python_code stringlengths 0 258k |
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"""Training algorithm track submission functions for LibriSpeech."""
from typing import Dict, Iterator, List, Tuple
import numpy as np
import torch
import torch.distributed.nn as dist_nn
from algorithmic_efficiency import spec
from algorithmic_efficiency.pytorch_utils import pytorch_setup
USE_PYTORCH_DDP = pytorch_s... |
from typing import Dict, Iterator, List, Tuple
import numpy as np
import torch
import torch.distributed.nn as dist_nn
from algorithmic_efficiency import spec
from algorithmic_efficiency.pytorch_utils import pytorch_setup
USE_PYTORCH_DDP = pytorch_setup()[0]
def get_batch_size(workload_name):
batch_sizes = {'wmt'... |
"""Training algorithm track submission functions for WMT."""
import functools
from typing import Dict, Iterator, List, Tuple
from flax import jax_utils
import jax
import jax.numpy as jnp
import optax
from algorithmic_efficiency import spec
def get_batch_size(workload_name):
batch_sizes = {'wmt': 128}
return ba... |
"""Training algorithm track submission functions for ImageNet."""
from typing import Dict, Iterator, List, Tuple
import torch
from torch.optim.lr_scheduler import CosineAnnealingLR
from torch.optim.lr_scheduler import LinearLR
from torch.optim.lr_scheduler import SequentialLR
from algorithmic_efficiency import spec
... |
"""Training algorithm track submission functions for ImageNet."""
import functools
from typing import Dict, Iterator, List, Tuple
from flax import jax_utils
import jax
from jax import lax
import jax.numpy as jnp
import optax
from algorithmic_efficiency import spec
def get_batch_size(workload_name):
# Return the ... |
"""Training algorithm track submission functions for LibriSpeech."""
import functools
from typing import Dict, Iterator, List, Tuple
from absl import logging
from flax import jax_utils
import jax
from jax import lax
import jax.numpy as jnp
import numpy as np
import optax
from algorithmic_efficiency import spec
_GRAD... |
"""Training algorithm track submission functions for LibriSpeech."""
from typing import Dict, Iterator, List, Tuple
import numpy as np
import torch
import torch.distributed.nn as dist_nn
from algorithmic_efficiency import spec
from algorithmic_efficiency.pytorch_utils import pytorch_setup
USE_PYTORCH_DDP = pytorch_s... |
from typing import Dict, Iterator, List, Tuple
import torch
import torch.distributed.nn as dist_nn
from algorithmic_efficiency import spec
from algorithmic_efficiency.pytorch_utils import pytorch_setup
USE_PYTORCH_DDP = pytorch_setup()[0]
def get_batch_size(workload_name):
# Return the global batch size.
batch... |
from typing import Dict, Iterator, List, Tuple
from flax import jax_utils
import jax
from jax import lax
import jax.numpy as jnp
import optax
from algorithmic_efficiency import spec
def get_batch_size(workload_name):
# Return the global batch size.
batch_sizes = {'ogbg': 2048}
return batch_sizes[workload_name... |
"""Training algorithm track submission functions for ImageNet."""
from typing import Dict, Iterator, List, Tuple
import torch
from torch.optim.lr_scheduler import CosineAnnealingLR
from torch.optim.lr_scheduler import LinearLR
from torch.optim.lr_scheduler import SequentialLR
from algorithmic_efficiency import spec
... |
"""Training algorithm track submission functions for ImageNet."""
import functools
from typing import Dict, Iterator, List, Tuple
from flax import jax_utils
import jax
from jax import lax
import jax.numpy as jnp
import optax
from algorithmic_efficiency import spec
def get_batch_size(workload_name):
# Return the ... |
from typing import Dict, Iterator, List, Tuple
import torch
from torch.optim.lr_scheduler import CosineAnnealingLR
from torch.optim.lr_scheduler import LinearLR
from torch.optim.lr_scheduler import SequentialLR
from algorithmic_efficiency import spec
def get_batch_size(workload_name):
batch_sizes = {'criteo1tb': ... |
"""Training algorithm track submission functions for Criteo1TB DLRM-Small."""
