python_code stringlengths 0 229k |
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from torchbenchmark.tasks import NLP
from torchbenchmark.util.framework.huggingface.model_factory import HuggingFaceModel
class Model(HuggingFaceModel):
task = NLP.LANGUAGE_MODELING
DEFAULT_TRAIN_BSIZE = 2
DEFAULT_EVAL_BSIZE = 1
def __init__(self, test, device, batch_size=None, extra_args=[]):
... |
import subprocess
import sys
import os
from torchbenchmark.util.framework.huggingface.patch_hf import patch_transformers, cache_model
def pip_install_requirements():
subprocess.check_call([sys.executable, '-m', 'pip', 'install', '-q', '-r', 'requirements.txt'])
if __name__ == '__main__':
pip_install_requirem... |
"""
HuggingFace Stable Diffusion model.
It requires users to specify "HUGGINGFACE_AUTH_TOKEN" in environment variable
to authorize login and agree HuggingFace terms and conditions.
"""
from torchbenchmark.tasks import COMPUTER_VISION
from torchbenchmark.util.model import BenchmarkModel
from torchbenchmark.util.framewor... |
from torchbenchmark.util.framework.diffusers import install_diffusers
from torchbenchmark.util.framework.huggingface.model_factory import HuggingFaceAuthMixin
import torch
import os
import warnings
MODEL_NAME = "stabilityai/stable-diffusion-2"
def load_model_checkpoint():
from diffusers import StableDiffusionPipel... |
import os
from torchbenchmark.tasks import COMPUTER_VISION
from torchbenchmark.util.framework.detectron2.model_factory import Detectron2Model
MODEL_NAME = os.path.basename(os.path.dirname(os.path.abspath(__file__)))
MODEL_DIR = os.path.abspath(os.path.dirname(__file__))
class Model(Detectron2Model):
task = COMPUT... |
import os
from torchbenchmark.util.framework.detectron2 import install_detectron2
MODEL_NAME = os.path.basename(os.path.dirname(os.path.abspath(__file__)))
MODEL_DIR = os.path.abspath(os.path.dirname(__file__))
if __name__ == '__main__':
install_detectron2(MODEL_NAME, MODEL_DIR)
|
import matplotlib
matplotlib.use("Agg")
import matplotlib.pylab as plt
import numpy as np
def save_figure_to_numpy(fig):
# save it to a numpy array.
data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
return data
def... |
import os
import time
import argparse
import math
from numpy import finfo
import torch
from .distributed import apply_gradient_allreduce
import torch.distributed as dist
from torch.utils.data.distributed import DistributedSampler
from torch.utils.data import DataLoader
from .model import Tacotron2
from .data_utils im... |
import tensorflow as tf
from text import symbols
def create_hparams(hparams_string=None, verbose=False):
"""Create model hyperparameters. Parse nondefault from given string."""
hparams = tf.contrib.training.HParams(
################################
# Experiment Parameters #
###... |
from .train_tacotron2 import load_model, prepare_dataloaders
import torch
from .loss_function import Tacotron2Loss
from argparse import Namespace
from .text import symbols
from pathlib import Path
from ...util.model import BenchmarkModel
from typing import Tuple
from contextlib import nullcontext
from torchbenchmark.ta... |
import torch
import numpy as np
from scipy.signal import get_window
import librosa.util as librosa_util
def window_sumsquare(window, n_frames, hop_length=200, win_length=800,
n_fft=800, dtype=np.float32, norm=None):
"""
# from librosa 0.6
Compute the sum-square envelope of a window fu... |
import random
import torch
from torch.utils.tensorboard import SummaryWriter
from plotting_utils import plot_alignment_to_numpy, plot_spectrogram_to_numpy
from plotting_utils import plot_gate_outputs_to_numpy
class Tacotron2Logger(SummaryWriter):
def __init__(self, logdir):
super(Tacotron2Logger, self).__... |
from math import sqrt
import torch
from torch.autograd import Variable
from torch import nn
from torch.nn import functional as F
from .layers import ConvNorm, LinearNorm
from .tacotron2_utils import to_gpu, get_mask_from_lengths
class LocationLayer(nn.Module):
def __init__(self, attention_n_filters, attention_ker... |
"""
BSD 3-Clause License
Copyright (c) 2017, Prem Seetharaman
All rights reserved.
* Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice,
this list of... |
import torch
import torch.distributed as dist
from torch.nn.modules import Module
from torch.autograd import Variable
def _flatten_dense_tensors(tensors):
"""Flatten dense tensors into a contiguous 1D buffer. Assume tensors are of
same dense type.
Since inputs are dense, the resulting tensor will be a conc... |
import random
import numpy as np
import torch
import torch.utils.data
from .layers import TacotronSTFT
from .tacotron2_utils import load_wav_to_torch, load_filepaths_and_text
from .text import text_to_sequence
class TextMelLoader(torch.utils.data.Dataset):
"""
1) loads audio,text pairs
2) normali... |
from torch import nn
class Tacotron2Loss(nn.Module):
def __init__(self):
super(Tacotron2Loss, self).__init__()
def forward(self, model_output, targets):
mel_target, gate_target = targets[0], targets[1]
mel_target.requires_grad = False
gate_target.requires_grad = False
... |
import os
from pathlib import Path
import subprocess
import sys
from utils import s3_utils
def check_data_dir():
current_dir = Path(os.path.dirname(os.path.realpath(__file__)))
tacotron2_data_dir = os.path.join(current_dir.parent.parent, "data", ".data", "tacotron2-minimal")
assert os.path.exists(tacotron... |
import torch
from librosa.filters import mel as librosa_mel_fn
from .audio_processing import dynamic_range_compression
from .audio_processing import dynamic_range_decompression
from .stft import STFT
class LinearNorm(torch.nn.Module):
def __init__(self, in_dim, out_dim, bias=True, w_init_gain='linear'):
s... |
import time
import torch
import sys
import subprocess
argslist = list(sys.argv)[1:]
num_gpus = torch.cuda.device_count()
argslist.append('--n_gpus={}'.format(num_gpus))
workers = []
job_id = time.strftime("%Y_%m_%d-%H%M%S")
argslist.append("--group_name=group_{}".format(job_id))
for i in range(num_gpus):
argslist... |
import numpy as np
from scipy.io.wavfile import read
import torch
from pathlib import Path
def get_mask_from_lengths(lengths):
max_len = torch.max(lengths).item()
ids = torch.arange(0, max_len, device=lengths.device)
mask = (ids < lengths.unsqueeze(1)).bool()
return mask
def load_wav_to_torch(full_p... |
import torch
class LossScaler:
def __init__(self, scale=1):
self.cur_scale = scale
# `params` is a list / generator of torch.Variable
def has_overflow(self, params):
return False
# `x` is a torch.Tensor
def _has_inf_or_nan(x):
return False
# `overflow` is boolean ind... |
# *****************************************************************************
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# * Redistributions... |
import copy
import torch
from glow import Invertible1x1Conv, remove
@torch.jit.script
def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
n_channels_int = n_channels[0]
in_act = input_a+input_b
t_act = torch.tanh(in_act[:, :n_channels_int, :])
s_act = torch.sigmoid(in_act[:, n_channels_... |
import sys
sys.path.append('tacotron2')
import torch
from layers import STFT
class Denoiser(torch.nn.Module):
""" Removes model bias from audio produced with waveglow """
def __init__(self, waveglow, filter_length=1024, n_overlap=4,
win_length=1024, mode='zeros'):
super(Denoiser, sel... |
# *****************************************************************************
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# * Redistributions... |
# *****************************************************************************
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# * Redistributions... |
# *****************************************************************************
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# * Redistributions... |
# *****************************************************************************
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# * Redistributions... |
import sys
import copy
import torch
def _check_model_old_version(model):
if hasattr(model.WN[0], 'res_layers') or hasattr(model.WN[0], 'cond_layers'):
return True
else:
return False
def _update_model_res_skip(old_model, new_model):
for idx in range(0, len(new_model.WN)):
wavenet =... |
import matplotlib
matplotlib.use("Agg")
import matplotlib.pylab as plt
import numpy as np
def save_figure_to_numpy(fig):
