-
-/*
-CPP Binding for CUDA OP
-*/
-
-// CUDA forward declarations
-torch::Tensor ngram_repeat_block_cuda_forward(
- torch::Tensor tokens,
- torch::Tensor lprobs,
- int bsz,
- int step,
- int beam_size,
- int no_repeat_ngram_size);
-
-#define CHECK_CUDA(x) \
- TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor")
-#define CHECK_CONTIGUOUS(x) \
- TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
-#define CHECK_INPUT(x) \
- CHECK_CUDA(x); \
- CHECK_CONTIGUOUS(x)
-
-// Input check and call to CUDA OP
-// Backward method not required
-torch::Tensor ngram_repeat_block_forward(
- torch::Tensor tokens,
- torch::Tensor lprobs,
- int bsz,
- int step,
- int beam_size,
- int no_repeat_ngram_size) {
- CHECK_INPUT(tokens);
- CHECK_INPUT(lprobs);
- assert(bsz > 0);
- assert(step >= 0);
- assert(beam_size > 0);
- assert(no_repeat_ngram_size > 0);
-
- return ngram_repeat_block_cuda_forward(
- tokens, lprobs, bsz, step, beam_size, no_repeat_ngram_size);
-}
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
- m.def(
- "forward",
- &ngram_repeat_block_forward,
- "No Repeat Ngram Block forward (CUDA)");
-}
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/tests/gpu/test_ema_gpu.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/tests/gpu/test_ema_gpu.py
deleted file mode 100644
index 337107d69a2626652d1f34321a555dde02b3c1a9..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/tests/gpu/test_ema_gpu.py
+++ /dev/null
@@ -1,200 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import unittest
-from copy import deepcopy
-from dataclasses import dataclass
-from typing import Optional
-
-import torch
-from fairseq.models.ema import EMA
-
-
-class DummyModule(torch.nn.Module):
- def __init__(self) -> None:
- """LightningModule for testing purposes
-
- Args:
- epoch_min_loss_override (int, optional): Pass in an epoch that will be set to the minimum
- validation loss for testing purposes (zero based). If None this is ignored. Defaults to None.
- """
- super().__init__()
- self.layer = torch.nn.Linear(in_features=32, out_features=2)
- self.another_layer = torch.nn.Linear(in_features=2, out_features=2)
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- x = self.layer(x)
- return self.another_layer(x)
-
-
-@dataclass
-class EMAConfig(object):
- ema_decay: float = 0.99
- ema_start_update: int = 0
- ema_fp32: bool = False
- ema_seed_model: Optional[str] = None
-
-
-@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU")
-class TestEMAGPU(unittest.TestCase):
- def assertTorchAllClose(self, x, y, atol=1e-8, rtol=1e-5, msg=None):
- diff = x.float() - y.float()
- diff_norm = torch.norm(diff)
- other_norm = torch.norm(y.float())
-
- if msg is None:
- msg = "|input - other| > {} + {} * |other|".format(
- atol, rtol
- )
-
- self.assertLessEqual(
- diff_norm,
- atol + rtol * other_norm,
- msg=msg,
- )
-
- def test_ema(self):
- model = DummyModule().cuda()
- optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
- state = deepcopy(model.state_dict())
- config = EMAConfig()
- ema = EMA(model, config)
-
- # set decay
- ema._set_decay(config.ema_decay)
- self.assertEqual(ema.get_decay(), config.ema_decay)
-
- # get model
- self.assertEqual(ema.get_model(), ema.model)
-
- # Since fp32 params is not used, it should be of size 0
- self.assertEqual(len(ema.fp32_params), 0)
-
- # EMA step
- x = torch.randn(32).cuda()
- y = model(x)
- loss = y.sum()
- loss.backward()
- optimizer.step()
-
- ema.step(model)
-
- ema_state_dict = ema.get_model().state_dict()
-
- for key, param in model.state_dict().items():
- prev_param = state[key]
- ema_param = ema_state_dict[key]
-
- if "version" in key:
- # Do not decay a model.version pytorch param
- continue
- self.assertTorchAllClose(
- ema_param,
- config.ema_decay * prev_param + (1 - config.ema_decay) * param,
- )
-
- # Since fp32 params is not used, it should be of size 0
- self.assertEqual(len(ema.fp32_params), 0)
-
- # Load EMA into model
- model2 = DummyModule().cuda()
- ema.reverse(model2)
-
- for key, param in model2.state_dict().items():
- ema_param = ema_state_dict[key]
- self.assertTrue(
- torch.allclose(ema_param, param)
- )
-
- def test_ema_fp32(self):
- model = DummyModule().cuda().half()
- optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
- state = deepcopy(model.state_dict())
- config = EMAConfig(ema_fp32=True)
- ema = EMA(model, config)
-
- x = torch.randn(32).cuda()
- y = model(x.half())
- loss = y.sum()
- loss.backward()
- optimizer.step()
-
- ema.step(model)
-
- for key, param in model.state_dict().items():
- prev_param = state[key]
- ema_param = ema.get_model().state_dict()[key]
-
- if "version" in key:
- # Do not decay a model.version pytorch param
- continue
- self.assertIn(key, ema.fp32_params)
-
- # EMA update is done in fp32, and hence the EMA param must be
- # closer to the EMA update done in fp32 than in fp16.