import functools
from typing import Dict, Iterator, List, Tuple
from flax import jax_utils
import jax
from jax import lax
import jax.numpy as jnp
import optax
from algorithmic_efficiency import spec
def get_batch_size(workload_name):
#... |
"""Training algorithm track submission functions for FastMRI."""
from typing import Dict, Iterator, List, Tuple
import torch
from torch.optim.lr_scheduler import StepLR
from algorithmic_efficiency import spec
def get_batch_size(workload_name):
# Return the global batch size.
batch_sizes = {'fastmri': 8}
retu... |
"""Training algorithm track submission functions for FastMRI in Jax."""
import functools
from typing import Dict, Iterator, List, Tuple
from flax import jax_utils
import jax
from jax import lax
import jax.numpy as jnp
import optax
from algorithmic_efficiency import spec
def get_batch_size(workload_name):
# Retur... |
"""Training algorithm track submission functions for CIFAR10."""
import functools
from typing import Dict, Iterator, List, Tuple
from flax import jax_utils
import jax
from jax import lax
import jax.numpy as jnp
import optax
from algorithmic_efficiency import spec
def get_batch_size(workload_name):
# Return the g... |
"""Training algorithm track submission functions for CIFAR10."""
from typing import Dict, Iterator, List, Tuple
import torch
from torch.optim.lr_scheduler import CosineAnnealingLR
from torch.optim.lr_scheduler import LinearLR
from torch.optim.lr_scheduler import SequentialLR
from algorithmic_efficiency import spec
... |
"""Template submission module.
See https://github.com/mlcommons/algorithmic-efficiency/blob/main/RULES.md#allowed-submissions
and https://github.com/mlcommons/algorithmic-efficiency/blob/main/RULES.md#disallowed-submissions
for guidelines.
"""
def init_optimizer_state(workload: spec.Workload,
... |
import json
import os
import re
from absl import logging
import pandas as pd
TRIAL_LINE_REGEX = '(.*) --- Tuning run (\d+)/(\d+) ---'
METRICS_LINE_REGEX = '(.*) Metrics: ({.*})'
TRIAL_DIR_REGEX = 'trial_(\d+)'
MEASUREMENTS_FILENAME = 'eval_measurements.csv'
#### File IO helper functions ###
def get_logfile_paths(lo... |
from absl.testing import absltest
import scoring_utils
TEST_LOGFILE = 'test_data/adamw_fastmri_jax_04-18-2023-13-10-58.log'
TEST_DIR = 'test_data/experiment_dir'
NUM_EVALS = 18
class Test(absltest.TestCase):
def test_get_trials_dict(self):
trials_dict = scoring_utils.get_trials_dict(TEST_LOGFILE)
self.ass... |
"""Performance and scoring code.
The three primary methods exposed by the `scoring` module are:
- `compute_performance_profiles`: generates performance profiles for a set of
submissions over all workloads as defined in the scoring rules:
https://github.com/mlcommons/algorithmic-efficiency/blob/main/RULES.md
- `com... |
import os
from absl import app
from absl import flags
from absl import logging
import scoring_utils
from algorithmic_efficiency import workloads
import scoring
flags.DEFINE_string(
'experiment_path',
None,
'Path to experiment directory containing workload directories.')
flags.DEFINE_string('submission_ta... |
"""Submission file for a LAMB optimizer with warmup+cosine LR in PyTorch."""
import math
from typing import Dict, Iterator, List, Tuple
from absl import logging
import torch
from torch import Tensor
from torch.optim.lr_scheduler import CosineAnnealingLR
from torch.optim.lr_scheduler import LinearLR
from torch.optim.l... |
"""Submission file for a LAMB optimizer with warmup+cosine LR in Jax."""