# save it to a numpy array.
data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
return data
def... |
import tensorflow as tf
from text import symbols
def create_hparams(hparams_string=None, verbose=False):
"""Create model hyperparameters. Parse nondefault from given string."""
hparams = tf.contrib.training.HParams(
################################
# Experiment Parameters #
###... |
import torch
import numpy as np
from scipy.signal import get_window
import librosa.util as librosa_util
def window_sumsquare(window, n_frames, hop_length=200, win_length=800,
n_fft=800, dtype=np.float32, norm=None):
"""
# from librosa 0.6
Compute the sum-square envelope of a window fu... |
import random
import torch.nn.functional as F
from tensorboardX import SummaryWriter
from plotting_utils import plot_alignment_to_numpy, plot_spectrogram_to_numpy
from plotting_utils import plot_gate_outputs_to_numpy
class Tacotron2Logger(SummaryWriter):
def __init__(self, logdir):
super(Tacotron2Logger, ... |
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 loss_scaler import DynamicLossScaler, LossScaler
FLOAT_TYPES = (torch.FloatTensor, torch.cuda.FloatTensor)
HALF_TYPES = (torch.H... |
import torch
from torch.autograd import Variable
from torch import nn
from torch.nn import functional as F
from layers import ConvNorm, LinearNorm
from utils import to_gpu, get_mask_from_lengths
from fp16_optimizer import fp32_to_fp16, fp16_to_fp32
class LocationLayer(nn.Module):
def __init__(self, attention_n_fi... |
"""
BSD 3-Clause License
Copyright (c) 2017, Prem Seetharaman
All rights reserved.
* Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice,
this list of... |
import torch
import torch.distributed as dist
from torch.nn.modules import Module
def _flatten_dense_tensors(tensors):
"""Flatten dense tensors into a contiguous 1D buffer. Assume tensors are of
same dense type.
Since inputs are dense, the resulting tensor will be a concatenated 1D
buffer. Element-wise... |
import random
import numpy as np
import torch
import torch.utils.data
import layers
from utils import load_wav_to_torch, load_filepaths_and_text
from text import text_to_sequence
class TextMelLoader(torch.utils.data.Dataset):
"""
1) loads audio,text pairs
2) normalizes text and converts them to s... |
from torch import nn
class Tacotron2Loss(nn.Module):
def __init__(self):
super(Tacotron2Loss, self).__init__()
def forward(self, model_output, targets):
mel_target, gate_target = targets[0], targets[1]
mel_target.requires_grad = False
gate_target.requires_grad = False
... |
import numpy as np
from scipy.io.wavfile import read
import torch
def get_mask_from_lengths(lengths):
max_len = torch.max(lengths)
ids = torch.arange(0, max_len).long().cuda()
mask = (ids < lengths.unsqueeze(1)).byte()
return mask
def load_wav_to_torch(full_path, sr):
sampling_rate, data = read(... |
import os
import time
import argparse
import math
from numpy import finfo
import torch
from distributed import DistributedDataParallel
from torch.utils.data.distributed import DistributedSampler
from torch.nn import DataParallel
from torch.utils.data import DataLoader
from fp16_optimizer import FP16_Optimizer
from m... |
import torch
from librosa.filters import mel as librosa_mel_fn
from audio_processing import dynamic_range_compression
from audio_processing import dynamic_range_decompression
from stft import STFT
