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Commit c0f05c10 authored by hepj's avatar hepj
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更新transformer代码

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PyTorch/NLP/new-Transformer/fairseq/checkpoint_utils.py 0 → 100644
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# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import ast
import collections
import contextlib
import inspect
import logging
import os
import re
import time
import traceback
from collections import OrderedDict
from pathlib import Path
from typing import Any, Dict, Optional, Union
import numpy as np
import torch
from fairseq.data import data_utils
from fairseq.dataclass.configs import CheckpointConfig
from fairseq.dataclass.utils import (
convert_namespace_to_omegaconf,
overwrite_args_by_name,
)
from fairseq.distributed.fully_sharded_data_parallel import FSDP, has_FSDP
from fairseq.file_io import PathManager
from fairseq.models import FairseqDecoder, FairseqEncoder
from omegaconf import DictConfig, OmegaConf, open_dict
logger = logging.getLogger(__name__)
def save_checkpoint(cfg: CheckpointConfig, trainer, epoch_itr, val_loss):
from fairseq import meters
# only one worker should attempt to create the required dir
if trainer.data_parallel_rank == 0:
os.makedirs(cfg.save_dir, exist_ok=True)
prev_best = getattr(save_checkpoint, "best", val_loss)
if val_loss is not None:
best_function = max if cfg.maximize_best_checkpoint_metric else min
save_checkpoint.best = best_function(val_loss, prev_best)
if cfg.no_save:
return
trainer.consolidate_optimizer() # TODO(SS): do we need this if no_save_optimizer_state
if not trainer.should_save_checkpoint_on_current_rank:
if trainer.always_call_state_dict_during_save_checkpoint:
trainer.state_dict()
return
write_timer = meters.StopwatchMeter()
write_timer.start()
epoch = epoch_itr.epoch
end_of_epoch = epoch_itr.end_of_epoch()
updates = trainer.get_num_updates()
logger.info(f"Preparing to save checkpoint for epoch {epoch} @ {updates} updates")
def is_better(a, b):
return a >= b if cfg.maximize_best_checkpoint_metric else a <= b
suffix = trainer.checkpoint_suffix
checkpoint_conds = collections.OrderedDict()
checkpoint_conds["checkpoint{}{}.pt".format(epoch, suffix)] = (
end_of_epoch and not cfg.no_epoch_checkpoints and epoch % cfg.save_interval == 0
)
checkpoint_conds["checkpoint_{}_{}{}.pt".format(epoch, updates, suffix)] = (
not end_of_epoch
and cfg.save_interval_updates > 0
and updates % cfg.save_interval_updates == 0
)
checkpoint_conds["checkpoint_best{}.pt".format(suffix)] = val_loss is not None and (
not hasattr(save_checkpoint, "best")
or is_better(val_loss, save_checkpoint.best)
)
if val_loss is not None and cfg.keep_best_checkpoints > 0:
worst_best = getattr(save_checkpoint, "best", None)
chkpts = checkpoint_paths(
cfg.save_dir,
pattern=r"checkpoint\.best_{}_(\d+\.?\d*){}\.pt".format(
cfg.best_checkpoint_metric, suffix
),
)
if len(chkpts) > 0:
p = chkpts[-1] if cfg.maximize_best_checkpoint_metric else chkpts[0]
worst_best = float(p.rsplit("_")[-1].replace("{}.pt".format(suffix), ""))
# add random digits to resolve ties
with data_utils.numpy_seed(epoch, updates, val_loss):
rand_sfx = np.random.randint(0, cfg.keep_best_checkpoints)
checkpoint_conds[
"checkpoint.best_{}_{:.3f}{}{}.pt".format(
cfg.best_checkpoint_metric, val_loss, rand_sfx, suffix
)
] = worst_best is None or is_better(val_loss, worst_best)
checkpoint_conds[
"checkpoint_last{}.pt".format(suffix)
] = not cfg.no_last_checkpoints
extra_state = {"train_iterator": epoch_itr.state_dict(), "val_loss": val_loss}
if hasattr(save_checkpoint, "best"):
extra_state.update({"best": save_checkpoint.best})
checkpoints = [
os.path.join(cfg.save_dir, fn) for fn, cond in checkpoint_conds.items() if cond
]
if len(checkpoints) > 0 and trainer.should_save_checkpoint_on_current_rank:
trainer.save_checkpoint(checkpoints[0], extra_state)
for cp in checkpoints[1:]:
if cfg.write_checkpoints_asynchronously:
# TODO[ioPath]: Need to implement a delayed asynchronous
# file copying/moving feature.
logger.warning(
f"ioPath is not copying {checkpoints[0]} to {cp} "
"since async write mode is on."
)
else:
assert PathManager.copy(
checkpoints[0], cp, overwrite=True
), f"Failed to copy {checkpoints[0]} to {cp}"
write_timer.stop()
logger.info(
"Saved checkpoint {} (epoch {} @ {} updates, score {}) (writing took {} seconds)".format(
checkpoints[0], epoch, updates, val_loss, write_timer.sum
)
)
if not end_of_epoch and cfg.keep_interval_updates > 0:
# remove old checkpoints; checkpoints are sorted in descending order
if cfg.keep_interval_updates_pattern == -1:
checkpoints = checkpoint_paths(
cfg.save_dir, pattern=r"checkpoint_\d+_(\d+){}\.pt".format(suffix)
)
else:
checkpoints = checkpoint_paths(
cfg.save_dir,
pattern=r"checkpoint_\d+_(\d+){}\.pt".format(suffix),
keep_match=True,
)
checkpoints = [
x[0]
for x in checkpoints
if x[1] % cfg.keep_interval_updates_pattern != 0
]
for old_chk in checkpoints[cfg.keep_interval_updates :]:
if os.path.lexists(old_chk):
os.remove(old_chk)
elif PathManager.exists(old_chk):
PathManager.rm(old_chk)
if cfg.keep_last_epochs > 0:
# remove old epoch checkpoints; checkpoints are sorted in descending order
checkpoints = checkpoint_paths(
cfg.save_dir, pattern=r"checkpoint(\d+){}\.pt".format(suffix)
)
for old_chk in checkpoints[cfg.keep_last_epochs :]:
if os.path.lexists(old_chk):
os.remove(old_chk)
elif PathManager.exists(old_chk):
PathManager.rm(old_chk)
if cfg.keep_best_checkpoints > 0:
# only keep the best N checkpoints according to validation metric
checkpoints = checkpoint_paths(
cfg.save_dir,
pattern=r"checkpoint\.best_{}_(\d+\.?\d*){}\.pt".format(
cfg.best_checkpoint_metric, suffix
),
)
if not cfg.maximize_best_checkpoint_metric:
checkpoints = checkpoints[::-1]
for old_chk in checkpoints[cfg.keep_best_checkpoints :]:
if os.path.lexists(old_chk):
os.remove(old_chk)
elif PathManager.exists(old_chk):
PathManager.rm(old_chk)
def load_checkpoint(cfg: CheckpointConfig, trainer, **passthrough_args):
"""
Load a checkpoint and restore the training iterator.
