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# 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 unittest
import numpy as np
import timeout_decorator # noqa
from transformers import OPTConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
os.environ["XLA_PYTHON_CLIENT_ALLOCATOR"] = "platform"
import jax
import jax.numpy as jnp
from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPT2Tokenizer
def prepare_opt_inputs_dict(config, input_ids, attention_mask=None, head_mask=None):
if attention_mask is None:
attention_mask = np.where(input_ids != config.pad_token_id, 1, 0)
return {
"input_ids": input_ids,
"attention_mask": attention_mask,
}
@require_flax
class FlaxOPTModelTester:
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,
embed_dim=16,
word_embed_proj_dim=16,
initializer_range=0.02,
):
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
self.embed_dim = embed_dim
self.word_embed_proj_dim = word_embed_proj_dim
self.initializer_range = initializer_range
self.is_encoder_decoder = False
def prepare_config_and_inputs(self):
input_ids = np.clip(ids_tensor([self.batch_size, self.seq_length - 1], self.vocab_size), 3, self.vocab_size)
input_ids = np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1), dtype=np.int64)), -1)
config = OPTConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
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,
embed_dim=self.embed_dim,
is_encoder_decoder=False,
word_embed_proj_dim=self.word_embed_proj_dim,
initializer_range=self.initializer_range,
use_cache=False,
)
inputs_dict = prepare_opt_inputs_dict(config, 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 check_use_cache_forward(self, model_class_name, config, inputs_dict):
max_length = 20
model = model_class_name(config)
input_ids = inputs_dict["input_ids"]
attention_mask = inputs_dict["attention_mask"]
past_key_values = model.init_cache(input_ids.shape[0], max_length)
attention_mask = jnp.ones((input_ids.shape[0], max_length), dtype="i4")
position_ids = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1)[None, :],
(input_ids.shape[0], input_ids.shape[-1] - 1),
)
outputs_cache = model(
input_ids[:, :-1],
attention_mask=attention_mask,
past_key_values=past_key_values,
position_ids=position_ids,
)
position_ids = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]], dtype="i4")
outputs_cache_next = model(
input_ids[:, -1:],
attention_mask=attention_mask,
past_key_values=outputs_cache.past_key_values,
position_ids=position_ids,
)
outputs = model(input_ids)
diff = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1e-3, msg=f"Max diff is {diff}")
def check_use_cache_forward_with_attn_mask(self, model_class_name, config, inputs_dict):
max_length = 20
model = model_class_name(config)
input_ids, attention_mask = (
inputs_dict["input_ids"],
inputs_dict["attention_mask"],
)
attention_mask_cache = jnp.concatenate(
[
attention_mask,
jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])),
],
axis=-1,
)
past_key_values = model.init_cache(input_ids.shape[0], max_length)
position_ids = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1)[None, :],
(input_ids.shape[0], input_ids.shape[-1] - 1),
)
outputs_cache = model(
input_ids[:, :-1],
attention_mask=attention_mask_cache,
past_key_values=past_key_values,
position_ids=position_ids,
)
position_ids = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]], dtype="i4")
outputs_cache_next = model(
input_ids[:, -1:],
past_key_values=outputs_cache.past_key_values,
attention_mask=attention_mask_cache,
position_ids=position_ids,
)
outputs = model(input_ids, attention_mask=attention_mask)
diff = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1e-3, msg=f"Max diff is {diff}")
@require_flax
class FlaxOPTModelTest(FlaxModelTesterMixin, unittest.TestCase, FlaxGenerationTesterMixin):
all_model_classes = (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
all_generative_model_classes = () if is_flax_available() else ()
def setUp(self):
self.model_tester = FlaxOPTModelTester(self)
def test_use_cache_forward(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(model_class, config, inputs_dict)
def test_use_cache_forward_with_attn_mask(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(model_class, config, inputs_dict)
@slow
def test_model_from_pretrained(self):
for model_class_name in self.all_model_classes:
model = model_class_name.from_pretrained("facebook/opt-125m")
input_ids = np.ones((1, 1)) * model.config.eos_token_id
outputs = model(input_ids)
self.assertIsNotNone(outputs)
@require_sentencepiece
@require_flax
class FlaxOPTModelIntegrationTests(unittest.TestCase):
@slow
def test_inference_no_head(self):
model = FlaxOPTModel.from_pretrained("facebook/opt-350m")
input_ids = jnp.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
output = model(input_ids=input_ids).last_hidden_state
expected_shape = (1, 11, 512)
self.assertEqual(output.shape, expected_shape)
expected_slice = jnp.array(
[[-0.2867, -1.9256, -0.3062], [-1.2711, -0.1337, -0.1897], [0.4109, 0.1187, -1.3142]]
)
self.assertTrue(jnp.allclose(output[:, :3, :3], expected_slice, atol=4e-2))
@require_flax
@slow
