python_code stringlengths 0 1.02M | repo_name stringlengths 9 48 | file_path stringlengths 5 114 |
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# coding=utf-8
# Copyright 2021 The Mesh TensorFlow Authors.
#
# 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 applicab... | mesh-master | mesh_tensorflow/auto_mtf/valid_layouts.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow Authors.
#
# 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 applicab... | mesh-master | mesh_tensorflow/auto_mtf/memory_estimator_test.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow Authors.
#
# 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 applicab... | mesh-master | mesh_tensorflow/auto_mtf/__init__.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow Authors.
#
# 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 applicab... | mesh-master | mesh_tensorflow/auto_mtf/layout_optimizer_test.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow Authors.
#
# 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 applicab... | mesh-master | mesh_tensorflow/auto_mtf/api.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow Authors.
#
# 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 applicab... | mesh-master | mesh_tensorflow/auto_mtf/print_cp_model_solution.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow Authors.
#
# 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 applicab... | mesh-master | mesh_tensorflow/auto_mtf/memory_estimator.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow Authors.
#
# 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 applicab... | mesh-master | mesh_tensorflow/auto_mtf/valid_layouts_test.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow Authors.
#
# 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 applicab... | mesh-master | mesh_tensorflow/auto_mtf/scheduler.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow Authors.
#
# 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 applicab... | mesh-master | mesh_tensorflow/auto_mtf/graph_interface_test.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow Authors.
#
# 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 applicab... | mesh-master | mesh_tensorflow/auto_mtf/api_test.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow Authors.
#
# 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 applicab... | mesh-master | mesh_tensorflow/auto_mtf/layout_optimizer.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow Authors.
#
# 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 applicab... | mesh-master | examples/mnist_dataset.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow Authors.
#
# 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 applicab... | mesh-master | examples/toy_model_tpu.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow Authors.
#
# 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 applicab... | mesh-master | examples/mnist.py |
from setuptools import setup, find_packages
setup(
name = 'autoregressive-linear-attention-cuda',
packages = find_packages(exclude=[]),
version = '0.0.1',
license='MIT',
description = 'Autoregressive Linear Attention CUDA kernel',
author = 'Phil Wang',
author_email = 'lucidrains@gmail.com',
long_descri... | autoregressive-linear-attention-cuda-main | setup.py |
autoregressive-linear-attention-cuda-main | autoregressive_linear_attention_cuda/__init__.py | |
autoregressive-linear-attention-cuda-main | autoregressive_linear_attention_cuda/autoregressive_linear_attention_cuda.py | |
from setuptools import setup, find_packages
setup(
name = 'retro-pytorch',
packages = find_packages(exclude=[]),
version = '0.3.8',
license='MIT',
description = 'RETRO - Retrieval Enhanced Transformer - Pytorch',
long_description_content_type = 'text/markdown',
author = 'Phil Wang',
author_email = 'luc... | RETRO-pytorch-main | setup.py |
from functools import partial
import torch
import torch.nn.functional as F
from torch import nn, einsum
from retro_pytorch.retrieval import BERT_VOCAB_SIZE
from einops import rearrange, repeat
# constants
MIN_DIM_HEAD = 32
# helper functions
def exists(val):
return val is not None
def default(val, d):
re... | RETRO-pytorch-main | retro_pytorch/retro_pytorch.py |
