python_code stringlengths 0 992k | repo_name stringlengths 8 46 | file_path stringlengths 5 162 |
|---|---|---|
import importlib
import numbers
import torch
from torch.nn.parameter import Parameter
from torch.nn import init
from torch.nn import functional as F
from apex._autocast_utils import _cast_if_autocast_enabled
global fused_layer_norm_cuda
fused_layer_norm_cuda = None
# Reference implementation from Huggingface
def m... | GeneSplice-main | GeneSplice/apex/apex/normalization/fused_layer_norm.py |
from .fused_layer_norm import FusedLayerNorm, MixedFusedLayerNorm, FusedRMSNorm, MixedFusedRMSNorm
| GeneSplice-main | GeneSplice/apex/apex/normalization/__init__.py |
from .fused_dense import *
| GeneSplice-main | GeneSplice/apex/apex/fused_dense/__init__.py |
import torch
from torch import nn
import fused_dense_cuda
from apex._autocast_utils import _cast_if_autocast_enabled
#implements fused GEMM+bias in forward pass using mlp_cuda from apex
class FusedDenseFunc(torch.autograd.Function):
@staticmethod
def forward(ctx, input, weight, bias):
ctx.save_for_back... | GeneSplice-main | GeneSplice/apex/apex/fused_dense/fused_dense.py |
# coding=utf-8
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless re... | GeneSplice-main | GeneSplice/apex/apex/transformer/enums.py |
# coding=utf-8
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless re... | GeneSplice-main | GeneSplice/apex/apex/transformer/parallel_state.py |
import logging
import os
def get_transformer_logger(name: str) -> logging.Logger:
name_wo_ext = os.path.splitext(name)[0]
return logging.getLogger(name_wo_ext)
def set_logging_level(verbosity) -> None:
"""Change logging severity.
Args:
verbosity
"""
from apex import _library_root_lo... | GeneSplice-main | GeneSplice/apex/apex/transformer/log_util.py |
from apex.transformer import amp
from apex.transformer import functional
from apex.transformer import parallel_state
from apex.transformer import pipeline_parallel
from apex.transformer import tensor_parallel
from apex.transformer import utils
from apex.transformer.enums import LayerType
from apex.transformer.enums imp... | GeneSplice-main | GeneSplice/apex/apex/transformer/__init__.py |
from torch import distributed as dist
HAS_UCC = hasattr(dist, "is_ucc_available") and dist.is_ucc_available()
if not HAS_UCC:
try:
import torch_ucc
HAS_UCC = True
except ImportError:
HAS_UCC = False
| GeneSplice-main | GeneSplice/apex/apex/transformer/_ucc_util.py |
"""Utility functions used by both `pipeline_parallel` and `tensor_parallel`"""
import torch
from apex.transformer import parallel_state
# `all_gather_into_tensor` is new placeholders for `_all_gather_base`.
# It requires the most recent version of PyTorch.
# The following 4 lines are for backward comparability with... | GeneSplice-main | GeneSplice/apex/apex/transformer/utils.py |
# coding=utf-8
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless re... | GeneSplice-main | GeneSplice/apex/apex/transformer/microbatches.py |
# coding=utf-8
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless re... | GeneSplice-main | GeneSplice/apex/apex/transformer/tensor_parallel/cross_entropy.py |
# coding=utf-8
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless r... | GeneSplice-main | GeneSplice/apex/apex/transformer/tensor_parallel/memory.py |
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appli... | GeneSplice-main | GeneSplice/apex/apex/transformer/tensor_parallel/__init__.py |
# coding=utf-8
# Copyright (c) 2021-22, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless... | GeneSplice-main | GeneSplice/apex/apex/transformer/tensor_parallel/random.py |
# coding=utf-8
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless re... | GeneSplice-main | GeneSplice/apex/apex/transformer/tensor_parallel/utils.py |
# coding=utf-8
# Copyright (c) 2021-22, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless... | GeneSplice-main | GeneSplice/apex/apex/transformer/tensor_parallel/layers.py |
# coding=utf-8
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless re... | GeneSplice-main | GeneSplice/apex/apex/transformer/tensor_parallel/data.py |
# coding=utf-8
# Copyright (c) 2021-22, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless... | GeneSplice-main | GeneSplice/apex/apex/transformer/tensor_parallel/mappings.py |
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
from apex.transformer.layers.layer_norm import FastLayerNorm
from apex.transformer.layers.layer_norm import FusedLayerNorm
from apex.transformer.layers.layer_norm import MixedFusedLayerNorm
__all__ = [
"FastLayerNorm",
"FusedLayerNorm",
"Mixed... | GeneSplice-main | GeneSplice/apex/apex/transformer/layers/__init__.py |
