id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
9,062 | import logging
from collections import defaultdict
from typing import Any, cast, Dict, List, Optional, Tuple, Union
from torch import nn
from torchrec.distributed.planner.constants import BIGINT_DTYPE, NUM_POOLINGS
from torchrec.distributed.planner.shard_estimators import _calculate_shard_io_sizes
from torchrec.distrib... | null |
9,063 | import logging
from collections import defaultdict
from typing import Any, cast, Dict, List, Optional, Tuple, Union
from torch import nn
from torchrec.distributed.planner.constants import BIGINT_DTYPE, NUM_POOLINGS
from torchrec.distributed.planner.shard_estimators import _calculate_shard_io_sizes
from torchrec.distrib... | null |
9,064 | import logging
from collections import defaultdict
from typing import Any, cast, Dict, List, Optional, Tuple, Union
from torch import nn
from torchrec.distributed.planner.constants import BIGINT_DTYPE, NUM_POOLINGS
from torchrec.distributed.planner.shard_estimators import _calculate_shard_io_sizes
from torchrec.distrib... | null |
9,065 | import logging
from collections import defaultdict
from typing import Any, cast, Dict, List, Optional, Tuple, Union
from torch import nn
from torchrec.distributed.planner.constants import BIGINT_DTYPE, NUM_POOLINGS
from torchrec.distributed.planner.shard_estimators import _calculate_shard_io_sizes
from torchrec.distrib... | null |
9,066 | import logging
import math
from typing import cast, Dict, List, Optional, Tuple, Type
import torch
import torchrec.optim as trec_optim
from torch import nn
from torchrec.distributed.embedding_types import EmbeddingComputeKernel
from torchrec.distributed.planner.constants import (
BATCHED_COPY_PERF_FACTOR,
BIGIN... | null |
9,067 | import logging
import math
from typing import cast, Dict, List, Optional, Tuple, Type
import torch
import torchrec.optim as trec_optim
from torch import nn
from torchrec.distributed.embedding_types import EmbeddingComputeKernel
from torchrec.distributed.planner.constants import (
BATCHED_COPY_PERF_FACTOR,
BIGIN... | Calculates estimated storage sizes for each sharded tensor, comprised of input, output, tensor, gradient, and optimizer sizes. Args: sharder (ModuleSharder[nn.Module]): sharder for module that supports sharding. sharding_type (str): provided ShardingType value. tensor (torch.Tensor): tensor to be sharded. compute_devic... |
9,068 | import logging
from typing import Dict, List, Optional, Tuple, Union
from torch import nn
from torchrec.distributed.embedding_types import EmbeddingComputeKernel
from torchrec.distributed.planner.constants import POOLING_FACTOR
from torchrec.distributed.planner.shard_estimators import (
EmbeddingPerfEstimator,
... | null |
9,069 | import logging
from typing import Dict, List, Optional, Tuple, Union
from torch import nn
from torchrec.distributed.embedding_types import EmbeddingComputeKernel
from torchrec.distributed.planner.constants import POOLING_FACTOR
from torchrec.distributed.planner.shard_estimators import (
EmbeddingPerfEstimator,
... | Gets corresponding partition by type for provided sharding type. Args: sharding_type (str): sharding type string. Returns: str: the corresponding `PartitionByType` value. |
9,070 | import logging
from typing import Dict, List, Optional, Tuple, Union
from torch import nn
from torchrec.distributed.embedding_types import EmbeddingComputeKernel
from torchrec.distributed.planner.constants import POOLING_FACTOR
from torchrec.distributed.planner.shard_estimators import (
EmbeddingPerfEstimator,
... | null |
9,071 | from typing import Optional
from torchrec.distributed.embedding_types import EmbeddingComputeKernel
UVM_CACHING_RATIO: float = 0.2
class EmbeddingComputeKernel(Enum):
DENSE = "dense"
FUSED = "fused"
FUSED_UVM = "fused_uvm"
FUSED_UVM_CACHING = "fused_uvm_caching"
QUANT = "quant"
QUANT_UVM = "qua... | Calculates the device bandwidth based on given compute device, compute kernel, and caching ratio. Args: compute_kernel (str): compute kernel. compute_device (str): compute device. hbm_mem_bw (float): the bandwidth of the device HBM. ddr_mem_bw (float): the bandwidth of the system DDR memory. caching_ratio (Optional[flo... |
9,072 | import copy
import heapq
import logging
from dataclasses import dataclass
from enum import Enum
from typing import cast, Dict, List, Optional
from torchrec.distributed.planner.perf_models import NoopPerfModel
from torchrec.distributed.planner.types import (
DeviceHardware,
PartitionByType,
Partitioner,
