python_code stringlengths 0 4.04M | repo_name stringlengths 7 58 | file_path stringlengths 5 147 |
|---|---|---|
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import dataclasses
from typing import Dict, List, NamedTuple, Tuple
import unittest
from co3d.dataset import data_types as... | co3d-main | tests/test_types.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import os
import unittest
import numpy as np
import tempfile
import torch
from pytorch3d.renderer.cameras imp... | co3d-main | tests/test_challenge_evaluate.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import os
import json
from joblib import Parallel, delayed
from collections import defaultdict
from tabulate i... | co3d-main | examples/print_co3d_stats.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import logging
import os
import torch
import math
import sys
import json
import random
from tqdm import tqdm
... | co3d-main | examples/show_co3d_dataset.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import logging
import os
import torch
import warnings
from tqdm import tqdm
from omegaconf import DictConfig
... | co3d-main | examples/example_co3d_challenge_submission.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import os
from dataset.download_dataset_impl import build_arg_parser, download_dataset
DEFAULT_LINK_LIST_FILE = os.path.... | co3d-main | co3d/download_dataset.py |
co3d-main | co3d/__init__.py | |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
| co3d-main | co3d/dataset/__init__.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import sys
import dataclasses
import gzip
import json
from dataclasses import dataclass, Field, MISSING
from ty... | co3d-main | co3d/dataset/data_types.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import torch
import copy
from pytorch3d.implicitron.dataset.dataset_base import FrameData
from co3d.challenge.... | co3d-main | co3d/dataset/utils.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import os
import shutil
import requests
import functools
import json
import warnings
from argparse import ArgumentParser
f... | co3d-main | co3d/dataset/download_dataset_impl.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import os
import glob
import argparse
import hashlib
import json
from typing import Optional
from multiprocessing import P... | co3d-main | co3d/dataset/check_checksum.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
"""
Evaluation of Implicitron models on CO3Dv2 challenge.
"""
import logging
import os
import torch
import js... | co3d-main | co3d/utils/evaluate_implicitron_model.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import dataclasses
import torch
from typing import Tuple
from pytorch3d.renderer.cameras import CamerasBase
fr... | co3d-main | co3d/utils/dbir_utils.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import os
import shutil
import logging
import errno
import pickle
import glob
import hashlib
import time
from ... | co3d-main | co3d/challenge/co3d_submission.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import os
import json
import logging
import numpy as np
import dbm
import functools
import h5py
from io impor... | co3d-main | co3d/challenge/io.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
| co3d-main | co3d/challenge/__init__.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import math
import numpy as np
import logging
import time
from typing import Optional
from typing import Tuple
... | co3d-main | co3d/challenge/metric_utils.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
from enum import Enum
import numpy as np
from dataclasses import dataclass
from typing import Optional
@data... | co3d-main | co3d/challenge/data_types.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import os
import zipfile
import glob
import logging
import multiprocessing
import numpy as np
import time
from... | co3d-main | co3d/challenge/utils.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
from io import StringIO
import os
import csv
from typing import List, Any
from .data_types import CO3DTask, C... | co3d-main | co3d/challenge/blank_predictions_results.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from pathlib import Path
from setuptools import setup # type: ignore
setup(
name="cc_net",
version="1.0.0",
pa... | cc_net-main | setup.py |
# 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.
#
"""
Main script to download a CC dump, remove duplicates, split by language and
filter the documents.
The pipeline parameters are described... | cc_net-main | cc_net/mine.py |
# 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.
#
"""
Creates mono-lingual corpus from Wikipedia.
"""
import functools
import re
import subprocess
import urllib.request
from pathlib import ... | cc_net-main | cc_net/get_wiki_cirrus.py |
# 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.
#
"""
Manipulate files containing one json per line.