from retro_pytorch.retro_pytorch import RETRO
from retro_pytorch.data import RETRODataset
from retro_pytorch.training import TrainingWrapper
| RETRO-pytorch-main | retro_pytorch/__init__.py |
import os
import numpy as np
from pathlib import Path
from shutil import rmtree
from contextlib import contextmanager
def is_true_env_flag(env_flag):
return os.getenv(env_flag, 'false').lower() in ('true', '1', 't')
def reset_folder_(p):
path = Path(p)
rmtree(path, ignore_errors = True)
path.mkdir(ex... | RETRO-pytorch-main | retro_pytorch/utils.py |
from pathlib import Path
from math import ceil
import torch
import torch.nn.functional as F
import logging
import numpy as np
from einops import rearrange
import faiss
from autofaiss import build_index
from retro_pytorch.utils import memmap, reset_folder_
# constants
SOS_ID = 101
EOS_ID = 102
BERT_MODEL_DIM = 768
... | RETRO-pytorch-main | retro_pytorch/retrieval.py |
from torch.optim import AdamW
def separate_weight_decayable_params(params):
no_wd_params = set([param for param in params if param.ndim < 2])
wd_params = set(params) - no_wd_params
return wd_params, no_wd_params
def get_optimizer(params, lr = 3e-4, wd = 1e-1, filter_by_requires_grad = False):
if filte... | RETRO-pytorch-main | retro_pytorch/optimizer.py |
import numpy as np
from functools import partial
import json
from pathlib import Path
import torch
from torch import nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
from retro_pytorch import RETRO, RETRODataset
from retro_pytorch.data import knn_to_retrieved_chunks
from retro_pytorch.optimi... | RETRO-pytorch-main | retro_pytorch/training.py |
from functools import partial
import numpy as np
import torch
from torch.utils.data import Dataset
from retro_pytorch.retrieval import EOS_ID
from retro_pytorch.utils import memmap
# knn to retrieved chunks
def knn_to_retrieved_chunks(
knns,
chunks_memmap,
*,
add_continuations,
num_chunks,
pa... | RETRO-pytorch-main | retro_pytorch/data.py |
from setuptools import setup, find_packages
setup(
name = 'fast-transformer-pytorch',
packages = find_packages(),
version = '0.0.4',
license='MIT',
description = 'Fast Transformer - Pytorch',
author = 'Phil Wang',
author_email = 'lucidrains@gmail.com',
url = 'https://github.com/lucidrains/fast-transfor... | fast-transformer-pytorch-main | setup.py |
import torch
import torch.nn.functional as F
from torch import nn, einsum
from einops import rearrange, reduce
from rotary_embedding_torch import apply_rotary_emb, RotaryEmbedding
# helper functions
def exists(val):
return val is not None
def default(val, d):
return val if exists(val) else d
# helper class... | fast-transformer-pytorch-main | fast_transformer_pytorch/fast_transformer_pytorch.py |
from fast_transformer_pytorch.fast_transformer_pytorch import FastTransformer
| fast-transformer-pytorch-main | fast_transformer_pytorch/__init__.py |
from setuptools import setup, find_packages
setup(
name = 'uformer-pytorch',
packages = find_packages(),
version = '0.0.8',
license='MIT',
description = 'Uformer - Pytorch',
author = 'Phil Wang',
author_email = 'lucidrains@gmail.com',
url = 'https://github.com/lucidrains/uformer-pytorch',
keywords = ... | uformer-pytorch-main | setup.py |
from uformer_pytorch.uformer_pytorch import Uformer
| uformer-pytorch-main | uformer_pytorch/__init__.py |
import math
from math import log, pi, sqrt
from functools import partial
import torch
from torch import nn, einsum
import torch.nn.functional as F
from einops import rearrange, repeat
# constants
List = nn.ModuleList
# helpers
def exists(val):
return val is not None
def default(val, d):
return val if exi... | uformer-pytorch-main | uformer_pytorch/uformer_pytorch.py |
import os
import gzip
import click
import re
import random
from math import ceil
from functools import partial
from itertools import islice, chain
from operator import itemgetter
from pyfaidx import Faidx
import numpy as np
from random import random
from pathlib import Path
import toml
from google.cloud import stora... | progen-main | generate_data.py |
from dotenv import load_dotenv
load_dotenv()
import click
import humanize
from jinja2 import Template
from pathlib import Path
import tqdm
import numpy as np
import toml
import jax