class FlaxOPTEmbeddingsTest(unittest.TestCase):
def setUp(self):
super().setUp()
self.path_model = "facebook/opt-350m"
def test_logits(self):
model = FlaxOPTForCausalLM.from_pretrained(self.path_model)
tokenizer = GPT2Tokenizer.from_pretrained(self.path_model)
prompts = [
"Today is a beautiful day and I want to",
"In the city of",
"Paris is the capital of France and",
"Computers and mobile phones have taken",
]
# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
inputs = tokenizer(prompts, return_tensors="jax", padding=True, add_special_tokens=False)
logits = model(inputs.input_ids, attention_mask=inputs.attention_mask)[0].mean(axis=-1)
logits_meta = jnp.array(
[
[1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670],
[-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822],
[0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703],
[6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
]
)
self.assertTrue(jnp.allclose(logits, logits_meta, atol=4e-2))
model = jax.jit(model)
logits = model(inputs.input_ids, attention_mask=inputs.attention_mask)[0].mean(axis=-1)
self.assertTrue(jnp.allclose(logits, logits_meta, atol=4e-2))
@require_flax
@slow
class FlaxOPTGenerationTest(unittest.TestCase):
@property
def prompts(self):
return [
"Today is a beautiful day and I want",
"In the city of",
"Paris is the capital of France and",
"Computers and mobile phones have taken",
]
def test_generation_pre_attn_layer_norm(self):
model_id = "facebook/opt-125m"
EXPECTED_OUTPUTS = [
"Today is a beautiful day and I want to",
"In the city of New York, the city",
"Paris is the capital of France and the capital",
"Computers and mobile phones have taken over the",
]
predicted_outputs = []
model = FlaxOPTForCausalLM.from_pretrained(model_id)
tokenizer = GPT2Tokenizer.from_pretrained(model_id)
for prompt in self.prompts:
input_ids = tokenizer(prompt, return_tensors="jax").input_ids
generated_ids = model.generate(input_ids, max_length=10)
generated_ids = generated_ids[0]
generated_string = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
predicted_outputs += generated_string
self.assertListEqual(predicted_outputs, EXPECTED_OUTPUTS)
def test_generation_post_attn_layer_norm(self):
model_id = "facebook/opt-350m"
EXPECTED_OUTPUTS = [
"Today is a beautiful day and I want to",
"In the city of San Francisco, the city",
"Paris is the capital of France and the capital",
"Computers and mobile phones have taken over the",
]
predicted_outputs = []
model = FlaxOPTForCausalLM.from_pretrained(model_id)
tokenizer = GPT2Tokenizer.from_pretrained(model_id)
for prompt in self.prompts:
input_ids = tokenizer(prompt, return_tensors="jax").input_ids
generated_ids = model.generate(input_ids, max_length=10)
generated_ids = generated_ids[0]
generated_string = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
predicted_outputs += generated_string
self.assertListEqual(predicted_outputs, EXPECTED_OUTPUTS)
def test_jitted_batch_generation(self):
model_id = "facebook/opt-125m"
EXPECTED_OUTPUTS = [
"Today is a beautiful day and I want to thank",
"In the city of Rome Canaver Canaver Canaver Canaver",
]
model = FlaxOPTForCausalLM.from_pretrained(model_id)
tokenizer = GPT2Tokenizer.from_pretrained(model_id)
inputs = tokenizer(
[
"Today is a beautiful day and I want to",
"In the city of",
],
return_tensors="jax",
padding=True,
)
jit_generate = jax.jit(model.generate)
output_sequences = jit_generate(inputs["input_ids"], attention_mask=inputs["attention_mask"]).sequences
output_string = tokenizer.batch_decode(output_sequences, skip_special_tokens=True)
self.assertIsNotNone(output_string, EXPECTED_OUTPUTS)
def test_batch_generation(self):
model_id = "facebook/opt-350m"
tokenizer = GPT2Tokenizer.from_pretrained(model_id)
model = FlaxOPTForCausalLM.from_pretrained(model_id)
tokenizer.padding_side = "left"
# use different length sentences to test batching
sentences = [
"Hello, my dog is a little",
"Today, I",
]
inputs = tokenizer(sentences, return_tensors="jax", padding=True)
input_ids = inputs["input_ids"]
outputs = model.generate(input_ids=input_ids, attention_mask=inputs["attention_mask"], trace=False)
inputs_non_padded = tokenizer(sentences[0], return_tensors="jax").input_ids
output_non_padded = model.generate(input_ids=inputs_non_padded)
num_paddings = inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].sum()
inputs_padded = tokenizer(sentences[1], return_tensors="jax").input_ids
output_padded = model.generate(input_ids=inputs_padded, max_length=model.config.max_length - num_paddings)
batch_out_sentence = tokenizer.batch_decode(outputs[0], skip_special_tokens=True)
non_padded_sentence = tokenizer.decode(output_non_padded[0][0], skip_special_tokens=True)
padded_sentence = tokenizer.decode(output_padded[0][0], skip_special_tokens=True)
expected_output_sentence = [
"Hello, my dog is a little bit of a dork.\nI'm a little bit",
"Today, I was in the middle of a conversation with a friend about the",
]
self.assertListEqual(expected_output_sentence, batch_out_sentence)
self.assertListEqual(batch_out_sentence, [non_padded_sentence, padded_sentence])
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