う場合には注意が必要です。
`xla_generate()`が常に同じ入力形状で動作するようにするには、トークナイザを呼び出す際に`padding`引数を指定できます。
```py
import tensorflow as tf
from transformers import AutoTokenizer, TFAutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2", padding_side="left", pad_token="</s>")
model = TFAutoModelForCausalLM.from_pretrained("openai-community/gpt2")
input_string = ["TensorFlow is"]
xla_generate = tf.function(model.generate, jit_compile=True)
# Here, we call the tokenizer with padding options.
tokenized_input = tokenizer(input_string, pad_to_multiple_of=8, padding=True, return_tensors="tf")
generated_tokens = xla_generate(**tokenized_input, num_beams=2)
decoded_text = tokenizer.decode(generated_tokens[0], skip_special_tokens=True)
print(f"Generated -- {decoded_text}")
```
これにより、`xla_generate()`への入力が常にトレースされた形状の入力を受け取ることを確認し、生成時間の高速化を実現できます。以下のコードでこれを確認できます：
```py
import time
import tensorflow as tf
from transformers import AutoTokenizer, TFAutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2", padding_side="left", pad_token="</s>")
model = TFAutoModelForCausalLM.from_pretrained("openai-community/gpt2")
xla_generate = tf.function(model.generate, jit_compile=True)
for input_string in ["TensorFlow is", "TensorFlow is a", "TFLite is a"]:
tokenized_input = tokenizer(input_string, pad_to_multiple_of=8, padding=True, return_tensors="tf")
start = time.time_ns()
generated_tokens = xla_generate(**tokenized_input, num_beams=2)
end = time.time_ns()
print(f"Execution time -- {(end - start) / 1e6:.1f} ms\n")
```
Tesla T4 GPUを使用すると、次のような出力が期待されます：
```bash
Execution time -- 30819.6 ms
Execution time -- 79.0 ms
Execution time -- 78.9 ms
```
最初の`xla_generate()`呼び出しはトレーシングのために時間がかかりますが、連続する呼び出しは桁違いに高速です。生成オプションのいかなる変更も、再トレーシングを引き起こし、生成時間の遅延を引き起こすことに注意してください。
このドキュメントでは、🤗 Transformersが提供するテキスト生成オプションをすべて網羅していません。高度なユースケースについてはドキュメンテーションを参照することをお勧めします。
## Additional Resources
ここでは、🤗 Transformersと一般的なXLAについてさらに詳しく学びたい場合のいくつかの追加リソースを提供します。
* [このColab Notebook](https://colab.research.google.com/github/huggingface/blog/blob/main/notebooks/91_tf_xla_generate.ipynb)では、XLA対応のエンコーダーデコーダー（[T5](https://huggingface.co/docs/transformers/model_doc/t5)など）およびデコーダー専用（[GPT2](https://huggingface.co/docs/transformers/model_doc/gpt2)など）テキスト生成モデルを試すための対話型デモが提供されています。
* [このブログ記事](https://huggingface.co/blog/tf-xla-generate)では、XLA対応モデルの比較ベンチマークの概要と、TensorFlowでのXLAについての友好的な紹介が提供されています。
* [このブログ記事](https://blog.tensorflow.org/2022/11/how-hugging-face-improved-text-generation-performance-with-xla.html)では、🤗 TransformersのTensorFlowモデルにXLAサポートを追加する際の設計哲学について説明しています。
* 一般的なXLAとTensorFlowグラフについて詳しく学ぶためのおすすめの投稿：
* [XLA: 機械学習用の最適化コンパイラ](https://www.tensorflow.org/xla)
* [グラフと`tf.function`の紹介](https://www.tensorflow.org/guide/intro_to_graphs)
* [`tf.function`を使用したパフォーマンス向上](https://www.tensorflow.org/guide/function)
| transformers/docs/source/ja/tf_xla.md/0 | {
"file_path": "transformers/docs/source/ja/tf_xla.md",
"repo_id": "transformers",
"token_count": 4413
} | 30 |
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# 큰 모델 인스턴스화 [[instantiating-a-big-model]]
매우 큰 사전훈련된 모델을 사용하려면, RAM 사용을 최소화해야 하는 과제가 있습니다. 일반적인 PyTorch 워크플로우는 다음과 같습니다:
1. 무작위 가중치로 모델을 생성합니다.
2. 사전훈련된 가중치를 불러옵니다.
3. 사전훈련된 가중치를 무작위 모델에 적용합니다.
1단계와 2단계 모두 모델의 전체 버전을 메모리에 적재해야 하며, 대부분 문제가 없지만 모델이 기가바이트급의 용량을 차지하기 시작하면 복사본 2개가 RAM을 초과하여 메모리 부족 이슈를 야기할 수 있습니다. 더 심각한 문제는 분산 학습을 위해 `torch.distributed`를 사용하는 경우, 프로세스마다 사전훈련된 모델을 로드하고 복사본을 2개씩 RAM에 저장한다는 것입니다.
<Tip>
무작위로 생성된 모델은 "비어 있는" (즉 그때 메모리에 있던 것으로 이뤄진) 텐서로 초기화되며 메모리 공간을 차지합니다. 초기화된 모델/파라미터의 종류에 적합한 분포(예: 정규 분포)에 따른 무작위 초기화는 가능한 한 빠르게 하기 위해 초기화되지 않은 가중치에 대해 3단계 이후에만 수행됩니다!
</Tip>
이 안내서에서는 Transformers가 이 문제를 해결하기 위해 제공하는 솔루션을 살펴봅니다. 주의할 점은 아직 활발히 개발 중인 분야이므로 여기서 설명하는 API가 앞으로 약간 변경될 수 있다는 것입니다.
## 샤딩된 체크포인트 [[sharded-checkpoints]]
4.18.0 버전 이후, 10GB 이상의 공간을 차지하는 모델 체크포인트는 자동으로 작은 조각들로 샤딩됩니다. `model.save_pretrained(save_dir)`를 실행할 때 하나의 단일 체크포인트를 가지게 될 대신, 여러 부분 체크포인트(각각의 크기는 10GB 미만)와 매개변수 이름을 해당 파일에 매핑하는 인덱스가 생성됩니다.
`max_shard_size` 매개변수로 샤딩 전 최대 크기를 제어할 수 있으므로, 이 예제를 위해 샤드 크기가 작은 일반 크기의 모델을 사용하겠습니다: 전통적인 BERT 모델을 사용해 봅시다.
```py
from transformers import AutoModel
model = AutoModel.from_pretrained("google-bert/bert-base-cased")
```
[`~PreTrainedModel.save_pretrained`]을 사용하여 모델을 저장하면, 모델의 구성과 가중치가 들어있는 두 개의 파일이 있는 새 폴더가 생성됩니다:
```py
>>> import os
>>> import tempfile
>>> with tempfile.TemporaryDirectory() as tmp_dir:
... model.save_pretrained(tmp_dir)
... print(sorted(os.listdir(tmp_dir)))
['config.json', 'pytorch_model.bin']
```
이제 최대 샤드 크기를 200MB로 사용해 봅시다:
```py
>>> with tempfile.TemporaryDirectory() as tmp_dir:
... model.save_pretrained(tmp_dir, max_shard_size="200MB")
... print(sorted(os.listdir(tmp_dir)))
['config.json', 'pytorch_model-00001-of-00003.bin', 'pytorch_model-00002-of-00003.bin', 'pytorch_model-00003-of-00003.bin', 'pytorch_model.bin.index.json']
```
모델의 구성에 더해, 세 개의 다른 가중치 파일과 파라미터 이름과 해당 파일의 매핑이 포함된 `index.json` 파일을 볼 수 있습니다. 이러한 체크포인트는 [`~PreTrainedModel.from_pretrained`] 메서드를 사용하여 완전히 다시 로드할 수 있습니다:
```py
>>> with tempfile.TemporaryDirectory() as tmp_dir:
... model.save_pretrained(tmp_dir, max_shard_size="200MB")
... new_model = AutoModel.from_pretrained(tmp_dir)
```
큰 모델의 경우 이러한 방식으로 처리하는 주된 장점은 위에서 보여준 흐름의 2단계에서, 각 샤드가 이전 샤드 다음에 로드되므로 메모리 사용량이 모델 크기와 가장 큰 샤드의 크기를 초과하지 않는다는 점입니다.
이 인덱스 파일은 키가 체크포인트에 있는지, 그리고 해당 가중치가 어디에 저장되어 있는지를 결정하는 데 사용됩니다. 이 인덱스를 json과 같이 로드하고 딕셔너리를 얻을 수 있습니다:
```py
>>> import json
>>> with tempfile.TemporaryDirectory() as tmp_dir:
... model.save_pretrained(tmp_dir, max_shard_size="200MB")
... with open(os.path.join(tmp_dir, "pytorch_model.bin.index.json"), "r") as f:
... index = json.load(f)
>>> print(index.keys())
dict_keys(['metadata', 'weight_map'])
```
메타데이터는 현재 모델의 총 크기만 포함됩니다. 앞으로 다른 정보를 추가할 계획입니다:
```py
>>> index["metadata"]
{'total_size': 433245184}
```
가중치 맵은 이 인덱스의 주요 부분으로, 각 매개변수 이름(PyTorch 모델 `state_dict`에서 보통 찾을 수 있는)을 해당 파일에 매핑합니다:
```py
>>> index["weight_map"]
{'embeddings.LayerNorm.bias': 'pytorch_model-00001-of-00003.bin',
'embeddings.LayerNorm.weight': 'pytorch_model-00001-of-00003.bin',
...
```
만약 [`~PreTrainedModel.from_pretrained`]를 사용하지 않고 모델 내에서 이러한 샤딩된 체크포인트를 직접 가져오려면 (전체 체크포인트를 위해 `model.load_state_dict()`를 수행하는 것처럼), [`~modeling_utils.load_sharded_checkpoint`]를 사용해야 합니다.
```py
>>> from transformers.modeling_utils import load_sharded_checkpoint
>>> with tempfile.TemporaryDirectory() as tmp_dir:
... model.save_pretrained(tmp_dir, max_shard_size="200MB")
... load_sharded_checkpoint(model, tmp_dir)
```
## 저(低)메모리 로딩 [[low-memory-loading]]
샤딩된 체크포인트는 위에서 언급한 작업 흐름의 2단계에서 메모리 사용량을 줄이지만, 저(低)메모리 설정에서 모델을 사용하기 위해 우리의 Accelerate 라이브러리를 기반으로 한 도구를 활용하는 것이 좋습니다.
자세한 사항은 다음 가이드를 참조해주세요: [Accelerate로 대규모 모델 가져오기 (영문)](../en/main_classes/model#large-model-loading) | transformers/docs/source/ko/big_models.md/0 | {
"file_path": "transformers/docs/source/ko/big_models.md",
"repo_id": "transformers",
"token_count": 4438
} | 31 |
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Licensed under the Apache License, Version 2.0 (the "License");
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# 모델 학습 해부하기 [[model-training-anatomy]]
모델 훈련 속도와 메모리 활용의 효율성을 향상시키기 위해 적용할 수 있는 성능 최적화 기술을 이해하려면 GPU가 훈련 중에 어떻게 활용되는지, 그리고 수행되는 연산에 따라 연산 강도가 어떻게 변하는지에 익숙해져야 합니다.
먼저 GPU 활용과 모델 훈련 실행에 대한 예시를 살펴보겠습니다. 데모를 위해 몇몇 라이브러리를 설치해야 합니다:
```bash
pip install transformers datasets accelerate nvidia-ml-py3
```
`nvidia-ml-py3` 라이브러리는 Python 내부에서 모델의 메모리 사용량을 모니터링할 수 있게 해줍니다. 터미널의 `nvidia-smi` 명령어에 익숙할 수 있는데, 이 라이브러리는 Python에서 직접 동일한 정보에 접근할 수 있게 해줍니다.
그 다음, 100과 30000 사이의 무작위 토큰 ID와 분류기를 위한 이진 레이블인 더미 데이터를 생성합니다.
길이가 각각 512인 총 512개의 시퀀스를 가져와 PyTorch 형식의 [`~datasets.Dataset`]에 저장합니다.
```py
>>> import numpy as np
>>> from datasets import Dataset
>>> seq_len, dataset_size = 512, 512
>>> dummy_data = {
... "input_ids": np.random.randint(100, 30000, (dataset_size, seq_len)),
... "labels": np.random.randint(0, 1, (dataset_size)),
... }
>>> ds = Dataset.from_dict(dummy_data)
>>> ds.set_format("pt")
```
GPU 활용 및 [`Trainer`]로 실행한 훈련 과정에 대한 요약 통계를 출력하기 위해 두 개의 도우미 함수를 정의하겠습니다:
```py
>>> from pynvml import *
>>> def print_gpu_utilization():
... nvmlInit()
... handle = nvmlDeviceGetHandleByIndex(0)
... info = nvmlDeviceGetMemoryInfo(handle)
... print(f"GPU memory occupied: {info.used//1024**2} MB.")
>>> def print_summary(result):
... print(f"Time: {result.metrics['train_runtime']:.2f}")
... print(f"Samples/second: {result.metrics['train_samples_per_second']:.2f}")
... print_gpu_utilization()
```
시작할 때 GPU 메모리가 비어 있는지 확인해 봅시다:
```py
>>> print_gpu_utilization()
GPU memory occupied: 0 MB.
```
좋습니다. 모델을 로드하기 전에는 예상대로 GPU 메모리가 점유되지 않았습니다. 그렇지 않다면 사용자의 기기에서 GPU 메모리를 사용하는 모든 프로세스를 중단해야 합니다. 그러나 사용자는 모든 여유 GPU 메모리를 사용할 수는 없습니다. 모델이 GPU에 로드될 때 커널도 로드되므로 1-2GB의 메모리를 차지할 수 있습니다. 얼마나 되는지 확인하기 위해 GPU에 작은 텐서를 로드하여 커널이 로드되도록 트리거합니다.
```py
>>> import torch
>>> torch.ones((1, 1)).to("cuda")
>>> print_gpu_utilization()
GPU memory occupied: 1343 MB.
```
커널만으로도 GPU 메모리의 1.3GB를 차지합니다. 이제 모델이 얼마나 많은 공간을 사용하는지 확인해 보겠습니다.
## 모델 로드 [[load-model]]
우선, `google-bert/bert-large-uncased` 모델을 로드합니다. 모델의 가중치를 직접 GPU에 로드해서 가중치만이 얼마나 많은 공간을 차지하는지 확인할 수 있습니다.
```py
>>> from transformers import AutoModelForSequenceClassification
>>> model = AutoModelForSequenceClassification.from_pretrained("google-bert/bert-large-uncased").to("cuda")
>>> print_gpu_utilization()
GPU memory occupied: 2631 MB.
```
모델의 가중치만으로도 GPU 메모리를 1.3 GB 차지하는 것을 볼 수 있습니다. 정확한 숫자는 사용하는 GPU에 따라 다릅니다. 최신 GPU에서는 모델 사용 속도를 높이는 최적화된 방식으로 가중치가 로드되므로, 모델이 더 많은 공간을 차지할 수 있습니다. 이제 `nvidia-smi` CLI와 동일한 결과를 얻는지 빠르게 확인할 수 있습니다:
```bash
nvidia-smi
```
```bash
Tue Jan 11 08:58:05 2022
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 460.91.03 Driver Version: 460.91.03 CUDA Version: 11.2 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 Tesla V100-SXM2... On | 00000000:00:04.0 Off | 0 |
| N/A 37C P0 39W / 300W | 2631MiB / 16160MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| 0 N/A N/A 3721 C ...nvs/codeparrot/bin/python 2629MiB |
+-----------------------------------------------------------------------------+
```
이전과 동일한 숫자가 출력되고 16GB 메모리를 가진 V100 GPU를 사용하고 있다는 것도 볼 수 있습니다. 그러므로 이제 모델 훈련을 시작하여 GPU 메모리 사용량이 어떻게 달라지는지 볼 수 있습니다. 우선 몇몇 표준 훈련 인수를 설정합니다:
```py
default_args = {
"output_dir": "tmp",
"evaluation_strategy": "steps",
"num_train_epochs": 1,
"log_level": "error",
"report_to": "none",
}
```
<Tip>
여러 실험을 실행할 계획이라면, 실험 간에 메모리를 제대로 비우기 위해서 Python 커널을 실험 사이마다 재시작해야 합니다.
</Tip>
## 기본 훈련에서의 메모리 활용 [[memory-utilization-at-vanilla-training]]
[`Trainer`]를 사용하여, GPU 성능 최적화 기술을 사용하지 않고 배치 크기가 4인 모델을 훈련시키겠습니다:
```py
>>> from transformers import TrainingArguments, Trainer, logging
>>> logging.set_verbosity_error()
>>> training_args = TrainingArguments(per_device_train_batch_size=4, **default_args)
>>> trainer = Trainer(model=model, args=training_args, train_dataset=ds)
>>> result = trainer.train()
>>> print_summary(result)
```
```
Time: 57.82
Samples/second: 8.86
GPU memory occupied: 14949 MB.
```
우리는 비교적 작은 배치 크기로도 전체 GPU 메모리를 거의 다 차지하는 것을 볼 수 있습니다. 그러나 배치 크기가 클수록 모델 수렴 속도가 빨라지고 최종 성능이 향상되는 경우가 많습니다. 그래서 이상적으로는 GPU 제한이 아닌 우리 모델의 요구사항에 맞게 배치 크기를 조정하려고 합니다. 흥미롭게도 우리는 모델의 크기보다 훨씬 더 많은 메모리를 사용합니다. 왜 이런 현상이 발생하는지 조금 더 잘 이해하기 위해 모델의 연산과 메모리 요구 사항을 살펴보겠습니다.
## 모델의 연산 해부하기 [[anatomy-of-models-operations]]
트랜스포머 아키텍처에는 연산 강도(compute-intensity)에 따라 그룹화된 3가지 주요 연산 그룹이 있습니다.
1. **텐서 축약(Tensor Contractions)**
선형 레이어와 멀티헤드 어텐션의 구성 요소는 모두 **행렬-행렬 곱셈(matrix-matrix multiplications)**을 일괄적으로 처리합니다. 이 연산은 트랜스포머 훈련에서 가장 연산 강도가 높은 부분입니다.
2. **통계 정규화(Statistical Normalizations)**
소프트맥스와 레이어 정규화는 텐서 축약보다 연산 강도가 낮습니다. 하나 이상의 **감소 연산(reduction operations)**을 포함하며, 그 결과는 map을 통해 적용됩니다.
3. **원소별 연산자(Element-wise Operators)**
그 외 연산자들, **편향(biases), 드롭아웃(dropout), 활성화 함수(activations), 잔차 연결(residual connections)**이 여기에 해당합니다. 이 연산들은 연산 강도가 가장 낮습니다.
이러한 지식은 성능 병목 현상을 분석할 때 도움이 될 수 있습니다.
이 내용은 [Data Movement Is All You Need: A Case Study on Optimizing Transformers 2020](https://arxiv.org/abs/2007.00072)을 참고하였습니다.
## 모델의 메모리 구조 [[anatomy-of-models-memory]]
모델을 훈련시키는 데는 단순히 GPU에 모델을 올리는 것보다 훨씬 더 많은 메모리를 사용한다는 것을 보았습니다. 이는 훈련 중 GPU 메모리를 사용하는 많은 구성 요소가 있기 때문입니다. GPU 메모리의 구성 요소는 다음과 같습니다:
1. 모델 가중치
2. 옵티마이저 상태
3. 그라디언트
4. 그라디언트 계산을 위해 저장된 순방향 활성화
5. 임시 버퍼
6. 기능별 메모리
AdamW를 사용하여 혼합 정밀도로 훈련된 일반적인 모델은 모델 파라미터당 18 바이트와 활성화 메모리가 필요합니다. 추론 단계에서는 옵티마이저와 그라디언트가 필요하지 않으므로 이들은 제외합니다. 따라서 혼합 정밀도 추론의 경우 모델 매개변수당 6 바이트와 활성화 메모리가 필요합니다.
자세히 살펴보겠습니다.
**모델 가중치:**
- fp32 훈련의 경우 매개 변수 수 * 4 바이트
- 혼합 정밀도 훈련의 경우 매개 변수 수 * 6 바이트 (메모리에 fp32와 fp16 두 가지 모델을 유지)
**옵티마이저 상태:**
- 일반 AdamW의 경우 매개 변수 수 * 8 바이트 (2가지 상태 유지)
- [bitsandbytes](https://github.com/TimDettmers/bitsandbytes)와 같은 8비트 AdamW 옵티마이저의 경우 매개 변수 수 * 2 바이트
- Momentum을 가진 SGD와 같은 옵티마이저의 경우 매개 변수 수 * 4 바이트 (하나의 상태만 유지)
**그라디언트**
- fp32 또는 혼합 정밀도 훈련의 경우 매개 변수 수 * 4 바이트 (그라디언트는 항상 fp32으로 유지됩니다.)
**순방향 활성화**
- 크기는 여러 요인에 따라 달라지며, 주요 요인은 시퀀스 길이, 은닉 상태의 크기 및 배치 크기입니다.
순방향 및 역방향 함수에서 전달 및 반환되는 입력과 출력이 있으며, 그라디언트 계산을 위해 저장된 순방향 활성화가 있습니다.
**임시 메모리**
더불어 모든 종류의 임시 변수는 연산이 완료되면 곧바로 해제되지만, 그 순간에는 추가 메모리가 필요할 수 있고 OOM을 유발할 수 있습니다. 따라서 코딩할 때 이러한 임시 변수에 대해 전략적으로 생각하고 때로는 더 이상 필요 없는 임시 변수를 즉시 명시적으로 메모리에서 제거하는 것이 중요합니다.
**기능별 메모리**
그런 다음, 소프트웨어에는 특별한 메모리 요구 사항이 있을 수 있습니다. 예를 들어, 빔 검색을 사용하여 텍스트를 생성할 때 소프트웨어는 입력과 출력 사본을 여러 개 유지해야 합니다.
**`forward` vs `backward` 실행 속도**
합성곱과 선형 레이어의 경우 순방향에 비해 역방향에서는 2배의 플롭스가 필요하므로 일반적으로 2배 정도 느리게 변환됩니다(역방향의 경우 사이즈가 부자연스럽기 때문에, 때로는 더욱 느릴 수도 있습니다). 활성화는 일반적으로 대역폭이 제한되어 있으며, 일반적으로 순방향보다 역방향에서 더 많은 데이터를 읽어야 합니다. (예를 들어, 순방향 활성화 시 한 번 씩 읽고 쓰지만, 역방향 활성화에서는 순방향 gradOutput과 출력에 대해 총 두 번 읽고 gradInput에 대해 한 번 씁니다.)
보다시피, GPU 메모리를 절약하거나 작업 속도를 높일 수 있는 몇 가지 방법이 있습니다.
이제 GPU 활용과 계산 속도에 영향을 주는 것이 무엇인지를 이해했으므로, [Methods and tools for efficient training on a single GPU](perf_train_gpu_one) 문서 페이지를 참조하여 성능 최적화 기법에 대해 알아보세요. | transformers/docs/source/ko/model_memory_anatomy.md/0 | {
"file_path": "transformers/docs/source/ko/model_memory_anatomy.md",
"repo_id": "transformers",
"token_count": 8877
} | 32 |
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# 이념과 목표 [[philosophy]]
🤗 Transformers는 다음과 같은 목적으로 만들어진 독자적인 라이브러리입니다:
- 대규모 Transformers 모델을 사용하거나 연구하거나 확장하려는 기계 학습 연구원 및 교육자를 위한 것입니다.
- 모델을 미세 조정하거나 제작용으로 사용하고자 하는 실전 개발자를 위한 것입니다.
- 특정 기계 학습 작업을 해결하기 위해 사전훈련된 모델을 다운로드하고 사용하기만 하려는 엔지니어를 위한 것입니다.
이 라이브러리는 두 가지 주요 목표를 가지고 설계되었습니다:
1. 사용하기 쉽고 빠르게 만드는 것:
- 학습해야 할 사용자 대상 추상화의 수를 제한했습니다. 실제로 거의 추상화가 없으며, 각 모델을 사용하기 위해 필요한 세 가지 표준 클래스인 [configuration](main_classes/configuration), [models](main_classes/model) 및 전처리 클래스인 ([tokenizer](main_classes/tokenizer)는 NLP용, [image processor](main_classes/image_processor)는 비전용, [feature extractor](main_classes/feature_extractor)는 오디오용, [processor](main_classes/processors)는 멀티모달 입력용)만 사용합니다.
- 이러한 클래스는 공통적인 `from_pretrained()` 메서드를 사용하여 미리 훈련된 인스턴스에서 간단하고 통일된 방식으로 초기화할 수 있습니다. 이 메소드는 미리 훈련된 체크포인트에서 관련 클래스 인스턴스와 관련 데이터(구성의 하이퍼파라미터, 토크나이저의 어휘, 모델의 가중치)를 (필요한 경우) 다운로드하고 캐시하며 가져옵니다. 체크포인트는 [Hugging Face Hub](https://huggingface.co/models)에서 제공되거나 사용자 자체의 저장된 체크포인트에서 제공됩니다.
- 이 세 가지 기본 클래스 위에 라이브러리는 [`pipeline`] API를 제공하여 주어진 작업에 대해 모델을 빠르게 추론하는 데 사용하고, [`Trainer`]를 제공하여 PyTorch 모델을 빠르게 훈련하거나 미세 조정할 수 있도록 합니다(모든 TensorFlow 모델은 `Keras.fit`과 호환됩니다).
- 결과적으로, 이 라이브러리는 신경망을 구축하기 위한 모듈식 도구 상자가 아닙니다. 라이브러리를 확장하거나 구축하려면 일반적인 Python, PyTorch, TensorFlow, Keras 모듈을 사용하고 라이브러리의 기본 클래스를 상속하여 모델 로딩 및 저장과 같은 기능을 재사용하면 됩니다. 모델에 대한 코딩 철학에 대해 더 자세히 알고 싶다면 [Repeat Yourself](https://huggingface.co/blog/transformers-design-philosophy) 블로그 글을 확인해보세요.
2. 원래 모델과 가능한 한 근접한 성능을 제공하는 최신 모델을 제공하는 것:
- 각 아키텍처에 대해 공식 저자가 제공한 결과를 재현하는 적어도 한 가지 예제를 제공합니다.
- 코드는 원래 코드와 가능한 한 유사하게 유지되므로 PyTorch 코드는 TensorFlow 코드로 변환되어 *pytorchic*하지 않을 수 있고, 그 반대의 경우도 마찬가지입니다.
기타 목표 몇 가지:
- 모델의 내부를 가능한 일관되게 노출시키기:
- 전체 은닉 상태와 어텐션 가중치에 대한 액세스를 단일 API를 사용하여 제공합니다.
- 전처리 클래스 및 기본 모델 API는 모델 간에 쉽게 전환할 수 있도록 표준화되어 있습니다.
- 미세 조정 및 모델 탐색을 위한 유망한 도구들을 주관적으로 선택하기:
- 미세 조정을 위해 어휘 및 임베딩에 새로운 토큰을 간단하고 일관된 방식으로 추가하는 방법을 제공합니다.
- Transformer 헤드를 마스킹하고 가지치기하는 간단한 방법을 제공합니다.
- PyTorch, TensorFlow 2.0 및 Flax 간에 쉽게 전환할 수 있도록 하여 하나의 프레임워크로 훈련하고 다른 프레임워크로 추론할 수 있게 합니다.
## 주요 개념 [[main-concepts]]
이 라이브러리는 각 모델에 대해 세 가지 유형의 클래스를 기반으로 구축되었습니다:
- **모델 클래스**는 라이브러리에서 제공하는 사전 훈련된 가중치와 함께 작동하는 PyTorch 모델([torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module)), Keras 모델([tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model)), JAX/Flax 모델([flax.linen.Module](https://flax.readthedocs.io/en/latest/api_reference/flax.linen/module.html))일 수 있습니다.
- **구성 클래스**는 모델을 구축하는 데 필요한 하이퍼파라미터(예: 레이어 수 및 은닉 크기)를 저장합니다. 구성 클래스를 직접 인스턴스화할 필요는 없습니다. 특히, 수정 없이 고 사전 학습된 모델을 사용하는 경우 모델을 생성하면 모델의 일부인 구성을 자동으로 인스턴스화됩니다.
- **전처리 클래스**는 원시 데이터를 모델이 수용하는 형식으로 변환합니다. [Tokenizer](main_classes/tokenizer)는 각 모델의 어휘를 저장하고, 문자열을 토큰 임베딩 인덱스 리스트로 인코딩하고 디코딩하기 위한 메소드를 제공합니다. [Image processors](main_classes/image_processor)는 비전 입력을 전처리하고, [feature extractors](main_classes/feature_extractor)는 오디오 입력을 전처리하며, [processor](main_classes/processors)는 멀티모달 입력을 처리합니다.
모든 이러한 클래스는 사전 훈련된 인스턴스에서 인스턴스화하고 로컬로 저장하며, 세 가지 메소드를 사용하여 Hub에서 공유할 수 있습니다:
- `from_pretrained()` 메소드를 사용하면 라이브러리 자체에서 제공하는 사전 훈련된 버전(지원되는 모델은 [Model Hub](https://huggingface.co/models)에서 찾을 수 있음)이나 사용자가 로컬로 저장한 경우(또는 서버에 저장한 경우)의 모델, 구성 및 전처리 클래스를 인스턴스화할 수 있습니다.
- `save_pretrained()` 메소드를 사용하면 모델, 구성 및 전처리 클래스를 로컬로 저장하여 `from_pretrained()`를 사용하여 다시 가져올 수 있습니다.
- `push_to_hub()` 메소드를 사용하면 모델, 구성 및 전처리 클래스를 Hub에 공유하여 모두에게 쉽게 접근할 수 있습니다.
| transformers/docs/source/ko/philosophy.md/0 | {
"file_path": "transformers/docs/source/ko/philosophy.md",
"repo_id": "transformers",
"token_count": 5165
} | 33 |
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the License. You may obtain a copy of the License at
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# TensorFlow 모델을 위한 XLA 통합 [[xla-integration-for-tensorflow-models]]
[[open-in-colab]]
XLA(Accelerated Linear Algebra)는 TensorFlow 모델의 실행 시간을 가속화하기 위한 컴파일러입니다. [공식 문서](https://www.tensorflow.org/xla)에 따르면 다음과 같습니다:
XLA(Accelerated Linear Algebra)는 선형 대수를 위한 도메인 특화 컴파일러로, TensorFlow 모델을 소스 코드 변경 없이 가속화할 수 있습니다.
TensorFlow에서 XLA를 사용하는 것은 간단합니다. XLA는 `tensorflow` 라이브러리 내에 패키지로 제공되며, [`tf.function`](https://www.tensorflow.org/guide/intro_to_graphs)과 같은 그래프 생성 함수에서 `jit_compile` 인수를 사용하여 활성화할 수 있습니다. `fit()` 및 `predict()`와 같은 Keras 메소드를 사용하는 경우, `jit_compile` 인수를 `model.compile()`에 전달하여 XLA를 간단하게 활성화할 수 있습니다. 그러나 XLA는 이러한 메소드에 국한되지 않고 임의의 `tf.function`을 가속화하는 데에도 사용할 수 있습니다.
🤗 Transformers에서는 [GPT2](https://huggingface.co/docs/transformers/model_doc/gpt2), [T5](https://huggingface.co/docs/transformers/model_doc/t5), [OPT](https://huggingface.co/docs/transformers/model_doc/opt)와 같은 모델의 텍스트 생성, 그리고 [Whisper](https://huggingface.co/docs/transformers/model_doc/whisper)와 같은 모델의 음성 처리를 포함하여 여러 TensorFlow 메소드가 XLA와 호환되도록 다시 작성되었습니다.
정확한 속도 향상은 모델에 따라 다르지만, 🤗 Transformers 내의 TensorFlow 텍스트 생성 모델의 경우 최대 100배의 속도 향상을 확인했습니다. 이 문서에서는 이러한 모델에 대해 XLA를 사용하여 최대 성능을 얻는 방법을 설명합니다. 또한 XLA 통합의 벤치마크 및 디자인 철학에 대한 추가 자료 링크도 제공할 것입니다.
## XLA를 사용하여 TF 함수 실행하기 [[running-tf-functions-with-xla]]
TensorFlow에서 다음과 같은 모델을 고려해 봅시다:
```py
import tensorflow as tf
model = tf.keras.Sequential(
[tf.keras.layers.Dense(10, input_shape=(10,), activation="relu"), tf.keras.layers.Dense(5, activation="softmax")]
)
```
위 모델은 차원이 `(10, )`인 입력을 받습니다. 다음과 같이 모델을 사용하여 순전파를 실행할 수 있습니다:
```py
# 모델에 대한 임의의 입력을 생성합니다.
batch_size = 16
input_vector_dim = 10
random_inputs = tf.random.normal((batch_size, input_vector_dim))
# 순전파를 실행합니다.
_ = model(random_inputs)
```
XLA로 컴파일된 함수로 순전파를 실행하려면 다음과 같이 해야 합니다:
```py
xla_fn = tf.function(model, jit_compile=True)
_ = xla_fn(random_inputs)
```
`model`의 기본 `call()` 함수는 XLA 그래프를 컴파일하는 데 사용됩니다. 그러나 다른 모델 함수를 XLA로 컴파일하려면 다음과 같이 할 수도 있습니다:
```py
my_xla_fn = tf.function(model.my_xla_fn, jit_compile=True)
```
## 🤗 Transformers에서 XLA를 사용하여 TF 텍스트 생성 모델 실행하기 [[running-a-tf-text-generation-model-with-xla-from-transformers]]
🤗 Transformers에서 XLA로 가속화된 생성을 활성화하려면 최신 버전의 `transformers`가 설치되어 있어야 합니다. 다음과 같이 설치할 수 있습니다:
```bash
pip install transformers --upgrade
```
그리고 다음 코드를 실행할 수 있습니다:
```py
import tensorflow as tf
from transformers import AutoTokenizer, TFAutoModelForCausalLM
# 최소 버전의 Transformers가 설치되어 있지 않다면 오류가 발생합니다.
from transformers.utils import check_min_version
check_min_version("4.21.0")
tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2", padding_side="left", pad_token="</s>")
model = TFAutoModelForCausalLM.from_pretrained("openai-community/gpt2")
input_string = ["TensorFlow is"]
# XLA 생성 함수를 만들기 위한 한 줄
xla_generate = tf.function(model.generate, jit_compile=True)
tokenized_input = tokenizer(input_string, return_tensors="tf")
generated_tokens = xla_generate(**tokenized_input, num_beams=2)
decoded_text = tokenizer.decode(generated_tokens[0], skip_special_tokens=True)
print(f"Generated -- {decoded_text}")
# Generated -- TensorFlow is an open-source, open-source, distributed-source application # framework for the
```
알 수 있듯이, `generate()`에서 XLA를 활성화하는 것은 단 한 줄의 코드입니다. 코드의 나머지 부분은 변경되지 않습니다. 그러나 위 코드 스니펫에서는 XLA에 특정한 몇 가지 주의할 점이 있습니다. XLA가 가져다줄 속도 향상을 실현하기 위해서는 이를 알고 있어야 합니다. 다음 섹션에서 이에 대해 논의합니다.
## 주의할 점 [[gotchas-to-be-aware-of]]
XLA 활성화 함수(`xla_generate()`와 같은)를 처음 실행할 때 내부적으로 계산 그래프를 추론하려고 하며, 이는 시간이 소요됩니다. 이 과정은 [“추적(tracing)”](https://www.tensorflow.org/guide/intro_to_graphs#when_is_a_function_tracing)이라고 알려져 있습니다.
생성 시간이 빠르지 않다는 것을 알 수 있을 것입니다. `xla_generate()`(또는 다른 XLA 활성화 함수)의 연속 호출은 함수에 전달된 입력이 초기에 구축된 계산 그래프와 동일한 형태를 따른다면, 계산 그래프를 추론할 필요가 없습니다. 이는 입력 형태가 고정된 모달리티(예: 이미지)에는 문제가 되지 않지만, 가변 입력 형태 모달리티(예: 텍스트)를 사용할 때 주의해야 합니다.
`xla_generate()`가 항상 동일한 입력 형태로 동작하도록 하려면, 토크나이저를 호출할 때 `padding` 인수를 지정할 수 있습니다.
```py
import tensorflow as tf
from transformers import AutoTokenizer, TFAutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2", padding_side="left", pad_token="</s>")
model = TFAutoModelForCausalLM.from_pretrained("openai-community/gpt2")
input_string = ["TensorFlow is"]
xla_generate = tf.function(model.generate, jit_compile=True)
# 여기서, padding 옵션이 있는 토크나이저를 호출합니다.
tokenized_input = tokenizer(input_string, pad_to_multiple_of=8, padding=True, return_tensors="tf")
generated_tokens = xla_generate(**tokenized_input, num_beams=2)
decoded_text = tokenizer.decode(generated_tokens[0], skip_special_tokens=True)
print(f"Generated -- {decoded_text}")
```
이렇게 하면 `xla_generate()`에 대한 입력이 항상 추적된 형태로 전달되어 생성 시간이 가속화됩니다. 다음 코드로 이를 확인할 수 있습니다:
```py
import time
import tensorflow as tf
from transformers import AutoTokenizer, TFAutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2", padding_side="left", pad_token="</s>")
model = TFAutoModelForCausalLM.from_pretrained("openai-community/gpt2")
xla_generate = tf.function(model.generate, jit_compile=True)
for input_string in ["TensorFlow is", "TensorFlow is a", "TFLite is a"]:
tokenized_input = tokenizer(input_string, pad_to_multiple_of=8, padding=True, return_tensors="tf")
start = time.time_ns()
generated_tokens = xla_generate(**tokenized_input, num_beams=2)
end = time.time_ns()
print(f"Execution time -- {(end - start) / 1e6:.1f} ms\n")
```
Tesla T4 GPU에서는 다음과 같은 출력을 예상할 수 있습니다:
```bash
Execution time -- 30819.6 ms
Execution time -- 79.0 ms
Execution time -- 78.9 ms
```
`xla_generate()`의 첫 번째 호출은 추적 때문에 시간이 오래 걸리지만, 연속 호출은 몇 배나 빠릅니다. 생성 옵션에 대한 어떤 변경이든 다시 추적을 유발하므로 생성 시간이 느려질 수 있음을 명심하세요.
이 문서에서는 🤗 Transformers에서 제공하는 모든 텍스트 생성 옵션을 다루지 않았습니다. 고급 사용 사례에 대해 문서를 참조하시기 바랍니다.
## 추가 자료 [[additional-resources]]
여기에 🤗 Transformers와 XLA에 대해 더 자세히 알고 싶은 경우 도움이 될 수 있는 몇 가지 추가 자료를 제공합니다.
* [이 Colab 노트북](https://colab.research.google.com/github/huggingface/blog/blob/main/notebooks/91_tf_xla_generate.ipynb)은 XLA와 호환되는 인코더-디코더([T5](https://huggingface.co/docs/transformers/model_doc/t5)와 같은) 및 디코더 전용([GPT2](https://huggingface.co/docs/transformers/model_doc/gpt2)와 같은) 텍스트 생성 모델을 실험해 볼 수 있는 대화형 데모를 제공합니다.
* [이 블로그 글](https://huggingface.co/blog/tf-xla-generate)은 TensorFlow에서 XLA에 대한 친절한 소개와 함께 XLA와 호환되는 모델의 비교 벤치마크에 대한 개요를 제공합니다.
* [이 블로그 글](https://blog.tensorflow.org/2022/11/how-hugging-face-improved-text-generation-performance-with-xla.html)은 🤗 Transformers의 TensorFlow 모델에 XLA 지원을 추가하는 것에 대한 디자인 철학을 논의합니다.
* XLA와 TensorFlow 그래프에 대해 더 자세히 알고 싶은 경우 추천하는 글:
* [XLA: 기계 학습을 위한 최적화 컴파일러](https://www.tensorflow.org/xla)
* [그래프 및 tf.function 소개](https://www.tensorflow.org/guide/intro_to_graphs)
* [tf.function으로 성능 향상하기](https://www.tensorflow.org/guide/function) | transformers/docs/source/ko/tf_xla.md/0 | {
"file_path": "transformers/docs/source/ko/tf_xla.md",
"repo_id": "transformers",
"token_count": 6235
} | 34 |
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除非适用法律要求或书面同意，否则按“按原样”分发的软件，无论是明示还是暗示的，都没有任何担保或条件。请参阅许可证以了解特定语言下的权限和限制。
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# 🤗 加速分布式训练
随着模型变得越来越大，并行性已经成为在有限硬件上训练更大模型和加速训练速度的策略，增加了数个数量级。在Hugging Face，我们创建了[🤗 加速](https://huggingface.co/docs/accelerate)库，以帮助用户在任何类型的分布式设置上轻松训练🤗 Transformers模型，无论是在一台机器上的多个GPU还是在多个机器上的多个GPU。在本教程中，了解如何自定义您的原生PyTorch训练循环，以启用分布式环境中的训练。
## 设置
通过安装🤗 加速开始:
```bash
pip install accelerate
```
然后导入并创建[`~accelerate.Accelerator`]对象。[`~accelerate.Accelerator`]将自动检测您的分布式设置类型，并初始化所有必要的训练组件。您不需要显式地将模型放在设备上。
```py
>>> from accelerate import Accelerator
>>> accelerator = Accelerator()
```
## 准备加速
下一步是将所有相关的训练对象传递给[`~accelerate.Accelerator.prepare`]方法。这包括您的训练和评估DataLoader、一个模型和一个优化器:
```py
>>> train_dataloader, eval_dataloader, model, optimizer = accelerator.prepare(
... train_dataloader, eval_dataloader, model, optimizer
... )
```
## 反向传播
最后一步是用🤗 加速的[`~accelerate.Accelerator.backward`]方法替换训练循环中的典型`loss.backward()`:
```py
>>> for epoch in range(num_epochs):
... for batch in train_dataloader:
... outputs = model(**batch)
... loss = outputs.loss
... accelerator.backward(loss)
... optimizer.step()
... lr_scheduler.step()
... optimizer.zero_grad()
... progress_bar.update(1)
```
如您在下面的代码中所见，您只需要添加四行额外的代码到您的训练循环中即可启用分布式训练！
```diff
+ from accelerate import Accelerator
from transformers import AdamW, AutoModelForSequenceClassification, get_scheduler
+ accelerator = Accelerator()
model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)
optimizer = AdamW(model.parameters(), lr=3e-5)
- device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
- model.to(device)
+ train_dataloader, eval_dataloader, model, optimizer = accelerator.prepare(
+ train_dataloader, eval_dataloader, model, optimizer
+ )
num_epochs = 3
num_training_steps = num_epochs * len(train_dataloader)
lr_scheduler = get_scheduler(
"linear",
optimizer=optimizer,
num_warmup_steps=0,
num_training_steps=num_training_steps
)
progress_bar = tqdm(range(num_training_steps))
model.train()
for epoch in range(num_epochs):
for batch in train_dataloader:
- batch = {k: v.to(device) for k, v in batch.items()}
outputs = model(**batch)
loss = outputs.loss
- loss.backward()
+ accelerator.backward(loss)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
progress_bar.update(1)
```
## 训练
在添加了相关代码行后，可以在脚本或笔记本（如Colaboratory）中启动训练。
### 用脚本训练
如果您从脚本中运行训练，请运行以下命令以创建和保存配置文件:
```bash
accelerate config
```
然后使用以下命令启动训练:
```bash
accelerate launch train.py
```
### 用笔记本训练
🤗 加速还可以在笔记本中运行，如果您计划使用Colaboratory的TPU，则可在其中运行。将负责训练的所有代码包装在一个函数中，并将其传递给[`~accelerate.notebook_launcher`]:
```py
>>> from accelerate import notebook_launcher
>>> notebook_launcher(training_function)
```
有关🤗 加速及其丰富功能的更多信息，请参阅[文档](https://huggingface.co/docs/accelerate)。 | transformers/docs/source/zh/accelerate.md/0 | {
"file_path": "transformers/docs/source/zh/accelerate.md",
"repo_id": "transformers",
"token_count": 2552
} | 35 |
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# 快速上手
[[open-in-colab]]
快来使用 🤗 Transformers 吧！无论你是开发人员还是日常用户，这篇快速上手教程都将帮助你入门并且向你展示如何使用 [`pipeline`] 进行推理，使用 [AutoClass](./model_doc/auto) 加载一个预训练模型和预处理器，以及使用 PyTorch 或 TensorFlow 快速训练一个模型。如果你是一个初学者，我们建议你接下来查看我们的教程或者[课程](https://huggingface.co/course/chapter1/1)，来更深入地了解在这里介绍到的概念。
在开始之前，确保你已经安装了所有必要的库：
```bash
!pip install transformers datasets
```
你还需要安装喜欢的机器学习框架：
<frameworkcontent>
<pt>
```bash
pip install torch
```
</pt>
<tf>
```bash
pip install tensorflow
```
</tf>
</frameworkcontent>
## Pipeline
<Youtube id="tiZFewofSLM"/>
使用 [`pipeline`] 是利用预训练模型进行推理的最简单的方式。你能够将 [`pipeline`] 开箱即用地用于跨不同模态的多种任务。来看看它支持的任务列表：
| **任务** | **描述** | **模态** | **Pipeline** |
|------------------------------|-----------------------------------|-----------------|-----------------------------------------------|
| 文本分类 | 为给定的文本序列分配一个标签 | NLP | pipeline(task="sentiment-analysis") |
| 文本生成 | 根据给定的提示生成文本 | NLP | pipeline(task="text-generation") |
| 命名实体识别 | 为序列里的每个 token 分配一个标签（人, 组织, 地址等等） | NLP | pipeline(task="ner") |
| 问答系统 | 通过给定的上下文和问题, 在文本中提取答案 | NLP | pipeline(task="question-answering") |
| 掩盖填充 | 预测出正确的在序列中被掩盖的token | NLP | pipeline(task="fill-mask") |
| 文本摘要 | 为文本序列或文档生成总结 | NLP | pipeline(task="summarization") |
| 文本翻译 | 将文本从一种语言翻译为另一种语言 | NLP | pipeline(task="translation") |
| 图像分类 | 为图像分配一个标签 | Computer vision | pipeline(task="image-classification") |
| 图像分割 | 为图像中每个独立的像素分配标签（支持语义、全景和实例分割） | Computer vision | pipeline(task="image-segmentation") |
| 目标检测 | 预测图像中目标对象的边界框和类别 | Computer vision | pipeline(task="object-detection") |
| 音频分类 | 给音频文件分配一个标签 | Audio | pipeline(task="audio-classification") |
| 自动语音识别 | 将音频文件中的语音提取为文本 | Audio | pipeline(task="automatic-speech-recognition") |
| 视觉问答 | 给定一个图像和一个问题，正确地回答有关图像的问题 | Multimodal | pipeline(task="vqa") |
创建一个 [`pipeline`] 实例并且指定你想要将它用于的任务，就可以开始了。你可以将 [`pipeline`] 用于任何一个上面提到的任务，如果想知道支持的任务的完整列表，可以查阅 [pipeline API 参考](./main_classes/pipelines)。不过, 在这篇教程中，你将把 [`pipeline`] 用在一个情感分析示例上：
```py
>>> from transformers import pipeline
>>> classifier = pipeline("sentiment-analysis")
```
[`pipeline`] 会下载并缓存一个用于情感分析的默认的[预训练模型](https://huggingface.co/distilbert/distilbert-base-uncased-finetuned-sst-2-english)和分词器。现在你可以在目标文本上使用 `classifier` 了：
```py
>>> classifier("We are very happy to show you the 🤗 Transformers library.")
[{'label': 'POSITIVE', 'score': 0.9998}]
```
如果你有不止一个输入，可以把所有输入放入一个列表然后传给[`pipeline`]，它将会返回一个字典列表：
```py
>>> results = classifier(["We are very happy to show you the 🤗 Transformers library.", "We hope you don't hate it."])
>>> for result in results:
... print(f"label: {result['label']}, with score: {round(result['score'], 4)}")
label: POSITIVE, with score: 0.9998
label: NEGATIVE, with score: 0.5309
```
[`pipeline`] 也可以为任何你喜欢的任务遍历整个数据集。在下面这个示例中，让我们选择自动语音识别作为我们的任务：
```py
>>> import torch
>>> from transformers import pipeline
>>> speech_recognizer = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-base-960h")
```
加载一个你想遍历的音频数据集（查阅 🤗 Datasets [快速开始](https://huggingface.co/docs/datasets/quickstart#audio) 获得更多信息）。比如，加载 [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14) 数据集：
```py
>>> from datasets import load_dataset, Audio
>>> dataset = load_dataset("PolyAI/minds14", name="en-US", split="train") # doctest: +IGNORE_RESULT
```
你需要确保数据集中的音频的采样率与 [`facebook/wav2vec2-base-960h`](https://huggingface.co/facebook/wav2vec2-base-960h) 训练用到的音频的采样率一致：
```py
>>> dataset = dataset.cast_column("audio", Audio(sampling_rate=speech_recognizer.feature_extractor.sampling_rate))
```
当调用 `"audio"` 列时, 音频文件将会自动加载并重采样。
从前四个样本中提取原始波形数组，将它作为列表传给 pipeline：
```py
>>> result = speech_recognizer(dataset[:4]["audio"])
>>> print([d["text"] for d in result])
['I WOULD LIKE TO SET UP A JOINT ACCOUNT WITH MY PARTNER HOW DO I PROCEED WITH DOING THAT', "FODING HOW I'D SET UP A JOIN TO HET WITH MY WIFE AND WHERE THE AP MIGHT BE", "I I'D LIKE TOY SET UP A JOINT ACCOUNT WITH MY PARTNER I'M NOT SEEING THE OPTION TO DO IT ON THE AP SO I CALLED IN TO GET SOME HELP CAN I JUST DO IT OVER THE PHONE WITH YOU AND GIVE YOU THE INFORMATION OR SHOULD I DO IT IN THE AP AND I'M MISSING SOMETHING UQUETTE HAD PREFERRED TO JUST DO IT OVER THE PHONE OF POSSIBLE THINGS", 'HOW DO I THURN A JOIN A COUNT']
```
对于输入非常庞大的大型数据集（比如语音或视觉），你会想到使用一个生成器，而不是一个将所有输入都加载进内存的列表。查阅 [pipeline API 参考](./main_classes/pipelines) 来获取更多信息。
### 在 pipeline 中使用另一个模型和分词器
[`pipeline`] 可以容纳 [Hub](https://huggingface.co/models) 中的任何模型，这让 [`pipeline`] 更容易适用于其他用例。比如，你想要一个能够处理法语文本的模型，就可以使用 Hub 上的标记来筛选出合适的模型。靠前的筛选结果会返回一个为情感分析微调的多语言的 [BERT 模型](https://huggingface.co/nlptown/bert-base-multilingual-uncased-sentiment)，你可以将它用于法语文本：
```py
>>> model_name = "nlptown/bert-base-multilingual-uncased-sentiment"
```
<frameworkcontent>
<pt>
使用 [`AutoModelForSequenceClassification`] 和 [`AutoTokenizer`] 来加载预训练模型和它关联的分词器（更多信息可以参考下一节的 `AutoClass`）：
```py
>>> from transformers import AutoTokenizer, AutoModelForSequenceClassification
>>> model = AutoModelForSequenceClassification.from_pretrained(model_name)
>>> tokenizer = AutoTokenizer.from_pretrained(model_name)
```
</pt>
<tf>
使用 [`TFAutoModelForSequenceClassification`] 和 [`AutoTokenizer`] 来加载预训练模型和它关联的分词器（更多信息可以参考下一节的 `TFAutoClass`）：
```py
>>> from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
>>> model = TFAutoModelForSequenceClassification.from_pretrained(model_name)
>>> tokenizer = AutoTokenizer.from_pretrained(model_name)
```
</tf>
</frameworkcontent>
在 [`pipeline`] 中指定模型和分词器，现在你就可以在法语文本上使用 `classifier` 了：
```py
>>> classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
>>> classifier("Nous sommes très heureux de vous présenter la bibliothèque 🤗 Transformers.")
