python_code stringlengths 0 258k |
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"""ImageNet-v2 tf.data input pipeline.
Uses TFDS https://www.tensorflow.org/datasets/catalog/imagenet_v2.
"""
import functools
from typing import Dict, Iterator, Tuple
import tensorflow_datasets as tfds
from algorithmic_efficiency import data_utils
from algorithmic_efficiency import spec
from algorithmic_efficiency... |
"""PyTorch implementation of ResNet.
Adapted from torchvision:
https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py.
"""
import collections
from typing import Any, Callable, List, Optional, Type, Union
import torch
from torch import nn
from torch import Tensor
from algorithmic_efficiency impor... |
"""ImageNet workload implemented in PyTorch."""
import contextlib
import functools
import itertools
import math
import os
import random
from typing import Dict, Iterator, Optional, Tuple
import numpy as np
import torch
import torch.distributed as dist
import torch.nn.functional as F
from torch.nn.parallel import Dist... |
"""PyTorch implementation of RandAugmentation.
Adapted from:
https://pytorch.org/vision/stable/_modules/torchvision/transforms/autoaugment.html.
"""
import math
from typing import Dict, List, Optional, Tuple
import numpy as np
import PIL
import torch
from torch import Tensor
from torchvision.transforms import functi... |
"""ImageNet input pipeline.
Forked from Flax example which can be found here:
https://github.com/google/flax/blob/main/examples/imagenet/input_pipeline.py.
"""
import functools
from typing import Dict, Iterator, Tuple
from flax import jax_utils
import jax
import tensorflow as tf
import tensorflow_datasets as tfds
f... |
"""Jax implementation of ResNet V1.
Adapted from Flax example:
https://github.com/google/flax/blob/main/examples/imagenet/models.py.
"""
import functools
from typing import Any, Callable, Tuple
from flax import linen as nn
import jax.numpy as jnp
from algorithmic_efficiency import spec
ModuleDef = nn.Module
clas... |
"""ImageNet workload implemented in Jax.
Forked from the Flax ImageNet Example v0.3.3
https://github.com/google/flax/tree/v0.3.3/examples/imagenet.
"""
import functools
import itertools
import math
from typing import Dict, Iterator, Optional, Tuple
from flax import jax_utils
import jax
from jax import lax
import jax... |
"""Jax implementation of RandAugmentation.
Adapted from:
https://github.com/google/init2winit/blob/master/init2winit/dataset_lib/autoaugment.py.
"""
import inspect
import math
import tensorflow as tf
from tensorflow_addons import image as contrib_image
# This signifies the max integer that the controller RNN could ... |
"""Data loader for pre-processed Criteo data.
Similar to how the NVIDIA example works, we split data from the last day into a
validation and test split (taking the first half for test and second half for
validation). See here for the NVIDIA example:
https://github.com/NVIDIA/DeepLearningExamples/blob/4e764dcd78732ebfe... |
"""Criteo1TB DLRM workload base class."""
import math
import os
from typing import Dict, Optional, Tuple
import jax
import torch.distributed as dist
from algorithmic_efficiency import spec
from algorithmic_efficiency.workloads.criteo1tb import input_pipeline
USE_PYTORCH_DDP = 'LOCAL_RANK' in os.environ
class BaseC... |
"""Pytorch implementation of DLRM-Small."""
import math
import torch
from torch import nn
def dot_interact(concat_features):
"""Performs feature interaction operation between dense or sparse features.
Input tensors represent dense or sparse features.
Pre-condition: The tensors have been stacked along dimensio... |
"""Criteo1TB workload implemented in PyTorch."""
import contextlib
from typing import Dict, Optional, Tuple
import jax
import torch
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP
from algorithmic_efficiency import param_utils
from algorithmic_efficiency import spec
from ... |
"""A JAX implementation of DLRM-Small."""
from typing import Sequence
import flax.linen as nn
from jax import nn as jnn
import jax.numpy as jnp
def dot_interact(concat_features):
"""Performs feature interaction operation between dense or sparse features.
Input tensors represent dense or sparse features.