from jax import nn, random, jit, tree_util, tree_map
from optax import adamw, clip_by_global_norm, chain, apply_updates, apply_every
fr... | progen-main | train.py |
from dotenv import load_dotenv
load_dotenv()
import click
import humanize
import jax
from jax import nn, random, jit, tree_util, numpy as np
from haiku import PRNGSequence
from progen_transformer import ProGen
from progen_transformer.data import decode_tokens, encode_tokens
from progen_transformer.utils import samp... | progen-main | sample.py |
import time
import os, errno
from pathlib import Path
from functools import partial
from google.cloud import storage
from cloudpickle import pickle
from progen_transformer.utils import clear_directory_, silentremove
# filesystem checkpoint fns
def file_reset_checkpoint(path):
clear_directory_(path)
def file_get... | progen-main | progen_transformer/checkpoint.py |
from functools import partial
import jax
from jax import random
from jax import nn
from jax.lax import stop_gradient
import jax.numpy as np
import jmp
import haiku as hk
from haiku import initializers
from einops import rearrange, repeat
from progen_transformer.utils import exists
# constants
ATTN_MASK_VALUE = -1e... | progen-main | progen_transformer/progen.py |
from progen_transformer.progen import ProGen
| progen-main | progen_transformer/__init__.py |
from math import ceil
import os, errno
from shutil import rmtree
import jax
from jax import random, nn, value_and_grad, vmap, pmap, jit, lax
from jax.lax import top_k
import jax.numpy as np
from einops import rearrange
# helper functions
def noop(x):
return x
def exists(val):
return val is not None
def lo... | progen-main | progen_transformer/utils.py |
import tensorflow as tf
import numpy as np
from functools import partial
from pathlib import Path
from contextlib import contextmanager
# writing tfrecords
def write(writer, values):
record_bytes = tf.train.Example(features = tf.train.Features(feature={
'seq': tf.train.Feature(bytes_list = tf.train.BytesL... | progen-main | progen_transformer/data.py |
from setuptools import setup, find_packages
setup(
name = 'einops-exts',
packages = find_packages(exclude=[]),
version = '0.0.4',
license='MIT',
description = 'Einops Extensions',
long_description_content_type = 'text/markdown',
author = 'Phil Wang',
author_email = 'lucidrains@gmail.com',
url = 'http... | einops-exts-main | setup.py |
from einops_exts.einops_exts import check_shape
from einops_exts.einops_exts import rearrange_many, repeat_many, reduce_many
from einops_exts.einops_exts import rearrange_with_anon_dims, repeat_with_anon_dims, reduce_with_anon_dims
| einops-exts-main | einops_exts/__init__.py |
import re
from torch import nn
from functools import wraps, partial
from einops import rearrange, reduce, repeat
# checking shape
# @nils-werner
# https://github.com/arogozhnikov/einops/issues/168#issuecomment-1042933838
def check_shape(tensor, pattern, **kwargs):
return rearrange(tensor, f"{pattern} -> {pattern... | einops-exts-main | einops_exts/einops_exts.py |
from torch import nn
from einops import rearrange
# for rearranging to and from a pattern
class EinopsToAndFrom(nn.Module):
def __init__(self, from_einops, to_einops, fn):
super().__init__()
self.from_einops = from_einops
self.to_einops = to_einops
self.fn = fn
if '...' in... | einops-exts-main | einops_exts/torch.py |
from setuptools import setup, find_packages
setup(
name = 'phenaki-pytorch',
packages = find_packages(exclude=[]),
version = '0.3.1',
license='MIT',
description = 'Phenaki - Pytorch',
author = 'Phil Wang',
author_email = 'lucidrains@gmail.com',
long_description_content_type = 'text/markdown',
url = '... | phenaki-pytorch-main | setup.py |
import math
import torch
import torch.nn.functional as F
from torch import nn, einsum
from beartype import beartype
from typing import Tuple
from einops import rearrange, repeat
# helpers
def exists(val):
return val is not None
def default(val, d):
return val if exists(val) else d
def leaky_relu(p = 0.1):... | phenaki-pytorch-main | phenaki_pytorch/attention.py |
import math
import copy
from pathlib import Path
from random import random, choices
from functools import partial
from collections import namedtuple
from multiprocessing import cpu_count
from beartype import beartype