[{'label': '5 stars', 'score': 0.7273}]
```
如果你没有找到适合你的模型，就需要在你的数据上微调一个预训练模型了。查看 [微调教程](./training) 来学习怎样进行微调。最后，微调完模型后，考虑一下在 Hub 上与社区 [分享](./model_sharing) 这个模型，把机器学习普及到每一个人! 🤗
## AutoClass
<Youtube id="AhChOFRegn4"/>
在幕后，是由 [`AutoModelForSequenceClassification`] 和 [`AutoTokenizer`] 一起支持你在上面用到的 [`pipeline`]。[AutoClass](./model_doc/auto) 是一个能够通过预训练模型的名称或路径自动查找其架构的快捷方式。你只需要为你的任务选择合适的 `AutoClass` 和它关联的预处理类。
让我们回过头来看上一节的示例，看看怎样使用 `AutoClass` 来重现使用 [`pipeline`] 的结果。
### AutoTokenizer
分词器负责预处理文本，将文本转换为用于输入模型的数字数组。有多个用来管理分词过程的规则，包括如何拆分单词和在什么样的级别上拆分单词（在 [分词器总结](./tokenizer_summary) 学习更多关于分词的信息）。要记住最重要的是你需要实例化的分词器要与模型的名称相同, 来确保和模型训练时使用相同的分词规则。
使用 [`AutoTokenizer`] 加载一个分词器:
```py
>>> from transformers import AutoTokenizer
>>> model_name = "nlptown/bert-base-multilingual-uncased-sentiment"
>>> tokenizer = AutoTokenizer.from_pretrained(model_name)
```
将文本传入分词器：
```py
>>> encoding = tokenizer("We are very happy to show you the 🤗 Transformers library.")
>>> print(encoding)
{'input_ids': [101, 11312, 10320, 12495, 19308, 10114, 11391, 10855, 10103, 100, 58263, 13299, 119, 102],
'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
```
分词器返回了含有如下内容的字典:
* [input_ids](./glossary#input-ids)：用数字表示的 token。
* [attention_mask](.glossary#attention-mask)：应该关注哪些 token 的指示。
分词器也可以接受列表作为输入，并填充和截断文本，返回具有统一长度的批次：
<frameworkcontent>
<pt>
```py
>>> pt_batch = tokenizer(
... ["We are very happy to show you the 🤗 Transformers library.", "We hope you don't hate it."],
... padding=True,
... truncation=True,
... max_length=512,
... return_tensors="pt",
... )
```
</pt>
<tf>
```py
>>> tf_batch = tokenizer(
... ["We are very happy to show you the 🤗 Transformers library.", "We hope you don't hate it."],
... padding=True,
... truncation=True,
... max_length=512,
... return_tensors="tf",
... )
```
</tf>
</frameworkcontent>
<Tip>
查阅[预处理](./preprocessing)教程来获得有关分词的更详细的信息，以及如何使用 [`AutoFeatureExtractor`] 和 [`AutoProcessor`] 来处理图像，音频，还有多模式输入。
</Tip>
### AutoModel
<frameworkcontent>
<pt>
🤗 Transformers 提供了一种简单统一的方式来加载预训练的实例. 这表示你可以像加载 [`AutoTokenizer`] 一样加载 [`AutoModel`]。唯一不同的地方是为你的任务选择正确的[`AutoModel`]。对于文本（或序列）分类，你应该加载[`AutoModelForSequenceClassification`]：
```py
>>> from transformers import AutoModelForSequenceClassification
>>> model_name = "nlptown/bert-base-multilingual-uncased-sentiment"
>>> pt_model = AutoModelForSequenceClassification.from_pretrained(model_name)
```
<Tip>
通过 [任务摘要](./task_summary) 查找 [`AutoModel`] 支持的任务.
</Tip>
现在可以把预处理好的输入批次直接送进模型。你只需要通过 `**` 来解包字典:
```py
>>> pt_outputs = pt_model(**pt_batch)
```
模型在 `logits` 属性输出最终的激活结果. 在 `logits` 上应用 softmax 函数来查询概率:
```py
>>> from torch import nn
>>> pt_predictions = nn.functional.softmax(pt_outputs.logits, dim=-1)
>>> print(pt_predictions)
tensor([[0.0021, 0.0018, 0.0115, 0.2121, 0.7725],
[0.2084, 0.1826, 0.1969, 0.1755, 0.2365]], grad_fn=<SoftmaxBackward0>)
```
</pt>
<tf>
🤗 Transformers 提供了一种简单统一的方式来加载预训练的实例。这表示你可以像加载 [`AutoTokenizer`] 一样加载 [`TFAutoModel`]。唯一不同的地方是为你的任务选择正确的 [`TFAutoModel`]，对于文本（或序列）分类，你应该加载 [`TFAutoModelForSequenceClassification`]：
```py
>>> from transformers import TFAutoModelForSequenceClassification
>>> model_name = "nlptown/bert-base-multilingual-uncased-sentiment"
>>> tf_model = TFAutoModelForSequenceClassification.from_pretrained(model_name)
```
<Tip>
通过 [任务摘要](./task_summary) 查找 [`AutoModel`] 支持的任务.
</Tip>
现在通过直接将字典的键传给张量，将预处理的输入批次传给模型。
```py
>>> tf_outputs = tf_model(tf_batch)
```
模型在 `logits` 属性输出最终的激活结果。在 `logits` 上应用softmax函数来查询概率：
```py
>>> import tensorflow as tf
>>> tf_predictions = tf.nn.softmax(tf_outputs.logits, axis=-1)
>>> tf_predictions # doctest: +IGNORE_RESULT
```
</tf>
</frameworkcontent>
<Tip>
所有 🤗 Transformers 模型（PyTorch 或 TensorFlow）在最终的激活函数（比如 softmax）*之前* 输出张量，
因为最终的激活函数常常与 loss 融合。模型的输出是特殊的数据类，所以它们的属性可以在 IDE 中被自动补全。模型的输出就像一个元组或字典（你可以通过整数、切片或字符串来索引它），在这种情况下，为 None 的属性会被忽略。
</Tip>
### 保存模型
<frameworkcontent>
<pt>
当你的模型微调完成，你就可以使用 [`PreTrainedModel.save_pretrained`] 把它和它的分词器保存下来：
```py
>>> pt_save_directory = "./pt_save_pretrained"
>>> tokenizer.save_pretrained(pt_save_directory) # doctest: +IGNORE_RESULT
>>> pt_model.save_pretrained(pt_save_directory)
```
当你准备再次使用这个模型时，就可以使用 [`PreTrainedModel.from_pretrained`] 加载它了：
```py
>>> pt_model = AutoModelForSequenceClassification.from_pretrained("./pt_save_pretrained")
```
</pt>
<tf>
当你的模型微调完成，你就可以使用 [`TFPreTrainedModel.save_pretrained`] 把它和它的分词器保存下来：
```py
>>> tf_save_directory = "./tf_save_pretrained"
>>> tokenizer.save_pretrained(tf_save_directory) # doctest: +IGNORE_RESULT
>>> tf_model.save_pretrained(tf_save_directory)
```
当你准备再次使用这个模型时，就可以使用 [`TFPreTrainedModel.from_pretrained`] 加载它了：
```py
>>> tf_model = TFAutoModelForSequenceClassification.from_pretrained("./tf_save_pretrained")
```
</tf>
</frameworkcontent>
🤗 Transformers 有一个特别酷的功能，它能够保存一个模型，并且将它加载为 PyTorch 或 TensorFlow 模型。`from_pt` 或 `from_tf` 参数可以将模型从一个框架转换为另一个框架：
<frameworkcontent>
<pt>
```py
>>> from transformers import AutoModel
>>> tokenizer = AutoTokenizer.from_pretrained(tf_save_directory)
>>> pt_model = AutoModelForSequenceClassification.from_pretrained(tf_save_directory, from_tf=True)
```
</pt>
<tf>
```py
>>> from transformers import TFAutoModel
>>> tokenizer = AutoTokenizer.from_pretrained(pt_save_directory)
>>> tf_model = TFAutoModelForSequenceClassification.from_pretrained(pt_save_directory, from_pt=True)
```
</tf>
</frameworkcontent>
## 自定义模型构建
你可以修改模型的配置类来改变模型的构建方式。配置指明了模型的属性，比如隐藏层或者注意力头的数量。当你从自定义的配置类初始化模型时，你就开始自定义模型构建了。模型属性是随机初始化的，你需要先训练模型，然后才能得到有意义的结果。
通过导入 [`AutoConfig`] 来开始，之后加载你想修改的预训练模型。在 [`AutoConfig.from_pretrained`] 中，你能够指定想要修改的属性，比如注意力头的数量：
```py
>>> from transformers import AutoConfig
>>> my_config = AutoConfig.from_pretrained("distilbert/distilbert-base-uncased", n_heads=12)
```
<frameworkcontent>
<pt>
使用 [`AutoModel.from_config`] 根据你的自定义配置创建一个模型：
```py
>>> from transformers import AutoModel
>>> my_model = AutoModel.from_config(my_config)
```
</pt>
<tf>
使用 [`TFAutoModel.from_config`] 根据你的自定义配置创建一个模型：
```py
>>> from transformers import TFAutoModel
>>> my_model = TFAutoModel.from_config(my_config)
```
</tf>
</frameworkcontent>
查阅 [创建一个自定义结构](./create_a_model) 指南获取更多关于构建自定义配置的信息。
## Trainer - PyTorch 优化训练循环
所有的模型都是标准的 [`torch.nn.Module`](https://pytorch.org/docs/stable/nn.html#torch.nn.Module)，所以你可以在任何典型的训练模型中使用它们。当你编写自己的训练循环时，🤗 Transformers 为 PyTorch 提供了一个 [`Trainer`] 类，它包含了基础的训练循环并且为诸如分布式训练，混合精度等特性增加了额外的功能。
取决于你的任务, 你通常可以传递以下的参数给 [`Trainer`]：
1. [`PreTrainedModel`] 或者 [`torch.nn.Module`](https://pytorch.org/docs/stable/nn.html#torch.nn.Module)：
```py
>>> from transformers import AutoModelForSequenceClassification
>>> model = AutoModelForSequenceClassification.from_pretrained("distilbert/distilbert-base-uncased")
```
2. [`TrainingArguments`] 含有你可以修改的模型超参数，比如学习率，批次大小和训练时的迭代次数。如果你没有指定训练参数，那么它会使用默认值：
```py
>>> from transformers import TrainingArguments
>>> training_args = TrainingArguments(
... output_dir="path/to/save/folder/",
... learning_rate=2e-5,
... per_device_train_batch_size=8,
... per_device_eval_batch_size=8,
... num_train_epochs=2,
... )
```
3. 一个预处理类，比如分词器，特征提取器或者处理器：
```py
>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert/distilbert-base-uncased")
```
4. 加载一个数据集：
```py
>>> from datasets import load_dataset
>>> dataset = load_dataset("rotten_tomatoes") # doctest: +IGNORE_RESULT
```
5. 创建一个给数据集分词的函数，并且使用 [`~datasets.Dataset.map`] 应用到整个数据集：
```py
>>> def tokenize_dataset(dataset):
... return tokenizer(dataset["text"])
>>> dataset = dataset.map(tokenize_dataset, batched=True)
```
6. 用来从数据集中创建批次的 [`DataCollatorWithPadding`]：
```py
>>> from transformers import DataCollatorWithPadding
>>> data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
```
现在把所有的类传给 [`Trainer`]：
```py
>>> from transformers import Trainer
>>> trainer = Trainer(
... model=model,
... args=training_args,
... train_dataset=dataset["train"],
... eval_dataset=dataset["test"],
... tokenizer=tokenizer,
... data_collator=data_collator,
... ) # doctest: +SKIP
```
一切准备就绪后，调用 [`~Trainer.train`] 进行训练：
```py
>>> trainer.train() # doctest: +SKIP
```
<Tip>
对于像翻译或摘要这些使用序列到序列模型的任务，用 [`Seq2SeqTrainer`] 和 [`Seq2SeqTrainingArguments`] 来替代。
</Tip>
你可以通过子类化 [`Trainer`] 中的方法来自定义训练循环。这样你就可以自定义像损失函数，优化器和调度器这样的特性。查阅 [`Trainer`] 参考手册了解哪些方法能够被子类化。
另一个自定义训练循环的方式是通过[回调](./main_classes/callbacks)。你可以使用回调来与其他库集成，查看训练循环来报告进度或提前结束训练。回调不会修改训练循环。如果想自定义损失函数等，就需要子类化 [`Trainer`] 了。
## 使用 Tensorflow 训练
所有模型都是标准的 [`tf.keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model)，所以你可以通过 [Keras](https://keras.io/) API 实现在 Tensorflow 中训练。🤗 Transformers 提供了 [`~TFPreTrainedModel.prepare_tf_dataset`] 方法来轻松地将数据集加载为 `tf.data.Dataset`，这样你就可以使用 Keras 的 [`compile`](https://keras.io/api/models/model_training_apis/#compile-method) 和 [`fit`](https://keras.io/api/models/model_training_apis/#fit-method) 方法马上开始训练。
1. 使用 [`TFPreTrainedModel`] 或者 [`tf.keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model) 来开始：
```py
>>> from transformers import TFAutoModelForSequenceClassification
>>> model = TFAutoModelForSequenceClassification.from_pretrained("distilbert/distilbert-base-uncased")
```
2. 一个预处理类，比如分词器，特征提取器或者处理器：
```py
>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert/distilbert-base-uncased")
```
3. 创建一个给数据集分词的函数
```py
>>> def tokenize_dataset(dataset):
... return tokenizer(dataset["text"]) # doctest: +SKIP
```
4. 使用 [`~datasets.Dataset.map`] 将分词器应用到整个数据集，之后将数据集和分词器传给 [`~TFPreTrainedModel.prepare_tf_dataset`]。如果你需要的话，也可以在这里改变批次大小和是否打乱数据集：
```py
>>> dataset = dataset.map(tokenize_dataset) # doctest: +SKIP
>>> tf_dataset = model.prepare_tf_dataset(
... dataset, batch_size=16, shuffle=True, tokenizer=tokenizer
... ) # doctest: +SKIP
```
5. 一切准备就绪后，调用 `compile` 和 `fit` 开始训练：
```py
>>> from tensorflow.keras.optimizers import Adam
>>> model.compile(optimizer=Adam(3e-5))
>>> model.fit(dataset) # doctest: +SKIP
```
## 接下来做什么?
现在你已经完成了 🤗 Transformers 的快速上手教程，来看看我们的指南并且学习如何做一些更具体的事情，比如写一个自定义模型，为某个任务微调一个模型以及如何使用脚本来训练模型。如果你有兴趣了解更多 🤗 Transformers 的核心章节，那就喝杯咖啡然后来看看我们的概念指南吧！
| transformers/docs/source/zh/quicktour.md/0 | {
"file_path": "transformers/docs/source/zh/quicktour.md",
"repo_id": "transformers",
"token_count": 12904
} | 36 |
<!---
Copyright 2021 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
-->
# Language model training examples
The following example showcases how to train a language model from scratch
using the JAX/Flax backend.
JAX/Flax allows you to trace pure functions and compile them into efficient, fused accelerator code on both GPU and TPU.
Models written in JAX/Flax are **immutable** and updated in a purely functional
way which enables simple and efficient model parallelism.
## Masked language modeling
In the following, we demonstrate how to train a bi-directional transformer model
using masked language modeling objective as introduced in [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805).
More specifically, we demonstrate how JAX/Flax can be leveraged
to pre-train [**`FacebookAI/roberta-base`**](https://huggingface.co/FacebookAI/roberta-base)
in Norwegian on a single TPUv3-8 pod.
The example script uses the 🤗 Datasets library. You can easily customize them to your needs if you need extra processing on your datasets.
To setup all relevant files for training, let's create a directory.
```bash
mkdir ./norwegian-roberta-base
```
### Train tokenizer
In the first step, we train a tokenizer to efficiently process the text input for the model. Similar to how it is shown in [How to train a new language model from scratch using Transformers and Tokenizers](https://huggingface.co/blog/how-to-train), we use a **`ByteLevelBPETokenizer`**.
The tokenizer is trained on the complete Norwegian dataset of OSCAR
and consequently saved in the cloned model directory.
This can take up to 10 minutes depending on your hardware ☕.
```python
from datasets import load_dataset
from tokenizers import trainers, Tokenizer, normalizers, ByteLevelBPETokenizer
# load dataset
dataset = load_dataset("oscar", "unshuffled_deduplicated_no", split="train")
# Instantiate tokenizer
tokenizer = ByteLevelBPETokenizer()
def batch_iterator(batch_size=1000):
for i in range(0, len(dataset), batch_size):
yield dataset[i: i + batch_size]["text"]
# Customized training
tokenizer.train_from_iterator(batch_iterator(), vocab_size=50265, min_frequency=2, special_tokens=[
"<s>",
"<pad>",
"</s>",
"<unk>",
"<mask>",
])
# Save files to disk
tokenizer.save("./norwegian-roberta-base/tokenizer.json")
```
### Create configuration
Next, we create the model's configuration file. This is as simple
as loading and storing [`**FacebookAI/roberta-base**`](https://huggingface.co/FacebookAI/roberta-base)
in the local model folder:
```python
from transformers import RobertaConfig
config = RobertaConfig.from_pretrained("FacebookAI/roberta-base", vocab_size=50265)
config.save_pretrained("./norwegian-roberta-base")
```
Great, we have set up our model repository. During training, we will automatically
push the training logs and model weights to the repo.
### Train model
Next we can run the example script to pretrain the model:
```bash
python run_mlm_flax.py \
--output_dir="./norwegian-roberta-base" \
--model_type="roberta" \
--config_name="./norwegian-roberta-base" \
--tokenizer_name="./norwegian-roberta-base" \
--dataset_name="oscar" \
--dataset_config_name="unshuffled_deduplicated_no" \
--max_seq_length="128" \
--weight_decay="0.01" \
--per_device_train_batch_size="128" \
--per_device_eval_batch_size="128" \
--learning_rate="3e-4" \
--warmup_steps="1000" \
--overwrite_output_dir \
--num_train_epochs="18" \
--adam_beta1="0.9" \
--adam_beta2="0.98" \
--logging_steps="500" \
--save_steps="2500" \
--eval_steps="2500" \
--push_to_hub
```
Training should converge at a loss and accuracy
of 1.78 and 0.64 respectively after 18 epochs on a single TPUv3-8.
This should take less than 18 hours.
Training statistics can be accessed on [tfhub.dev](https://tensorboard.dev/experiment/GdYmdak2TWeVz0DDRYOrrg).
For a step-by-step walkthrough of how to do masked language modeling in Flax, please have a
look at [this](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/masked_language_modeling_flax.ipynb) google colab.
## Causal language modeling
In the following, we demonstrate how to train an auto-regressive causal transformer model
in JAX/Flax.
More specifically, we pretrain a randomly initialized [**`openai-community/gpt2`**](https://huggingface.co/openai-community/gpt2) model in Norwegian on a single TPUv3-8.
to pre-train 124M [**`openai-community/gpt2`**](https://huggingface.co/openai-community/gpt2)
in Norwegian on a single TPUv3-8 pod.
The example script uses the 🤗 Datasets library. You can easily customize them to your needs if you need extra processing on your datasets.
To setup all relevant files for training, let's create a directory.
```bash
mkdir ./norwegian-gpt2
```
### Train tokenizer
In the first step, we train a tokenizer to efficiently process the text input for the model. Similar to how it is shown in [How to train a new language model from scratch using Transformers and Tokenizers](https://huggingface.co/blog/how-to-train), we use a **`ByteLevelBPETokenizer`**.
The tokenizer is trained on the complete Norwegian dataset of OSCAR
and consequently saved in the cloned model directory.
This can take up to 10 minutes depending on your hardware ☕.
```python
from datasets import load_dataset
from tokenizers import trainers, Tokenizer, normalizers, ByteLevelBPETokenizer
# load dataset
dataset = load_dataset("oscar", "unshuffled_deduplicated_no", split="train")
# Instantiate tokenizer
tokenizer = ByteLevelBPETokenizer()
def batch_iterator(batch_size=1000):
for i in range(0, len(dataset), batch_size):
yield dataset[i: i + batch_size]["text"]
# Customized training
tokenizer.train_from_iterator(batch_iterator(), vocab_size=50257, min_frequency=2, special_tokens=[
"<s>",
"<pad>",
"</s>",
"<unk>",
"<mask>",
])
# Save files to disk
tokenizer.save("./norwegian-gpt2/tokenizer.json")
```
### Create configuration
Next, we create the model's configuration file. This is as simple
as loading and storing [`**openai-community/gpt2**`](https://huggingface.co/openai-community/gpt2)
in the local model folder:
```python
from transformers import GPT2Config
config = GPT2Config.from_pretrained("openai-community/gpt2", resid_pdrop=0.0, embd_pdrop=0.0, attn_pdrop=0.0, vocab_size=50257)
config.save_pretrained("./norwegian-gpt2")
```
Great, we have set up our model repository. During training, we will now automatically
push the training logs and model weights to the repo.
### Train model
Finally, we can run the example script to pretrain the model:
```bash
python run_clm_flax.py \
--output_dir="./norwegian-gpt2" \
--model_type="openai-community/gpt2" \
--config_name="./norwegian-gpt2" \
--tokenizer_name="./norwegian-gpt2" \
--dataset_name="oscar" \
--dataset_config_name="unshuffled_deduplicated_no" \
--do_train --do_eval \
--block_size="512" \
--per_device_train_batch_size="64" \
--per_device_eval_batch_size="64" \
--learning_rate="5e-3" --warmup_steps="1000" \
--adam_beta1="0.9" --adam_beta2="0.98" --weight_decay="0.01" \
--overwrite_output_dir \
--num_train_epochs="20" \
--logging_steps="500" \
--save_steps="2500" \
--eval_steps="2500" \
--push_to_hub
```
Training should converge at a loss and perplexity
of 3.24 and 25.72 respectively after 20 epochs on a single TPUv3-8.
This should take less than ~21 hours.
Training statistics can be accessed on [tfhub.de](https://tensorboard.dev/experiment/2zEhLwJ0Qp2FAkI3WVH9qA).
For a step-by-step walkthrough of how to do causal language modeling in Flax, please have a
look at [this](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/causal_language_modeling_flax.ipynb) google colab.
## T5-like span-masked language modeling
In the following, we demonstrate how to train a T5 model using the span-masked language model
objective as proposed in the [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683).
More specifically, we demonstrate how JAX/Flax can be leveraged
to pre-train [**`google/t5-v1_1-base`**](https://huggingface.co/google/t5-v1_1-base)
in Norwegian on a single TPUv3-8 pod.
The example script uses the 🤗 Datasets library. You can easily customize them to your needs if you need extra processing on your datasets.
Let's start by creating a model repository to save the trained model and logs.
Here we call the model `"norwegian-t5-base"`, but you can change the model name as you like.
To setup all relevant files for training, let's create a directory.
```bash
cd ./norwegian-t5-base
```
### Train tokenizer
In the first step, we train a tokenizer to efficiently process the text input for the model.
We make use of the [tokenizers](https://github.com/huggingface/tokenizers) library to train
a sentencepiece unigram tokenizer as shown in [t5_tokenizer_model.py](https://github.com/huggingface/transformers/tree/main/examples/flax/language-modeling/t5_tokenizer_model.py)
which is heavily inspired from [yandex-research/DeDLOC's tokenizer model](https://github.com/yandex-research/DeDLOC/blob/5c994bc64e573702a9a79add3ecd68b38f14b548/sahajbert/tokenizer/tokenizer_model.py) .
The tokenizer is trained on the complete Norwegian dataset of OSCAR
and consequently saved in the cloned model directory.
This can take up to 120 minutes depending on your hardware ☕☕☕ .
```python
import datasets
from t5_tokenizer_model import SentencePieceUnigramTokenizer
vocab_size = 32_000
input_sentence_size = None
# Initialize a dataset
dataset = datasets.load_dataset("oscar", name="unshuffled_deduplicated_no", split="train")
tokenizer = SentencePieceUnigramTokenizer(unk_token="<unk>", eos_token="</s>", pad_token="<pad>")
# Build an iterator over this dataset
def batch_iterator(input_sentence_size=None):
if input_sentence_size is None:
input_sentence_size = len(dataset)
batch_length = 100
for i in range(0, input_sentence_size, batch_length):
yield dataset[i: i + batch_length]["text"]
# Train tokenizer
tokenizer.train_from_iterator(
iterator=batch_iterator(input_sentence_size=input_sentence_size),
vocab_size=vocab_size,
show_progress=True,
)
# Save files to disk
tokenizer.save("./norwegian-t5-base/tokenizer.json")
```
### Create configuration
Next, we create the model's configuration file. This is as simple
as loading and storing [`**google/t5-v1_1-base**`](https://huggingface.co/google/t5-v1_1-base)
in the local model folder:
```python
from transformers import T5Config
config = T5Config.from_pretrained("google/t5-v1_1-base", vocab_size=tokenizer.get_vocab_size())
config.save_pretrained("./norwegian-t5-base")
```
Great, we have set up our model repository. During training, we will automatically
push the training logs and model weights to the repo.
### Train model
Next we can run the example script to pretrain the model:
```bash
python run_t5_mlm_flax.py \
--output_dir="./norwegian-t5-base" \
--model_type="t5" \
--config_name="./norwegian-t5-base" \
--tokenizer_name="./norwegian-t5-base" \
--dataset_name="oscar" \
--dataset_config_name="unshuffled_deduplicated_no" \
--max_seq_length="512" \
--per_device_train_batch_size="32" \
--per_device_eval_batch_size="32" \
--adafactor \
--learning_rate="0.005" \
--weight_decay="0.001" \
--warmup_steps="2000" \
--overwrite_output_dir \
--logging_steps="500" \
--save_steps="10000" \
--eval_steps="2500" \
--push_to_hub
```
Training should converge at a loss and accuracy
of 2.36 and 57.0 respectively after 3 epochs on a single TPUv3-8.
This should take around 4.5 hours.
Training statistics can be accessed on directly on the 🤗 [hub](https://huggingface.co/patrickvonplaten/t5-base-norwegian/tensorboard)
## BART: Denoising language modeling
In the following, we demonstrate how to train a BART model
using denoising language modeling objective as introduced in [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/abs/1910.13461).
More specifically, we demonstrate how JAX/Flax can be leveraged
to pre-train [**`bart-base`**](https://huggingface.co/facebook/bart-base)
in Norwegian on a single TPUv3-8 pod.
The example script uses the 🤗 Datasets library. You can easily customize them to your needs if you need extra processing on your datasets.
To setup all relevant files for training, let's create a directory.
```bash
mkdir ./norwegian-bart-base
```
### Train tokenizer
In the first step, we train a tokenizer to efficiently process the text input for the model. Similar to how it is shown in [How to train a new language model from scratch using Transformers and Tokenizers](https://huggingface.co/blog/how-to-train), we use a **`ByteLevelBPETokenizer`**.
The tokenizer is trained on the complete Norwegian dataset of OSCAR
and consequently saved in the cloned model directory.
This can take up to 10 minutes depending on your hardware ☕.
```python
from datasets import load_dataset
from tokenizers import trainers, Tokenizer, normalizers, ByteLevelBPETokenizer
# load dataset
dataset = load_dataset("oscar", "unshuffled_deduplicated_no", split="train")
# Instantiate tokenizer
tokenizer = ByteLevelBPETokenizer()
def batch_iterator(batch_size=1000):
for i in range(0, len(dataset), batch_size):
yield dataset[i: i + batch_size]["text"]
# Customized training
tokenizer.train_from_iterator(batch_iterator(), vocab_size=50265, min_frequency=2, special_tokens=[
"<s>",
"<pad>",
"</s>",
"<unk>",
"<mask>",
])
# Save files to disk
tokenizer.save("./norwegian-bart-base/tokenizer.json")
```
### Create configuration
Next, we create the model's configuration file. This is as simple
as loading and storing [`**facebook/bart-base**`](https://huggingface.co/facebook/bart-base)
in the local model folder:
```python
from transformers import BartConfig
config = BartConfig.from_pretrained("facebook/bart-base", vocab_size=50265)
config.save_pretrained("./norwegian-bart-base")
```
Great, we have set up our model repository. During training, we will automatically
push the training logs and model weights to the repo.
### Train model
Next we can run the example script to pretrain the model:
```bash
python run_bart_dlm_flax.py \
--output_dir="./norwegian-bart-base" \
--config_name="./norwegian-bart-base" \
--tokenizer_name="./norwegian-bart-base" \
--dataset_name="oscar" \
--dataset_config_name="unshuffled_deduplicated_no" \
--max_seq_length="1024" \
--per_device_train_batch_size="32" \
--per_device_eval_batch_size="32" \
--learning_rate="1e-4" \
--warmup_steps="2000" \
--overwrite_output_dir \
--logging_steps="500" \
--save_steps="2000" \
--eval_steps="2000" \
--push_to_hub
```
Training should converge at a loss and accuracy
of 1.36 and 0.77 respectively after 3 epochs on a single TPUv3-8.
This should take less than 6 hours.
Training statistics can be accessed on [tfhub.dev](https://tensorboard.dev/experiment/Maw62QlaSXWS0MOf2V2lbg/).
## Runtime evaluation
We also ran masked language modeling using PyTorch/XLA on a TPUv3-8, and PyTorch on 8 V100 GPUs. We report the
overall training time below.
For reproducibility, we state the training commands used for PyTorch/XLA and PyTorch further below.
| Task | [TPU v3-8 (Flax)](https://tensorboard.dev/experiment/GdYmdak2TWeVz0DDRYOrrg/) | [TPU v3-8 (Pytorch/XLA)](https://tensorboard.dev/experiment/7Jq1kcQQRAmy12KOdXek7A/)| [8 GPU (PyTorch)](https://tensorboard.dev/experiment/PJneV8FQRxa2unPw1QnVHA) |
|-------|-----------|------------|------------|
| MLM | 15h32m | 23h46m | 44h14m |
*All experiments are ran on Google Cloud Platform.
GPU experiments are ran without further optimizations besides JAX
transformations. GPU experiments are ran with full precision (fp32). "TPU v3-8"
are 8 TPU cores on 4 chips (each chips has 2 cores), while "8 GPU" are 8 GPU chips.
### Script to run MLM with PyTorch/XLA on TPUv3-8
For comparison one can run the same pre-training with PyTorch/XLA on TPU. To set up PyTorch/XLA on Cloud TPU VMs, please
refer to [this](https://cloud.google.com/tpu/docs/pytorch-xla-ug-tpu-vm) guide.
Having created the tokenizer and configuration in `norwegian-roberta-base`, we create the following symbolic links:
```bash
ln -s ~/transformers/examples/pytorch/language-modeling/run_mlm.py ./
ln -s ~/transformers/examples/pytorch/xla_spawn.py ./
```
, set the following environment variables:
```bash
export XRT_TPU_CONFIG="localservice;0;localhost:51011"
unset LD_PRELOAD
export NUM_TPUS=8
export TOKENIZERS_PARALLELISM=0
export MODEL_DIR="./norwegian-roberta-base"
mkdir -p ${MODEL_DIR}
```
, and start training as follows:
```bash
python3 xla_spawn.py --num_cores ${NUM_TPUS} run_mlm.py --output_dir="./runs" \
--model_type="roberta" \
--config_name="${MODEL_DIR}" \
--tokenizer_name="${MODEL_DIR}" \
--dataset_name="oscar" \
--dataset_config_name="unshuffled_deduplicated_no" \
--max_seq_length="128" \
--weight_decay="0.01" \
--per_device_train_batch_size="128" \
--per_device_eval_batch_size="128" \
--learning_rate="3e-4" \
--warmup_steps="1000" \
--overwrite_output_dir \
--num_train_epochs="18" \
--adam_beta1="0.9" \
--adam_beta2="0.98" \
--do_train \
--do_eval \
--logging_steps="500" \
--evaluation_strategy="epoch" \
--report_to="tensorboard" \
--save_strategy="no"
```
### Script to compare pre-training with PyTorch on 8 GPU V100's
For comparison you can run the same pre-training with PyTorch on GPU. Note that we have to make use of `gradient_accumulation`
because the maximum batch size that fits on a single V100 GPU is 32 instead of 128.
Having created the tokenizer and configuration in `norwegian-roberta-base`, we create the following symbolic links:
```bash
ln -s ~/transformers/examples/pytorch/language-modeling/run_mlm.py ./
```
, set some environment variables:
```bash
export NUM_GPUS=8
export TOKENIZERS_PARALLELISM=0
export MODEL_DIR="./norwegian-roberta-base"
mkdir -p ${MODEL_DIR}
```
, and can start training as follows:
```bash
python3 -m torch.distributed.launch --nproc_per_node ${NUM_GPUS} run_mlm.py \
--output_dir="${MODEL_DIR}" \
--model_type="roberta" \
--config_name="${MODEL_DIR}" \
--tokenizer_name="${MODEL_DIR}" \
--dataset_name="oscar" \
--dataset_config_name="unshuffled_deduplicated_no" \
--max_seq_length="128" \
--weight_decay="0.01" \
--per_device_train_batch_size="32" \
--per_device_eval_batch_size="32" \
--gradient_accumulation="4" \
--learning_rate="3e-4" \
--warmup_steps="1000" \
--overwrite_output_dir \
--num_train_epochs="18" \
--adam_beta1="0.9" \
--adam_beta2="0.98" \
--do_train \
--do_eval \
--logging_steps="500" \
--evaluation_strategy="steps" \
--report_to="tensorboard" \
--save_strategy="no"
```
| transformers/examples/flax/language-modeling/README.md/0 | {
"file_path": "transformers/examples/flax/language-modeling/README.md",
"repo_id": "transformers",
"token_count": 6875
} | 37 |
#!/usr/bin/env python
# coding=utf-8
# Copyright 2021 The HuggingFace Team All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Fine-tuning the library models for summarization.
"""
# You can also adapt this script on your own sequence to sequence task. Pointers for this are left as comments.
import json
import logging
import math
import os
import sys
import time
import warnings
from dataclasses import asdict, dataclass, field
from enum import Enum
from functools import partial
from pathlib import Path
from typing import Callable, Optional
import datasets
import evaluate
import jax
import jax.numpy as jnp
import nltk # Here to have a nice missing dependency error message early on
import numpy as np
import optax
from datasets import Dataset, load_dataset
from filelock import FileLock
from flax import jax_utils, traverse_util
from flax.jax_utils import pad_shard_unpad, unreplicate
from flax.training import train_state
from flax.training.common_utils import get_metrics, onehot, shard, shard_prng_key
from huggingface_hub import Repository, create_repo
from tqdm import tqdm
import transformers
from transformers import (
CONFIG_MAPPING,
FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
AutoConfig,
AutoTokenizer,
FlaxAutoModelForSeq2SeqLM,
HfArgumentParser,
is_tensorboard_available,
)
from transformers.utils import is_offline_mode, send_example_telemetry
logger = logging.getLogger(__name__)
try:
nltk.data.find("tokenizers/punkt")
except (LookupError, OSError):
if is_offline_mode():
raise LookupError(
"Offline mode: run this script without TRANSFORMERS_OFFLINE first to download nltk data files"
)
with FileLock(".lock") as lock:
nltk.download("punkt", quiet=True)
MODEL_CONFIG_CLASSES = list(FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.keys())
MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class TrainingArguments:
output_dir: str = field(
metadata={"help": "The output directory where the model predictions and checkpoints will be written."},
)
overwrite_output_dir: bool = field(
default=False,
metadata={
"help": (
"Overwrite the content of the output directory. "
"Use this to continue training if output_dir points to a checkpoint directory."
)
},
)
do_train: bool = field(default=False, metadata={"help": "Whether to run training."})
do_eval: bool = field(default=False, metadata={"help": "Whether to run eval on the dev set."})
do_predict: bool = field(default=False, metadata={"help": "Whether to run predictions on the test set."})
per_device_train_batch_size: int = field(
default=8, metadata={"help": "Batch size per GPU/TPU core/CPU for training."}
)
per_device_eval_batch_size: int = field(
default=8, metadata={"help": "Batch size per GPU/TPU core/CPU for evaluation."}
)
learning_rate: float = field(default=5e-5, metadata={"help": "The initial learning rate for AdamW."})
weight_decay: float = field(default=0.0, metadata={"help": "Weight decay for AdamW if we apply some."})
adam_beta1: float = field(default=0.9, metadata={"help": "Beta1 for AdamW optimizer"})
adam_beta2: float = field(default=0.999, metadata={"help": "Beta2 for AdamW optimizer"})
adam_epsilon: float = field(default=1e-8, metadata={"help": "Epsilon for AdamW optimizer."})
label_smoothing_factor: float = field(
default=0.0, metadata={"help": "The label smoothing epsilon to apply (zero means no label smoothing)."}
)
adafactor: bool = field(default=False, metadata={"help": "Whether or not to replace AdamW by Adafactor."})
num_train_epochs: float = field(default=3.0, metadata={"help": "Total number of training epochs to perform."})
warmup_steps: int = field(default=0, metadata={"help": "Linear warmup over warmup_steps."})
logging_steps: int = field(default=500, metadata={"help": "Log every X updates steps."})
save_steps: int = field(default=500, metadata={"help": "Save checkpoint every X updates steps."})
eval_steps: int = field(default=None, metadata={"help": "Run an evaluation every X steps."})
seed: int = field(default=42, metadata={"help": "Random seed that will be set at the beginning of training."})
push_to_hub: bool = field(
default=False, metadata={"help": "Whether or not to upload the trained model to the model hub after training."}
)
hub_model_id: str = field(
default=None, metadata={"help": "The name of the repository to keep in sync with the local `output_dir`."}
)
hub_token: str = field(default=None, metadata={"help": "The token to use to push to the Model Hub."})
gradient_checkpointing: bool = field(
default=False,
metadata={
"help": "If True, use gradient checkpointing to save memory at the expense of slower backward pass."
},
)
def __post_init__(self):
if self.output_dir is not None:
self.output_dir = os.path.expanduser(self.output_dir)
def to_dict(self):
"""
Serializes this instance while replace `Enum` by their values (for JSON serialization support). It obfuscates
the token values by removing their value.
"""
d = asdict(self)
for k, v in d.items():
if isinstance(v, Enum):
d[k] = v.value
if isinstance(v, list) and len(v) > 0 and isinstance(v[0], Enum):
d[k] = [x.value for x in v]
if k.endswith("_token"):
d[k] = f"<{k.upper()}>"
return d
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
"""
model_name_or_path: Optional[str] = field(
default=None,
metadata={
"help": (
"The model checkpoint for weights initialization. Don't set if you want to train a model from scratch."
)
},
)
model_type: Optional[str] = field(
default=None,
metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)},
)
config_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
)
tokenizer_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
)
cache_dir: Optional[str] = field(
default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
)
use_fast_tokenizer: bool = field(
default=True,
metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
)
dtype: Optional[str] = field(
default="float32",
metadata={
"help": (
"Floating-point format in which the model weights should be initialized and trained. Choose one of"
" `[float32, float16, bfloat16]`."
)
},
)
token: str = field(
default=None,
metadata={
"help": (
"The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
"generated when running `huggingface-cli login` (stored in `~/.huggingface`)."
)
},
)
use_auth_token: bool = field(
default=None,
metadata={
"help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead."
},
)
trust_remote_code: bool = field(
default=False,
metadata={
"help": (
"Whether or not to allow for custom models defined on the Hub in their own modeling files. This option "
"should only be set to `True` for repositories you trust and in which you have read the code, as it will "
"execute code present on the Hub on your local machine."
)
},
)
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
"""
dataset_name: Optional[str] = field(
default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
)
dataset_config_name: Optional[str] = field(
default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
)
text_column: Optional[str] = field(
default=None,
metadata={"help": "The name of the column in the datasets containing the full texts (for summarization)."},
)
summary_column: Optional[str] = field(
default=None,
metadata={"help": "The name of the column in the datasets containing the summaries (for summarization)."},
)
train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."})
validation_file: Optional[str] = field(
default=None,
metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
)
test_file: Optional[str] = field(
default=None,
metadata={"help": "An optional input predict data file to do prediction on (a text file)."},
)
max_source_length: Optional[int] = field(
default=1024,
metadata={
"help": (
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
},
)
max_target_length: Optional[int] = field(
default=128,
metadata={
"help": (
"The maximum total sequence length for target text after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
},
)
val_max_target_length: Optional[int] = field(
default=None,
metadata={
"help": (
"The maximum total sequence length for validation target text after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded. Will default to `max_target_length`. "
"This argument is also used to override the `max_length` param of `model.generate`, which is used "
"during evaluation."
)
},
)
max_train_samples: Optional[int] = field(
default=None,
metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
},
)
max_eval_samples: Optional[int] = field(
default=None,
metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
)
},
)
max_predict_samples: Optional[int] = field(
default=None,
metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of prediction examples to this "
"value if set."
)
},
)
preprocessing_num_workers: Optional[int] = field(
default=None,
metadata={"help": "The number of processes to use for the preprocessing."},
)
source_prefix: Optional[str] = field(
default=None, metadata={"help": "A prefix to add before every source text (useful for T5 models)."}
)
predict_with_generate: bool = field(
default=False, metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}
)
num_beams: Optional[int] = field(
default=1,
metadata={
"help": (
"Number of beams to use for evaluation. This argument will be passed to `model.generate`, "
"which is used during evaluation."
)
},
)
overwrite_cache: bool = field(
default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
)
def __post_init__(self):
if (
self.dataset_name is None
and self.train_file is None
and self.validation_file is None
and self.test_file is None
):
raise ValueError("Need either a dataset name or a training, validation, or test file.")
else:
if self.train_file is not None:
extension = self.train_file.split(".")[-1]
assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
if self.validation_file is not None:
extension = self.validation_file.split(".")[-1]
assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
if self.test_file is not None:
extension = self.test_file.split(".")[-1]
assert extension in ["csv", "json"], "`test_file` should be a csv or a json file."
if self.val_max_target_length is None:
self.val_max_target_length = self.max_target_length
summarization_name_mapping = {
"amazon_reviews_multi": ("review_body", "review_title"),
"big_patent": ("description", "abstract"),
"cnn_dailymail": ("article", "highlights"),
"orange_sum": ("text", "summary"),
"pn_summary": ("article", "summary"),
"psc": ("extract_text", "summary_text"),
"samsum": ("dialogue", "summary"),
"thaisum": ("body", "summary"),
"xglue": ("news_body", "news_title"),
"xsum": ("document", "summary"),
"wiki_summary": ("article", "highlights"),
}
class TrainState(train_state.TrainState):
dropout_rng: jnp.ndarray
def replicate(self):
return jax_utils.replicate(self).replace(dropout_rng=shard_prng_key(self.dropout_rng))
def data_loader(rng: jax.random.PRNGKey, dataset: Dataset, batch_size: int, shuffle: bool = False, drop_last=True):
"""
Returns batches of size `batch_size` from `dataset`. If `drop_last` is set to `False`, the final batch may be incomplete,
and range in size from 1 to `batch_size`. Shuffle batches if `shuffle` is `True`.
"""
if shuffle:
batch_idx = jax.random.permutation(rng, len(dataset))
batch_idx = np.asarray(batch_idx)
else:
batch_idx = np.arange(len(dataset))
if drop_last:
steps_per_epoch = len(dataset) // batch_size
batch_idx = batch_idx[: steps_per_epoch * batch_size] # Skip incomplete batch.
batch_idx = batch_idx.reshape((steps_per_epoch, batch_size))
else:
steps_per_epoch = math.ceil(len(dataset) / batch_size)
batch_idx = np.array_split(batch_idx, steps_per_epoch)
for idx in batch_idx:
batch = dataset[idx]
batch = {k: np.array(v) for k, v in batch.items()}
yield batch
def write_metric(summary_writer, train_metrics, eval_metrics, train_time, step):
summary_writer.scalar("train_time", train_time, step)
train_metrics = get_metrics(train_metrics)
for key, vals in train_metrics.items():
tag = f"train_{key}"
for i, val in enumerate(vals):
summary_writer.scalar(tag, val, step - len(vals) + i + 1)
for metric_name, value in eval_metrics.items():
summary_writer.scalar(f"eval_{metric_name}", value, step)
def create_learning_rate_fn(
train_ds_size: int, train_batch_size: int, num_train_epochs: int, num_warmup_steps: int, learning_rate: float
) -> Callable[[int], jnp.ndarray]:
"""Returns a linear warmup, linear_decay learning rate function."""
steps_per_epoch = train_ds_size // train_batch_size
num_train_steps = steps_per_epoch * num_train_epochs
warmup_fn = optax.linear_schedule(init_value=0.0, end_value=learning_rate, transition_steps=num_warmup_steps)
decay_fn = optax.linear_schedule(
init_value=learning_rate, end_value=0, transition_steps=num_train_steps - num_warmup_steps
)
schedule_fn = optax.join_schedules(schedules=[warmup_fn, decay_fn], boundaries=[num_warmup_steps])
return schedule_fn
def main():
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
else:
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
if model_args.use_auth_token is not None:
warnings.warn(
"The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead.",
FutureWarning,
)
if model_args.token is not None:
raise ValueError("`token` and `use_auth_token` are both specified. Please set only the argument `token`.")
model_args.token = model_args.use_auth_token
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("run_summarization", model_args, data_args, framework="flax")
if (
os.path.exists(training_args.output_dir)
and os.listdir(training_args.output_dir)
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. "
"Use --overwrite_output_dir to overcome."
)
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
# Setup logging, we only want one process per machine to log things on the screen.
logger.setLevel(logging.INFO if jax.process_index() == 0 else logging.ERROR)
if jax.process_index() == 0:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# Set the verbosity to info of the Transformers logger (on main process only):
logger.info(f"Training/evaluation parameters {training_args}")
# Handle the repository creation
if training_args.push_to_hub:
# Retrieve of infer repo_name
repo_name = training_args.hub_model_id
if repo_name is None:
repo_name = Path(training_args.output_dir).absolute().name
# Create repo and retrieve repo_id
repo_id = create_repo(repo_name, exist_ok=True, token=training_args.hub_token).repo_id
# Clone repo locally
repo = Repository(training_args.output_dir, clone_from=repo_id, token=training_args.hub_token)
# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files this script will use the first column for the full texts and the second column for the
# summaries (unless you specify column names for this with the `text_column` and `summary_column` arguments).
#
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
dataset = load_dataset(
data_args.dataset_name,
data_args.dataset_config_name,
cache_dir=model_args.cache_dir,
keep_in_memory=False,
token=model_args.token,
)
else:
data_files = {}
if data_args.train_file is not None:
data_files["train"] = data_args.train_file
extension = data_args.train_file.split(".")[-1]
if data_args.validation_file is not None:
data_files["validation"] = data_args.validation_file
extension = data_args.validation_file.split(".")[-1]
if data_args.test_file is not None:
data_files["test"] = data_args.test_file
extension = data_args.test_file.split(".")[-1]
dataset = load_dataset(
extension,
data_files=data_files,
cache_dir=model_args.cache_dir,
token=model_args.token,
)
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.
# Load pretrained model and tokenizer
if model_args.config_name:
config = AutoConfig.from_pretrained(
model_args.config_name,
cache_dir=model_args.cache_dir,
token=model_args.token,
trust_remote_code=model_args.trust_remote_code,
)
elif model_args.model_name_or_path:
config = AutoConfig.from_pretrained(
model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
token=model_args.token,
trust_remote_code=model_args.trust_remote_code,
)
else:
config = CONFIG_MAPPING[model_args.model_type]()
logger.warning("You are instantiating a new config instance from scratch.")
if model_args.tokenizer_name:
tokenizer = AutoTokenizer.from_pretrained(
model_args.tokenizer_name,
cache_dir=model_args.cache_dir,
use_fast=model_args.use_fast_tokenizer,
token=model_args.token,
trust_remote_code=model_args.trust_remote_code,
)
elif model_args.model_name_or_path:
tokenizer = AutoTokenizer.from_pretrained(
model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
use_fast=model_args.use_fast_tokenizer,
token=model_args.token,
trust_remote_code=model_args.trust_remote_code,
)
else:
raise ValueError(
"You are instantiating a new tokenizer from scratch. This is not supported by this script. "
"You can do it from another script, save it, and load it from here, using --tokenizer_name."