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
# NOTE(mkozuki): This file defines two LayerNorm that are compatible with Megatron-LM.
# while avoiding introducing the breaking change of `"sequence_parallel_enabled"` attribute into apex.normalization.FusedLayerNorm
# and apex.contrib.layer_norm.FastLaye... | GeneSplice-main | GeneSplice/apex/apex/transformer/layers/layer_norm.py |
import time
import torch
class _Timer:
"""Timer."""
def __init__(self, name):
self.name_ = name
self.elapsed_ = 0.0
self.started_ = False
self.start_time = time.time()
def start(self):
"""Start the timer."""
assert not self.started_, "timer has already be... | GeneSplice-main | GeneSplice/apex/apex/transformer/pipeline_parallel/_timers.py |
from apex.transformer.pipeline_parallel.schedules import get_forward_backward_func
from apex.transformer.pipeline_parallel.schedules.common import build_model
__all__ = [
"get_forward_backward_func",
"build_model",
]
| GeneSplice-main | GeneSplice/apex/apex/transformer/pipeline_parallel/__init__.py |
# coding=utf-8
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless re... | GeneSplice-main | GeneSplice/apex/apex/transformer/pipeline_parallel/utils.py |
# coding=utf-8
# Copyright (c) 2021-22, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless... | GeneSplice-main | GeneSplice/apex/apex/transformer/pipeline_parallel/p2p_communication.py |
import contextlib
from typing import Any, List, Optional, Sequence, Union
import warnings
import torch
from apex.transformer import parallel_state
from apex.transformer.enums import ModelType
from apex.transformer.pipeline_parallel import p2p_communication
from apex.transformer.pipeline_parallel.p2p_communication imp... | GeneSplice-main | GeneSplice/apex/apex/transformer/pipeline_parallel/schedules/fwd_bwd_pipelining_without_interleaving.py |
import contextlib
from typing import List, Union, Optional
import torch
from apex.transformer.pipeline_parallel.utils import listify_model
from apex.transformer.pipeline_parallel.utils import get_num_microbatches
from apex.transformer.pipeline_parallel.utils import get_kth_microbatch
from apex.transformer.pipeline_pa... | GeneSplice-main | GeneSplice/apex/apex/transformer/pipeline_parallel/schedules/fwd_bwd_no_pipelining.py |
from apex.transformer import parallel_state
from apex.transformer.pipeline_parallel.utils import get_num_microbatches
from apex.transformer.pipeline_parallel.schedules.fwd_bwd_no_pipelining import (
forward_backward_no_pipelining,
)
from apex.transformer.pipeline_parallel.schedules.fwd_bwd_pipelining_with_interleav... | GeneSplice-main | GeneSplice/apex/apex/transformer/pipeline_parallel/schedules/__init__.py |
from typing import Any, Callable, Dict, List, Tuple, Union, Optional, Sequence
import torch
from torch.autograd.variable import Variable
from apex.normalization.fused_layer_norm import FusedLayerNorm
from apex.transformer import parallel_state
from apex.transformer.enums import ModelType
from apex.transformer.pipelin... | GeneSplice-main | GeneSplice/apex/apex/transformer/pipeline_parallel/schedules/common.py |
import contextlib
from typing import Any, Callable, List, Optional, Sequence, Union
import warnings
import torch
from apex.transformer import parallel_state
from apex.transformer.pipeline_parallel import p2p_communication
from apex.transformer.pipeline_parallel.schedules.common import Batch
from apex.transformer.pipe... | GeneSplice-main | GeneSplice/apex/apex/transformer/pipeline_parallel/schedules/fwd_bwd_pipelining_with_interleaving.py |
# coding=utf-8
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless re... | GeneSplice-main | GeneSplice/apex/apex/transformer/testing/arguments.py |
GeneSplice-main | GeneSplice/apex/apex/transformer/testing/__init__.py | |
# coding=utf-8
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless re... | GeneSplice-main | GeneSplice/apex/apex/transformer/testing/commons.py |
import contextlib
import torch
from apex.transformer import tensor_parallel
from apex.transformer.enums import AttnMaskType
from apex.transformer.enums import ModelType
from apex.transformer.layers import FusedLayerNorm as LayerNorm
from apex.transformer.testing.global_vars import get_args
from apex.transformer.testi... | GeneSplice-main | GeneSplice/apex/apex/transformer/testing/standalone_bert.py |