from beartype.door import is_bearable
from beartype.vale import Is
from typing import Optional, List, ... | phenaki-pytorch-main | phenaki_pytorch/phenaki_trainer.py |
from pathlib import Path
import copy
import math
from functools import wraps
import torch
import torch.nn.functional as F
from torch import nn, einsum
from torch.autograd import grad as torch_grad
import torchvision
from einops import rearrange, repeat, pack, unpack
from einops.layers.torch import Rearrange
from ve... | phenaki-pytorch-main | phenaki_pytorch/cvivit.py |
from math import sqrt
from random import choice
from pathlib import Path
from shutil import rmtree
from beartype import beartype
import torch
from torch import nn
from torch.utils.data import Dataset, DataLoader, random_split
import torchvision.transforms as T
from torchvision.datasets import ImageFolder
from torchv... | phenaki-pytorch-main | phenaki_pytorch/cvivit_trainer.py |
import torch
import transformers
from transformers import T5Tokenizer, T5EncoderModel, T5Config
# less warning messages since only using encoder
transformers.logging.set_verbosity_error()
# helper functions
def exists(val):
return val is not None
# config
MAX_LENGTH = 256
DEFAULT_T5_NAME = 'google/t5-v1_1-ba... | phenaki-pytorch-main | phenaki_pytorch/t5.py |
import math
import functools
from contextlib import nullcontext
from functools import partial, wraps
from typing import Optional, List, Union
from beartype import beartype
import torch
import torch.nn.functional as F
from torch import nn, einsum
from einops import rearrange, repeat, pack, unpack
from einops.layers.t... | phenaki-pytorch-main | phenaki_pytorch/phenaki_pytorch.py |
from phenaki_pytorch.phenaki_pytorch import Phenaki, CViViT, MaskGit, TokenCritic, make_video
from phenaki_pytorch.cvivit_trainer import CViViTTrainer
from phenaki_pytorch.phenaki_trainer import PhenakiTrainer
| phenaki-pytorch-main | phenaki_pytorch/__init__.py |
from torch.optim import AdamW, Adam
def separate_weight_decayable_params(params):
wd_params, no_wd_params = [], []
for param in params:
param_list = no_wd_params if param.ndim < 2 else wd_params
param_list.append(param)
return wd_params, no_wd_params
def get_optimizer(
params,
lr =... | phenaki-pytorch-main | phenaki_pytorch/optimizer.py |
from pathlib import Path
import cv2
from PIL import Image
from functools import partial
from typing import Tuple, List
from beartype.door import is_bearable
import numpy as np
import torch
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader as PytorchDataLoader
from torchvision import t... | phenaki-pytorch-main | phenaki_pytorch/data.py |
from setuptools import setup, find_packages
setup(
name = 'adjacent-attention-pytorch',
packages = find_packages(),
version = '0.0.12',
license='MIT',
description = 'Adjacent Attention Network - Pytorch',
long_description_content_type = 'text/markdown',
author = 'Phil Wang',
author_email = 'lucidrains@... | adjacent-attention-network-main | setup.py |
from adjacent_attention_network.adjacent_attention_network import AdjacentAttentionNetwork
| adjacent-attention-network-main | adjacent_attention_network/__init__.py |
import torch
import torch.nn.functional as F
from torch import nn, einsum
from einops import rearrange, repeat
from isab_pytorch import ISAB
# helpers
def exists(val):
return val is not None
def batched_index_select(values, indices):
last_dim = values.shape[-1]
return values.gather(1, indices[:, :, None... | adjacent-attention-network-main | adjacent_attention_network/adjacent_attention_network.py |
# Adapted from https://github.com/NVIDIA/apex/blob/master/setup.py
import sys
import warnings
import os
from pathlib import Path
from setuptools import setup, find_packages
import subprocess
import torch
from torch.utils.cpp_extension import BuildExtension, CppExtension, CUDAExtension, CUDA_HOME
with open("README.m... | flash-attention-main | setup.py |
# Copied from https://github.com/NVIDIA/apex/tree/master/csrc/megatron
# We add the case where seqlen = 4k and seqlen = 8k
import os
import subprocess
import torch
from setuptools import setup
from torch.utils.cpp_extension import BuildExtension, CUDAExtension, CUDA_HOME
def get_cuda_bare_metal_version(cuda_dir):