)
if model_args.model_name_or_path:
model = FlaxAutoModelForSeq2SeqLM.from_pretrained(
model_args.model_name_or_path,
config=config,
seed=training_args.seed,
dtype=getattr(jnp, model_args.dtype),
token=model_args.token,
trust_remote_code=model_args.trust_remote_code,
)
else:
model = FlaxAutoModelForSeq2SeqLM.from_config(
config,
seed=training_args.seed,
dtype=getattr(jnp, model_args.dtype),
trust_remote_code=model_args.trust_remote_code,
)
if training_args.gradient_checkpointing:
model.enable_gradient_checkpointing()
if model.config.decoder_start_token_id is None:
raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined")
prefix = data_args.source_prefix if data_args.source_prefix is not None else ""
# Preprocessing the datasets.
# We need to tokenize inputs and targets.
if training_args.do_train:
if "train" not in dataset:
raise ValueError("--do_train requires a train dataset")
column_names = dataset["train"].column_names
elif training_args.do_eval:
if "validation" not in dataset:
raise ValueError("--do_eval requires a validation dataset")
column_names = dataset["validation"].column_names
elif training_args.do_predict:
if "test" not in dataset:
raise ValueError("--do_predict requires a test dataset")
column_names = dataset["test"].column_names
else:
logger.info("There is nothing to do. Please pass `do_train`, `do_eval` and/or `do_predict`.")
return
# Get the column names for input/target.
dataset_columns = summarization_name_mapping.get(data_args.dataset_name, None)
if data_args.text_column is None:
text_column = dataset_columns[0] if dataset_columns is not None else column_names[0]
else:
text_column = data_args.text_column
if text_column not in column_names:
raise ValueError(
f"--text_column' value '{data_args.text_column}' needs to be one of: {', '.join(column_names)}"
)
if data_args.summary_column is None:
summary_column = dataset_columns[1] if dataset_columns is not None else column_names[1]
else:
summary_column = data_args.summary_column
if summary_column not in column_names:
raise ValueError(
f"--summary_column' value '{data_args.summary_column}' needs to be one of: {', '.join(column_names)}"
)
# Temporarily set max_target_length for training.
max_target_length = data_args.max_target_length
# In Flax, for seq2seq models we need to pass `decoder_input_ids`
# as the Flax models don't accept `labels`, we need to prepare the decoder_input_ids here
# for that dynamically import the `shift_tokens_right` function from the model file
model_module = __import__(model.__module__, fromlist=["shift_tokens_tight"])
shift_tokens_right_fn = getattr(model_module, "shift_tokens_right")
# Setting padding="max_length" as we need fixed length inputs for jitted functions
def preprocess_function(examples):
inputs = examples[text_column]
targets = examples[summary_column]
inputs = [prefix + inp for inp in inputs]
model_inputs = tokenizer(
inputs, max_length=data_args.max_source_length, padding="max_length", truncation=True, return_tensors="np"
)
# Setup the tokenizer for targets
labels = tokenizer(
text_target=targets,
max_length=max_target_length,
padding="max_length",
truncation=True,
return_tensors="np",
)
model_inputs["labels"] = labels["input_ids"]
decoder_input_ids = shift_tokens_right_fn(
labels["input_ids"], config.pad_token_id, config.decoder_start_token_id
)
model_inputs["decoder_input_ids"] = np.asarray(decoder_input_ids)
# We need decoder_attention_mask so we can ignore pad tokens from loss
model_inputs["decoder_attention_mask"] = labels["attention_mask"]
return model_inputs
if training_args.do_train:
train_dataset = dataset["train"]
if data_args.max_train_samples is not None:
max_train_samples = min(len(train_dataset), data_args.max_train_samples)
train_dataset = train_dataset.select(range(max_train_samples))
train_dataset = train_dataset.map(
preprocess_function,
batched=True,
num_proc=data_args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on train dataset",
)
if training_args.do_eval:
max_target_length = data_args.val_max_target_length
eval_dataset = dataset["validation"]
if data_args.max_eval_samples is not None:
max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples)
eval_dataset = eval_dataset.select(range(max_eval_samples))
eval_dataset = eval_dataset.map(
preprocess_function,
batched=True,
num_proc=data_args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on validation dataset",
)
if training_args.do_predict:
max_target_length = data_args.val_max_target_length
predict_dataset = dataset["test"]
if data_args.max_predict_samples is not None:
max_predict_samples = min(len(predict_dataset), data_args.max_predict_samples)
predict_dataset = predict_dataset.select(range(max_predict_samples))
predict_dataset = predict_dataset.map(
preprocess_function,
batched=True,
num_proc=data_args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on prediction dataset",
)
# Metric
metric = evaluate.load("rouge", cache_dir=model_args.cache_dir)
def postprocess_text(preds, labels):
preds = [pred.strip() for pred in preds]
labels = [label.strip() for label in labels]
# rougeLSum expects newline after each sentence
preds = ["\n".join(nltk.sent_tokenize(pred)) for pred in preds]
labels = ["\n".join(nltk.sent_tokenize(label)) for label in labels]
return preds, labels
def compute_metrics(preds, labels):
decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)
decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
# Some simple post-processing
decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)
result = metric.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)
result = {k: round(v * 100, 4) for k, v in result.items()}
prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]
result["gen_len"] = np.mean(prediction_lens)
return result
# Enable tensorboard only on the master node
has_tensorboard = is_tensorboard_available()
if has_tensorboard and jax.process_index() == 0:
try:
from flax.metrics.tensorboard import SummaryWriter
summary_writer = SummaryWriter(log_dir=Path(training_args.output_dir))
except ImportError as ie:
has_tensorboard = False
logger.warning(
f"Unable to display metrics through TensorBoard because some package are not installed: {ie}"
)
else:
logger.warning(
"Unable to display metrics through TensorBoard because the package is not installed: "
"Please run pip install tensorboard to enable."
)
# Initialize our training
rng = jax.random.PRNGKey(training_args.seed)
rng, dropout_rng = jax.random.split(rng)
# Store some constant
num_epochs = int(training_args.num_train_epochs)
train_batch_size = int(training_args.per_device_train_batch_size) * jax.device_count()
per_device_eval_batch_size = int(training_args.per_device_eval_batch_size)
eval_batch_size = per_device_eval_batch_size * jax.device_count()
steps_per_epoch = len(train_dataset) // train_batch_size
total_train_steps = steps_per_epoch * num_epochs
# Create learning rate schedule
linear_decay_lr_schedule_fn = create_learning_rate_fn(
len(train_dataset),
train_batch_size,
training_args.num_train_epochs,
training_args.warmup_steps,
training_args.learning_rate,
)
# We use Optax's "masking" functionality to not apply weight decay
# to bias and LayerNorm scale parameters. decay_mask_fn returns a
# mask boolean with the same structure as the parameters.
# The mask is True for parameters that should be decayed.
def decay_mask_fn(params):
flat_params = traverse_util.flatten_dict(params)
# find out all LayerNorm parameters
layer_norm_candidates = ["layernorm", "layer_norm", "ln"]
layer_norm_named_params = {
layer[-2:]
for layer_norm_name in layer_norm_candidates
for layer in flat_params.keys()
if layer_norm_name in "".join(layer).lower()
}
flat_mask = {path: (path[-1] != "bias" and path[-2:] not in layer_norm_named_params) for path in flat_params}
return traverse_util.unflatten_dict(flat_mask)
# create adam optimizer
adamw = optax.adamw(
learning_rate=linear_decay_lr_schedule_fn,
b1=training_args.adam_beta1,
b2=training_args.adam_beta2,
eps=training_args.adam_epsilon,
weight_decay=training_args.weight_decay,
mask=decay_mask_fn,
)
# Setup train state
state = TrainState.create(apply_fn=model.__call__, params=model.params, tx=adamw, dropout_rng=dropout_rng)
# label smoothed cross entropy
def loss_fn(logits, labels, padding_mask, label_smoothing_factor=0.0):
"""
The label smoothing implementation is adapted from Flax's official example:
https://github.com/google/flax/blob/87a211135c6a377c8f29048a1cac3840e38b9da4/examples/wmt/train.py#L104
"""
vocab_size = logits.shape[-1]
confidence = 1.0 - label_smoothing_factor
low_confidence = (1.0 - confidence) / (vocab_size - 1)
normalizing_constant = -(
confidence * jnp.log(confidence) + (vocab_size - 1) * low_confidence * jnp.log(low_confidence + 1e-20)
)
soft_labels = onehot(labels, vocab_size, on_value=confidence, off_value=low_confidence)
loss = optax.softmax_cross_entropy(logits, soft_labels)
loss = loss - normalizing_constant
# ignore padded tokens from loss
loss = loss * padding_mask
loss = loss.sum()
num_labels = padding_mask.sum()
return loss, num_labels
# Define gradient update step fn
def train_step(state, batch, label_smoothing_factor=0.0):
dropout_rng, new_dropout_rng = jax.random.split(state.dropout_rng)
def compute_loss(params):
labels = batch.pop("labels")
logits = state.apply_fn(**batch, params=params, dropout_rng=dropout_rng, train=True)[0]
loss, num_labels = loss_fn(logits, labels, batch["decoder_attention_mask"], label_smoothing_factor)
return loss, num_labels
grad_fn = jax.value_and_grad(compute_loss, has_aux=True)
(loss, num_labels), grad = grad_fn(state.params)
num_labels = jax.lax.psum(num_labels, "batch")
# true loss = total loss / total samples
loss = jax.lax.psum(loss, "batch")
loss = jax.tree_util.tree_map(lambda x: x / num_labels, loss)
# true grad = total grad / total samples
grad = jax.lax.psum(grad, "batch")
grad = jax.tree_util.tree_map(lambda x: x / num_labels, grad)
new_state = state.apply_gradients(grads=grad, dropout_rng=new_dropout_rng)
metrics = {"loss": loss, "learning_rate": linear_decay_lr_schedule_fn(state.step)}
return new_state, metrics
# Define eval fn
def eval_step(params, batch, label_smoothing_factor=0.0):
labels = batch.pop("labels")
logits = model(**batch, params=params, train=False)[0]
loss, num_labels = loss_fn(logits, labels, batch["decoder_attention_mask"], label_smoothing_factor)
num_labels = jax.lax.psum(num_labels, "batch")
# true loss = total loss / total samples
loss = jax.lax.psum(loss, "batch")
loss = jax.tree_util.tree_map(lambda x: x / num_labels, loss)
metrics = {"loss": loss}
return metrics
# Define generation function
max_length = (
data_args.val_max_target_length if data_args.val_max_target_length is not None else model.config.max_length
)
num_beams = data_args.num_beams if data_args.num_beams is not None else model.config.num_beams
gen_kwargs = {"max_length": max_length, "num_beams": num_beams}
def generate_step(params, batch):
model.params = params
output_ids = model.generate(batch["input_ids"], attention_mask=batch["attention_mask"], **gen_kwargs)
return output_ids.sequences
# Create parallel version of the train and eval step
p_train_step = jax.pmap(
partial(train_step, label_smoothing_factor=training_args.label_smoothing_factor), "batch", donate_argnums=(0,)
)
p_eval_step = jax.pmap(partial(eval_step, label_smoothing_factor=training_args.label_smoothing_factor), "batch")
p_generate_step = jax.pmap(generate_step, "batch")
# Replicate the train state on each device
state = state.replicate()
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num Epochs = {num_epochs}")
logger.info(f" Instantaneous batch size per device = {training_args.per_device_train_batch_size}")
logger.info(f" Total train batch size (w. parallel & distributed) = {train_batch_size}")
logger.info(f" Total optimization steps = {total_train_steps}")
train_time = 0
epochs = tqdm(range(num_epochs), desc=f"Epoch ... (1/{num_epochs})", position=0)
for epoch in epochs:
# ======================== Training ================================
train_start = time.time()
# Create sampling rng
rng, input_rng = jax.random.split(rng)
train_metrics = []
# Generate an epoch by shuffling sampling indices from the train dataset
train_loader = data_loader(input_rng, train_dataset, train_batch_size, shuffle=True)
steps_per_epoch = len(train_dataset) // train_batch_size
# train
for _ in tqdm(range(steps_per_epoch), desc="Training...", position=1, leave=False):
batch = next(train_loader)
batch = shard(batch)
state, train_metric = p_train_step(state, batch)
train_metrics.append(train_metric)
train_time += time.time() - train_start
train_metric = unreplicate(train_metric)
epochs.write(
f"Epoch... ({epoch + 1}/{num_epochs} | Loss: {train_metric['loss']}, Learning Rate:"
f" {train_metric['learning_rate']})"
)
# ======================== Evaluating ==============================
eval_metrics = []
eval_preds = []
eval_labels = []
eval_loader = data_loader(input_rng, eval_dataset, eval_batch_size, drop_last=False)
eval_steps = math.ceil(len(eval_dataset) / eval_batch_size)
for _ in tqdm(range(eval_steps), desc="Evaluating...", position=2, leave=False):
# Model forward
batch = next(eval_loader)
labels = batch["labels"]
metrics = pad_shard_unpad(p_eval_step, static_return=True)(
state.params, batch, min_device_batch=per_device_eval_batch_size
)
eval_metrics.append(metrics)
# generation
if data_args.predict_with_generate:
generated_ids = pad_shard_unpad(p_generate_step)(state.params, batch)
eval_preds.extend(jax.device_get(generated_ids.reshape(-1, gen_kwargs["max_length"])))
eval_labels.extend(labels)
# normalize eval metrics
eval_metrics = get_metrics(eval_metrics)
eval_metrics = jax.tree_util.tree_map(jnp.mean, eval_metrics)
# compute ROUGE metrics
rouge_desc = ""
if data_args.predict_with_generate:
rouge_metrics = compute_metrics(eval_preds, eval_labels)
eval_metrics.update(rouge_metrics)
rouge_desc = " ".join([f"Eval {key}: {value} |" for key, value in rouge_metrics.items()])
# Print metrics and update progress bar
desc = f"Epoch... ({epoch + 1}/{num_epochs} | Eval Loss: {eval_metrics['loss']} | {rouge_desc})"
epochs.write(desc)
epochs.desc = desc
# Save metrics
if has_tensorboard and jax.process_index() == 0:
cur_step = epoch * (len(train_dataset) // train_batch_size)
write_metric(summary_writer, train_metrics, eval_metrics, train_time, cur_step)
# save checkpoint after each epoch and push checkpoint to the hub
if jax.process_index() == 0:
params = jax.device_get(jax.tree_util.tree_map(lambda x: x[0], state.params))
model.save_pretrained(training_args.output_dir, params=params)
tokenizer.save_pretrained(training_args.output_dir)
if training_args.push_to_hub:
repo.push_to_hub(commit_message=f"Saving weights and logs of epoch {epoch}", blocking=False)
# ======================== Prediction loop ==============================
if training_args.do_predict:
logger.info("*** Predict ***")
pred_metrics = []
pred_generations = []
pred_labels = []
pred_loader = data_loader(input_rng, predict_dataset, eval_batch_size, drop_last=False)
pred_steps = math.ceil(len(predict_dataset) / eval_batch_size)
for _ in tqdm(range(pred_steps), desc="Predicting...", position=2, leave=False):
# Model forward
batch = next(pred_loader)
labels = batch["labels"]
metrics = pad_shard_unpad(p_eval_step, static_return=True)(
state.params, batch, min_device_batch=per_device_eval_batch_size
)
pred_metrics.append(metrics)
# generation
if data_args.predict_with_generate:
generated_ids = pad_shard_unpad(p_generate_step)(state.params, batch)
pred_generations.extend(jax.device_get(generated_ids.reshape(-1, gen_kwargs["max_length"])))
pred_labels.extend(labels)
# normalize prediction metrics
pred_metrics = get_metrics(pred_metrics)
pred_metrics = jax.tree_util.tree_map(jnp.mean, pred_metrics)
# compute ROUGE metrics
rouge_desc = ""
if data_args.predict_with_generate:
rouge_metrics = compute_metrics(pred_generations, pred_labels)
pred_metrics.update(rouge_metrics)
rouge_desc = " ".join([f"Predict {key}: {value} |" for key, value in rouge_metrics.items()])
# Print metrics
desc = f"Predict Loss: {pred_metrics['loss']} | {rouge_desc})"
logger.info(desc)
# save final metrics in json
if jax.process_index() == 0:
rouge_metrics = {f"test_{metric_name}": value for metric_name, value in rouge_metrics.items()}
path = os.path.join(training_args.output_dir, "test_results.json")
with open(path, "w") as f:
json.dump(rouge_metrics, f, indent=4, sort_keys=True)
if __name__ == "__main__":
main()
| transformers/examples/flax/summarization/run_summarization_flax.py/0 | {
"file_path": "transformers/examples/flax/summarization/run_summarization_flax.py",
"repo_id": "transformers",
"token_count": 18177
} | 38 |
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Finetuning the library models for multiple choice (Bert, Roberta, XLNet)."""
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
logger = logging.getLogger(__name__)
def simple_accuracy(preds, labels):
return (preds == labels).mean()
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
"""
model_name_or_path: str = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
)
config_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
)
tokenizer_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
)
cache_dir: Optional[str] = field(
default=None,
metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
)
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
"""
task_name: str = field(metadata={"help": "The name of the task to train on: " + ", ".join(processors.keys())})
data_dir: str = field(metadata={"help": "Should contain the data files for the task."})
max_seq_length: int = field(
default=128,
metadata={
"help": (
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
},
)
overwrite_cache: bool = field(
default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
)
def main():
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir)
and os.listdir(training_args.output_dir)
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
" --overwrite_output_dir to overcome."
)
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN,
)
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
training_args.local_rank,
training_args.device,
training_args.n_gpu,
bool(training_args.local_rank != -1),
training_args.fp16,
)
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info("Training/evaluation parameters %s", training_args)
# Set seed
set_seed(training_args.seed)
try:
processor = processors[data_args.task_name]()
label_list = processor.get_labels()
num_labels = len(label_list)
except KeyError:
raise ValueError("Task not found: %s" % (data_args.task_name))
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
config = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path,
num_labels=num_labels,
finetuning_task=data_args.task_name,
cache_dir=model_args.cache_dir,
)
tokenizer = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
)
model = AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path,
from_tf=bool(".ckpt" in model_args.model_name_or_path),
config=config,
cache_dir=model_args.cache_dir,
)
# Get datasets
train_dataset = (
MultipleChoiceDataset(
data_dir=data_args.data_dir,
tokenizer=tokenizer,
task=data_args.task_name,
max_seq_length=data_args.max_seq_length,
overwrite_cache=data_args.overwrite_cache,
mode=Split.train,
)
if training_args.do_train
else None
)
eval_dataset = (
MultipleChoiceDataset(
data_dir=data_args.data_dir,
tokenizer=tokenizer,
task=data_args.task_name,
max_seq_length=data_args.max_seq_length,
overwrite_cache=data_args.overwrite_cache,
mode=Split.dev,
)
if training_args.do_eval
else None
)
def compute_metrics(p: EvalPrediction) -> Dict:
preds = np.argmax(p.predictions, axis=1)
return {"acc": simple_accuracy(preds, p.label_ids)}
# Data collator
data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8) if training_args.fp16 else None
# Initialize our Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
compute_metrics=compute_metrics,
data_collator=data_collator,
)
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path) else None
)
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_master():
tokenizer.save_pretrained(training_args.output_dir)
# Evaluation
results = {}
if training_args.do_eval:
logger.info("*** Evaluate ***")
result = trainer.evaluate()
output_eval_file = os.path.join(training_args.output_dir, "eval_results.txt")
if trainer.is_world_master():
with open(output_eval_file, "w") as writer:
logger.info("***** Eval results *****")
for key, value in result.items():
logger.info(" %s = %s", key, value)
writer.write("%s = %s\n" % (key, value))
results.update(result)
return results
def _mp_fn(index):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| transformers/examples/legacy/multiple_choice/run_multiple_choice.py/0 | {
"file_path": "transformers/examples/legacy/multiple_choice/run_multiple_choice.py",
"repo_id": "transformers",
"token_count": 3303
} | 39 |
#!/usr/bin/env python
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""BERT finetuning runner.
Finetuning the library models for multiple choice on SWAG (Bert).
"""
import argparse
import csv
import glob
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm, trange
import transformers
from transformers import (
WEIGHTS_NAME,
AdamW,
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
get_linear_schedule_with_warmup,
)
from transformers.trainer_utils import is_main_process
try:
from torch.utils.tensorboard import SummaryWriter
except ImportError:
from tensorboardX import SummaryWriter
logger = logging.getLogger(__name__)
class SwagExample(object):
"""A single training/test example for the SWAG dataset."""
def __init__(self, swag_id, context_sentence, start_ending, ending_0, ending_1, ending_2, ending_3, label=None):
self.swag_id = swag_id
self.context_sentence = context_sentence
self.start_ending = start_ending
self.endings = [
ending_0,
ending_1,
ending_2,
ending_3,
]
self.label = label
def __str__(self):
return self.__repr__()
def __repr__(self):
attributes = [
"swag_id: {}".format(self.swag_id),
"context_sentence: {}".format(self.context_sentence),
"start_ending: {}".format(self.start_ending),
"ending_0: {}".format(self.endings[0]),
"ending_1: {}".format(self.endings[1]),
"ending_2: {}".format(self.endings[2]),
"ending_3: {}".format(self.endings[3]),
]
if self.label is not None:
attributes.append("label: {}".format(self.label))
return ", ".join(attributes)
class InputFeatures(object):
def __init__(self, example_id, choices_features, label):
self.example_id = example_id
self.choices_features = [
{"input_ids": input_ids, "input_mask": input_mask, "segment_ids": segment_ids}
for _, input_ids, input_mask, segment_ids in choices_features
]
self.label = label
def read_swag_examples(input_file, is_training=True):
with open(input_file, "r", encoding="utf-8") as f:
lines = list(csv.reader(f))
if is_training and lines[0][-1] != "label":
raise ValueError("For training, the input file must contain a label column.")
examples = [
SwagExample(
swag_id=line[2],
context_sentence=line[4],
start_ending=line[5], # in the swag dataset, the
# common beginning of each
# choice is stored in "sent2".
ending_0=line[7],
ending_1=line[8],
ending_2=line[9],
ending_3=line[10],
label=int(line[11]) if is_training else None,
)
for line in lines[1:] # we skip the line with the column names
]
return examples
def convert_examples_to_features(examples, tokenizer, max_seq_length, is_training):
"""Loads a data file into a list of `InputBatch`s."""
# Swag is a multiple choice task. To perform this task using Bert,
# we will use the formatting proposed in "Improving Language
# Understanding by Generative Pre-Training" and suggested by
# @jacobdevlin-google in this issue
# https://github.com/google-research/bert/issues/38.
#
# Each choice will correspond to a sample on which we run the
# inference. For a given Swag example, we will create the 4
# following inputs:
# - [CLS] context [SEP] choice_1 [SEP]
# - [CLS] context [SEP] choice_2 [SEP]
# - [CLS] context [SEP] choice_3 [SEP]
# - [CLS] context [SEP] choice_4 [SEP]
# The model will output a single value for each input. To get the
# final decision of the model, we will run a softmax over these 4
# outputs.
features = []
for example_index, example in tqdm(enumerate(examples)):
context_tokens = tokenizer.tokenize(example.context_sentence)
start_ending_tokens = tokenizer.tokenize(example.start_ending)
choices_features = []
for ending_index, ending in enumerate(example.endings):
# We create a copy of the context tokens in order to be
# able to shrink it according to ending_tokens
context_tokens_choice = context_tokens[:]
ending_tokens = start_ending_tokens + tokenizer.tokenize(ending)
# Modifies `context_tokens_choice` and `ending_tokens` in
# place so that the total length is less than the
# specified length. Account for [CLS], [SEP], [SEP] with
# "- 3"
_truncate_seq_pair(context_tokens_choice, ending_tokens, max_seq_length - 3)
tokens = ["[CLS]"] + context_tokens_choice + ["[SEP]"] + ending_tokens + ["[SEP]"]
segment_ids = [0] * (len(context_tokens_choice) + 2) + [1] * (len(ending_tokens) + 1)
input_ids = tokenizer.convert_tokens_to_ids(tokens)
input_mask = [1] * len(input_ids)
# Zero-pad up to the sequence length.
padding = [0] * (max_seq_length - len(input_ids))
input_ids += padding
input_mask += padding
segment_ids += padding
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
assert len(segment_ids) == max_seq_length
choices_features.append((tokens, input_ids, input_mask, segment_ids))
label = example.label
if example_index < 5:
logger.info("*** Example ***")
logger.info("swag_id: {}".format(example.swag_id))
for choice_idx, (tokens, input_ids, input_mask, segment_ids) in enumerate(choices_features):
logger.info("choice: {}".format(choice_idx))
logger.info("tokens: {}".format(" ".join(tokens)))
logger.info("input_ids: {}".format(" ".join(map(str, input_ids))))
logger.info("input_mask: {}".format(" ".join(map(str, input_mask))))
logger.info("segment_ids: {}".format(" ".join(map(str, segment_ids))))
if is_training:
logger.info("label: {}".format(label))
features.append(InputFeatures(example_id=example.swag_id, choices_features=choices_features, label=label))
return features
def _truncate_seq_pair(tokens_a, tokens_b, max_length):
"""Truncates a sequence pair in place to the maximum length."""
# This is a simple heuristic which will always truncate the longer sequence
# one token at a time. This makes more sense than truncating an equal percent
# of tokens from each, since if one sequence is very short then each token
# that's truncated likely contains more information than a longer sequence.
while True:
total_length = len(tokens_a) + len(tokens_b)
if total_length <= max_length:
break
if len(tokens_a) > len(tokens_b):
tokens_a.pop()
else:
tokens_b.pop()
def accuracy(out, labels):
outputs = np.argmax(out, axis=1)
return np.sum(outputs == labels)
def select_field(features, field):
return [[choice[field] for choice in feature.choices_features] for feature in features]
def set_seed(args):
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if args.n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
def load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=False):
if args.local_rank not in [-1, 0]:
torch.distributed.barrier() # Make sure only the first process in distributed training process the dataset, and the others will use the cache
# Load data features from cache or dataset file
input_file = args.predict_file if evaluate else args.train_file
cached_features_file = os.path.join(
os.path.dirname(input_file),
"cached_{}_{}_{}".format(
"dev" if evaluate else "train",
list(filter(None, args.model_name_or_path.split("/"))).pop(),
str(args.max_seq_length),
),
)
if os.path.exists(cached_features_file) and not args.overwrite_cache and not output_examples:
logger.info("Loading features from cached file %s", cached_features_file)
features = torch.load(cached_features_file)
else:
logger.info("Creating features from dataset file at %s", input_file)
examples = read_swag_examples(input_file)
features = convert_examples_to_features(examples, tokenizer, args.max_seq_length, not evaluate)
if args.local_rank in [-1, 0]:
logger.info("Saving features into cached file %s", cached_features_file)
torch.save(features, cached_features_file)
if args.local_rank == 0:
torch.distributed.barrier() # Make sure only the first process in distributed training process the dataset, and the others will use the cache
# Convert to Tensors and build dataset
all_input_ids = torch.tensor(select_field(features, "input_ids"), dtype=torch.long)
all_input_mask = torch.tensor(select_field(features, "input_mask"), dtype=torch.long)
all_segment_ids = torch.tensor(select_field(features, "segment_ids"), dtype=torch.long)
all_label = torch.tensor([f.label for f in features], dtype=torch.long)
if evaluate:
dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label)
else:
dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label)
if output_examples:
return dataset, examples, features
return dataset
def train(args, train_dataset, model, tokenizer):
"""Train the model"""
if args.local_rank in [-1, 0]:
tb_writer = SummaryWriter()
args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset)
train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size)
if args.max_steps > 0:
t_total = args.max_steps
args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1
else:
t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs
# Prepare optimizer and schedule (linear warmup and decay)
no_decay = ["bias", "LayerNorm.weight"]
optimizer_grouped_parameters = [
{
"params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
"weight_decay": args.weight_decay,
},
{"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], "weight_decay": 0.0},
]
optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
scheduler = get_linear_schedule_with_warmup(
optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total
)
if args.fp16:
try:
from apex import amp
except ImportError:
raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level)
# multi-gpu training (should be after apex fp16 initialization)
if args.n_gpu > 1:
model = torch.nn.DataParallel(model)
# Distributed training (should be after apex fp16 initialization)
if args.local_rank != -1:
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True
)
# Train!
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_dataset))
logger.info(" Num Epochs = %d", args.num_train_epochs)
logger.info(" Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size)
logger.info(
" Total train batch size (w. parallel, distributed & accumulation) = %d",
args.train_batch_size
* args.gradient_accumulation_steps
* (torch.distributed.get_world_size() if args.local_rank != -1 else 1),
)
logger.info(" Gradient Accumulation steps = %d", args.gradient_accumulation_steps)
logger.info(" Total optimization steps = %d", t_total)
global_step = 0
tr_loss, logging_loss = 0.0, 0.0
model.zero_grad()
train_iterator = trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0])
set_seed(args) # Added here for reproducibility
for _ in train_iterator:
epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])
for step, batch in enumerate(epoch_iterator):
model.train()
batch = tuple(t.to(args.device) for t in batch)
inputs = {
"input_ids": batch[0],
"attention_mask": batch[1],
# 'token_type_ids': None if args.model_type == 'xlm' else batch[2],
"token_type_ids": batch[2],
"labels": batch[3],
}
# if args.model_type in ['xlnet', 'xlm']:
# inputs.update({'cls_index': batch[5],
# 'p_mask': batch[6]})
outputs = model(**inputs)
loss = outputs[0] # model outputs are always tuple in transformers (see doc)
if args.n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu parallel (not distributed) training
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
if args.fp16:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
else:
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
tr_loss += loss.item()
if (step + 1) % args.gradient_accumulation_steps == 0:
optimizer.step()
scheduler.step() # Update learning rate schedule
model.zero_grad()
global_step += 1
if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:
# Log metrics
if (
args.local_rank == -1 and args.evaluate_during_training
): # Only evaluate when single GPU otherwise metrics may not average well
results = evaluate(args, model, tokenizer)
for key, value in results.items():
tb_writer.add_scalar("eval_{}".format(key), value, global_step)
tb_writer.add_scalar("lr", scheduler.get_lr()[0], global_step)
tb_writer.add_scalar("loss", (tr_loss - logging_loss) / args.logging_steps, global_step)
logging_loss = tr_loss
if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:
# Save model checkpoint
output_dir = os.path.join(args.output_dir, "checkpoint-{}".format(global_step))
model_to_save = (
model.module if hasattr(model, "module") else model
) # Take care of distributed/parallel training
model_to_save.save_pretrained(output_dir)
tokenizer.save_vocabulary(output_dir)
torch.save(args, os.path.join(output_dir, "training_args.bin"))
logger.info("Saving model checkpoint to %s", output_dir)
if args.max_steps > 0 and global_step > args.max_steps:
epoch_iterator.close()
break
if args.max_steps > 0 and global_step > args.max_steps:
train_iterator.close()
break
if args.local_rank in [-1, 0]:
tb_writer.close()
return global_step, tr_loss / global_step
def evaluate(args, model, tokenizer, prefix=""):
dataset, examples, features = load_and_cache_examples(args, tokenizer, evaluate=True, output_examples=True)
if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
os.makedirs(args.output_dir)
args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
# Note that DistributedSampler samples randomly
eval_sampler = SequentialSampler(dataset) if args.local_rank == -1 else DistributedSampler(dataset)
eval_dataloader = DataLoader(dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)
# Eval!
logger.info("***** Running evaluation {} *****".format(prefix))
logger.info(" Num examples = %d", len(dataset))
logger.info(" Batch size = %d", args.eval_batch_size)
eval_loss, eval_accuracy = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
for batch in tqdm(eval_dataloader, desc="Evaluating"):
model.eval()
batch = tuple(t.to(args.device) for t in batch)
with torch.no_grad():
inputs = {
"input_ids": batch[0],
"attention_mask": batch[1],
# 'token_type_ids': None if args.model_type == 'xlm' else batch[2] # XLM don't use segment_ids
"token_type_ids": batch[2],
"labels": batch[3],
}
# if args.model_type in ['xlnet', 'xlm']:
# inputs.update({'cls_index': batch[4],
# 'p_mask': batch[5]})
outputs = model(**inputs)
tmp_eval_loss, logits = outputs[:2]
eval_loss += tmp_eval_loss.mean().item()
logits = logits.detach().cpu().numpy()
label_ids = inputs["labels"].to("cpu").numpy()
tmp_eval_accuracy = accuracy(logits, label_ids)
eval_accuracy += tmp_eval_accuracy
nb_eval_steps += 1
nb_eval_examples += inputs["input_ids"].size(0)
eval_loss = eval_loss / nb_eval_steps
eval_accuracy = eval_accuracy / nb_eval_examples
result = {"eval_loss": eval_loss, "eval_accuracy": eval_accuracy}
output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
with open(output_eval_file, "w") as writer:
logger.info("***** Eval results *****")
for key in sorted(result.keys()):
logger.info("%s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
return result
def main():
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--train_file", default=None, type=str, required=True, help="SWAG csv for training. E.g., train.csv"
)
parser.add_argument(
"--predict_file",
default=None,
type=str,
required=True,
help="SWAG csv for predictions. E.g., val.csv or test.csv",
)
parser.add_argument(
"--model_name_or_path",
default=None,
type=str,
required=True,
help="Path to pretrained model or model identifier from huggingface.co/models",
)
parser.add_argument(
"--output_dir",
default=None,
type=str,
required=True,
help="The output directory where the model checkpoints and predictions will be written.",
)
# Other parameters
parser.add_argument(
"--config_name", default="", type=str, help="Pretrained config name or path if not the same as model_name"
)
parser.add_argument(
"--tokenizer_name",
default="",
type=str,
help="Pretrained tokenizer name or path if not the same as model_name",
)
parser.add_argument(
"--max_seq_length",
default=384,
type=int,
help=(
"The maximum total input sequence length after tokenization. Sequences "
"longer than this will be truncated, and sequences shorter than this will be padded."
),
)
parser.add_argument("--do_train", action="store_true", help="Whether to run training.")
parser.add_argument("--do_eval", action="store_true", help="Whether to run eval on the dev set.")
parser.add_argument(
"--evaluate_during_training", action="store_true", help="Rul evaluation during training at each logging step."
)
parser.add_argument("--per_gpu_train_batch_size", default=8, type=int, help="Batch size per GPU/CPU for training.")
parser.add_argument(
"--per_gpu_eval_batch_size", default=8, type=int, help="Batch size per GPU/CPU for evaluation."
)
parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.")
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.")
parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.")
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
parser.add_argument(
"--num_train_epochs", default=3.0, type=float, help="Total number of training epochs to perform."
)
parser.add_argument(
"--max_steps",
default=-1,
type=int,
help="If > 0: set total number of training steps to perform. Override num_train_epochs.",
)
parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.")
parser.add_argument("--logging_steps", type=int, default=50, help="Log every X updates steps.")
parser.add_argument("--save_steps", type=int, default=50, help="Save checkpoint every X updates steps.")
parser.add_argument(
"--eval_all_checkpoints",
action="store_true",
help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number",
)
parser.add_argument("--no_cuda", action="store_true", help="Whether not to use CUDA when available")
parser.add_argument(
"--overwrite_output_dir", action="store_true", help="Overwrite the content of the output directory"
)
parser.add_argument(
"--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets"
)
parser.add_argument("--seed", type=int, default=42, help="random seed for initialization")
parser.add_argument("--local_rank", type=int, default=-1, help="local_rank for distributed training on gpus")
parser.add_argument(
"--fp16",
action="store_true",
help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit",
)
parser.add_argument(
"--fp16_opt_level",
type=str,
default="O1",
help=(
"For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. "
"See details at https://nvidia.github.io/apex/amp.html"
),
)
parser.add_argument("--server_ip", type=str, default="", help="Can be used for distant debugging.")
parser.add_argument("--server_port", type=str, default="", help="Can be used for distant debugging.")
args = parser.parse_args()
if (
os.path.exists(args.output_dir)
and os.listdir(args.output_dir)
and args.do_train
and not args.overwrite_output_dir
):
raise ValueError(
"Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format(
args.output_dir
)
)
# Setup distant debugging if needed
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach")
ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
ptvsd.wait_for_attach()
# Setup CUDA, GPU & distributed training
if args.local_rank == -1 or args.no_cuda:
device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
args.n_gpu = 0 if args.no_cuda else torch.cuda.device_count()
else: # Initializes the distributed backend which will take care of synchronizing nodes/GPUs
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
torch.distributed.init_process_group(backend="nccl")
args.n_gpu = 1
args.device = device
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN,
)
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
args.local_rank,
device,
args.n_gpu,
bool(args.local_rank != -1),
args.fp16,
)
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(args.local_rank):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Set seed
set_seed(args)
# Load pretrained model and tokenizer
if args.local_rank not in [-1, 0]:
torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab
config = AutoConfig.from_pretrained(args.config_name if args.config_name else args.model_name_or_path)
tokenizer = AutoTokenizer.from_pretrained(
args.tokenizer_name if args.tokenizer_name else args.model_name_or_path,
)
model = AutoModelForMultipleChoice.from_pretrained(
args.model_name_or_path, from_tf=bool(".ckpt" in args.model_name_or_path), config=config
)
if args.local_rank == 0:
torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab
model.to(args.device)
logger.info("Training/evaluation parameters %s", args)
# Training
if args.do_train:
train_dataset = load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=False)
global_step, tr_loss = train(args, train_dataset, model, tokenizer)
logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)
# Save the trained model and the tokenizer
if args.local_rank == -1 or torch.distributed.get_rank() == 0:
logger.info("Saving model checkpoint to %s", args.output_dir)
# Save a trained model, configuration and tokenizer using `save_pretrained()`.
# They can then be reloaded using `from_pretrained()`
model_to_save = (
model.module if hasattr(model, "module") else model
) # Take care of distributed/parallel training
model_to_save.save_pretrained(args.output_dir)
tokenizer.save_pretrained(args.output_dir)
# Good practice: save your training arguments together with the trained model
torch.save(args, os.path.join(args.output_dir, "training_args.bin"))
# Load a trained model and vocabulary that you have fine-tuned
model = AutoModelForMultipleChoice.from_pretrained(args.output_dir)
tokenizer = AutoTokenizer.from_pretrained(args.output_dir)
model.to(args.device)
# Evaluation - we can ask to evaluate all the checkpoints (sub-directories) in a directory
results = {}
if args.do_eval and args.local_rank in [-1, 0]:
if args.do_train:
checkpoints = [args.output_dir]
else:
# if do_train is False and do_eval is true, load model directly from pretrained.
checkpoints = [args.model_name_or_path]
if args.eval_all_checkpoints:
checkpoints = [
os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + "/**/" + WEIGHTS_NAME, recursive=True))
]
logger.info("Evaluate the following checkpoints: %s", checkpoints)
for checkpoint in checkpoints:
# Reload the model
global_step = checkpoint.split("-")[-1] if len(checkpoints) > 1 else ""
model = AutoModelForMultipleChoice.from_pretrained(checkpoint)
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model.to(args.device)
# Evaluate
result = evaluate(args, model, tokenizer, prefix=global_step)
result = {k + ("_{}".format(global_step) if global_step else ""): v for k, v in result.items()}
results.update(result)
logger.info("Results: {}".format(results))
return results
if __name__ == "__main__":
main()
| transformers/examples/legacy/run_swag.py/0 | {
"file_path": "transformers/examples/legacy/run_swag.py",
"repo_id": "transformers",
"token_count": 12598
} | 40 |
#!/usr/bin/env python
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Fill examples with bitext up to max_tokens without breaking up examples.
[['I went', 'yo fui'],
['to the store', 'a la tienda']
]
=> ['I went to the store', 'yo fui a la tienda']
"""
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def pack_examples(tok, src_examples, tgt_examples, max_tokens=1024):
finished_src, finished_tgt = [], []
sorted_examples = list(zip(src_examples, tgt_examples))
new_src, new_tgt = sorted_examples[0]
def is_too_big(strang):
return tok(strang, return_tensors="pt").input_ids.shape[1] > max_tokens
for src, tgt in tqdm(sorted_examples[1:]):
cand_src = new_src + " " + src
cand_tgt = new_tgt + " " + tgt
if is_too_big(cand_src) or is_too_big(cand_tgt): # cant fit, finalize example
finished_src.append(new_src)
finished_tgt.append(new_tgt)
new_src, new_tgt = src, tgt
else: # can fit, keep adding
new_src, new_tgt = cand_src, cand_tgt
# cleanup
if new_src:
assert new_tgt
finished_src.append(new_src)
finished_tgt.append(new_tgt)
return finished_src, finished_tgt
def pack_data_dir(tok, data_dir: Path, max_tokens, save_path):
save_path = Path(save_path)
save_path.mkdir(exist_ok=True)
for split in ["train"]:
src_path, tgt_path = data_dir / f"{split}.source", data_dir / f"{split}.target"
src_docs = [x.rstrip() for x in Path(src_path).open().readlines()]
tgt_docs = [x.rstrip() for x in Path(tgt_path).open().readlines()]
packed_src, packed_tgt = pack_examples(tok, src_docs, tgt_docs, max_tokens)
print(f"packed {split} split from {len(src_docs)} examples -> {len(packed_src)}.")
Path(save_path / f"{split}.source").open("w").write("\n".join(packed_src))
Path(save_path / f"{split}.target").open("w").write("\n".join(packed_tgt))
for split in ["val", "test"]:
src_path, tgt_path = data_dir / f"{split}.source", data_dir / f"{split}.target"
shutil.copyfile(src_path, save_path / f"{split}.source")
shutil.copyfile(tgt_path, save_path / f"{split}.target")
def packer_cli():
parser = argparse.ArgumentParser()
parser.add_argument("--tok_name", type=str, help="like facebook/bart-large-cnn,google-t5/t5-base, etc.")
parser.add_argument("--max_seq_len", type=int, default=128)
parser.add_argument("--data_dir", type=str)
parser.add_argument("--save_path", type=str)
args = parser.parse_args()
tokenizer = AutoTokenizer.from_pretrained(args.tok_name)
return pack_data_dir(tokenizer, Path(args.data_dir), args.max_seq_len, args.save_path)
if __name__ == "__main__":
packer_cli()
| transformers/examples/legacy/seq2seq/pack_dataset.py/0 | {
"file_path": "transformers/examples/legacy/seq2seq/pack_dataset.py",
"repo_id": "transformers",
"token_count": 1363
} | 41 |
if ! [ -f ./dev.txt ]; then
echo "Download dev dataset...."
curl -L -o ./dev.txt 'https://github.com/UniversalDependencies/UD_English-EWT/raw/master/en_ewt-ud-dev.conllu'
fi
if ! [ -f ./test.txt ]; then
echo "Download test dataset...."
curl -L -o ./test.txt 'https://github.com/UniversalDependencies/UD_English-EWT/raw/master/en_ewt-ud-test.conllu'
fi
if ! [ -f ./train.txt ]; then
echo "Download train dataset...."
curl -L -o ./train.txt 'https://github.com/UniversalDependencies/UD_English-EWT/raw/master/en_ewt-ud-train.conllu'
fi
export MAX_LENGTH=200
export BERT_MODEL=bert-base-uncased
export OUTPUT_DIR=postagger-model
export BATCH_SIZE=32
export NUM_EPOCHS=3
export SAVE_STEPS=750
export SEED=1
python3 run_ner.py \
--task_type POS \
--data_dir . \
--model_name_or_path $BERT_MODEL \
--output_dir $OUTPUT_DIR \
--max_seq_length $MAX_LENGTH \
--num_train_epochs $NUM_EPOCHS \
--per_gpu_train_batch_size $BATCH_SIZE \
--save_steps $SAVE_STEPS \
--seed $SEED \
--do_train \
--do_eval \
--do_predict
| transformers/examples/legacy/token-classification/run_pos.sh/0 | {
"file_path": "transformers/examples/legacy/token-classification/run_pos.sh",
"repo_id": "transformers",
"token_count": 416
} | 42 |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Finetuning any 🤗 Transformers model for image classification leveraging 🤗 Accelerate."""
import argparse
import json
import logging
import math
import os
from pathlib import Path
import datasets
import evaluate
import torch
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import set_seed
from datasets import load_dataset
from huggingface_hub import Repository, create_repo
from torch.utils.data import DataLoader
from torchvision.transforms import (
CenterCrop,
Compose,
Lambda,
Normalize,
RandomHorizontalFlip,
RandomResizedCrop,
Resize,
ToTensor,
)
from tqdm.auto import tqdm
import transformers
from transformers import AutoConfig, AutoImageProcessor, AutoModelForImageClassification, SchedulerType, get_scheduler
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.39.0.dev0")
logger = get_logger(__name__)
require_version("datasets>=2.0.0", "To fix: pip install -r examples/pytorch/image-classification/requirements.txt")
def parse_args():
parser = argparse.ArgumentParser(description="Fine-tune a Transformers model on an image classification dataset")
parser.add_argument(
"--dataset_name",
type=str,
default="cifar10",
help=(
"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,"
" dataset)."
),
)
parser.add_argument("--train_dir", type=str, default=None, help="A folder containing the training data.")
parser.add_argument("--validation_dir", type=str, default=None, help="A folder containing the validation data.")
parser.add_argument(
"--max_train_samples",
type=int,
default=None,
help=(
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
),
)
parser.add_argument(
"--max_eval_samples",
type=int,
default=None,
help=(
"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
),
)
parser.add_argument(
"--train_val_split",
type=float,
default=0.15,
help="Percent to split off of train for validation",
)
parser.add_argument(
"--model_name_or_path",
type=str,
help="Path to pretrained model or model identifier from huggingface.co/models.",
default="google/vit-base-patch16-224-in21k",
)
parser.add_argument(
"--per_device_train_batch_size",
type=int,
default=8,
help="Batch size (per device) for the training dataloader.",
)
parser.add_argument(
"--per_device_eval_batch_size",
type=int,
default=8,
help="Batch size (per device) for the evaluation dataloader.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=5e-5,
help="Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.")
parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.")
parser.add_argument(
"--max_train_steps",
type=int,
default=None,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"--lr_scheduler_type",
type=SchedulerType,
default="linear",
help="The scheduler type to use.",
choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"],
)
parser.add_argument(
"--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.")
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument(
"--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`."
)
parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.")
parser.add_argument(
"--trust_remote_code",
type=bool,
default=False,
help=(
"Whether or not to allow for custom models defined on the Hub in their own modeling files. This option "
"should only be set to `True` for repositories you trust and in which you have read the code, as it will "
"execute code present on the Hub on your local machine."
),
)
parser.add_argument(
"--checkpointing_steps",
type=str,
default=None,
help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.",
)
parser.add_argument(
"--resume_from_checkpoint",
type=str,
default=None,
help="If the training should continue from a checkpoint folder.",
)
parser.add_argument(
"--with_tracking",
action="store_true",
help="Whether to enable experiment trackers for logging.",
)
parser.add_argument(
"--report_to",
type=str,
default="all",
help=(
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`,'
' `"wandb"`, `"comet_ml"` and `"clearml"`. Use `"all"` (default) to report to all integrations. '
"Only applicable when `--with_tracking` is passed."
),
)
parser.add_argument(
"--ignore_mismatched_sizes",
action="store_true",
help="Whether or not to enable to load a pretrained model whose head dimensions are different.",
)
parser.add_argument(
"--image_column_name",
type=str,
default="image",
help="The name of the dataset column containing the image data. Defaults to 'image'.",
)
parser.add_argument(
"--label_column_name",
type=str,
default="label",
help="The name of the dataset column containing the labels. Defaults to 'label'.",
)
args = parser.parse_args()
# Sanity checks
if args.dataset_name is None and args.train_dir is None and args.validation_dir is None:
raise ValueError("Need either a dataset name or a training/validation folder.")
if args.push_to_hub or args.with_tracking:
if args.output_dir is None:
raise ValueError(
"Need an `output_dir` to create a repo when `--push_to_hub` or `with_tracking` is specified."
)
if args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
return args
def main():
args = parse_args()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("run_image_classification_no_trainer", args)
# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
# If we're using tracking, we also need to initialize it here and it will by default pick up all supported trackers
# in the environment
accelerator_log_kwargs = {}
if args.with_tracking:
accelerator_log_kwargs["log_with"] = args.report_to
accelerator_log_kwargs["project_dir"] = args.output_dir
accelerator = Accelerator(gradient_accumulation_steps=args.gradient_accumulation_steps, **accelerator_log_kwargs)
logger.info(accelerator.state)
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger.info(accelerator.state, main_process_only=False)
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Handle the repository creation
if accelerator.is_main_process:
if args.push_to_hub:
# Retrieve of infer repo_name
repo_name = args.hub_model_id
if repo_name is None:
repo_name = Path(args.output_dir).absolute().name
# Create repo and retrieve repo_id
repo_id = create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id
# Clone repo locally
repo = Repository(args.output_dir, clone_from=repo_id, token=args.hub_token)
with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
if "step_*" not in gitignore:
gitignore.write("step_*\n")
if "epoch_*" not in gitignore:
gitignore.write("epoch_*\n")
elif args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
accelerator.wait_for_everyone()
# Get the datasets: you can either provide your own training and evaluation files (see below)
# or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
# download the dataset.
if args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
dataset = load_dataset(args.dataset_name)
else:
data_files = {}
if args.train_dir is not None:
data_files["train"] = os.path.join(args.train_dir, "**")
if args.validation_dir is not None:
data_files["validation"] = os.path.join(args.validation_dir, "**")
dataset = load_dataset(
"imagefolder",
data_files=data_files,
)
# See more about loading custom images at
# https://huggingface.co/docs/datasets/v2.0.0/en/image_process#imagefolder.
dataset_column_names = dataset["train"].column_names if "train" in dataset else dataset["validation"].column_names
if args.image_column_name not in dataset_column_names:
raise ValueError(
f"--image_column_name {args.image_column_name} not found in dataset '{args.dataset_name}'. "
"Make sure to set `--image_column_name` to the correct audio column - one of "
f"{', '.join(dataset_column_names)}."
)
if args.label_column_name not in dataset_column_names:
raise ValueError(
f"--label_column_name {args.label_column_name} not found in dataset '{args.dataset_name}'. "
"Make sure to set `--label_column_name` to the correct text column - one of "
f"{', '.join(dataset_column_names)}."