import os
import sys
import unittest
from packaging.version import Version, parse
import torch
from torch import distributed as dist
from torch.utils import collect_env
from torch.testing._internal import common_utils
from torch.testing._internal import common_distributed
from apex.transformer._ucc_util import HAS_UC... | GeneSplice-main | GeneSplice/apex/apex/transformer/testing/distributed_test_base.py |
# Copyright (c) 2021-22, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | GeneSplice-main | GeneSplice/apex/apex/transformer/testing/standalone_gpt.py |
# coding=utf-8
# Copyright (c) 2021-22, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless... | GeneSplice-main | GeneSplice/apex/apex/transformer/testing/standalone_transformer_lm.py |
# coding=utf-8
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless re... | GeneSplice-main | GeneSplice/apex/apex/transformer/testing/global_vars.py |
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appli... | GeneSplice-main | GeneSplice/apex/apex/transformer/amp/grad_scaler.py |
from apex.transformer.amp.grad_scaler import GradScaler
__all__ = [
"GradScaler",
]
| GeneSplice-main | GeneSplice/apex/apex/transformer/amp/__init__.py |
from apex.transformer._data._batchsampler import MegatronPretrainingRandomSampler
from apex.transformer._data._batchsampler import MegatronPretrainingSampler
__all__ = [
"MegatronPretrainingRandomSampler",
"MegatronPretrainingSampler",
]
| GeneSplice-main | GeneSplice/apex/apex/transformer/_data/__init__.py |
"""BatchSampler implementations for POC of dynamic batch size or rampup_batch_size support.
Implementations are based on https://github.com/NVIDIA/Megatron-LM/blob/bcd605f8570ebeeb0436c115ebbfafc3c5a40ae5/megatron/data/data_samplers.py.
""" # NOQA
import abc
import torch
__all__ = [
"MegatronPretrainingSampler... | GeneSplice-main | GeneSplice/apex/apex/transformer/_data/_batchsampler.py |
# coding=utf-8
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless re... | GeneSplice-main | GeneSplice/apex/apex/transformer/functional/fused_softmax.py |
from apex.transformer.functional.fused_softmax import FusedScaleMaskSoftmax
__all__ = [
"FusedScaleMaskSoftmax",
]
| GeneSplice-main | GeneSplice/apex/apex/transformer/functional/__init__.py |
from .fp16util import (
BN_convert_float,
network_to_half,
prep_param_lists,
model_grads_to_master_grads,
master_params_to_model_params,
tofp16,
to_python_float,
clip_grad_norm,
convert_module,
convert_network,
FP16Model,
)
from .fp16_optimizer import FP16_Optimizer
from .lo... | GeneSplice-main | GeneSplice/apex/apex/fp16_utils/__init__.py |
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
class tofp16(nn.Module):
"""
Utility module that implements::
def forward(self, input):
return input.half()
"""
def __init__(self):
... | GeneSplice-main | GeneSplice/apex/apex/fp16_utils/fp16util.py |
import torch
from torch import nn
from torch.autograd import Variable
from torch.nn.parameter import Parameter
from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
from ..amp._amp_state import _amp_state, maybe_print
from ..amp.scaler import LossScaler
from ..multi_tensor_apply import multi_tensor... | GeneSplice-main | GeneSplice/apex/apex/fp16_utils/fp16_optimizer.py |
import torch
# item() is a recent addition, so this helps with backward compatibility.
def to_python_float(t):
if hasattr(t, 'item'):
return t.item()
else:
return t[0]
class LossScaler:
"""
Class that manages a static loss scale. This class is intended to interact with
:class:`FP1... | GeneSplice-main | GeneSplice/apex/apex/fp16_utils/loss_scaler.py |
from .multi_tensor_apply import MultiTensorApply
multi_tensor_applier = MultiTensorApply(2048*32)
| GeneSplice-main | GeneSplice/apex/apex/multi_tensor_apply/__init__.py |
import torch
class MultiTensorApply(object):
available = False
warned = False
def __init__(self, chunk_size):
try:
import amp_C
MultiTensorApply.available = True
self.chunk_size = chunk_size
except ImportError as err:
MultiTensorApply.availab... | GeneSplice-main | GeneSplice/apex/apex/multi_tensor_apply/multi_tensor_apply.py |
GeneSplice-main | GeneSplice/apex/apex/contrib/__init__.py | |
import torch
import fused_index_mul_2d
class IndexMul2d_(torch.autograd.Function):
'''
Currently only support index in dimension 0 with a 2-dimension tensor.