Pre-c... |
"""Criteo1TB workload implemented in Jax."""
import functools
from typing import Dict, Optional, Tuple
from flax import jax_utils
import jax
import jax.numpy as jnp
from algorithmic_efficiency import param_utils
from algorithmic_efficiency import spec
from algorithmic_efficiency.workloads.criteo1tb.criteo1tb_jax impo... |
"""FastMRI knee singlecoil input pipeline."""
import datetime
import functools
import glob
import os
import h5py
import jax
import tensorflow as tf
from algorithmic_efficiency import data_utils
_TRAIN_DIR = 'knee_singlecoil_train'
_VAL_DIR = 'knee_singlecoil_val'
_EVAL_SEED = 0
def _process_example(kspace,
... |
"""FastMRI workload parent class."""
import math
from typing import Optional
from algorithmic_efficiency import spec
from algorithmic_efficiency.workloads.fastmri import input_pipeline
class BaseFastMRIWorkload(spec.Workload):
@property
def target_metric_name(self) -> str:
"""The name of the target metric ... |
"""U-Net Model.
Adapted from fastMRI:
https://github.com/facebookresearch/fastMRI/blob/main/fastmri/models/unet.py
"""
from typing import Optional
import torch
from torch import nn
from torch import Tensor
from torch.nn import functional as F
from algorithmic_efficiency import init_utils
class UNet(nn.Module):
... |
"""Structural similarity index calculation in PyTorch, ported from Jax."""
import functools
import functorch
import torch
import torch.nn.functional as F
from torchvision.transforms.functional import pad as pad_fn
from algorithmic_efficiency.pytorch_utils import pytorch_setup
DEVICE = pytorch_setup()[2]
def ssim(... |
"""FastMRI workload implemented in PyTorch."""
import contextlib
import math
from typing import Dict, Optional, Tuple
import torch
import torch.distributed as dist
import torch.nn.functional as F
from torch.nn.parallel import DistributedDataParallel as DDP
from algorithmic_efficiency import param_utils
from algorith... |
"""Jax / Flax implementation of FastMRI U-Net.
Forked from
https://github.com/google/init2winit/blob/master/init2winit/model_lib/unet.py
Original implementation:
github.com/facebookresearch/fastMRI/blob/main/fastmri/models/unet.py
Training:
github.com/facebookresearch/fastMRI/blob/main/fastmri/pl_modules/unet_module... |
"""Structural similarity index calculation in Jax."""
import functools
import jax
import jax.numpy as jnp
def ssim(logits, targets, mean=None, std=None, volume_max=None):
"""Computes example-wise structural similarity for a batch.
NOTE(dsuo): we use the same (default) arguments to `structural_similarity`
as ... |
"""FastMRI workload implemented in Jax."""
import functools
import math
from typing import Dict, Optional, Tuple
from flax import jax_utils
import jax
import jax.numpy as jnp
from algorithmic_efficiency import param_utils
from algorithmic_efficiency import spec
import algorithmic_efficiency.random_utils as prng
from... |
"""CIFAR workload parent class."""
import abc
import math
from typing import Any, Dict, Iterator, Optional, Tuple
import jax
import torch
from algorithmic_efficiency import spec
from algorithmic_efficiency.pytorch_utils import pytorch_setup
import algorithmic_efficiency.random_utils as prng
USE_PYTORCH_DDP, _, _, _... |
"""CIFAR input pipeline.
Forked from Flax example which can be found here:
https://github.com/google/flax/blob/main/examples/imagenet/input_pipeline.py
and adjusted to work for CIFAR10.
"""
import functools
from typing import Dict, Iterator, Tuple
from flax import jax_utils
import jax
import tensorflow as tf
import ... |
"""Jax implementation of ResNet V1 for CIFAR.
Adapted from Flax example:
https://github.com/google/flax/blob/main/examples/imagenet/models.py.
"""
import functools
from typing import Any, Callable, Tuple
from flax import linen as nn
import jax.numpy as jnp
from algorithmic_efficiency import spec
from algorithmic_ef... |
"""CIFAR workload implemented in Jax."""
import functools
from typing import Any, Dict, Iterator, Optional, Tuple
from flax import jax_utils
from flax import linen as nn
import jax
from jax import lax
import jax.numpy as jnp
import optax
import tensorflow_datasets as tfds
from algorithmic_efficiency import param_uti... |
"""PyTorch implementation of ResNet for CIFAR.
Adapted from torchvision:
https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py.
"""
import collections
from typing import Any, Callable, List, Optional, Type, Union
import torch
from torch import nn
from algorithmic_efficiency import spec
from alg... |
"""CIFAR10 workload implemented in PyTorch."""
import contextlib
import functools
import random
from typing import Any, Dict, Optional, Tuple
import torch
import torch.distributed as dist
import torch.nn.functional as F
from torch.nn.parallel import DistributedDataParallel as DDP
from torchvision import transforms
fr... |
"""Data preprocessing for LibriSpeech.