*passthrough_args* will be passed through to
``trainer.get_train_iterator``.
"""
reset_optimizer = cfg.reset_optimizer
reset_lr_scheduler = cfg.reset_lr_scheduler
optimizer_overrides = ast.literal_eval(cfg.optimizer_overrides)
reset_meters = cfg.reset_meters
reset_dataloader = cfg.reset_dataloader
if cfg.finetune_from_model is not None and (
reset_optimizer or reset_lr_scheduler or reset_meters or reset_dataloader
):
raise ValueError(
"--finetune-from-model can not be set together with either --reset-optimizer"
" or reset_lr_scheduler or reset_meters or reset_dataloader"
)
suffix = trainer.checkpoint_suffix
if (
cfg.restore_file == "checkpoint_last.pt"
): # default value of restore_file is 'checkpoint_last.pt'
checkpoint_path = os.path.join(
cfg.save_dir, "checkpoint_last{}.pt".format(suffix)
)
first_launch = not PathManager.exists(checkpoint_path)
if first_launch and getattr(cfg, "continue_once", None) is not None:
checkpoint_path = cfg.continue_once
elif cfg.finetune_from_model is not None and first_launch:
# if there is no last checkpoint to restore, start the finetune from pretrained model
# else just use usual logic to load checkpoint, e.g. restart from last checkpoint and etc.
if PathManager.exists(cfg.finetune_from_model):
checkpoint_path = cfg.finetune_from_model
reset_optimizer = True
reset_lr_scheduler = True
reset_meters = True
reset_dataloader = True
logger.info(
f"loading pretrained model from {checkpoint_path}: "
"optimizer, lr scheduler, meters, dataloader will be reset"
)
else:
raise ValueError(
f"--finetune-from-model {cfg.finetune_from_model} does not exist"
)
elif suffix is not None:
checkpoint_path = cfg.restore_file.replace(".pt", suffix + ".pt")
else:
checkpoint_path = cfg.restore_file
if cfg.restore_file != "checkpoint_last.pt" and cfg.finetune_from_model:
raise ValueError(
"--finetune-from-model and --restore-file (non-default value) "
"can not be specified together: " + str(cfg)
)
extra_state = trainer.load_checkpoint(
checkpoint_path,
reset_optimizer,
reset_lr_scheduler,
optimizer_overrides,
reset_meters=reset_meters,
)
if (
extra_state is not None
and "best" in extra_state
and not reset_optimizer
and not reset_meters
):
save_checkpoint.best = extra_state["best"]
if extra_state is not None and not reset_dataloader:
# restore iterator from checkpoint
itr_state = extra_state["train_iterator"]
epoch_itr = trainer.get_train_iterator(
epoch=itr_state["epoch"], load_dataset=True, **passthrough_args
)
epoch_itr.load_state_dict(itr_state)
else:
epoch_itr = trainer.get_train_iterator(
epoch=1, load_dataset=True, **passthrough_args
)
trainer.lr_step(epoch_itr.epoch)
return extra_state, epoch_itr
def load_checkpoint_to_cpu(path, arg_overrides=None, load_on_all_ranks=False):
"""Loads a checkpoint to CPU (with upgrading for backward compatibility).
If doing single-GPU training or if the checkpoint is only being loaded by at
most one process on each node (current default behavior is for only rank 0
to read the checkpoint from disk), load_on_all_ranks should be False to
avoid errors from torch.distributed not having been initialized or
torch.distributed.barrier() hanging.
If all processes on each node may be loading the checkpoint
simultaneously, load_on_all_ranks should be set to True to avoid I/O
conflicts.
There's currently no support for > 1 but < all processes loading the
checkpoint on each node.
"""
local_path = PathManager.get_local_path(path)
# The locally cached file returned by get_local_path() may be stale for
# remote files that are periodically updated/overwritten (ex:
# checkpoint_last.pt) - so we remove the local copy, sync across processes
# (if needed), and then download a fresh copy.
if local_path != path and PathManager.path_requires_pathmanager(path):
try:
os.remove(local_path)
except FileNotFoundError:
# With potentially multiple processes removing the same file, the
# file being missing is benign (missing_ok isn't available until
# Python 3.8).
pass
if load_on_all_ranks:
torch.distributed.barrier()
local_path = PathManager.get_local_path(path)
with open(local_path, "rb") as f:
state = torch.load(f, map_location=torch.device("cpu"))
if "args" in state and state["args"] is not None and arg_overrides is not None:
args = state["args"]
for arg_name, arg_val in arg_overrides.items():
setattr(args, arg_name, arg_val)
if "cfg" in state and state["cfg"] is not None:
# hack to be able to set Namespace in dict config. this should be removed when we update to newer
# omegaconf version that supports object flags, or when we migrate all existing models
from omegaconf import _utils
old_primitive = _utils.is_primitive_type
_utils.is_primitive_type = lambda _: True
state["cfg"] = OmegaConf.create(state["cfg"])
_utils.is_primitive_type = old_primitive
OmegaConf.set_struct(state["cfg"], True)
if arg_overrides is not None:
overwrite_args_by_name(state["cfg"], arg_overrides)
state = _upgrade_state_dict(state)
return state
def load_model_ensemble(
filenames,
arg_overrides: Optional[Dict[str, Any]] = None,
task=None,
strict=True,
suffix="",
num_shards=1,
state=None,
):
"""Loads an ensemble of models.