"""
import argparse
import collections
import contextlib
import functools
import glob
imp... | cc_net-main | cc_net/jsonql.py |
# 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 functools
import itertools
import logging
import os
import sys
import time
import warnings
from pathlib import Path
from typing impor... | cc_net-main | cc_net/execution.py |
# 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 sys
import time
import warnings
from typing import Iterable, Iterator, Sequence, Sized, Tuple, Type
import numpy as np
HASH_TYPE: T... | cc_net-main | cc_net/flat_hash_set.py |
# 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 base64
import hashlib
import itertools
import urllib.parse
from pathlib import Path
from typing import Dict, Iterable, List, Optional... | cc_net-main | cc_net/minify.py |
# 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 re
import unicodedata
UNICODE_PUNCT = {
"，": ",",
"。": ".",
"、": ",",
"„": '"',
"”": '"',
"“": '"',
"«":... | cc_net-main | cc_net/text_normalizer.py |
# 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 logging
import subprocess
from pathlib import Path
from typing import List
import func_argparse
import numpy as np
from cc_net impo... | cc_net-main | cc_net/regroup.py |
# 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 argparse
import time
from pathlib import Path
from typing import Dict, List, Optional, Sequence, Tuple, Union
import kenlm # type: ... | cc_net-main | cc_net/perplexity.py |
# 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.
#
| cc_net-main | cc_net/__init__.py |
# 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 time
from typing import Dict, Optional
import sacremoses # type: ignore
from cc_net import jsonql, text_normalizer
class RobustT... | cc_net-main | cc_net/tokenizer.py |
# 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.
#
"""
Tools to remove duplicate paragraphs across one or several shards.
"""
import argparse
import gc
import hashlib
import logging
import m... | cc_net-main | cc_net/dedup.py |
# 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 contextlib
import functools
import logging
import re
import tempfile
import time
import urllib.request
from pathlib import Path
from ... | cc_net-main | cc_net/process_wet_file.py |
# 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 func_argparse
import cc_net.mine
def main():
func_argparse.parse_and_call(cc_net.mine.get_main_parser())
if __name__ == "__... | cc_net-main | cc_net/__main__.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import collections
from pathlib import Path
from typing import Dict, Optional
import fasttext # type: igno... | cc_net-main | cc_net/split_by_lang.py |
# 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 contextlib
import functools
import gzip
import logging
import multiprocessing
from collections import defaultdict
from pathlib import... | cc_net-main | cc_net/tools/dl_cc_100.py |
cc_net-main | cc_net/tools/__init__.py | |
# 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.
#
"""
This code is used to train a fastText classifier to label document with DMOZ categories.
The data, distributed under the cc-by 3.0 lice... | cc_net-main | cc_net/tools/make_dmoz_corpus.py |
# 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.
#
"""
Tools to search sentences in CC similar to sentences in another corpus.
"""
import functools
import logging
import math
import subproce... | cc_net-main | cc_net/tools/expand_corpus.py |
# 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 json
from pathlib import Path
from typing import Iterable, Sequence
from cc_net import dedup, jsonql
from cc_net.dedup import str_ha... | cc_net-main | tests/test_dedup.py |
# 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 cc_net.text_normalizer as txt
def test_unicode_punct():
weird = "，。、„”“«»１」「《》´∶：？！（）；–—．～’…━〈〉【】％"
replaced = ',.,""""""""... | cc_net-main | tests/test_normalizer.py |
# 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.
#
from pathlib import Path
from cc_net import process_wet_file
def test_parsing():
sample = Path(__file__).parent / "data" / "sample.wa... | cc_net-main | tests/test_parse_wet_file.py |
# 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 numpy as np
import pytest
from cc_net.flat_hash_set import HASH_TYPE, FlatHashSet, NaiveHashSet
def as_dict(flat_hash_set) -> dict... | cc_net-main | tests/test_flat_hash_set.py |
# 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 pytest
def _request_is_disabled(self, *args, **kwargs):
raise Exception(
f"Your code tried to call 'request' with: {arg... | cc_net-main | tests/conftest.py |