)
# If we don't have a validation split, split off a percentage of train as validation.
args.train_val_split = None if "validation" in dataset.keys() else args.train_val_split
if isinstance(args.train_val_split, float) and args.train_val_split > 0.0:
split = dataset["train"].train_test_split(args.train_val_split)
dataset["train"] = split["train"]
dataset["validation"] = split["test"]
# Prepare label mappings.
# We'll include these in the model's config to get human readable labels in the Inference API.
labels = dataset["train"].features[args.label_column_name].names
label2id = {label: str(i) for i, label in enumerate(labels)}
id2label = {str(i): label for i, label in enumerate(labels)}
# Load pretrained model and image processor
#
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
config = AutoConfig.from_pretrained(
args.model_name_or_path,
num_labels=len(labels),
i2label=id2label,
label2id=label2id,
finetuning_task="image-classification",
trust_remote_code=args.trust_remote_code,
)
image_processor = AutoImageProcessor.from_pretrained(
args.model_name_or_path,
trust_remote_code=args.trust_remote_code,
)
model = AutoModelForImageClassification.from_pretrained(
args.model_name_or_path,
from_tf=bool(".ckpt" in args.model_name_or_path),
config=config,
ignore_mismatched_sizes=args.ignore_mismatched_sizes,
trust_remote_code=args.trust_remote_code,
)
# Preprocessing the datasets
# Define torchvision transforms to be applied to each image.
if "shortest_edge" in image_processor.size:
size = image_processor.size["shortest_edge"]
else:
size = (image_processor.size["height"], image_processor.size["width"])
normalize = (
Normalize(mean=image_processor.image_mean, std=image_processor.image_std)
if hasattr(image_processor, "image_mean") and hasattr(image_processor, "image_std")
else Lambda(lambda x: x)
)
train_transforms = Compose(
[
RandomResizedCrop(size),
RandomHorizontalFlip(),
ToTensor(),
normalize,
]
)
val_transforms = Compose(
[
Resize(size),
CenterCrop(size),
ToTensor(),
normalize,
]
)
def preprocess_train(example_batch):
"""Apply _train_transforms across a batch."""
example_batch["pixel_values"] = [
train_transforms(image.convert("RGB")) for image in example_batch[args.image_column_name]
]
return example_batch
def preprocess_val(example_batch):
"""Apply _val_transforms across a batch."""
example_batch["pixel_values"] = [
val_transforms(image.convert("RGB")) for image in example_batch[args.image_column_name]
]
return example_batch
with accelerator.main_process_first():
if args.max_train_samples is not None:
dataset["train"] = dataset["train"].shuffle(seed=args.seed).select(range(args.max_train_samples))
# Set the training transforms
train_dataset = dataset["train"].with_transform(preprocess_train)
if args.max_eval_samples is not None:
dataset["validation"] = dataset["validation"].shuffle(seed=args.seed).select(range(args.max_eval_samples))
# Set the validation transforms
eval_dataset = dataset["validation"].with_transform(preprocess_val)
# DataLoaders creation:
def collate_fn(examples):
pixel_values = torch.stack([example["pixel_values"] for example in examples])
labels = torch.tensor([example[args.label_column_name] for example in examples])
return {"pixel_values": pixel_values, "labels": labels}
train_dataloader = DataLoader(
train_dataset, shuffle=True, collate_fn=collate_fn, batch_size=args.per_device_train_batch_size
)
eval_dataloader = DataLoader(eval_dataset, collate_fn=collate_fn, batch_size=args.per_device_eval_batch_size)
# Optimizer
# Split weights in two groups, one with weight decay and the other not.
no_decay = ["bias", "LayerNorm.weight"]
optimizer_grouped_parameters = [
{
"params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
"weight_decay": args.weight_decay,
},
{
"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
"weight_decay": 0.0,
},
]
optimizer = torch.optim.AdamW(optimizer_grouped_parameters, lr=args.learning_rate)
# Scheduler and math around the number of training steps.
overrode_max_train_steps = False
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if args.max_train_steps is None:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
overrode_max_train_steps = True
lr_scheduler = get_scheduler(
name=args.lr_scheduler_type,
optimizer=optimizer,
num_warmup_steps=args.num_warmup_steps * accelerator.num_processes,
num_training_steps=args.max_train_steps
if overrode_max_train_steps
else args.max_train_steps * accelerator.num_processes,
)
# Prepare everything with our `accelerator`.
model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare(
model, optimizer, train_dataloader, eval_dataloader, lr_scheduler
)
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if overrode_max_train_steps:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
# Afterwards we recalculate our number of training epochs
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# Figure out how many steps we should save the Accelerator states
checkpointing_steps = args.checkpointing_steps
if checkpointing_steps is not None and checkpointing_steps.isdigit():
checkpointing_steps = int(checkpointing_steps)
# We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process.
if args.with_tracking:
experiment_config = vars(args)
# TensorBoard cannot log Enums, need the raw value
experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value
accelerator.init_trackers("image_classification_no_trainer", experiment_config)
# Get the metric function
metric = evaluate.load("accuracy")
# Train!
total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num Epochs = {args.num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {args.per_device_train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {args.max_train_steps}")
# Only show the progress bar once on each machine.
progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process)
completed_steps = 0
starting_epoch = 0
# Potentially load in the weights and states from a previous save
if args.resume_from_checkpoint:
if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
checkpoint_path = args.resume_from_checkpoint
path = os.path.basename(args.resume_from_checkpoint)
else:
# Get the most recent checkpoint
dirs = [f.name for f in os.scandir(os.getcwd()) if f.is_dir()]
dirs.sort(key=os.path.getctime)
path = dirs[-1] # Sorts folders by date modified, most recent checkpoint is the last
checkpoint_path = path
path = os.path.basename(checkpoint_path)
accelerator.print(f"Resumed from checkpoint: {checkpoint_path}")
accelerator.load_state(checkpoint_path)
# Extract `epoch_{i}` or `step_{i}`
training_difference = os.path.splitext(path)[0]
if "epoch" in training_difference:
starting_epoch = int(training_difference.replace("epoch_", "")) + 1
resume_step = None
completed_steps = starting_epoch * num_update_steps_per_epoch
else:
# need to multiply `gradient_accumulation_steps` to reflect real steps
resume_step = int(training_difference.replace("step_", "")) * args.gradient_accumulation_steps
starting_epoch = resume_step // len(train_dataloader)
completed_steps = resume_step // args.gradient_accumulation_steps
resume_step -= starting_epoch * len(train_dataloader)
# update the progress_bar if load from checkpoint
progress_bar.update(completed_steps)
for epoch in range(starting_epoch, args.num_train_epochs):
model.train()
if args.with_tracking:
total_loss = 0
if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
# We skip the first `n` batches in the dataloader when resuming from a checkpoint
active_dataloader = accelerator.skip_first_batches(train_dataloader, resume_step)
else:
active_dataloader = train_dataloader
for step, batch in enumerate(active_dataloader):
with accelerator.accumulate(model):
outputs = model(**batch)
loss = outputs.loss
# We keep track of the loss at each epoch
if args.with_tracking:
total_loss += loss.detach().float()
accelerator.backward(loss)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
progress_bar.update(1)
completed_steps += 1
if isinstance(checkpointing_steps, int):
if completed_steps % checkpointing_steps == 0:
output_dir = f"step_{completed_steps}"
if args.output_dir is not None:
output_dir = os.path.join(args.output_dir, output_dir)
accelerator.save_state(output_dir)
if args.push_to_hub and epoch < args.num_train_epochs - 1:
accelerator.wait_for_everyone()
unwrapped_model = accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(
args.output_dir,
is_main_process=accelerator.is_main_process,
save_function=accelerator.save,
)
if accelerator.is_main_process:
image_processor.save_pretrained(args.output_dir)
repo.push_to_hub(
commit_message=f"Training in progress {completed_steps} steps",
blocking=False,
auto_lfs_prune=True,
)
if completed_steps >= args.max_train_steps:
break
model.eval()
for step, batch in enumerate(eval_dataloader):
with torch.no_grad():
outputs = model(**batch)
predictions = outputs.logits.argmax(dim=-1)
predictions, references = accelerator.gather_for_metrics((predictions, batch["labels"]))
metric.add_batch(
predictions=predictions,
references=references,
)
eval_metric = metric.compute()
logger.info(f"epoch {epoch}: {eval_metric}")
if args.with_tracking:
accelerator.log(
{
"accuracy": eval_metric,
"train_loss": total_loss.item() / len(train_dataloader),
"epoch": epoch,
"step": completed_steps,
},
step=completed_steps,
)
if args.push_to_hub and epoch < args.num_train_epochs - 1:
accelerator.wait_for_everyone()
unwrapped_model = accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(
args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save
)
if accelerator.is_main_process:
image_processor.save_pretrained(args.output_dir)
repo.push_to_hub(
commit_message=f"Training in progress epoch {epoch}", blocking=False, auto_lfs_prune=True
)
if args.checkpointing_steps == "epoch":
output_dir = f"epoch_{epoch}"
if args.output_dir is not None:
output_dir = os.path.join(args.output_dir, output_dir)
accelerator.save_state(output_dir)
if args.with_tracking:
accelerator.end_training()
if args.output_dir is not None:
accelerator.wait_for_everyone()
unwrapped_model = accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(
args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save
)
if accelerator.is_main_process:
image_processor.save_pretrained(args.output_dir)
if args.push_to_hub:
repo.push_to_hub(commit_message="End of training", auto_lfs_prune=True)
all_results = {f"eval_{k}": v for k, v in eval_metric.items()}
with open(os.path.join(args.output_dir, "all_results.json"), "w") as f:
json.dump(all_results, f)
if __name__ == "__main__":
main()
| transformers/examples/pytorch/image-classification/run_image_classification_no_trainer.py/0 | {
"file_path": "transformers/examples/pytorch/image-classification/run_image_classification_no_trainer.py",
"repo_id": "transformers",
"token_count": 11204
} | 43 |
#!/usr/bin/env python
# coding=utf-8
# Copyright The HuggingFace Team and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Fine-tuning the library models for multiple choice.
"""
# You can also adapt this script on your own multiple choice task. Pointers for this are left as comments.
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import PaddingStrategy, check_min_version, send_example_telemetry
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.39.0.dev0")
logger = logging.getLogger(__name__)
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
"""
model_name_or_path: str = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
)
config_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
)
tokenizer_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
)
cache_dir: Optional[str] = field(
default=None,
metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
)
use_fast_tokenizer: bool = field(
default=True,
metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
)
model_revision: str = field(
default="main",
metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
)
token: str = field(
default=None,
metadata={
"help": (
"The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
"generated when running `huggingface-cli login` (stored in `~/.huggingface`)."
)
},
)
use_auth_token: bool = field(
default=None,
metadata={
"help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead."
},
)
trust_remote_code: bool = field(
default=False,
metadata={
"help": (
"Whether or not to allow for custom models defined on the Hub in their own modeling files. This option "
"should only be set to `True` for repositories you trust and in which you have read the code, as it will "
"execute code present on the Hub on your local machine."
)
},
)
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
"""
train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."})
validation_file: Optional[str] = field(
default=None,
metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
)
overwrite_cache: bool = field(
default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
)
preprocessing_num_workers: Optional[int] = field(
default=None,
metadata={"help": "The number of processes to use for the preprocessing."},
)
max_seq_length: Optional[int] = field(
default=None,
metadata={
"help": (
"The maximum total input sequence length after tokenization. If passed, sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
},
)
pad_to_max_length: bool = field(
default=False,
metadata={
"help": (
"Whether to pad all samples to the maximum sentence length. "
"If False, will pad the samples dynamically when batching to the maximum length in the batch. More "
"efficient on GPU but very bad for TPU."
)
},
)
max_train_samples: Optional[int] = field(
default=None,
metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
},
)
max_eval_samples: Optional[int] = field(
default=None,
metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
)
},
)
def __post_init__(self):
if self.train_file is not None:
extension = self.train_file.split(".")[-1]
assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
if self.validation_file is not None:
extension = self.validation_file.split(".")[-1]
assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
@dataclass
class DataCollatorForMultipleChoice:
"""
Data collator that will dynamically pad the inputs for multiple choice received.
Args:
tokenizer ([`PreTrainedTokenizer`] or [`PreTrainedTokenizerFast`]):
The tokenizer used for encoding the data.
padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `True`):
Select a strategy to pad the returned sequences (according to the model's padding side and padding index)
among:
- `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence
if provided).
- `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
acceptable input length for the model if that argument is not provided.
- `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
lengths).
max_length (`int`, *optional*):
Maximum length of the returned list and optionally padding length (see above).
pad_to_multiple_of (`int`, *optional*):
If set will pad the sequence to a multiple of the provided value.
This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=
7.5 (Volta).
"""
tokenizer: PreTrainedTokenizerBase
padding: Union[bool, str, PaddingStrategy] = True
max_length: Optional[int] = None
pad_to_multiple_of: Optional[int] = None
def __call__(self, features):
label_name = "label" if "label" in features[0].keys() else "labels"
labels = [feature.pop(label_name) for feature in features]
batch_size = len(features)
num_choices = len(features[0]["input_ids"])
flattened_features = [
[{k: v[i] for k, v in feature.items()} for i in range(num_choices)] for feature in features
]
flattened_features = list(chain(*flattened_features))
batch = self.tokenizer.pad(
flattened_features,
padding=self.padding,
max_length=self.max_length,
pad_to_multiple_of=self.pad_to_multiple_of,
return_tensors="pt",
)
# Un-flatten
batch = {k: v.view(batch_size, num_choices, -1) for k, v in batch.items()}
# Add back labels
batch["labels"] = torch.tensor(labels, dtype=torch.int64)
return batch
def main():
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
else:
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
if model_args.use_auth_token is not None:
warnings.warn(
"The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead.",
FutureWarning,
)
if model_args.token is not None:
raise ValueError("`token` and `use_auth_token` are both specified. Please set only the argument `token`.")
model_args.token = model_args.use_auth_token
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("run_swag", model_args, data_args)
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
handlers=[logging.StreamHandler(sys.stdout)],
)
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
log_level = training_args.get_process_log_level()
logger.setLevel(log_level)
datasets.utils.logging.set_verbosity(log_level)
transformers.utils.logging.set_verbosity(log_level)
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
+ f"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}"
)
logger.info(f"Training/evaluation parameters {training_args}")
# Detecting last checkpoint.
last_checkpoint = None
if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
last_checkpoint = get_last_checkpoint(training_args.output_dir)
if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. "
"Use --overwrite_output_dir to overcome."
)
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
)
# Set seed before initializing model.
set_seed(training_args.seed)
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.train_file is not None or data_args.validation_file is not None:
data_files = {}
if data_args.train_file is not None:
data_files["train"] = data_args.train_file
extension = data_args.train_file.split(".")[-1]
if data_args.validation_file is not None:
data_files["validation"] = data_args.validation_file
extension = data_args.validation_file.split(".")[-1]
raw_datasets = load_dataset(
extension,
data_files=data_files,
cache_dir=model_args.cache_dir,
token=model_args.token,
)
else:
# Downloading and loading the swag dataset from the hub.
raw_datasets = load_dataset(
"swag",
"regular",
cache_dir=model_args.cache_dir,
token=model_args.token,
)
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.
# Load pretrained model and tokenizer
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
config = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
token=model_args.token,
trust_remote_code=model_args.trust_remote_code,
)
tokenizer = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
use_fast=model_args.use_fast_tokenizer,
revision=model_args.model_revision,
token=model_args.token,
trust_remote_code=model_args.trust_remote_code,
)
model = AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path,
from_tf=bool(".ckpt" in model_args.model_name_or_path),
config=config,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
token=model_args.token,
trust_remote_code=model_args.trust_remote_code,
)
# When using your own dataset or a different dataset from swag, you will probably need to change this.
ending_names = [f"ending{i}" for i in range(4)]
context_name = "sent1"
question_header_name = "sent2"
if data_args.max_seq_length is None:
max_seq_length = tokenizer.model_max_length
if max_seq_length > 1024:
logger.warning(
"The chosen tokenizer supports a `model_max_length` that is longer than the default `block_size` value"
" of 1024. If you would like to use a longer `block_size` up to `tokenizer.model_max_length` you can"
" override this default with `--block_size xxx`."
)
max_seq_length = 1024
else:
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the "
f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."
)
max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)
# Preprocessing the datasets.
def preprocess_function(examples):
first_sentences = [[context] * 4 for context in examples[context_name]]
question_headers = examples[question_header_name]
second_sentences = [
[f"{header} {examples[end][i]}" for end in ending_names] for i, header in enumerate(question_headers)
]
# Flatten out
first_sentences = list(chain(*first_sentences))
second_sentences = list(chain(*second_sentences))
# Tokenize
tokenized_examples = tokenizer(
first_sentences,
second_sentences,
truncation=True,
max_length=max_seq_length,
padding="max_length" if data_args.pad_to_max_length else False,
)
# Un-flatten
return {k: [v[i : i + 4] for i in range(0, len(v), 4)] for k, v in tokenized_examples.items()}
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError("--do_train requires a train dataset")
train_dataset = raw_datasets["train"]
if data_args.max_train_samples is not None:
max_train_samples = min(len(train_dataset), data_args.max_train_samples)
train_dataset = train_dataset.select(range(max_train_samples))
with training_args.main_process_first(desc="train dataset map pre-processing"):
train_dataset = train_dataset.map(
preprocess_function,
batched=True,
num_proc=data_args.preprocessing_num_workers,
load_from_cache_file=not data_args.overwrite_cache,
)
if training_args.do_eval:
if "validation" not in raw_datasets:
raise ValueError("--do_eval requires a validation dataset")
eval_dataset = raw_datasets["validation"]
if data_args.max_eval_samples is not None:
max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples)
eval_dataset = eval_dataset.select(range(max_eval_samples))
with training_args.main_process_first(desc="validation dataset map pre-processing"):
eval_dataset = eval_dataset.map(
preprocess_function,
batched=True,
num_proc=data_args.preprocessing_num_workers,
load_from_cache_file=not data_args.overwrite_cache,
)
# Data collator
data_collator = (
default_data_collator
if data_args.pad_to_max_length
else DataCollatorForMultipleChoice(tokenizer=tokenizer, pad_to_multiple_of=8 if training_args.fp16 else None)
)
# Metric
def compute_metrics(eval_predictions):
predictions, label_ids = eval_predictions
preds = np.argmax(predictions, axis=1)
return {"accuracy": (preds == label_ids).astype(np.float32).mean().item()}
# Initialize our Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset if training_args.do_train else None,
eval_dataset=eval_dataset if training_args.do_eval else None,
tokenizer=tokenizer,
data_collator=data_collator,
compute_metrics=compute_metrics,
)
# Training
if training_args.do_train:
checkpoint = None
if training_args.resume_from_checkpoint is not None:
checkpoint = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
checkpoint = last_checkpoint
train_result = trainer.train(resume_from_checkpoint=checkpoint)
trainer.save_model() # Saves the tokenizer too for easy upload
metrics = train_result.metrics
max_train_samples = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset)
)
metrics["train_samples"] = min(max_train_samples, len(train_dataset))
trainer.log_metrics("train", metrics)
trainer.save_metrics("train", metrics)
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("*** Evaluate ***")
metrics = trainer.evaluate()
max_eval_samples = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset)
metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset))
trainer.log_metrics("eval", metrics)
trainer.save_metrics("eval", metrics)
kwargs = {
"finetuned_from": model_args.model_name_or_path,
"tasks": "multiple-choice",
"dataset_tags": "swag",
"dataset_args": "regular",
"dataset": "SWAG",
"language": "en",
}
if training_args.push_to_hub:
trainer.push_to_hub(**kwargs)
else:
trainer.create_model_card(**kwargs)
def _mp_fn(index):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| transformers/examples/pytorch/multiple-choice/run_swag.py/0 | {
"file_path": "transformers/examples/pytorch/multiple-choice/run_swag.py",
"repo_id": "transformers",
"token_count": 8510
} | 44 |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Finetuning any 🤗 Transformers model supported by AutoModelForSemanticSegmentation for semantic segmentation."""
import argparse
import json
import math
import os
import random
from pathlib import Path
import datasets
import evaluate
import numpy as np
import torch
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import set_seed
from datasets import load_dataset
from huggingface_hub import Repository, create_repo, hf_hub_download
from PIL import Image
from torch.utils.data import DataLoader
from torchvision import transforms
from torchvision.transforms import functional
from tqdm.auto import tqdm
import transformers
from transformers import (
AutoConfig,
AutoImageProcessor,
AutoModelForSemanticSegmentation,
SchedulerType,
default_data_collator,
get_scheduler,
)
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.39.0.dev0")
logger = get_logger(__name__)
require_version("datasets>=2.0.0", "To fix: pip install -r examples/pytorch/semantic-segmentation/requirements.txt")
def pad_if_smaller(img, size, fill=0):
min_size = min(img.size)
if min_size < size:
original_width, original_height = img.size
pad_height = size - original_height if original_height < size else 0
pad_width = size - original_width if original_width < size else 0
img = functional.pad(img, (0, 0, pad_width, pad_height), fill=fill)
return img
class Compose:
def __init__(self, transforms):
self.transforms = transforms
def __call__(self, image, target):
for t in self.transforms:
image, target = t(image, target)
return image, target
class Identity:
def __init__(self):
pass
def __call__(self, image, target):
return image, target
class Resize:
def __init__(self, size):
self.size = size
def __call__(self, image, target):
image = functional.resize(image, self.size)
target = functional.resize(target, self.size, interpolation=transforms.InterpolationMode.NEAREST)
return image, target
class RandomResize:
def __init__(self, min_size, max_size=None):
self.min_size = min_size
if max_size is None:
max_size = min_size
self.max_size = max_size
def __call__(self, image, target):
size = random.randint(self.min_size, self.max_size)
image = functional.resize(image, size)
target = functional.resize(target, size, interpolation=transforms.InterpolationMode.NEAREST)
return image, target
class RandomCrop:
def __init__(self, size):
self.size = size
def __call__(self, image, target):
image = pad_if_smaller(image, self.size)
target = pad_if_smaller(target, self.size, fill=255)
crop_params = transforms.RandomCrop.get_params(image, (self.size, self.size))
image = functional.crop(image, *crop_params)
target = functional.crop(target, *crop_params)
return image, target
class RandomHorizontalFlip:
def __init__(self, flip_prob):
self.flip_prob = flip_prob
def __call__(self, image, target):
if random.random() < self.flip_prob:
image = functional.hflip(image)
target = functional.hflip(target)
return image, target
class PILToTensor:
def __call__(self, image, target):
image = functional.pil_to_tensor(image)
target = torch.as_tensor(np.array(target), dtype=torch.int64)
return image, target
class ConvertImageDtype:
def __init__(self, dtype):
self.dtype = dtype
def __call__(self, image, target):
image = functional.convert_image_dtype(image, self.dtype)
return image, target
class Normalize:
def __init__(self, mean, std):
self.mean = mean
self.std = std
def __call__(self, image, target):
image = functional.normalize(image, mean=self.mean, std=self.std)
return image, target
class ReduceLabels:
def __call__(self, image, target):
if not isinstance(target, np.ndarray):
target = np.array(target).astype(np.uint8)
# avoid using underflow conversion
target[target == 0] = 255
target = target - 1
target[target == 254] = 255
target = Image.fromarray(target)
return image, target
def parse_args():
parser = argparse.ArgumentParser(description="Finetune a transformers model on a text classification task")
parser.add_argument(
"--model_name_or_path",
type=str,
help="Path to a pretrained model or model identifier from huggingface.co/models.",
default="nvidia/mit-b0",
)
parser.add_argument(
"--dataset_name",
type=str,
help="Name of the dataset on the hub.",
default="segments/sidewalk-semantic",
)
parser.add_argument(
"--reduce_labels",
action="store_true",
help="Whether or not to reduce all labels by 1 and replace background by 255.",
)
parser.add_argument(
"--train_val_split",
type=float,
default=0.15,
help="Fraction of the dataset to be used for validation.",
)
parser.add_argument(
"--cache_dir",
type=str,
help="Path to a folder in which the model and dataset will be cached.",
)
parser.add_argument(
"--use_auth_token",
action="store_true",
help="Whether to use an authentication token to access the model repository.",
)
parser.add_argument(
"--per_device_train_batch_size",
type=int,
default=8,
help="Batch size (per device) for the training dataloader.",
)
parser.add_argument(
"--per_device_eval_batch_size",
type=int,
default=8,
help="Batch size (per device) for the evaluation dataloader.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=5e-5,
help="Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument(
"--adam_beta1",
type=float,
default=0.9,
help="Beta1 for AdamW optimizer",
)
parser.add_argument(
"--adam_beta2",
type=float,
default=0.999,
help="Beta2 for AdamW optimizer",
)
parser.add_argument(
"--adam_epsilon",
type=float,
default=1e-8,
help="Epsilon for AdamW optimizer",
)
parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.")
parser.add_argument(
"--max_train_steps",
type=int,
default=None,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"--lr_scheduler_type",
type=SchedulerType,
default="polynomial",
help="The scheduler type to use.",
choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"],
)
parser.add_argument(
"--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.")
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument(
"--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`."
)
parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.")
parser.add_argument(
"--trust_remote_code",
type=bool,
default=False,
help=(
"Whether or not to allow for custom models defined on the Hub in their own modeling files. This option "
"should only be set to `True` for repositories you trust and in which you have read the code, as it will "
"execute code present on the Hub on your local machine."
),
)
parser.add_argument(
"--checkpointing_steps",
type=str,
default=None,
help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.",
)
parser.add_argument(
"--resume_from_checkpoint",
type=str,
default=None,
help="If the training should continue from a checkpoint folder.",
)
parser.add_argument(
"--with_tracking",
required=False,
action="store_true",
help="Whether to enable experiment trackers for logging.",
)
parser.add_argument(
"--report_to",
type=str,
default="all",
help=(
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`,'
' `"wandb"`, `"comet_ml"` and `"clearml"`. Use `"all"` (default) to report to all integrations. '
"Only applicable when `--with_tracking` is passed."
),
)
args = parser.parse_args()
# Sanity checks
if args.push_to_hub or args.with_tracking:
if args.output_dir is None:
raise ValueError(
"Need an `output_dir` to create a repo when `--push_to_hub` or `with_tracking` is specified."
)
if args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
return args
def main():
args = parse_args()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("run_semantic_segmentation_no_trainer", args)
# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
# If we're using tracking, we also need to initialize it here and it will by default pick up all supported trackers
# in the environment
accelerator_log_kwargs = {}
if args.with_tracking:
accelerator_log_kwargs["log_with"] = args.report_to
accelerator_log_kwargs["project_dir"] = args.output_dir
accelerator = Accelerator(gradient_accumulation_steps=args.gradient_accumulation_steps, **accelerator_log_kwargs)
logger.info(accelerator.state, main_process_only=False)
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
# We set device_specific to True as we want different data augmentation per device.
if args.seed is not None:
set_seed(args.seed, device_specific=True)
# Handle the repository creation
if accelerator.is_main_process:
if args.push_to_hub:
# Retrieve of infer repo_name
repo_name = args.hub_model_id
if repo_name is None:
repo_name = Path(args.output_dir).absolute().name
# Create repo and retrieve repo_id
repo_id = create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id
# Clone repo locally
repo = Repository(args.output_dir, clone_from=repo_id, token=args.hub_token)
with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
if "step_*" not in gitignore:
gitignore.write("step_*\n")
if "epoch_*" not in gitignore:
gitignore.write("epoch_*\n")
elif args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
accelerator.wait_for_everyone()
# Load dataset
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
# download the dataset.
# TODO support datasets from local folders
dataset = load_dataset(args.dataset_name, cache_dir=args.cache_dir)
# Rename column names to standardized names (only "image" and "label" need to be present)
if "pixel_values" in dataset["train"].column_names:
dataset = dataset.rename_columns({"pixel_values": "image"})
if "annotation" in dataset["train"].column_names:
dataset = dataset.rename_columns({"annotation": "label"})
# If we don't have a validation split, split off a percentage of train as validation.
args.train_val_split = None if "validation" in dataset.keys() else args.train_val_split
if isinstance(args.train_val_split, float) and args.train_val_split > 0.0:
split = dataset["train"].train_test_split(args.train_val_split)
dataset["train"] = split["train"]
dataset["validation"] = split["test"]
# Prepare label mappings.
# We'll include these in the model's config to get human readable labels in the Inference API.
if args.dataset_name == "scene_parse_150":
repo_id = "huggingface/label-files"
filename = "ade20k-id2label.json"
else:
repo_id = args.dataset_name
filename = "id2label.json"
id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r"))
id2label = {int(k): v for k, v in id2label.items()}
label2id = {v: k for k, v in id2label.items()}
# Load pretrained model and image processor
config = AutoConfig.from_pretrained(
args.model_name_or_path, id2label=id2label, label2id=label2id, trust_remote_code=args.trust_remote_code
)
image_processor = AutoImageProcessor.from_pretrained(
args.model_name_or_path, trust_remote_code=args.trust_remote_code
)
model = AutoModelForSemanticSegmentation.from_pretrained(
args.model_name_or_path, config=config, trust_remote_code=args.trust_remote_code
)
# Preprocessing the datasets
# Define torchvision transforms to be applied to each image + target.
# Not that straightforward in torchvision: https://github.com/pytorch/vision/issues/9
# Currently based on official torchvision references: https://github.com/pytorch/vision/blob/main/references/segmentation/transforms.py
if "shortest_edge" in image_processor.size:
# We instead set the target size as (shortest_edge, shortest_edge) to here to ensure all images are batchable.
size = (image_processor.size["shortest_edge"], image_processor.size["shortest_edge"])
else:
size = (image_processor.size["height"], image_processor.size["width"])
train_transforms = Compose(
[
ReduceLabels() if args.reduce_labels else Identity(),
RandomCrop(size=size),
RandomHorizontalFlip(flip_prob=0.5),
PILToTensor(),
ConvertImageDtype(torch.float),
Normalize(mean=image_processor.image_mean, std=image_processor.image_std),
]
)
# Define torchvision transform to be applied to each image.
# jitter = ColorJitter(brightness=0.25, contrast=0.25, saturation=0.25, hue=0.1)
val_transforms = Compose(
[
ReduceLabels() if args.reduce_labels else Identity(),
Resize(size=size),
PILToTensor(),
ConvertImageDtype(torch.float),
Normalize(mean=image_processor.image_mean, std=image_processor.image_std),
]
)
def preprocess_train(example_batch):
pixel_values = []
labels = []
for image, target in zip(example_batch["image"], example_batch["label"]):
image, target = train_transforms(image.convert("RGB"), target)
pixel_values.append(image)
labels.append(target)
encoding = {}
encoding["pixel_values"] = torch.stack(pixel_values)
encoding["labels"] = torch.stack(labels)
return encoding
def preprocess_val(example_batch):
pixel_values = []
labels = []
for image, target in zip(example_batch["image"], example_batch["label"]):
image, target = val_transforms(image.convert("RGB"), target)
pixel_values.append(image)
labels.append(target)
encoding = {}
encoding["pixel_values"] = torch.stack(pixel_values)
encoding["labels"] = torch.stack(labels)
return encoding
with accelerator.main_process_first():
train_dataset = dataset["train"].with_transform(preprocess_train)
eval_dataset = dataset["validation"].with_transform(preprocess_val)
train_dataloader = DataLoader(
train_dataset, shuffle=True, collate_fn=default_data_collator, batch_size=args.per_device_train_batch_size
)
eval_dataloader = DataLoader(
eval_dataset, collate_fn=default_data_collator, batch_size=args.per_device_eval_batch_size
)
# Optimizer
optimizer = torch.optim.AdamW(
list(model.parameters()),
lr=args.learning_rate,
betas=[args.adam_beta1, args.adam_beta2],
eps=args.adam_epsilon,
)
# Figure out how many steps we should save the Accelerator states
checkpointing_steps = args.checkpointing_steps
if checkpointing_steps is not None and checkpointing_steps.isdigit():
checkpointing_steps = int(checkpointing_steps)
# Scheduler and math around the number of training steps.
overrode_max_train_steps = False
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if args.max_train_steps is None:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
overrode_max_train_steps = True
lr_scheduler = get_scheduler(
name=args.lr_scheduler_type,
optimizer=optimizer,
num_warmup_steps=args.num_warmup_steps * accelerator.num_processes,
num_training_steps=args.max_train_steps
if overrode_max_train_steps
else args.max_train_steps * accelerator.num_processes,
)
# Prepare everything with our `accelerator`.
model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare(
model, optimizer, train_dataloader, eval_dataloader, lr_scheduler
)
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if overrode_max_train_steps:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
# Afterwards we recalculate our number of training epochs
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# Instantiate metric
metric = evaluate.load("mean_iou", cache_dir=args.cache_dir)
# We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process.
if args.with_tracking:
experiment_config = vars(args)
# TensorBoard cannot log Enums, need the raw value
experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value
accelerator.init_trackers("semantic_segmentation_no_trainer", experiment_config)
# Train!
total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num Epochs = {args.num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {args.per_device_train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {args.max_train_steps}")
# Only show the progress bar once on each machine.
progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process)
completed_steps = 0
starting_epoch = 0
# Potentially load in the weights and states from a previous save
if args.resume_from_checkpoint:
if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
checkpoint_path = args.resume_from_checkpoint
path = os.path.basename(args.resume_from_checkpoint)
else:
# Get the most recent checkpoint
dirs = [f.name for f in os.scandir(os.getcwd()) if f.is_dir()]
dirs.sort(key=os.path.getctime)
path = dirs[-1] # Sorts folders by date modified, most recent checkpoint is the last
checkpoint_path = path
path = os.path.basename(checkpoint_path)
accelerator.print(f"Resumed from checkpoint: {checkpoint_path}")
accelerator.load_state(checkpoint_path)
# Extract `epoch_{i}` or `step_{i}`
training_difference = os.path.splitext(path)[0]
if "epoch" in training_difference:
starting_epoch = int(training_difference.replace("epoch_", "")) + 1
resume_step = None
completed_steps = starting_epoch * num_update_steps_per_epoch
else:
# need to multiply `gradient_accumulation_steps` to reflect real steps
resume_step = int(training_difference.replace("step_", "")) * args.gradient_accumulation_steps
starting_epoch = resume_step // len(train_dataloader)
completed_steps = resume_step // args.gradient_accumulation_steps
resume_step -= starting_epoch * len(train_dataloader)
# update the progress_bar if load from checkpoint
progress_bar.update(completed_steps)
for epoch in range(starting_epoch, args.num_train_epochs):
model.train()
if args.with_tracking:
total_loss = 0
if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
# We skip the first `n` batches in the dataloader when resuming from a checkpoint
active_dataloader = accelerator.skip_first_batches(train_dataloader, resume_step)
else:
active_dataloader = train_dataloader
for step, batch in enumerate(active_dataloader):
with accelerator.accumulate(model):
outputs = model(**batch)
loss = outputs.loss
# We keep track of the loss at each epoch
if args.with_tracking:
total_loss += loss.detach().float()
accelerator.backward(loss)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
progress_bar.update(1)
completed_steps += 1
if isinstance(checkpointing_steps, int):
if completed_steps % checkpointing_steps == 0:
output_dir = f"step_{completed_steps}"
if args.output_dir is not None:
output_dir = os.path.join(args.output_dir, output_dir)
accelerator.save_state(output_dir)
if args.push_to_hub and epoch < args.num_train_epochs - 1:
accelerator.wait_for_everyone()
unwrapped_model = accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(
args.output_dir,
is_main_process=accelerator.is_main_process,
save_function=accelerator.save,
)
if accelerator.is_main_process:
image_processor.save_pretrained(args.output_dir)
repo.push_to_hub(
commit_message=f"Training in progress {completed_steps} steps",
blocking=False,
auto_lfs_prune=True,
)
if completed_steps >= args.max_train_steps:
break
logger.info("***** Running evaluation *****")
model.eval()
for step, batch in enumerate(tqdm(eval_dataloader, disable=not accelerator.is_local_main_process)):
with torch.no_grad():
outputs = model(**batch)
upsampled_logits = torch.nn.functional.interpolate(
outputs.logits, size=batch["labels"].shape[-2:], mode="bilinear", align_corners=False
)
predictions = upsampled_logits.argmax(dim=1)
predictions, references = accelerator.gather_for_metrics((predictions, batch["labels"]))
metric.add_batch(
predictions=predictions,
references=references,
)
eval_metrics = metric.compute(
num_labels=len(id2label),
ignore_index=255,
reduce_labels=False, # we've already reduced the labels before
)
logger.info(f"epoch {epoch}: {eval_metrics}")
if args.with_tracking:
accelerator.log(
{
"mean_iou": eval_metrics["mean_iou"],
"mean_accuracy": eval_metrics["mean_accuracy"],
"overall_accuracy": eval_metrics["overall_accuracy"],
"train_loss": total_loss.item() / len(train_dataloader),
"epoch": epoch,
"step": completed_steps,
},
step=completed_steps,
)
if args.push_to_hub and epoch < args.num_train_epochs - 1:
accelerator.wait_for_everyone()
unwrapped_model = accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(
args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save
)
if accelerator.is_main_process:
image_processor.save_pretrained(args.output_dir)
repo.push_to_hub(
commit_message=f"Training in progress epoch {epoch}", blocking=False, auto_lfs_prune=True
)
if args.checkpointing_steps == "epoch":
output_dir = f"epoch_{epoch}"
if args.output_dir is not None:
output_dir = os.path.join(args.output_dir, output_dir)
accelerator.save_state(output_dir)
if args.with_tracking:
accelerator.end_training()
if args.output_dir is not None:
accelerator.wait_for_everyone()
unwrapped_model = accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(
args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save
)
if accelerator.is_main_process:
image_processor.save_pretrained(args.output_dir)
if args.push_to_hub:
repo.push_to_hub(commit_message="End of training", auto_lfs_prune=True)
all_results = {
f"eval_{k}": v.tolist() if isinstance(v, np.ndarray) else v for k, v in eval_metrics.items()
}
with open(os.path.join(args.output_dir, "all_results.json"), "w") as f:
json.dump(all_results, f)
if __name__ == "__main__":
main()
| transformers/examples/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py/0 | {
"file_path": "transformers/examples/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py",
"repo_id": "transformers",
"token_count": 11989
} | 45 |
# Text Summarization with Pretrained Encoders
This folder contains part of the code necessary to reproduce the results on abstractive summarization from the article [Text Summarization with Pretrained Encoders](https://arxiv.org/pdf/1908.08345.pdf) by [Yang Liu](https://nlp-yang.github.io/) and [Mirella Lapata](https://homepages.inf.ed.ac.uk/mlap/). It can also be used to summarize any document.
The original code can be found on the Yang Liu's [github repository](https://github.com/nlpyang/PreSumm).
The model is loaded with the pre-trained weights for the abstractive summarization model trained on the CNN/Daily Mail dataset with an extractive and then abstractive tasks.
## Setup
```bash
git clone https://github.com/huggingface/transformers && cd transformers
pip install .
pip install nltk py-rouge
cd examples/seq2seq/bertabs
```
## Reproduce the authors' ROUGE score
To be able to reproduce the authors' results on the CNN/Daily Mail dataset you first need to download both CNN and Daily Mail datasets [from Kyunghyun Cho's website](https://cs.nyu.edu/~kcho/DMQA/) (the links next to "Stories") in the same folder. Then uncompress the archives by running:
```bash
tar -xvf cnn_stories.tgz && tar -xvf dailymail_stories.tgz
```
And move all the stories to the same folder. We will refer as `$DATA_PATH` the path to where you uncompressed both archive. Then run the following in the same folder as `run_summarization.py`:
```bash
python run_summarization.py \
--documents_dir $DATA_PATH \
--summaries_output_dir $SUMMARIES_PATH \ # optional
--no_cuda false \
--batch_size 4 \
--min_length 50 \
--max_length 200 \
--beam_size 5 \
--alpha 0.95 \
--block_trigram true \
--compute_rouge true
```
The scripts executes on GPU if one is available and if `no_cuda` is not set to `true`. Inference on multiple GPUs is not supported yet. The ROUGE scores will be displayed in the console at the end of evaluation and written in a `rouge_scores.txt` file. The script takes 30 hours to compute with a single Tesla V100 GPU and a batch size of 10 (300,000 texts to summarize).
## Summarize any text
Put the documents that you would like to summarize in a folder (the path to which is referred to as `$DATA_PATH` below) and run the following in the same folder as `run_summarization.py`:
```bash
python run_summarization.py \
--documents_dir $DATA_PATH \
--summaries_output_dir $SUMMARIES_PATH \ # optional
--no_cuda false \
--batch_size 4 \
--min_length 50 \
--max_length 200 \
--beam_size 5 \
--alpha 0.95 \
--block_trigram true \
```
You may want to play around with `min_length`, `max_length` and `alpha` to suit your use case. If you want to compute ROUGE on another dataset you will need to tweak the stories/summaries import in `utils_summarization.py` and tell it where to fetch the reference summaries.
| transformers/examples/research_projects/bertabs/README.md/0 | {
"file_path": "transformers/examples/research_projects/bertabs/README.md",
"repo_id": "transformers",
"token_count": 909
} | 46 |
#!/bin/bash
export CUDA_VISIBLE_DEVICES=0
PATH_TO_DATA=/h/xinji/projects/GLUE
MODEL_TYPE=bert # bert or roberta
MODEL_SIZE=base # base or large
DATASET=MRPC # SST-2, MRPC, RTE, QNLI, QQP, or MNLI
MODEL_NAME=${MODEL_TYPE}-${MODEL_SIZE}
if [ $MODEL_TYPE = 'bert' ]
then
MODEL_NAME=${MODEL_NAME}-uncased
fi
python -u run_glue_deebert.py \
--model_type $MODEL_TYPE \
--model_name_or_path ./saved_models/${MODEL_TYPE}-${MODEL_SIZE}/$DATASET/two_stage \
--task_name $DATASET \
--do_eval \
--do_lower_case \
--data_dir $PATH_TO_DATA/$DATASET \
--output_dir ./saved_models/${MODEL_TYPE}-${MODEL_SIZE}/$DATASET/two_stage \
--plot_data_dir ./results/ \
--max_seq_length 128 \
--eval_each_highway \
--eval_highway \
--overwrite_cache \
--per_gpu_eval_batch_size=1
| transformers/examples/research_projects/deebert/eval_deebert.sh/0 | {
"file_path": "transformers/examples/research_projects/deebert/eval_deebert.sh",
"repo_id": "transformers",
"token_count": 360
} | 47 |
# coding=utf-8
# Copyright 2019-present, the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Preprocessing script before training DistilBERT.
Specific to BERT -> DistilBERT.
"""
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description=(
"Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned"
" Distillation"
)
)
parser.add_argument("--model_type", default="bert", choices=["bert"])
parser.add_argument("--model_name", default="bert-base-uncased", type=str)
parser.add_argument("--dump_checkpoint", default="serialization_dir/tf_bert-base-uncased_0247911.pth", type=str)
parser.add_argument("--vocab_transform", action="store_true")
args = parser.parse_args()
if args.model_type == "bert":
model = BertForMaskedLM.from_pretrained(args.model_name)
prefix = "bert"
else:
raise ValueError('args.model_type should be "bert".')
state_dict = model.state_dict()
compressed_sd = {}
for w in ["word_embeddings", "position_embeddings"]:
compressed_sd[f"distilbert.embeddings.{w}.weight"] = state_dict[f"{prefix}.embeddings.{w}.weight"]
for w in ["weight", "bias"]:
compressed_sd[f"distilbert.embeddings.LayerNorm.{w}"] = state_dict[f"{prefix}.embeddings.LayerNorm.{w}"]
std_idx = 0
for teacher_idx in [0, 2, 4, 7, 9, 11]:
for w in ["weight", "bias"]:
compressed_sd[f"distilbert.transformer.layer.{std_idx}.attention.q_lin.{w}"] = state_dict[
f"{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"
]
compressed_sd[f"distilbert.transformer.layer.{std_idx}.attention.k_lin.{w}"] = state_dict[
f"{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"
]
compressed_sd[f"distilbert.transformer.layer.{std_idx}.attention.v_lin.{w}"] = state_dict[
f"{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"
]
compressed_sd[f"distilbert.transformer.layer.{std_idx}.attention.out_lin.{w}"] = state_dict[
f"{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"
]
compressed_sd[f"distilbert.transformer.layer.{std_idx}.sa_layer_norm.{w}"] = state_dict[
f"{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"
]
compressed_sd[f"distilbert.transformer.layer.{std_idx}.ffn.lin1.{w}"] = state_dict[
f"{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"
]
compressed_sd[f"distilbert.transformer.layer.{std_idx}.ffn.lin2.{w}"] = state_dict[
f"{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"
]
compressed_sd[f"distilbert.transformer.layer.{std_idx}.output_layer_norm.{w}"] = state_dict[
f"{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"
]
std_idx += 1
compressed_sd["vocab_projector.weight"] = state_dict["cls.predictions.decoder.weight"]
compressed_sd["vocab_projector.bias"] = state_dict["cls.predictions.bias"]
if args.vocab_transform:
for w in ["weight", "bias"]:
compressed_sd[f"vocab_transform.{w}"] = state_dict[f"cls.predictions.transform.dense.{w}"]
compressed_sd[f"vocab_layer_norm.{w}"] = state_dict[f"cls.predictions.transform.LayerNorm.{w}"]
print(f"N layers selected for distillation: {std_idx}")
print(f"Number of params transferred for distillation: {len(compressed_sd.keys())}")
print(f"Save transferred checkpoint to {args.dump_checkpoint}.")
torch.save(compressed_sd, args.dump_checkpoint)
| transformers/examples/research_projects/distillation/scripts/extract_distilbert.py/0 | {
"file_path": "transformers/examples/research_projects/distillation/scripts/extract_distilbert.py",
"repo_id": "transformers",
"token_count": 1864
} | 48 |
# Information Gain Filtration(IGF)
Authors @Tuko @mraunak
This folder contains the code how to implement IGF for finetuning on GPT-2.
## What is IGF?
Here we present a general fine-tuning method that we call information gain filtration for improving the overall training efficiency and final
performance of language model fine-tuning(see paper below). The method is an alternative fine-tuning method that trains
a secondary model (e.g., a simple convolutional network) to predict the amount of information
gained over a given pre-trained model. The secondary model is lightweight and trained to
predict the Information Gain measure. Information Gain is defined as the change in a loss
function for a model before and after an SGD update with a sample (Equation X in the paper).
A small subset of the training set named the “objective” set, is used to measure information
gain on the pre-trained model, and consequently to train the secondary model. After
training, the model is used for filtering samples for the fine-tuning process. Therefore,
a high information gain value would suggest a sample is informative, whereas a low value
would suggest a non-informative sample that should be filtered out. Thus, a thresholding
strategy is defined to select informative samples. With such a strategy, samples are filtered
and once enough samples are selected to form a mini-batch and a usual fine-tuning/optimization
step is applied. The filtration process is repeated until the fine-tuning process is over.
Paper [Selecting Informative Contexts Improves Language Model Finetuning](https://arxiv.org/abs/2005.00175)
# Results
Several experiments were conducted to show the robustness of the IGF method versus the
standard fine-tuning process. For example, we achieve a median perplexity of 54.0 on the
Books dataset compared to 57.3 for standard fine-tuning on GPT-2 Small. The code was
implemented using the Transformers library and Pytorch. While the method may seem more
expensive, we saw enough evidence that it may lead to a performance benefit in the final models.
Figure 1: Comparing IGF to Standard Fine-tuning:
IGF with constant (p < 10−3 , t-test) and shifting(p < 10−6 , t-test) thresholding significantly outperform standard fine-tuning. The left-hand figure shows
test-set perplexity after each fine-tuning batch, averaged over 50 runs (error bars denote ± one standard error). The right-hand figure shows the perplexity of each
method after 60 batches. IGF with shifting thresholding (red) clearly improves over standard batched fine-tuning with Adam
## How to use this project?
To fine-tune a transformer model with IGF on a language modeling task, use the following script:
- `model_name_or_path`: Path to pretrained model or model identifier from huggingface.co/models
- `data_file`: A jbl file containing tokenized data which can be split as objective dataset,
train_dataset and test_dataset
- `igf_data_file`: A jbl file containing the context and information gain pairs to train secondary learner.
- `context_len`: The maximum total input sequence length after tokenization. Sequences longer
than this will be truncated, sequences shorter will be padded.
- `size_objective_set`: Number of articles that are long enough to be used as our objective set"
- `min_len`: The minimum length of the article to be used as objective set
- `trim`: Truncate the example if it exceeds context length
- `eval_freq`: Secondary model evaluation can be triggered at eval_freq
- `max_steps`: To calculate training epochs
- `number`: The number of examples split to be used as objective_set/test_data
- `secondary_learner_batch_size`: The batch size of training data for secondary learner
- `secondary_learner_max_epochs`: The number of epochs to train secondary learner
- `recopy_model`: Reset the model to the original pretrained GPT-2 weights after each iteration
- `eval_interval`: Decay the selectivity of our secondary learner filter from"
1 standard deviation above average to 1 below average after eval_interval(10) batches"
```python
python run_clm_igf.py\
--model_name_or_path "openai-community/gpt2" \
--data_file="data/tokenized_stories_train_wikitext103" \
--igf_data_file="data/IGF_values" \
--context_len 32 \
--size_objective_set 100 \
--min_len 1026 \
--trim True \
--eval_freq 100 \
--max_steps 1000 \
--secondary_learner_batch_size 128 \
--secondary_learner_max_epochs 15 \
--number 100 \
--recopy_model \
--eval_interval 10 \
```
## Citation
If you find the resource useful, please cite the following paper
```bibtex
@inproceedings{antonello-etal-2021-selecting,
title = "Selecting Informative Contexts Improves Language Model Fine-tuning",
author = "Antonello, Richard and Beckage, Nicole and Turek, Javier and Huth, Alexander",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.87",
doi = "10.18653/v1/2021.acl-long.87",
pages = "1072--1085",
}
```
| transformers/examples/research_projects/information-gain-filtration/README.md/0 | {
"file_path": "transformers/examples/research_projects/information-gain-filtration/README.md",
"repo_id": "transformers",
"token_count": 1461
} | 49 |
#!/usr/bin/env python
# coding=utf-8
# Copyright 2021 The HuggingFace Team All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Fine-tuning the library models for masked language modeling (BERT, ALBERT, RoBERTa...) with whole word masking on a
text file or a dataset.
Here is the full list of checkpoints on the hub that can be fine-tuned by this script:
https://huggingface.co/models?filter=fill-mask
"""
import logging
import os
import sys
import time
from collections import defaultdict
from dataclasses import dataclass, field
# You can also adapt this script on your own masked language modeling task. Pointers for this are left as comments.
from pathlib import Path
from typing import Dict, List, Optional, Tuple
import datasets
import flax
import jax
import jax.numpy as jnp
import numpy as np
import optax
from datasets import load_dataset
from flax import jax_utils, traverse_util
from flax.training import train_state
from flax.training.common_utils import get_metrics, onehot, shard
from tqdm import tqdm
from transformers import (
CONFIG_MAPPING,
FLAX_MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
AutoTokenizer,
FlaxAutoModelForMaskedLM,
HfArgumentParser,
PreTrainedTokenizerBase,
TensorType,
TrainingArguments,
is_tensorboard_available,
set_seed,
)
if datasets.__version__ <= "1.8.0":
raise ValueError("Make sure to upgrade `datasets` to a version >= 1.9.0 to use dataset streaming")
MODEL_CONFIG_CLASSES = list(FLAX_MODEL_FOR_MASKED_LM_MAPPING.keys())
MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
"""
model_name_or_path: Optional[str] = field(
default=None,
metadata={
"help": (
"The model checkpoint for weights initialization. Don't set if you want to train a model from scratch."
)
},
)
model_type: Optional[str] = field(
default=None,
metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)},
)
config_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
)
tokenizer_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
)
cache_dir: Optional[str] = field(
default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
)
use_fast_tokenizer: bool = field(
default=True,
metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
)
dtype: Optional[str] = field(
default="float32",
metadata={
"help": (
"Floating-point format in which the model weights should be initialized and trained. Choose one of"
" `[float32, float16, bfloat16]`."
)
},
)
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
"""
dataset_name: Optional[str] = field(
default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
)
dataset_config_name: Optional[str] = field(
default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
)
train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."})
validation_file: Optional[str] = field(
default=None,
metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
)
train_ref_file: Optional[str] = field(
default=None,
metadata={"help": "An optional input train ref data file for whole word masking in Chinese."},
)
validation_ref_file: Optional[str] = field(
default=None,
metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."},
)
overwrite_cache: bool = field(
default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
)
validation_split_percentage: Optional[int] = field(
default=5,
metadata={
"help": "The percentage of the train set used as validation set in case there's no validation split"
},
)
max_seq_length: Optional[int] = field(
default=None,
metadata={
"help": (
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated. Default to the max input length of the model."
)
},
)
preprocessing_num_workers: Optional[int] = field(
default=None,
metadata={"help": "The number of processes to use for the preprocessing."},
)
mlm_probability: float = field(
default=0.15, metadata={"help": "Ratio of tokens to mask for masked language modeling loss"}
)
pad_to_max_length: bool = field(
default=False,
metadata={
"help": (
"Whether to pad all samples to `max_seq_length`. "
"If False, will pad the samples dynamically when batching to the maximum length in the batch."
)
},
)
line_by_line: bool = field(
default=False,
metadata={"help": "Whether distinct lines of text in the dataset are to be handled as distinct sequences."},
)
text_column_name: str = field(
default="text", metadata={"help": "The name of the column to retrieve the training text."}
)
shuffle_buffer_size: int = field(
default=10000, metadata={"help": "The number of examples to pre-load for shuffling."}
)
num_train_steps: int = field(default=50000, metadata={"help": "The number of training steps."})
num_eval_samples: int = field(default=50000, metadata={"help": "The number of samples to be used for evaluation"})
def __post_init__(self):
if self.dataset_name is None and self.train_file is None and self.validation_file is None:
raise ValueError("Need either a dataset name or a training/validation file.")
else:
if self.train_file is not None:
extension = self.train_file.split(".")[-1]
assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
if self.validation_file is not None:
extension = self.validation_file.split(".")[-1]
assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."
@flax.struct.dataclass
class FlaxDataCollatorForLanguageModeling:
"""
Data collator used for language modeling. Inputs are dynamically padded to the maximum length of a batch if they
are not all of the same length.
Args:
tokenizer (:class:`~transformers.PreTrainedTokenizer` or :class:`~transformers.PreTrainedTokenizerFast`):
The tokenizer used for encoding the data.
mlm_probability (:obj:`float`, `optional`, defaults to 0.15):
The probability with which to (randomly) mask tokens in the input.
.. note::
For best performance, this data collator should be used with a dataset having items that are dictionaries or
BatchEncoding, with the :obj:`"special_tokens_mask"` key, as returned by a
:class:`~transformers.PreTrainedTokenizer` or a :class:`~transformers.PreTrainedTokenizerFast` with the
argument :obj:`return_special_tokens_mask=True`.
"""
tokenizer: PreTrainedTokenizerBase
mlm_probability: float = 0.15
def __post_init__(self):
if self.tokenizer.mask_token is None:
raise ValueError(
"This tokenizer does not have a mask token which is necessary for masked language modeling. "
"You should pass `mlm=False` to train on causal language modeling instead."