class LinearNorm(torch.nn.Module):
def __init__(self, in_dim, out_dim, bias=True, w_init_gain='linear'):
supe... |
import time
import torch
import sys
import subprocess
argslist = list(sys.argv)[1:]
num_gpus = torch.cuda.device_count()
argslist.append('--n_gpus={}'.format(num_gpus))
workers = []
job_id = time.strftime("%Y_%m_%d-%H%M%S")
argslist.append("--group_name=group_{}".format(job_id))
for i in range(num_gpus):
argslist... |
import torch
class LossScaler:
def __init__(self, scale=1):
self.cur_scale = scale
# `params` is a list / generator of torch.Variable
def has_overflow(self, params):
return False
# `x` is a torch.Tensor
def _has_inf_or_nan(x):
return False
# `overflow` is boolean ind... |
""" from https://github.com/keithito/tacotron """
import re
valid_symbols = [
'AA', 'AA0', 'AA1', 'AA2', 'AE', 'AE0', 'AE1', 'AE2', 'AH', 'AH0', 'AH1', 'AH2',
'AO', 'AO0', 'AO1', 'AO2', 'AW', 'AW0', 'AW1', 'AW2', 'AY', 'AY0', 'AY1', 'AY2',
'B', 'CH', 'D', 'DH', 'EH', 'EH0', 'EH1', 'EH2', 'ER', 'ER0', 'ER1', 'E... |
""" from https://github.com/keithito/tacotron """
import re
from text import cleaners
from text.symbols import symbols
# Mappings from symbol to numeric ID and vice versa:
_symbol_to_id = {s: i for i, s in enumerate(symbols)}
_id_to_symbol = {i: s for i, s in enumerate(symbols)}
# Regular expression matching text en... |
""" from https://github.com/keithito/tacotron """
import inflect
import re
_inflect = inflect.engine()
_comma_number_re = re.compile(r'([0-9][0-9\,]+[0-9])')
_decimal_number_re = re.compile(r'([0-9]+\.[0-9]+)')
_pounds_re = re.compile(r'£([0-9\,]*[0-9]+)')
_dollars_re = re.compile(r'\$([0-9\.\,]*[0-9]+)')
_ordinal_r... |
""" from https://github.com/keithito/tacotron """
'''
Defines the set of symbols used in text input to the model.
The default is a set of ASCII characters that works well for English or text that has been run through Unidecode. For other data, you can modify _characters. See TRAINING_DATA.md for details. '''
from tex... |
""" from https://github.com/keithito/tacotron """
'''
Cleaners are transformations that run over the input text at both training and eval time.
Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
hyperparameter. Some cleaners are English-specific. You'll typically want to use... |
""" from https://github.com/keithito/tacotron """
import re
valid_symbols = [
'AA', 'AA0', 'AA1', 'AA2', 'AE', 'AE0', 'AE1', 'AE2', 'AH', 'AH0', 'AH1', 'AH2',
'AO', 'AO0', 'AO1', 'AO2', 'AW', 'AW0', 'AW1', 'AW2', 'AY', 'AY0', 'AY1', 'AY2',
'B', 'CH', 'D', 'DH', 'EH', 'EH0', 'EH1', 'EH2', 'ER', 'ER0', 'ER1', 'E... |
""" from https://github.com/keithito/tacotron """
import re
from . import cleaners
from .symbols import symbols
# Mappings from symbol to numeric ID and vice versa:
_symbol_to_id = {s: i for i, s in enumerate(symbols)}
_id_to_symbol = {i: s for i, s in enumerate(symbols)}
# Regular expression matching text enclosed ... |
""" from https://github.com/keithito/tacotron """
import inflect
import re
_inflect = inflect.engine()
_comma_number_re = re.compile(r'([0-9][0-9\,]+[0-9])')
_decimal_number_re = re.compile(r'([0-9]+\.[0-9]+)')
_pounds_re = re.compile(r'£([0-9\,]*[0-9]+)')
_dollars_re = re.compile(r'\$([0-9\.\,]*[0-9]+)')
_ordinal_r... |
""" from https://github.com/keithito/tacotron """
'''
Defines the set of symbols used in text input to the model.
