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import shutil import struct from functools import lru_cache import numpy as np import torch from fairseq.dataclass.constants import DATASET_IMPL_CHOICES from fairseq.data.fasta_dataset import FastaDataset from fairseq.file_io import PathManager from . import FairseqDataset from typing import Union class IndexedDataset(...
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import shutil import struct from functools import lru_cache import numpy as np import torch from fairseq.dataclass.constants import DATASET_IMPL_CHOICES from fairseq.data.fasta_dataset import FastaDataset from fairseq.file_io import PathManager from . import FairseqDataset from typing import Union class IndexedDataset(...
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import shutil import struct from functools import lru_cache import numpy as np import torch from fairseq.dataclass.constants import DATASET_IMPL_CHOICES from fairseq.data.fasta_dataset import FastaDataset from fairseq.file_io import PathManager from . import FairseqDataset from typing import Union def read_longs(f, n)...
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import shutil import struct from functools import lru_cache import numpy as np import torch from fairseq.dataclass.constants import DATASET_IMPL_CHOICES from fairseq.data.fasta_dataset import FastaDataset from fairseq.file_io import PathManager from . import FairseqDataset from typing import Union def write_longs(f, a...
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import shutil import struct from functools import lru_cache import numpy as np import torch from fairseq.dataclass.constants import DATASET_IMPL_CHOICES from fairseq.data.fasta_dataset import FastaDataset from fairseq.file_io import PathManager from . import FairseqDataset from typing import Union _code_to_dtype = { ...
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import shutil import struct from functools import lru_cache import numpy as np import torch from fairseq.dataclass.constants import DATASET_IMPL_CHOICES from fairseq.data.fasta_dataset import FastaDataset from fairseq.file_io import PathManager from . import FairseqDataset from typing import Union def _warmup_mmap_fil...
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import shutil import struct from functools import lru_cache import numpy as np import torch from fairseq.dataclass.constants import DATASET_IMPL_CHOICES from fairseq.data.fasta_dataset import FastaDataset from fairseq.file_io import PathManager from . import FairseqDataset from typing import Union def index_file_path(p...
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from argparse import Namespace from typing import Union from fairseq.dataclass import FairseqDataclass from fairseq.dataclass.utils import populate_dataclass, merge_with_parent from hydra.core.config_store import ConfigStore from omegaconf import DictConfig REGISTRIES = {} def populate_dataclass( dataclass: Fairse...
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from dataclasses import dataclass, field import itertools import json import logging import os from typing import Optional from argparse import Namespace from omegaconf import II import numpy as np from fairseq import metrics, utils from fairseq.data import ( AppendTokenDataset, ConcatDataset, LanguagePairD...
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import contextlib import logging import os from collections import OrderedDict import torch from fairseq import metrics, options, utils from fairseq.data import ( Dictionary, LanguagePairDataset, RoundRobinZipDatasets, TransformEosLangPairDataset, ) from fairseq.models import FairseqMultiModel from fair...
Return language token index.
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import argparse from typing import Callable, List, Optional import torch from fairseq import utils from fairseq.data.indexed_dataset import get_available_dataset_impl from fairseq.dataclass.configs import ( CheckpointConfig, CommonConfig, CommonEvalConfig, DatasetConfig, DistributedTrainingConfig, ...
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import argparse from typing import Callable, List, Optional import torch from fairseq import utils from fairseq.data.indexed_dataset import get_available_dataset_impl from fairseq.dataclass.configs import ( CheckpointConfig, CommonConfig, CommonEvalConfig, DatasetConfig, DistributedTrainingConfig, ...
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import argparse from typing import Callable, List, Optional import torch from fairseq import utils from fairseq.data.indexed_dataset import get_available_dataset_impl from fairseq.dataclass.configs import ( CheckpointConfig, CommonConfig, CommonEvalConfig, DatasetConfig, DistributedTrainingConfig, ...