Modified from https://github.com/lsari/librispeech_100.
"""
import multiprocessing.dummy
import os
from os.path import exists
import sys
import threading
import time
from absl import flags
from absl import logging
import numpy as np
import pandas as pd
from pydub import AudioSeg... |
r"""MLCommons dataset setup script.
If you already have a copy of a dataset(s), you can skip download it and provide
the path when running your algorithm with submission_runner.py via --data_dir.
Note that in order to avoid potential accidental deletion, this script does NOT
delete any intermediate temporary files (s... |
"""Sentence Piece Tokenizer and ops for tokenizing / de-tokenizing a dataset.
Forked from:
https://github.com/google/flax/blob/b60f7f45b90f8fc42a88b1639c9cc88a40b298d3/examples/lm1b/tokenizer.py
"""
import os
import tempfile
from typing import Dict
from absl import flags
from absl import logging
import sentencepiece... |
"""Test for the equality of the SSIM calculation in Jax and PyTorch."""
import os
from typing import Tuple
from absl.testing import absltest
from absl.testing import parameterized
import jax.numpy as jnp
import numpy as np
import torch
from algorithmic_efficiency.pytorch_utils import pytorch_setup
from algorithmic_e... |
import jax
import numpy as np
import pytest
# isort: skip_file
# pylint:disable=line-too-long
from algorithmic_efficiency.workloads.cifar.cifar_jax.workload import CifarWorkload as JaxCifarWorkload
from algorithmic_efficiency.workloads.cifar.cifar_pytorch.workload import CifarWorkload as PyTorchCifarWorkload
from algo... |
import jax
import pytest
from absl import logging
from algorithmic_efficiency import spec
# isort: skip_file
# pylint:disable=line-too-long
from algorithmic_efficiency.workloads.cifar.cifar_jax.workload import CifarWorkload as JaxCifarWorkload
from algorithmic_efficiency.workloads.cifar.cifar_pytorch.workload import ... |
"""
Runs 10 steps of SGD for each workload and compares results.
Run it as:
python3 test_traindiffs.py
"""
import pickle
from subprocess import DEVNULL
from subprocess import run
from subprocess import STDOUT
from absl import flags
from absl.testing import absltest
FLAGS = flags.FLAGS
WORKLOADS = [
'imagenet_r... |
import jax
import jax.numpy as jnp
import jax.random as jax_rng
import jraph
import pytest
import torch
from algorithmic_efficiency.workloads.criteo1tb.criteo1tb_jax.models import \
DlrmSmall as JaxDlrmSmall
from algorithmic_efficiency.workloads.criteo1tb.criteo1tb_pytorch.models import \
DlrmSmall as PyTorchD... |
"""Check whether the __version__ attribute is set correctly."""
import algorithmic_efficiency
def test_version_attribute():
"""Check whether __version__ exists and is a valid string."""
assert hasattr(algorithmic_efficiency, "__version__")
version = algorithmic_efficiency.__version__
assert isinstance(versi... |
"""Tests for submission.py for baselines.
This is an end-to-end test for all baselines on MNIST in PyTorch and Jax that
requires the dataset to be available.
"""
import copy
import os
import sys
from absl import flags
from absl import logging
from absl.testing import absltest
from absl.testing import parameterized
... |
"""Test that each reference submission can run a train and eval step.
This is a brief test that runs the for the workload and reference submission
code for one train and one eval step for all workloads, without the real data
iterator because it is not realistic to have all datasets available at testing
time. For end-t... |
"""Tests for submission_runner.py.
This is an end-to-end test for MNIST in PyTorch and Jax that requires the
dataset to be available. For testing the workload and reference submission code
for all workloads, see reference_algorithm_tests.py.
"""
import copy
import os
import sys
from absl import flags
from absl import... |
from collections import Counter
import pprint
def jax_like_pytorch_statedict(model, state_dict, keys=None):
if keys is None:
keys = []
c = Counter()
children = list(model.children())
for k, v in model.named_parameters():
if '.' not in k:
state_dict[(*keys, k)] = v
for i in children:
num_p... |
import torch
from algorithmic_efficiency import spec
from reference_algorithms.target_setting_algorithms.data_selection import \
data_selection # pylint: disable=unused-import
from reference_algorithms.target_setting_algorithms.pytorch_submission_base import \
update_params # pylint: disable=unused-import
... |
from flax import jax_utils
import jax
import numpy as np
import torch
from tests.modeldiffs.torch2jax_utils import Torch2Jax
from tests.modeldiffs.torch2jax_utils import value_transform
#pylint: disable=dangerous-default-value
def torch2jax(jax_workload,
pytorch_workload,
key_transform=No... |
from flax import jax_utils
import jax
import jax.numpy as jnp
import optax
from algorithmic_efficiency import spec
from reference_algorithms.target_setting_algorithms.data_selection import \
data_selection # pylint: disable=unused-import
from reference_algorithms.target_setting_algorithms.jax_submission_base impo... |