)
def __call__(self, examples: List[Dict[str, np.ndarray]]) -> Dict[str, np.ndarray]:
# Handle dict or lists with proper padding and conversion to tensor.
batch = self.tokenizer.pad(examples, return_tensors=TensorType.NUMPY)
# If special token mask has been preprocessed, pop it from the dict.
special_tokens_mask = batch.pop("special_tokens_mask", None)
batch["input_ids"], batch["labels"] = self.mask_tokens(
batch["input_ids"], special_tokens_mask=special_tokens_mask
)
return batch
def mask_tokens(
self, inputs: np.ndarray, special_tokens_mask: Optional[np.ndarray]
) -> Tuple[jnp.ndarray, jnp.ndarray]:
"""
Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original.
"""
labels = inputs.copy()
# We sample a few tokens in each sequence for MLM training (with probability `self.mlm_probability`)
probability_matrix = np.full(labels.shape, self.mlm_probability)
special_tokens_mask = special_tokens_mask.astype("bool")
probability_matrix[special_tokens_mask] = 0.0
masked_indices = np.random.binomial(1, probability_matrix).astype("bool")
labels[~masked_indices] = -100 # We only compute loss on masked tokens
# 80% of the time, we replace masked input tokens with tokenizer.mask_token ([MASK])
indices_replaced = np.random.binomial(1, np.full(labels.shape, 0.8)).astype("bool") & masked_indices
inputs[indices_replaced] = self.tokenizer.convert_tokens_to_ids(self.tokenizer.mask_token)
# 10% of the time, we replace masked input tokens with random word
indices_random = np.random.binomial(1, np.full(labels.shape, 0.5)).astype("bool")
indices_random &= masked_indices & ~indices_replaced
random_words = np.random.randint(self.tokenizer.vocab_size, size=labels.shape, dtype="i4")
inputs[indices_random] = random_words[indices_random]
# The rest of the time (10% of the time) we keep the masked input tokens unchanged
return inputs, labels
def generate_batch_splits(samples_idx: np.ndarray, batch_size: int) -> np.ndarray:
num_samples = len(samples_idx)
samples_to_remove = num_samples % batch_size
if samples_to_remove != 0:
samples_idx = samples_idx[:-samples_to_remove]
sections_split = num_samples // batch_size
batch_idx = np.split(samples_idx, sections_split)
return batch_idx
def advance_iter_and_group_samples(train_iterator, num_samples, max_seq_length):
"""
The training iterator is advanced so that after groupifying the samples,
`num_samples` of length `max_seq_length` are returned.
"""
num_total_tokens = max_seq_length * num_samples
samples = defaultdict(list)
i = 0
while i < num_total_tokens:
tokenized_samples = next(train_iterator)
i += len(tokenized_samples["input_ids"])
# concatenate tokenized samples to list (excluding "id" and "text")
samples = {
k: samples[k] + tokenized_samples[k] for k in ["input_ids", "attention_mask", "special_tokens_mask"]
}
# Concatenated tokens are split to lists of length `max_seq_length`.
# Note that remainedr of % max_seq_length are thrown away.
def group_texts(examples):
result = {
k: [t[i : i + max_seq_length] for i in range(0, num_total_tokens, max_seq_length)]
for k, t in examples.items()
}
return result
grouped_samples = group_texts(samples)
return grouped_samples
def write_train_metric(summary_writer, train_metrics, train_time, step):
summary_writer.scalar("train_time", train_time, step)
train_metrics = get_metrics(train_metrics)
for key, vals in train_metrics.items():
tag = f"train_{key}"
for i, val in enumerate(vals):
summary_writer.scalar(tag, val, step - len(vals) + i + 1)
def write_eval_metric(summary_writer, eval_metrics, step):
for metric_name, value in eval_metrics.items():
summary_writer.scalar(f"eval_{metric_name}", value, step)
if __name__ == "__main__":
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
else:
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir)
and os.listdir(training_args.output_dir)
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. "
"Use --overwrite_output_dir to overcome."
)
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
level="INFO",
datefmt="[%X]",
)
# Log on each process the small summary:
logger = logging.getLogger(__name__)
logger.warning(
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+ f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
)
# Set the verbosity to info of the Transformers logger (on main process only):
logger.info(f"Training/evaluation parameters {training_args}")
# Set seed before initializing model.
set_seed(training_args.seed)
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
dataset = load_dataset(
data_args.dataset_name,
data_args.dataset_config_name,
cache_dir=model_args.cache_dir,
streaming=True,
split="train",
)
if model_args.config_name:
config = AutoConfig.from_pretrained(model_args.config_name, cache_dir=model_args.cache_dir)
elif model_args.model_name_or_path:
config = AutoConfig.from_pretrained(model_args.model_name_or_path, cache_dir=model_args.cache_dir)
else:
config = CONFIG_MAPPING[model_args.model_type]()
logger.warning("You are instantiating a new config instance from scratch.")
if model_args.tokenizer_name:
tokenizer = AutoTokenizer.from_pretrained(
model_args.tokenizer_name, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
)
elif model_args.model_name_or_path:
tokenizer = AutoTokenizer.from_pretrained(
model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
)
else:
raise ValueError(
"You are instantiating a new tokenizer from scratch. This is not supported by this script. "
"You can do it from another script, save it, and load it from here, using --tokenizer_name."
)
# Otherwise, we tokenize every text, then concatenate them together before splitting them in smaller parts.
# We use `return_special_tokens_mask=True` because DataCollatorForLanguageModeling (see below) is more
# efficient when it receives the `special_tokens_mask`.
def tokenize_function(examples):
return tokenizer(examples[data_args.text_column_name], return_special_tokens_mask=True)
tokenized_datasets = dataset.map(tokenize_function, batched=True, remove_columns=list(dataset.features.keys()))
shuffle_seed = training_args.seed
tokenized_datasets = tokenized_datasets.shuffle(buffer_size=data_args.shuffle_buffer_size, seed=shuffle_seed)
has_tensorboard = is_tensorboard_available()
if has_tensorboard and jax.process_index() == 0:
try:
from flax.metrics.tensorboard import SummaryWriter
except ImportError as ie:
has_tensorboard = False
logger.warning(
f"Unable to display metrics through TensorBoard because some package are not installed: {ie}"
)
summary_writer = SummaryWriter(log_dir=Path(training_args.output_dir))
# Data collator
# This one will take care of randomly masking the tokens.
data_collator = FlaxDataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=data_args.mlm_probability)
# Initialize our training
rng = jax.random.PRNGKey(training_args.seed)
dropout_rngs = jax.random.split(rng, jax.local_device_count())
if model_args.model_name_or_path:
model = FlaxAutoModelForMaskedLM.from_pretrained(
model_args.model_name_or_path, config=config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype)
)
else:
model = FlaxAutoModelForMaskedLM.from_config(
config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype)
)
# Store some constant
num_epochs = int(training_args.num_train_epochs)
train_batch_size = int(training_args.per_device_train_batch_size) * jax.device_count()
eval_batch_size = int(training_args.per_device_eval_batch_size) * jax.device_count()
# define number steps per stream epoch
num_train_steps = data_args.num_train_steps
# Create learning rate schedule
warmup_fn = optax.linear_schedule(
init_value=0.0, end_value=training_args.learning_rate, transition_steps=training_args.warmup_steps
)
decay_fn = optax.linear_schedule(
init_value=training_args.learning_rate,
end_value=0,
transition_steps=num_train_steps - training_args.warmup_steps,
)
linear_decay_lr_schedule_fn = optax.join_schedules(
schedules=[warmup_fn, decay_fn], boundaries=[training_args.warmup_steps]
)
# We use Optax's "masking" functionality to not apply weight decay
# to bias and LayerNorm scale parameters. decay_mask_fn returns a
# mask boolean with the same structure as the parameters.
# The mask is True for parameters that should be decayed.
# Note that this mask is specifically adapted for FlaxBERT-like models.
# For other models, one should correct the layer norm parameter naming
# accordingly.
def decay_mask_fn(params):
flat_params = traverse_util.flatten_dict(params)
flat_mask = {path: (path[-1] != "bias" and path[-2:] != ("LayerNorm", "scale")) for path in flat_params}
return traverse_util.unflatten_dict(flat_mask)
# create adam optimizer
adamw = optax.adamw(
learning_rate=linear_decay_lr_schedule_fn,
b1=training_args.adam_beta1,
b2=training_args.adam_beta2,
eps=training_args.adam_epsilon,
weight_decay=training_args.weight_decay,
mask=decay_mask_fn,
)
# Setup train state
state = train_state.TrainState.create(apply_fn=model.__call__, params=model.params, tx=adamw)
# Define gradient update step fn
def train_step(state, batch, dropout_rng):
dropout_rng, new_dropout_rng = jax.random.split(dropout_rng)
def loss_fn(params):
labels = batch.pop("labels")
logits = state.apply_fn(**batch, params=params, dropout_rng=dropout_rng, train=True)[0]
# compute loss, ignore padded input tokens
label_mask = jnp.where(labels > 0, 1.0, 0.0)
loss = optax.softmax_cross_entropy(logits, onehot(labels, logits.shape[-1])) * label_mask
# take average
loss = loss.sum() / label_mask.sum()
return loss
grad_fn = jax.value_and_grad(loss_fn)
loss, grad = grad_fn(state.params)
grad = jax.lax.pmean(grad, "batch")
new_state = state.apply_gradients(grads=grad)
metrics = jax.lax.pmean(
{"loss": loss, "learning_rate": linear_decay_lr_schedule_fn(state.step)}, axis_name="batch"
)
return new_state, metrics, new_dropout_rng
# Create parallel version of the train step
p_train_step = jax.pmap(train_step, "batch", donate_argnums=(0,))
# Define eval fn
def eval_step(params, batch):
labels = batch.pop("labels")
logits = model(**batch, params=params, train=False)[0]
# compute loss, ignore padded input tokens
label_mask = jnp.where(labels > 0, 1.0, 0.0)
loss = optax.softmax_cross_entropy(logits, onehot(labels, logits.shape[-1])) * label_mask
# compute accuracy
accuracy = jnp.equal(jnp.argmax(logits, axis=-1), labels) * label_mask
# summarize metrics
metrics = {"loss": loss.sum(), "accuracy": accuracy.sum(), "normalizer": label_mask.sum()}
metrics = jax.lax.psum(metrics, axis_name="batch")
return metrics
p_eval_step = jax.pmap(eval_step, "batch", donate_argnums=(0,))
# Replicate the train state on each device
state = jax_utils.replicate(state)
train_time = 0
train_start = time.time()
train_metrics = []
eval_metrics = []
training_iter = iter(tokenized_datasets)
max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)
eval_samples = advance_iter_and_group_samples(training_iter, data_args.num_eval_samples, max_seq_length)
steps = tqdm(range(num_train_steps), desc="Training...", position=0)
for step in range(num_train_steps):
# ======================== Training ================================
try:
samples = advance_iter_and_group_samples(training_iter, train_batch_size, max_seq_length)
except StopIteration:
# Once the end of the dataset stream is reached, the training iterator
# is reinitialized and reshuffled and a new eval dataset is randomly chosen.
shuffle_seed += 1
tokenized_datasets.set_epoch(shuffle_seed)
training_iter = iter(tokenized_datasets)
eval_dataset = advance_iter_and_group_samples(training_iter, data_args.num_eval_samples, max_seq_length)
samples = advance_iter_and_group_samples(training_iter, train_batch_size, max_seq_length)
# process input samples
model_inputs = data_collator(samples)
# Model forward
model_inputs = shard(model_inputs.data)
state, train_metric, dropout_rngs = p_train_step(state, model_inputs, dropout_rngs)
train_metrics.append(train_metric)
if step % training_args.logging_steps == 0 and step > 0:
steps.write(
f"Step... ({step} | Loss: {train_metric['loss'].mean()}, Learning Rate:"
f" {train_metric['learning_rate'].mean()})"
)
train_time += time.time() - train_start
if has_tensorboard and jax.process_index() == 0:
write_train_metric(summary_writer, train_metrics, train_time, step)
train_metrics = []
# ======================== Evaluating ==============================
if step % training_args.eval_steps == 0 and step > 0:
# Avoid using jax.numpy here in case of TPU training
eval_samples_idx = np.arange(data_args.num_eval_samples)
eval_batch_idx = generate_batch_splits(eval_samples_idx, eval_batch_size)
for i, batch_idx in enumerate(tqdm(eval_batch_idx, desc="Evaluating ...", position=1)):
# process input samples
batch_eval_samples = {k: [v[idx] for idx in batch_idx] for k, v in eval_samples.items()}
model_inputs = data_collator(batch_eval_samples)
# Model forward
model_inputs = shard(model_inputs.data)
metrics = p_eval_step(state.params, model_inputs)
eval_metrics.append(metrics)
# normalize eval metrics
eval_metrics = get_metrics(eval_metrics)
eval_metrics = jax.tree_util.tree_map(jnp.sum, eval_metrics)
eval_normalizer = eval_metrics.pop("normalizer")
eval_metrics = jax.tree_util.tree_map(lambda x: x / eval_normalizer, eval_metrics)
# Update progress bar
steps.desc = (
f"Step... ({step + 1}/{num_train_steps} | Loss: {eval_metrics['loss']}, Acc:"
f" {eval_metrics['accuracy']})"
)
if has_tensorboard and jax.process_index() == 0:
write_eval_metric(summary_writer, eval_metrics, step)
eval_metrics = []
# save checkpoint after each epoch and push checkpoint to the hub
if jax.process_index() == 0:
params = jax.device_get(jax.tree_util.tree_map(lambda x: x[0], state.params))
model.save_pretrained(
training_args.output_dir,
params=params,
push_to_hub=training_args.push_to_hub,
commit_message=f"Saving weights and logs of step {step+1}",
)
# update tqdm bar
steps.update(1)
| transformers/examples/research_projects/jax-projects/dataset-streaming/run_mlm_flax_stream.py/0 | {
"file_path": "transformers/examples/research_projects/jax-projects/dataset-streaming/run_mlm_flax_stream.py",
"repo_id": "transformers",
"token_count": 10617
} | 50 |
import functools
import math
import os # noqa: F401
from random import choice, randint
from time import time
import datasets # noqa: F401
import faiss # noqa: F401
import numpy as np
import pandas as pd
import torch
import torch.utils.checkpoint as checkpoint
from elasticsearch import Elasticsearch # noqa: F401
from elasticsearch.helpers import bulk, streaming_bulk # noqa: F401
from torch import nn
from torch.utils.data import DataLoader, Dataset, RandomSampler, SequentialSampler
from tqdm import tqdm
from transformers import AdamW, AutoModel, AutoModelForSeq2SeqLM, AutoTokenizer, get_linear_schedule_with_warmup
pd.set_option("display.max_colwidth", None)
###############
# Sparse index
###############
def make_es_index_snippets(es_client, passages_dset, index_name="english_wiki_kilt_snippets_100w"):
index_config = {
"settings": {
"number_of_shards": 1,
"analysis": {"analyzer": {"stop_standard": {"type": "standard", " stopwords": "_english_"}}},
},
"mappings": {
"properties": {
"article_title": {"type": "text", "analyzer": "standard", "similarity": "BM25"},
"section_title": {"type": "text", "analyzer": "standard", "similarity": "BM25"},
"passage_text": {"type": "text", "analyzer": "standard", "similarity": "BM25"},
}
},
}
es_client.indices.create(index=index_name, body=index_config)
number_of_docs = passages_dset.num_rows
progress = tqdm(unit="docs", total=number_of_docs)
successes = 0
def passage_generator():
for passage in passages_dset:
yield passage
# create the ES index
for ok, action in streaming_bulk(
client=es_client,
index=index_name,
actions=passage_generator(),
):
progress.update(1)
successes += ok
print("Indexed %d documents" % (successes,))
def query_es_index(question, es_client, index_name="english_wiki_kilt_snippets_100w", n_results=10, min_length=20):
q = question.lower()
banned = ["how", "why", "what", "where", "which", "do", "does", "is", "?", "eli5", "eli5:"]
q = " ".join([w for w in q.split() if w not in banned])
response = es_client.search(
index=index_name,
body={
"query": {
"multi_match": {
"query": q,
"fields": ["article_title", "section_title", "passage_text^2"],
"type": "cross_fields",
}
},
"size": 2 * n_results,
},
)
hits = response["hits"]["hits"]
support_doc = "<P> " + " <P> ".join([hit["_source"]["passage_text"] for hit in hits])
res_list = [{k: hit["_source"][k] for k in hit["_source"] if k != "passage_text"} for hit in hits]
for r, hit in zip(res_list, hits):
r["passage_id"] = hit["_id"]
r["score"] = hit["_score"]
r["passage_text"] = hit["_source"]["passage_text"]
res_list = [res for res in res_list if len(res["passage_text"].split()) > min_length][:n_results]
return support_doc, res_list
###############
# ELI5 retriever training
###############
class ELI5DatasetQARetriver(Dataset):
def __init__(self, examples_array, extra_answer_threshold=3, min_answer_length=64, training=True, n_samples=None):
self.data = examples_array
self.answer_thres = extra_answer_threshold
self.min_length = min_answer_length
self.training = training
self.n_samples = self.data.num_rows if n_samples is None else n_samples
def __len__(self):
return self.n_samples
def make_example(self, idx):
example = self.data[idx]
question = example["title"]
if self.training:
answers = [a for i, (a, sc) in enumerate(zip(example["answers"]["text"], example["answers"]["score"]))]
answer_tab = choice(answers).split(" ")
start_idx = randint(0, max(0, len(answer_tab) - self.min_length))
answer_span = " ".join(answer_tab[start_idx:])
else:
answer_span = example["answers"]["text"][0]
return (question, answer_span)
def __getitem__(self, idx):
return self.make_example(idx % self.data.num_rows)
class RetrievalQAEmbedder(nn.Module):
def __init__(self, sent_encoder, dim):
super(RetrievalQAEmbedder, self).__init__()
self.sent_encoder = sent_encoder
self.output_dim = 128
self.project_q = nn.Linear(dim, self.output_dim, bias=False)
self.project_a = nn.Linear(dim, self.output_dim, bias=False)
self.ce_loss = nn.CrossEntropyLoss(reduction="mean")
def embed_sentences_checkpointed(self, input_ids, attention_mask, checkpoint_batch_size=-1):
# reproduces BERT forward pass with checkpointing
if checkpoint_batch_size < 0 or input_ids.shape[0] < checkpoint_batch_size:
return self.sent_encoder(input_ids, attention_mask=attention_mask)[1]
else:
# prepare implicit variables
device = input_ids.device
input_shape = input_ids.size()
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
head_mask = [None] * self.sent_encoder.config.num_hidden_layers
extended_attention_mask: torch.Tensor = self.sent_encoder.get_extended_attention_mask(
attention_mask, input_shape
)
# define function for checkpointing
def partial_encode(*inputs):
encoder_outputs = self.sent_encoder.encoder(
inputs[0],
attention_mask=inputs[1],
head_mask=head_mask,
)
sequence_output = encoder_outputs[0]
pooled_output = self.sent_encoder.pooler(sequence_output)
return pooled_output
# run embedding layer on everything at once
embedding_output = self.sent_encoder.embeddings(
input_ids=input_ids, position_ids=None, token_type_ids=token_type_ids, inputs_embeds=None
)
# run encoding and pooling on one mini-batch at a time
pooled_output_list = []
for b in range(math.ceil(input_ids.shape[0] / checkpoint_batch_size)):
b_embedding_output = embedding_output[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
b_attention_mask = extended_attention_mask[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
pooled_output = checkpoint.checkpoint(partial_encode, b_embedding_output, b_attention_mask)
pooled_output_list.append(pooled_output)
return torch.cat(pooled_output_list, dim=0)
def embed_questions(self, q_ids, q_mask, checkpoint_batch_size=-1):
q_reps = self.embed_sentences_checkpointed(q_ids, q_mask, checkpoint_batch_size)
return self.project_q(q_reps)
def embed_answers(self, a_ids, a_mask, checkpoint_batch_size=-1):
a_reps = self.embed_sentences_checkpointed(a_ids, a_mask, checkpoint_batch_size)
return self.project_a(a_reps)
def forward(self, q_ids, q_mask, a_ids, a_mask, checkpoint_batch_size=-1):
device = q_ids.device
q_reps = self.embed_questions(q_ids, q_mask, checkpoint_batch_size)
a_reps = self.embed_answers(a_ids, a_mask, checkpoint_batch_size)
compare_scores = torch.mm(q_reps, a_reps.t())
loss_qa = self.ce_loss(compare_scores, torch.arange(compare_scores.shape[1]).to(device))
loss_aq = self.ce_loss(compare_scores.t(), torch.arange(compare_scores.shape[0]).to(device))
loss = (loss_qa + loss_aq) / 2
return loss
def make_qa_retriever_model(model_name="google/bert_uncased_L-8_H-512_A-8", from_file=None, device="cuda:0"):
tokenizer = AutoTokenizer.from_pretrained(model_name)
bert_model = AutoModel.from_pretrained(model_name).to(device)
# run bert_model on a dummy batch to get output dimension
d_ids = torch.LongTensor(
[[bert_model.config.bos_token_id if bert_model.config.bos_token_id is not None else 1]]
).to(device)
d_mask = torch.LongTensor([[1]]).to(device)
sent_dim = bert_model(d_ids, attention_mask=d_mask)[1].shape[-1]
qa_embedder = RetrievalQAEmbedder(bert_model, sent_dim).to(device)
if from_file is not None:
param_dict = torch.load(from_file) # has model weights, optimizer, and scheduler states
qa_embedder.load_state_dict(param_dict["model"])
return tokenizer, qa_embedder
def make_qa_retriever_batch(qa_list, tokenizer, max_len=64, device="cuda:0"):
q_ls = [q for q, a in qa_list]
a_ls = [a for q, a in qa_list]
q_toks = tokenizer(q_ls, max_length=max_len, padding="max_length", truncation=True)
q_ids, q_mask = (
torch.LongTensor(q_toks["input_ids"]).to(device),
torch.LongTensor(q_toks["attention_mask"]).to(device),
)
a_toks = tokenizer(a_ls, max_length=max_len, padding="max_length", truncation=True)
a_ids, a_mask = (
torch.LongTensor(a_toks["input_ids"]).to(device),
torch.LongTensor(a_toks["attention_mask"]).to(device),
)
return (q_ids, q_mask, a_ids, a_mask)
def train_qa_retriever_epoch(model, dataset, tokenizer, optimizer, scheduler, args, e=0):
model.train()
# make iterator
train_sampler = RandomSampler(dataset)
model_collate_fn = functools.partial(
make_qa_retriever_batch, tokenizer=tokenizer, max_len=args.max_length, device="cuda:0"
)
data_loader = DataLoader(dataset, batch_size=args.batch_size, sampler=train_sampler, collate_fn=model_collate_fn)
epoch_iterator = tqdm(data_loader, desc="Iteration", disable=True)
# accumulate loss since last print
loc_steps = 0
loc_loss = 0.0
st_time = time()
for step, batch in enumerate(epoch_iterator):
q_ids, q_mask, a_ids, a_mask = batch
pre_loss = model(q_ids, q_mask, a_ids, a_mask, checkpoint_batch_size=args.checkpoint_batch_size)
loss = pre_loss.sum()
# optimizer
loss.backward()
optimizer.step()
scheduler.step()
model.zero_grad()
# some printing within the epoch
loc_loss += loss.item()
loc_steps += 1
if step % args.print_freq == 0 or step == 1:
print(
"{:2d} {:5d} of {:5d} \t L: {:.3f} \t -- {:.3f}".format(
e,
step,
len(dataset) // args.batch_size,
loc_loss / loc_steps,
time() - st_time,
)
)
loc_loss = 0
loc_steps = 0
def train_qa_retriever_joint_epoch(model, dataset_list, tokenizer, optimizer, scheduler, args, e=0):
model.train()
model_collate_fn = functools.partial(
make_qa_retriever_batch, tokenizer=tokenizer, max_len=args.max_length, device="cuda:0"
)
# make iterator
train_samplers = [RandomSampler(dataset) for dataset in dataset_list]
data_loaders = [
DataLoader(dataset, batch_size=args.batch_size, sampler=train_sampler, collate_fn=model_collate_fn)
for dataset, train_sampler in zip(dataset_list, train_samplers)
]
iterators = [iter(dloader) for dloader in data_loaders]
joint_iter = zip(*iterators)
# accumulate loss since last print
loc_steps = 0
loc_loss = 0.0
st_time = time()
for step, (batches,) in enumerate(zip(joint_iter)):
for batch in batches:
q_ids, q_mask, a_ids, a_mask = batch
loss = model(q_ids, q_mask, a_ids, a_mask, checkpoint_batch_size=args.checkpoint_batch_size)
# optimizer
loss.backward()
optimizer.step()
scheduler.step()
model.zero_grad()
# some printing within the epoch
loc_loss += loss.item()
loc_steps += 1
if step % args.print_freq == 0:
print(
"{:2d} {:5d} of {:5d} \t L: {:.3f} \t -- {:.3f}".format(
e,
step,
len(dataset_list[0]) // args.batch_size,
loc_loss / loc_steps,
time() - st_time,
)
)
loc_loss = 0
loc_steps = 0
def evaluate_qa_retriever(model, dataset, tokenizer, args):
model.eval()
# make iterator
eval_sampler = SequentialSampler(dataset)
model_collate_fn = functools.partial(
make_qa_retriever_batch, tokenizer=tokenizer, max_len=args.max_length, device="cuda:0"
)
data_loader = DataLoader(dataset, batch_size=args.batch_size, sampler=eval_sampler, collate_fn=model_collate_fn)
epoch_iterator = tqdm(data_loader, desc="Iteration", disable=True)
tot_loss = 0.0
with torch.no_grad():
for step, batch in enumerate(epoch_iterator):
q_ids, q_mask, a_ids, a_mask = batch
loss = model(q_ids, q_mask, a_ids, a_mask)
tot_loss += loss.item()
return tot_loss / (step + 1)
def train_qa_retriever(qar_model, qar_tokenizer, qar_train_dset, qar_valid_dset, qar_args):
qar_optimizer = AdamW(qar_model.parameters(), lr=qar_args.learning_rate, eps=1e-8)
qar_scheduler = get_linear_schedule_with_warmup(
qar_optimizer,
num_warmup_steps=100,
num_training_steps=(qar_args.num_epochs + 1) * math.ceil(len(qar_train_dset) / qar_args.batch_size),
)
for e in range(qar_args.num_epochs):
train_qa_retriever_epoch(qar_model, qar_train_dset, qar_tokenizer, qar_optimizer, qar_scheduler, qar_args, e)
m_save_dict = {
"model": qar_model.state_dict(),
"optimizer": qar_optimizer.state_dict(),
"scheduler": qar_scheduler.state_dict(),
}
print("Saving model {}".format(qar_args.model_save_name))
torch.save(m_save_dict, "{}_{}.pth".format(qar_args.model_save_name, e))
eval_loss = evaluate_qa_retriever(qar_model, qar_valid_dset, qar_tokenizer, qar_args)
print("Evaluation loss epoch {:4d}: {:.3f}".format(e, eval_loss))
###############
# ELI5 seq2seq model training
###############
class ELI5DatasetS2S(Dataset):
def __init__(
self, examples_array, make_doc_fun=None, extra_answer_threshold=3, document_cache=None, training=True
):
self.training = training
self.data = examples_array
self.make_doc_function = make_doc_fun
self.document_cache = {} if document_cache is None else document_cache
assert not (make_doc_fun is None and document_cache is None)
# make index of specific question-answer pairs from multi-answers
if self.training:
self.qa_id_list = [
(i, j)
for i, qa in enumerate(self.data)
for j, (a, sc) in enumerate(zip(qa["answers"]["text"], qa["answers"]["score"]))
if j == 0 or sc >= extra_answer_threshold
]
else:
self.qa_id_list = [(i, 0) for i in range(self.data.num_rows)]
def __len__(self):
return len(self.qa_id_list)
def make_example(self, idx):
i, j = self.qa_id_list[idx]
example = self.data[i]
question = example["title"] + " " + example["selftext"]
answer = example["answers"]["text"][j]
q_id = example["q_id"]
if self.make_doc_function is not None:
self.document_cache[q_id] = self.document_cache.get(q_id, self.make_doc_function(example["title"]))
document = self.document_cache[q_id]
in_st = "question: {} context: {}".format(
question.lower().replace(" --t--", "").strip(),
document.lower().strip(),
)
out_st = answer
return (in_st, out_st)
def __getitem__(self, idx):
return self.make_example(idx)
def make_qa_s2s_model(model_name="facebook/bart-large", from_file=None, device="cuda:0"):
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name).to(device)
if from_file is not None:
param_dict = torch.load(from_file) # has model weights, optimizer, and scheduler states
model.load_state_dict(param_dict["model"])
return tokenizer, model
def make_qa_s2s_batch(qa_list, tokenizer, max_len=64, max_a_len=360, device="cuda:0"):
q_ls = [q for q, a in qa_list]
a_ls = [a for q, a in qa_list]
q_toks = tokenizer(q_ls, max_length=max_len, padding="max_length", truncation=True)
q_ids, q_mask = (
torch.LongTensor(q_toks["input_ids"]).to(device),
torch.LongTensor(q_toks["attention_mask"]).to(device),
)
a_toks = tokenizer(a_ls, max_length=min(max_len, max_a_len), padding="max_length", truncation=True)
a_ids, a_mask = (
torch.LongTensor(a_toks["input_ids"]).to(device),
torch.LongTensor(a_toks["attention_mask"]).to(device),
)
lm_labels = a_ids[:, 1:].contiguous().clone()
lm_labels[a_mask[:, 1:].contiguous() == 0] = -100
model_inputs = {
"input_ids": q_ids,
"attention_mask": q_mask,
"decoder_input_ids": a_ids[:, :-1].contiguous(),
"lm_labels": lm_labels,
}
return model_inputs
def train_qa_s2s_epoch(model, dataset, tokenizer, optimizer, scheduler, args, e=0, curriculum=False):
model.train()
# make iterator
if curriculum:
train_sampler = SequentialSampler(dataset)
else:
train_sampler = RandomSampler(dataset)
model_collate_fn = functools.partial(
make_qa_s2s_batch, tokenizer=tokenizer, max_len=args.max_length, device="cuda:0"
)
data_loader = DataLoader(dataset, batch_size=args.batch_size, sampler=train_sampler, collate_fn=model_collate_fn)
epoch_iterator = tqdm(data_loader, desc="Iteration", disable=True)
# accumulate loss since last print
loc_steps = 0
loc_loss = 0.0
st_time = time()
for step, batch_inputs in enumerate(epoch_iterator):
pre_loss = model(**batch_inputs)[0]
loss = pre_loss.sum() / pre_loss.shape[0]
loss.backward()
# optimizer
if step % args.backward_freq == 0:
optimizer.step()
scheduler.step()
model.zero_grad()
# some printing within the epoch
loc_loss += loss.item()
loc_steps += 1
if step % args.print_freq == 0 or step == 1:
print(
"{:2d} {:5d} of {:5d} \t L: {:.3f} \t -- {:.3f}".format(
e,
step,
len(dataset) // args.batch_size,
loc_loss / loc_steps,
time() - st_time,
)
)
loc_loss = 0
loc_steps = 0
def eval_qa_s2s_epoch(model, dataset, tokenizer, args):
model.eval()
# make iterator
train_sampler = SequentialSampler(dataset)
model_collate_fn = functools.partial(
make_qa_s2s_batch, tokenizer=tokenizer, max_len=args.max_length, device="cuda:0"
)
data_loader = DataLoader(dataset, batch_size=args.batch_size, sampler=train_sampler, collate_fn=model_collate_fn)
epoch_iterator = tqdm(data_loader, desc="Iteration", disable=True)
# accumulate loss since last print
loc_steps = 0
loc_loss = 0.0
st_time = time()
with torch.no_grad():
for step, batch_inputs in enumerate(epoch_iterator):
pre_loss = model(**batch_inputs)[0]
loss = pre_loss.sum() / pre_loss.shape[0]
loc_loss += loss.item()
loc_steps += 1
if step % args.print_freq == 0:
print(
"{:5d} of {:5d} \t L: {:.3f} \t -- {:.3f}".format(
step,
len(dataset) // args.batch_size,
loc_loss / loc_steps,
time() - st_time,
)
)
print(
"Total \t L: {:.3f} \t -- {:.3f}".format(
loc_loss / loc_steps,
time() - st_time,
)
)
def train_qa_s2s(qa_s2s_model, qa_s2s_tokenizer, s2s_train_dset, s2s_valid_dset, s2s_args):
s2s_optimizer = AdamW(qa_s2s_model.parameters(), lr=s2s_args.learning_rate, eps=1e-8)
s2s_scheduler = get_linear_schedule_with_warmup(
s2s_optimizer,
num_warmup_steps=400,
num_training_steps=(s2s_args.num_epochs + 1) * math.ceil(len(s2s_train_dset) / s2s_args.batch_size),
)
for e in range(s2s_args.num_epochs):
train_qa_s2s_epoch(
qa_s2s_model,
s2s_train_dset,
qa_s2s_tokenizer,
s2s_optimizer,
s2s_scheduler,
s2s_args,
e,
curriculum=(e == 0),
)
m_save_dict = {
"model": qa_s2s_model.state_dict(),
"optimizer": s2s_optimizer.state_dict(),
"scheduler": s2s_scheduler.state_dict(),
}
print("Saving model {}".format(s2s_args.model_save_name))
eval_qa_s2s_epoch(qa_s2s_model, s2s_valid_dset, qa_s2s_tokenizer, s2s_args)
torch.save(m_save_dict, "{}_{}.pth".format(s2s_args.model_save_name, e))
# generate answer from input "question: ... context: <p> ..."
def qa_s2s_generate(
question_doc,
qa_s2s_model,
qa_s2s_tokenizer,
num_answers=1,
num_beams=None,
min_len=64,
max_len=256,
do_sample=False,
temp=1.0,
top_p=None,
top_k=None,
max_input_length=512,
device="cuda:0",
):
model_inputs = make_qa_s2s_batch(
[(question_doc, "A")],
qa_s2s_tokenizer,
max_input_length,
device=device,
)
n_beams = num_answers if num_beams is None else max(num_beams, num_answers)
generated_ids = qa_s2s_model.generate(
input_ids=model_inputs["input_ids"],
attention_mask=model_inputs["attention_mask"],
min_length=min_len,
max_length=max_len,
do_sample=do_sample,
early_stopping=True,
num_beams=1 if do_sample else n_beams,
temperature=temp,
top_k=top_k,
top_p=top_p,
eos_token_id=qa_s2s_tokenizer.eos_token_id,
no_repeat_ngram_size=3,
num_return_sequences=num_answers,
decoder_start_token_id=qa_s2s_tokenizer.bos_token_id,
)
return [qa_s2s_tokenizer.decode(ans_ids, skip_special_tokens=True).strip() for ans_ids in generated_ids]
###############
# ELI5-trained retrieval model usage
###############
def embed_passages_for_retrieval(passages, tokenizer, qa_embedder, max_length=128, device="cuda:0"):
a_toks = tokenizer(passages, max_length=max_length, padding="max_length", truncation=True)
a_ids, a_mask = (
torch.LongTensor(a_toks["input_ids"]).to(device),
torch.LongTensor(a_toks["attention_mask"]).to(device),
)
with torch.no_grad():
a_reps = qa_embedder.embed_answers(a_ids, a_mask).cpu().type(torch.float)
return a_reps.numpy()
def embed_questions_for_retrieval(q_ls, tokenizer, qa_embedder, device="cuda:0"):
q_toks = tokenizer(q_ls, max_length=128, padding="max_length", truncation=True)
q_ids, q_mask = (
torch.LongTensor(q_toks["input_ids"]).to(device),
torch.LongTensor(q_toks["attention_mask"]).to(device),
)
with torch.no_grad():
q_reps = qa_embedder.embed_questions(q_ids, q_mask).cpu().type(torch.float)
return q_reps.numpy()
def make_qa_dense_index(
qa_embedder,
tokenizer,
passages_dset,
batch_size=512,
max_length=128,
index_name="kilt_passages_reps.dat",
dtype="float32",
device="cuda:0",
):
st_time = time()
fp = np.memmap(index_name, dtype=dtype, mode="w+", shape=(passages_dset.num_rows, 128))
n_batches = math.ceil(passages_dset.num_rows / batch_size)
for i in range(n_batches):
passages = list(passages_dset[i * batch_size : (i + 1) * batch_size]["passage_text"])
reps = embed_passages_for_retrieval(passages, tokenizer, qa_embedder, max_length, device)
fp[i * batch_size : (i + 1) * batch_size] = reps
if i % 50 == 0:
print(i, time() - st_time)
def evaluate_retriever(qa_list, retriever_func, scoring_func, n_ret=10, verbose=False):
total_retriever_time = 0.0
total_retriever_score = 0.0
st_time = time()
for i, (question, answer) in enumerate(qa_list):
r_time = time()
retrieved_passages = retriever_func(question, n_ret)
total_retriever_time += time() - r_time
total_retriever_score += scoring_func(retrieved_passages, answer)
if verbose and ((i + 1) % 500 == 0 or i <= 1):
print(
"{:03d}: S-{:.4f} T-{:.4f} | {:.2f}".format(
i + 1, total_retriever_score / (i + 1), total_retriever_time / (i + 1), time() - st_time
)
)
return {"idf_recall": total_retriever_score / (i + 1), "retrieval_time": total_retriever_time / (i + 1)}
# build a support document for the question out of Wikipedia snippets
def query_qa_dense_index(
question, qa_embedder, tokenizer, wiki_passages, wiki_index, n_results=10, min_length=20, device="cuda:0"
):
q_rep = embed_questions_for_retrieval([question], tokenizer, qa_embedder, device=device)
D, I = wiki_index.search(q_rep, 2 * n_results)
res_passages = [wiki_passages[int(i)] for i in I[0]]
support_doc = "<P> " + " <P> ".join([p["passage_text"] for p in res_passages])
res_list = [{k: p[k] for k in wiki_passages.column_names} for p in res_passages]
res_list = [res for res in res_list if len(res["passage_text"].split()) > min_length][:n_results]
for r, sc in zip(res_list, D[0]):
r["score"] = float(sc)
return support_doc, res_list
def batch_query_qa_dense_index(questions, qa_embedder, tokenizer, wiki_passages, wiki_index, n_results=10):
q_rep = embed_questions_for_retrieval(questions, tokenizer, qa_embedder)
D, I = wiki_index.search(q_rep, n_results)
res_passages_lst = [[wiki_passages[int(i)] for i in i_lst] for i_lst in I]
support_doc_lst = [
"<P> " + " <P> ".join([p["passage_text"] for p in res_passages]) for res_passages in res_passages_lst
]
all_res_lists = []
for res_passages, dl in zip(res_passages_lst, D):
res_list = [{k: p[k] for k in wiki_passages.column_names} for p in res_passages]
for r, sc in zip(res_list, dl):
r["score"] = float(sc)
all_res_lists += [res_list[:]]
return support_doc_lst, all_res_lists
# find nearest neighbors of an answer or declarative text in Wikipedia snippets
def query_qa_dense_index_nn(passage, qa_embedder, tokenizer, wiki_passages, wiki_index, n_results=10, min_length=20):
a_rep = embed_passages_for_retrieval([passage], tokenizer, qa_embedder)
D, I = wiki_index.search(a_rep, 2 * n_results)
res_passages = [wiki_passages[int(i)] for i in I[0]]
support_doc = "<P> " + " <P> ".join([p["passage_text"] for p in res_passages])
res_list = [{k: p[k] for k in wiki_passages.column_names} for p in res_passages]
res_list = [res for res in res_list if len(res["passage_text"].split()) > min_length][:n_results]
for r, sc, i in zip(res_list, D[0], I[0]):
r["passage_id"] = int(i)
r["score"] = float(sc)
return support_doc, res_list
def batch_query_qa_dense_index_nn(passages, qa_embedder, tokenizer, wiki_passages, wiki_index, n_results=10):
a_reps = embed_passages_for_retrieval(passages, tokenizer, qa_embedder)
D, I = wiki_index.search(a_reps, n_results)
res_passages_lst = [[wiki_passages[int(i)] for i in i_lst] for i_lst in I]
support_doc_lst = [
"<P> " + " <P> ".join([p["passage_text"] for p in res_passages]) for res_passages in res_passages_lst
]
all_res_lists = []
for res_passages, dl, il in zip(res_passages_lst, D, I):
res_list = [{k: p[k] for k in wiki_passages.column_names} for p in res_passages]
for r, sc, i in zip(res_list, dl, il):
r["passage_id"] = int(i)
r["score"] = float(sc)
all_res_lists += [res_list[:]]
return support_doc_lst, all_res_lists
| transformers/examples/research_projects/longform-qa/eli5_utils.py/0 | {
"file_path": "transformers/examples/research_projects/longform-qa/eli5_utils.py",
"repo_id": "transformers",
"token_count": 13301
} | 51 |
# coding=utf-8
# Copyright 2020 The HuggingFace Team All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Fine-tuning the library models for masked language modeling (BERT, ALBERT, RoBERTa...) with whole word masking on a
text file or a dataset.
Here is the full list of checkpoints on the hub that can be fine-tuned by this script:
https://huggingface.co/models?filter=fill-mask
"""
# You can also adapt this script on your own masked language modeling task. Pointers for this are left as comments.
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
AutoModelForMaskedLM,
AutoTokenizer,
DataCollatorForWholeWordMask,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process
logger = logging.getLogger(__name__)
MODEL_CONFIG_CLASSES = list(MODEL_FOR_MASKED_LM_MAPPING.keys())
MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
"""
model_name_or_path: Optional[str] = field(
default=None,
metadata={
"help": (
"The model checkpoint for weights initialization. Don't set if you want to train a model from scratch."
)
},
)
model_type: Optional[str] = field(
default=None,
metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)},
)
config_overrides: Optional[str] = field(
default=None,
metadata={
"help": (
"Override some existing default config settings when a model is trained from scratch. Example: "
"n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
)
},
)
config_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
)
tokenizer_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
)
cache_dir: Optional[str] = field(
default=None,
metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
)
use_fast_tokenizer: bool = field(
default=True,
metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
)
model_revision: str = field(
default="main",
metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
)
use_auth_token: bool = field(
default=False,
metadata={
"help": (
"Will use the token generated when running `huggingface-cli login` (necessary to use this script "
"with private models)."
)
},
)
def __post_init__(self):
if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
raise ValueError(
"--config_overrides can't be used in combination with --config_name or --model_name_or_path"
)
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
"""
dataset_name: Optional[str] = field(
default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
)
dataset_config_name: Optional[str] = field(
default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
)
train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."})
validation_file: Optional[str] = field(
default=None,
metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
)
train_ref_file: Optional[str] = field(
default=None,
metadata={"help": "An optional input train ref data file for whole word masking in Chinese."},
)
validation_ref_file: Optional[str] = field(
default=None,
metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."},
)
overwrite_cache: bool = field(
default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
)
validation_split_percentage: Optional[int] = field(
default=5,
metadata={
"help": "The percentage of the train set used as validation set in case there's no validation split"
},
)
max_seq_length: Optional[int] = field(
default=None,
metadata={
"help": (
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated. Default to the max input length of the model."
)
},
)
preprocessing_num_workers: Optional[int] = field(
default=None,
metadata={"help": "The number of processes to use for the preprocessing."},
)
mlm_probability: float = field(
default=0.15, metadata={"help": "Ratio of tokens to mask for masked language modeling loss"}
)
pad_to_max_length: bool = field(
default=False,
metadata={
"help": (
"Whether to pad all samples to `max_seq_length`. "
"If False, will pad the samples dynamically when batching to the maximum length in the batch."
)
},
)
def __post_init__(self):
if self.train_file is not None:
extension = self.train_file.split(".")[-1]
assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
if self.validation_file is not None:
extension = self.validation_file.split(".")[-1]
assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."
def add_chinese_references(dataset, ref_file):
with open(ref_file, "r", encoding="utf-8") as f:
refs = [json.loads(line) for line in f.read().splitlines() if (len(line) > 0 and not line.isspace())]
assert len(dataset) == len(refs)
dataset_dict = {c: dataset[c] for c in dataset.column_names}
dataset_dict["chinese_ref"] = refs
return Dataset.from_dict(dataset_dict)
def main():
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
else:
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
# Detecting last checkpoint.
last_checkpoint = None
if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
last_checkpoint = get_last_checkpoint(training_args.output_dir)
if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. "
"Use --overwrite_output_dir to overcome."
)
elif last_checkpoint is not None:
logger.info(
f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
)
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
handlers=[logging.StreamHandler(sys.stdout)],
)
logger.setLevel(logging.INFO if is_main_process(training_args.local_rank) else logging.WARN)
# Log on each process the small summary:
logger.warning(
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+ f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
)
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info("Training/evaluation parameters %s", training_args)
# Set seed before initializing model.
set_seed(training_args.seed)
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
#
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)
if "validation" not in datasets.keys():
datasets["validation"] = load_dataset(
data_args.dataset_name,
data_args.dataset_config_name,
split=f"train[:{data_args.validation_split_percentage}%]",
)
datasets["train"] = load_dataset(
data_args.dataset_name,
data_args.dataset_config_name,
split=f"train[{data_args.validation_split_percentage}%:]",
)
else:
data_files = {}
if data_args.train_file is not None:
data_files["train"] = data_args.train_file
extension = data_args.train_file.split(".")[-1]
if data_args.validation_file is not None:
data_files["validation"] = data_args.validation_file
extension = data_args.validation_file.split(".")[-1]
if extension == "txt":
extension = "text"
datasets = load_dataset(extension, data_files=data_files)
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
config_kwargs = {
"cache_dir": model_args.cache_dir,
"revision": model_args.model_revision,
"use_auth_token": True if model_args.use_auth_token else None,
}
if model_args.config_name:
config = AutoConfig.from_pretrained(model_args.config_name, **config_kwargs)
elif model_args.model_name_or_path:
config = AutoConfig.from_pretrained(model_args.model_name_or_path, **config_kwargs)
else:
config = CONFIG_MAPPING[model_args.model_type]()
logger.warning("You are instantiating a new config instance from scratch.")
if model_args.config_overrides is not None:
logger.info(f"Overriding config: {model_args.config_overrides}")
config.update_from_string(model_args.config_overrides)
logger.info(f"New config: {config}")
tokenizer_kwargs = {
"cache_dir": model_args.cache_dir,
"use_fast": model_args.use_fast_tokenizer,
"revision": model_args.model_revision,
"use_auth_token": True if model_args.use_auth_token else None,
}
if model_args.tokenizer_name:
tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, **tokenizer_kwargs)
elif model_args.model_name_or_path:
tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path, **tokenizer_kwargs)
else:
raise ValueError(
"You are instantiating a new tokenizer from scratch. This is not supported by this script. "
"You can do it from another script, save it, and load it from here, using --tokenizer_name."
)
if model_args.model_name_or_path:
model = AutoModelForMaskedLM.from_pretrained(
model_args.model_name_or_path,
from_tf=bool(".ckpt" in model_args.model_name_or_path),
config=config,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
token=True if model_args.use_auth_token else None,
)
else:
logger.info("Training new model from scratch")
model = AutoModelForMaskedLM.from_config(config)
model.resize_token_embeddings(len(tokenizer))
# Preprocessing the datasets.
# First we tokenize all the texts.
if training_args.do_train:
column_names = datasets["train"].column_names
else:
column_names = datasets["validation"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]
padding = "max_length" if data_args.pad_to_max_length else False
def tokenize_function(examples):
# Remove empty lines
examples["text"] = [line for line in examples["text"] if len(line) > 0 and not line.isspace()]
return tokenizer(examples["text"], padding=padding, truncation=True, max_length=data_args.max_seq_length)
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
num_proc=data_args.preprocessing_num_workers,
remove_columns=[text_column_name],
load_from_cache_file=not data_args.overwrite_cache,
)
# Add the chinese references if provided
if data_args.train_ref_file is not None:
tokenized_datasets["train"] = add_chinese_references(tokenized_datasets["train"], data_args.train_ref_file)
if data_args.validation_ref_file is not None:
tokenized_datasets["validation"] = add_chinese_references(
tokenized_datasets["validation"], data_args.validation_ref_file
)
# If we have ref files, need to avoid it removed by trainer
has_ref = data_args.train_ref_file or data_args.validation_ref_file
if has_ref:
training_args.remove_unused_columns = False
# Data collator
# This one will take care of randomly masking the tokens.
data_collator = DataCollatorForWholeWordMask(tokenizer=tokenizer, mlm_probability=data_args.mlm_probability)
# Initialize our Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_datasets["train"] if training_args.do_train else None,
eval_dataset=tokenized_datasets["validation"] if training_args.do_eval else None,
tokenizer=tokenizer,
data_collator=data_collator,
)
# Training
if training_args.do_train:
if last_checkpoint is not None:
checkpoint = last_checkpoint
elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path):
checkpoint = model_args.model_name_or_path
else:
checkpoint = None
train_result = trainer.train(resume_from_checkpoint=checkpoint)
trainer.save_model() # Saves the tokenizer too for easy upload
output_train_file = os.path.join(training_args.output_dir, "train_results.txt")
if trainer.is_world_process_zero():
with open(output_train_file, "w") as writer:
logger.info("***** Train results *****")
for key, value in sorted(train_result.metrics.items()):
logger.info(f" {key} = {value}")
writer.write(f"{key} = {value}\n")
# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
trainer.state.save_to_json(os.path.join(training_args.output_dir, "trainer_state.json"))
# Evaluation
results = {}
if training_args.do_eval:
logger.info("*** Evaluate ***")
eval_output = trainer.evaluate()
perplexity = math.exp(eval_output["eval_loss"])
results["perplexity"] = perplexity
output_eval_file = os.path.join(training_args.output_dir, "eval_results_mlm_wwm.txt")
if trainer.is_world_process_zero():
with open(output_eval_file, "w") as writer:
logger.info("***** Eval results *****")
for key, value in sorted(results.items()):
logger.info(f" {key} = {value}")
writer.write(f"{key} = {value}\n")
return results
def _mp_fn(index):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| transformers/examples/research_projects/mlm_wwm/run_mlm_wwm.py/0 | {
"file_path": "transformers/examples/research_projects/mlm_wwm/run_mlm_wwm.py",
"repo_id": "transformers",
"token_count": 7241
} | 52 |
# Sample script to finetune RAG using Ray for distributed retrieval.
# Add parent directory to python path to access lightning_base.py
export PYTHONPATH="../":"${PYTHONPATH}"
#creates the custom knowlegebase
python use_own_knowledge_dataset.py \
--csv_path /DIR/SQUAD-KB/squad-kb.csv \
--output_dir /DIR/SQUAD-KB
# Start a single-node Ray cluster.
ray start --head
# A sample finetuning run, you need to specify data_dir, output_dir and model_name_or_path
# run ./examples/rag/finetune_rag_ray.sh --help to see all the possible options
python finetune_rag.py \
--data_dir /DIR/squad-training-data \
--output_dir /DIR/model_checkpoints \
--model_name_or_path facebook/rag-token-base \
--model_type rag_token \
--fp16 \
--gpus 2 \
--profile \
--do_train \
--end2end \
--do_predict \
--n_val -1 \
--train_batch_size 4 \
--eval_batch_size 1 \
--max_source_length 128 \
--max_target_length 25 \
--val_max_target_length 25 \
--test_max_target_length 25 \
--label_smoothing 0.1 \
--dropout 0.1 \
--attention_dropout 0.1 \
--weight_decay 0.001 \
--adam_epsilon 1e-08 \
--max_grad_norm 0.1 \
--lr_scheduler polynomial \
--learning_rate 3e-05 \
--num_train_epochs 10 \
--warmup_steps 500 \
--gradient_accumulation_steps 8 \
--distributed_retriever ray \
--num_retrieval_workers 4 \
--passages_path /DIR/SQUAD-KB/my_knowledge_dataset \
--index_path /DIR/SQUAD-KB/my_knowledge_dataset_hnsw_index.faiss \
--index_name custom \
--context_encoder_name facebook/dpr-ctx_encoder-multiset-base \
--csv_path /DIR/SQUAD-KB/squad-kb.csv \
--index_gpus 1 \
--gpu_order [5,6,7,8,9,0,1,2,3,4] \
--shard_dir ./test_dir/kb-shards \
--indexing_freq 500
# Stop the Ray cluster.
ray stop
#this script was used to test the SQuAD data.