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import argparse from typing import Callable, List, Optional import torch from fairseq import utils from fairseq.data.indexed_dataset import get_available_dataset_impl from fairseq.dataclass.configs import ( CheckpointConfig, CommonConfig, CommonEvalConfig, DatasetConfig, DistributedTrainingConfig, ...
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import argparse from typing import Callable, List, Optional import torch from fairseq import utils from fairseq.data.indexed_dataset import get_available_dataset_impl from fairseq.dataclass.configs import ( CheckpointConfig, CommonConfig, CommonEvalConfig, DatasetConfig, DistributedTrainingConfig, ...
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import argparse from typing import Callable, List, Optional import torch from fairseq import utils from fairseq.data.indexed_dataset import get_available_dataset_impl from fairseq.dataclass.configs import ( CheckpointConfig, CommonConfig, CommonEvalConfig, DatasetConfig, DistributedTrainingConfig, ...
Args: parser (ArgumentParser): the parser input_args (List[str]): strings to parse, defaults to sys.argv parse_known (bool): only parse known arguments, similar to `ArgumentParser.parse_known_args` suppress_defaults (bool): parse while ignoring all default values modify_parser (Optional[Callable[[ArgumentParser], None]...
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import logging from hydra.core.config_store import ConfigStore from fairseq.dataclass.configs import FairseqConfig from omegaconf import DictConfig, OmegaConf logger = logging.getLogger(__name__) class FairseqConfig(FairseqDataclass): def hydra_init(cfg_name="config") -> None: cs = ConfigStore.instance() cs....
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import logging from hydra.core.config_store import ConfigStore from fairseq.dataclass.configs import FairseqConfig from omegaconf import DictConfig, OmegaConf class FairseqConfig(FairseqDataclass): common: CommonConfig = CommonConfig() common_eval: CommonEvalConfig = CommonEvalConfig() distributed_training...
This function adds default values that are stored in dataclasses that hydra doesn't know about
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import ast import inspect import logging import os import re from argparse import ArgumentError, ArgumentParser, Namespace from dataclasses import _MISSING_TYPE, MISSING from enum import Enum from typing import Any, Dict, List, Optional, Tuple, Type from fairseq.dataclass import FairseqDataclass from fairseq.dataclass....
convert a dataclass instance to tailing parser arguments
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import ast import inspect import logging import os import re from argparse import ArgumentError, ArgumentParser, Namespace from dataclasses import _MISSING_TYPE, MISSING from enum import Enum from typing import Any, Dict, List, Optional, Tuple, Type from fairseq.dataclass import FairseqDataclass from fairseq.dataclass....
Convert a flat argparse.Namespace to a structured DictConfig.
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import ast import inspect import logging import os import re from argparse import ArgumentError, ArgumentParser, Namespace from dataclasses import _MISSING_TYPE, MISSING from enum import Enum from typing import Any, Dict, List, Optional, Tuple, Type from fairseq.dataclass import FairseqDataclass from fairseq.dataclass....
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import io import logging import os import pickle import random import socket import struct import subprocess import warnings from argparse import Namespace from collections import OrderedDict from dataclasses import dataclass from typing import Any, Dict, List, Mapping, Optional import torch import torch.distributed as...
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import io import logging import os import pickle import random import socket import struct import subprocess import warnings from argparse import Namespace from collections import OrderedDict from dataclasses import dataclass from typing import Any, Dict, List, Mapping, Optional import torch import torch.distributed as...
Broadcast an arbitrary Python object to other workers.
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import types import torch class FusedAdamV1(torch.optim.Optimizer): """ Implements Adam algorithm. Currently GPU-only. Requires Apex to be installed via ``python setup.py install --cuda_ext --cpp_ext``. It has been proposed in `Adam: A Method for Stochastic Optimization`_. Compared to the original v...
Look for the FusedAdam optimizer from apex. We first try to load the "contrib" interface, which is a bit faster than the main interface, but is technically deprecated.