- self.assertLessEqual(
- torch.norm(
- ema_param.float() -
- (config.ema_decay * prev_param.float() + (1 - config.ema_decay) * param.float()).half().float()
- ),
- torch.norm(
- ema_param.float() -
- (config.ema_decay * prev_param + (1 - config.ema_decay) * param).float()
- ),
- )
- self.assertTorchAllClose(
- ema_param,
- (config.ema_decay * prev_param.float() + (1 - config.ema_decay) * param.float()).half(),
- )
-
- def test_ema_fp16(self):
- model = DummyModule().cuda().half()
- optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
- state = deepcopy(model.state_dict())
- config = EMAConfig(ema_fp32=False)
- ema = EMA(model, config)
-
- # Since fp32 params is not used, it should be of size 0
- self.assertEqual(len(ema.fp32_params), 0)
-
- x = torch.randn(32).cuda()
- y = model(x.half())
- loss = y.sum()
- loss.backward()
- optimizer.step()
-
- ema.step(model)
-
- for key, param in model.state_dict().items():
- prev_param = state[key]
- ema_param = ema.get_model().state_dict()[key]
-
- if "version" in key:
- # Do not decay a model.version pytorch param
- continue
-
- # EMA update is done in fp16, and hence the EMA param must be
- # closer to the EMA update done in fp16 than in fp32.
- self.assertLessEqual(
- torch.norm(
- ema_param.float() -
- (config.ema_decay * prev_param + (1 - config.ema_decay) * param).float()
- ),
- torch.norm(
- ema_param.float() -
- (config.ema_decay * prev_param.float() + (1 - config.ema_decay) * param.float()).half().float()
- ),
- )
- self.assertTorchAllClose(
- ema_param,
- config.ema_decay * prev_param + (1 - config.ema_decay) * param,
- )
-
- # Since fp32 params is not used, it should be of size 0
- self.assertEqual(len(ema.fp32_params), 0)
-
-
-if __name__ == "__main__":
- unittest.main()
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/fully_sharded_data_parallel/README.md b/spaces/OFA-Sys/OFA-vqa/fairseq/examples/fully_sharded_data_parallel/README.md
deleted file mode 100644
index b9e44fef48bee5faeee27b3d1d1b1eb96b6a477f..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/fully_sharded_data_parallel/README.md
+++ /dev/null
@@ -1,177 +0,0 @@
-# Fully Sharded Data Parallel (FSDP)
-
-## Overview
-Recent work by [Microsoft](https://arxiv.org/abs/1910.02054) and
-[Google](https://arxiv.org/abs/2004.13336) has shown that data parallel
-training can be made significantly more efficient by sharding the model
-parameters and optimizer state across data parallel workers. These ideas are
-encapsulated in the new **`FullyShardedDataParallel` (FSDP)** wrapper provided
-by [fairscale](https://github.com/facebookresearch/fairscale/).
-
-Compared to PyTorch DDP:
-* FSDP produces identical results as PyTorch DDP (it's still synchronous data parallel training)
-* FSDP shards parameters (FP16 + FP32) and optimizer state across data parallel GPUs
-* FSDP is faster than PyTorch DDP because the optimizer step is sharded, and the communication can be overlapped with the forward pass
-* FSDP enables training 13B parameter models on 8 GPUs and 175B parameter models on 128 GPUs
-
-FSDP is fully supported in fairseq via the following new arguments:
-* `--ddp-backend=fully_sharded`: enables full sharding via FSDP
-* `--cpu-offload`: offloads the optimizer state and FP32 model copy to CPU (combine with `--optimizer=cpu_adam`)
-* `--no-reshard-after-forward`: increases training speed for large models (1B+ params) and is similar to ZeRO stage 2
-* other popular options (`--fp16`, `--update-freq`, `--checkpoint-activations`, `--offload-activations`, etc.) continue to work as normal
-
-Limitations
-
-FSDP currently has several limitations compared to fairseq's default DDP backend (PyTorch DDP):
-* while FSDP is full compatible with pointwise Optimizers (e.g., Adam, AdamW, Adadelta, Adamax, SGD, etc.), it is not currently compatible with non-pointwise Optimizers (e.g., Adagrad, Adafactor, LAMB, etc.)