The shape of indexed in1 must be same with in2. Now this kernel does not support broadcast.
The datatype must be float32 or float16.
'''
... | GeneSplice-main | GeneSplice/apex/apex/contrib/index_mul_2d/index_mul_2d.py |
from .index_mul_2d import index_mul_2d
| GeneSplice-main | GeneSplice/apex/apex/contrib/index_mul_2d/__init__.py |
from .sparse_masklib import create_mask
from .asp import ASP
| GeneSplice-main | GeneSplice/apex/apex/contrib/sparsity/__init__.py |
import types
import torch
from .sparse_masklib import create_mask
from .permutation_lib import Permutation
torchvision_imported=True
try:
import torchvision
except ImportError:
print("[ASP][Warning] torchvision cannot be imported.")
torchvision_imported=False
import json
import os
import string
import tim... | GeneSplice-main | GeneSplice/apex/apex/contrib/sparsity/asp.py |
import os
import torch
import json
import string
import time
import numpy as np
import sys
import builtins as __builtin__
import io
try:
from .permutation_search_kernels import accelerated_search_for_good_permutation, sum_after_2_to_4
print("[ASP][Info] permutation_search_kernels can be imported.")
except Impor... | GeneSplice-main | GeneSplice/apex/apex/contrib/sparsity/permutation_lib.py |
import sys
import torch
import numpy as np
import collections
from itertools import permutations
""" compute density (helper fn to compute % NNZs in a tensor) """
def fill(x):
return float(x.nonzero().size(0))/torch.numel(x)
""" reshape matrix into m-dimensional vectors: (h,w) -> (hw/m, m) """
def reshape_1d(mat... | GeneSplice-main | GeneSplice/apex/apex/contrib/sparsity/sparse_masklib.py |
from .permutation_utilities import *
################################################################################################################
# Exhaustive
# Try them all
# - order of columns within a group doesn't matter
# - order of groups doesn't matter
# - we can eliminate effective duplicates by de... | GeneSplice-main | GeneSplice/apex/apex/contrib/sparsity/permutation_search_kernels/exhaustive_search.py |
from .permutation_utilities import *
################################################################################################################
# Greedy Channel Swaps - iterative, deterministic, can be parallelized
# 1. Build a map of the magnitude improvement of involved stripes for all pairs of channel swaps... | GeneSplice-main | GeneSplice/apex/apex/contrib/sparsity/permutation_search_kernels/channel_swap.py |
import numpy as np
from .permutation_utilities import *
from .exhaustive_search import Exhaustive_Search
def accelerated_search_for_good_permutation(matrix_group, options=None, verbosity=0):
"""This function is used to call the permutation search CUDA kernels.