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from collections import OrderedDict from fairseq import utils from fairseq.models import ( FairseqMultiModel, register_model, register_model_architecture, ) from fairseq.models.transformer import ( Embedding, TransformerDecoder, TransformerEncoder, TransformerModel, base_architecture, ) ...
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import math import torch import torch.nn as nn import torch.nn.functional as F from fairseq import utils from fairseq.models import ( FairseqEncoder, FairseqEncoderDecoderModel, FairseqIncrementalDecoder, register_model, register_model_architecture, ) from fairseq.modules import ( AdaptiveSoftma...
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import math import torch import torch.nn as nn import torch.nn.functional as F from fairseq import utils from fairseq.models import ( FairseqEncoder, FairseqEncoderDecoderModel, FairseqIncrementalDecoder, register_model, register_model_architecture, ) from fairseq.modules import ( AdaptiveSoftma...
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import math import torch import torch.nn as nn import torch.nn.functional as F from fairseq import utils from fairseq.models import ( FairseqEncoder, FairseqEncoderDecoderModel, FairseqIncrementalDecoder, register_model, register_model_architecture, ) from fairseq.modules import ( AdaptiveSoftma...
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import math import torch import torch.nn as nn import torch.nn.functional as F from fairseq import utils from fairseq.models import ( FairseqEncoder, FairseqEncoderDecoderModel, FairseqIncrementalDecoder, register_model, register_model_architecture, ) from fairseq.modules import ( AdaptiveSoftma...
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import math import torch import torch.nn as nn import torch.nn.functional as F from fairseq import utils from fairseq.models import ( FairseqEncoder, FairseqEncoderDecoderModel, FairseqIncrementalDecoder, register_model, register_model_architecture, ) from fairseq.modules import ( AdaptiveSoftma...
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import math import torch import torch.nn as nn import torch.nn.functional as F from fairseq import utils from fairseq.models import ( FairseqEncoder, FairseqEncoderDecoderModel, FairseqIncrementalDecoder, register_model, register_model_architecture, ) from fairseq.modules import ( AdaptiveSoftma...
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import logging import torch import torch.nn as nn import torch.nn.functional as F from fairseq import utils from fairseq.models import ( FairseqEncoder, FairseqEncoderModel, register_model, register_model_architecture, ) from fairseq.models.transformer import TransformerEncoder from fairseq.modules impo...
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import logging import torch import torch.nn as nn import torch.nn.functional as F from fairseq import utils from fairseq.models import ( FairseqEncoder, FairseqEncoderModel, register_model, register_model_architecture, ) from fairseq.models.transformer import TransformerEncoder from fairseq.modules impo...
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import logging import torch import torch.nn as nn import torch.nn.functional as F from fairseq import utils from fairseq.models import ( FairseqEncoder, FairseqEncoderModel, register_model, register_model_architecture, ) from fairseq.models.transformer import TransformerEncoder from fairseq.modules impo...
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import logging import torch import torch.nn as nn import torch.nn.functional as F from fairseq import utils from fairseq.models import ( FairseqEncoder, FairseqEncoderModel, register_model, register_model_architecture, ) from fairseq.models.transformer import TransformerEncoder from fairseq.modules impo...
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import math from typing import Any, Dict, List, Optional, Tuple import torch import torch.nn as nn from fairseq import utils from fairseq.models import ( FairseqEncoder, FairseqEncoderDecoderModel, FairseqIncrementalDecoder, register_model, register_model_architecture, ) from fairseq.modules import ...
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import math from typing import Any, Dict, List, Optional, Tuple import torch import torch.nn as nn from fairseq import utils from fairseq.models import ( FairseqEncoder, FairseqEncoderDecoderModel, FairseqIncrementalDecoder, register_model, register_model_architecture, ) from fairseq.modules import ...