import os
# Disable GPU access for both jax and pytorch.
os.environ['CUDA_VISIBLE_DEVICES'] = ''
import jax
import torch
from algorithmic_efficiency import spec
from algorithmic_efficiency.workloads.librispeech_deepspeech.librispeech_jax.workload import \
LibriSpeechDeepSpeechWorkload as JaxWorkload
from algorit... |
import os
# Disable GPU access for both jax and pytorch.
os.environ['CUDA_VISIBLE_DEVICES'] = ''
import jax
import torch
from algorithmic_efficiency import spec
from algorithmic_efficiency.workloads.wmt.wmt_jax.workload import \
WmtWorkload as JaxWorkload
from algorithmic_efficiency.workloads.wmt.wmt_pytorch.wor... |
import os
# Disable GPU access for both jax and pytorch.
os.environ['CUDA_VISIBLE_DEVICES'] = ''
import jax
import torch
from algorithmic_efficiency import spec
from algorithmic_efficiency.workloads.imagenet_vit.imagenet_jax.workload import \
ImagenetVitWorkload as JaxWorkload
from algorithmic_efficiency.workloa... |
import os
# Disable GPU access for both jax and pytorch.
os.environ['CUDA_VISIBLE_DEVICES'] = ''
import jax
import torch
from algorithmic_efficiency import spec
from algorithmic_efficiency.workloads.librispeech_conformer.librispeech_jax.workload import \
LibriSpeechConformerWorkload as JaxWorkload
from algorithm... |
import os
# Disable GPU access for both jax and pytorch.
os.environ['CUDA_VISIBLE_DEVICES'] = ''
import jax
import jraph
import numpy as np
import torch
from algorithmic_efficiency import spec
from algorithmic_efficiency.workloads.ogbg.ogbg_jax.workload import \
OgbgWorkload as JaxWorkload
from algorithmic_effic... |
import os
# Disable GPU access for both jax and pytorch.
os.environ['CUDA_VISIBLE_DEVICES'] = ''
import jax
import torch
from algorithmic_efficiency import spec
from algorithmic_efficiency.workloads.imagenet_resnet.imagenet_jax.workload import \
ImagenetResNetWorkload as JaxWorkload
from algorithmic_efficiency.w... |
import os
# Disable GPU access for both jax and pytorch.
os.environ['CUDA_VISIBLE_DEVICES'] = ''
import jax
import numpy as np
import torch
from algorithmic_efficiency import spec
from algorithmic_efficiency.workloads.criteo1tb.criteo1tb_jax.workload import \
Criteo1TbDlrmSmallWorkload as JaxWorkload
from algori... |
import os
# Disable GPU access for both jax and pytorch.
os.environ['CUDA_VISIBLE_DEVICES'] = ''
import jax
import torch
from algorithmic_efficiency import spec
from algorithmic_efficiency.workloads.fastmri.fastmri_jax.workload import \
FastMRIWorkload as JaxWorkload
from algorithmic_efficiency.workloads.fastmri... |
"""Tests for imagenet_resnet/imagenet_jax/workload.py."""
from absl.testing import absltest
import jax
import jax.numpy as jnp
from algorithmic_efficiency import spec
from algorithmic_efficiency.workloads.imagenet_resnet.imagenet_jax.workload import \
ImagenetResNetWorkload
def _pytree_total_diff(pytree_a, pytr... |
"""Update submission function in Jax."""
import functools
from typing import Dict, List, Tuple
import jax
from jax import lax
import jax.numpy as jnp
import optax
from algorithmic_efficiency import spec
_GRAD_CLIP_EPS = 1e-6
@functools.partial(
jax.pmap,
axis_name='batch',
in_axes=(None, None, 0, 0, 0,... |
"""Submission file for an AdamW optimizer with warmup+cosine LR in PyTorch."""
import torch
from algorithmic_efficiency import spec
from reference_algorithms.target_setting_algorithms import cosine_warmup
from reference_algorithms.target_setting_algorithms.data_selection import \
data_selection # pylint: disable... |
"""Submission file for a NAdamW optimizer in PyTorch."""