Args:
filenames (List[str]): checkpoint files to load
arg_overrides (Dict[str,Any], optional): override model args that
were used during model training
task (fairseq.tasks.FairseqTask, optional): task to use for loading
"""
assert not (
strict and num_shards > 1
), "Cannot load state dict with strict=True and checkpoint shards > 1"
ensemble, args, _task = load_model_ensemble_and_task(
filenames,
arg_overrides,
task,
strict,
suffix,
num_shards,
state,
)
return ensemble, args
def get_maybe_sharded_checkpoint_filename(
filename: str, suffix: str, shard_idx: int, num_shards: int
) -> str:
orig_filename = filename
filename = filename.replace(".pt", suffix + ".pt")
fsdp_filename = filename[:-3] + f"-shard{shard_idx}.pt"
model_parallel_filename = orig_filename[:-3] + f"_part{shard_idx}.pt"
if PathManager.exists(fsdp_filename):
return fsdp_filename
elif num_shards > 1:
return model_parallel_filename
else:
return filename
def load_model_ensemble_and_task(
filenames,
arg_overrides: Optional[Dict[str, Any]] = None,
task=None,
strict=True,
suffix="",
num_shards=1,
state=None,
):
assert state is None or len(filenames) == 1
from fairseq import tasks
assert not (
strict and num_shards > 1
), "Cannot load state dict with strict=True and checkpoint shards > 1"
ensemble = []
cfg = None
for filename in filenames:
orig_filename = filename
model_shard_state = {"shard_weights": [], "shard_metadata": []}
assert num_shards > 0
st = time.time()
for shard_idx in range(num_shards):
filename = get_maybe_sharded_checkpoint_filename(
orig_filename, suffix, shard_idx, num_shards
)
if not PathManager.exists(filename):
raise IOError("Model file not found: {}".format(filename))
if state is None:
state = load_checkpoint_to_cpu(filename, arg_overrides)
if "args" in state and state["args"] is not None:
cfg = convert_namespace_to_omegaconf(state["args"])
elif "cfg" in state and state["cfg"] is not None:
cfg = state["cfg"]
else:
raise RuntimeError(
f"Neither args nor cfg exist in state keys = {state.keys()}"
)
if task is None:
task = tasks.setup_task(cfg.task)
if "task_state" in state:
task.load_state_dict(state["task_state"])
if "fsdp_metadata" in state and num_shards > 1:
model_shard_state["shard_weights"].append(state["model"])
model_shard_state["shard_metadata"].append(state["fsdp_metadata"])
# check FSDP import before the code goes too far
if not has_FSDP:
raise ImportError(
"Cannot find FullyShardedDataParallel. "
"Please install fairscale with: pip install fairscale"
)
if shard_idx == num_shards - 1:
consolidated_model_state = FSDP.consolidate_shard_weights(
shard_weights=model_shard_state["shard_weights"],
shard_metadata=model_shard_state["shard_metadata"],
)
model = task.build_model(cfg.model)
if (
"optimizer_history" in state
and len(state["optimizer_history"]) > 0
and "num_updates" in state["optimizer_history"][-1]
):
model.set_num_updates(
state["optimizer_history"][-1]["num_updates"]
)
model.load_state_dict(
consolidated_model_state, strict=strict, model_cfg=cfg.model
)
else:
# model parallel checkpoint or unsharded checkpoint
# support old external tasks
argspec = inspect.getfullargspec(task.build_model)
if "from_checkpoint" in argspec.args:
model = task.build_model(cfg.model, from_checkpoint=True)
else:
model = task.build_model(cfg.model)
if (
"optimizer_history" in state
and len(state["optimizer_history"]) > 0
and "num_updates" in state["optimizer_history"][-1]
):
model.set_num_updates(state["optimizer_history"][-1]["num_updates"])
model.load_state_dict(
state["model"], strict=strict, model_cfg=cfg.model
)
# reset state so it gets loaded for the next model in ensemble
state = None
if shard_idx % 10 == 0 and shard_idx > 0:
elapsed = time.time() - st
logger.info(
f"Loaded {shard_idx} shards in {elapsed:.2f}s, {elapsed / (shard_idx+1):.2f}s/shard"
)
# build model for ensemble
ensemble.append(model)
return ensemble, cfg, task
def load_model_ensemble_and_task_from_hf_hub(
model_id,
cache_dir: Optional[str] = None,
arg_overrides: Optional[Dict[str, Any]] = None,
**kwargs: Any,
):
try:
from huggingface_hub import snapshot_download
except ImportError:
raise ImportError(
"You need to install huggingface_hub to use `load_from_hf_hub`. "
"See https://pypi.org/project/huggingface-hub/ for installation."
)
library_name = "fairseq"
cache_dir = cache_dir or (Path.home() / ".cache" / library_name).as_posix()
cache_dir = snapshot_download(
model_id, cache_dir=cache_dir, library_name=library_name, **kwargs
)
_arg_overrides = arg_overrides or {}
_arg_overrides["data"] = cache_dir
return load_model_ensemble_and_task(
[p.as_posix() for p in Path(cache_dir).glob("*.pt")],
arg_overrides=_arg_overrides,
)
def checkpoint_paths(path, pattern=r"checkpoint(\d+)\.pt", keep_match=False):
"""Retrieves all checkpoints found in `path` directory.
Checkpoints are identified by matching filename to the specified pattern. If
the pattern contains groups, the result will be sorted by the first group in
descending order.
"""
pt_regexp = re.compile(pattern)
files = PathManager.ls(path)
entries = []
for i, f in enumerate(files):
m = pt_regexp.fullmatch(f)
if m is not None:
idx = float(m.group(1)) if len(m.groups()) > 0 else i
entries.append((idx, m.group(0)))
if keep_match:
return [(os.path.join(path, x[1]), x[0]) for x in sorted(entries, reverse=True)]
else:
return [os.path.join(path, x[1]) for x in sorted(entries, reverse=True)]
def torch_persistent_save(obj, filename, async_write: bool = False):
if async_write:
with PathManager.opena(filename, "wb") as f:
_torch_persistent_save(obj, f)
else:
if PathManager.supports_rename(filename):
# do atomic save
with PathManager.open(filename + ".tmp", "wb") as f:
_torch_persistent_save(obj, f)
PathManager.rename(filename + ".tmp", filename)
else:
# fallback to non-atomic save
with PathManager.open(filename, "wb") as f:
_torch_persistent_save(obj, f)
def _torch_persistent_save(obj, f):
if isinstance(f, str):
with PathManager.open(f, "wb") as h:
torch_persistent_save(obj, h)
return
for i in range(3):
try:
return torch.save(obj, f)
except Exception:
if i == 2:
logger.error(traceback.format_exc())
raise
def _upgrade_state_dict(state):
"""Helper for upgrading old model checkpoints."""