-* FSDP depends on flattening the parameters, so models that currently require `--fp16-no-flatten-grads` may not be supported
-
-See the [fairscale docs](https://fairscale.readthedocs.io/en/latest/api/nn/fsdp_tips.html) for a more detailed
-explanation of these and other limitations.
-
-
How it works
-
-
-
-See the [fairscale docs](https://fairscale.readthedocs.io/en/latest/api/nn/fsdp_tips.html) for a more detailed
-explanation of how FSDP works.
-
-
Example output
-
-```
-(...)
-2021-03-08 12:29:51 | INFO | fairseq_cli.train | num. model params: 13,110,865,920 (num. trained: 13,110,865,920)
-(...)
-2021-03-08 12:29:51 | INFO | fairseq_cli.train | training on 1 devices (GPUs/TPUs)
-2021-03-08 12:29:51 | INFO | fairseq_cli.train | max tokens per GPU = None and batch size per GPU = 8
-(...)
-Adam Optimizer #0 is created with AVX2 arithmetic capability.
-Config: alpha=0.000100, betas=(0.900000, 0.980000), weight_decay=0.000000, adam_w=1
-(...)
-2021-03-08 12:31:36 | INFO | train_inner | {"epoch": 1, "update": 0.0, "loss": "16.475", "ppl": "91120.8", "wps": "0", "ups": "0", "wpb": "16384", "bsz": "8", "num_updates": "1", "lr": "2e-05", "gnorm": "20.751", "loss_scale": "4", "train_wall": "99", "gb_free": "9.3", "wall": "105"}
-2021-03-08 12:32:33 | INFO | train_inner | {"epoch": 1, "update": 0.0, "loss": "16.446", "ppl": "89281.6", "wps": "288.7", "ups": "0.02", "wpb": "16384", "bsz": "8", "num_updates": "2", "lr": "4e-05", "gnorm": "19.777", "loss_scale": "4", "train_wall": "57", "gb_free": "9.3", "wall": "161"}
-2021-03-08 12:33:12 | INFO | fairseq.trainer | NOTE: gradient overflow detected, ignoring gradient, setting loss scale to: 2.0
-2021-03-08 12:33:51 | INFO | fairseq.trainer | NOTE: gradient overflow detected, ignoring gradient, setting loss scale to: 1.0
-2021-03-08 12:34:45 | INFO | train_inner | {"epoch": 1, "update": 0.001, "loss": "25.22", "ppl": "3.90691e+07", "wps": "123.4", "ups": "0.01", "wpb": "16384", "bsz": "8", "num_updates": "3", "lr": "6e-05", "gnorm": "131.281", "loss_scale": "1", "train_wall": "133", "gb_free": "9.3", "wall": "294"}
-2021-03-08 12:35:43 | INFO | train_inner | {"epoch": 1, "update": 0.001, "loss": "18.079", "ppl": "276809", "wps": "285.5", "ups": "0.02", "wpb": "16384", "bsz": "8", "num_updates": "4", "lr": "8e-05", "gnorm": "13.776", "loss_scale": "1", "train_wall": "57", "gb_free": "9.3", "wall": "351"}
-2021-03-08 12:36:35 | INFO | train_inner | {"epoch": 1, "update": 0.001, "loss": "23.729", "ppl": "1.39088e+07", "wps": "316.7", "ups": "0.02", "wpb": "16384", "bsz": "8", "num_updates": "5", "lr": "0.0001", "gnorm": "72.774", "loss_scale": "1", "train_wall": "52", "gb_free": "9.3", "wall": "403"}