users can provide prefer search strategy by provid... | GeneSplice-main | GeneSplice/apex/apex/contrib/sparsity/permutation_search_kernels/call_permutation_search_kernels.py |
from .call_permutation_search_kernels import accelerated_search_for_good_permutation
from .permutation_utilities import sum_after_2_to_4 | GeneSplice-main | GeneSplice/apex/apex/contrib/sparsity/permutation_search_kernels/__init__.py |
import numpy as np
import time
import subprocess
import math
gpus_tested = False
gpus_found = 0
kernels_found = True
try:
import permutation_search_cuda as permutation_search_cuda_kernels
print(f"Found permutation search CUDA kernels")
except ImportError:
try:
from . import permutation_search_... | GeneSplice-main | GeneSplice/apex/apex/contrib/sparsity/permutation_search_kernels/permutation_utilities.py |
from collections import OrderedDict
import torch
from apex.optimizers import FusedAdam
from apex.contrib.sparsity import ASP
def build_model(args):
od = OrderedDict()
for i in range(args.num_layers):
if i == 0:
od['linear_layer_%d' % (i+1)] = torch.nn.Linear(args.input_features, args.hidde... | GeneSplice-main | GeneSplice/apex/apex/contrib/sparsity/test/toy_problem.py |
import torch
import torch.onnx
from apex.contrib.sparsity.permutation_lib import Permutation
"""
Functional and behavioral correctness checking for network permutations
Each test class is a torch.nn.Module with three required members:
- self.input_shape is used to populate a dummy input
- self.expected_C_params indica... | GeneSplice-main | GeneSplice/apex/apex/contrib/sparsity/test/test_permutation_application.py |
from collections import OrderedDict
import torch
from apex.optimizers import FusedAdam
from apex.contrib.sparsity import ASP
def build_model(args):
od = OrderedDict()
for i in range(args.num_layers):
if i == 0:
od['linear_layer_%d' % (i+1)] = torch.nn.Linear(args.input_features, args.hidde... | GeneSplice-main | GeneSplice/apex/apex/contrib/sparsity/test/checkpointing_test_part2.py |
from collections import OrderedDict
import torch
from apex.optimizers import FusedAdam
from apex.contrib.sparsity import ASP
def build_model(args):
od = OrderedDict()
for i in range(args.num_layers):
if i == 0:
od['linear_layer_%d' % (i+1)] = torch.nn.Linear(args.input_features, args.hidde... | GeneSplice-main | GeneSplice/apex/apex/contrib/sparsity/test/checkpointing_test_part1.py |
from collections import OrderedDict
import torch
from apex.optimizers import FusedAdam
from apex.contrib.sparsity import ASP
#
# Reference run for checkpointing test (part1 + part2)
#
def build_model(args):
od = OrderedDict()
for i in range(args.num_layers):
if i == 0:
od['linear_layer_%d... | GeneSplice-main | GeneSplice/apex/apex/contrib/sparsity/test/checkpointing_test_reference.py |
import numpy as np
import time
import sys
# permutation-specifics
sys.path.append("../")
from permutation_search_kernels.permutation_utilities import *
from permutation_search_kernels.exhaustive_search import Exhaustive_Search
from permutation_search_kernels.channel_swap import Channel_Swap
# Arguments
import argpars... | GeneSplice-main | GeneSplice/apex/apex/contrib/sparsity/permutation_tests/permutation_test.py |
try:
import torch
import bnp
from .batch_norm import BatchNorm2d_NHWC
del torch
del bnp
del batch_norm
except ImportError as err:
print("apex was installed without --bnp flag, contrib.groupbn is not available")
| GeneSplice-main | GeneSplice/apex/apex/contrib/groupbn/__init__.py |
import torch
import numpy as np
from torch.nn.modules.batchnorm import _BatchNorm
import bnp
class bn_NHWC_impl(torch.autograd.Function):
@staticmethod
def forward(ctx, x, s, b, rm, riv, mini_m, mini_riv, ret_cta, mom, epsilon, fuse_relu, is_train, bn_group, my_data, pair_data, magic, pair_data2, pair_data3, ... | GeneSplice-main | GeneSplice/apex/apex/contrib/groupbn/batch_norm.py |
from .batch_norm import GroupBatchNorm2d | GeneSplice-main | GeneSplice/apex/apex/contrib/cudnn_gbn/__init__.py |
import torch
from torch.nn.modules.batchnorm import _BatchNorm