# add optimizer_history
if "optimizer_history" not in state:
state["optimizer_history"] = [
{"criterion_name": "CrossEntropyCriterion", "best_loss": state["best_loss"]}
]
state["last_optimizer_state"] = state["optimizer"]
del state["optimizer"]
del state["best_loss"]
# move extra_state into sub-dictionary
if "epoch" in state and "extra_state" not in state:
state["extra_state"] = {
"epoch": state["epoch"],
"batch_offset": state["batch_offset"],
"val_loss": state["val_loss"],
}
del state["epoch"]
del state["batch_offset"]
del state["val_loss"]
# reduce optimizer history's memory usage (only keep the last state)
if "optimizer" in state["optimizer_history"][-1]:
state["last_optimizer_state"] = state["optimizer_history"][-1]["optimizer"]
for optim_hist in state["optimizer_history"]:
del optim_hist["optimizer"]
# record the optimizer class name
if "optimizer_name" not in state["optimizer_history"][-1]:
state["optimizer_history"][-1]["optimizer_name"] = "FairseqNAG"
# move best_loss into lr_scheduler_state
if "lr_scheduler_state" not in state["optimizer_history"][-1]:
state["optimizer_history"][-1]["lr_scheduler_state"] = {
"best": state["optimizer_history"][-1]["best_loss"]
}
del state["optimizer_history"][-1]["best_loss"]
# keep track of number of updates
if "num_updates" not in state["optimizer_history"][-1]:
state["optimizer_history"][-1]["num_updates"] = 0
# use stateful training data iterator
if "train_iterator" not in state["extra_state"]:
state["extra_state"]["train_iterator"] = {
"epoch": state["extra_state"].get("epoch", 0),
"iterations_in_epoch": state["extra_state"].get("batch_offset", 0),
}
# backward compatibility, cfg updates
if "args" in state and state["args"] is not None:
# old model checkpoints may not have separate source/target positions
if hasattr(state["args"], "max_positions") and not hasattr(
state["args"], "max_source_positions"
):
state["args"].max_source_positions = state["args"].max_positions
state["args"].max_target_positions = state["args"].max_positions
# default to translation task
if not hasattr(state["args"], "task"):
state["args"].task = "translation"
# --raw-text and --lazy-load are deprecated
if getattr(state["args"], "raw_text", False):
state["args"].dataset_impl = "raw"
elif getattr(state["args"], "lazy_load", False):
state["args"].dataset_impl = "lazy"
# epochs start at 1
if state["extra_state"]["train_iterator"] is not None:
state["extra_state"]["train_iterator"]["epoch"] = max(
state["extra_state"]["train_iterator"].get("epoch", 1), 1
)
# --remove-bpe ==> --postprocess
if hasattr(state["args"], "remove_bpe"):
state["args"].post_process = state["args"].remove_bpe
# --min-lr ==> --stop-min-lr
if hasattr(state["args"], "min_lr"):
state["args"].stop_min_lr = state["args"].min_lr
del state["args"].min_lr
# binary_cross_entropy / kd_binary_cross_entropy => wav2vec criterion
if hasattr(state["args"], "criterion") and state["args"].criterion in [
"binary_cross_entropy",
"kd_binary_cross_entropy",
]:
state["args"].criterion = "wav2vec"
# remove log_keys if it's None (criteria will supply a default value of [])
if hasattr(state["args"], "log_keys") and state["args"].log_keys is None:
delattr(state["args"], "log_keys")
# speech_pretraining => audio pretraining
if (
hasattr(state["args"], "task")
and state["args"].task == "speech_pretraining"
):
state["args"].task = "audio_pretraining"
# audio_cpc => wav2vec
if hasattr(state["args"], "arch") and state["args"].arch == "audio_cpc":
state["args"].arch = "wav2vec"
# convert legacy float learning rate to List[float]
if hasattr(state["args"], "lr") and isinstance(state["args"].lr, float):
state["args"].lr = [state["args"].lr]
# convert task data arg to a string instead of List[string]
if (
hasattr(state["args"], "data")
and isinstance(state["args"].data, list)
and len(state["args"].data) > 0
):
state["args"].data = state["args"].data[0]
state["cfg"] = convert_namespace_to_omegaconf(state["args"])
if "cfg" in state and state["cfg"] is not None:
cfg = state["cfg"]
with open_dict(cfg):
# any upgrades for Hydra-based configs
if (
"task" in cfg
and "eval_wer_config" in cfg.task
and isinstance(cfg.task.eval_wer_config.print_alignment, bool)
):
cfg.task.eval_wer_config.print_alignment = "hard"
if "generation" in cfg and isinstance(cfg.generation.print_alignment, bool):
cfg.generation.print_alignment = (
"hard" if cfg.generation.print_alignment else None
)
if (
"model" in cfg
and "w2v_args" in cfg.model
and cfg.model.w2v_args is not None
and (
hasattr(cfg.model.w2v_args, "task") or "task" in cfg.model.w2v_args
)
and hasattr(cfg.model.w2v_args.task, "eval_wer_config")
and cfg.model.w2v_args.task.eval_wer_config is not None
and isinstance(
cfg.model.w2v_args.task.eval_wer_config.print_alignment, bool
)
):
cfg.model.w2v_args.task.eval_wer_config.print_alignment = "hard"
return state
def prune_state_dict(state_dict, model_cfg: Optional[DictConfig]):
"""Prune the given state_dict if desired for LayerDrop
(https://arxiv.org/abs/1909.11556).
Training with LayerDrop allows models to be robust to pruning at inference
time. This function prunes state_dict to allow smaller models to be loaded
from a larger model and re-maps the existing state_dict for this to occur.
It's called by functions that load models from checkpoints and does not
need to be called directly.
"""
arch = None
if model_cfg is not None:
arch = (
model_cfg._name
if isinstance(model_cfg, DictConfig)
else getattr(model_cfg, "arch", None)
)
if not model_cfg or arch is None or arch == "ptt_transformer":
# args should not be none, but don't crash if it is.
return state_dict
encoder_layers_to_keep = getattr(model_cfg, "encoder_layers_to_keep", None)
decoder_layers_to_keep = getattr(model_cfg, "decoder_layers_to_keep", None)
if not encoder_layers_to_keep and not decoder_layers_to_keep:
return state_dict
# apply pruning
logger.info(
"Pruning model to specified layer configuration - this works best if the model was trained with LayerDrop"
)
def create_pruning_pass(layers_to_keep, layer_name):
keep_layers = sorted(
int(layer_string) for layer_string in layers_to_keep.split(",")
)
mapping_dict = {}
for i in range(len(keep_layers)):
mapping_dict[str(keep_layers[i])] = str(i)
regex = re.compile(r"^{layer}.*\.layers\.(\d+)".format(layer=layer_name))
return {"substitution_regex": regex, "mapping_dict": mapping_dict}
pruning_passes = []
if encoder_layers_to_keep:
pruning_passes.append(create_pruning_pass(encoder_layers_to_keep, "encoder"))
if decoder_layers_to_keep:
pruning_passes.append(create_pruning_pass(decoder_layers_to_keep, "decoder"))
new_state_dict = {}
for layer_name in state_dict.keys():
match = re.search(r"\.layers\.(\d+)\.", layer_name)
# if layer has no number in it, it is a supporting layer, such as an
# embedding
if not match:
new_state_dict[layer_name] = state_dict[layer_name]
continue
# otherwise, layer should be pruned.
original_layer_number = match.group(1)
# figure out which mapping dict to replace from
for pruning_pass in pruning_passes:
if original_layer_number in pruning_pass["mapping_dict"] and pruning_pass[
"substitution_regex"
].search(layer_name):
new_layer_number = pruning_pass["mapping_dict"][original_layer_number]
substitution_match = pruning_pass["substitution_regex"].search(
layer_name
)
new_state_key = (
layer_name[: substitution_match.start(1)]
+ new_layer_number
+ layer_name[substitution_match.end(1) :]
)
new_state_dict[new_state_key] = state_dict[layer_name]