The default is a set of ASCII characters that works well for English or text that has been run through Unidecode. For other data, you can modify _characters. See TRAINING_DATA.md for details. '''
from . i... |
""" from https://github.com/keithito/tacotron """
'''
Cleaners are transformations that run over the input text at both training and eval time.
Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
hyperparameter. Some cleaners are English-specific. You'll typically want to use... |
import os
import json
import torch
import kaldi_io
import dataclasses
from .speech_transformer.transformer.decoder import Decoder
from .speech_transformer.transformer.encoder import Encoder
from .speech_transformer.transformer import Transformer
from .speech_transformer.transformer.optimizer import TransformerOptimize... |
#!/usr/bin/env python
#
# The SpeechTransformer model copied from https://github.com/kaituoxu/Speech-Transformer, commit e684777.
# The model only supports CUDA and eager mode.
# The input data files in the input_data/ directory are generated with a minimized aishell data
# containing the following files in the origina... |
import sys
import subprocess
from utils import s3_utils
def pip_install_requirements():
subprocess.check_call([sys.executable, '-m', 'pip', 'install', '-q', '-r', 'requirements.txt'])
if __name__ == '__main__':
s3_utils.checkout_s3_data("INPUT_TARBALLS", "speech_transformer_inputs.tar.gz", decompress=True)
... |
import torch
import torch.nn as nn
import torch.nn.functional as F
from .attention import MultiHeadAttention
from .module import PositionalEncoding, PositionwiseFeedForward
from ..utils import (IGNORE_ID, get_attn_key_pad_mask, get_attn_pad_mask,
get_non_pad_mask, get_subsequent_mask, pad_list)
cl... |
import numpy as np
import torch
import torch.nn as nn
class MultiHeadAttention(nn.Module):
''' Multi-Head Attention module '''
def __init__(self, n_head, d_model, d_k, d_v, dropout=0.1):
super().__init__()
self.n_head = n_head
self.d_k = d_k
self.d_v = d_v
self.w_qs ... |
from .transformer import *
|
import torch.nn as nn
from .attention import MultiHeadAttention
from .module import PositionalEncoding, PositionwiseFeedForward
from ..utils import get_non_pad_mask, get_attn_pad_mask
class Encoder(nn.Module):
"""Encoder of Transformer including self-attention and feed forward.
"""
def __init__(self, d_... |
import torch
import torch.nn.functional as F
from ..utils import IGNORE_ID
def cal_performance(pred, gold, smoothing=0.0):
"""Calculate cross entropy loss, apply label smoothing if needed.
Args:
pred: N x T x C, score before softmax
gold: N x T
"""
pred = pred.view(-1, pred.size(2))
... |
import torch
import torch.nn as nn
from .decoder import Decoder
from .encoder import Encoder
class Transformer(nn.Module):
"""An encoder-decoder framework only includes attention.
"""
def __init__(self, encoder, decoder):
super(Transformer, self).__init__()
self.encoder = encoder
... |
"""A wrapper class for optimizer"""
import torch
class TransformerOptimizer:
"""A simple wrapper class for learning rate scheduling"""
def __init__(self, optimizer, k, d_model, warmup_steps=4000):
self.optimizer = optimizer
self.k = k
self.init_lr = d_model ** (-0.5)
self.warm... |
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class PositionalEncoding(nn.Module):
"""Implement the positional encoding (PE) function.