#change the dir paramater acording to your prefernece.
#please use the same device ordere when running CUDA_VISIBLE_DEVICES=5,6,7,8,9,0,1,2,3,4 sh finetune_rag_ray_end2end.sh | transformers/examples/research_projects/rag-end2end-retriever/finetune_rag_ray_end2end.sh/0 | {
"file_path": "transformers/examples/research_projects/rag-end2end-retriever/finetune_rag_ray_end2end.sh",
"repo_id": "transformers",
"token_count": 876
} | 53 |
import os
import sys
sys.path.insert(1, os.path.dirname(os.path.realpath(__file__)))
| transformers/examples/research_projects/rag/__init__.py/0 | {
"file_path": "transformers/examples/research_projects/rag/__init__.py",
"repo_id": "transformers",
"token_count": 34
} | 54 |
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transformers import BartTokenizer, RagTokenizer, T5Tokenizer
def encode_line(tokenizer, line, max_length, padding_side, pad_to_max_length=True, return_tensors="pt"):
extra_kw = {"add_prefix_space": True} if isinstance(tokenizer, BartTokenizer) and not line.startswith(" ") else {}
tokenizer.padding_side = padding_side
return tokenizer(
[line],
max_length=max_length,
padding="max_length" if pad_to_max_length else None,
truncation=True,
return_tensors=return_tensors,
add_special_tokens=True,
**extra_kw,
)
def trim_batch(
input_ids,
pad_token_id,
attention_mask=None,
):
"""Remove columns that are populated exclusively by pad_token_id"""
keep_column_mask = input_ids.ne(pad_token_id).any(dim=0)
if attention_mask is None:
return input_ids[:, keep_column_mask]
else:
return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class Seq2SeqDataset(Dataset):
def __init__(
self,
tokenizer,
data_dir,
max_source_length,
max_target_length,
type_path="train",
n_obs=None,
src_lang=None,
tgt_lang=None,
prefix="",
):
super().__init__()
self.src_file = Path(data_dir).joinpath(type_path + ".source")
self.tgt_file = Path(data_dir).joinpath(type_path + ".target")
self.src_lens = self.get_char_lens(self.src_file)
self.max_source_length = max_source_length
self.max_target_length = max_target_length
assert min(self.src_lens) > 0, f"found empty line in {self.src_file}"
self.tokenizer = tokenizer
self.prefix = prefix
if n_obs is not None:
self.src_lens = self.src_lens[:n_obs]
self.src_lang = src_lang
self.tgt_lang = tgt_lang
def __len__(self):
return len(self.src_lens)
def __getitem__(self, index) -> Dict[str, torch.Tensor]:
index = index + 1 # linecache starts at 1
source_line = self.prefix + linecache.getline(str(self.src_file), index).rstrip("\n")
tgt_line = linecache.getline(str(self.tgt_file), index).rstrip("\n")
assert source_line, f"empty source line for index {index}"
assert tgt_line, f"empty tgt line for index {index}"
# Need to add eos token manually for T5
if isinstance(self.tokenizer, T5Tokenizer):
source_line += self.tokenizer.eos_token
tgt_line += self.tokenizer.eos_token
# Pad source and target to the right
source_tokenizer = (
self.tokenizer.question_encoder if isinstance(self.tokenizer, RagTokenizer) else self.tokenizer
)
target_tokenizer = self.tokenizer.generator if isinstance(self.tokenizer, RagTokenizer) else self.tokenizer
source_inputs = encode_line(source_tokenizer, source_line, self.max_source_length, "right")
target_inputs = encode_line(target_tokenizer, tgt_line, self.max_target_length, "right")
source_ids = source_inputs["input_ids"].squeeze()
target_ids = target_inputs["input_ids"].squeeze()
src_mask = source_inputs["attention_mask"].squeeze()
return {
"input_ids": source_ids,
"attention_mask": src_mask,
"decoder_input_ids": target_ids,
}
@staticmethod
def get_char_lens(data_file):
return [len(x) for x in Path(data_file).open().readlines()]
def collate_fn(self, batch) -> Dict[str, torch.Tensor]:
input_ids = torch.stack([x["input_ids"] for x in batch])
masks = torch.stack([x["attention_mask"] for x in batch])
target_ids = torch.stack([x["decoder_input_ids"] for x in batch])
tgt_pad_token_id = (
self.tokenizer.generator.pad_token_id
if isinstance(self.tokenizer, RagTokenizer)
else self.tokenizer.pad_token_id
)
src_pad_token_id = (
self.tokenizer.question_encoder.pad_token_id
if isinstance(self.tokenizer, RagTokenizer)
else self.tokenizer.pad_token_id
)
y = trim_batch(target_ids, tgt_pad_token_id)
source_ids, source_mask = trim_batch(input_ids, src_pad_token_id, attention_mask=masks)
batch = {
"input_ids": source_ids,
"attention_mask": source_mask,
"decoder_input_ids": y,
}
return batch
logger = getLogger(__name__)
def flatten_list(summary_ids: List[List]):
return list(itertools.chain.from_iterable(summary_ids))
def save_git_info(folder_path: str) -> None:
"""Save git information to output_dir/git_log.json"""
repo_infos = get_git_info()
save_json(repo_infos, os.path.join(folder_path, "git_log.json"))
def save_json(content, path, indent=4, **json_dump_kwargs):
with open(path, "w") as f:
json.dump(content, f, indent=indent, **json_dump_kwargs)
def load_json(path):
with open(path) as f:
return json.load(f)
def get_git_info():
repo = git.Repo(search_parent_directories=True)
repo_infos = {
"repo_id": str(repo),
"repo_sha": str(repo.head.object.hexsha),
"repo_branch": str(repo.active_branch),
"hostname": str(socket.gethostname()),
}
return repo_infos
def lmap(f: Callable, x: Iterable) -> List:
"""list(map(f, x))"""
return list(map(f, x))
def pickle_save(obj, path):
"""pickle.dump(obj, path)"""
with open(path, "wb") as f:
return pickle.dump(obj, f)
def normalize_answer(s):
"""Lower text and remove punctuation, articles and extra whitespace."""
def remove_articles(text):
return re.sub(r"\b(a|an|the)\b", " ", text)
def white_space_fix(text):
return " ".join(text.split())
def remove_punc(text):
exclude = set(string.punctuation)
return "".join(ch for ch in text if ch not in exclude)
def lower(text):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(s))))
def f1_score(prediction, ground_truth):
prediction_tokens = normalize_answer(prediction).split()
ground_truth_tokens = normalize_answer(ground_truth).split()
common = Counter(prediction_tokens) & Counter(ground_truth_tokens)
num_same = sum(common.values())
if num_same == 0:
return 0
precision = 1.0 * num_same / len(prediction_tokens)
recall = 1.0 * num_same / len(ground_truth_tokens)
f1 = (2 * precision * recall) / (precision + recall)
return f1
def exact_match_score(prediction, ground_truth):
return normalize_answer(prediction) == normalize_answer(ground_truth)
def calculate_exact_match(output_lns: List[str], reference_lns: List[str]) -> Dict:
assert len(output_lns) == len(reference_lns)
em = 0
for hypo, pred in zip(output_lns, reference_lns):
em += exact_match_score(hypo, pred)
if len(output_lns) > 0:
em /= len(output_lns)
return {"em": em}
def is_rag_model(model_prefix):
return model_prefix.startswith("rag")
def set_extra_model_params(extra_params, hparams, config):
equivalent_param = {p: p for p in extra_params}
# T5 models don't have `dropout` param, they have `dropout_rate` instead
equivalent_param["dropout"] = "dropout_rate"
for p in extra_params:
if getattr(hparams, p, None):
if not hasattr(config, p) and not hasattr(config, equivalent_param[p]):
logger.info("config doesn't have a `{}` attribute".format(p))
delattr(hparams, p)
continue
set_p = p if hasattr(config, p) else equivalent_param[p]
setattr(config, set_p, getattr(hparams, p))
delattr(hparams, p)
return hparams, config
| transformers/examples/research_projects/rag/utils_rag.py/0 | {
"file_path": "transformers/examples/research_projects/rag/utils_rag.py",
"repo_id": "transformers",
"token_count": 3495
} | 55 |
#!/usr/bin/env python
import os
from pathlib import Path
from typing import Dict, List
import fire
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from transformers.utils.logging import get_logger
logger = get_logger(__name__)
def remove_prefix(text: str, prefix: str):
if text.startswith(prefix):
return text[len(prefix) :]
return text # or whatever
def sanitize(sd):
return {remove_prefix(k, "model."): v for k, v in sd.items()}
def average_state_dicts(state_dicts: List[Dict[str, torch.Tensor]]):
new_sd = {}
for k in state_dicts[0].keys():
tensors = [sd[k] for sd in state_dicts]
new_t = sum(tensors) / len(tensors)
assert isinstance(new_t, torch.Tensor)
new_sd[k] = new_t
return new_sd
def convert_pl_to_hf(pl_ckpt_path: str, hf_src_model_dir: str, save_path: str) -> None:
"""Cleanup a pytorch-lightning .ckpt file or experiment dir and save a huggingface model with that state dict.
Silently allows extra pl keys (like teacher.) Puts all ckpt models into CPU RAM at once!
Args:
pl_ckpt_path (:obj:`str`): Path to a .ckpt file saved by pytorch_lightning or dir containing ckpt files.
If a directory is passed, all .ckpt files inside it will be averaged!
hf_src_model_dir (:obj:`str`): Path to a directory containing a correctly shaped checkpoint
save_path (:obj:`str`): Directory to save the new model
"""
hf_model = AutoModelForSeq2SeqLM.from_pretrained(hf_src_model_dir)
if os.path.isfile(pl_ckpt_path):
ckpt_files = [pl_ckpt_path]
else:
assert os.path.isdir(pl_ckpt_path)
ckpt_files = list(Path(pl_ckpt_path).glob("*.ckpt"))
assert ckpt_files, f"could not find any ckpt files inside the {pl_ckpt_path} directory"
if len(ckpt_files) > 1:
logger.info(f"averaging the weights of {ckpt_files}")
state_dicts = [sanitize(torch.load(x, map_location="cpu")["state_dict"]) for x in ckpt_files]
state_dict = average_state_dicts(state_dicts)
missing, unexpected = hf_model.load_state_dict(state_dict, strict=False)
assert not missing, f"missing keys: {missing}"
hf_model.save_pretrained(save_path)
try:
tok = AutoTokenizer.from_pretrained(hf_src_model_dir)
tok.save_pretrained(save_path)
except Exception:
pass
# dont copy tokenizer if cant
if __name__ == "__main__":
fire.Fire(convert_pl_to_hf)
| transformers/examples/research_projects/seq2seq-distillation/convert_pl_checkpoint_to_hf.py/0 | {
"file_path": "transformers/examples/research_projects/seq2seq-distillation/convert_pl_checkpoint_to_hf.py",
"repo_id": "transformers",
"token_count": 1017
} | 56 |
#!/usr/bin/env bash
export PYTHONPATH="../":"${PYTHONPATH}"
export BS=32
export GAS=1
python finetune.py \
--learning_rate=3e-5 \
--fp16 \
--gpus 1 \
--do_train \
--do_predict \
--val_check_interval 0.25 \
--n_val 500 \
--num_train_epochs 2 \
--freeze_encoder --freeze_embeds --data_dir cnn_dm \
--max_target_length 142 --val_max_target_length=142 \
--train_batch_size=$BS --eval_batch_size=$BS --gradient_accumulation_steps=$GAS \
--model_name_or_path sshleifer/student_cnn_12_6 \
--tokenizer_name facebook/bart-large \
--warmup_steps 500 \
--output_dir distilbart-cnn-12-6 \
"$@"
| transformers/examples/research_projects/seq2seq-distillation/train_distilbart_cnn.sh/0 | {
"file_path": "transformers/examples/research_projects/seq2seq-distillation/train_distilbart_cnn.sh",
"repo_id": "transformers",
"token_count": 292
} | 57 |
"""
coding=utf-8
Copyright 2018, Antonio Mendoza Hao Tan, Mohit Bansal, Huggingface team :)
Adapted From Facebook Inc, Detectron2
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.import copy
"""
import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from io import BytesIO
from pathlib import Path
from urllib.parse import urlparse
from zipfile import ZipFile, is_zipfile
import cv2
import numpy as np
import requests
import wget
from filelock import FileLock
from huggingface_hub.utils import insecure_hashlib
from PIL import Image
from tqdm.auto import tqdm
from yaml import Loader, dump, load
try:
import torch
_torch_available = True
except ImportError:
_torch_available = False
try:
from torch.hub import _get_torch_home
torch_cache_home = _get_torch_home()
except ImportError:
torch_cache_home = os.path.expanduser(
os.getenv("TORCH_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "torch"))
)
default_cache_path = os.path.join(torch_cache_home, "transformers")
CLOUDFRONT_DISTRIB_PREFIX = "https://cdn.huggingface.co"
S3_BUCKET_PREFIX = "https://s3.amazonaws.com/models.huggingface.co/bert"
PATH = "/".join(str(Path(__file__).resolve()).split("/")[:-1])
CONFIG = os.path.join(PATH, "config.yaml")
ATTRIBUTES = os.path.join(PATH, "attributes.txt")
OBJECTS = os.path.join(PATH, "objects.txt")
PYTORCH_PRETRAINED_BERT_CACHE = os.getenv("PYTORCH_PRETRAINED_BERT_CACHE", default_cache_path)
PYTORCH_TRANSFORMERS_CACHE = os.getenv("PYTORCH_TRANSFORMERS_CACHE", PYTORCH_PRETRAINED_BERT_CACHE)
TRANSFORMERS_CACHE = os.getenv("TRANSFORMERS_CACHE", PYTORCH_TRANSFORMERS_CACHE)
WEIGHTS_NAME = "pytorch_model.bin"
CONFIG_NAME = "config.yaml"
def load_labels(objs=OBJECTS, attrs=ATTRIBUTES):
vg_classes = []
with open(objs) as f:
for object in f.readlines():
vg_classes.append(object.split(",")[0].lower().strip())
vg_attrs = []
with open(attrs) as f:
for object in f.readlines():
vg_attrs.append(object.split(",")[0].lower().strip())
return vg_classes, vg_attrs
def load_checkpoint(ckp):
r = OrderedDict()
with open(ckp, "rb") as f:
ckp = pkl.load(f)["model"]
for k in copy.deepcopy(list(ckp.keys())):
v = ckp.pop(k)
if isinstance(v, np.ndarray):
v = torch.tensor(v)
else:
assert isinstance(v, torch.tensor), type(v)
r[k] = v
return r
class Config:
_pointer = {}
def __init__(self, dictionary: dict, name: str = "root", level=0):
self._name = name
self._level = level
d = {}
for k, v in dictionary.items():
if v is None:
raise ValueError()
k = copy.deepcopy(k)
v = copy.deepcopy(v)
if isinstance(v, dict):
v = Config(v, name=k, level=level + 1)
d[k] = v
setattr(self, k, v)
self._pointer = d
def __repr__(self):
return str(list((self._pointer.keys())))
def __setattr__(self, key, val):
self.__dict__[key] = val
self.__dict__[key.upper()] = val
levels = key.split(".")
last_level = len(levels) - 1
pointer = self._pointer
if len(levels) > 1:
for i, l in enumerate(levels):
if hasattr(self, l) and isinstance(getattr(self, l), Config):
setattr(getattr(self, l), ".".join(levels[i:]), val)
if l == last_level:
pointer[l] = val
else:
pointer = pointer[l]
def to_dict(self):
return self._pointer
def dump_yaml(self, data, file_name):
with open(f"{file_name}", "w") as stream:
dump(data, stream)
def dump_json(self, data, file_name):
with open(f"{file_name}", "w") as stream:
json.dump(data, stream)
@staticmethod
def load_yaml(config):
with open(config) as stream:
data = load(stream, Loader=Loader)
return data
def __str__(self):
t = " "
if self._name != "root":
r = f"{t * (self._level-1)}{self._name}:\n"
else:
r = ""
level = self._level
for i, (k, v) in enumerate(self._pointer.items()):
if isinstance(v, Config):
r += f"{t * (self._level)}{v}\n"
self._level += 1
else:
r += f"{t * (self._level)}{k}: {v} ({type(v).__name__})\n"
self._level = level
return r[:-1]
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path: str, **kwargs):
config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
return cls(config_dict)
@classmethod
def get_config_dict(cls, pretrained_model_name_or_path: str, **kwargs):
cache_dir = kwargs.pop("cache_dir", None)
force_download = kwargs.pop("force_download", False)
resume_download = kwargs.pop("resume_download", False)
proxies = kwargs.pop("proxies", None)
local_files_only = kwargs.pop("local_files_only", False)
if os.path.isdir(pretrained_model_name_or_path):
config_file = os.path.join(pretrained_model_name_or_path, CONFIG_NAME)
elif os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path):
config_file = pretrained_model_name_or_path
else:
config_file = hf_bucket_url(pretrained_model_name_or_path, filename=CONFIG_NAME, use_cdn=False)
try:
# Load from URL or cache if already cached
resolved_config_file = cached_path(
config_file,
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
resume_download=resume_download,
local_files_only=local_files_only,
)
# Load config dict
if resolved_config_file is None:
raise EnvironmentError
config_file = Config.load_yaml(resolved_config_file)
except EnvironmentError:
msg = "Can't load config for"
raise EnvironmentError(msg)
if resolved_config_file == config_file:
print("loading configuration file from path")
else:
print("loading configuration file cache")
return Config.load_yaml(resolved_config_file), kwargs
# quick compare tensors
def compare(in_tensor):
out_tensor = torch.load("dump.pt", map_location=in_tensor.device)
n1 = in_tensor.numpy()
n2 = out_tensor.numpy()[0]
print(n1.shape, n1[0, 0, :5])
print(n2.shape, n2[0, 0, :5])
assert np.allclose(n1, n2, rtol=0.01, atol=0.1), (
f"{sum([1 for x in np.isclose(n1, n2, rtol=0.01, atol=0.1).flatten() if x is False])/len(n1.flatten())*100:.4f} %"
" element-wise mismatch"
)
raise Exception("tensors are all good")
# Hugging face functions below
def is_remote_url(url_or_filename):
parsed = urlparse(url_or_filename)
return parsed.scheme in ("http", "https")
def hf_bucket_url(model_id: str, filename: str, use_cdn=True) -> str:
endpoint = CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
legacy_format = "/" not in model_id
if legacy_format:
return f"{endpoint}/{model_id}-{filename}"
else:
return f"{endpoint}/{model_id}/{filename}"
def http_get(
url,
temp_file,
proxies=None,
resume_size=0,
user_agent=None,
):
ua = "python/{}".format(sys.version.split()[0])
if _torch_available:
ua += "; torch/{}".format(torch.__version__)
if isinstance(user_agent, dict):
ua += "; " + "; ".join("{}/{}".format(k, v) for k, v in user_agent.items())
elif isinstance(user_agent, str):
ua += "; " + user_agent
headers = {"user-agent": ua}
if resume_size > 0:
headers["Range"] = "bytes=%d-" % (resume_size,)
response = requests.get(url, stream=True, proxies=proxies, headers=headers)
if response.status_code == 416: # Range not satisfiable
return
content_length = response.headers.get("Content-Length")
total = resume_size + int(content_length) if content_length is not None else None
progress = tqdm(
unit="B",
unit_scale=True,
total=total,
initial=resume_size,
desc="Downloading",
)
for chunk in response.iter_content(chunk_size=1024):
if chunk: # filter out keep-alive new chunks
progress.update(len(chunk))
temp_file.write(chunk)
progress.close()
def get_from_cache(
url,
cache_dir=None,
force_download=False,
proxies=None,
etag_timeout=10,
resume_download=False,
user_agent=None,
local_files_only=False,
):
if cache_dir is None:
cache_dir = TRANSFORMERS_CACHE
if isinstance(cache_dir, Path):
cache_dir = str(cache_dir)
os.makedirs(cache_dir, exist_ok=True)
etag = None
if not local_files_only:
try:
response = requests.head(url, allow_redirects=True, proxies=proxies, timeout=etag_timeout)
if response.status_code == 200:
etag = response.headers.get("ETag")
except (EnvironmentError, requests.exceptions.Timeout):
# etag is already None
pass
filename = url_to_filename(url, etag)
# get cache path to put the file
cache_path = os.path.join(cache_dir, filename)
# etag is None = we don't have a connection, or url doesn't exist, or is otherwise inaccessible.
# try to get the last downloaded one
if etag is None:
if os.path.exists(cache_path):
return cache_path
else:
matching_files = [
file
for file in fnmatch.filter(os.listdir(cache_dir), filename + ".*")
if not file.endswith(".json") and not file.endswith(".lock")
]
if len(matching_files) > 0:
return os.path.join(cache_dir, matching_files[-1])
else:
# If files cannot be found and local_files_only=True,
# the models might've been found if local_files_only=False
# Notify the user about that
if local_files_only:
raise ValueError(
"Cannot find the requested files in the cached path and outgoing traffic has been"
" disabled. To enable model look-ups and downloads online, set 'local_files_only'"
" to False."
)
return None
# From now on, etag is not None.
if os.path.exists(cache_path) and not force_download:
return cache_path
# Prevent parallel downloads of the same file with a lock.
lock_path = cache_path + ".lock"
with FileLock(lock_path):
# If the download just completed while the lock was activated.
if os.path.exists(cache_path) and not force_download:
# Even if returning early like here, the lock will be released.
return cache_path
if resume_download:
incomplete_path = cache_path + ".incomplete"
@contextmanager
def _resumable_file_manager():
with open(incomplete_path, "a+b") as f:
yield f
temp_file_manager = _resumable_file_manager
if os.path.exists(incomplete_path):
resume_size = os.stat(incomplete_path).st_size
else:
resume_size = 0
else:
temp_file_manager = partial(tempfile.NamedTemporaryFile, dir=cache_dir, delete=False)
resume_size = 0
# Download to temporary file, then copy to cache dir once finished.
# Otherwise you get corrupt cache entries if the download gets interrupted.
with temp_file_manager() as temp_file:
print(
"%s not found in cache or force_download set to True, downloading to %s",
url,
temp_file.name,
)
http_get(
url,
temp_file,
proxies=proxies,
resume_size=resume_size,
user_agent=user_agent,
)
os.replace(temp_file.name, cache_path)
meta = {"url": url, "etag": etag}
meta_path = cache_path + ".json"
with open(meta_path, "w") as meta_file:
json.dump(meta, meta_file)
return cache_path
def url_to_filename(url, etag=None):
url_bytes = url.encode("utf-8")
url_hash = insecure_hashlib.sha256(url_bytes)
filename = url_hash.hexdigest()
if etag:
etag_bytes = etag.encode("utf-8")
etag_hash = insecure_hashlib.sha256(etag_bytes)
filename += "." + etag_hash.hexdigest()
if url.endswith(".h5"):
filename += ".h5"
return filename
def cached_path(
url_or_filename,
cache_dir=None,
force_download=False,
proxies=None,
resume_download=False,
user_agent=None,
extract_compressed_file=False,
force_extract=False,
local_files_only=False,
):
if cache_dir is None:
cache_dir = TRANSFORMERS_CACHE
if isinstance(url_or_filename, Path):
url_or_filename = str(url_or_filename)
if isinstance(cache_dir, Path):
cache_dir = str(cache_dir)
if is_remote_url(url_or_filename):
# URL, so get it from the cache (downloading if necessary)
output_path = get_from_cache(
url_or_filename,
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
resume_download=resume_download,
user_agent=user_agent,
local_files_only=local_files_only,
)
elif os.path.exists(url_or_filename):
# File, and it exists.
output_path = url_or_filename
elif urlparse(url_or_filename).scheme == "":
# File, but it doesn't exist.
raise EnvironmentError("file {} not found".format(url_or_filename))
else:
# Something unknown
raise ValueError("unable to parse {} as a URL or as a local path".format(url_or_filename))
if extract_compressed_file:
if not is_zipfile(output_path) and not tarfile.is_tarfile(output_path):
return output_path
# Path where we extract compressed archives
# We avoid '.' in dir name and add "-extracted" at the end: "./model.zip" => "./model-zip-extracted/"
output_dir, output_file = os.path.split(output_path)
output_extract_dir_name = output_file.replace(".", "-") + "-extracted"
output_path_extracted = os.path.join(output_dir, output_extract_dir_name)
if os.path.isdir(output_path_extracted) and os.listdir(output_path_extracted) and not force_extract:
return output_path_extracted
# Prevent parallel extractions
lock_path = output_path + ".lock"
with FileLock(lock_path):
shutil.rmtree(output_path_extracted, ignore_errors=True)
os.makedirs(output_path_extracted)
if is_zipfile(output_path):
with ZipFile(output_path, "r") as zip_file:
zip_file.extractall(output_path_extracted)
zip_file.close()
elif tarfile.is_tarfile(output_path):
tar_file = tarfile.open(output_path)
tar_file.extractall(output_path_extracted)
tar_file.close()
else:
raise EnvironmentError("Archive format of {} could not be identified".format(output_path))
return output_path_extracted
return output_path
def get_data(query, delim=","):
assert isinstance(query, str)
if os.path.isfile(query):
with open(query) as f:
data = eval(f.read())
else:
req = requests.get(query)
try:
data = requests.json()
except Exception:
data = req.content.decode()
assert data is not None, "could not connect"
try:
data = eval(data)
except Exception:
data = data.split("\n")
req.close()
return data
def get_image_from_url(url):
response = requests.get(url)
img = np.array(Image.open(BytesIO(response.content)))
return img
# to load legacy frcnn checkpoint from detectron
def load_frcnn_pkl_from_url(url):
fn = url.split("/")[-1]
if fn not in os.listdir(os.getcwd()):
wget.download(url)
with open(fn, "rb") as stream:
weights = pkl.load(stream)
model = weights.pop("model")
new = {}
for k, v in model.items():
new[k] = torch.from_numpy(v)
if "running_var" in k:
zero = torch.tensor([0])
k2 = k.replace("running_var", "num_batches_tracked")
new[k2] = zero
return new
def get_demo_path():
print(f"{os.path.abspath(os.path.join(PATH, os.pardir))}/demo.ipynb")
def img_tensorize(im, input_format="RGB"):
assert isinstance(im, str)
if os.path.isfile(im):
img = cv2.imread(im)
else:
img = get_image_from_url(im)
assert img is not None, f"could not connect to: {im}"
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
if input_format == "RGB":
img = img[:, :, ::-1]
return img
def chunk(images, batch=1):
return (images[i : i + batch] for i in range(0, len(images), batch))
| transformers/examples/research_projects/visual_bert/utils.py/0 | {
"file_path": "transformers/examples/research_projects/visual_bert/utils.py",
"repo_id": "transformers",
"token_count": 8356
} | 58 |
#!/usr/bin/env bash
python run_asr.py \
--output_dir="./wav2vec2-large-lv60-timit-asr" \
--num_train_epochs="30" \
--per_device_train_batch_size="2" \
--per_device_eval_batch_size="2" \
--gradient_accumulation_steps="4" \
--evaluation_strategy="steps" \
--save_steps="500" \
--eval_steps="100" \
--logging_steps="50" \
--learning_rate="5e-4" \
--warmup_steps="3000" \
--model_name_or_path="facebook/wav2vec2-large-lv60" \
--fp16 \
--dataset_name="timit_asr" \
--train_split_name="train" \
--validation_split_name="test" \
--orthography="timit" \
--preprocessing_num_workers="$(nproc)" \
--group_by_length \
--freeze_feature_extractor \
--verbose_logging \
| transformers/examples/research_projects/wav2vec2/finetune_large_lv60_timit_asr.sh/0 | {
"file_path": "transformers/examples/research_projects/wav2vec2/finetune_large_lv60_timit_asr.sh",
"repo_id": "transformers",
"token_count": 276
} | 59 |
<!---
Copyright 2021 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
-->
# Examples
This folder contains actively maintained examples of the use of 🤗 Transformers organized into different ML tasks. All examples in this folder are **TensorFlow** examples and are written using native Keras. If you've previously only used 🤗 Transformers via `TFTrainer`, we highly recommend taking a look at the new style - we think it's a big improvement!
In addition, all scripts here now support the [🤗 Datasets](https://github.com/huggingface/datasets) library - you can grab entire datasets just by changing one command-line argument!
## A note on code folding
Most of these examples have been formatted with #region blocks. In IDEs such as PyCharm and VSCode, these blocks mark
named regions of code that can be folded for easier viewing. If you find any of these scripts overwhelming or difficult
to follow, we highly recommend beginning with all regions folded and then examining regions one at a time!
## The Big Table of Tasks
Here is the list of all our examples:
| Task | Example datasets |
|---|---|
| [**`language-modeling`**](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/language-modeling) | WikiText-2
| [**`multiple-choice`**](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/multiple-choice) | SWAG
| [**`question-answering`**](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/question-answering) | SQuAD
| [**`summarization`**](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/summarization) | XSum
| [**`text-classification`**](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/text-classification) | GLUE
| [**`token-classification`**](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/token-classification) | CoNLL NER
| [**`translation`**](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/translation) | WMT
## Coming soon
- **Colab notebooks** to easily run through these scripts!
| transformers/examples/tensorflow/README.md/0 | {
"file_path": "transformers/examples/tensorflow/README.md",
"repo_id": "transformers",
"token_count": 731
} | 60 |
#!/usr/bin/env python
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Script for training a Unigram tokenizer."""
import argparse
import logging
import datasets
from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers, processors
from tokenizers.models import Unigram
from tokenizers.trainers import UnigramTrainer
from transformers import AlbertTokenizerFast
logger = logging.getLogger(__name__)
def parse_args():
parser = argparse.ArgumentParser(description="Train a unigram tokenizer on the wikitext dataset.")
parser.add_argument(
"--dataset_name",
type=str,
default="wikitext",
help="Name of the training. Explore datasets at: hf.co/datasets.",
)
parser.add_argument(
"--dataset_config", type=str, default="wikitext-103-raw-v1", help="Configuration name of the dataset."
)
parser.add_argument(
"--batch_size",
type=int,
default=1000,
help="Batch size during training.",
)
parser.add_argument(
"--vocab_size",
type=int,
default=10048,
help="Size of the desired vocabulary.",
)
parser.add_argument(
"--limit",
default=None,
type=int,
help="Limit the number of shards (used for debugging).",
)
parser.add_argument(
"--export_to_hub",
action="store_true",
)
args = parser.parse_args()
return args
def main(args):
dataset = datasets.load_dataset(args.dataset_name, args.dataset_config, split="train")
if args.limit is not None:
max_train_samples = min(len(dataset), args.limit)
dataset = dataset.select(range(max_train_samples))
logger.info(f"Limiting the dataset to {args.limit} entries.")
def batch_iterator():
for i in range(0, len(dataset), args.batch_size):
yield dataset[i : i + args.batch_size]["text"]
# Prepare the tokenizer.
tokenizer = Tokenizer(Unigram())
tokenizer.normalizer = normalizers.Sequence([normalizers.Replace("``", '"'), normalizers.Replace("''", '"')])
tokenizer.pre_tokenizer = pre_tokenizers.Metaspace()
# Prepare the trainer.
trainer = UnigramTrainer(
unk_token="<unk>",
special_tokens=["[CLS]", "[SEP]", "<unk>", "<pad>", "[MASK]"],
vocab_size=args.vocab_size,
)
logger.info("Training the tokenizer.")
tokenizer.train_from_iterator(batch_iterator(), trainer=trainer)
logger.info("Tokenizer training complete!")
cls_token_id = tokenizer.token_to_id("[CLS]")
sep_token_id = tokenizer.token_to_id("[SEP]")
tokenizer.post_processor = processors.TemplateProcessing(
single="[CLS]:0 $A:0 [SEP]:0",
pair="[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1",
special_tokens=[
("[CLS]", cls_token_id),
("[SEP]", sep_token_id),
],
)
tokenizer.decoder = decoders.Metaspace()
if args.export_to_hub:
logger.info("Exporting the trained tokenizer to Hub.")
new_tokenizer = AlbertTokenizerFast(tokenizer_object=tokenizer)
new_tokenizer.push_to_hub("unigram-tokenizer-dataset")
if __name__ == "__main__":
args = parse_args()
main(args)
| transformers/examples/tensorflow/language-modeling-tpu/train_unigram.py/0 | {
"file_path": "transformers/examples/tensorflow/language-modeling-tpu/train_unigram.py",
"repo_id": "transformers",
"token_count": 1489
} | 61 |
<!---
Copyright 2021 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
-->
# Text classification examples
This folder contains some scripts showing examples of *text classification* with the 🤗 Transformers library.
For straightforward use-cases you may be able to use these scripts without modification, although we have also
included comments in the code to indicate areas that you may need to adapt to your own projects.
## run_text_classification.py
This script handles perhaps the single most common use-case for this entire library: Training an NLP classifier
on your own training data. This can be whatever you want - you could classify text as abusive/hateful or
allowable, or forum posts as spam or not-spam, or classify the genre of a headline as politics, sports or any
number of other categories. Any task that involves classifying natural language into two or more different categories
can work with this! You can even do regression, such as predicting the score on a 1-10 scale that a user gave,
given the text of their review.
The preferred input format is either a CSV or newline-delimited JSON file that contains a `sentence1` and
`label` field. If your task involves comparing two texts (for example, if your classifier
is deciding whether two sentences are paraphrases of each other, or were written by the same author) then you should also include a `sentence2` field in each example. If you do not have a `sentence1` field then the script will assume the non-label fields are the input text, which
may not always be what you want, especially if you have more than two fields!
Here is a snippet of a valid input JSON file, though note that your texts can be much longer than these, and are not constrained
(despite the field name) to being single grammatical sentences:
```json
{"sentence1": "COVID-19 vaccine updates: How is the rollout proceeding?", "label": "news"}
{"sentence1": "Manchester United celebrates Europa League success", "label": "sports"}
```
### Usage notes
If your inputs are long (more than ~60-70 words), you may wish to increase the `--max_seq_length` argument
beyond the default value of 128. The maximum supported value for most models is 512 (about 200-300 words),
and some can handle even longer. This will come at a cost in runtime and memory use, however.
We assume that your labels represent *categories*, even if they are integers, since text classification
is a much more common task than text regression. If your labels are floats, however, the script will assume
you want to do regression. This is something you can edit yourself if your use-case requires it!
After training, the model will be saved to `--output_dir`. Once your model is trained, you can get predictions
by calling the script without a `--train_file` or `--validation_file`; simply pass it the output_dir containing
the trained model and a `--test_file` and it will write its predictions to a text file for you.
### Multi-GPU and TPU usage
By default, the script uses a `MirroredStrategy` and will use multiple GPUs effectively if they are available. TPUs
can also be used by passing the name of the TPU resource with the `--tpu` argument.
### Memory usage and data loading
One thing to note is that all data is loaded into memory in this script. Most text classification datasets are small
enough that this is not an issue, but if you have a very large dataset you will need to modify the script to handle
data streaming. This is particularly challenging for TPUs, given the stricter requirements and the sheer volume of data
required to keep them fed. A full explanation of all the possible pitfalls is a bit beyond this example script and
README, but for more information you can see the 'Input Datasets' section of
[this document](https://www.tensorflow.org/guide/tpu).
### Example command
```bash
python run_text_classification.py \
--model_name_or_path distilbert/distilbert-base-cased \
--train_file training_data.json \
--validation_file validation_data.json \
--output_dir output/ \
--test_file data_to_predict.json
```
## run_glue.py
This script handles training on the GLUE dataset for various text classification and regression tasks. The GLUE datasets will be loaded automatically, so you only need to specify the task you want (with the `--task_name` argument). You can also supply your own files for prediction with the `--predict_file` argument, for example if you want to train a model on GLUE for e.g. paraphrase detection and then predict whether your own data contains paraphrases or not. Please ensure the names of your input fields match the names of the features in the relevant GLUE dataset - you can see a list of the column names in the `task_to_keys` dict in the `run_glue.py` file.
### Usage notes
The `--do_train`, `--do_eval` and `--do_predict` arguments control whether training, evaluations or predictions are performed. After training, the model will be saved to `--output_dir`. Once your model is trained, you can call the script without the `--do_train` or `--do_eval` arguments to quickly get predictions from your saved model.
### Multi-GPU and TPU usage
By default, the script uses a `MirroredStrategy` and will use multiple GPUs effectively if they are available. TPUs
can also be used by passing the name of the TPU resource with the `--tpu` argument.
### Memory usage and data loading
One thing to note is that all data is loaded into memory in this script. Most text classification datasets are small
enough that this is not an issue, but if you have a very large dataset you will need to modify the script to handle
data streaming. This is particularly challenging for TPUs, given the stricter requirements and the sheer volume of data
required to keep them fed. A full explanation of all the possible pitfalls is a bit beyond this example script and
README, but for more information you can see the 'Input Datasets' section of
[this document](https://www.tensorflow.org/guide/tpu).
### Example command
```bash
python run_glue.py \
--model_name_or_path distilbert/distilbert-base-cased \
--task_name mnli \
--do_train \
--do_eval \
--do_predict \
--predict_file data_to_predict.json
```
| transformers/examples/tensorflow/text-classification/README.md/0 | {
"file_path": "transformers/examples/tensorflow/text-classification/README.md",
"repo_id": "transformers",
"token_count": 1674
} | 62 |
#!/usr/bin/env python
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# You may need to add --master_addr $MASTER_ADDR --master_port $MASTER_PORT if using a custom addr:port
#
# You can also use the rdzv API: --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT --rdzv_backend c10d
#
# use torch.distributed.launch instead of torch.distributed.run for torch < 1.9
#
# If you get a hanging in `barrier` calls you have some network issues, you may try to debug this with:
#
# NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# which should tell you what's going on behind the scenes.
#
#
# This script can be run via `srun` in the SLURM environment as well. Here is a SLURM script that
# runs on 2 nodes of 4 gpus per node:
#
# #SBATCH --job-name=test-nodes # name
# #SBATCH --nodes=2 # nodes
# #SBATCH --ntasks-per-node=1 # crucial - only 1 task per dist per node!
# #SBATCH --cpus-per-task=10 # number of cores per tasks
# #SBATCH --gres=gpu:4 # number of gpus
# #SBATCH --time 0:05:00 # maximum execution time (HH:MM:SS)
# #SBATCH --output=%x-%j.out # output file name
#
# GPUS_PER_NODE=4
# MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
# MASTER_PORT=6000
#
# srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \
# --nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \
# --master_addr $MASTER_ADDR --master_port $MASTER_PORT \
# torch-distributed-gpu-test.py'
#
import fcntl
import os
import socket
import torch
import torch.distributed as dist
def printflock(*msgs):
"""solves multi-process interleaved print problem"""
with open(__file__, "r") as fh:
fcntl.flock(fh, fcntl.LOCK_EX)
try:
print(*msgs)
finally:
fcntl.flock(fh, fcntl.LOCK_UN)
local_rank = int(os.environ["LOCAL_RANK"])
torch.cuda.set_device(local_rank)
device = torch.device("cuda", local_rank)
hostname = socket.gethostname()
gpu = f"[{hostname}-{local_rank}]"
try:
# test distributed
dist.init_process_group("nccl")
dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
dist.barrier()
# test cuda is available and can allocate memory
torch.cuda.is_available()
torch.ones(1).cuda(local_rank)
# global rank
rank = dist.get_rank()
world_size = dist.get_world_size()
printflock(f"{gpu} is OK (global rank: {rank}/{world_size})")
dist.barrier()
if rank == 0:
printflock(f"pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}")
except Exception:
printflock(f"{gpu} is broken")
raise
| transformers/scripts/distributed/torch-distributed-gpu-test.py/0 | {
"file_path": "transformers/scripts/distributed/torch-distributed-gpu-test.py",
"repo_id": "transformers",
"token_count": 1240
} | 63 |
<!---
Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
-->
Setup transformers following instructions in README.md, (I would fork first).
```bash
git clone [email protected]:huggingface/transformers.git
cd transformers
pip install -e .
pip install pandas GitPython wget
```
Get required metadata
```bash
curl https://cdn-datasets.huggingface.co/language_codes/language-codes-3b2.csv > language-codes-3b2.csv
curl https://cdn-datasets.huggingface.co/language_codes/iso-639-3.csv > iso-639-3.csv
```
Install Tatoeba-Challenge repo inside transformers
```bash
git clone [email protected]:Helsinki-NLP/Tatoeba-Challenge.git
```
To convert a few models, call the conversion script from command line:
```bash
python src/transformers/models/marian/convert_marian_tatoeba_to_pytorch.py --models heb-eng eng-heb --save_dir converted
```
To convert lots of models you can pass your list of Tatoeba model names to `resolver.convert_models` in a python client or script.
```python
from transformers.convert_marian_tatoeba_to_pytorch import TatoebaConverter
resolver = TatoebaConverter(save_dir='converted')
resolver.convert_models(['heb-eng', 'eng-heb'])
```
### Upload converted models
Since version v3.5.0, the model sharing workflow is switched to git-based system . Refer to [model sharing doc](https://huggingface.co/transformers/main/model_sharing.html#model-sharing-and-uploading) for more details.
To upload all converted models,
1. Install [git-lfs](https://git-lfs.github.com/).
2. Login to `huggingface-cli`
```bash
huggingface-cli login
```
3. Run the `upload_models` script
```bash
./scripts/tatoeba/upload_models.sh
```
### Modifications
- To change naming logic, change the code near `os.rename`. The model card creation code may also need to change.
- To change model card content, you must modify `TatoebaCodeResolver.write_model_card`
| transformers/scripts/tatoeba/README.md/0 | {
"file_path": "transformers/scripts/tatoeba/README.md",
"repo_id": "transformers",
"token_count": 761
} | 64 |
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
_has_cookiecutter = True
except ImportError:
_has_cookiecutter = False
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
def add_new_model_command_factory(args: Namespace):
return AddNewModelCommand(args.testing, args.testing_file, path=args.path)
class AddNewModelCommand(BaseTransformersCLICommand):
@staticmethod
def register_subcommand(parser: ArgumentParser):
add_new_model_parser = parser.add_parser("add-new-model")
add_new_model_parser.add_argument("--testing", action="store_true", help="If in testing mode.")
add_new_model_parser.add_argument("--testing_file", type=str, help="Configuration file on which to run.")
add_new_model_parser.add_argument(
"--path", type=str, help="Path to cookiecutter. Should only be used for testing purposes."
)
add_new_model_parser.set_defaults(func=add_new_model_command_factory)
def __init__(self, testing: bool, testing_file: str, path=None, *args):
self._testing = testing
self._testing_file = testing_file
self._path = path
def run(self):
warnings.warn(
"The command `transformers-cli add-new-model` is deprecated and will be removed in v5 of Transformers. "
"It is not actively maintained anymore, so might give a result that won't pass all tests and quality "
"checks, you should use `transformers-cli add-new-model-like` instead."
)
if not _has_cookiecutter:
raise ImportError(
"Model creation dependencies are required to use the `add_new_model` command. Install them by running "
"the following at the root of your `transformers` clone:\n\n\t$ pip install -e .[modelcreation]\n"
)
# Ensure that there is no other `cookiecutter-template-xxx` directory in the current working directory
directories = [directory for directory in os.listdir() if "cookiecutter-template-" == directory[:22]]
if len(directories) > 0:
raise ValueError(
"Several directories starting with `cookiecutter-template-` in current working directory. "
"Please clean your directory by removing all folders starting with `cookiecutter-template-` or "
"change your working directory."
)
path_to_transformer_root = (
Path(__file__).parent.parent.parent.parent if self._path is None else Path(self._path).parent.parent
)
path_to_cookiecutter = path_to_transformer_root / "templates" / "adding_a_new_model"
# Execute cookiecutter
if not self._testing:
cookiecutter(str(path_to_cookiecutter))
else:
with open(self._testing_file, "r") as configuration_file:
testing_configuration = json.load(configuration_file)
cookiecutter(
str(path_to_cookiecutter if self._path is None else self._path),
no_input=True,
extra_context=testing_configuration,
)
directory = [directory for directory in os.listdir() if "cookiecutter-template-" in directory[:22]][0]
# Retrieve configuration
with open(directory + "/configuration.json", "r") as configuration_file:
configuration = json.load(configuration_file)
lowercase_model_name = configuration["lowercase_modelname"]
generate_tensorflow_pytorch_and_flax = configuration["generate_tensorflow_pytorch_and_flax"]
os.remove(f"{directory}/configuration.json")
output_pytorch = "PyTorch" in generate_tensorflow_pytorch_and_flax
output_tensorflow = "TensorFlow" in generate_tensorflow_pytorch_and_flax
output_flax = "Flax" in generate_tensorflow_pytorch_and_flax
model_dir = f"{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}"
os.makedirs(model_dir, exist_ok=True)
os.makedirs(f"{path_to_transformer_root}/tests/models/{lowercase_model_name}", exist_ok=True)
# Tests require submodules as they have parent imports
with open(f"{path_to_transformer_root}/tests/models/{lowercase_model_name}/__init__.py", "w"):
pass
shutil.move(
f"{directory}/__init__.py",
f"{model_dir}/__init__.py",
)
shutil.move(
f"{directory}/configuration_{lowercase_model_name}.py",
f"{model_dir}/configuration_{lowercase_model_name}.py",
)
def remove_copy_lines(path):
with open(path, "r") as f:
lines = f.readlines()
with open(path, "w") as f:
for line in lines:
if "# Copied from transformers." not in line:
f.write(line)
if output_pytorch:
if not self._testing:
remove_copy_lines(f"{directory}/modeling_{lowercase_model_name}.py")
shutil.move(
f"{directory}/modeling_{lowercase_model_name}.py",
f"{model_dir}/modeling_{lowercase_model_name}.py",
)
shutil.move(
f"{directory}/test_modeling_{lowercase_model_name}.py",
f"{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_{lowercase_model_name}.py",
)
else:
os.remove(f"{directory}/modeling_{lowercase_model_name}.py")
os.remove(f"{directory}/test_modeling_{lowercase_model_name}.py")
if output_tensorflow:
if not self._testing:
remove_copy_lines(f"{directory}/modeling_tf_{lowercase_model_name}.py")
shutil.move(
f"{directory}/modeling_tf_{lowercase_model_name}.py",
f"{model_dir}/modeling_tf_{lowercase_model_name}.py",
)
shutil.move(
f"{directory}/test_modeling_tf_{lowercase_model_name}.py",
f"{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_tf_{lowercase_model_name}.py",
)
else:
os.remove(f"{directory}/modeling_tf_{lowercase_model_name}.py")
os.remove(f"{directory}/test_modeling_tf_{lowercase_model_name}.py")
if output_flax:
if not self._testing:
remove_copy_lines(f"{directory}/modeling_flax_{lowercase_model_name}.py")
shutil.move(
f"{directory}/modeling_flax_{lowercase_model_name}.py",
f"{model_dir}/modeling_flax_{lowercase_model_name}.py",
)
shutil.move(
f"{directory}/test_modeling_flax_{lowercase_model_name}.py",
f"{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_flax_{lowercase_model_name}.py",
)
else:
os.remove(f"{directory}/modeling_flax_{lowercase_model_name}.py")
os.remove(f"{directory}/test_modeling_flax_{lowercase_model_name}.py")
shutil.move(
f"{directory}/{lowercase_model_name}.md",
f"{path_to_transformer_root}/docs/source/en/model_doc/{lowercase_model_name}.md",
)
shutil.move(
f"{directory}/tokenization_{lowercase_model_name}.py",
f"{model_dir}/tokenization_{lowercase_model_name}.py",
)
shutil.move(
f"{directory}/tokenization_fast_{lowercase_model_name}.py",
f"{model_dir}/tokenization_{lowercase_model_name}_fast.py",
)
from os import fdopen, remove
from shutil import copymode, move
from tempfile import mkstemp
def replace(original_file: str, line_to_copy_below: str, lines_to_copy: List[str]):
# Create temp file
fh, abs_path = mkstemp()
line_found = False
with fdopen(fh, "w") as new_file:
with open(original_file) as old_file:
for line in old_file:
new_file.write(line)
if line_to_copy_below in line:
line_found = True
for line_to_copy in lines_to_copy:
new_file.write(line_to_copy)
if not line_found:
raise ValueError(f"Line {line_to_copy_below} was not found in file.")
# Copy the file permissions from the old file to the new file
copymode(original_file, abs_path)
# Remove original file
remove(original_file)
# Move new file
move(abs_path, original_file)
def skip_units(line):
return (
("generating PyTorch" in line and not output_pytorch)
or ("generating TensorFlow" in line and not output_tensorflow)
or ("generating Flax" in line and not output_flax)
)
def replace_in_files(path_to_datafile):
with open(path_to_datafile) as datafile:
lines_to_copy = []
skip_file = False
skip_snippet = False
for line in datafile:
if "# To replace in: " in line and "##" not in line:
file_to_replace_in = line.split('"')[1]
skip_file = skip_units(line)
elif "# Below: " in line and "##" not in line:
line_to_copy_below = line.split('"')[1]
skip_snippet = skip_units(line)
elif "# End." in line and "##" not in line:
if not skip_file and not skip_snippet:
replace(file_to_replace_in, line_to_copy_below, lines_to_copy)
lines_to_copy = []
elif "# Replace with" in line and "##" not in line:
lines_to_copy = []
elif "##" not in line:
lines_to_copy.append(line)
remove(path_to_datafile)
replace_in_files(f"{directory}/to_replace_{lowercase_model_name}.py")
os.rmdir(directory)
| transformers/src/transformers/commands/add_new_model.py/0 | {
"file_path": "transformers/src/transformers/commands/add_new_model.py",
"repo_id": "transformers",
"token_count": 5123
} | 65 |
# coding=utf-8
# Copyright 2018 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Convert slow tokenizers checkpoints in fast (serialization format of the `tokenizers` library)"""
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
logger = logging.get_logger(__name__)
TOKENIZER_CLASSES = {name: getattr(transformers, name + "Fast") for name in SLOW_TO_FAST_CONVERTERS}
def convert_slow_checkpoint_to_fast(tokenizer_name, checkpoint_name, dump_path, force_download):
if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
raise ValueError(f"Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys())}.")
if tokenizer_name is None:
tokenizer_names = TOKENIZER_CLASSES
else:
tokenizer_names = {tokenizer_name: getattr(transformers, tokenizer_name + "Fast")}
logger.info(f"Loading tokenizer classes: {tokenizer_names}")
for tokenizer_name in tokenizer_names:
tokenizer_class = TOKENIZER_CLASSES[tokenizer_name]
add_prefix = True
if checkpoint_name is None:
checkpoint_names = list(tokenizer_class.max_model_input_sizes.keys())
else:
checkpoint_names = [checkpoint_name]
logger.info(f"For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}")
for checkpoint in checkpoint_names:
logger.info(f"Loading {tokenizer_class.__class__.__name__} {checkpoint}")
# Load tokenizer
tokenizer = tokenizer_class.from_pretrained(checkpoint, force_download=force_download)
# Save fast tokenizer
logger.info(f"Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}")
# For organization names we create sub-directories
if "/" in checkpoint:
checkpoint_directory, checkpoint_prefix_name = checkpoint.split("/")
dump_path_full = os.path.join(dump_path, checkpoint_directory)
elif add_prefix:
checkpoint_prefix_name = checkpoint
dump_path_full = dump_path
else:
checkpoint_prefix_name = None
dump_path_full = dump_path
logger.info(f"=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}")
if checkpoint in list(tokenizer.pretrained_vocab_files_map.values())[0]:
file_path = list(tokenizer.pretrained_vocab_files_map.values())[0][checkpoint]
next_char = file_path.split(checkpoint)[-1][0]
if next_char == "/":
dump_path_full = os.path.join(dump_path_full, checkpoint_prefix_name)
checkpoint_prefix_name = None
logger.info(f"=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}")
file_names = tokenizer.save_pretrained(
dump_path_full, legacy_format=False, filename_prefix=checkpoint_prefix_name
)
logger.info(f"=> File names {file_names}")
for file_name in file_names:
if not file_name.endswith("tokenizer.json"):
os.remove(file_name)
logger.info(f"=> removing {file_name}")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--dump_path", default=None, type=str, required=True, help="Path to output generated fast tokenizer files."