import math
from typing import List
import torch
from torch import Tensor
from algorithmic_efficiency import spec
from reference_algorithms.target_setting_algorithms import cosine_warmup
from reference_algorithms.target_setting_algorithms.data_selection import... |
"""Submission file for a SGD with Nesterov optimizer in Jax."""
from typing import Callable
from flax import jax_utils
import jax
import jax.numpy as jnp
import optax
from algorithmic_efficiency import spec
from reference_algorithms.target_setting_algorithms.data_selection import \
data_selection # pylint: disa... |
"""Submission file for a SGD with HeavyBall momentum optimizer in PyTorch."""
import torch
from torch.optim.lr_scheduler import LambdaLR
from algorithmic_efficiency import spec
from reference_algorithms.target_setting_algorithms.data_selection import \
data_selection # pylint: disable=unused-import
from referenc... |
"""Collection of the target-setting runs for all workloads."""
|
"""Submission file for an AdamW optimizer with warmup+cosine LR in Jax."""
from flax import jax_utils
import jax
import jax.numpy as jnp
import optax
from algorithmic_efficiency import spec
from reference_algorithms.target_setting_algorithms import cosine_warmup
from reference_algorithms.target_setting_algorithms.data... |
"""Submission file for a NAdamW optimizer with warmup+cosine LR in Jax."""
from typing import Any, Callable, NamedTuple, Optional, Union
import chex
from flax import jax_utils
import jax
import jax.numpy as jnp
import optax
from algorithmic_efficiency import spec
from reference_algorithms.target_setting_algorithms i... |
"""Implementions of a linear warmup then cosine decay LR schedule."""
import optax
from torch.optim.lr_scheduler import CosineAnnealingLR
from torch.optim.lr_scheduler import LinearLR
from torch.optim.lr_scheduler import SequentialLR
def jax_cosine_warmup(step_hint: int, hyperparameters):
# Create learning rate sc... |
from typing import Dict, Iterator, Tuple
from algorithmic_efficiency import spec
def data_selection(
workload: spec.Workload,
input_queue: Iterator[Dict[str, spec.Tensor]],
optimizer_state: spec.OptimizerState,
current_param_container: spec.ParameterContainer,
model_state: spec.ModelAuxiliaryStat... |
"""Batch size selection submission function."""
def get_batch_size(workload_name):
# Return the global batch size.
if workload_name == 'criteo1tb':
return 262_144
elif workload_name == 'fastmri':
return 32
elif workload_name == 'imagenet_resnet':
return 1024
elif workload_name == 'imagenet_vit':... |
"""Batch size and update submission functions in PyTorch."""
from typing import Dict, List, Tuple
from absl import logging
import torch
import torch.distributed.nn as dist_nn
from algorithmic_efficiency import spec
from algorithmic_efficiency.pytorch_utils import pytorch_setup
USE_PYTORCH_DDP = pytorch_setup()[0]
... |
"""Submission file for a SGD with Nesterov optimizer in PyTorch."""
import torch
from torch.optim.lr_scheduler import LambdaLR
from algorithmic_efficiency import spec
from reference_algorithms.target_setting_algorithms.data_selection import \
data_selection # pylint: disable=unused-import
from reference_algorith... |
"""Submission file for a SGD with HeavyBall momentum optimizer in Jax."""
from typing import Callable
from flax import jax_utils
import jax
import jax.numpy as jnp
import optax
from algorithmic_efficiency import spec
from reference_algorithms.target_setting_algorithms.data_selection import \
data_selection # py... |
"""Training algorithm track submission functions for MNIST."""
import functools
from typing import Dict, Iterator, List, Tuple
from flax import jax_utils
import jax
from jax import lax
import jax.numpy as jnp
import optax
from algorithmic_efficiency import spec
def get_batch_size(workload_name):
# Return the glo... |
"""Training algorithm track submission functions for MNIST."""
from typing import Dict, Iterator, List, Tuple
import torch
from algorithmic_efficiency import spec
def get_batch_size(workload_name):
# Return the global batch size.
batch_sizes = {'mnist': 1024}
return batch_sizes[workload_name]
def init_opti... |
"""Training algorithm track submission functions for LibriSpeech."""
import functools
from typing import Dict, Iterator, List, Tuple
from absl import logging
from flax import jax_utils
import jax
from jax import lax
import jax.numpy as jnp
import numpy as np
import optax
from algorithmic_efficiency import spec
_GRAD... |
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