... | null |
9,073 | import copy
import heapq
import logging
from dataclasses import dataclass
from enum import Enum
from typing import cast, Dict, List, Optional
from torchrec.distributed.planner.perf_models import NoopPerfModel
from torchrec.distributed.planner.types import (
DeviceHardware,
PartitionByType,
Partitioner,
... | null |
9,074 | import copy
import heapq
import logging
from dataclasses import dataclass
from enum import Enum
from typing import cast, Dict, List, Optional
from torchrec.distributed.planner.perf_models import NoopPerfModel
from torchrec.distributed.planner.types import (
DeviceHardware,
PartitionByType,
Partitioner,
... | null |
9,075 | import copy
import heapq
import logging
from dataclasses import dataclass
from enum import Enum
from typing import cast, Dict, List, Optional
from torchrec.distributed.planner.perf_models import NoopPerfModel
from torchrec.distributed.planner.types import (
DeviceHardware,
PartitionByType,
Partitioner,
... | null |
9,076 | import abc
from dataclasses import dataclass
from enum import Enum, unique
from typing import Any, Dict, Generic, Iterator, List, Optional, TypeVar
import torch
from fbgemm_gpu.split_table_batched_embeddings_ops_training import EmbeddingLocation
from torch import fx, nn
from torch.nn.modules.module import _addindent
fr... | null |
9,077 | import abc
import logging
from collections import defaultdict, OrderedDict
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
import torch.distributed as dist
from torch import nn
from torchrec.distributed.embedding_types import (
EmbeddingComputeKernel,
GroupedEmbeddingConfig,
ShardedE... | It is possible for there to be multiple shards from a table on a single rank. We accumulate them in key_to_local_shards. Repeat shards should have identical global ShardedTensorMetadata. |
9,078 | import copy
import logging
from typing import Any, Dict, Iterator, List, Optional, Tuple
import torch
import torch.distributed as dist
from fbgemm_gpu.split_embedding_configs import SparseType
from fbgemm_gpu.split_table_batched_embeddings_ops_inference import (
EmbeddingLocation,
IntNBitTableBatchedEmbeddingBa... | null |
9,079 | import copy
import logging
from typing import Any, Dict, Iterator, List, Optional, Tuple
import torch
import torch.distributed as dist
from fbgemm_gpu.split_embedding_configs import SparseType
from fbgemm_gpu.split_table_batched_embeddings_ops_inference import (
EmbeddingLocation,
IntNBitTableBatchedEmbeddingBa... | null |
9,080 | import copy
import logging
from typing import Any, Dict, Iterator, List, Optional, Tuple
import torch
import torch.distributed as dist
from fbgemm_gpu.split_embedding_configs import SparseType
from fbgemm_gpu.split_table_batched_embeddings_ops_inference import (
EmbeddingLocation,
IntNBitTableBatchedEmbeddingBa... | null |
9,081 | import argparse
import copy
import logging
import os
import time
from functools import partial
from typing import List, Optional, Tuple
import torch
from torchrec.distributed.benchmark.benchmark_utils import (
benchmark_module,
BenchmarkResult,
CompileMode,
DLRM_NUM_EMBEDDINGS_PER_FEATURE,
EMBEDDING... | null |
9,082 | import argparse
import copy
import logging
import os
import time
from functools import partial
from typing import List, Optional, Tuple
import torch
from torchrec.distributed.benchmark.benchmark_utils import (
benchmark_module,
BenchmarkResult,
CompileMode,
DLRM_NUM_EMBEDDINGS_PER_FEATURE,
EMBEDDING... | null |
9,083 | import argparse
import contextlib
import copy
import gc
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import (
Any,
Callable,
ContextManager,
Dict,
List,
Optional,
Tuple,
TypeVar,
Union,
)
import torch
from torch import multiprocessing a... | null |
9,084 | import argparse
import contextlib
import copy
import gc
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import (
Any,
Callable,
ContextManager,
Dict,
List,
Optional,
Tuple,
TypeVar,
Union,
)
import torch
from torch import multiprocessing a... | null |
9,085 | import argparse
import logging
import os
import time
from functools import partial
from typing import List, Tuple
import torch
from torchrec.distributed.benchmark.benchmark_utils import (
benchmark_module,
BenchmarkResult,
CompileMode,
DLRM_NUM_EMBEDDINGS_PER_FEATURE,
EMBEDDING_DIM,
get_tables,
... | null |
9,086 | import argparse
import logging
import os
import time
from functools import partial
from typing import List, Tuple
import torch
from torchrec.distributed.benchmark.benchmark_utils import (