)
parser.add_argument(
"--tokenizer_name",
default=None,
type=str,
help=(
f"Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will "
"download and convert all the checkpoints from AWS."
),
)
parser.add_argument(
"--checkpoint_name",
default=None,
type=str,
help="Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.",
)
parser.add_argument(
"--force_download",
action="store_true",
help="Re-download checkpoints.",
)
args = parser.parse_args()
convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)
| transformers/src/transformers/convert_slow_tokenizers_checkpoints_to_fast.py/0 | {
"file_path": "transformers/src/transformers/convert_slow_tokenizers_checkpoints_to_fast.py",
"repo_id": "transformers",
"token_count": 1993
} | 66 |
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Integration with Deepspeed - kept for backward compatiblity, if you plan to make any edit, make sure to modify the file
in `integrations/deepspeed` instead.
Check: https://github.com/huggingface/transformers/pull/25599
"""
import warnings
warnings.warn(
"transformers.deepspeed module is deprecated and will be removed in a future version. Please import deepspeed modules directly from transformers.integrations",
FutureWarning,
)
# Backward compatibility imports, to make sure all those objects can be found in integrations/deepspeed
from .integrations.deepspeed import ( # noqa
HfDeepSpeedConfig,
HfTrainerDeepSpeedConfig,
deepspeed_config,
deepspeed_init,
deepspeed_load_checkpoint,
deepspeed_optim_sched,
is_deepspeed_available,
is_deepspeed_zero3_enabled,
set_hf_deepspeed_config,
unset_hf_deepspeed_config,
)
| transformers/src/transformers/deepspeed.py/0 | {
"file_path": "transformers/src/transformers/deepspeed.py",
"repo_id": "transformers",
"token_count": 432
} | 67 |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class BaseStreamer:
"""
Base class from which `.generate()` streamers should inherit.
"""
def put(self, value):
"""Function that is called by `.generate()` to push new tokens"""
raise NotImplementedError()
def end(self):
"""Function that is called by `.generate()` to signal the end of generation"""
raise NotImplementedError()
class TextStreamer(BaseStreamer):
"""
Simple text streamer that prints the token(s) to stdout as soon as entire words are formed.
<Tip warning={true}>
The API for the streamer classes is still under development and may change in the future.
</Tip>
Parameters:
tokenizer (`AutoTokenizer`):
The tokenized used to decode the tokens.
skip_prompt (`bool`, *optional*, defaults to `False`):
Whether to skip the prompt to `.generate()` or not. Useful e.g. for chatbots.
decode_kwargs (`dict`, *optional*):
Additional keyword arguments to pass to the tokenizer's `decode` method.
Examples:
```python
>>> from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
>>> tok = AutoTokenizer.from_pretrained("openai-community/gpt2")
>>> model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2")
>>> inputs = tok(["An increasing sequence: one,"], return_tensors="pt")
>>> streamer = TextStreamer(tok)
>>> # Despite returning the usual output, the streamer will also print the generated text to stdout.
>>> _ = model.generate(**inputs, streamer=streamer, max_new_tokens=20)
An increasing sequence: one, two, three, four, five, six, seven, eight, nine, ten, eleven,
```
"""
def __init__(self, tokenizer: "AutoTokenizer", skip_prompt: bool = False, **decode_kwargs):
self.tokenizer = tokenizer
self.skip_prompt = skip_prompt
self.decode_kwargs = decode_kwargs
# variables used in the streaming process
self.token_cache = []
self.print_len = 0
self.next_tokens_are_prompt = True
def put(self, value):
"""
Receives tokens, decodes them, and prints them to stdout as soon as they form entire words.
"""
if len(value.shape) > 1 and value.shape[0] > 1:
raise ValueError("TextStreamer only supports batch size 1")
elif len(value.shape) > 1:
value = value[0]
if self.skip_prompt and self.next_tokens_are_prompt:
self.next_tokens_are_prompt = False
return
# Add the new token to the cache and decodes the entire thing.
self.token_cache.extend(value.tolist())
text = self.tokenizer.decode(self.token_cache, **self.decode_kwargs)
# After the symbol for a new line, we flush the cache.
if text.endswith("\n"):
printable_text = text[self.print_len :]
self.token_cache = []
self.print_len = 0
# If the last token is a CJK character, we print the characters.
elif len(text) > 0 and self._is_chinese_char(ord(text[-1])):
printable_text = text[self.print_len :]
self.print_len += len(printable_text)
# Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
# which may change with the subsequent token -- there are probably smarter ways to do this!)
else:
printable_text = text[self.print_len : text.rfind(" ") + 1]
self.print_len += len(printable_text)
self.on_finalized_text(printable_text)
def end(self):
"""Flushes any remaining cache and prints a newline to stdout."""
# Flush the cache, if it exists
if len(self.token_cache) > 0:
text = self.tokenizer.decode(self.token_cache, **self.decode_kwargs)
printable_text = text[self.print_len :]
self.token_cache = []
self.print_len = 0
else:
printable_text = ""
self.next_tokens_are_prompt = True
self.on_finalized_text(printable_text, stream_end=True)
def on_finalized_text(self, text: str, stream_end: bool = False):
"""Prints the new text to stdout. If the stream is ending, also prints a newline."""
print(text, flush=True, end="" if not stream_end else None)
def _is_chinese_char(self, cp):
"""Checks whether CP is the codepoint of a CJK character."""
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
# despite its name. The modern Korean Hangul alphabet is a different block,
# as is Japanese Hiragana and Katakana. Those alphabets are used to write
# space-separated words, so they are not treated specially and handled
# like the all of the other languages.
if (
(cp >= 0x4E00 and cp <= 0x9FFF)
or (cp >= 0x3400 and cp <= 0x4DBF) #
or (cp >= 0x20000 and cp <= 0x2A6DF) #
or (cp >= 0x2A700 and cp <= 0x2B73F) #
or (cp >= 0x2B740 and cp <= 0x2B81F) #
or (cp >= 0x2B820 and cp <= 0x2CEAF) #
or (cp >= 0xF900 and cp <= 0xFAFF)
or (cp >= 0x2F800 and cp <= 0x2FA1F) #
): #
return True
return False
class TextIteratorStreamer(TextStreamer):
"""
Streamer that stores print-ready text in a queue, to be used by a downstream application as an iterator. This is
useful for applications that benefit from acessing the generated text in a non-blocking way (e.g. in an interactive
Gradio demo).
<Tip warning={true}>
The API for the streamer classes is still under development and may change in the future.
</Tip>
Parameters:
tokenizer (`AutoTokenizer`):
The tokenized used to decode the tokens.
skip_prompt (`bool`, *optional*, defaults to `False`):
Whether to skip the prompt to `.generate()` or not. Useful e.g. for chatbots.
timeout (`float`, *optional*):
The timeout for the text queue. If `None`, the queue will block indefinitely. Useful to handle exceptions
in `.generate()`, when it is called in a separate thread.
decode_kwargs (`dict`, *optional*):
Additional keyword arguments to pass to the tokenizer's `decode` method.
Examples:
```python
>>> from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
>>> from threading import Thread
>>> tok = AutoTokenizer.from_pretrained("openai-community/gpt2")
>>> model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2")
>>> inputs = tok(["An increasing sequence: one,"], return_tensors="pt")
>>> streamer = TextIteratorStreamer(tok)
>>> # Run the generation in a separate thread, so that we can fetch the generated text in a non-blocking way.
>>> generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=20)
>>> thread = Thread(target=model.generate, kwargs=generation_kwargs)
>>> thread.start()
>>> generated_text = ""
>>> for new_text in streamer:
... generated_text += new_text
>>> generated_text
'An increasing sequence: one, two, three, four, five, six, seven, eight, nine, ten, eleven,'
```
"""
def __init__(
self, tokenizer: "AutoTokenizer", skip_prompt: bool = False, timeout: Optional[float] = None, **decode_kwargs
):
super().__init__(tokenizer, skip_prompt, **decode_kwargs)
self.text_queue = Queue()
self.stop_signal = None
self.timeout = timeout
def on_finalized_text(self, text: str, stream_end: bool = False):
"""Put the new text in the queue. If the stream is ending, also put a stop signal in the queue."""
self.text_queue.put(text, timeout=self.timeout)
if stream_end:
self.text_queue.put(self.stop_signal, timeout=self.timeout)
def __iter__(self):
return self
def __next__(self):
value = self.text_queue.get(timeout=self.timeout)
if value == self.stop_signal:
raise StopIteration()
else:
return value
| transformers/src/transformers/generation/streamers.py/0 | {
"file_path": "transformers/src/transformers/generation/streamers.py",
"repo_id": "transformers",
"token_count": 3625
} | 68 |
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Integration with Deepspeed
"""
import copy
import importlib.metadata as importlib_metadata
import importlib.util
import weakref
from functools import partialmethod
from ..dependency_versions_check import dep_version_check
from ..utils import is_accelerate_available, is_torch_available, logging
if is_torch_available():
import torch
logger = logging.get_logger(__name__)
def is_deepspeed_available():
package_exists = importlib.util.find_spec("deepspeed") is not None
# Check we're not importing a "deepspeed" directory somewhere but the actual library by trying to grab the version
# AND checking it has an author field in the metadata that is HuggingFace.
if package_exists:
try:
_ = importlib_metadata.metadata("deepspeed")
return True
except importlib_metadata.PackageNotFoundError:
return False
if is_accelerate_available() and is_deepspeed_available():
from accelerate.utils.deepspeed import HfDeepSpeedConfig as DeepSpeedConfig
else:
# Inherits from a dummy `object` if accelerate is not available, so that python succeeds to import this file.
# Deepspeed glue code will never inherit this dummy object as it checks if accelerate is available.
from builtins import object as DeepSpeedConfig
class HfDeepSpeedConfig(DeepSpeedConfig):
"""
This object contains a DeepSpeed configuration dictionary and can be quickly queried for things like zero stage.
A `weakref` of this object is stored in the module's globals to be able to access the config from areas where
things like the Trainer object is not available (e.g. `from_pretrained` and `_get_resized_embeddings`). Therefore
it's important that this object remains alive while the program is still running.
[`Trainer`] uses the `HfTrainerDeepSpeedConfig` subclass instead. That subclass has logic to sync the configuration
with values of [`TrainingArguments`] by replacing special placeholder values: `"auto"`. Without this special logic
the DeepSpeed configuration is not modified in any way.
Args:
config_file_or_dict (`Union[str, Dict]`): path to DeepSpeed config file or dict.
"""
def __init__(self, config_file_or_dict):
# set global weakref object
set_hf_deepspeed_config(self)
dep_version_check("accelerate")
dep_version_check("deepspeed")
super().__init__(config_file_or_dict)
class HfTrainerDeepSpeedConfig(HfDeepSpeedConfig):
"""
The `HfTrainerDeepSpeedConfig` object is meant to be created during `TrainingArguments` object creation and has the
same lifespan as the latter.
"""
def __init__(self, config_file_or_dict):
super().__init__(config_file_or_dict)
self._dtype = None
self.mismatches = []
def dtype(self):
if self._dtype is None:
raise ValueError("trainer_config_process() wasn't called yet to tell dtype")
return self._dtype
def is_auto(self, ds_key_long):
val = self.get_value(ds_key_long)
if val is None:
return False
else:
return val == "auto"
def fill_match(self, ds_key_long, hf_val, hf_key=None, must_match=True):
"""
A utility method that massages the config file and can optionally verify that the values match.
1. Replace "auto" values with `TrainingArguments` value.
2. If it wasn't "auto" and `must_match` is true, then check that DS config matches Trainer
config values and if mismatched add the entry to `self.mismatched` - will assert during
`trainer_config_finalize` for one or more mismatches.
"""
config, ds_key = self.find_config_node(ds_key_long)
if config is None:
return
if config.get(ds_key) == "auto":
config[ds_key] = hf_val
return
if not must_match:
return
ds_val = config.get(ds_key)
if ds_val is not None and ds_val != hf_val:
self.mismatches.append(f"- ds {ds_key_long}={ds_val} vs hf {hf_key}={hf_val}")
fill_only = partialmethod(fill_match, must_match=False)
def trainer_config_process(self, args, auto_find_batch_size=False):
"""
Adjust the config with `TrainingArguments` values. This stage is run during `TrainingArguments` object
creation.
"""
# DeepSpeed does:
# train_batch_size = world_size * train_micro_batch_size_per_gpu * gradient_accumulation_steps
train_batch_size = args.world_size * args.per_device_train_batch_size * args.gradient_accumulation_steps
self.fill_match(
"train_micro_batch_size_per_gpu",
args.per_device_train_batch_size,
"per_device_train_batch_size",
not auto_find_batch_size,
)
self.fill_match(
"gradient_accumulation_steps",
args.gradient_accumulation_steps,
"gradient_accumulation_steps",
)
self.fill_match(
"train_batch_size",
train_batch_size,
"train_batch_size (calculated)",
not auto_find_batch_size,
)
self.fill_match("gradient_clipping", args.max_grad_norm, "max_grad_norm")
self.fill_match("optimizer.params.lr", args.learning_rate, "learning_rate")
self.fill_match(
"optimizer.params.betas",
[args.adam_beta1, args.adam_beta2],
"adam_beta1+adam_beta2",
)
self.fill_match("optimizer.params.eps", args.adam_epsilon, "adam_epsilon")
self.fill_match("optimizer.params.weight_decay", args.weight_decay, "weight_decay")
self.fill_only("scheduler.params.warmup_min_lr", 0) # not a trainer arg
self.fill_match("scheduler.params.warmup_max_lr", args.learning_rate, "learning_rate")
# total_num_steps - will get set in trainer_config_finalize
# fp16
if args.fp16 or args.fp16_full_eval:
fp16_backend = "apex" if args.fp16_backend == "apex" else "amp"
else:
fp16_backend = None
if args.save_on_each_node:
# deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
self.config["checkpoint"] = self.config.get("checkpoint", {})
self.config["checkpoint"]["use_node_local_storage"] = args.save_on_each_node
# amp: similar to the pytorch native amp - it has a bunch of optional params but we won't set
# any here unless the user did the work
self.fill_match(
"fp16.enabled",
((args.fp16 or args.fp16_full_eval) and fp16_backend == "amp"),
"fp16|fp16_full_eval+fp16_backend(amp)",
)
# apex: delegates amp work to apex (which needs to be available), but it cannot be used with any
# ZeRO features
self.fill_match("amp.enabled", fp16_backend == "apex", "fp16+fp16_backend(apex)")
self.fill_match("amp.opt_level", args.fp16_opt_level, "fp16_opt_level")
self.fill_match("bf16.enabled", (args.bf16 or args.bf16_full_eval), "bf16|bf16_full_eval")
# deepspeed's default mode is fp16 unless there is a config that says differently
if self.is_true("bf16.enabled"):
self._dtype = torch.bfloat16
elif self.is_false("fp16.enabled"):
self._dtype = torch.float32
else:
self._dtype = torch.float16
def trainer_config_finalize(self, args, model, num_training_steps):
"""
This stage is run after we have the model and know num_training_steps.
Now we can complete the configuration process.
"""
# zero
# deal with config keys that use `auto` value and rely on model's hidden_size
hidden_size_based_keys = [
"zero_optimization.reduce_bucket_size",
"zero_optimization.stage3_prefetch_bucket_size",
"zero_optimization.stage3_param_persistence_threshold",
]
hidden_size_auto_keys = [x for x in hidden_size_based_keys if self.is_auto(x)]
if len(hidden_size_auto_keys) > 0:
if hasattr(model.config, "hidden_size"):
hidden_size = model.config.hidden_size
elif hasattr(model.config, "hidden_sizes"):
# if there are many hidden sizes pick the largest one
hidden_size = max(model.config.hidden_sizes)
else:
raise ValueError(
"The model's config file has neither `hidden_size` nor `hidden_sizes` entry, "
"therefore it's not possible to automatically fill out the following `auto` entries "
f"in the DeepSpeed config file: {hidden_size_auto_keys}. You can fix that by replacing "
"`auto` values for these keys with an integer value of your choice."
)
self.fill_only("zero_optimization.reduce_bucket_size", hidden_size * hidden_size)
if self.is_zero3():
# automatically assign the optimal config values based on model config
self.fill_only(
"zero_optimization.stage3_prefetch_bucket_size",
0.9 * hidden_size * hidden_size,
)
self.fill_only(
"zero_optimization.stage3_param_persistence_threshold",
10 * hidden_size,
)
# scheduler
self.fill_match(
"scheduler.params.total_num_steps",
num_training_steps,
"num_training_steps (calculated)",
)
self.fill_match(
"scheduler.params.warmup_num_steps",
args.get_warmup_steps(num_training_steps),
"warmup_steps",
)
if len(self.mismatches) > 0:
mismatches = "\n".join(self.mismatches)
raise ValueError(
"Please correct the following DeepSpeed config values that mismatch TrainingArguments"
f" values:\n{mismatches}\nThe easiest method is to set these DeepSpeed config values to 'auto'."
)
# keep the config object global to be able to access it anywhere during TrainingArguments life-cycle
_hf_deepspeed_config_weak_ref = None
def set_hf_deepspeed_config(hf_deepspeed_config_obj):
# this is a special weakref global object to allow us to get to Deepspeed config from APIs
# that don't have an easy way to get to the Deepspeed config outside of the Trainer domain.
global _hf_deepspeed_config_weak_ref
# will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
_hf_deepspeed_config_weak_ref = weakref.ref(hf_deepspeed_config_obj)
def unset_hf_deepspeed_config():
# useful for unit tests to ensure the global state doesn't leak - call from `tearDown` method
global _hf_deepspeed_config_weak_ref
_hf_deepspeed_config_weak_ref = None
def is_deepspeed_zero3_enabled():
if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
return _hf_deepspeed_config_weak_ref().is_zero3()
else:
return False
def deepspeed_config():
if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
return _hf_deepspeed_config_weak_ref().config
else:
return None
def deepspeed_optim_sched(trainer, hf_deepspeed_config, args, num_training_steps, model_parameters):
"""
A convenience wrapper that deals with optimizer and lr scheduler configuration.
"""
from accelerate.utils import DummyOptim, DummyScheduler
config = hf_deepspeed_config.config
# Mixing and matching DS schedulers and optimizers is supported unless Offload is enabled in which case it's:
# 1. DS scheduler + DS optimizer: Yes
# 2. HF scheduler + HF optimizer: Mostly*
# 3. DS scheduler + HF optimizer: Mostly*
# 4. HF scheduler + DS optimizer: Yes
#
# Mostly*: All non-native DeepSpeed optimizers that have both CPU and GPU implementation should work (except LAMB)
optimizer = None
if "optimizer" in config:
if args.adafactor:
raise ValueError(
"--adafactor was passed, but also found `optimizer` configured in the DeepSpeed config. "
"Only one optimizer can be configured."
)
optimizer = DummyOptim(params=model_parameters)
else:
if hf_deepspeed_config.is_offload():
logger.info(
"Detected ZeRO Offload and non-DeepSpeed optimizers: This combination should work as long as the"
" custom optimizer has both CPU and GPU implementation (except LAMB)"
)
# ds supports Adam, OneBitAdam, and Lamb optimizers and can import other optimizers from torch.
# But trainer uses AdamW by default.
optimizer = trainer.create_optimizer()
# To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
config["zero_allow_untested_optimizer"] = True
lr_scheduler = None
if "scheduler" in config:
lr_scheduler = DummyScheduler(optimizer)
else:
if isinstance(optimizer, DummyOptim):
def _lr_scheduler_callable(optimizer):
# create a shallow copy first, so later modifications do not affect original trainer
trainer_copy = copy.copy(trainer)
# at the time _lr_scheduler_callable is called, trainer.lr_scheduler has been set
# update it to None so that we can re-create a new scheduler
trainer_copy.lr_scheduler = None
lr_scheduler = trainer_copy.create_scheduler(
num_training_steps=num_training_steps, optimizer=optimizer
)
return lr_scheduler
lr_scheduler = DummyScheduler(optimizer, lr_scheduler_callable=_lr_scheduler_callable)
else:
lr_scheduler = trainer.create_scheduler(num_training_steps=num_training_steps, optimizer=optimizer)
return optimizer, lr_scheduler
def deepspeed_init(trainer, num_training_steps, inference=False):
"""
Init DeepSpeed, after updating the DeepSpeed configuration with any relevant Trainer's args.
If `resume_from_checkpoint` was passed then an attempt to resume from a previously saved checkpoint will be made.
Args:
trainer: Trainer object
num_training_steps: per single gpu
resume_from_checkpoint: path to a checkpoint if to resume from after normal DeepSpeedEngine load
inference: launch in inference mode (no optimizer and no lr scheduler)
auto_find_batch_size: whether to ignore the `train_micro_batch_size_per_gpu` argument as it's being
set automatically by the auto batch size finder
Returns: optimizer, lr_scheduler
We may use `deepspeed_init` more than once during the life of Trainer, when we do - it's a temp hack based on:
https://github.com/microsoft/DeepSpeed/issues/1394#issuecomment-937405374 until Deepspeed fixes a bug where it
can't resume from a checkpoint after it did some stepping https://github.com/microsoft/DeepSpeed/issues/1612
"""
from deepspeed.utils import logger as ds_logger
model = trainer.model
args = trainer.args
hf_deepspeed_config = trainer.accelerator.state.deepspeed_plugin.hf_ds_config
# resume config update - some bits like `model` and `num_training_steps` only become available during train
hf_deepspeed_config.trainer_config_finalize(args, model, num_training_steps)
# set the Deepspeed log level consistent with the Trainer
ds_logger.setLevel(args.get_process_log_level())
if inference:
# only Z3 makes sense for the inference
if not hf_deepspeed_config.is_zero3():
raise ValueError("ZeRO inference only makes sense with ZeRO Stage 3 - please adjust your config")
# in case the training config is re-used for inference
hf_deepspeed_config.del_config_sub_tree("optimizer")
hf_deepspeed_config.del_config_sub_tree("lr_scheduler")
optimizer, lr_scheduler = None, None
model_parameters = None
else:
trainer.optimizer = None # important for when deepspeed_init is used as re-init
model_parameters = list(filter(lambda p: p.requires_grad, model.parameters()))
optimizer, lr_scheduler = deepspeed_optim_sched(
trainer, hf_deepspeed_config, args, num_training_steps, model_parameters
)
# keep for quick debug:
# from pprint import pprint; pprint(config)
return optimizer, lr_scheduler
def deepspeed_load_checkpoint(deepspeed_engine, checkpoint_path, load_module_strict=True):
# it's possible that the user is trying to resume from model_path, which doesn't necessarily
# contain a deepspeed checkpoint. e.g. examples just check if the dir exists and assume it's
# a resume from a checkpoint and not just a local pretrained weight. So we check here if the
# path contains what looks like a deepspeed checkpoint
import glob
deepspeed_checkpoint_dirs = sorted(glob.glob(f"{checkpoint_path}/global_step*"))
if len(deepspeed_checkpoint_dirs) > 0:
logger.info(f"Attempting to resume from {checkpoint_path}")
# this magically updates self.optimizer and self.lr_scheduler
load_path, _ = deepspeed_engine.load_checkpoint(
checkpoint_path,
load_module_strict=load_module_strict,
load_optimizer_states=True,
load_lr_scheduler_states=True,
)
if load_path is None:
raise ValueError(f"[deepspeed] failed to resume from checkpoint {checkpoint_path}")
else:
raise ValueError(f"Can't find a valid checkpoint at {checkpoint_path}")
| transformers/src/transformers/integrations/deepspeed.py/0 | {
"file_path": "transformers/src/transformers/integrations/deepspeed.py",
"repo_id": "transformers",
"token_count": 7316
} | 69 |
/*!
**************************************************************************************************
* Deformable DETR
* Copyright (c) 2020 SenseTime. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
**************************************************************************************************
* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
**************************************************************************************************
*/
#include <vector>
#include <cuda.h>
#include <cuda_runtime.h>
#include <cstdio>
#include <algorithm>
#include <cstring>
#include <ATen/ATen.h>
#include <ATen/cuda/CUDAContext.h>
#include <THC/THCAtomics.cuh>
#define CUDA_KERNEL_LOOP(i, n) \
for (int i = blockIdx.x * blockDim.x + threadIdx.x; \
i < (n); \
i += blockDim.x * gridDim.x)
at::Tensor ms_deform_attn_cuda_forward(
const at::Tensor &value,
const at::Tensor &spatial_shapes,
const at::Tensor &level_start_index,
const at::Tensor &sampling_loc,
const at::Tensor &attn_weight,
const int im2col_step)
{
AT_ASSERTM(value.is_contiguous(), "value tensor has to be contiguous");
AT_ASSERTM(spatial_shapes.is_contiguous(), "spatial_shapes tensor has to be contiguous");
AT_ASSERTM(level_start_index.is_contiguous(), "level_start_index tensor has to be contiguous");
AT_ASSERTM(sampling_loc.is_contiguous(), "sampling_loc tensor has to be contiguous");
AT_ASSERTM(attn_weight.is_contiguous(), "attn_weight tensor has to be contiguous");
AT_ASSERTM(value.type().is_cuda(), "value must be a CUDA tensor");
AT_ASSERTM(spatial_shapes.type().is_cuda(), "spatial_shapes must be a CUDA tensor");
AT_ASSERTM(level_start_index.type().is_cuda(), "level_start_index must be a CUDA tensor");
AT_ASSERTM(sampling_loc.type().is_cuda(), "sampling_loc must be a CUDA tensor");
AT_ASSERTM(attn_weight.type().is_cuda(), "attn_weight must be a CUDA tensor");
const int batch = value.size(0);
const int spatial_size = value.size(1);
const int num_heads = value.size(2);
const int channels = value.size(3);
const int num_levels = spatial_shapes.size(0);
const int num_query = sampling_loc.size(1);
const int num_point = sampling_loc.size(4);
const int im2col_step_ = std::min(batch, im2col_step);
AT_ASSERTM(batch % im2col_step_ == 0, "batch(%d) must divide im2col_step(%d)", batch, im2col_step_);
auto output = at::zeros({batch, num_query, num_heads, channels}, value.options());
const int batch_n = im2col_step_;
auto output_n = output.view({batch/im2col_step_, batch_n, num_query, num_heads, channels});
auto per_value_size = spatial_size * num_heads * channels;
auto per_sample_loc_size = num_query * num_heads * num_levels * num_point * 2;
auto per_attn_weight_size = num_query * num_heads * num_levels * num_point;
for (int n = 0; n < batch/im2col_step_; ++n)
{
auto columns = output_n.select(0, n);
AT_DISPATCH_FLOATING_TYPES(value.type(), "ms_deform_attn_forward_cuda", ([&] {
ms_deformable_im2col_cuda(at::cuda::getCurrentCUDAStream(),
value.data<scalar_t>() + n * im2col_step_ * per_value_size,
spatial_shapes.data<int64_t>(),
level_start_index.data<int64_t>(),
sampling_loc.data<scalar_t>() + n * im2col_step_ * per_sample_loc_size,
attn_weight.data<scalar_t>() + n * im2col_step_ * per_attn_weight_size,
batch_n, spatial_size, num_heads, channels, num_levels, num_query, num_point,
columns.data<scalar_t>());
}));
}
output = output.view({batch, num_query, num_heads*channels});
return output;
}
std::vector<at::Tensor> ms_deform_attn_cuda_backward(
const at::Tensor &value,
const at::Tensor &spatial_shapes,
const at::Tensor &level_start_index,
const at::Tensor &sampling_loc,
const at::Tensor &attn_weight,
const at::Tensor &grad_output,
const int im2col_step)
{
AT_ASSERTM(value.is_contiguous(), "value tensor has to be contiguous");
AT_ASSERTM(spatial_shapes.is_contiguous(), "spatial_shapes tensor has to be contiguous");
AT_ASSERTM(level_start_index.is_contiguous(), "level_start_index tensor has to be contiguous");
AT_ASSERTM(sampling_loc.is_contiguous(), "sampling_loc tensor has to be contiguous");
AT_ASSERTM(attn_weight.is_contiguous(), "attn_weight tensor has to be contiguous");
AT_ASSERTM(grad_output.is_contiguous(), "grad_output tensor has to be contiguous");
AT_ASSERTM(value.type().is_cuda(), "value must be a CUDA tensor");
AT_ASSERTM(spatial_shapes.type().is_cuda(), "spatial_shapes must be a CUDA tensor");
AT_ASSERTM(level_start_index.type().is_cuda(), "level_start_index must be a CUDA tensor");
AT_ASSERTM(sampling_loc.type().is_cuda(), "sampling_loc must be a CUDA tensor");
AT_ASSERTM(attn_weight.type().is_cuda(), "attn_weight must be a CUDA tensor");
AT_ASSERTM(grad_output.type().is_cuda(), "grad_output must be a CUDA tensor");
const int batch = value.size(0);
const int spatial_size = value.size(1);
const int num_heads = value.size(2);
const int channels = value.size(3);
const int num_levels = spatial_shapes.size(0);
const int num_query = sampling_loc.size(1);
const int num_point = sampling_loc.size(4);
const int im2col_step_ = std::min(batch, im2col_step);
AT_ASSERTM(batch % im2col_step_ == 0, "batch(%d) must divide im2col_step(%d)", batch, im2col_step_);
auto grad_value = at::zeros_like(value);
auto grad_sampling_loc = at::zeros_like(sampling_loc);
auto grad_attn_weight = at::zeros_like(attn_weight);
const int batch_n = im2col_step_;
auto per_value_size = spatial_size * num_heads * channels;
auto per_sample_loc_size = num_query * num_heads * num_levels * num_point * 2;
auto per_attn_weight_size = num_query * num_heads * num_levels * num_point;
auto grad_output_n = grad_output.view({batch/im2col_step_, batch_n, num_query, num_heads, channels});
for (int n = 0; n < batch/im2col_step_; ++n)
{
auto grad_output_g = grad_output_n.select(0, n);
AT_DISPATCH_FLOATING_TYPES(value.type(), "ms_deform_attn_backward_cuda", ([&] {
ms_deformable_col2im_cuda(at::cuda::getCurrentCUDAStream(),
grad_output_g.data<scalar_t>(),
value.data<scalar_t>() + n * im2col_step_ * per_value_size,
spatial_shapes.data<int64_t>(),
level_start_index.data<int64_t>(),
sampling_loc.data<scalar_t>() + n * im2col_step_ * per_sample_loc_size,
attn_weight.data<scalar_t>() + n * im2col_step_ * per_attn_weight_size,
batch_n, spatial_size, num_heads, channels, num_levels, num_query, num_point,
grad_value.data<scalar_t>() + n * im2col_step_ * per_value_size,
grad_sampling_loc.data<scalar_t>() + n * im2col_step_ * per_sample_loc_size,
grad_attn_weight.data<scalar_t>() + n * im2col_step_ * per_attn_weight_size);
}));
}
return {
grad_value, grad_sampling_loc, grad_attn_weight
};
}
const int CUDA_NUM_THREADS = 1024;
inline int GET_BLOCKS(const int N, const int num_threads)
{
return (N + num_threads - 1) / num_threads;
}
template <typename scalar_t>
__device__ scalar_t ms_deform_attn_im2col_bilinear(const scalar_t* &bottom_data,
const int &height, const int &width, const int &nheads, const int &channels,
const scalar_t &h, const scalar_t &w, const int &m, const int &c)
{
const int h_low = floor(h);
const int w_low = floor(w);
const int h_high = h_low + 1;
const int w_high = w_low + 1;
const scalar_t lh = h - h_low;
const scalar_t lw = w - w_low;
const scalar_t hh = 1 - lh, hw = 1 - lw;
const int w_stride = nheads * channels;
const int h_stride = width * w_stride;
const int h_low_ptr_offset = h_low * h_stride;
const int h_high_ptr_offset = h_low_ptr_offset + h_stride;
const int w_low_ptr_offset = w_low * w_stride;
const int w_high_ptr_offset = w_low_ptr_offset + w_stride;
const int base_ptr = m * channels + c;
scalar_t v1 = 0;
if (h_low >= 0 && w_low >= 0)
{
const int ptr1 = h_low_ptr_offset + w_low_ptr_offset + base_ptr;
v1 = bottom_data[ptr1];
}
scalar_t v2 = 0;
if (h_low >= 0 && w_high <= width - 1)
{
const int ptr2 = h_low_ptr_offset + w_high_ptr_offset + base_ptr;
v2 = bottom_data[ptr2];
}
scalar_t v3 = 0;
if (h_high <= height - 1 && w_low >= 0)
{
const int ptr3 = h_high_ptr_offset + w_low_ptr_offset + base_ptr;
v3 = bottom_data[ptr3];
}
scalar_t v4 = 0;
if (h_high <= height - 1 && w_high <= width - 1)
{
const int ptr4 = h_high_ptr_offset + w_high_ptr_offset + base_ptr;
v4 = bottom_data[ptr4];
}
const scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw;
const scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4);
return val;
}
template <typename scalar_t>
__device__ void ms_deform_attn_col2im_bilinear(const scalar_t* &bottom_data,
const int &height, const int &width, const int &nheads, const int &channels,
const scalar_t &h, const scalar_t &w, const int &m, const int &c,
const scalar_t &top_grad,
const scalar_t &attn_weight,
scalar_t* &grad_value,
scalar_t* grad_sampling_loc,
scalar_t* grad_attn_weight)
{
const int h_low = floor(h);
const int w_low = floor(w);
const int h_high = h_low + 1;
const int w_high = w_low + 1;
const scalar_t lh = h - h_low;
const scalar_t lw = w - w_low;
const scalar_t hh = 1 - lh, hw = 1 - lw;
const int w_stride = nheads * channels;
const int h_stride = width * w_stride;
const int h_low_ptr_offset = h_low * h_stride;
const int h_high_ptr_offset = h_low_ptr_offset + h_stride;
const int w_low_ptr_offset = w_low * w_stride;
const int w_high_ptr_offset = w_low_ptr_offset + w_stride;
const int base_ptr = m * channels + c;
const scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw;
const scalar_t top_grad_value = top_grad * attn_weight;
scalar_t grad_h_weight = 0, grad_w_weight = 0;
scalar_t v1 = 0;
if (h_low >= 0 && w_low >= 0)
{
const int ptr1 = h_low_ptr_offset + w_low_ptr_offset + base_ptr;
v1 = bottom_data[ptr1];
grad_h_weight -= hw * v1;
grad_w_weight -= hh * v1;
atomicAdd(grad_value+ptr1, w1*top_grad_value);
}
scalar_t v2 = 0;
if (h_low >= 0 && w_high <= width - 1)
{
const int ptr2 = h_low_ptr_offset + w_high_ptr_offset + base_ptr;
v2 = bottom_data[ptr2];
grad_h_weight -= lw * v2;
grad_w_weight += hh * v2;
atomicAdd(grad_value+ptr2, w2*top_grad_value);
}
scalar_t v3 = 0;
if (h_high <= height - 1 && w_low >= 0)
{
const int ptr3 = h_high_ptr_offset + w_low_ptr_offset + base_ptr;
v3 = bottom_data[ptr3];
grad_h_weight += hw * v3;
grad_w_weight -= lh * v3;
atomicAdd(grad_value+ptr3, w3*top_grad_value);
}
scalar_t v4 = 0;
if (h_high <= height - 1 && w_high <= width - 1)
{
const int ptr4 = h_high_ptr_offset + w_high_ptr_offset + base_ptr;
v4 = bottom_data[ptr4];
grad_h_weight += lw * v4;
grad_w_weight += lh * v4;
atomicAdd(grad_value+ptr4, w4*top_grad_value);
}
const scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4);
*grad_attn_weight = top_grad * val;
*grad_sampling_loc = width * grad_w_weight * top_grad_value;
*(grad_sampling_loc + 1) = height * grad_h_weight * top_grad_value;
}
template <typename scalar_t>
__device__ void ms_deform_attn_col2im_bilinear_gm(const scalar_t* &bottom_data,
const int &height, const int &width, const int &nheads, const int &channels,
const scalar_t &h, const scalar_t &w, const int &m, const int &c,
const scalar_t &top_grad,
const scalar_t &attn_weight,
scalar_t* &grad_value,
scalar_t* grad_sampling_loc,
scalar_t* grad_attn_weight)
{
const int h_low = floor(h);
const int w_low = floor(w);
const int h_high = h_low + 1;
const int w_high = w_low + 1;
const scalar_t lh = h - h_low;
const scalar_t lw = w - w_low;
const scalar_t hh = 1 - lh, hw = 1 - lw;
const int w_stride = nheads * channels;
const int h_stride = width * w_stride;
const int h_low_ptr_offset = h_low * h_stride;
const int h_high_ptr_offset = h_low_ptr_offset + h_stride;
const int w_low_ptr_offset = w_low * w_stride;
const int w_high_ptr_offset = w_low_ptr_offset + w_stride;
const int base_ptr = m * channels + c;
const scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw;
const scalar_t top_grad_value = top_grad * attn_weight;
scalar_t grad_h_weight = 0, grad_w_weight = 0;
scalar_t v1 = 0;
if (h_low >= 0 && w_low >= 0)
{
const int ptr1 = h_low_ptr_offset + w_low_ptr_offset + base_ptr;
v1 = bottom_data[ptr1];
grad_h_weight -= hw * v1;
grad_w_weight -= hh * v1;
atomicAdd(grad_value+ptr1, w1*top_grad_value);
}
scalar_t v2 = 0;
if (h_low >= 0 && w_high <= width - 1)
{
const int ptr2 = h_low_ptr_offset + w_high_ptr_offset + base_ptr;
v2 = bottom_data[ptr2];
grad_h_weight -= lw * v2;
grad_w_weight += hh * v2;
atomicAdd(grad_value+ptr2, w2*top_grad_value);
}
scalar_t v3 = 0;
if (h_high <= height - 1 && w_low >= 0)
{
const int ptr3 = h_high_ptr_offset + w_low_ptr_offset + base_ptr;
v3 = bottom_data[ptr3];
grad_h_weight += hw * v3;
grad_w_weight -= lh * v3;
atomicAdd(grad_value+ptr3, w3*top_grad_value);
}
scalar_t v4 = 0;
if (h_high <= height - 1 && w_high <= width - 1)
{
const int ptr4 = h_high_ptr_offset + w_high_ptr_offset + base_ptr;
v4 = bottom_data[ptr4];
grad_h_weight += lw * v4;
grad_w_weight += lh * v4;
atomicAdd(grad_value+ptr4, w4*top_grad_value);
}
const scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4);
atomicAdd(grad_attn_weight, top_grad * val);
atomicAdd(grad_sampling_loc, width * grad_w_weight * top_grad_value);
atomicAdd(grad_sampling_loc + 1, height * grad_h_weight * top_grad_value);
}
template <typename scalar_t>
__global__ void ms_deformable_im2col_gpu_kernel(const int n,
const scalar_t *data_value,
const int64_t *data_spatial_shapes,
const int64_t *data_level_start_index,
const scalar_t *data_sampling_loc,
const scalar_t *data_attn_weight,
const int batch_size,
const int spatial_size,
const int num_heads,
const int channels,
const int num_levels,
const int num_query,
const int num_point,
scalar_t *data_col)
{
CUDA_KERNEL_LOOP(index, n)
{
int _temp = index;
const int c_col = _temp % channels;
_temp /= channels;
const int sampling_index = _temp;
const int m_col = _temp % num_heads;
_temp /= num_heads;
const int q_col = _temp % num_query;
_temp /= num_query;
const int b_col = _temp;
scalar_t *data_col_ptr = data_col + index;
int data_weight_ptr = sampling_index * num_levels * num_point;
int data_loc_w_ptr = data_weight_ptr << 1;
const int qid_stride = num_heads * channels;
const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;
scalar_t col = 0;
for (int l_col=0; l_col < num_levels; ++l_col)
{
const int level_start_id = data_level_start_index[l_col];
const int spatial_h_ptr = l_col << 1;
const int spatial_h = data_spatial_shapes[spatial_h_ptr];
const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];
const scalar_t *data_value_ptr = data_value + (data_value_ptr_init_offset + level_start_id * qid_stride);
for (int p_col=0; p_col < num_point; ++p_col)
{
const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];
const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];
const scalar_t weight = data_attn_weight[data_weight_ptr];
const scalar_t h_im = loc_h * spatial_h - 0.5;
const scalar_t w_im = loc_w * spatial_w - 0.5;
if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)
{
col += ms_deform_attn_im2col_bilinear(data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col) * weight;
}
data_weight_ptr += 1;
data_loc_w_ptr += 2;
}
}
*data_col_ptr = col;
}
}
template <typename scalar_t, unsigned int blockSize>
__global__ void ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1(const int n,
const scalar_t *grad_col,
const scalar_t *data_value,
const int64_t *data_spatial_shapes,
const int64_t *data_level_start_index,
const scalar_t *data_sampling_loc,
const scalar_t *data_attn_weight,
const int batch_size,
const int spatial_size,
const int num_heads,
const int channels,
const int num_levels,
const int num_query,
const int num_point,
scalar_t *grad_value,
scalar_t *grad_sampling_loc,
scalar_t *grad_attn_weight)
{
CUDA_KERNEL_LOOP(index, n)
{
__shared__ scalar_t cache_grad_sampling_loc[blockSize * 2];
__shared__ scalar_t cache_grad_attn_weight[blockSize];
unsigned int tid = threadIdx.x;
int _temp = index;
const int c_col = _temp % channels;
_temp /= channels;
const int sampling_index = _temp;
const int m_col = _temp % num_heads;
_temp /= num_heads;
const int q_col = _temp % num_query;
_temp /= num_query;
const int b_col = _temp;
const scalar_t top_grad = grad_col[index];
int data_weight_ptr = sampling_index * num_levels * num_point;
int data_loc_w_ptr = data_weight_ptr << 1;
const int grad_sampling_ptr = data_weight_ptr;
grad_sampling_loc += grad_sampling_ptr << 1;
grad_attn_weight += grad_sampling_ptr;
const int grad_weight_stride = 1;
const int grad_loc_stride = 2;
const int qid_stride = num_heads * channels;
const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;
for (int l_col=0; l_col < num_levels; ++l_col)
{
const int level_start_id = data_level_start_index[l_col];
const int spatial_h_ptr = l_col << 1;
const int spatial_h = data_spatial_shapes[spatial_h_ptr];
const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];
const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;
const scalar_t *data_value_ptr = data_value + value_ptr_offset;
scalar_t *grad_value_ptr = grad_value + value_ptr_offset;
for (int p_col=0; p_col < num_point; ++p_col)
{
const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];
const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];
const scalar_t weight = data_attn_weight[data_weight_ptr];
const scalar_t h_im = loc_h * spatial_h - 0.5;
const scalar_t w_im = loc_w * spatial_w - 0.5;
*(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0;
*(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0;
*(cache_grad_attn_weight+threadIdx.x)=0;
if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)
{
ms_deform_attn_col2im_bilinear(
data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col,
top_grad, weight, grad_value_ptr,
cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x);
}
__syncthreads();
if (tid == 0)
{
scalar_t _grad_w=cache_grad_sampling_loc[0], _grad_h=cache_grad_sampling_loc[1], _grad_a=cache_grad_attn_weight[0];
int sid=2;
for (unsigned int tid = 1; tid < blockSize; ++tid)
{
_grad_w += cache_grad_sampling_loc[sid];
_grad_h += cache_grad_sampling_loc[sid + 1];
_grad_a += cache_grad_attn_weight[tid];
sid += 2;
}
*grad_sampling_loc = _grad_w;
*(grad_sampling_loc + 1) = _grad_h;
*grad_attn_weight = _grad_a;
}
__syncthreads();
data_weight_ptr += 1;
data_loc_w_ptr += 2;
grad_attn_weight += grad_weight_stride;
grad_sampling_loc += grad_loc_stride;
}
}
}
}
template <typename scalar_t, unsigned int blockSize>
__global__ void ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2(const int n,
const scalar_t *grad_col,
const scalar_t *data_value,
const int64_t *data_spatial_shapes,
const int64_t *data_level_start_index,
const scalar_t *data_sampling_loc,
const scalar_t *data_attn_weight,
const int batch_size,
const int spatial_size,
const int num_heads,
const int channels,
const int num_levels,
const int num_query,
const int num_point,
scalar_t *grad_value,
scalar_t *grad_sampling_loc,
scalar_t *grad_attn_weight)
{
CUDA_KERNEL_LOOP(index, n)
{
__shared__ scalar_t cache_grad_sampling_loc[blockSize * 2];
__shared__ scalar_t cache_grad_attn_weight[blockSize];
unsigned int tid = threadIdx.x;
int _temp = index;
const int c_col = _temp % channels;
_temp /= channels;
const int sampling_index = _temp;
const int m_col = _temp % num_heads;
_temp /= num_heads;
const int q_col = _temp % num_query;
_temp /= num_query;
const int b_col = _temp;
const scalar_t top_grad = grad_col[index];
int data_weight_ptr = sampling_index * num_levels * num_point;
int data_loc_w_ptr = data_weight_ptr << 1;
const int grad_sampling_ptr = data_weight_ptr;
grad_sampling_loc += grad_sampling_ptr << 1;
grad_attn_weight += grad_sampling_ptr;
const int grad_weight_stride = 1;
const int grad_loc_stride = 2;
const int qid_stride = num_heads * channels;
const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;
for (int l_col=0; l_col < num_levels; ++l_col)
{
const int level_start_id = data_level_start_index[l_col];
const int spatial_h_ptr = l_col << 1;
const int spatial_h = data_spatial_shapes[spatial_h_ptr];
const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];
const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;
const scalar_t *data_value_ptr = data_value + value_ptr_offset;
scalar_t *grad_value_ptr = grad_value + value_ptr_offset;
for (int p_col=0; p_col < num_point; ++p_col)
{
const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];
const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];
const scalar_t weight = data_attn_weight[data_weight_ptr];
const scalar_t h_im = loc_h * spatial_h - 0.5;
const scalar_t w_im = loc_w * spatial_w - 0.5;
*(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0;
*(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0;
*(cache_grad_attn_weight+threadIdx.x)=0;
if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)
{
ms_deform_attn_col2im_bilinear(
data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col,
top_grad, weight, grad_value_ptr,
cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x);
}
__syncthreads();
for (unsigned int s=blockSize/2; s>0; s>>=1)
{
if (tid < s) {
const unsigned int xid1 = tid << 1;
const unsigned int xid2 = (tid + s) << 1;
cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + s];
cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2];
cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1];
}
__syncthreads();
}
if (tid == 0)
{
*grad_sampling_loc = cache_grad_sampling_loc[0];
*(grad_sampling_loc + 1) = cache_grad_sampling_loc[1];
*grad_attn_weight = cache_grad_attn_weight[0];
}
__syncthreads();
data_weight_ptr += 1;
data_loc_w_ptr += 2;
grad_attn_weight += grad_weight_stride;
grad_sampling_loc += grad_loc_stride;
}
}
}
}
template <typename scalar_t>
__global__ void ms_deformable_col2im_gpu_kernel_shm_reduce_v1(const int n,
const scalar_t *grad_col,
const scalar_t *data_value,
const int64_t *data_spatial_shapes,
const int64_t *data_level_start_index,
const scalar_t *data_sampling_loc,
const scalar_t *data_attn_weight,
const int batch_size,
const int spatial_size,
const int num_heads,
const int channels,
const int num_levels,
const int num_query,
const int num_point,
scalar_t *grad_value,
scalar_t *grad_sampling_loc,
scalar_t *grad_attn_weight)
{
CUDA_KERNEL_LOOP(index, n)
{
extern __shared__ int _s[];
scalar_t* cache_grad_sampling_loc = (scalar_t*)_s;
scalar_t* cache_grad_attn_weight = cache_grad_sampling_loc + 2 * blockDim.x;
unsigned int tid = threadIdx.x;
int _temp = index;
const int c_col = _temp % channels;
_temp /= channels;
const int sampling_index = _temp;
const int m_col = _temp % num_heads;
_temp /= num_heads;
const int q_col = _temp % num_query;
_temp /= num_query;
const int b_col = _temp;
const scalar_t top_grad = grad_col[index];
int data_weight_ptr = sampling_index * num_levels * num_point;
int data_loc_w_ptr = data_weight_ptr << 1;
const int grad_sampling_ptr = data_weight_ptr;
grad_sampling_loc += grad_sampling_ptr << 1;
grad_attn_weight += grad_sampling_ptr;
const int grad_weight_stride = 1;
const int grad_loc_stride = 2;
const int qid_stride = num_heads * channels;
const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;
for (int l_col=0; l_col < num_levels; ++l_col)
{
const int level_start_id = data_level_start_index[l_col];
const int spatial_h_ptr = l_col << 1;
const int spatial_h = data_spatial_shapes[spatial_h_ptr];
const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];
const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;
const scalar_t *data_value_ptr = data_value + value_ptr_offset;
scalar_t *grad_value_ptr = grad_value + value_ptr_offset;
for (int p_col=0; p_col < num_point; ++p_col)
{
const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];
const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];
const scalar_t weight = data_attn_weight[data_weight_ptr];
const scalar_t h_im = loc_h * spatial_h - 0.5;
const scalar_t w_im = loc_w * spatial_w - 0.5;
*(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0;
*(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0;
*(cache_grad_attn_weight+threadIdx.x)=0;
if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)
{
ms_deform_attn_col2im_bilinear(
data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col,
top_grad, weight, grad_value_ptr,
cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x);
}
__syncthreads();
if (tid == 0)
{
scalar_t _grad_w=cache_grad_sampling_loc[0], _grad_h=cache_grad_sampling_loc[1], _grad_a=cache_grad_attn_weight[0];
int sid=2;
for (unsigned int tid = 1; tid < blockDim.x; ++tid)
{
_grad_w += cache_grad_sampling_loc[sid];
_grad_h += cache_grad_sampling_loc[sid + 1];
_grad_a += cache_grad_attn_weight[tid];
sid += 2;
}
*grad_sampling_loc = _grad_w;
*(grad_sampling_loc + 1) = _grad_h;
*grad_attn_weight = _grad_a;
}
__syncthreads();
data_weight_ptr += 1;
data_loc_w_ptr += 2;
grad_attn_weight += grad_weight_stride;
grad_sampling_loc += grad_loc_stride;
}
}
}
}
template <typename scalar_t>
__global__ void ms_deformable_col2im_gpu_kernel_shm_reduce_v2(const int n,
const scalar_t *grad_col,
const scalar_t *data_value,
const int64_t *data_spatial_shapes,
const int64_t *data_level_start_index,
const scalar_t *data_sampling_loc,
const scalar_t *data_attn_weight,
const int batch_size,
const int spatial_size,
const int num_heads,
const int channels,
const int num_levels,
const int num_query,
const int num_point,
scalar_t *grad_value,
scalar_t *grad_sampling_loc,
scalar_t *grad_attn_weight)
{
CUDA_KERNEL_LOOP(index, n)
{
extern __shared__ int _s[];
scalar_t* cache_grad_sampling_loc = (scalar_t*)_s;
scalar_t* cache_grad_attn_weight = cache_grad_sampling_loc + 2 * blockDim.x;
unsigned int tid = threadIdx.x;
int _temp = index;
const int c_col = _temp % channels;
_temp /= channels;
const int sampling_index = _temp;