import functools
from typing import Dict, Iterator, List, Tuple
from flax import jax_utils
import jax
from jax import lax
import jax.numpy as jnp
import optax
from algorithmic_efficiency import spec
_GRAD_CLIP_EPS = 1e-6
def scale_by_learnin... |
"""Submission file for an NAdamW optimizer with warmup+cosine LR in PyTorch."""
import math
from typing import Dict, Iterator, List, Tuple
from absl import logging
import torch
from torch import Tensor
import torch.distributed.nn as dist_nn
from torch.optim.lr_scheduler import CosineAnnealingLR
from torch.optim.lr_sc... |
"""Submission file for an NAdamW optimizer with warmup+cosine LR in Jax."""
import functools
# isort: off
# We have to turn off isort here to resolve a conflict between isort and yapf.
from typing import (Any,
Callable,
Dict,
Iterator,
Li... |
"""Submission file for an AdamW optimizer with warmup+cosine LR in PyTorch."""
from typing import Dict, Iterator, List, Tuple
from absl import logging
import torch
import torch.distributed.nn as dist_nn
from torch.optim.lr_scheduler import CosineAnnealingLR
from torch.optim.lr_scheduler import LinearLR
from torch.opt... |
"""Submission file for an AdamW optimizer with warmup+cosine LR in Jax."""
import functools
from typing import Dict, Iterator, List, Tuple
from flax import jax_utils
import jax
from jax import lax
import jax.numpy as jnp
import optax
from algorithmic_efficiency import spec
_GRAD_CLIP_EPS = 1e-6
def init_optimizer... |
"""Submission file for Adafactor in PyTorch."""
from functools import partial
from typing import Dict, Iterator, List, Tuple
from absl import logging
import torch
import torch.distributed.nn as dist_nn
from torch.optim.lr_scheduler import CosineAnnealingLR
from torch.optim.lr_scheduler import LinearLR
from torch.opti... |
"""Submission file for an Adafactor optimizer with warmup+cosine LR in Jax."""
import functools
from typing import Dict, Iterator, List, Tuple
from flax import jax_utils
import jax
from jax import lax
import jax.numpy as jnp
import optax
from algorithmic_efficiency import spec
from baselines.adafactor.jax.sharded_ad... |
# coding=utf-8
# Copyright 2023 The init2winit 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 applicable la... |
"""Submission file for a SAM optimizer with warmup+cosine LR in PyTorch."""
from typing import Callable, Dict, Iterator, List, Tuple
from absl import logging
import torch
import torch.distributed.nn as dist_nn
from torch.optim.lr_scheduler import CosineAnnealingLR
from torch.optim.lr_scheduler import LinearLR
from to... |
"""Submission file for a SAM optimizer with warmup+cosine LR in Jax."""
import functools
from typing import Dict, Iterator, List, Optional, Tuple
from flax import jax_utils
import jax
from jax import lax
import jax.numpy as jnp
import optax
from algorithmic_efficiency import spec
_GRAD_CLIP_EPS = 1e-6
# Copied fr... |
"""Submission file for a SGD with HeavyBall momentum optimizer in PyTorch."""
from typing import Callable, Dict, Iterator, List, Tuple
from absl import logging
import optax
import torch
import torch.distributed.nn as dist_nn
from torch.optim.lr_scheduler import LambdaLR
from algorithmic_efficiency import spec
from a... |
"""Submission file for a SGD with HeavyBall momentum optimizer in Jax."""
import functools
from typing import Callable, Dict, Iterator, List, Tuple
from flax import jax_utils
import jax
from jax import lax
import jax.numpy as jnp
import optax
from algorithmic_efficiency import spec
_GRAD_CLIP_EPS = 1e-6
def init_... |
"""Submission file for a SGD with Nesterov momentum optimizer in PyTorch."""