benchmark_module,
BenchmarkResult,
CompileMode,
DLRM_NUM_EMBEDDINGS_PER_FEATURE,
EMBEDDING_DIM,
get_tables,
... | null |
9,087 | import argparse
import logging
import os
import time
from functools import partial
from typing import List, Tuple
import torch
from torchrec.distributed.benchmark.benchmark_utils import (
benchmark_module,
BenchmarkResult,
CompileMode,
DLRM_NUM_EMBEDDINGS_PER_FEATURE,
EMBEDDING_DIM,
get_tables,
... | null |
9,088 | from typing import Any, Callable, Dict, List, Optional, Set, Tuple, TypeVar
import torch
import torch.distributed as dist
from fbgemm_gpu.permute_pooled_embedding_modules_split import (
PermutePooledEmbeddingsSplit,
)
from torchrec.distributed.dist_data import EmbeddingsAllToOne
from torchrec.distributed.embedding... | null |
9,089 | import math
from typing import Any, cast, Dict, List, Optional, Tuple, TypeVar, Union
import torch
import torch.distributed as dist
from torchrec.distributed.dist_data import (
EmbeddingsAllToOneReduce,
KJTAllToAll,
KJTOneToAll,
PooledEmbeddingsReduceScatter,
VariableBatchPooledEmbeddingsReduceScatt... | null |
9,090 | import math
from typing import Any, cast, Dict, List, Optional, Tuple, TypeVar, Union
import torch
import torch.distributed as dist
from torchrec.distributed.dist_data import (
EmbeddingsAllToOneReduce,
KJTAllToAll,
KJTOneToAll,
PooledEmbeddingsReduceScatter,
VariableBatchPooledEmbeddingsReduceScatt... | null |
9,091 | import math
from typing import Any, cast, Dict, List, Optional, Tuple, TypeVar, Union
import torch
import torch.distributed as dist
from torchrec.distributed.dist_data import (
EmbeddingsAllToOneReduce,
KJTAllToAll,
KJTOneToAll,
PooledEmbeddingsReduceScatter,
VariableBatchPooledEmbeddingsReduceScatt... | null |
9,092 | from collections import OrderedDict
from dataclasses import dataclass, field
from typing import Any, cast, Dict, Iterator, List, Optional, Set, Tuple, Type, TypeVar
import torch
import torch.distributed as dist
from torch import nn
from torch.nn.parallel import DistributedDataParallel
from torchrec.distributed.comm imp... | null |
9,093 | import abc
import operator
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch.autograd.profiler import record_function
from torch.fx._pytree import register_pytree_flatten_spec, TreeSpec
from torch.utils._pytree import GetAttrKey, KeyEntry, register_pytree_node
from torchrec.st... | null |
9,094 | import abc
import operator
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch.autograd.profiler import record_function
from torch.fx._pytree import register_pytree_flatten_spec, TreeSpec
from torch.utils._pytree import GetAttrKey, KeyEntry, register_pytree_node
from torchrec.st... | null |
9,095 | import abc
import operator
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch.autograd.profiler import record_function
from torch.fx._pytree import register_pytree_flatten_spec, TreeSpec
from torch.utils._pytree import GetAttrKey, KeyEntry, register_pytree_node
from torchrec.st... | null |
9,096 | import abc
import operator
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch.autograd.profiler import record_function
from torch.fx._pytree import register_pytree_flatten_spec, TreeSpec
from torch.utils._pytree import GetAttrKey, KeyEntry, register_pytree_node
from torchrec.st... | null |
9,097 | import abc
import operator
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch.autograd.profiler import record_function
from torch.fx._pytree import register_pytree_flatten_spec, TreeSpec
from torch.utils._pytree import GetAttrKey, KeyEntry, register_pytree_node
from torchrec.st... | null |
9,098 | import abc
import operator
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch.autograd.profiler import record_function
from torch.fx._pytree import register_pytree_flatten_spec, TreeSpec
from torch.utils._pytree import GetAttrKey, KeyEntry, register_pytree_node
from torchrec.st... | null |
9,099 | import abc
import operator
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch.autograd.profiler import record_function
from torch.fx._pytree import register_pytree_flatten_spec, TreeSpec
from torch.utils._pytree import GetAttrKey, KeyEntry, register_pytree_node
from torchrec.st... | null |
9,100 | import abc
import operator
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch.autograd.profiler import record_function
from torch.fx._pytree import register_pytree_flatten_spec, TreeSpec
from torch.utils._pytree import GetAttrKey, KeyEntry, register_pytree_node