const int m_col = _temp % num_heads;
_temp /= num_heads;
const int q_col = _temp % num_query;
_temp /= num_query;
const int b_col = _temp;
const scalar_t top_grad = grad_col[index];
int data_weight_ptr = sampling_index * num_levels * num_point;
int data_loc_w_ptr = data_weight_ptr << 1;
const int grad_sampling_ptr = data_weight_ptr;
grad_sampling_loc += grad_sampling_ptr << 1;
grad_attn_weight += grad_sampling_ptr;
const int grad_weight_stride = 1;
const int grad_loc_stride = 2;
const int qid_stride = num_heads * channels;
const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;
for (int l_col=0; l_col < num_levels; ++l_col)
{
const int level_start_id = data_level_start_index[l_col];
const int spatial_h_ptr = l_col << 1;
const int spatial_h = data_spatial_shapes[spatial_h_ptr];
const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];
const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;
const scalar_t *data_value_ptr = data_value + value_ptr_offset;
scalar_t *grad_value_ptr = grad_value + value_ptr_offset;
for (int p_col=0; p_col < num_point; ++p_col)
{
const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];
const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];
const scalar_t weight = data_attn_weight[data_weight_ptr];
const scalar_t h_im = loc_h * spatial_h - 0.5;
const scalar_t w_im = loc_w * spatial_w - 0.5;
*(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0;
*(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0;
*(cache_grad_attn_weight+threadIdx.x)=0;
if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)
{
ms_deform_attn_col2im_bilinear(
data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col,
top_grad, weight, grad_value_ptr,
cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x);
}
__syncthreads();
for (unsigned int s=blockDim.x/2, spre=blockDim.x; s>0; s>>=1, spre>>=1)
{
if (tid < s) {
const unsigned int xid1 = tid << 1;
const unsigned int xid2 = (tid + s) << 1;
cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + s];
cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2];
cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1];
if (tid + (s << 1) < spre)
{
cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + (s << 1)];
cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2 + (s << 1)];
cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1 + (s << 1)];
}
}
__syncthreads();
}
if (tid == 0)
{
*grad_sampling_loc = cache_grad_sampling_loc[0];
*(grad_sampling_loc + 1) = cache_grad_sampling_loc[1];
*grad_attn_weight = cache_grad_attn_weight[0];
}
__syncthreads();
data_weight_ptr += 1;
data_loc_w_ptr += 2;
grad_attn_weight += grad_weight_stride;
grad_sampling_loc += grad_loc_stride;
}
}
}
}
template <typename scalar_t>
__global__ void ms_deformable_col2im_gpu_kernel_shm_reduce_v2_multi_blocks(const int n,
const scalar_t *grad_col,
const scalar_t *data_value,
const int64_t *data_spatial_shapes,
const int64_t *data_level_start_index,
const scalar_t *data_sampling_loc,
const scalar_t *data_attn_weight,
const int batch_size,
const int spatial_size,
const int num_heads,
const int channels,
const int num_levels,
const int num_query,
const int num_point,
scalar_t *grad_value,
scalar_t *grad_sampling_loc,
scalar_t *grad_attn_weight)
{
CUDA_KERNEL_LOOP(index, n)
{
extern __shared__ int _s[];
scalar_t* cache_grad_sampling_loc = (scalar_t*)_s;
scalar_t* cache_grad_attn_weight = cache_grad_sampling_loc + 2 * blockDim.x;
unsigned int tid = threadIdx.x;
int _temp = index;
const int c_col = _temp % channels;
_temp /= channels;
const int sampling_index = _temp;
const int m_col = _temp % num_heads;
_temp /= num_heads;
const int q_col = _temp % num_query;
_temp /= num_query;
const int b_col = _temp;
const scalar_t top_grad = grad_col[index];
int data_weight_ptr = sampling_index * num_levels * num_point;
int data_loc_w_ptr = data_weight_ptr << 1;
const int grad_sampling_ptr = data_weight_ptr;
grad_sampling_loc += grad_sampling_ptr << 1;
grad_attn_weight += grad_sampling_ptr;
const int grad_weight_stride = 1;
const int grad_loc_stride = 2;
const int qid_stride = num_heads * channels;
const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;
for (int l_col=0; l_col < num_levels; ++l_col)
{
const int level_start_id = data_level_start_index[l_col];
const int spatial_h_ptr = l_col << 1;
const int spatial_h = data_spatial_shapes[spatial_h_ptr];
const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];
const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;
const scalar_t *data_value_ptr = data_value + value_ptr_offset;
scalar_t *grad_value_ptr = grad_value + value_ptr_offset;
for (int p_col=0; p_col < num_point; ++p_col)
{
const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];
const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];
const scalar_t weight = data_attn_weight[data_weight_ptr];
const scalar_t h_im = loc_h * spatial_h - 0.5;
const scalar_t w_im = loc_w * spatial_w - 0.5;
*(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0;
*(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0;
*(cache_grad_attn_weight+threadIdx.x)=0;
if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)
{
ms_deform_attn_col2im_bilinear(
data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col,
top_grad, weight, grad_value_ptr,
cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x);
}
__syncthreads();
for (unsigned int s=blockDim.x/2, spre=blockDim.x; s>0; s>>=1, spre>>=1)
{
if (tid < s) {
const unsigned int xid1 = tid << 1;
const unsigned int xid2 = (tid + s) << 1;
cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + s];
cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2];
cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1];
if (tid + (s << 1) < spre)
{
cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + (s << 1)];
cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2 + (s << 1)];
cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1 + (s << 1)];
}
}
__syncthreads();
}
if (tid == 0)
{
atomicAdd(grad_sampling_loc, cache_grad_sampling_loc[0]);
atomicAdd(grad_sampling_loc + 1, cache_grad_sampling_loc[1]);
atomicAdd(grad_attn_weight, cache_grad_attn_weight[0]);
}
__syncthreads();
data_weight_ptr += 1;
data_loc_w_ptr += 2;
grad_attn_weight += grad_weight_stride;
grad_sampling_loc += grad_loc_stride;
}
}
}
}
template <typename scalar_t>
__global__ void ms_deformable_col2im_gpu_kernel_gm(const int n,
const scalar_t *grad_col,
const scalar_t *data_value,
const int64_t *data_spatial_shapes,
const int64_t *data_level_start_index,
const scalar_t *data_sampling_loc,
const scalar_t *data_attn_weight,
const int batch_size,
const int spatial_size,
const int num_heads,
const int channels,
const int num_levels,
const int num_query,
const int num_point,
scalar_t *grad_value,
scalar_t *grad_sampling_loc,
scalar_t *grad_attn_weight)
{
CUDA_KERNEL_LOOP(index, n)
{
int _temp = index;
const int c_col = _temp % channels;
_temp /= channels;
const int sampling_index = _temp;
const int m_col = _temp % num_heads;
_temp /= num_heads;
const int q_col = _temp % num_query;
_temp /= num_query;
const int b_col = _temp;
const scalar_t top_grad = grad_col[index];
int data_weight_ptr = sampling_index * num_levels * num_point;
int data_loc_w_ptr = data_weight_ptr << 1;
const int grad_sampling_ptr = data_weight_ptr;
grad_sampling_loc += grad_sampling_ptr << 1;
grad_attn_weight += grad_sampling_ptr;
const int grad_weight_stride = 1;
const int grad_loc_stride = 2;
const int qid_stride = num_heads * channels;
const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;
for (int l_col=0; l_col < num_levels; ++l_col)
{
const int level_start_id = data_level_start_index[l_col];
const int spatial_h_ptr = l_col << 1;
const int spatial_h = data_spatial_shapes[spatial_h_ptr];
const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];
const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;
const scalar_t *data_value_ptr = data_value + value_ptr_offset;
scalar_t *grad_value_ptr = grad_value + value_ptr_offset;
for (int p_col=0; p_col < num_point; ++p_col)
{
const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];
const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];
const scalar_t weight = data_attn_weight[data_weight_ptr];
const scalar_t h_im = loc_h * spatial_h - 0.5;
const scalar_t w_im = loc_w * spatial_w - 0.5;
if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)
{
ms_deform_attn_col2im_bilinear_gm(
data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col,
top_grad, weight, grad_value_ptr,
grad_sampling_loc, grad_attn_weight);
}
data_weight_ptr += 1;
data_loc_w_ptr += 2;
grad_attn_weight += grad_weight_stride;
grad_sampling_loc += grad_loc_stride;
}
}
}
}
template <typename scalar_t>
void ms_deformable_im2col_cuda(cudaStream_t stream,
const scalar_t* data_value,
const int64_t* data_spatial_shapes,
const int64_t* data_level_start_index,
const scalar_t* data_sampling_loc,
const scalar_t* data_attn_weight,
const int batch_size,
const int spatial_size,
const int num_heads,
const int channels,
const int num_levels,
const int num_query,
const int num_point,
scalar_t* data_col)
{
const int num_kernels = batch_size * num_query * num_heads * channels;
const int num_actual_kernels = batch_size * num_query * num_heads * channels;
const int num_threads = CUDA_NUM_THREADS;
ms_deformable_im2col_gpu_kernel<scalar_t>
<<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
0, stream>>>(
num_kernels, data_value, data_spatial_shapes, data_level_start_index, data_sampling_loc, data_attn_weight,
batch_size, spatial_size, num_heads, channels, num_levels, num_query, num_point, data_col);
cudaError_t err = cudaGetLastError();
if (err != cudaSuccess)
{
printf("error in ms_deformable_im2col_cuda: %s\n", cudaGetErrorString(err));
}
}
template <typename scalar_t>
void ms_deformable_col2im_cuda(cudaStream_t stream,
const scalar_t* grad_col,
const scalar_t* data_value,
const int64_t * data_spatial_shapes,
const int64_t * data_level_start_index,
const scalar_t * data_sampling_loc,
const scalar_t * data_attn_weight,
const int batch_size,
const int spatial_size,
const int num_heads,
const int channels,
const int num_levels,
const int num_query,
const int num_point,
scalar_t* grad_value,
scalar_t* grad_sampling_loc,
scalar_t* grad_attn_weight)
{
const int num_threads = (channels > CUDA_NUM_THREADS)?CUDA_NUM_THREADS:channels;
const int num_kernels = batch_size * num_query * num_heads * channels;
const int num_actual_kernels = batch_size * num_query * num_heads * channels;
if (channels > 1024)
{
if ((channels & 1023) == 0)
{
ms_deformable_col2im_gpu_kernel_shm_reduce_v2_multi_blocks<scalar_t>
<<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
num_threads*3*sizeof(scalar_t), stream>>>(
num_kernels,
grad_col,
data_value,
data_spatial_shapes,
data_level_start_index,
data_sampling_loc,
data_attn_weight,
batch_size,
spatial_size,
num_heads,
channels,
num_levels,
num_query,
num_point,
grad_value,
grad_sampling_loc,
grad_attn_weight);
}
else
{
ms_deformable_col2im_gpu_kernel_gm<scalar_t>
<<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
0, stream>>>(
num_kernels,
grad_col,
data_value,
data_spatial_shapes,
data_level_start_index,
data_sampling_loc,
data_attn_weight,
batch_size,
spatial_size,
num_heads,
channels,
num_levels,
num_query,
num_point,
grad_value,
grad_sampling_loc,
grad_attn_weight);
}
}
else{
switch(channels)
{
case 1:
ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1<scalar_t, 1>
<<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
0, stream>>>(
num_kernels,
grad_col,
data_value,
data_spatial_shapes,
data_level_start_index,
data_sampling_loc,
data_attn_weight,
batch_size,
spatial_size,
num_heads,
channels,
num_levels,
num_query,
num_point,
grad_value,
grad_sampling_loc,
grad_attn_weight);
break;
case 2:
ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1<scalar_t, 2>
<<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
0, stream>>>(
num_kernels,
grad_col,
data_value,
data_spatial_shapes,
data_level_start_index,
data_sampling_loc,
data_attn_weight,
batch_size,
spatial_size,
num_heads,
channels,
num_levels,
num_query,
num_point,
grad_value,
grad_sampling_loc,
grad_attn_weight);
break;
case 4:
ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1<scalar_t, 4>
<<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
0, stream>>>(
num_kernels,
grad_col,
data_value,
data_spatial_shapes,
data_level_start_index,
data_sampling_loc,
data_attn_weight,
batch_size,
spatial_size,
num_heads,
channels,
num_levels,
num_query,
num_point,
grad_value,
grad_sampling_loc,
grad_attn_weight);
break;
case 8:
ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1<scalar_t, 8>
<<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
0, stream>>>(
num_kernels,
grad_col,
data_value,
data_spatial_shapes,
data_level_start_index,
data_sampling_loc,
data_attn_weight,
batch_size,
spatial_size,
num_heads,
channels,
num_levels,
num_query,
num_point,
grad_value,
grad_sampling_loc,
grad_attn_weight);
break;
case 16:
ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1<scalar_t, 16>
<<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
0, stream>>>(
num_kernels,
grad_col,
data_value,
data_spatial_shapes,
data_level_start_index,
data_sampling_loc,
data_attn_weight,
batch_size,
spatial_size,
num_heads,
channels,
num_levels,
num_query,
num_point,
grad_value,
grad_sampling_loc,
grad_attn_weight);
break;
case 32:
ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1<scalar_t, 32>
<<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
0, stream>>>(
num_kernels,
grad_col,
data_value,
data_spatial_shapes,
data_level_start_index,
data_sampling_loc,
data_attn_weight,
batch_size,
spatial_size,
num_heads,
channels,
num_levels,
num_query,
num_point,
grad_value,
grad_sampling_loc,
grad_attn_weight);
break;
case 64:
ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2<scalar_t, 64>
<<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
0, stream>>>(
num_kernels,
grad_col,
data_value,
data_spatial_shapes,
data_level_start_index,
data_sampling_loc,
data_attn_weight,
batch_size,
spatial_size,
num_heads,
channels,
num_levels,
num_query,
num_point,
grad_value,
grad_sampling_loc,
grad_attn_weight);
break;
case 128:
ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2<scalar_t, 128>
<<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
0, stream>>>(
num_kernels,
grad_col,
data_value,
data_spatial_shapes,
data_level_start_index,
data_sampling_loc,
data_attn_weight,
batch_size,
spatial_size,
num_heads,
channels,
num_levels,
num_query,
num_point,
grad_value,
grad_sampling_loc,
grad_attn_weight);
break;
case 256:
ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2<scalar_t, 256>
<<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
0, stream>>>(
num_kernels,
grad_col,
data_value,
data_spatial_shapes,
data_level_start_index,
data_sampling_loc,
data_attn_weight,
batch_size,
spatial_size,
num_heads,
channels,
num_levels,
num_query,
num_point,
grad_value,
grad_sampling_loc,
grad_attn_weight);
break;
case 512:
ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2<scalar_t, 512>
<<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
0, stream>>>(
num_kernels,
grad_col,
data_value,
data_spatial_shapes,
data_level_start_index,
data_sampling_loc,
data_attn_weight,
batch_size,
spatial_size,
num_heads,
channels,
num_levels,
num_query,
num_point,
grad_value,
grad_sampling_loc,
grad_attn_weight);
break;
case 1024:
ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2<scalar_t, 1024>
<<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
0, stream>>>(
num_kernels,
grad_col,
data_value,
data_spatial_shapes,
data_level_start_index,
data_sampling_loc,
data_attn_weight,
batch_size,
spatial_size,
num_heads,
channels,
num_levels,
num_query,
num_point,
grad_value,
grad_sampling_loc,
grad_attn_weight);
break;
default:
if (channels < 64)
{
ms_deformable_col2im_gpu_kernel_shm_reduce_v1<scalar_t>
<<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
num_threads*3*sizeof(scalar_t), stream>>>(
num_kernels,
grad_col,
data_value,
data_spatial_shapes,
data_level_start_index,
data_sampling_loc,
data_attn_weight,
batch_size,
spatial_size,
num_heads,
channels,
num_levels,
num_query,
num_point,
grad_value,
grad_sampling_loc,
grad_attn_weight);
}
else
{
ms_deformable_col2im_gpu_kernel_shm_reduce_v2<scalar_t>
<<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
num_threads*3*sizeof(scalar_t), stream>>>(
num_kernels,
grad_col,
data_value,
data_spatial_shapes,
data_level_start_index,
data_sampling_loc,
data_attn_weight,
batch_size,
spatial_size,
num_heads,
channels,
num_levels,
num_query,
num_point,
grad_value,
grad_sampling_loc,
grad_attn_weight);
}
}
}
cudaError_t err = cudaGetLastError();
if (err != cudaSuccess)
{
printf("error in ms_deformable_col2im_cuda: %s\n", cudaGetErrorString(err));
}
}
| transformers/src/transformers/kernels/deta/cuda/ms_deform_attn_cuda.cuh/0 | {
"file_path": "transformers/src/transformers/kernels/deta/cuda/ms_deform_attn_cuda.cuh",
"repo_id": "transformers",
"token_count": 34691
} | 70 |
// File from https://github.com/mlpen/YOSO/blob/main/encoders/backbones/efficient_attentions/yoso/yoso_v1/cuda/fast_lsh_cumulation.cu
#include <torch/extension.h>
#include <ATen/ATen.h>
#include "fast_lsh_cumulation.h"
#include "fast_lsh_cumulation_cuda.h"
#include "common_cuda.h"
#include "common.h"
#include <vector>
//////////////////////////////////////////////////////////////////////////////////////////////////
//////////////////////////////////////////////////////////////////////////////////////////////////
std::vector<at::Tensor> fast_hash_ver1_kernel(
at::Tensor query_mask,
at::Tensor query_vector,
at::Tensor key_mask,
at::Tensor key_vector,
int num_hash_f,
int hash_code_len,
bool use_cuda
) {
int batch_size = query_vector.size(0);
int num_query = query_vector.size(1);
int num_key = key_vector.size(1);
int vector_dim = query_vector.size(2);
int num_hash_per_part = vector_dim / hash_code_len;
int num_part = max(1, ceil_divide(num_hash_f, num_hash_per_part));
at::Tensor Dmat = 2 * at::randint(0, 2, {batch_size, 3, num_part, vector_dim}, query_mask.options()) - 1;
at::Tensor query_hash_code = at::zeros({batch_size, num_query, num_hash_f}, query_mask.options());
at::Tensor key_hash_code = at::zeros({batch_size, num_key, num_hash_f}, key_mask.options());
int *query_mask_ptr = query_mask.data_ptr<int>();
float *query_vector_ptr = query_vector.data_ptr<float>();
int *key_mask_ptr = key_mask.data_ptr<int>();
float *key_vector_ptr = key_vector.data_ptr<float>();
int *Dmat_ptr = Dmat.data_ptr<int>();
int *query_hash_code_ptr = query_hash_code.data_ptr<int>();
int *key_hash_code_ptr = key_hash_code.data_ptr<int>();
if (use_cuda) {
{
dim3 threads(vector_dim);
dim3 blocks(num_part, num_query, batch_size);
int shared_mem = vector_dim * sizeof(float);
fast_hash_ver1_cuda_kernel<<<blocks, threads, shared_mem>>>(
query_mask_ptr,
query_vector_ptr,
Dmat_ptr,
query_hash_code_ptr,
batch_size,
num_query,
vector_dim,
num_part,
num_hash_f,
hash_code_len
);
}
{
dim3 threads(vector_dim);
dim3 blocks(num_part, num_key, batch_size);
int shared_mem = vector_dim * sizeof(float);
fast_hash_ver1_cuda_kernel<<<blocks, threads, shared_mem>>>(
key_mask_ptr,
key_vector_ptr,
Dmat_ptr,
key_hash_code_ptr,
batch_size,
num_key,
vector_dim,
num_part,
num_hash_f,
hash_code_len
);
}
}
return {query_hash_code, key_hash_code};
}
at::Tensor lsh_cumulation_ver1_kernel(
at::Tensor query_mask,
at::Tensor query_hash_code,
at::Tensor key_mask,
at::Tensor key_hash_code,
at::Tensor value,
int hashtable_capacity,
bool use_cuda
) {
int batch_size = query_hash_code.size(0);
int num_hash_f = query_hash_code.size(2);
int num_query = query_hash_code.size(1);
int num_key = key_hash_code.size(1);
int value_dim = value.size(2);
at::Tensor hashtable_value = at::empty({batch_size, num_hash_f, hashtable_capacity, WARP_SIZE}, value.options());
at::Tensor cumulation_value = at::zeros({batch_size, num_query, value_dim}, value.options());
if (use_cuda) {
int threads_x = WARP_SIZE;
int threads_y = OPTIMAL_THREADS_PER_BLOCK / WARP_SIZE;
int block_x_step1 = num_key / threads_y;
int block_x_step2 = num_query / threads_y;
int block_y = batch_size;
dim3 threads(threads_x, threads_y);
dim3 blocks_step1(block_x_step1, block_y);
dim3 blocks_step2(block_x_step2, block_y);
int *query_mask_ptr = query_mask.data_ptr<int>();
int *query_hash_code_ptr = query_hash_code.data_ptr<int>();
int *key_mask_ptr = key_mask.data_ptr<int>();
int *key_hash_code_ptr = key_hash_code.data_ptr<int>();
float *value_ptr = value.data_ptr<float>();
float *hashtable_value_ptr = hashtable_value.data_ptr<float>();
float *cumulation_value_ptr = cumulation_value.data_ptr<float>();
for (int value_offset = 0; value_offset < value_dim; value_offset = value_offset + WARP_SIZE) {
cudaMemset(hashtable_value_ptr, 0, (batch_size * num_hash_f * hashtable_capacity * WARP_SIZE) * sizeof(float));
lsh_cumulation_ver1_step1_cuda_kernel<<<blocks_step1, threads>>>(
key_mask_ptr,
key_hash_code_ptr,
value_ptr,
hashtable_value_ptr,
batch_size,
num_hash_f,
hashtable_capacity,
num_key,
value_dim,
value_offset
);
lsh_cumulation_ver1_step2_cuda_kernel<<<blocks_step2, threads>>>(
query_mask_ptr,
query_hash_code_ptr,
hashtable_value_ptr,
cumulation_value_ptr,
batch_size,
num_hash_f,
hashtable_capacity,
num_query,
value_dim,
value_offset
);
}
}
return cumulation_value;
}
at::Tensor lsh_weighted_cumulation_ver1_kernel(
at::Tensor query_mask,
at::Tensor query_hash_code,
at::Tensor query_weight,
at::Tensor key_mask,
at::Tensor key_hash_code,
at::Tensor key_weight,
at::Tensor value,
int hashtable_capacity,
bool use_cuda
) {
int batch_size = query_hash_code.size(0);
int num_hash_f = query_hash_code.size(2);
int num_query = query_hash_code.size(1);
int num_key = key_hash_code.size(1);
int value_dim = value.size(2);
int weight_dim = query_weight.size(2);
at::Tensor hashtable_value = at::zeros({batch_size, num_hash_f, hashtable_capacity, WARP_SIZE}, value.options());
at::Tensor cumulation_value = at::zeros({batch_size, num_query, value_dim}, value.options());
if (use_cuda) {
int threads_x = WARP_SIZE;
int threads_y = OPTIMAL_THREADS_PER_BLOCK / WARP_SIZE;
int block_x_step1 = num_key / threads_y;
int block_x_step2 = num_query / threads_y;
int block_y = batch_size;
dim3 threads(threads_x, threads_y);
dim3 blocks_step1(block_x_step1, block_y);
dim3 blocks_step2(block_x_step2, block_y);
int *query_mask_ptr = query_mask.data_ptr<int>();
int *query_hash_code_ptr = query_hash_code.data_ptr<int>();
float *query_weight_ptr = query_weight.data_ptr<float>();
int *key_mask_ptr = key_mask.data_ptr<int>();
int *key_hash_code_ptr = key_hash_code.data_ptr<int>();
float *key_weight_ptr = key_weight.data_ptr<float>();
float *value_ptr = value.data_ptr<float>();
float *hashtable_value_ptr = hashtable_value.data_ptr<float>();
float *cumulation_value_ptr = cumulation_value.data_ptr<float>();
for (int value_offset = 0; value_offset < value_dim; value_offset = value_offset + WARP_SIZE) {
for (int weight_idx = 0; weight_idx < weight_dim; weight_idx++) {
cudaMemset(hashtable_value_ptr, 0, (batch_size * num_hash_f * hashtable_capacity * WARP_SIZE) * sizeof(float));
lsh_weighted_cumulation_ver1_step1_cuda_kernel<<<blocks_step1, threads>>>(
key_mask_ptr,
key_hash_code_ptr,
key_weight_ptr,
value_ptr,
hashtable_value_ptr,
batch_size,
num_hash_f,
hashtable_capacity,
num_key,
value_dim,
weight_dim,
value_offset,
weight_idx
);
lsh_weighted_cumulation_ver1_step2_cuda_kernel<<<blocks_step2, threads>>>(
query_mask_ptr,
query_hash_code_ptr,
query_weight_ptr,
hashtable_value_ptr,
cumulation_value_ptr,
batch_size,
num_hash_f,
hashtable_capacity,
num_query,
value_dim,
weight_dim,
value_offset,
weight_idx
);
}
}
}
return cumulation_value;
}
at::Tensor lsh_weighted_cumulation_ver2_kernel(
at::Tensor query_mask,
at::Tensor query_hash_code,
at::Tensor query_weight,
at::Tensor key_mask,
at::Tensor key_hash_code,
at::Tensor key_weight,
at::Tensor value,
int hashtable_capacity,
bool use_cuda
) {
int batch_size = query_hash_code.size(0);
int num_hash_f = query_hash_code.size(2);
int num_query = query_hash_code.size(1);
int num_key = key_hash_code.size(1);
int value_dim = value.size(2);
int weight_dim = query_weight.size(2);
at::Tensor count_sort_table = at::zeros({batch_size, num_hash_f, hashtable_capacity}, query_hash_code.options());
at::Tensor key_sorted_idxes = at::zeros({batch_size, num_hash_f, num_key}, query_hash_code.options());
at::Tensor query_info = at::zeros({batch_size, num_query, 2, num_hash_f}, query_hash_code.options());
at::Tensor cumulation_value = at::zeros({batch_size, num_query, value_dim}, value.options());
if (use_cuda) {
int *query_mask_ptr = query_mask.data_ptr<int>();
int *query_hash_code_ptr = query_hash_code.data_ptr<int>();
float *query_weight_ptr = query_weight.data_ptr<float>();
int *key_mask_ptr = key_mask.data_ptr<int>();
int *key_hash_code_ptr = key_hash_code.data_ptr<int>();
float *key_weight_ptr = key_weight.data_ptr<float>();
float *value_ptr = value.data_ptr<float>();
int *count_sort_table_ptr = count_sort_table.data_ptr<int>();
int *key_sorted_idxes_ptr = key_sorted_idxes.data_ptr<int>();
int *query_info_ptr = query_info.data_ptr<int>();
float *cumulation_value_ptr = cumulation_value.data_ptr<float>();
{
dim3 threads_step13(num_hash_f, max(1, OPTIMAL_THREADS_PER_BLOCK / num_hash_f));
dim3 blocks_step13(num_key / max(1, OPTIMAL_THREADS_PER_BLOCK / num_hash_f), batch_size);
dim3 threads_step2(min(hashtable_capacity, OPTIMAL_THREADS_PER_BLOCK));
dim3 blocks_step2(num_hash_f, batch_size);
int shared_mem = hashtable_capacity * sizeof(float);
count_sort_step1_cuda_kernel<<<blocks_step13, threads_step13>>>(
key_mask_ptr,
key_hash_code_ptr,
count_sort_table_ptr,
batch_size,
num_hash_f,
hashtable_capacity,
num_key
);
count_sort_step2_cuda_kernel<<<blocks_step2, threads_step2, shared_mem>>>(
count_sort_table_ptr,
batch_size,
num_hash_f,
hashtable_capacity
);
count_sort_step3_cuda_kernel<<<blocks_step13, threads_step13>>>(
key_mask_ptr,
key_hash_code_ptr,
count_sort_table_ptr,
key_sorted_idxes_ptr,
batch_size,
num_hash_f,
hashtable_capacity,
num_key
);
}
{
dim3 threads(num_hash_f, max(1, OPTIMAL_THREADS_PER_BLOCK / num_hash_f));
dim3 blocks(num_query / max(1, OPTIMAL_THREADS_PER_BLOCK / num_hash_f), batch_size);
extract_query_info_cuda_kernel<<<blocks, threads>>>(
query_mask_ptr,
query_hash_code_ptr,
count_sort_table_ptr,
query_info_ptr,
batch_size,
num_hash_f,
hashtable_capacity,
num_query
);
}
{
dim3 threads(WARP_SIZE, OPTIMAL_THREADS_PER_BLOCK / WARP_SIZE);
dim3 blocks(num_query, num_hash_f, batch_size);
int shared_mem = (weight_dim + WARP_SIZE) * sizeof(float);
lsh_weighted_cumulation_ver2_step2_cuda_kernel<<<blocks, threads, shared_mem>>>(
query_mask_ptr,
query_info_ptr,
key_sorted_idxes_ptr,
query_weight_ptr,
key_weight_ptr,
value_ptr,
cumulation_value_ptr,
batch_size,
num_hash_f,
num_query,
num_key,
value_dim,
weight_dim
);
}
}
return cumulation_value;
}
at::Tensor lsh_weighted_cumulation_ver3_kernel(
at::Tensor query_mask,
at::Tensor query_hash_code,
at::Tensor query_weight,
at::Tensor key_mask,
at::Tensor key_hash_code,
at::Tensor key_weight,
at::Tensor value,
int hashtable_capacity,
bool use_cuda
) {
int batch_size = query_hash_code.size(0);
int num_hash_f = query_hash_code.size(2);
int num_query = query_hash_code.size(1);
int num_key = key_hash_code.size(1);
int value_dim = value.size(2);
int weight_dim = query_weight.size(2);
at::Tensor count_sort_table = at::zeros({batch_size, num_hash_f, hashtable_capacity}, query_hash_code.options());
at::Tensor query_sorted_idxes = at::zeros({batch_size, num_hash_f, num_query}, query_hash_code.options());
at::Tensor key_info = at::zeros({batch_size, num_key, 2, num_hash_f}, query_hash_code.options());
at::Tensor cumulation_value = at::zeros({batch_size, num_query, value_dim}, value.options());
if (use_cuda) {
int *query_mask_ptr = query_mask.data_ptr<int>();
int *query_hash_code_ptr = query_hash_code.data_ptr<int>();
float *query_weight_ptr = query_weight.data_ptr<float>();
int *key_mask_ptr = key_mask.data_ptr<int>();
int *key_hash_code_ptr = key_hash_code.data_ptr<int>();
float *key_weight_ptr = key_weight.data_ptr<float>();
float *value_ptr = value.data_ptr<float>();
int *count_sort_table_ptr = count_sort_table.data_ptr<int>();
int *query_sorted_idxes_ptr = query_sorted_idxes.data_ptr<int>();
int *key_info_ptr = key_info.data_ptr<int>();
float *cumulation_value_ptr = cumulation_value.data_ptr<float>();
{
dim3 threads_step13(num_hash_f, max(1, OPTIMAL_THREADS_PER_BLOCK / num_hash_f));
dim3 blocks_step13(num_query / max(1, OPTIMAL_THREADS_PER_BLOCK / num_hash_f), batch_size);
dim3 threads_step2(min(hashtable_capacity, OPTIMAL_THREADS_PER_BLOCK));
dim3 blocks_step2(num_hash_f, batch_size);
int shared_mem = hashtable_capacity * sizeof(float);
count_sort_step1_cuda_kernel<<<blocks_step13, threads_step13>>>(
query_mask_ptr,
query_hash_code_ptr,
count_sort_table_ptr,
batch_size,
num_hash_f,
hashtable_capacity,
num_query
);
count_sort_step2_cuda_kernel<<<blocks_step2, threads_step2, shared_mem>>>(
count_sort_table_ptr,
batch_size,
num_hash_f,
hashtable_capacity
);
count_sort_step3_cuda_kernel<<<blocks_step13, threads_step13>>>(
query_mask_ptr,
query_hash_code_ptr,
count_sort_table_ptr,
query_sorted_idxes_ptr,
batch_size,
num_hash_f,
hashtable_capacity,
num_query
);
}
{
dim3 threads(num_hash_f, max(1, OPTIMAL_THREADS_PER_BLOCK / num_hash_f));
dim3 blocks(num_key / max(1, OPTIMAL_THREADS_PER_BLOCK / num_hash_f), batch_size);
extract_query_info_cuda_kernel<<<blocks, threads>>>(
key_mask_ptr,
key_hash_code_ptr,
count_sort_table_ptr,
key_info_ptr,
batch_size,
num_hash_f,
hashtable_capacity,
num_key
);
}
{
dim3 threads(WARP_SIZE, OPTIMAL_THREADS_PER_BLOCK / WARP_SIZE);
dim3 blocks(num_key, num_hash_f, batch_size);
int shared_mem = (weight_dim + value_dim + WARP_SIZE) * sizeof(float);
lsh_weighted_cumulation_ver3_step2_cuda_kernel<<<blocks, threads, shared_mem>>>(
query_sorted_idxes_ptr,
key_mask_ptr,
key_info_ptr,
query_weight_ptr,
key_weight_ptr,
value_ptr,
cumulation_value_ptr,
batch_size,
num_hash_f,
num_query,
num_key,
value_dim,
weight_dim
);
}
}
return cumulation_value;
}
at::Tensor lsh_weighted_cumulation_ver4_kernel(
at::Tensor query_mask,
at::Tensor query_hash_code,
at::Tensor query_weight,
at::Tensor key_mask,
at::Tensor key_hash_code,
at::Tensor key_weight,
at::Tensor value,
int hashtable_capacity,
bool use_cuda
) {
int batch_size = query_hash_code.size(0);
int num_hash_f = query_hash_code.size(2);
int num_query = query_hash_code.size(1);
int num_key = key_hash_code.size(1);
int value_dim = value.size(2);
int weight_dim = query_weight.size(2);
at::Tensor count_sort_table = at::zeros({batch_size, num_hash_f, hashtable_capacity}, query_hash_code.options());
at::Tensor query_sorted_idxes = at::zeros({batch_size, num_hash_f, num_query}, query_hash_code.options());
at::Tensor key_info = at::zeros({batch_size, num_key, 2, num_hash_f}, query_hash_code.options());
at::Tensor cumulation_value = at::zeros({batch_size, num_query, value_dim}, value.options());
if (use_cuda) {
int *query_mask_ptr = query_mask.data_ptr<int>();
int *query_hash_code_ptr = query_hash_code.data_ptr<int>();
float *query_weight_ptr = query_weight.data_ptr<float>();
int *key_mask_ptr = key_mask.data_ptr<int>();
int *key_hash_code_ptr = key_hash_code.data_ptr<int>();
float *key_weight_ptr = key_weight.data_ptr<float>();
float *value_ptr = value.data_ptr<float>();
int *count_sort_table_ptr = count_sort_table.data_ptr<int>();
int *query_sorted_idxes_ptr = query_sorted_idxes.data_ptr<int>();
int *key_info_ptr = key_info.data_ptr<int>();
float *cumulation_value_ptr = cumulation_value.data_ptr<float>();
{
dim3 threads_step13(num_hash_f, max(1, OPTIMAL_THREADS_PER_BLOCK / num_hash_f));
dim3 blocks_step13(num_query / max(1, OPTIMAL_THREADS_PER_BLOCK / num_hash_f), batch_size);
dim3 threads_step2(min(hashtable_capacity, OPTIMAL_THREADS_PER_BLOCK));
dim3 blocks_step2(num_hash_f, batch_size);
int shared_mem = hashtable_capacity * sizeof(float);
count_sort_step1_cuda_kernel<<<blocks_step13, threads_step13>>>(
query_mask_ptr,
query_hash_code_ptr,
count_sort_table_ptr,
batch_size,
num_hash_f,
hashtable_capacity,
num_query
);
count_sort_step2_cuda_kernel<<<blocks_step2, threads_step2, shared_mem>>>(
count_sort_table_ptr,
batch_size,
num_hash_f,
hashtable_capacity
);
count_sort_step3_cuda_kernel<<<blocks_step13, threads_step13>>>(
query_mask_ptr,
query_hash_code_ptr,
count_sort_table_ptr,
query_sorted_idxes_ptr,
batch_size,
num_hash_f,
hashtable_capacity,
num_query
);
}
{
dim3 threads(num_hash_f, max(1, OPTIMAL_THREADS_PER_BLOCK / num_hash_f));
dim3 blocks(num_key / max(1, OPTIMAL_THREADS_PER_BLOCK / num_hash_f), batch_size);
extract_query_info_cuda_kernel<<<blocks, threads>>>(
key_mask_ptr,
key_hash_code_ptr,
count_sort_table_ptr,
key_info_ptr,
batch_size,
num_hash_f,
hashtable_capacity,
num_key
);
}
{
dim3 threads(WARP_SIZE, OPTIMAL_THREADS_PER_BLOCK / WARP_SIZE);
dim3 blocks(num_key, batch_size);
int shared_mem = (weight_dim + value_dim + 2 * num_hash_f) * sizeof(float);
lsh_weighted_cumulation_ver4_step2_cuda_kernel<<<blocks, threads, shared_mem>>>(
query_sorted_idxes_ptr,
key_mask_ptr,
key_info_ptr,
query_weight_ptr,
key_weight_ptr,
value_ptr,
cumulation_value_ptr,
batch_size,
num_hash_f,
num_query,
num_key,
value_dim,
weight_dim
);
}
}
return cumulation_value;
}
| transformers/src/transformers/kernels/yoso/fast_lsh_cumulation.cu/0 | {
"file_path": "transformers/src/transformers/kernels/yoso/fast_lsh_cumulation.cu",
"repo_id": "transformers",
"token_count": 8662
} | 71 |
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_import_structure = {
"configuration_albert": ["ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "AlbertConfig", "AlbertOnnxConfig"],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_albert"] = ["AlbertTokenizer"]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_albert_fast"] = ["AlbertTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_albert"] = [
"ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"AlbertForMaskedLM",
"AlbertForMultipleChoice",
"AlbertForPreTraining",
"AlbertForQuestionAnswering",
"AlbertForSequenceClassification",
"AlbertForTokenClassification",
"AlbertModel",
"AlbertPreTrainedModel",
"load_tf_weights_in_albert",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_albert"] = [
"TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFAlbertForMaskedLM",
"TFAlbertForMultipleChoice",
"TFAlbertForPreTraining",
"TFAlbertForQuestionAnswering",
"TFAlbertForSequenceClassification",
"TFAlbertForTokenClassification",
"TFAlbertMainLayer",
"TFAlbertModel",
"TFAlbertPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_flax_albert"] = [
"FlaxAlbertForMaskedLM",
"FlaxAlbertForMultipleChoice",
"FlaxAlbertForPreTraining",
"FlaxAlbertForQuestionAnswering",
"FlaxAlbertForSequenceClassification",
"FlaxAlbertForTokenClassification",
"FlaxAlbertModel",
"FlaxAlbertPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_albert import AlbertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_albert_fast import AlbertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_albert import (
ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
AlbertForMaskedLM,
AlbertForMultipleChoice,
AlbertForPreTraining,
AlbertForQuestionAnswering,
AlbertForSequenceClassification,
AlbertForTokenClassification,
AlbertModel,
AlbertPreTrainedModel,
load_tf_weights_in_albert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_albert import (
TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFAlbertForMaskedLM,
TFAlbertForMultipleChoice,
TFAlbertForPreTraining,
TFAlbertForQuestionAnswering,
TFAlbertForSequenceClassification,
TFAlbertForTokenClassification,
TFAlbertMainLayer,
TFAlbertModel,
TFAlbertPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_albert import (
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForPreTraining,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertModel,
FlaxAlbertPreTrainedModel,
)
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| transformers/src/transformers/models/albert/__init__.py/0 | {
"file_path": "transformers/src/transformers/models/albert/__init__.py",
"repo_id": "transformers",
"token_count": 2259
} | 72 |
# coding=utf-8
# Copyright 2022 WenXiang ZhongzhiCheng LedellWu LiuGuang BoWenZhang The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Image/Text processor class for AltCLIP
"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class AltCLIPProcessor(ProcessorMixin):
r"""
Constructs a AltCLIP processor which wraps a CLIP image processor and a XLM-Roberta tokenizer into a single
processor.
[`AltCLIPProcessor`] offers all the functionalities of [`CLIPImageProcessor`] and [`XLMRobertaTokenizerFast`]. See
the [`~AltCLIPProcessor.__call__`] and [`~AltCLIPProcessor.decode`] for more information.
Args:
image_processor ([`CLIPImageProcessor`], *optional*):
The image processor is a required input.
tokenizer ([`XLMRobertaTokenizerFast`], *optional*):
The tokenizer is a required input.
"""
attributes = ["image_processor", "tokenizer"]
image_processor_class = "CLIPImageProcessor"
tokenizer_class = ("XLMRobertaTokenizer", "XLMRobertaTokenizerFast")
def __init__(self, image_processor=None, tokenizer=None, **kwargs):
feature_extractor = None
if "feature_extractor" in kwargs:
warnings.warn(
"The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
" instead.",
FutureWarning,
)
feature_extractor = kwargs.pop("feature_extractor")
image_processor = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("You need to specify an `image_processor`.")
if tokenizer is None:
raise ValueError("You need to specify a `tokenizer`.")
super().__init__(image_processor, tokenizer)
def __call__(self, text=None, images=None, return_tensors=None, **kwargs):
"""
Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
and `kwargs` arguments to XLMRobertaTokenizerFast's [`~XLMRobertaTokenizerFast.__call__`] if `text` is not
`None` to encode the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to
CLIPImageProcessor's [`~CLIPImageProcessor.__call__`] if `images` is not `None`. Please refer to the doctsring
of the above two methods for more information.
Args:
text (`str`, `List[str]`, `List[List[str]]`):
The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
(pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
`is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a
number of channels, H and W are image height and width.
return_tensors (`str` or [`~utils.TensorType`], *optional*):
If set, will return tensors of a particular framework. Acceptable values are:
- `'tf'`: Return TensorFlow `tf.constant` objects.
- `'pt'`: Return PyTorch `torch.Tensor` objects.
- `'np'`: Return NumPy `np.ndarray` objects.
- `'jax'`: Return JAX `jnp.ndarray` objects.
Returns:
[`BatchEncoding`]: A [`BatchEncoding`] with the following fields:
- **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
- **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
`return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
`None`).
- **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
"""
if text is None and images is None:
raise ValueError("You have to specify either text or images. Both cannot be none.")
if text is not None:
encoding = self.tokenizer(text, return_tensors=return_tensors, **kwargs)
if images is not None:
image_features = self.image_processor(images, return_tensors=return_tensors, **kwargs)
if text is not None and images is not None:
encoding["pixel_values"] = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**image_features), tensor_type=return_tensors)
def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to XLMRobertaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`].
Please refer to the docstring of this method for more information.
"""
return self.tokenizer.batch_decode(*args, **kwargs)
def decode(self, *args, **kwargs):
"""
This method forwards all its arguments to XLMRobertaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please
refer to the docstring of this method for more information.
"""
return self.tokenizer.decode(*args, **kwargs)
@property
def model_input_names(self):
tokenizer_input_names = self.tokenizer.model_input_names
image_processor_input_names = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
| transformers/src/transformers/models/altclip/processing_altclip.py/0 | {
"file_path": "transformers/src/transformers/models/altclip/processing_altclip.py",
"repo_id": "transformers",
"token_count": 2484
} | 73 |
# coding=utf-8
# Copyright 2020 The Facebook AI Research Team Authors and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
# See all BART models at https://huggingface.co/models?filter=bart
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/vocab.json",
"facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/vocab.json",
"facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json",
"facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json",
"facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json",
"yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json",
},
"merges_file": {
"facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/merges.txt",
"facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/merges.txt",
"facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt",
"facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt",
"facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt",
"yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt",
},
"tokenizer_file": {
"facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json",
"facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json",
"facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json",
"facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json",
"facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json",
"yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json",
},
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"facebook/bart-base": 1024,
"facebook/bart-large": 1024,
"facebook/bart-large-mnli": 1024,
"facebook/bart-large-cnn": 1024,
"facebook/bart-large-xsum": 1024,
"yjernite/bart_eli5": 1024,
}
class BartTokenizerFast(PreTrainedTokenizerFast):
r"""
Construct a "fast" BART tokenizer (backed by HuggingFace's *tokenizers* library), derived from the GPT-2 tokenizer,
using byte-level Byte-Pair-Encoding.
This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
be encoded differently whether it is at the beginning of the sentence (without space) or not:
```python
>>> from transformers import BartTokenizerFast
>>> tokenizer = BartTokenizerFast.from_pretrained("facebook/bart-base")
>>> tokenizer("Hello world")["input_ids"]
[0, 31414, 232, 2]
>>> tokenizer(" Hello world")["input_ids"]
[0, 20920, 232, 2]
```
You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer or when you
call it on some text, but since the model was not pretrained this way, it might yield a decrease in performance.
<Tip>
When used with `is_split_into_words=True`, this tokenizer needs to be instantiated with `add_prefix_space=True`.
</Tip>
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods.
Args:
vocab_file (`str`):
Path to the vocabulary file.
merges_file (`str`):
Path to the merges file.
errors (`str`, *optional*, defaults to `"replace"`):
Paradigm to follow when decoding bytes to UTF-8. See
[bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
bos_token (`str`, *optional*, defaults to `"<s>"`):
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the beginning of
sequence. The token used is the `cls_token`.
</Tip>
eos_token (`str`, *optional*, defaults to `"</s>"`):
The end of sequence token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the end of sequence.
The token used is the `sep_token`.
</Tip>
sep_token (`str`, *optional*, defaults to `"</s>"`):
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.
cls_token (`str`, *optional*, defaults to `"<s>"`):
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
unk_token (`str`, *optional*, defaults to `"<unk>"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
pad_token (`str`, *optional*, defaults to `"<pad>"`):
The token used for padding, for example when batching sequences of different lengths.
mask_token (`str`, *optional*, defaults to `"<mask>"`):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.
add_prefix_space (`bool`, *optional*, defaults to `False`):
Whether or not to add an initial space to the input. This allows to treat the leading word just as any
other word. (BART tokenizer detect beginning of words by the preceding space).
trim_offsets (`bool`, *optional*, defaults to `True`):
Whether the post processing step should trim offsets to avoid including whitespaces.
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
model_input_names = ["input_ids", "attention_mask"]
slow_tokenizer_class = BartTokenizer
def __init__(
self,
vocab_file=None,
merges_file=None,
tokenizer_file=None,
errors="replace",
bos_token="<s>",
eos_token="</s>",
sep_token="</s>",
cls_token="<s>",
unk_token="<unk>",
pad_token="<pad>",
mask_token="<mask>",
add_prefix_space=False,
trim_offsets=True,
**kwargs,
):
# we have to specify that this tokens is special otherwise adding it will reset the normalized flag to `False` in `add_special_tokens`
mask_token = (
AddedToken(mask_token, lstrip=True, normalized=True, special=True)
if isinstance(mask_token, str)
else mask_token
)
super().__init__(
vocab_file,
merges_file,
tokenizer_file=tokenizer_file,
errors=errors,
bos_token=bos_token,
eos_token=eos_token,
sep_token=sep_token,
cls_token=cls_token,
unk_token=unk_token,
pad_token=pad_token,
mask_token=mask_token,
add_prefix_space=add_prefix_space,
trim_offsets=trim_offsets,
**kwargs,
)
pre_tok_state = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
if pre_tok_state.get("add_prefix_space", add_prefix_space) != add_prefix_space:
pre_tok_class = getattr(pre_tokenizers, pre_tok_state.pop("type"))
pre_tok_state["add_prefix_space"] = add_prefix_space
self.backend_tokenizer.pre_tokenizer = pre_tok_class(**pre_tok_state)
self.add_prefix_space = add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
tokenizer_component = "post_processor"
tokenizer_component_instance = getattr(self.backend_tokenizer, tokenizer_component, None)
if tokenizer_component_instance:
state = json.loads(tokenizer_component_instance.__getstate__())
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
state["sep"] = tuple(state["sep"])
if "cls" in state:
state["cls"] = tuple(state["cls"])
changes_to_apply = False
if state.get("add_prefix_space", add_prefix_space) != add_prefix_space:
state["add_prefix_space"] = add_prefix_space
changes_to_apply = True
if state.get("trim_offsets", trim_offsets) != trim_offsets:
state["trim_offsets"] = trim_offsets
changes_to_apply = True
if changes_to_apply:
component_class = getattr(processors, state.pop("type"))
new_value = component_class(**state)
setattr(self.backend_tokenizer, tokenizer_component, new_value)
@property
def mask_token(self) -> str:
"""
`str`: Mask token, to use when training a model with masked-language modeling. Log an error if used while not
having been set.
BART tokenizer has a special mask token to be usable in the fill-mask pipeline. The mask token will greedily
comprise the space before the *<mask>*.
"""
if self._mask_token is None:
if self.verbose:
logger.error("Using mask_token, but it is not set yet.")
return None
return str(self._mask_token)
@mask_token.setter
def mask_token(self, value):
"""
Overriding the default behavior of the mask token to have it eat the space before it.
This is needed to preserve backward compatibility with all the previously used models based on Bart.
"""
# Mask token behave like a normal word, i.e. include the space before it
# So we set lstrip to True
value = AddedToken(value, lstrip=True, rstrip=False) if isinstance(value, str) else value
self._mask_token = value
def _batch_encode_plus(self, *args, **kwargs) -> BatchEncoding:
is_split_into_words = kwargs.get("is_split_into_words", False)
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
"to use it with pretokenized inputs."
)
return super()._batch_encode_plus(*args, **kwargs)
def _encode_plus(self, *args, **kwargs) -> BatchEncoding:
is_split_into_words = kwargs.get("is_split_into_words", False)
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
"to use it with pretokenized inputs."
)
return super()._encode_plus(*args, **kwargs)
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
files = self._tokenizer.model.save(save_directory, name=filename_prefix)
return tuple(files)
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
output = [self.bos_token_id] + token_ids_0 + [self.eos_token_id]
if token_ids_1 is None:
return output
return output + [self.eos_token_id] + token_ids_1 + [self.eos_token_id]
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task. BART does not
make use of token type ids, therefore a list of zeros is returned.
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of zeros.
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return len(cls + token_ids_0 + sep) * [0]
return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
| transformers/src/transformers/models/bart/tokenization_bart_fast.py/0 | {
"file_path": "transformers/src/transformers/models/bart/tokenization_bart_fast.py",
"repo_id": "transformers",
"token_count": 5760
} | 74 |
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