# Since layers are now pruned, *_layers_to_keep are no longer needed.
# This is more of "It would make it work fix" rather than a proper fix.
if isinstance(model_cfg, DictConfig):
context = open_dict(model_cfg)
else:
context = contextlib.ExitStack()
with context:
if hasattr(model_cfg, "encoder_layers_to_keep"):
model_cfg.encoder_layers_to_keep = None
if hasattr(model_cfg, "decoder_layers_to_keep"):
model_cfg.decoder_layers_to_keep = None
return new_state_dict
def load_pretrained_component_from_model(
component: Union[FairseqEncoder, FairseqDecoder],
checkpoint: str,
strict: bool = True,
):
"""
Load a pretrained FairseqEncoder or FairseqDecoder from checkpoint into the
provided `component` object. If state_dict fails to load, there may be a
mismatch in the architecture of the corresponding `component` found in the
`checkpoint` file.
"""
if not PathManager.exists(checkpoint):
raise IOError("Model file not found: {}".format(checkpoint))
state = load_checkpoint_to_cpu(checkpoint)
if isinstance(component, FairseqEncoder):
component_type = "encoder"
elif isinstance(component, FairseqDecoder):
component_type = "decoder"
else:
raise ValueError(
"component to load must be either a FairseqEncoder or "
"FairseqDecoder. Loading other component types are not supported."
)
component_state_dict = OrderedDict()
for key in state["model"].keys():
if key.startswith(component_type):
# encoder.input_layers.0.0.weight --> input_layers.0.0.weight
component_subkey = key[len(component_type) + 1 :]
component_state_dict[component_subkey] = state["model"][key]
component.load_state_dict(component_state_dict, strict=strict)
return component
def verify_checkpoint_directory(save_dir: str) -> None:
if not os.path.exists(save_dir):
os.makedirs(save_dir, exist_ok=True)
temp_file_path = os.path.join(save_dir, "dummy")
try:
with open(temp_file_path, "w"):
pass
except OSError as e:
logger.warning(
"Unable to access checkpoint save directory: {}".format(save_dir)
)
raise e
else:
os.remove(temp_file_path)
def save_ema_as_checkpoint(src_path, dst_path):
state = load_ema_from_checkpoint(src_path)
torch_persistent_save(state, dst_path)
def load_ema_from_checkpoint(fpath):
"""Loads exponential moving averaged (EMA) checkpoint from input and
returns a model with ema weights.
Args:
fpath: A string path of checkpoint to load from.
Returns:
A dict of string keys mapping to various values. The 'model' key
from the returned dict should correspond to an OrderedDict mapping
string parameter names to torch Tensors.
"""
params_dict = collections.OrderedDict()
new_state = None
with PathManager.open(fpath, "rb") as f:
new_state = torch.load(
f,
map_location=(
lambda s, _: torch.serialization.default_restore_location(s, "cpu")
),
)
# EMA model is stored in a separate "extra state"
model_params = new_state["extra_state"]["ema"]
for key in list(model_params.keys()):
p = model_params[key]
if isinstance(p, torch.HalfTensor):
p = p.float()
if key not in params_dict:
params_dict[key] = p.clone()
# NOTE: clone() is needed in case of p is a shared parameter
else:
raise ValueError("Key {} is repeated in EMA model params.".format(key))
if len(params_dict) == 0:
raise ValueError(
f"Input checkpoint path '{fpath}' does not contain "
"ema model weights, is this model trained with EMA?"
)
new_state["model"] = params_dict
return new_state
PyTorch/NLP/new-Transformer/fairseq/clib/cuda/ngram_repeat_block_cuda.cpp 0 → 100644
View file @ c0f05c10
/*
Copyright (c) Microsoft Corporation.
Licensed under the MIT License.
*/
#include <torch/extension.h>
#include <vector>
/*
CPP Binding for CUDA OP
*/
// CUDA forward declarations
torch::Tensor ngram_repeat_block_cuda_forward(
torch::Tensor tokens,
torch::Tensor lprobs,
int bsz,
int step,
int beam_size,
int no_repeat_ngram_size);
#define CHECK_CUDA(x) \
TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor")
#define CHECK_CONTIGUOUS(x) \
TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
#define CHECK_INPUT(x) \
CHECK_CUDA(x); \
CHECK_CONTIGUOUS(x)
// Input check and call to CUDA OP
// Backward method not required
torch::Tensor ngram_repeat_block_forward(
torch::Tensor tokens,
torch::Tensor lprobs,
int bsz,
int step,
int beam_size,
int no_repeat_ngram_size) {
CHECK_INPUT(tokens);
CHECK_INPUT(lprobs);
assert(bsz > 0);
assert(step >= 0);
assert(beam_size > 0);
assert(no_repeat_ngram_size > 0);
return ngram_repeat_block_cuda_forward(
tokens, lprobs, bsz, step, beam_size, no_repeat_ngram_size);
}
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
m.def(
"forward",
&ngram_repeat_block_forward,
"No Repeat Ngram Block forward (CUDA)");
}
PyTorch/NLP/new-Transformer/fairseq/clib/cuda/ngram_repeat_block_cuda_kernel.cu 0 → 100644
View file @ c0f05c10
/*
Copyright (c) Microsoft Corporation.
Licensed under the MIT License.
*/
/*
Kernel implementation for blocking repeated n-grams.