... | flash-attention-main | csrc/fused_softmax/setup.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... | flash-attention-main | csrc/xentropy/setup.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... | flash-attention-main | csrc/layer_norm/setup.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... | flash-attention-main | csrc/rotary/setup.py |
import os
import subprocess
import torch
from setuptools import setup
from torch.utils.cpp_extension import BuildExtension, CUDAExtension, CUDA_HOME
def get_cuda_bare_metal_version(cuda_dir):
raw_output = subprocess.check_output([cuda_dir + "/bin/nvcc", "-V"], universal_newlines=True)
output = raw_output.spl... | flash-attention-main | csrc/fused_dense_lib/setup.py |
import math
import torch
import torch.nn.functional as F
import pytest
from einops import rearrange
from flash_attn.layers.rotary import apply_rotary_emb_func, apply_rotary_emb_torch
is_sm8x = torch.cuda.get_device_capability('cuda') >= (8, 0)
@pytest.mark.parametrize('dtype', ([torch.float16] if not is_sm8x else... | flash-attention-main | tests/test_rotary.py |
import math
from functools import partial
import torch
import torch.nn.functional as F
import pytest
from einops import rearrange, repeat
from flash_attn.flash_attn_interface import flash_attn_func, flash_attn_unpadded_qkvpacked_func, _get_block_size, flash_attn_unpadded_kvpacked_func, flash_attn_unpadded_func
from... | flash-attention-main | tests/test_flash_attn.py |
# Run test with:
# torchrun --no_python --nproc_per_node=8 pytest -q -s tests/losses/test_cross_entropy_parallel.py
import math
import torch
import torch.nn.functional as F
import pytest
from apex.transformer import parallel_state
from apex.transformer import tensor_parallel
from flash_attn.losses.cross_entropy_par... | flash-attention-main | tests/losses/test_cross_entropy_parallel.py |
import math
import torch
import torch.nn.functional as F
import pytest
from einops import rearrange
from flass_attn.losses.cross_entropy_apex import CrossEntropyLossApex
is_sm8x = torch.cuda.get_device_capability('cuda')[0] >= 8
@pytest.mark.parametrize('dtype', [torch.float16, torch.float32] + ([torch.bfloat16] ... | flash-attention-main | tests/losses/test_cross_entropy_apex.py |
import math
import torch
import torch.nn.functional as F
import pytest
from einops import rearrange
from flash_attn.ops.layer_norm import DropoutAddLayerNorm, dropout_add_layer_norm
is_sm8x = torch.cuda.get_device_capability('cuda')[0] >= 8
@pytest.mark.parametrize('has_rowscale', [True, False])
# @pytest.mark.pa... | flash-attention-main | tests/ops/test_dropout_layer_norm.py |
import math
import torch
import torch.nn.functional as F
import pytest
from einops import rearrange
from flash_attn.ops.fused_dense import FusedDenseTD, FusedDenseGeluDenseTD
from flash_attn.ops.fused_dense import FusedDenseResidual, FusedDenseResGeluDense
@pytest.mark.parametrize('dtype', [torch.float16, torch.bf... | flash-attention-main | tests/ops/test_fused_dense.py |
from functools import partial
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange, repeat
from flash_attn.utils.benchmark import benchmark_forward, benchmark_all, pytorch_profiler
from flash_attn.flash_attn_interface import flash_attn_unpadded_qkvpacked_func
# f... | flash-attention-main | benchmarks/benchmark_causal.py |
from functools import partial
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange, repeat
from flash_attn.utils.benchmark import benchmark_all, benchmark_forward, benchmark_backward, benchmark_combined
from flash_attn.bert_padding import unpad_input, pad_input
f... | flash-attention-main | benchmarks/benchmark_flash_attention.py |
# [2022-10-23] Copied from https://github.com/NVIDIA/apex/blob/master/apex/transformer/functional/fused_softmax.py
# for benchmarking.
# We added support for seqlen=2k and seqlen=4k
# coding=utf-8
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "L... | flash-attention-main | flash_attn/fused_softmax.py |
# Adapted from https://github.com/mlcommons/training_results_v1.1/blob/main/NVIDIA/benchmarks/bert/implementations/pytorch/fmha.py
import torch
import torch.nn as nn
import flash_attn_cuda
def convert_blockmask(blockmask, causal):
"""Convert from the 0-1 format to the format used by the CUDA code.