from torchrec.st... | Permutes a tensor by segments according to recat tensor. For variable stride tensors we permute across length per key, which reduces the number of permute indices and lengthens each sequence. `keyed_jagged_index_select_dim1` more efficiently parallelizes work for each permute index and sequence across multiple thread b... |
9,101 | import abc
import operator
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch.autograd.profiler import record_function
from torch.fx._pytree import register_pytree_flatten_spec, TreeSpec
from torch.utils._pytree import GetAttrKey, KeyEntry, register_pytree_node
from torchrec.st... | null |
9,102 | import abc
import operator
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch.autograd.profiler import record_function
from torch.fx._pytree import register_pytree_flatten_spec, TreeSpec
from torch.utils._pytree import GetAttrKey, KeyEntry, register_pytree_node
from torchrec.st... | null |
9,103 | import abc
import operator
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch.autograd.profiler import record_function
from torch.fx._pytree import register_pytree_flatten_spec, TreeSpec
from torch.utils._pytree import GetAttrKey, KeyEntry, register_pytree_node
from torchrec.st... | null |
9,104 | import abc
import operator
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch.autograd.profiler import record_function
from torch.fx._pytree import register_pytree_flatten_spec, TreeSpec
from torch.utils._pytree import GetAttrKey, KeyEntry, register_pytree_node
from torchrec.st... | null |
9,105 | import abc
import operator
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch.autograd.profiler import record_function
from torch.fx._pytree import register_pytree_flatten_spec, TreeSpec
from torch.utils._pytree import GetAttrKey, KeyEntry, register_pytree_node
from torchrec.st... | null |
9,106 | import abc
import operator
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch.autograd.profiler import record_function
from torch.fx._pytree import register_pytree_flatten_spec, TreeSpec
from torch.utils._pytree import GetAttrKey, KeyEntry, register_pytree_node
from torchrec.st... | null |
9,107 | import abc
import operator
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch.autograd.profiler import record_function
from torch.fx._pytree import register_pytree_flatten_spec, TreeSpec
from torch.utils._pytree import GetAttrKey, KeyEntry, register_pytree_node
from torchrec.st... | null |
9,108 | import abc
import operator
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch.autograd.profiler import record_function
from torch.fx._pytree import register_pytree_flatten_spec, TreeSpec
from torch.utils._pytree import GetAttrKey, KeyEntry, register_pytree_node
from torchrec.st... | null |
9,109 | import abc
import operator
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch.autograd.profiler import record_function
from torch.fx._pytree import register_pytree_flatten_spec, TreeSpec
from torch.utils._pytree import GetAttrKey, KeyEntry, register_pytree_node
from torchrec.st... | null |
9,110 | import abc
import operator
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch.autograd.profiler import record_function
from torch.fx._pytree import register_pytree_flatten_spec, TreeSpec
from torch.utils._pytree import GetAttrKey, KeyEntry, register_pytree_node
from torchrec.st... | null |
9,111 | import abc
import operator
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch.autograd.profiler import record_function
from torch.fx._pytree import register_pytree_flatten_spec, TreeSpec
from torch.utils._pytree import GetAttrKey, KeyEntry, register_pytree_node
from torchrec.st... | null |
9,112 | import abc
import operator
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch.autograd.profiler import record_function
from torch.fx._pytree import register_pytree_flatten_spec, TreeSpec
from torch.utils._pytree import GetAttrKey, KeyEntry, register_pytree_node
from torchrec.st... | This function checks if two JaggedTensors are equal by comparing their internal representations. The comparison is done by comparing the values of the internal representations themselves. For optional fields, None values are treated as equal. Args: jt_1 (JaggedTensor): the first JaggedTensor jt_2 (JaggedTensor): the se... |
9,113 | import abc
import operator
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch.autograd.profiler import record_function
from torch.fx._pytree import register_pytree_flatten_spec, TreeSpec