*/
#include <cuda.h>
#include <cuda_runtime.h>
#include <math.h>
#include <torch/extension.h>
#include <vector>
// Ban repeated ngrams of length = 'no_repeat_ngram_size'
__global__ void banRepeatedTokens(
long* __restrict__ tokens,
float* __restrict__ lprobs,
int max_predict_len,
int vocab_size,
int no_repeat_ngram_size) {
auto row = blockIdx.x;
auto col = threadIdx.x;
auto start = row * (max_predict_len) + col;
// Each thread compares ngram starting from
// thread index with final ngram starting from
// step - no_repeat_ngram_size +2
auto check_start_pos = blockDim.x;
auto lprob_start = row * vocab_size;
bool is_banned = true;
extern __shared__ long tokens_shm[];
tokens_shm[col] = tokens[start];
if (col == blockDim.x - 1) {
for (int i = 1; i < no_repeat_ngram_size; i++) {
if (col + i < max_predict_len) {
tokens_shm[col + i] = tokens[start + i];
}
}
}
__syncthreads();
for (int k = 0; k < no_repeat_ngram_size - 1; k++) {
if (tokens_shm[col + k] != tokens_shm[check_start_pos + k]) {
is_banned = false;
}
}
if (is_banned == true) {
auto token_to_be_banned = tokens_shm[col + no_repeat_ngram_size - 1];
lprobs[lprob_start + token_to_be_banned] = -INFINITY;
}
}
// Allocate blocks and threads based on
// batch size and sequence length and launch
// kernel
torch::Tensor ngram_repeat_block_cuda_forward(
const torch::Tensor tokens,
torch::Tensor lprobs,
int bsz,
int step,
int beam_size,
int no_repeat_ngram_size) {
int threads = step - no_repeat_ngram_size + 2;
if (threads <= 0)
return lprobs;
int max_predict_len = tokens.size(1);
int vocab_size = lprobs.size(1);
auto token_ptr = tokens.data_ptr<long>();
auto lprob_ptr = lprobs.data_ptr<float>();
int blocks = bsz * beam_size;
int shared_mem_size = (step + 1) * sizeof(long);
// Launching N blocks where N is number of samples in a batch (beams*bsz)
// Launching T threads where T is number of previous ngrams in a sample
// Allocating shared mem per block for fastser access of input tokens since
// each token will be accessed N times to compare with current Ngram where
// N is Ngram size.
banRepeatedTokens<<<blocks, threads, shared_mem_size>>>(
token_ptr, lprob_ptr, max_predict_len, vocab_size, no_repeat_ngram_size);
return lprobs;
}
PyTorch/NLP/new-Transformer/fairseq/clib/libbase/balanced_assignment.cpp 0 → 100644
View file @ c0f05c10
/**
* Copyright 2017-present, Facebook, Inc.
* All rights reserved.
*
* This source code is licensed under the license found in the
* LICENSE file in the root directory of this source tree.
*/
/*
C++ code for solving the linear assignment problem.
Based on the Auction Algorithm from
https://dspace.mit.edu/bitstream/handle/1721.1/3265/P-2108-26912652.pdf and the
implementation from: https://github.com/bkj/auction-lap Adapted to be more
efficient when each worker is looking for k jobs instead of 1.
*/
#include <torch/extension.h>
#include <iostream>
using namespace torch::indexing;
torch::Tensor balanced_assignment(torch::Tensor job_and_worker_to_score) {
int max_iterations = 100;
torch::Tensor epsilon =
(job_and_worker_to_score.max() - job_and_worker_to_score.min()) / 50;
epsilon.clamp_min_(1e-04);
torch::Tensor worker_and_job_to_score =
job_and_worker_to_score.detach().transpose(0, 1).contiguous();
int num_workers = worker_and_job_to_score.size(0);
int num_jobs = worker_and_job_to_score.size(1);
auto device = worker_and_job_to_score.device();
int jobs_per_worker = num_jobs / num_workers;
torch::Tensor value = worker_and_job_to_score.clone();
int counter = 0;
torch::Tensor max_value = worker_and_job_to_score.max();
torch::Tensor bid_indices;
torch::Tensor cost = worker_and_job_to_score.new_zeros({1, num_jobs});
torch::Tensor bids =
worker_and_job_to_score.new_empty({num_workers, num_jobs});
torch::Tensor bid_increments =
worker_and_job_to_score.new_empty({num_workers, jobs_per_worker});
torch::Tensor top_values =
worker_and_job_to_score.new_empty({num_workers, jobs_per_worker + 1});
torch::Tensor high_bids = worker_and_job_to_score.new_empty({num_jobs});
torch::Tensor top_index = top_values.to(torch::kLong);
torch::Tensor high_bidders = top_index.new_empty({num_jobs});
torch::Tensor have_bids = high_bidders.to(torch::kBool);
torch::Tensor jobs_indices =
torch::arange({num_jobs}, torch::dtype(torch::kLong).device(device));
torch::Tensor true_tensor =
torch::ones({1}, torch::dtype(torch::kBool).device(device));
while (true) {
bids.zero_();
torch::topk_out(top_values, top_index, value, jobs_per_worker + 1, 1);
// Each worker bids the difference in value between that job and the k+1th
// job
torch::sub_out(
bid_increments,
top_values.index({Slice(None, None), Slice(0, jobs_per_worker)}),
top_values.index({Slice(None, None), jobs_per_worker}).unsqueeze(1));
bid_increments.add_(epsilon);
bids.scatter_(
1,
top_index.index({Slice(None, None), Slice(0, jobs_per_worker)}),
bid_increments);
if (counter < max_iterations && counter > 0) {
// Put in a minimal bid to retain items from the last round if no-one else
// bids for them this round
bids.view(-1).index_put_({bid_indices}, epsilon);
}
// Find the highest bidding worker per job
torch::max_out(high_bids, high_bidders, bids, 0);
torch::gt_out(have_bids, high_bids, 0);
if (have_bids.all().item<bool>()) {
// All jobs were bid for
break;
}
// Make popular items more expensive
cost.add_(high_bids);
torch::sub_out(value, worker_and_job_to_score, cost);
bid_indices = ((high_bidders * num_jobs) + jobs_indices).index({have_bids});
if (counter < max_iterations) {
// Make sure that this item will be in the winning worker's top-k next
// time.
value.view(-1).index_put_({bid_indices}, max_value);
} else {
// Suboptimal approximation that converges quickly from current solution
value.view(-1).index_put_(
{bid_indices}, worker_and_job_to_score.view(-1).index({bid_indices}));
}
counter += 1;
}
return top_index.index({Slice(None, None), Slice(0, jobs_per_worker)})
.reshape(-1);
}
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
m.def("balanced_assignment", &balanced_assignment, "Balanced Assignment");
}
PyTorch/NLP/new-Transformer/fairseq/clib/libbleu/libbleu.cpp 0 → 100644
View file @ c0f05c10
/**
* Copyright 2017-present, Facebook, Inc.
* All rights reserved.
*
* This source code is licensed under the license found in the
* LICENSE file in the root directory of this source tree.