# 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.
#
#
| cc_net-main | tests/__init__.py |
# 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 time
from cc_net import jsonql, regroup
def check_regroup(tmp_path, regroup_fn, check_blocks_boundaries=False):
n_shards = 4
... | cc_net-main | tests/test_regroup.py |
# 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 io
from pathlib import Path
from typing import Sequence
import numpy as np
import pytest
from cc_net import jsonql
def bar(small_... | cc_net-main | tests/test_jsonql.py |
# 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 json
from pathlib import Path
import pytest
import cc_net
import cc_net.minify as minify
from cc_net import jsonql, process_wet_fil... | cc_net-main | tests/test_minify.py |
# 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 inspect
import pickle
from pathlib import Path
import pytest
from cc_net import dedup, jsonql, perplexity, split_by_lang, tokenizer... | cc_net-main | tests/test_transformer.py |
# Adapted from https://github.com/NVIDIA/apex/blob/master/setup.py
import torch
from torch.utils.cpp_extension import BuildExtension, CppExtension, CUDAExtension, CUDA_HOME
from setuptools import setup, find_packages
import subprocess
import sys
import warnings
import os
# ninja build does not work unless include_dir... | m2-main | csrc/flashmm/setup.py |
import torch
import torch.nn.functional as F
from einops import rearrange
import math
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
from flashmm import mm_block_fwd, hyena_filter_fwd, exp_mod_in_place_fwd
def ref_mm_block(
u,
linear, out_linear,
x1_s, x2_s, v_s,
... | m2-main | csrc/flashmm/test_flash_mm.py |
import math
import re
import numpy as np
# N = 8192
N = 16384
# The case of 0 / N is special, we want to simplify it to 0 / 2 instead of 0 / 1
numerator = np.arange(1, N // 8 + 1)
gcd = np.gcd(numerator, N)
num = numerator // gcd
denom = N // gcd
lut_vals = ['T_2_0'] + [f'T_{d}_{n}' for n, d in zip(num, denom)]
lut_... | m2-main | csrc/flashmm/lut_code_gen.py |
# Copyright 2022 MosaicML Examples authors
# SPDX-License-Identifier: Apache-2.0
import os
import sys
from typing import Optional, cast
# Add folder root to path to allow us to use relative imports regardless of what directory the script is run from
sys.path.append(os.path.dirname(os.path.realpath(__file__)))
import... | m2-main | bert/benchmark_fwd.py |
# Copyright 2022 MosaicML Examples authors
# SPDX-License-Identifier: Apache-2.0
import os
import sys
# Add folder root to path to allow us to use relative imports regardless of what directory the script is run from
sys.path.append(os.path.dirname(os.path.realpath(__file__)))
try:
import torch
# yapf: disabl... | m2-main | bert/__init__.py |
# Copyright 2022 MosaicML Examples authors
# SPDX-License-Identifier: Apache-2.0
import copy
import gc
import multiprocessing as mp
import os
import sys
import time
from collections import defaultdict
from concurrent.futures import ProcessPoolExecutor as Pool
from multiprocessing.managers import DictProxy, SyncManager... | m2-main | bert/glue.py |
# Copyright 2022 MosaicML Examples authors
# SPDX-License-Identifier: Apache-2.0
import os
import sys
from typing import Optional, cast
# Add folder root to path to allow us to use relative imports regardless of what directory the script is run from
sys.path.append(os.path.dirname(os.path.realpath(__file__)))
import... | m2-main | bert/main.py |
from transformers import BertConfig
class BertConfig(BertConfig):
def __init__(
self,
alibi_starting_size: int = 512,
attention_probs_dropout_prob: float = 0.0,
# mlp
use_glu_mlp: bool = True,
use_monarch_mlp: bool = False,
monarch_mlp_nblocks: int = 4,
... | m2-main | bert/src/configuration_bert.py |
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018-2021, NVIDIA CORPORATION. All rights reserved.
# Copyright (c) 2022, Tri Dao.
# Copyright (c) 2023, MosaicML.
# Copyright (c) 2023, Dan Fu and Simran Arora.
import copy
import logging
import math
import os
import ... | m2-main | bert/src/bert_layers.py |
# Adapted from https://github.com/HazyResearch/flash-attention/blob/main/flash_attn/bert_padding.py
# Adapted from https://github.com/mlcommons/training_results_v1.1/blob/main/NVIDIA/benchmarks/bert/implementations/pytorch/padding.py
"""
Functions for FlashAttention padding and unpadding
"""
from typing import Tup... | m2-main | bert/src/bert_padding.py |
# Copyright 2022 MosaicML Examples authors
# SPDX-License-Identifier: Apache-2.0
"""Implements a Hugging Face BERT wrapped inside a :class:`.ComposerModel`."""