from torch.utils._pytree import GetAttrKey, KeyEntry, register_pytree_node
from torchrec.st... | This function checks if two KeyedJaggedTensors are equal by comparing their internal representations. The comparison is done by comparing the values of the internal representations themselves. For optional fields, None values are treated as equal. We compare the keys by ensuring that they have the same length and that ... |
9,114 | import abc
import operator
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch.autograd.profiler import record_function
from torch.fx._pytree import register_pytree_flatten_spec, TreeSpec
from torch.utils._pytree import GetAttrKey, KeyEntry, register_pytree_node
from torchrec.st... | null |
9,115 | import abc
import operator
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch.autograd.profiler import record_function
from torch.fx._pytree import register_pytree_flatten_spec, TreeSpec
from torch.utils._pytree import GetAttrKey, KeyEntry, register_pytree_node
from torchrec.st... | null |
9,116 | import abc
import operator
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch.autograd.profiler import record_function
from torch.fx._pytree import register_pytree_flatten_spec, TreeSpec
from torch.utils._pytree import GetAttrKey, KeyEntry, register_pytree_node
from torchrec.st... | null |
9,117 | import abc
import operator
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch.autograd.profiler import record_function
from torch.fx._pytree import register_pytree_flatten_spec, TreeSpec
from torch.utils._pytree import GetAttrKey, KeyEntry, register_pytree_node
from torchrec.st... | null |
9,118 | import abc
import operator
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch.autograd.profiler import record_function
from torch.fx._pytree import register_pytree_flatten_spec, TreeSpec
from torch.utils._pytree import GetAttrKey, KeyEntry, register_pytree_node
from torchrec.st... | null |
9,119 | import abc
import operator
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch.autograd.profiler import record_function
from torch.fx._pytree import register_pytree_flatten_spec, TreeSpec
from torch.utils._pytree import GetAttrKey, KeyEntry, register_pytree_node
from torchrec.st... | null |
9,120 | import abc
import operator
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch.autograd.profiler import record_function
from torch.fx._pytree import register_pytree_flatten_spec, TreeSpec
from torch.utils._pytree import GetAttrKey, KeyEntry, register_pytree_node
from torchrec.st... | null |
9,121 | import abc
import operator
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch.autograd.profiler import record_function
from torch.fx._pytree import register_pytree_flatten_spec, TreeSpec
from torch.utils._pytree import GetAttrKey, KeyEntry, register_pytree_node
from torchrec.st... | null |
9,122 | import csv
import math
import random
from dataclasses import dataclass
from functools import partial
from io import IOBase
from typing import Any, Callable, Iterable, Iterator, List, Sequence, Tuple, TypeVar
import torch
from iopath.common.file_io import PathManager, PathManagerFactory
from torch.utils.data import func... | null |
9,123 | import csv
import math
import random
from dataclasses import dataclass
from functools import partial
from io import IOBase
from typing import Any, Callable, Iterable, Iterator, List, Sequence, Tuple, TypeVar
import torch
from iopath.common.file_io import PathManager, PathManagerFactory
from torch.utils.data import func... | Via uniform random sampling, generates two IterDataPipe instances representing disjoint train and val splits of the given IterDataPipe. Args: datapipe (IterDataPipe): datapipe to split. train_perc (float): value in range (0.0, 1.0) specifying target proportion of datapipe samples to include in train split. Note that th... |
9,124 | import csv
import math
import random
from dataclasses import dataclass
from functools import partial
from io import IOBase
from typing import Any, Callable, Iterable, Iterator, List, Sequence, Tuple, TypeVar
import torch
from iopath.common.file_io import PathManager, PathManagerFactory
from torch.utils.data import func... | null |
9,125 | import csv
import math
import random
from dataclasses import dataclass
from functools import partial
from io import IOBase
from typing import Any, Callable, Iterable, Iterator, List, Sequence, Tuple, TypeVar
import torch
from iopath.common.file_io import PathManager, PathManagerFactory
from torch.utils.data import func... | null |