0 means th... | flash-attention-main | flash_attn/flash_blocksparse_attn_interface.py |
import math
import torch
import torch.nn as nn
from einops import rearrange
import hydra
from flash_attn.flash_blocksparse_attn_interface import flash_blocksparse_attn_func
from flash_attn.flash_blocksparse_attn_interface import convert_blockmask
from flash_attn.bert_padding import unpad_input, pad_input, index_firs... | flash-attention-main | flash_attn/flash_blocksparse_attention.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... | flash-attention-main | flash_attn/bert_padding.py |
flash-attention-main | flash_attn/__init__.py | |
# [2022-10-23] Downloaded from https://github.com/openai/triton/blob/master/python/tutorials/06-fused-attention.py
# for benchmarking.
# We fixed a few dtype cast to make it work for bf16
"""
Fused Attention
===============
This is a Triton implementation of the Flash Attention algorithm
(see: Dao et al., https://arxi... | flash-attention-main | flash_attn/flash_attn_triton_og.py |
import math
import torch
import torch.nn as nn
from einops import rearrange
from flash_attn.flash_attn_interface import flash_attn_unpadded_qkvpacked_func
from flash_attn.bert_padding import unpad_input, pad_input, index_first_axis
class FlashAttention(nn.Module):
"""Implement the scaled dot product attention w... | flash-attention-main | flash_attn/flash_attention.py |
"""
*Experimental* implementation of FlashAttention in Triton.
We use the FlashAttention implementation from Phil Tillet a starting point.
https://github.com/openai/triton/blob/master/python/tutorials/06-fused-attention.py
Changes:
- Implement both causal and non-causal attention.
- Implement both self-attention and ... | flash-attention-main | flash_attn/flash_attn_triton.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... | flash-attention-main | flash_attn/flash_attn_interface.py |
import torch
import torch.nn as nn
import xentropy_cuda_lib
# https://github.com/NVIDIA/apex/blob/master/apex/contrib/xentropy/softmax_xentropy.py
class SoftmaxCrossEntropyLossFn(torch.autograd.Function):
@staticmethod
def forward(ctx, logits, labels, smoothing=0.0, padding_idx=0, inplace_backward=False):
... | flash-attention-main | flash_attn/losses/cross_entropy_apex.py |
# Inspired by https://github.com/NVIDIA/apex/blob/master/apex/transformer/tensor_parallel/cross_entropy.py
# But we make it much faster: we compute the local loss and the LSE, and by exchanging the LSE and
# the losses we can get the global loss. There's no need to do it step by step
# (compute local max, exchange, com... | flash-attention-main | flash_attn/losses/cross_entropy_parallel.py |
# Inspired by https://github.com/facebookresearch/xformers/blob/main/xformers/components/positional_embedding/rotary.py
from typing import Tuple
import math
import torch
from einops import rearrange, repeat
import rotary_emb
def rotate_half(x):
x1, x2 = x.chunk(2, dim=-1)
return torch.cat((-x2, x1), dim=-... | flash-attention-main | flash_attn/layers/rotary.py |
# Copyright (c) 2022, Tri Dao.
""" 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, **kwinputs):
""" Use Pytorch Benchmark on the forwa... | flash-attention-main | flash_attn/utils/benchmark.py |
# Copyright (c) 2022, Tri Dao.
# Inspired by / adapted from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
import math
from functools import partial
from copy import deepcopy
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.init import tr... | flash-attention-main | flash_attn/models/vit.py |
# Copyright (c) 2022, Tri Dao.
import math
from functools import partial
from collections import namedtuple
from collections.abc import Sequence
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers.models.gpt2.configuration_gpt2 import GPT2Config
from flash_attn.modules.mha import M... | flash-attention-main | flash_attn/models/gpt.py |