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import math from typing import Any, Dict, List, Optional, Tuple import torch import torch.nn as nn from fairseq import utils from fairseq.models import ( FairseqEncoder, FairseqEncoderDecoderModel, FairseqIncrementalDecoder, register_model, register_model_architecture, ) from fairseq.modules import ...
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import math from typing import Any, Dict, List, Optional, Tuple import torch import torch.nn as nn from fairseq import utils from fairseq.models import ( FairseqEncoder, FairseqEncoderDecoderModel, FairseqIncrementalDecoder, register_model, register_model_architecture, ) from fairseq.modules import ...
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import math from typing import Any, Dict, List, Optional, Tuple import torch import torch.nn as nn from fairseq import utils from fairseq.models import ( FairseqEncoder, FairseqEncoderDecoderModel, FairseqIncrementalDecoder, register_model, register_model_architecture, ) from fairseq.modules import ...
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from dataclasses import dataclass, field from typing import Optional from fairseq import options, utils from fairseq.dataclass import ChoiceEnum, FairseqDataclass from fairseq.models import ( FairseqLanguageModel, register_model, register_model_architecture, ) from fairseq.models.transformer import Embeddin...
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from dataclasses import dataclass, field from typing import Optional from fairseq import options, utils from fairseq.dataclass import ChoiceEnum, FairseqDataclass from fairseq.models import ( FairseqLanguageModel, register_model, register_model_architecture, ) from fairseq.models.transformer import Embeddin...
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from dataclasses import dataclass, field from typing import Optional from fairseq import options, utils from fairseq.dataclass import ChoiceEnum, FairseqDataclass from fairseq.models import ( FairseqLanguageModel, register_model, register_model_architecture, ) from fairseq.models.transformer import Embeddin...
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from dataclasses import dataclass, field from typing import Optional from fairseq import options, utils from fairseq.dataclass import ChoiceEnum, FairseqDataclass from fairseq.models import ( FairseqLanguageModel, register_model, register_model_architecture, ) from fairseq.models.transformer import Embeddin...
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from dataclasses import dataclass, field from typing import Optional from fairseq import options, utils from fairseq.dataclass import ChoiceEnum, FairseqDataclass from fairseq.models import ( FairseqLanguageModel, register_model, register_model_architecture, ) from fairseq.models.transformer import Embeddin...
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from dataclasses import dataclass, field from typing import Optional from fairseq import options, utils from fairseq.dataclass import ChoiceEnum, FairseqDataclass from fairseq.models import ( FairseqLanguageModel, register_model, register_model_architecture, ) from fairseq.models.transformer import Embeddin...
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from dataclasses import dataclass, field from typing import Optional from fairseq import options, utils from fairseq.dataclass import ChoiceEnum, FairseqDataclass from fairseq.models import ( FairseqLanguageModel, register_model, register_model_architecture, ) from fairseq.models.transformer import Embeddin...
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from fairseq.models import register_model, register_model_architecture from fairseq.models.transformer import ( TransformerModel, base_architecture, transformer_wmt_en_de_big, ) def base_architecture(args): def transformer_align(args): args.alignment_heads = getattr(args, "alignment_heads", 1) arg...
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import torch import torch.nn.functional as F from fairseq import utils from fairseq.iterative_refinement_generator import DecoderOut from fairseq.models import register_model, register_model_architecture from fairseq.models.nat import FairseqNATDecoder, FairseqNATEncoder, FairseqNATModel, ensemble_decoder, ensemble_enc...
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import torch import torch.nn.functional as F from fairseq import utils from fairseq.iterative_refinement_generator import DecoderOut from fairseq.models import register_model, register_model_architecture from fairseq.models.nat import FairseqNATDecoder, FairseqNATEncoder, FairseqNATModel, ensemble_decoder, ensemble_enc...
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import torch import torch.nn.functional as F from fairseq import utils from fairseq.iterative_refinement_generator import DecoderOut from fairseq.models import register_model, register_model_architecture from fairseq.models.nat import FairseqNATDecoder, FairseqNATEncoder, FairseqNATModel, ensemble_decoder, ensemble_enc...