*/
#include <array>
#include <cstdio>
#include <cstring>
#include <map>
// NOLINTNEXTLINE
typedef struct {
size_t reflen;
size_t predlen;
size_t match1;
size_t count1;
size_t match2;
size_t count2;
size_t match3;
size_t count3;
size_t match4;
size_t count4;
} bleu_stat;
// left trim (remove pad)
void bleu_ltrim(size_t* len, int** sent, int pad) {
size_t start = 0;
while (start < *len) {
if (*(*sent + start) != pad) {
break;
}
start++;
}
*sent += start;
*len -= start;
}
// right trim remove (eos)
void bleu_rtrim(size_t* len, int** sent, int pad, int eos) {
size_t end = *len - 1;
while (end > 0) {
if (*(*sent + end) != eos && *(*sent + end) != pad) {
break;
}
end--;
}
*len = end + 1;
}
// left and right trim
void bleu_trim(size_t* len, int** sent, int pad, int eos) {
bleu_ltrim(len, sent, pad);
bleu_rtrim(len, sent, pad, eos);
}
size_t bleu_hash(int len, int* data) {
size_t h = 14695981039346656037ul;
size_t prime = 0x100000001b3;
char* b = (char*)data;
size_t blen = sizeof(int) * len;
while (blen-- > 0) {
h ^= *b++;
h *= prime;
}
return h;
}
void bleu_addngram(
size_t* ntotal,
size_t* nmatch,
size_t n,
size_t reflen,
int* ref,
size_t predlen,
int* pred) {
if (predlen < n) {
return;
}
predlen = predlen - n + 1;
(*ntotal) += predlen;
if (reflen < n) {
return;
}
reflen = reflen - n + 1;
std::map<size_t, size_t> count;
while (predlen > 0) {
size_t w = bleu_hash(n, pred++);
count[w]++;
predlen--;
}
while (reflen > 0) {
size_t w = bleu_hash(n, ref++);
if (count[w] > 0) {
(*nmatch)++;
count[w] -= 1;
}
reflen--;
}
}
extern "C" {
#ifdef _WIN64
__declspec(dllexport)
#endif
void bleu_zero_init(bleu_stat* stat) {
std::memset(stat, 0, sizeof(bleu_stat));
}
#ifdef _WIN64
__declspec(dllexport)
#endif
void bleu_one_init(bleu_stat* stat) {
bleu_zero_init(stat);
stat->count1 = 0;
stat->count2 = 1;
stat->count3 = 1;
stat->count4 = 1;
stat->match1 = 0;
stat->match2 = 1;
stat->match3 = 1;
stat->match4 = 1;
}
#ifdef _WIN64
__declspec(dllexport)
#endif
void bleu_add(
bleu_stat* stat,
size_t reflen,
int* ref,
size_t predlen,
int* pred,
int pad,
int eos) {
bleu_trim(&reflen, &ref, pad, eos);
bleu_trim(&predlen, &pred, pad, eos);
stat->reflen += reflen;
stat->predlen += predlen;
bleu_addngram(&stat->count1, &stat->match1, 1, reflen, ref, predlen, pred);
bleu_addngram(&stat->count2, &stat->match2, 2, reflen, ref, predlen, pred);
bleu_addngram(&stat->count3, &stat->match3, 3, reflen, ref, predlen, pred);
bleu_addngram(&stat->count4, &stat->match4, 4, reflen, ref, predlen, pred);
}
}
PyTorch/NLP/new-Transformer/fairseq/clib/libbleu/module.cpp 0 → 100644
View file @ c0f05c10
/**
* Copyright 2017-present, Facebook, Inc.
* All rights reserved.
*
* This source code is licensed under the license found in the
* LICENSE file in the root directory of this source tree.
*/
#include <Python.h>
static PyMethodDef method_def[] = {{NULL, NULL, 0, NULL}}; // NOLINT
static struct PyModuleDef module_def = {
PyModuleDef_HEAD_INIT,
"libbleu", /* name of module */
// NOLINTNEXTLINE
NULL, /* module documentation, may be NULL */
-1, /* size of per-interpreter state of the module,
or -1 if the module keeps state in global variables. */
method_def}; // NOLINT
#if PY_MAJOR_VERSION == 2
PyMODINIT_FUNC init_libbleu()
#else
PyMODINIT_FUNC PyInit_libbleu()
#endif
{
PyObject* m = PyModule_Create(&module_def);
if (!m) {
return NULL;
}
return m;
}
PyTorch/NLP/new-Transformer/fairseq/clib/libnat/edit_dist.cpp 0 → 100644
View file @ c0f05c10
/**
* Copyright 2017-present, Facebook, Inc.
* All rights reserved.
*
* This source code is licensed under the license found in the
* LICENSE file in the root directory of this source tree.
*/
#include <pybind11/detail/common.h>
#include <pybind11/pybind11.h>
#include <torch/torch.h> // @manual=//caffe2:torch_extension
#include <algorithm>
#include <cstdint>
#include <iosfwd>
#include <memory>
#include <new>
#include <string>
#include <utility>
#include <vector>
using namespace ::std;
vector<vector<uint32_t>> edit_distance2_with_dp(
vector<uint32_t>& x,
vector<uint32_t>& y) {
uint32_t lx = x.size();
uint32_t ly = y.size();
vector<vector<uint32_t>> d(lx + 1, vector<uint32_t>(ly + 1));
for (uint32_t i = 0; i < lx + 1; i++) {
d[i][0] = i;
}
for (uint32_t j = 0; j < ly + 1; j++) {
d[0][j] = j;
}
for (uint32_t i = 1; i < lx + 1; i++) {
for (uint32_t j = 1; j < ly + 1; j++) {
d[i][j] =
min(min(d[i - 1][j], d[i][j - 1]) + 1,
d[i - 1][j - 1] + 2 * (x.at(i - 1) == y.at(j - 1) ? 0 : 1));
}
}
return d;
}
vector<vector<uint32_t>> edit_distance2_backtracking(
vector<vector<uint32_t>>& d,
vector<uint32_t>& x,
vector<uint32_t>& y,
uint32_t terminal_symbol) {
vector<uint32_t> seq;
vector<vector<uint32_t>> edit_seqs(x.size() + 2, vector<uint32_t>());
/*
edit_seqs:
0~x.size() cell is the insertion sequences
last cell is the delete sequence
*/
if (x.size() == 0) {
edit_seqs.at(0) = y;
return edit_seqs;
}
uint32_t i = d.size() - 1;
uint32_t j = d.at(0).size() - 1;
while ((i >= 0) && (j >= 0)) {
if ((i == 0) && (j == 0)) {
break;
}
if ((j > 0) && (d.at(i).at(j - 1) < d.at(i).at(j))) {
seq.push_back(1); // insert
seq.push_back(y.at(j - 1));
j--;
} else if ((i > 0) && (d.at(i - 1).at(j) < d.at(i).at(j))) {
seq.push_back(2); // delete
seq.push_back(x.at(i - 1));