from torch.nn import functional as F
from torch import Tensor
import peer_memory_cuda as pm
import cudnn_gbn_lib
from torch.cuda.amp import custom_fwd, custom_bwd
class _GroupBatchNorm2d(torch.autograd.Function):
@staticmethod
@custom_fwd
def ... | GeneSplice-main | GeneSplice/apex/apex/contrib/cudnn_gbn/batch_norm.py |
GeneSplice-main | GeneSplice/apex/apex/contrib/test/__init__.py | |
GeneSplice-main | GeneSplice/apex/apex/contrib/test/index_mul_2d/__init__.py | |
import random
import unittest
import torch
HAS_INDEX_MUL_2D_RELU = None
try:
from apex.contrib.index_mul_2d import index_mul_2d
except ImportError as e:
HAS_INDEX_MUL_2D_RELU = False
else:
HAS_INDEX_MUL_2D_RELU = True
@unittest.skipIf(not HAS_INDEX_MUL_2D_RELU, "`apex.contrib.index_mul_2d` is not found.... | GeneSplice-main | GeneSplice/apex/apex/contrib/test/index_mul_2d/test_index_mul_2d.py |
import copy
import typing
import unittest
import torch
import torch.nn as nn
from torch.testing._internal import common_utils
SKIP_TEST = None
from apex.transformer.testing.distributed_test_base import NcclDistributedTestBase
try:
from apex.contrib.cudnn_gbn import GroupBatchNorm2d as GBN
except ImportError as e:... | GeneSplice-main | GeneSplice/apex/apex/contrib/test/cudnn_gbn/test_cudnn_gbn_with_two_gpus.py |
GeneSplice-main | GeneSplice/apex/apex/contrib/test/cudnn_gbn/__init__.py | |
import unittest
import torch
import torch.nn.functional as F
reference_available = True
try:
from torchvision.ops.focal_loss import sigmoid_focal_loss
except ImportError:
reference_available = False
SKIP_TEST = None
try:
from apex.contrib.focal_loss import focal_loss
except ImportError as e:
SKIP_TES... | GeneSplice-main | GeneSplice/apex/apex/contrib/test/focal_loss/test_focal_loss.py |
GeneSplice-main | GeneSplice/apex/apex/contrib/test/focal_loss/__init__.py | |
GeneSplice-main | GeneSplice/apex/apex/contrib/test/xentropy/__init__.py | |
import unittest
import random
import time
import numpy as np
import torch
SKIP_TEST = None
try:
from apex.contrib import xentropy as label_smoothing
except ImportError as e:
SKIP_TEST = e
def label_smoothing_raw(x, target, padding_idx, smoothing):
logprobs = torch.nn.functional.log_softmax(x, dim=-1, d... | GeneSplice-main | GeneSplice/apex/apex/contrib/test/xentropy/test_label_smoothing.py |
import unittest
import os
import torch
from torch.testing._internal import common_utils
from torch.testing._internal.common_device_type import instantiate_device_type_tests
SKIP_TEST = None
try:
from apex import fused_dense
except ImportError as e:
SKIP_TEST = e
@unittest.skipIf(SKIP_TEST, f"{SKIP_TEST}")
c... | GeneSplice-main | GeneSplice/apex/apex/contrib/test/fused_dense/test_fused_dense.py |
GeneSplice-main | GeneSplice/apex/apex/contrib/test/layer_norm/__init__.py | |
import unittest
import torch
SKIP_TEST = None
try:
from apex.contrib.layer_norm.layer_norm import FastLayerNorm
import fast_layer_norm as fln
except ImportError as e:
SKIP_TEST = e
class GPUTimer:
def __init__(self, stream):
self.start_ = torch.cuda.Event(enable_timing=True)
self.sto... | GeneSplice-main | GeneSplice/apex/apex/contrib/test/layer_norm/test_fast_layer_norm.py |
import os
import inspect
import torch
from torch.cuda.amp import GradScaler
from torch.testing._internal import common_utils
from apex.parallel.distributed import flat_dist_call
from apex.contrib.optimizers.distributed_fused_lamb import DistributedFusedLAMB
from apex.transformer.testing.distributed_test_base import Ncc... | GeneSplice-main | GeneSplice/apex/apex/contrib/test/optimizers/test_distributed_fused_lamb.py |
GeneSplice-main | GeneSplice/apex/apex/contrib/test/optimizers/__init__.py | |
from contextlib import contextmanager
import io
import unittest
import torch
from torch.testing._internal import common_utils
SKIP_TEST = None
try:
from apex.contrib.optimizers.distributed_fused_adam import DistributedFusedAdam
except ImportError as e:
SKIP_TEST = e