from __future__ import annotations
from typing import Optional
from composer.metrics.nlp import (BinaryF1Score, LanguageCrossEntropy,
... | m2-main | bert/src/hf_bert.py |
# Copyright 2022 MosaicML Examples authors
# SPDX-License-Identifier: Apache-2.0
import os
import sys
import torch
# Add src folder root to path to allow us to use relative imports regardless of what directory the script is run from
sys.path.append(os.path.dirname(os.path.realpath(__file__)))
# yapf: disable
from b... | m2-main | bert/src/__init__.py |
# Copyright 2022 MosaicML Examples authors
# SPDX-License-Identifier: Apache-2.0
"""Triton implementation of Flash Attention.
# Copyright (c) 2022, Tri Dao.
#
# 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... | m2-main | bert/src/flash_attn_triton.py |
# Copyright 2022 MosaicML Examples authors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import os
import sys
from typing import Optional
# Add src folder root to path to allow us to use relative imports regardless of what directory the script is run from
sys.path.append(os.path.dirname(os... | m2-main | bert/src/create_bert.py |
# Copyright 2022 MosaicML Examples authors
# SPDX-License-Identifier: Apache-2.0
"""Build a StreamingTextDataset dataset and dataloader for training."""
import os
from itertools import islice
from typing import Any, Callable, Dict, List, Optional, Sequence, Union
import numpy as np
import torch
import transformers
f... | m2-main | bert/src/text_data.py |
# Copyright 2022 MosaicML Examples authors
# SPDX-License-Identifier: Apache-2.0
"""Streaming dataset conversion scripts for C4 and The Pile."""
import os
import platform
import warnings
from argparse import ArgumentParser, Namespace
from dataclasses import dataclass
from enum import Enum
from typing import Dict, Iter... | m2-main | bert/src/convert_dataset.py |
# Adapted from https://github.com/HazyResearch/hippo/blob/datasets/benchmark/utils.py
""" Useful functions for writing test code. """
import torch
import torch.utils.benchmark as benchmark
def benchmark_forward(fn, *inputs, repeats = 10, desc='', verbose=True, amp=False,
amp_dtype=torch.float16... | m2-main | bert/src/benchmark/benchmark.py |
m2-main | bert/src/benchmark/__init__.py | |
# Copyright 2022 MosaicML Examples authors
# SPDX-License-Identifier: Apache-2.0
# """Contains GLUE job objects for the simple_glue_trainer."""
import atexit
import copy
import gc
import multiprocessing as mp
import os
import sys
from multiprocessing import managers
from typing import Any, Dict, List, Optional, Union,... | m2-main | bert/src/glue/finetuning_jobs.py |
# Copyright 2022 MosaicML Examples authors
# SPDX-License-Identifier: Apache-2.0
| m2-main | bert/src/glue/__init__.py |
# Copyright 2022 MosaicML Examples authors
# SPDX-License-Identifier: Apache-2.0
import logging
from composer.utils import MissingConditionalImportError, dist
_task_column_names = {
'cola': ('sentence', None),
'mnli': ('premise', 'hypothesis'),
'mrpc': ('sentence1', 'sentence2'),
'qnli': ('question',... | m2-main | bert/src/glue/data.py |
def create_param_groups(cfg, model):
'''Create sets of parameter groups based on whether parameter has `_optim` attribute.'''