-2021-03-08 12:37:28 | INFO | train_inner | {"epoch": 1, "update": 0.001, "loss": "20.429", "ppl": "1.41203e+06", "wps": "307.6", "ups": "0.02", "wpb": "16384", "bsz": "8", "num_updates": "6", "lr": "8e-05", "gnorm": "60.846", "loss_scale": "1", "train_wall": "53", "gb_free": "9.3", "wall": "456"}
-2021-03-08 12:38:27 | INFO | train_inner | {"epoch": 1, "update": 0.001, "loss": "18.965", "ppl": "511684", "wps": "279.4", "ups": "0.02", "wpb": "16384", "bsz": "8", "num_updates": "7", "lr": "6e-05", "gnorm": "22.687", "loss_scale": "1", "train_wall": "59", "gb_free": "9.3", "wall": "515"}
-2021-03-08 12:39:18 | INFO | train_inner | {"epoch": 1, "update": 0.001, "loss": "18.345", "ppl": "332887", "wps": "319.1", "ups": "0.02", "wpb": "16384", "bsz": "8", "num_updates": "8", "lr": "4e-05", "gnorm": "8.451", "loss_scale": "1", "train_wall": "51", "gb_free": "9.3", "wall": "566"}
-2021-03-08 12:40:11 | INFO | train_inner | {"epoch": 1, "update": 0.002, "loss": "18.262", "ppl": "314336", "wps": "305.9", "ups": "0.02", "wpb": "16384", "bsz": "8", "num_updates": "9", "lr": "2e-05", "gnorm": "6.457", "loss_scale": "1", "train_wall": "54", "gb_free": "9.3", "wall": "620"}
-2021-03-08 12:41:04 | INFO | train_inner | {"epoch": 1, "update": 0.002, "loss": "17.556", "ppl": "192686", "wps": "311.8", "ups": "0.02", "wpb": "16384", "bsz": "8", "num_updates": "10", "lr": "0", "gnorm": "5.796", "loss_scale": "1", "train_wall": "53", "gb_free": "9.3", "wall": "673"}
-2021-03-08 12:41:04 | INFO | fairseq_cli.train | Stopping training due to num_updates: 10 >= max_update: 10
-2021-03-08 12:41:04 | INFO | fairseq_cli.train | begin validation on "valid" subset
-2021-03-08 12:43:15 | INFO | valid | {"epoch": 1, "valid_loss": "17.953", "valid_ppl": "253807", "valid_wps": "1868.4", "valid_wpb": "15400.2", "valid_bsz": "7.6", "valid_num_updates": "10"}
-2021-03-08 12:43:15 | INFO | fairseq_cli.train | end of epoch 1 (average epoch stats below)
-2021-03-08 12:43:15 | INFO | train | {"epoch": 1, "train_loss": "19.351", "train_ppl": "668509", "train_wps": "210.9", "train_ups": "0.01", "train_wpb": "16384", "train_bsz": "8", "train_num_updates": "10", "train_lr": "0", "train_gnorm": "36.26", "train_loss_scale": "1", "train_train_wall": "667", "train_gb_free": "9.3", "train_wall": "804"}
-2021-03-08 12:43:15 | INFO | fairseq_cli.train | done training in 798.6 seconds
-```
-
-
Example output
-
-```
-(...)
-2021-03-08 18:04:09 | INFO | fairseq_cli.train | num. model params: 13,110,865,920 (num. trained: 13,110,865,920)
-(...)
-2021-03-08 18:04:09 | INFO | fairseq_cli.train | training on 8 devices (GPUs/TPUs)
-2021-03-08 18:04:09 | INFO | fairseq_cli.train | max tokens per GPU = None and batch size per GPU = 8
-(...)
-Adam Optimizer #0 is created with AVX2 arithmetic capability.
-Config: alpha=0.000100, betas=(0.900000, 0.980000), weight_decay=0.000000, adam_w=1
-(...)