9,126 | import os
import shutil
import time
from typing import Any, Callable, Dict, Iterable, Iterator, List, Optional, Tuple, Union
import numpy as np
import torch
import torch.utils.data.datapipes as dp
from iopath.common.file_io import PathManager, PathManagerFactory
from pyre_extensions import none_throws
from torch.utils.... | `Criteo 1TB Click Logs <https://ailab.criteo.com/download-criteo-1tb-click-logs-dataset/>`_ Dataset Args: paths (Iterable[str]): local paths to TSV files that constitute the Criteo 1TB dataset. row_mapper (Optional[Callable[[List[str]], Any]]): function to apply to each split TSV line. open_kw: options to pass to under... |
9,127 | import os
import shutil
import time
from typing import Any, Callable, Dict, Iterable, Iterator, List, Optional, Tuple, Union
import numpy as np
import torch
import torch.utils.data.datapipes as dp
from iopath.common.file_io import PathManager, PathManagerFactory
from pyre_extensions import none_throws
from torch.utils.... | `Kaggle/Criteo Display Advertising <https://www.kaggle.com/c/criteo-display-ad-challenge/>`_ Dataset Args: path (str): local path to train or test dataset file. row_mapper (Optional[Callable[[List[str]], Any]]): function to apply to each split TSV line. open_kw: options to pass to underlying invocation of iopath.common... |
9,128 | import argparse
import os
import sys
from multiprocessing import Manager, Process
from typing import List
import numpy as np
from torchrec.datasets.criteo import BinaryCriteoUtils
def parse_args(argv: List[str]) -> argparse.Namespace:
parser = argparse.ArgumentParser(description="Shuffle preprocessed npy dataset."... | null |
9,129 | import argparse
import os
import sys
from multiprocessing import Manager, Process
from typing import List
import numpy as np
from torchrec.datasets.criteo import BinaryCriteoUtils
def count_rows(rows_per_file, path, day):
day_file = os.path.join(path, f"day_{day}_labels.npy")
data = np.load(day_file)
num_r... | null |
9,130 | import argparse
import math
import os
import time
from typing import Sequence
import numpy as np
from tqdm import tqdm
from utils.criteo_constant import (
CAT_FEATURE_COUNT,
DEFAULT_INT_NAMES,
NUM_EMBEDDINGS_PER_FEATURE,
)
def split_binary_file(
binary_file_path: str,
output_dir: str,
categorica... | null |
9,131 | import argparse
import os
import shutil
import subprocess
import time
import numpy as np
import nvtabular as nvt
from utils.criteo_constant import DAYS, DEFAULT_COLUMN_NAMES, DEFAULT_LABEL_NAME
from utils.dask import setup_dask
dtypes = {c: np.int32 for c in DEFAULT_COLUMN_NAMES[:14] + [DEFAULT_LABEL_NAME]}
dtypes.upda... | null |
9,132 | import argparse
import os
import shutil
import subprocess
import time
import numpy as np
import nvtabular as nvt
from utils.criteo_constant import DAYS, DEFAULT_COLUMN_NAMES, DEFAULT_LABEL_NAME
from utils.dask import setup_dask
def parse_args():
parser = argparse.ArgumentParser(description="Convert criteo tsv to p... | null |
9,133 | import argparse
import os
import shutil
import time
import numpy as np
import nvtabular as nvt
from merlin.io import Shuffle
from utils.criteo_constant import (
DAYS,
DEFAULT_CAT_NAMES,
DEFAULT_COLUMN_NAMES,
DEFAULT_INT_NAMES,
DEFAULT_LABEL_NAME,
NUM_EMBEDDINGS_PER_FEATURE_DICT,
)
from utils.das... | null |
9,134 | import argparse
import glob
import os
import time
import numpy as np
import pandas as pd
import tqdm
from joblib import delayed, Parallel
from utils.criteo_constant import (
DEFAULT_CAT_NAMES,
DEFAULT_COLUMN_NAMES,
DEFAULT_INT_NAMES,
DEFAULT_LABEL_NAME,
)
DEFAULT_LABEL_NAME = "label"
DEFAULT_INT_NAMES:... | null |
9,135 | import os
import shutil
import numba
from dask.distributed import Client
from dask_cuda import LocalCUDACluster
from nvtabular.utils import device_mem_size
def setup_dask(dask_workdir):
if os.path.exists(dask_workdir):
shutil.rmtree(dask_workdir)
os.makedirs(dask_workdir)
device_limit_frac = 0.8 ... | null |
9,136 | import argparse
import os
import sys
from typing import List
from torchrec.datasets.criteo import BinaryCriteoUtils
def parse_args(argv: List[str]) -> argparse.Namespace:
parser = argparse.ArgumentParser(
description="Criteo sparse -> contiguous preprocessing script. "
)
parser.add_argument(
... | null |
9,137 | import argparse
import os
import sys
from typing import List
from torchrec.datasets.criteo import BinaryCriteoUtils
def parse_args(argv: List[str]) -> argparse.Namespace:
parser = argparse.ArgumentParser(
description="Criteo tsv -> npy preprocessing script."