if not any(hasattr(p, '_optim') for p in model.parameters()):
return model.parameters()
special_params = set()
other_params = set()
param_dict = {pn: p for pn, p... | m2-main | bert/src/optim/create_param_groups.py |
m2-main | bert/src/optim/__init__.py | |
import json
import math
from tqdm import tqdm
from collections import defaultdict
directory = # Enter path to your data directory here
new_directory = # Enter output path here
val_pct = 0.0005 # Percentage of data to use for validation
index = f"{directory}/train/index.json"
with open(index, "r") as f:
index = js... | m2-main | bert/src/utils/create_val_split.py |
m2-main | bert/src/utils/__init__.py | |
""" Utils for the training loop. Copied from https://github.com/HazyResearch/transformers/blob/master/src/utils/utils.py """
import torch.nn as nn
class OptimModule(nn.Module):
""" Interface for Module that allows registering buffers/parameters with configurable optimizer hyperparameters """
def register(self... | m2-main | bert/src/utils/train.py |
# Adapted from https://github.com/HazyResearch/fly/tree/master/src/models/layers
import math
import torch
import torch.nn as nn
from einops import rearrange
from src.mm.structured_linear import StructuredLinear
from src.mm.blockdiag_multiply import blockdiag_multiply
class BlockdiagLinear(StructuredLinear):
de... | m2-main | bert/src/mm/blockdiag_linear.py |
# Copyright (c) 2023, Dan Fu and Simran Arora.
# Adapted from https://github.com/HazyResearch/safari/blob/main/src/models/sequence/hyena.py
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
import opt_einsum as oe
contract = oe.contract
from src.utils.train ... | m2-main | bert/src/mm/hyena_utils.py |
# Adapted from https://github.com/HazyResearch/fly/tree/master/src/models/layers
import math
import numpy as np
import torch
from torch.nn import functional as F
from einops import rearrange
def blockdiag_butterfly_multiply_reference(x, w1_bfly, w2_bfly, version=2):
"""
This implementation is slow but more... | m2-main | bert/src/mm/blockdiag_butterfly_multiply.py |
# Copyright (c) 2023, Dan Fu and Simran Arora.
# Adapted from https://github.com/HazyResearch/safari/blob/main/src/models/sequence/hyena.py
import torch.nn as nn
from einops import rearrange
import opt_einsum as oe
contract = oe.contract
from src.mm.hyena_utils import HyenaFilter
class MonarchMixerSequenceMixing(nn... | m2-main | bert/src/mm/monarch_mixer_sequence_mixer.py |
# Adapted from https://github.com/HazyResearch/fly/tree/master/src/models/layers
import numpy as np
import torch
from torch.nn import functional as F
from einops import rearrange
def blockdiag_weight_to_dense_weight(weight):
"""
Argumments:
weight: (nblocks, out / nblocks, in / blocks)
Return:
... | m2-main | bert/src/mm/blockdiag_multiply.py |
m2-main | bert/src/mm/__init__.py | |
# Adapted from https://github.com/HazyResearch/fly/tree/master/src/models/layers
import math
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
class StructuredLinear(nn.Module):
def __init__(self, in_features, out_features, bias=True, devi... | m2-main | bert/src/mm/structured_linear.py |
# Copyright (c) 2023, Dan Fu and Simran Arora.
import torch
import torch.nn as nn
import math
from einops import rearrange
import opt_einsum as oe
contract = oe.contract
from flashmm import mm_block_fwd, hyena_filter_fwd, exp_mod_in_place_fwd
from src.utils.train import OptimModule
def fast_mm_block(
u,
line... | m2-main | bert/src/mm/flash_mm.py |
import torch
from einops import rearrange
def low_rank_project(M, rank):
"""Supports batches of matrices as well.
"""
U, S, Vt = torch.linalg.svd(M)
S_sqrt = S[..., :rank].sqrt()
U = U[..., :rank] * rearrange(S_sqrt, '... rank -> ... 1 rank')
Vt = rearrange(S_sqrt, '... rank -> ... rank 1') *... | m2-main | bert/src/ops/low_rank.py |
import math
import torch
from einops import rearrange
def butterfly_factor_to_matrix(twiddle: torch.Tensor, factor_index: int) -> torch.Tensor:
"""
Let b be the base (most commonly 2).