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import torch import torch.nn.functional as F from fairseq import utils from fairseq.iterative_refinement_generator import DecoderOut from fairseq.models import register_model, register_model_architecture from fairseq.models.nat import FairseqNATDecoder, FairseqNATEncoder, FairseqNATModel, ensemble_decoder, ensemble_enc...
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import logging import os import signal import threading import torch import torch.nn as nn from torch.nn.parallel import DistributedDataParallel from fairseq.distributed import ( DistributedTimeoutWrapper, LegacyDistributedDataParallel, ModuleProxyWrapper, TPUDistributedDataParallel, ) _GOSSIP_DISABLED ...
Wrap a *model* to support distributed data parallel training. This is similar to the built-in DistributedDataParallel, but allows additional configuration of the DistributedDataParallel class to use, and also provides easier access to the wrapped model by forwarding requests for missing attributes to the wrapped model....
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from fairseq import utils from fairseq.models import ( FairseqLanguageModel, register_model, register_model_architecture, ) from fairseq.models.fconv import FConvDecoder def base_lm_architecture(args): args.dropout = getattr(args, "dropout", 0.1) args.decoder_embed_dim = getattr(args, "decoder_embed...
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from fairseq import utils from fairseq.models import ( FairseqLanguageModel, register_model, register_model_architecture, ) from fairseq.models.fconv import FConvDecoder def base_lm_architecture(args): args.dropout = getattr(args, "dropout", 0.1) args.decoder_embed_dim = getattr(args, "decoder_embed...
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import logging import math from typing import Dict, List, Optional, Tuple import torch.nn as nn from fairseq import checkpoint_utils, utils from fairseq.data.data_utils import lengths_to_padding_mask from fairseq.models import ( FairseqEncoder, FairseqEncoderDecoderModel, register_model, register_model_...
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import logging import math from typing import Dict, List, Optional, Tuple import torch.nn as nn from fairseq import checkpoint_utils, utils from fairseq.data.data_utils import lengths_to_padding_mask from fairseq.models import ( FairseqEncoder, FairseqEncoderDecoderModel, register_model, register_model_...
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import logging import math from typing import Dict, List, Optional, Tuple import torch.nn as nn from fairseq import checkpoint_utils, utils from fairseq.data.data_utils import lengths_to_padding_mask from fairseq.models import ( FairseqEncoder, FairseqEncoderDecoderModel, register_model, register_model_...
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import logging import math from typing import Dict, List, Optional, Tuple import torch.nn as nn from fairseq import checkpoint_utils, utils from fairseq.data.data_utils import lengths_to_padding_mask from fairseq.models import ( FairseqEncoder, FairseqEncoderDecoderModel, register_model, register_model_...
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import logging import math from typing import Dict, List, Optional, Tuple import torch import torch.nn as nn import torch.nn.functional as F from fairseq.data.data_utils import lengths_to_padding_mask from fairseq import checkpoint_utils, utils from fairseq.models import ( FairseqEncoder, FairseqEncoderDecoderM...
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import logging import math from typing import Dict, List, Optional, Tuple import torch import torch.nn as nn import torch.nn.functional as F from fairseq.data.data_utils import lengths_to_padding_mask from fairseq import checkpoint_utils, utils from fairseq.models import ( FairseqEncoder, FairseqEncoderDecoderM...
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import math import torch import torch.nn as nn import torch.nn.functional as F from fairseq import utils from fairseq.models import ( FairseqEncoder, FairseqEncoderDecoderModel, FairseqIncrementalDecoder, register_model, register_model_architecture, ) from fairseq.modules import ( AdaptiveSoftma...
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from argparse import Namespace import contextlib import copy import math import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from dataclasses import dataclass, field from omegaconf import MISSING, II, open_dict from typing import Any from fairseq import checkpoint_utils, tasks, utils f...