)
parser.add_argument(
"--input_... | null |
9,138 | import os
from typing import Any, Callable, Dict, List, Optional, Union
from torch.utils.data import IterDataPipe
from torchrec.datasets.utils import LoadFiles, ReadLinesFromCSV, safe_cast
def _default_row_mapper(example: List[str]) -> Dict[str, Union[float, int, str]]:
return {
DEFAULT_COLUMN_NAMES[idx]: C... | `MovieLens 20M <https://grouplens.org/datasets/movielens/20m/>`_ Dataset Args: root (str): local path to root directory containing MovieLens 20M dataset files. include_movies_data (bool): if True, adds movies data to each line. row_mapper (Optional[Callable[[List[str]], Any]]): function to apply to each split line. ope... |
9,139 | import os
from typing import Any, Callable, Dict, List, Optional, Union
from torch.utils.data import IterDataPipe
from torchrec.datasets.utils import LoadFiles, ReadLinesFromCSV, safe_cast
def _default_row_mapper(example: List[str]) -> Dict[str, Union[float, int, str]]:
return {
DEFAULT_COLUMN_NAMES[idx]: C... | `MovieLens 25M <https://grouplens.org/datasets/movielens/25m/>`_ Dataset Args: root (str): local path to root directory containing MovieLens 25M dataset files. include_movies_data (bool): if True, adds movies data to each line. row_mapper (Optional[Callable[[List[str]], Any]]): function to apply to each split line. ope... |
9,140 | from typing import Any, Dict, Iterable, List
import torch
from torch import Tensor
from torch.optim.optimizer import Optimizer
def _single_tensor_adagrad(
params: List[Tensor],
grads: List[Tensor],
state_sums: List[Tensor],
state_steps: List[Tensor],
*,
lr: float,
weight_decay: float,
lr... | r"""Functional API that performs Adagrad algorithm computation. See :class:`~torch.optim.Adagrad` for details. |
9,141 | import logging
import math
from dataclasses import dataclass
from enum import Enum, unique
from typing import Any, List, Tuple
import torch
from torchrec.optim.keyed import KeyedOptimizer, OptimizerWrapper
class WarmupPolicy(Enum):
NONE = "none"
LINEAR = "linear"
CONSTANT = "constant"
POLY = "poly"
... | null |
9,142 | import logging
import math
from dataclasses import dataclass
from enum import Enum, unique
from typing import Any, List, Tuple
import torch
from torchrec.optim.keyed import KeyedOptimizer, OptimizerWrapper
class WarmupPolicy(Enum):
class WarmupStage:
def _get_multiplier(stage: WarmupStage, iter: int) -> float:
mul... | null |
9,143 | from typing import Any, Dict, Iterable, Type
from warnings import warn
import torch
The provided code snippet includes necessary dependencies for implementing the `apply_optimizer_in_backward` function. Write a Python function `def apply_optimizer_in_backward( optimizer_class: Type[torch.optim.Optimizer], para... | NOTE: This API is deprecated. Please use Pytorch Distributed's _apply_optimizer_in_backward instead. Upon backwards(), parameters will fire the corresponding optimizer Each parameter will have the optimizer_class and optimizer_kwargs attached to _optimizer and _optimizer_kwargs. Note - gradients for these parameters wi... |
9,144 | from typing import Dict, List, Optional, Tuple
import torch
from torch import nn
from torchrec.datasets.utils import Batch
from torchrec.modules.crossnet import LowRankCrossNet
from torchrec.modules.embedding_modules import EmbeddingBagCollection
from torchrec.modules.mlp import MLP
from torchrec.sparse.jagged_tensor i... | Simple implementation of math.comb for Python 3.7 compatibility. |
9,145 | import argparse
import concurrent.futures
import json
import os
import subprocess
from typing import List
from usort import config as usort_config, usort
from utils import as_posix, LintMessage, LintSeverity
class LintSeverity(str, Enum):
ERROR = "error"
WARNING = "warning"
ADVICE = "advice"
DISABLED =... | null |
9,146 | import argparse
import concurrent.futures
import json
import logging
import os
import subprocess
import sys
import time
from typing import BinaryIO, List
from utils import as_posix, IS_WINDOWS, LintMessage, LintSeverity
def run_command(
args: List[str],
*,
stdin: BinaryIO,
retries: int,
timeout: int... | null |
9,147 | import argparse
import logging