from typing import Callable, Dict, Iterator, List, Tuple
from absl import logging
import optax
import torch
import torch.distributed.nn as dist_nn
from torch.optim.lr_scheduler import LambdaLR
from algorithmic_efficiency import spec
from al... |
"""Submission file for a SGD with Nesterov momentum optimizer in Jax."""
import functools
from typing import Callable, Dict, Iterator, List, Tuple
from flax import jax_utils
import jax
from jax import lax
import jax.numpy as jnp
import optax
from algorithmic_efficiency import spec
_GRAD_CLIP_EPS = 1e-6
def init_o... |
"""Submission file for a Shampoo optimizer with warmup+cosine LR in Jax."""
import functools
from typing import Dict, Iterator, List, Tuple
from flax import jax_utils
import jax
from jax import lax
import jax.numpy as jnp
import optax
from algorithmic_efficiency import spec
from baselines.shampoo.jax.distributed_sha... |
# coding=utf-8
# Copyright 2022 The Google Research 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... |
import argparse
import os
import yaml
from collections import OrderedDict
import cwrap_parser
import nn_parse
import native_parse
import preprocess_declarations
import function_wrapper
import copy_wrapper
from code_template import CodeTemplate
# This file is the top-level entry point for code generation in ATen.
#... |
# this code should be common among cwrap and ATen preprocessing
# for now, I have put it in one place but right now is copied out of cwrap
from copy import deepcopy
from itertools import product
def parse_arguments(args):
new_args = []
for arg in args:
# Simple arg declaration of form "<type> <name>"... |
import re
from copy import deepcopy
from function_wrapper import TYPE_FORMAL_GENERIC
import common_with_cwrap
type_map = {
'floating_point': [
'Float',
'Double',
'Half',
],
'integral': [
'Byte',
'Char',
'Short',
'Int',
'Long'
],
}
all_typ... |
import re
# match $identifier or ${identifier} and replace with value in env
# If this identifier is at the beginning of whitespace on a line
# and its value is a list then it is treated as
# block subsitution by indenting to that depth and putting each element
# of the list on its own line
# if the identifier is on a... |
import copy
import re
import common_with_cwrap
import yaml
from collections import OrderedDict, defaultdict
try:
# use faster C loader if available
from yaml import CLoader as Loader
except ImportError:
from yaml import Loader
# matches `name`, `params` in `name(params)`
NAME_PARAM_REGEX = r'(\w+)\((.*)\... |
# HEY! Trying to understand what this file does? Read
# "what has to be done to add a Operation ..." first!
import re
from code_template import CodeTemplate
try:
import typing # noqa: F401
except ImportError:
raise RuntimeError(
'Missing build dependency: Unable to import the `typing` module. '
... |
import yaml
# follows similar logic to cwrap, ignores !inc, and just looks for [[]]
def parse(filename):
with open(filename, 'r') as file:
declaration_lines = []
declarations = []
in_declaration = False
for line in file.readlines():
line = line.rstrip()
if ... |
from optparse import OptionParser
parser = OptionParser()
parser.add_option('-o', '--output', help='where to write the result file.',
action='store', default='.')
options, _ = parser.parse_args()
files = [
# '../../csrc/cudnn/cuDNN.cwrap',
'../../csrc/generic/TensorMethods.cwrap',
# '../... |
import re
import yaml
try:
# use faster C loader if available
from yaml import CLoader as Loader
except ImportError:
from yaml import Loader
def parse_default(s):
if s.lower() == 'true':
return True
elif s.lower() == 'false':
return False
elif s == 'nullptr':
return s
... |
from code_template import CodeTemplate
from function_wrapper import nested_dict
FILE = CodeTemplate("""\
#include "ATen/Config.h"
#include "TH/TH.h"
#if AT_CUDA_ENABLED()
#undef THNN_
#include "THC/THC.h"
#endif
#include "ATen/Utils.h"
${copy_includes}
namespace at {
${copy_functions}
}
""")
COPY = CodeTemplate("... |
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