PE(pos, 2i) = sin(pos/(10000^(2i/dmodel)))
PE(pos, 2i+1) = cos(pos/(10000^(2i/dmodel)))
"""
def __init__(self, d_model, max_l... |
#!/usr/bin/env python
# encoding: utf-8
# Copyright 2017 Johns Hopkins University (Shinji Watanabe)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
import json
import argparse
import logging
from utils import process_dict
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_ar... |
#!/usr/bin/env python2
# encoding: utf-8
# Copyright 2017 Johns Hopkins University (Shinji Watanabe)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
import sys
import json
import argparse
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--key', '-k', type=str,
... |
#!/usr/bin/env python
# Apache 2.0
import sys
import argparse
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--exclude', '-v', dest='exclude',
action='store_true', help='exclude filter words')
parser.add_argument('filt', type=str, help='filter l... |
#!/usr/bin/env python2
# encoding: utf-8
# Copyright 2017 Johns Hopkins University (Shinji Watanabe)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
import argparse
import json
import logging
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('jsons', type=str, nar... |
from .utils import *
|
#!/usr/bin/env python3
IGNORE_ID = -1
def pad_list(xs, pad_value):
# From: espnet/src/nets/e2e_asr_th.py: pad_list()
n_batch = len(xs)
max_len = max(x.size(0) for x in xs)
pad = xs[0].new(n_batch, max_len, * xs[0].size()[1:]).fill_(pad_value)
for i in range(n_batch):
pad[i, :xs[i].size(0)]... |
from .data import *
|
"""
Logic:
1. AudioDataLoader generate a minibatch from AudioDataset, the size of this
minibatch is AudioDataLoader's batchsize. For now, we always set
AudioDataLoader's batchsize as 1. The real minibatch size we care about is
set in AudioDataset's __init__(...). So actually, we generate the
information of... |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# This software may be used and distributed according to the terms of the GNU General Public License version 3.
from typing import List
import torch
from .tokenizer import Tokenizer
from .model import Transformer
class LLaMA:
def __init__(self, model: Transf... |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# This software may be used and distributed according to the terms of the GNU General Public License version 3.
from ...util.model import BenchmarkModel
from torchbenchmark.tasks import NLP
import torch
from .model import ModelArgs, Transformer
import torch
class... |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# This software may be used and distributed according to the terms of the GNU General Public License version 3.
from typing import Optional, Tuple
from dataclasses import dataclass
import math
import torch
from torch import nn
import torch.nn.functional as F
@dat... |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# This software may be used and distributed according to the terms of the GNU General Public License version 3.
from sentencepiece import SentencePieceProcessor
from logging import getLogger
from typing import List
import os
logger = getLogger()
class Tokenizer:... |
import subprocess
import sys
def pip_install_requirements():
subprocess.check_call([sys.executable, '-m', 'pip', 'install', '-q', '-r', 'requirements.txt'])
if __name__ == '__main__':
pip_install_requirements() |
from torchbenchmark.util.framework.timm.model_factory import TimmModel
from torchbenchmark.tasks import COMPUTER_VISION
class Model(TimmModel):
task = COMPUTER_VISION.CLASSIFICATION
DEFAULT_TRAIN_BSIZE = 32
DEFAULT_EVAL_BSIZE = 32
def __init__(self, test, device, batch_size=None, extra_args=[]):
... |
"""
Generate a fully specified benchmark configuration file, given a lightweight
specification and a complete source of benchmark data.
Specification File
------------------
Score hierarchy input intended to be as easy to construct as possible,
relying on automatic inference of unspecified weights, benchmark configs,
... |
"""
Compute TorchBench Score V2.
"""
import re
import math
import yaml
import importlib
import itertools
from pathlib import Path
from typing import List, Optional
TORCHBENCH_V2_REF_DATA = Path(__file__).parent.joinpath("configs/v2/config-v2.yaml")
TORCHBENCH_V2_DEFAULT_THRESHOLD = 0.07
TORCHBENCH_V2_DEFAULT_TARGET = ... |
"""
Compute the benchmark score given a frozen score configuration and current benchmark data.
"""
import argparse
import json
import math
import sys
import os
import re
import yaml
import importlib
from tabulate import tabulate
from pathlib import Path
from collections import defaultdict
TARGET_SCORE_DEFAULT = 1000
... |
"""
Compute the benchmark score given a frozen score configuration and current benchmark data.