from apex.transformer.testing.distributed_t... | GeneSplice-main | GeneSplice/apex/apex/contrib/test/optimizers/test_dist_adam.py |
import unittest
import torch
from torch.testing._internal import common_utils
from apex.transformer.testing.distributed_test_base import NcclDistributedTestBase
SKIP_TEST = None
try:
from apex.contrib.bottleneck import Bottleneck, SpatialBottleneck
from apex.contrib.bottleneck import HaloExchangerPeer
fro... | GeneSplice-main | GeneSplice/apex/apex/contrib/test/bottleneck/test_bottleneck_module.py |
GeneSplice-main | GeneSplice/apex/apex/contrib/test/bottleneck/__init__.py | |
GeneSplice-main | GeneSplice/apex/apex/contrib/test/conv_bias_relu/__init__.py | |
import copy
import math
import random
import unittest
import torch
import torch.nn.functional as F
HAS_CONV_BIAS_RELU = None
try:
from apex.contrib.conv_bias_relu import ConvBiasReLU, ConvBias, ConvBiasMaskReLU, ConvFrozenScaleBiasReLU
except ImportError as e:
HAS_CONV_BIAS_RELU = False
else:
HAS_CONV_BIA... | GeneSplice-main | GeneSplice/apex/apex/contrib/test/conv_bias_relu/test_conv_bias_relu.py |
import unittest
import torch
SKIP_TEST = None
try:
from apex.contrib.multihead_attn import SelfMultiheadAttn
except ImportError as e:
SKIP_TEST = e
@unittest.skipIf(SKIP_TEST, f"{SKIP_TEST}")
class SelfMultiheadAttnNormAddTest(unittest.TestCase):
def setUp(self, seed=1234):
torch.manual_seed(see... | GeneSplice-main | GeneSplice/apex/apex/contrib/test/multihead_attn/test_self_multihead_attn_norm_add.py |
GeneSplice-main | GeneSplice/apex/apex/contrib/test/multihead_attn/__init__.py | |
import unittest
import torch
import torch.nn.functional as F
SKIP_TEST = None
try:
from apex.contrib.multihead_attn import fast_mask_softmax_dropout_func
except ImportError as e:
SKIP_TEST = e
@unittest.skipIf(SKIP_TEST, f"{SKIP_TEST}")
class FusedSoftmaxTest(unittest.TestCase):
def setUp(self, seed=123... | GeneSplice-main | GeneSplice/apex/apex/contrib/test/multihead_attn/test_mha_fused_softmax.py |
import unittest
import torch
SKIP_TEST = None
try:
from apex.contrib.multihead_attn import EncdecMultiheadAttn
except ImportError as e:
SKIP_TEST = e
@unittest.skipIf(SKIP_TEST, f"{SKIP_TEST}")
class EncdecMultiheadAttnTest(unittest.TestCase):
def setUp(self, seed=1234):
torch.manual_seed(seed)
... | GeneSplice-main | GeneSplice/apex/apex/contrib/test/multihead_attn/test_encdec_multihead_attn.py |
import unittest
import torch
SKIP_TEST = None
try:
from apex.contrib.multihead_attn import SelfMultiheadAttn
except ImportError as e:
SKIP_TEST = e
@unittest.skipIf(SKIP_TEST, f"{SKIP_TEST}")
class SelfMultiheadAttnTest(unittest.TestCase):
def setUp(self, seed=1234):
torch.manual_seed(seed)
... | GeneSplice-main | GeneSplice/apex/apex/contrib/test/multihead_attn/test_fast_self_multihead_attn_bias.py |
import unittest
import torch
SKIP_TEST = None
try:
from apex.contrib.multihead_attn import EncdecMultiheadAttn
except ImportError as e:
SKIP_TEST = e
@unittest.skipIf(SKIP_TEST, f"{SKIP_TEST}")
class EncdecMultiheadAttnNormAddTest(unittest.TestCase):
def setUp(self, seed=1234):
torch.manual_seed... | GeneSplice-main | GeneSplice/apex/apex/contrib/test/multihead_attn/test_encdec_multihead_attn_norm_add.py |
import unittest
import torch
SKIP_TEST = None
try:
from apex.contrib.multihead_attn import SelfMultiheadAttn
except ImportError as e:
SKIP_TEST = e
@unittest.skipIf(SKIP_TEST, f"{SKIP_TEST}")
class SelfMultiheadAttnTest(unittest.TestCase):
def setUp(self, seed=1234):
torch.manual_seed(seed)
... | GeneSplice-main | GeneSplice/apex/apex/contrib/test/multihead_attn/test_self_multihead_attn.py |
import random
import unittest
import torch
SKIP_TEST = None
try:
from apex.contrib.clip_grad import clip_grad_norm_
except ImportError as e:
SKIP_TEST = e
def make_params(
num_params,
sizes=[1,2,3,4,5],
num_dims=[1,2,3],
dtypes=[torch.float32],
devices=['cuda'],
... | GeneSplice-main | GeneSplice/apex/apex/contrib/test/clip_grad/test_clip_grad.py |
GeneSplice-main | GeneSplice/apex/apex/contrib/test/clip_grad/__init__.py |
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