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from argparse import Namespace import contextlib import copy import math import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from dataclasses import dataclass, field from omegaconf import MISSING, II, open_dict from typing import Any from fairseq import checkpoint_utils, tasks, utils f...
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import logging import torch import torch.nn as nn import torch.nn.functional as F from fairseq import utils from fairseq.models import ( FairseqEncoder, FairseqEncoderModel, register_model, register_model_architecture, ) from fairseq.modules import ( LayerNorm, SinusoidalPositionalEmbedding, ...
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import logging import torch import torch.nn as nn import torch.nn.functional as F from fairseq import utils from fairseq.models import ( FairseqEncoder, FairseqEncoderModel, register_model, register_model_architecture, ) from fairseq.modules import ( LayerNorm, SinusoidalPositionalEmbedding, ...
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import os import sys import time import logging from tqdm import tqdm import torch from fairseq import utils, tasks, options from fairseq.checkpoint_utils import load_model_ensemble_and_task from fairseq.dataclass.utils import convert_namespace_to_omegaconf from torch import Tensor from typing import Dict, List, Option...
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import os import sys import time import logging from tqdm import tqdm import torch from fairseq import utils, tasks, options from fairseq.checkpoint_utils import load_model_ensemble_and_task from fairseq.dataclass.utils import convert_namespace_to_omegaconf from torch import Tensor from typing import Dict, List, Option...
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import argparse import collections import os import re import torch from fairseq.file_io import PathManager class PathManager: def open( path: str, mode: str = "r", buffering: int = -1, encoding: Optional[str] = None, errors: Optional[str] = None, ...
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import argparse import logging import math import os import sys from typing import Dict, Optional, Any, List, Tuple, Callable import numpy as np import torch from fairseq import ( checkpoint_utils, distributed_utils, options, quantization_utils, tasks, utils, ) from fairseq.data import iterators...
Train the model for one epoch and return validation losses.
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import argparse import logging import math import os import sys from typing import Dict, Optional, Any, List, Tuple, Callable import numpy as np import torch from fairseq import ( checkpoint_utils, distributed_utils, options, quantization_utils, tasks, utils, ) from fairseq.data import iterators...
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import logging import os import sys from argparse import Namespace from itertools import chain import torch from fairseq import checkpoint_utils, distributed_utils, options, utils from fairseq.dataclass.utils import convert_namespace_to_omegaconf from fairseq.logging import metrics, progress_bar from omegaconf import D...
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import logging import os import shutil import sys from collections import Counter from itertools import zip_longest from multiprocessing import Pool from fairseq import options, tasks, utils from fairseq.binarizer import Binarizer from fairseq.data import indexed_dataset def dataset_dest_file(args, output_prefix, lang,...
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import logging import os import shutil import sys from collections import Counter from itertools import zip_longest from multiprocessing import Pool from fairseq import options, tasks, utils from fairseq.binarizer import Binarizer from fairseq.data import indexed_dataset def dataset_dest_file(args, output_prefix, lang,...
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import logging import os import shutil import sys from collections import Counter from itertools import zip_longest from multiprocessing import Pool from fairseq import options, tasks, utils from fairseq.binarizer import Binarizer from fairseq.data import indexed_dataset class Binarizer: def binarize( ...
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import logging import os import shutil import sys from collections import Counter from itertools import zip_longest from multiprocessing import Pool from fairseq import options, tasks, utils from fairseq.binarizer import Binarizer from fairseq.data import indexed_dataset def main(args): utils.import_user_module(arg...
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import logging import os import sys from fairseq.dataclass.initialize import add_defaults, hydra_init from fairseq_cli.train import main as pre_main from fairseq import distributed_utils, metrics from fairseq.dataclass.configs import FairseqConfig import hydra from hydra.core.hydra_config import HydraConfig import torc...