"""
import argparse
import json
import math
import sys
import os
import re
import yaml
import importlib
from enum import Enum
from tabulate import tabulate
from pathlib import Path
from collections import defaultdict
from ty... |
"""
Compute the benchmark score given a frozen score configuration and current benchmark data.
"""
import argparse
import json
import math
import sys
import os
import re
import yaml
import importlib
from tabulate import tabulate
from pathlib import Path
from collections import defaultdict
from .generate_score_config ... |
from accelerate.utils.dataclasses import DeepSpeedPlugin
import torch
import math
import os
from pathlib import Path
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
from torch.utils.data import DataLoader
from torchbenchmark.util.e2emodel ... |
import subprocess
import sys
def pip_install_requirements():
subprocess.check_call([sys.executable, '-m', 'pip', 'install', '-q', '-r', 'requirements.txt'])
if __name__ == '__main__':
pip_install_requirements()
|
import torch
import math
import os
from pathlib import Path
from torch.utils.data import DataLoader
from ...util.model import BenchmarkModel
from torchbenchmark.tasks import NLP
from accelerate import Accelerator
from transformers import (
AdamW,
AutoConfig,
AutoModelForSequenceClassification,
AutoToken... |
from accelerate.utils.dataclasses import DeepSpeedPlugin
import functools
import torch
import numpy as np
import math
import os
from pathlib import Path
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
from torch.distributed.fsdp.wrap impor... |
import subprocess
import sys
def pip_install_requirements():
subprocess.check_call([sys.executable, '-m', 'pip', 'install', '-q', '-r', 'requirements.txt'])
if __name__ == '__main__':
pip_install_requirements()
|
# upstream repo: https://github.com/kuangliu/pytorch-cifar
import torch
import torchvision
import torchvision.transforms as transforms
from torchbenchmark.util.e2emodel import E2EBenchmarkModel
from torchbenchmark.tasks import COMPUTER_VISION
import os
from tqdm import tqdm
from pathlib import Path
# setup environmen... |
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_planes, planes, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(
in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
... |
import os
import sys
import torch
import subprocess
from pathlib import Path
from dataclasses import dataclass
from torchbenchmark.util.e2emodel import E2EBenchmarkModel
from typing import Optional, List
CURRENT_DIR = Path(os.path.dirname(os.path.realpath(__file__)))
FAMBENCH_ROOT = CURRENT_DIR.parent.parent.parent.... |
import sys
import subprocess
def pip_install_requirements():
subprocess.check_call([sys.executable, '-m', 'pip', 'install', '-q', '-r', 'requirements.txt'])
if __name__ == '__main__':
pip_install_requirements()
|
import importlib
import sys
from urllib import request
from typing import List, Dict
TORCH_DEPS = ['torch', 'torchvision', 'torchaudio']
proxy_suggestion = "Unable to verify https connectivity, " \
"required for setup.\n" \
"Do you need to use a proxy?"
class add_path():
def... |
"""gitutils.py
Utils for getting git-related information.
"""
import git
import re
import os
import time
import subprocess
from datetime import datetime
from typing import Optional, List
# Assume the nightly branch commit message is in the following format
# Hash in the parentheses links to the commit on the master ... |
from typing import Any, List, Optional
import boto3
import os
import json
import yaml
from pathlib import Path
USERBENCHMARK_S3_BUCKET = "ossci-metrics"
USERBENCHMARK_S3_OBJECT = "torchbench-userbenchmark"
REPO_ROOT = Path(__file__).parent.parent
class S3Client:
def __init__(self, bucket, object):
self.s3... |
"""
Utilities for building pytorch and torch* domain packages
"""
import os
import sys
import shutil
import subprocess
from dataclasses import dataclass
from pathlib import Path
from typing import List, Dict
CLEANUP_ROUND = 5
@dataclass
class TorchRepo:
name: str
origin_url: str
main_branch: str
src_p... |
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