-2021-03-08 18:05:06 | INFO | train_inner | {"epoch": 1, "update": 0.001, "loss": "16.408", "ppl": "86945.6", "wps": "0", "ups": "0", "wpb": "131072", "bsz": "64", "num_updates": "1", "lr": "2e-05", "gnorm": "18.27", "loss_scale": "4", "train_wall": "47", "gb_free": "9.3", "wall": "56"}
-2021-03-08 18:05:45 | INFO | train_inner | {"epoch": 1, "update": 0.002, "loss": "16.352", "ppl": "83644.3", "wps": "3283.4", "ups": "0.03", "wpb": "131072", "bsz": "64", "num_updates": "2", "lr": "4e-05", "gnorm": "18.411", "loss_scale": "4", "train_wall": "40", "gb_free": "9.3", "wall": "96"}
-2021-03-08 18:06:21 | INFO | fairseq.trainer | NOTE: gradient overflow detected, ignoring gradient, setting loss scale to: 2.0
-2021-03-08 18:06:56 | INFO | fairseq.trainer | NOTE: gradient overflow detected, ignoring gradient, setting loss scale to: 1.0
-2021-03-08 18:07:37 | INFO | train_inner | {"epoch": 1, "update": 0.006, "loss": "23.682", "ppl": "1.34537e+07", "wps": "1176.6", "ups": "0.01", "wpb": "131072", "bsz": "64", "num_updates": "3", "lr": "6e-05", "gnorm": "119.682", "loss_scale": "1", "train_wall": "111", "gb_free": "9.3", "wall": "208"}
-2021-03-08 18:08:18 | INFO | train_inner | {"epoch": 1, "update": 0.007, "loss": "18.988", "ppl": "519921", "wps": "3189.1", "ups": "0.02", "wpb": "131072", "bsz": "64", "num_updates": "4", "lr": "8e-05", "gnorm": "14.934", "loss_scale": "1", "train_wall": "41", "gb_free": "9.3", "wall": "249"}
-2021-03-08 18:08:59 | INFO | train_inner | {"epoch": 1, "update": 0.008, "loss": "20.08", "ppl": "1.10798e+06", "wps": "3223.1", "ups": "0.02", "wpb": "131072", "bsz": "64", "num_updates": "5", "lr": "0.0001", "gnorm": "59.92", "loss_scale": "1", "train_wall": "41", "gb_free": "9.3", "wall": "289"}
-2021-03-08 18:09:39 | INFO | train_inner | {"epoch": 1, "update": 0.009, "loss": "18.323", "ppl": "327980", "wps": "3256.6", "ups": "0.02", "wpb": "131072", "bsz": "64", "num_updates": "6", "lr": "8e-05", "gnorm": "37.425", "loss_scale": "1", "train_wall": "40", "gb_free": "9.3", "wall": "330"}
-2021-03-08 18:10:20 | INFO | train_inner | {"epoch": 1, "update": 0.01, "loss": "17.264", "ppl": "157354", "wps": "3188.7", "ups": "0.02", "wpb": "131072", "bsz": "64", "num_updates": "7", "lr": "6e-05", "gnorm": "10.824", "loss_scale": "1", "train_wall": "41", "gb_free": "9.3", "wall": "371"}
-2021-03-08 18:11:01 | INFO | train_inner | {"epoch": 1, "update": 0.011, "loss": "16.794", "ppl": "113647", "wps": "3230", "ups": "0.02", "wpb": "131072", "bsz": "64", "num_updates": "8", "lr": "4e-05", "gnorm": "5.616", "loss_scale": "1", "train_wall": "41", "gb_free": "9.3", "wall": "411"}
-2021-03-08 18:11:39 | INFO | train_inner | {"epoch": 1, "update": 0.012, "loss": "16.706", "ppl": "106938", "wps": "3384", "ups": "0.03", "wpb": "131072", "bsz": "64", "num_updates": "9", "lr": "2e-05", "gnorm": "5.318", "loss_scale": "1", "train_wall": "39", "gb_free": "9.3", "wall": "450"}
-2021-03-08 18:12:19 | INFO | train_inner | {"epoch": 1, "update": 0.013, "loss": "16.548", "ppl": "95796.2", "wps": "3274.4", "ups": "0.02", "wpb": "131072", "bsz": "64", "num_updates": "10", "lr": "0", "gnorm": "5.22", "loss_scale": "1", "train_wall": "40", "gb_free": "9.3", "wall": "490"}
-2021-03-08 18:12:19 | INFO | fairseq_cli.train | Stopping training due to num_updates: 10 >= max_update: 10
-2021-03-08 18:12:19 | INFO | fairseq_cli.train | begin validation on "valid" subset
-2021-03-08 18:12:45 | INFO | valid | {"epoch": 1, "valid_loss": "16.624", "valid_ppl": "101000", "valid_wps": "10855.9", "valid_wpb": "123202", "valid_bsz": "60.5", "valid_num_updates": "10"}
-2021-03-08 18:12:45 | INFO | fairseq_cli.train | end of epoch 1 (average epoch stats below)
-2021-03-08 18:12:45 | INFO | train | {"epoch": 1, "train_loss": "18.114", "train_ppl": "283776", "train_wps": "2567.8", "train_ups": "0.02", "train_wpb": "131072", "train_bsz": "64", "train_num_updates": "10", "train_lr": "0", "train_gnorm": "29.562", "train_loss_scale": "1", "train_train_wall": "480", "train_gb_free": "9.3", "train_wall": "516"}
-2021-03-08 18:12:45 | INFO | fairseq_cli.train | done training in 509.9 seconds
-```
-
-
' if cover else ""
- '
-