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import logging import math import os import sys from argparse import Namespace from typing import Iterable, List, Optional import torch import fairseq from fairseq import checkpoint_utils, distributed_utils, options, tasks, utils from fairseq.dataclass.utils import convert_namespace_to_omegaconf from fairseq.logging im...
Args: models (List[~fairseq.models.FairseqModel]): list of models to evaluate. Models are essentially `nn.Module` instances, but must be compatible with fairseq's `SequenceScorer`. source_dictionary (~fairseq.data.Dictionary): dictionary for applying any relevant post processing or outputing word probs/stats. batch_ite...
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import ast import logging import math import os import sys from argparse import Namespace from itertools import chain import numpy as np import torch from fairseq import checkpoint_utils, options, scoring, tasks, utils from fairseq.dataclass.utils import convert_namespace_to_omegaconf from fairseq.logging import progre...
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import logging, os, sys import time import torch from torch import Tensor from typing import Dict, List, Optional import copy from tqdm import tqdm from omegaconf import open_dict import fairseq from fairseq.checkpoint_utils import load_model_ensemble_and_task from fairseq import utils from fairseq.data import data_uti...
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import logging, os, sys import time import torch from torch import Tensor from typing import Dict, List, Optional import copy from tqdm import tqdm from omegaconf import open_dict import fairseq from fairseq.checkpoint_utils import load_model_ensemble_and_task from fairseq import utils from fairseq.data import data_uti...
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import logging, os, sys import time import torch from torch import Tensor from typing import Dict, List, Optional import copy from tqdm import tqdm from omegaconf import open_dict import fairseq from fairseq.checkpoint_utils import load_model_ensemble_and_task from fairseq import utils from fairseq.data import data_uti...
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import logging, os, sys import time import torch from torch import Tensor from typing import Dict, List, Optional import copy from tqdm import tqdm from omegaconf import open_dict import fairseq from fairseq.checkpoint_utils import load_model_ensemble_and_task from fairseq import utils from fairseq.data import data_uti...
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import logging, os, sys import time import torch from torch import Tensor from typing import Dict, List, Optional import copy from tqdm import tqdm from omegaconf import open_dict import fairseq from fairseq.checkpoint_utils import load_model_ensemble_and_task from fairseq import utils from fairseq.data import data_uti...
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import ast import logging import math import os import sys import editdistance import numpy as np import torch from fairseq import checkpoint_utils, options, progress_bar, tasks, utils from fairseq.data.data_utils import post_process from fairseq.logging.meters import StopwatchMeter, TimeMeter logger = logging.getLogge...
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import ast import logging import math import os import sys import editdistance import numpy as np import torch from fairseq import checkpoint_utils, options, progress_bar, tasks, utils from fairseq.data.data_utils import post_process from fairseq.logging.meters import StopwatchMeter, TimeMeter def main(args, task=None,...
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import argparse import math from collections.abc import Iterable import torch import torch.nn as nn from examples.speech_recognition.data.data_utils import lengths_to_encoder_padding_mask from fairseq import utils from fairseq.models import ( FairseqEncoder, FairseqEncoderDecoderModel, FairseqEncoderModel, ...
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from __future__ import absolute_import, division, print_function, unicode_literals import re from collections import deque from enum import Enum import numpy as np class WERTransformer(object): def __init__(self, hyp_str, ref_str, verbose=True): def process(self, input): def report_result(self): def...
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import torch import torch.nn as nn import torch.nn.functional as F from examples.simultaneous_translation.modules.monotonic_transformer_layer import ( TransformerMonotonicDecoderLayer, TransformerMonotonicEncoderLayer, ) from fairseq.models import register_model, register_model_architecture from fairseq.models....
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import torch import torch.nn as nn import torch.nn.functional as F from examples.simultaneous_translation.modules.monotonic_transformer_layer import ( TransformerMonotonicDecoderLayer, TransformerMonotonicEncoderLayer, ) from fairseq.models import register_model, register_model_architecture from fairseq.models....