import os
import subprocess
import sys
import time
from typing import List
def run_command(args: List[str]) -> "subprocess.CompletedProcess[bytes]":
logging.debug("$ %s", " ".join(args))
start_time = time.monotonic()
try:
return subprocess.run(args, check=True)
fi... | null |
9,148 | import os
import tensorrt as trt
import pycuda.autoinit
import pycuda.driver as cuda
from calibrator import Calibrator
from torch.autograd import Variable
import torch
import numpy as np
import time
TRT_LOGGER = trt.Logger(trt.Logger.VERBOSE)
class Calibrator(trt.IInt8EntropyCalibrator2):
def __init__(self, input_... | Attempts to load a serialized engine if available, otherwise builds a new TensorRT engine and saves it. |
9,149 | import os
import tensorrt as trt
import pycuda.autoinit
import pycuda.driver as cuda
from calibrator import Calibrator
from torch.autograd import Variable
import torch
import numpy as np
import time
class HostDeviceMem(object):
def __init__(self, host_mem, device_mem):
"""Within this context, host_mom means... | null |
9,150 | import os
import tensorrt as trt
import pycuda.autoinit
import pycuda.driver as cuda
from calibrator import Calibrator
from torch.autograd import Variable
import torch
import numpy as np
import time
class HostDeviceMem(object):
def __init__(self, host_mem, device_mem):
def __str__(self):
def __repr__(sel... | null |
9,151 | import os
import tensorrt as trt
import pycuda.autoinit
import pycuda.driver as cuda
from calibrator import Calibrator
from torch.autograd import Variable
import torch
import numpy as np
import time
def do_inference(context, bindings, inputs, outputs, stream, batch_size=1):
# Transfer data from CPU to GPU.
[cu... | null |
9,152 | import os
import tensorrt as trt
import pycuda.autoinit
import pycuda.driver as cuda
from calibrator import Calibrator
from torch.autograd import Variable
import torch
import numpy as np
import time
def do_inference_v2(context, bindings, inputs, outputs, stream, h_, w_, binding_id):
# set the input dimensions
... | null |
9,153 | import os
import tensorrt as trt
import pycuda.autoinit
import pycuda.driver as cuda
from calibrator import Calibrator
from torch.autograd import Variable
import torch
import numpy as np
import time
def postprocess_the_outputs(h_outputs, shape_of_output):
h_outputs = h_outputs.reshape(*shape_of_output).copy()
... | null |
9,154 | import os
import tensorrt as trt
import pycuda.autoinit
import pycuda.driver as cuda
from calibrator import Calibrator
from torch.autograd import Variable
import torch
import numpy as np
import time
def to_numpy(tensor):
return (
tensor.detach().cpu().numpy() if tensor.requires_grad else tensor.cpu().numpy... | null |
9,155 | import numpy as np
import torch
import torch.nn as nn
import util_trt
from calibrator import SegBatchStream
def evaluate_trt(segmentation_module_trt, loader, cfg, gpu, result_queue_trt):
# pbar = tqdm(total=len(loader))
for batch_data in loader:
# process data
batch_data = batch_data[0]
... | null |
9,156 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys
import math
import os
import argparse
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
from torch.autogr... | null |
9,157 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys
import math
import os
import argparse
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
from torch.autogr... | null |
9,158 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys
import math
import os
import argparse
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
from torch.autogr... | null |
9,159 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys
import math
import os
import argparse
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
from torch.autogr... | null |
9,160 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys
import os
import math
import argparse
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
from torch.autogr... | null |
9,161 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys
import os
import math
import argparse
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
from torch.autogr... | null |
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