Parameters:
twiddle: (n // b, b, b)
factor_index: an int from 0 to log_b(n) - 1
"""
n_div_b, b, ... | m2-main | bert/src/ops/butterfly_factor.py |
import torch
import torch.nn as nn
import torch.nn.functional as F
import flash_attn_cuda
def _get_block_size(device, head_dim, is_dropout):
assert head_dim % 8 == 0 and head_dim <= 128
return 256 if head_dim <= 64 else 128
def _flash_attn_forward(q, k, v, out, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max... | m2-main | bert/src/ops/bert_flashattention.py |
# Adapted from https://github.com/mlcommons/training_results_v1.1/blob/main/NVIDIA/benchmarks/bert/implementations/pytorch/padding.py
import torch
import torch.nn.functional as F
from einops import rearrange, repeat
class IndexFirstAxis(torch.autograd.Function):
@staticmethod
def forward(ctx, input, indice... | m2-main | bert/src/ops/bert_padding.py |
import numpy as np
import torch
from torch.nn import functional as F
from einops import rearrange
from src.ops.low_rank import low_rank_project
def blockdiag_weight_to_dense_weight(weight):
"""
Argumments:
weight: (nblocks, out / nblocks, in / blocks)
Return:
dense_weight: (out / in)
... | m2-main | bert/src/ops/blockdiag_multiply.py |
import math
import torch
import torch.nn as nn
from einops import rearrange
from src.models.layers.blockdiag_butterfly_multiply import blockdiag_butterfly_multiply
# from src.ops.low_rank import low_rank_project
# Copied here so it's more self-contained
def low_rank_project(M, rank):
"""Supports batches of mat... | m2-main | bert/src/ops/blockdiag_butterfly_projection.py |
import torch
@torch.jit.script
def jit_dropout_add(x, residual, prob):
# type: (Tensor, Tensor, float) -> Tensor
return torch.nn.functional.dropout(x, p=prob, training=True) + residual
def fused_dropout_add(x, residual, prob, is_training) :
# type: (Tensor, Tensor, float, bool) -> Tensor
if is_train... | m2-main | bert/src/ops/fused_dropout_add.py |
# Adapted from https://github.com/NVIDIA/apex/blob/master/apex/fused_dense/fused_dense.py
# On the backward pass, we don't use the fused kernel from cublasLt since that's a bit slower.
# Instead we use the regular backward from F.linear.
# We also make it work with pytorch amp.
# TD [2022-02-27] The fused backward is a... | m2-main | bert/src/ops/fused_dense.py |
import torch
from einops import rearrange
from src.ops.low_rank import low_rank_project
def blockdiag_butterfly_multiply_einsum_simple(x, w1_bfly, w2_bfly):
"""
Arguments:
x: (batch, n)
w1_bfly: (k, j, i), where k = n / i
w2_bfly: (j, l, k)
Outputs:
out: (batch, m), where... | m2-main | bert/src/ops/blockdiag_butterfly_einsum.py |
import torch
from softmaxlib import additive_masked_softmax_dropout_forward
from softmaxlib import masked_scale_softmax_backward_recompute
from src.ops.triton.softmax_dropout import softmax_dropout
class _fused_softmax_dropout(torch.autograd.Function):
@staticmethod
def forward(ctx, x, p, mask, return_drop... | m2-main | bert/src/ops/fused_softmax_dropout.py |
from PIL import Image, ImageEnhance, ImageOps
import numpy as np
from torchvision import transforms
import random
norm_stats = {
"imagenet": ([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
"clevr": ([0.47097, 0.46812, 0.46185, 0], [0.08974, 0.08686, 0.09197, 1]),
"cxr": ([0.48865], [0.24621]),
"poet": ... | observational-main | transforms.py |
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import models
import json
import os
import pickle
from emmental.scorer import Scorer
from emmental.task import EmmentalTask
from emmental.data import EmmentalDataLoader
from emmental.utils.utils import pred_to_prob,... | observational-main | emmental_utils.py |
import logging
import os, sys
import argparse
import emmental
from emmental import Meta
from emmental.learner import EmmentalLearner
from emmental.model import EmmentalModel
from emmental.utils.parse_args import parse_args, parse_args_to_config
from emmental_utils import (
fetch_dataloaders,
create_tasks,
... | observational-main | train_emmental.py |
observational-main | __init__.py | |
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import cv2
import pydicom
def plot_heatmap(source, img_pth, heatmap):
figure, axs = plt.subplots(nrows=1, ncols=2)
if source == "cxr":
ds = pydicom.dcmread(img_pth)
img = ds.pixel_array
else:
img = plt.imread(img_pth... | observational-main | viz_utils.py |
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