# Adapted from https://github.com/NVIDIA/apex/blob/master/apex/fused_dense/fused_dense.py
# We make it work with pytorch amp and with bfloat16.
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.cuda.amp import custom_bwd, custom_fwd
# import fused_dense_cuda # from apex
import fused_dense... | flash-attention-main | flash_attn/ops/fused_dense.py |
# Adapted from https://github.com/NVIDIA/apex/blob/master/apex/contrib/layer_norm/layer_norm.py
import torch
from torch.nn import init
# from apex._autocast_utils import _cast_if_autocast_enabled
import dropout_layer_norm
def _dropout_add_layer_norm_forward(x0, x1, gamma, beta, rowscale, dropout_p, epsilon,
... | flash-attention-main | flash_attn/ops/layer_norm.py |
# Copied from https://github.com/mlcommons/training_results_v1.1/blob/main/NVIDIA/benchmarks/bert/implementations/pytorch/model/layers/activations.py
import math
import torch
from torch import nn
# 1/sqrt(2*pi)-> 0.3989423
# 1/sqrt(2) -> 0.70710678
# sqrt(2/pi) -> 0.79788456
# this function is tanh approximation... | flash-attention-main | flash_attn/ops/gelu_activation.py |
# Adapted on https://github.com/ELS-RD/kernl/blob/main/src/kernl/implementations/linear_layer.py
# and https://github.com/openai/triton/blob/master/python/triton/ops/matmul.py
from typing import Optional
import torch
import triton
import triton.language as tl
from torch.autograd.function import FunctionCtx
from torch.... | flash-attention-main | flash_attn/ops/triton/linear.py |
# Adapted from https://github.com/facebookresearch/xformers/blob/main/xformers/triton/k_activations.py
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
import math
from e... | flash-attention-main | flash_attn/ops/triton/k_activations.py |
# The triton fused matmul + sqrelu is faster for fp16 but slower for bf16, compared
# to naive implementation.
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.cuda.amp import custom_bwd, custom_fwd
import fused_dense_lib as fused_dense_cuda
from flash_attn.ops.triton.linear import triton... | flash-attention-main | flash_attn/ops/triton/mlp.py |
# Copyright (c) 2022, Tri Dao.
import torch
import torch.nn as nn
from einops import repeat
class GPT2Embeddings(nn.Module):
def __init__(self, embed_dim, vocab_size, max_position_embeddings, padding_idx=None):
"""
If max_position_embeddings <= 0, there's no position embeddings
"""
... | flash-attention-main | flash_attn/modules/embedding.py |
# Copyright (c) 2022, Tri Dao.
import torch
import torch.nn as nn
import torch.nn.functional as F
try:
from flash_attn.ops.fused_dense import fused_dense_gelu_dense_function_td
from flash_attn.ops.fused_dense import fused_dense_res_gelu_dense_function_td
except ImportError:
fused_dense_gelu_dense_function... | flash-attention-main | flash_attn/modules/mlp.py |
# Copyright (c) 2022, Tri Dao.
from typing import Optional
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
from torchvision.ops import StochasticDepth
from flash_attn.modules.mha import MHA
from flash_attn.modules.mlp import Mlp
try:
fro... | flash-attention-main | flash_attn/modules/block.py |
# Copyright (c) 2022, Tri Dao.
import math
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
try:
from flash_attn.flash_attn_interface import flash_attn_unpadded_qkvpacked_func
from flash_attn.flash_attn_interface import flash_attn_... | flash-attention-main | flash_attn/modules/mha.py |
from setuptools import setup, find_packages
setup(
name = 'feedback-transformer-pytorch',
packages = find_packages(),
version = '0.0.11',
license='MIT',
description = 'Implementation of Feedback Transformer in Pytorch',
author = 'Phil Wang',
author_email = 'lucidrains@gmail.com',
url = ... | feedback-transformer-pytorch-main | setup.py |
from feedback_transformer_pytorch.feedback_transformer_pytorch import FeedbackTransformer
| feedback-transformer-pytorch-main | feedback_transformer_pytorch/__init__.py |
import math
from collections import namedtuple
import torch
from torch import nn, einsum
import torch.nn.functional as F
from einops import rearrange
# constants
Memory = namedtuple('Memory', ['keys', 'values'])
# helpers
def exists(val):
return val is not None
def default(val, d):
return val if exists(va... | feedback-transformer-pytorch-main | feedback_transformer_pytorch/feedback_transformer_pytorch.py |
from setuptools import setup, find_packages
setup(
name = 'memory-transformer-xl',
packages = find_packages(exclude=['examples']),
version = '0.1.0',
license='MIT',
description = 'Memory Transformer-XL, a variant of Transformer-XL that uses linear attention update long term memory',
author = 'Phil Wang',
... | memory-transformer-xl-master | setup.py |
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