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import torch import torch.nn as nn import torch.nn.functional as F from examples.simultaneous_translation.modules.monotonic_transformer_layer import ( TransformerMonotonicDecoderLayer, TransformerMonotonicEncoderLayer, ) from fairseq.models import register_model, register_model_architecture from fairseq.models....
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import torch import torch.nn as nn import torch.nn.functional as F from examples.simultaneous_translation.modules.monotonic_transformer_layer import ( TransformerMonotonicDecoderLayer, TransformerMonotonicEncoderLayer, ) from fairseq.models import register_model, register_model_architecture from fairseq.models....
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import torch def safe_cumprod(tensor, dim: int, eps: float = 1e-10): """ An implementation of cumprod to prevent precision issue. cumprod(x) = [x1, x1x2, x1x2x3, ....] = [exp(log(x1)), exp(log(x1) + log(x2)), exp(log(x1) + log(x2) + log(x3)), ...] = exp(cumsum(log(x))) """ if (tensor + e...
Implementing exclusive cumprod. There is cumprod in pytorch, however there is no exclusive mode. cumprod(x) = [x1, x1x2, x2x3x4, ..., prod_{i=1}^n x_i] exclusive means cumprod(x) = [1, x1, x1x2, x1x2x3, ..., prod_{i=1}^{n-1} x_i]
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import torch The provided code snippet includes necessary dependencies for implementing the `moving_sum` function. Write a Python function `def moving_sum(x, start_idx: int, end_idx: int)` to solve the following problem: From MONOTONIC CHUNKWISE ATTENTION https://arxiv.org/pdf/1712.05382.pdf Equation (18) x = [x_1, x_...
From MONOTONIC CHUNKWISE ATTENTION https://arxiv.org/pdf/1712.05382.pdf Equation (18) x = [x_1, x_2, ..., x_N] MovingSum(x, start_idx, end_idx)_n = Sigma_{m=n−(start_idx−1)}^{n+end_idx-1} x_m for n in {1, 2, 3, ..., N} x : src_len, batch_size start_idx : start idx end_idx : end idx Example src_len = 5 batch_size = 3 x ...
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import argparse import json import sys from scorers import build_scorer from tornado import ioloop, web DEFAULT_HOSTNAME = "localhost" DEFAULT_PORT = 12321 def add_args(): parser = argparse.ArgumentParser() # fmt: off parser.add_argument('--hostname', type=str, default=DEFAULT_HOSTNAME, ...
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import argparse import json import sys from scorers import build_scorer from tornado import ioloop, web DEFAULT_HOSTNAME = "localhost" DEFAULT_PORT = 12321 class EvalSessionHandler(ScorerHandler): def post(self): self.scorer.reset() def get(self): r = json.dumps(self.scorer.get_info()) s...
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import argparse from agents import build_agent from client import SimulSTEvaluationService, SimulSTLocalEvaluationService from fairseq.registry import REGISTRIES DEFAULT_HOSTNAME = "localhost" DEFAULT_PORT = 12321 REGISTRIES = {} def get_args(): parser = argparse.ArgumentParser() parser.add_argument( ...
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from typing import NamedTuple, List from urllib.parse import urlparse import os, sys import subprocess from subprocess import check_call, check_output import glob import wget import re import multiprocessing as mp from functools import partial import pathlib from collections import OrderedDict to_data_path = f'{WORKDI...
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from fairseq.models import register_model, register_model_architecture from fairseq.models.multilingual_transformer import MultilingualTransformerModel from fairseq.models.transformer import ( TransformerDecoder, TransformerEncoder, base_architecture, ) from .latent_transformer import LatentTransformerDecod...
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import csv from pathlib import Path import zipfile from functools import reduce from multiprocessing import cpu_count from typing import Any, Dict, List, Optional, Union import numpy as np import pandas as pd import sentencepiece as sp from fairseq.data.audio.audio_utils import _get_kaldi_fbank, _get_torchaudio_fbank f...
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