i--;
} else {
seq.push_back(3); // keep
seq.push_back(x.at(i - 1));
i--;
j--;
}
}
uint32_t prev_op, op, s, word;
prev_op = 0, s = 0;
for (uint32_t k = 0; k < seq.size() / 2; k++) {
op = seq.at(seq.size() - 2 * k - 2);
word = seq.at(seq.size() - 2 * k - 1);
if (prev_op != 1) {
s++;
}
if (op == 1) // insert
{
edit_seqs.at(s - 1).push_back(word);
} else if (op == 2) // delete
{
edit_seqs.at(x.size() + 1).push_back(1);
} else {
edit_seqs.at(x.size() + 1).push_back(0);
}
prev_op = op;
}
for (uint32_t k = 0; k < edit_seqs.size(); k++) {
if (edit_seqs[k].size() == 0) {
edit_seqs[k].push_back(terminal_symbol);
}
}
return edit_seqs;
}
vector<vector<uint32_t>> edit_distance2_backtracking_with_delete(
vector<vector<uint32_t>>& d,
vector<uint32_t>& x,
vector<uint32_t>& y,
uint32_t terminal_symbol,
uint32_t deletion_symbol) {
vector<uint32_t> seq;
vector<vector<uint32_t>> edit_seqs(x.size() + 1, vector<uint32_t>());
/*
edit_seqs:
0~x.size() cell is the insertion sequences
last cell is the delete sequence
*/
if (x.size() == 0) {
edit_seqs.at(0) = y;
return edit_seqs;
}
uint32_t i = d.size() - 1;
uint32_t j = d.at(0).size() - 1;
while ((i >= 0) && (j >= 0)) {
if ((i == 0) && (j == 0)) {
break;
}
if ((j > 0) && (d.at(i).at(j - 1) < d.at(i).at(j))) {
seq.push_back(1); // insert
seq.push_back(y.at(j - 1));
j--;
} else if ((i > 0) && (d.at(i - 1).at(j) < d.at(i).at(j))) {
seq.push_back(2); // delete
seq.push_back(x.at(i - 1));
i--;
} else {
seq.push_back(3); // keep
seq.push_back(x.at(i - 1));
i--;
j--;
}
}
uint32_t prev_op, op, s, word;
prev_op = 0, s = 0;
for (uint32_t k = 0; k < seq.size() / 2; k++) {
op = seq.at(seq.size() - 2 * k - 2);
word = seq.at(seq.size() - 2 * k - 1);
if (prev_op != 1) {
s++;
}
if (op == 1) // insert
{
edit_seqs.at(s - 1).push_back(word);
} else if (op == 2) // delete
{
edit_seqs.at(s - 1).push_back(deletion_symbol);
}
prev_op = op;
}
for (uint32_t k = 0; k < edit_seqs.size(); k++) {
if (edit_seqs.at(k).size() == 0) {
edit_seqs.at(k).push_back(terminal_symbol);
}
}
return edit_seqs;
}
vector<uint32_t> compute_ed2(
vector<vector<uint32_t>>& xs,
vector<vector<uint32_t>>& ys) {
vector<uint32_t> distances(xs.size());
for (uint32_t i = 0; i < xs.size(); i++) {
vector<vector<uint32_t>> d = edit_distance2_with_dp(xs.at(i), ys.at(i));
distances.at(i) = d.at(xs.at(i).size()).at(ys.at(i).size());
}
return distances;
}
vector<vector<vector<uint32_t>>> suggested_ed2_path(
vector<vector<uint32_t>>& xs,
vector<vector<uint32_t>>& ys,
uint32_t terminal_symbol) {
vector<vector<vector<uint32_t>>> seq(xs.size());
for (uint32_t i = 0; i < xs.size(); i++) {
vector<vector<uint32_t>> d = edit_distance2_with_dp(xs.at(i), ys.at(i));
seq.at(i) =
edit_distance2_backtracking(d, xs.at(i), ys.at(i), terminal_symbol);
}
return seq;
}
vector<vector<vector<uint32_t>>> suggested_ed2_path_with_delete(
vector<vector<uint32_t>>& xs,
vector<vector<uint32_t>>& ys,
uint32_t terminal_symbol,
uint32_t deletion_symbol) {
vector<vector<vector<uint32_t>>> seq(xs.size());
for (uint32_t i = 0; i < xs.size(); i++) {
vector<vector<uint32_t>> d = edit_distance2_with_dp(xs.at(i), ys.at(i));
seq.at(i) = edit_distance2_backtracking_with_delete(
d, xs.at(i), ys.at(i), terminal_symbol, deletion_symbol);
}
return seq;
}
PYBIND11_MODULE(libnat, m) {
m.def("compute_ed2", &compute_ed2, "compute_ed2");
m.def("suggested_ed2_path", &suggested_ed2_path, "suggested_ed2_path");
m.def(
"suggested_ed2_path_with_delete",
&suggested_ed2_path_with_delete,
"suggested_ed2_path_with_delete");
}
PyTorch/NLP/new-Transformer/fairseq/clib/libnat_cuda/binding.cpp 0 → 100644
View file @ c0f05c10
/**
* Copyright 2017-present, Facebook, Inc.
* All rights reserved.
*
* This source code is licensed under the license found in the
* LICENSE file in the root directory of this source tree.
*/
/*
This code is partially adpoted from
https://github.com/1ytic/pytorch-edit-distance
*/
#include <torch/types.h>
#include "edit_dist.h"
#ifndef TORCH_CHECK
#define TORCH_CHECK AT_CHECK
#endif
#define CHECK_CUDA(x) \
TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor")
#define CHECK_CONTIGUOUS(x) \
TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
#define CHECK_INPUT(x) \
CHECK_CUDA(x); \
CHECK_CONTIGUOUS(x)
torch::Tensor LevenshteinDistance(
torch::Tensor source,
torch::Tensor target,
torch::Tensor source_length,
torch::Tensor target_length) {
CHECK_INPUT(source);
CHECK_INPUT(target);
CHECK_INPUT(source_length);
CHECK_INPUT(target_length);
return LevenshteinDistanceCuda(source, target, source_length, target_length);
}
torch::Tensor GenerateDeletionLabel(
torch::Tensor source,
torch::Tensor operations) {
CHECK_INPUT(source);
CHECK_INPUT(operations);
return GenerateDeletionLabelCuda(source, operations);
}
std::pair<torch::Tensor, torch::Tensor> GenerateInsertionLabel(
torch::Tensor target,
torch::Tensor operations) {
CHECK_INPUT(target);
CHECK_INPUT(operations);
return GenerateInsertionLabelCuda(target, operations);
}
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
m.def("levenshtein_distance", &LevenshteinDistance, "Levenshtein distance");
m.def(
"generate_deletion_labels",
&GenerateDeletionLabel,
"Generate Deletion Label");
m.def(
"generate_insertion_labels",
&GenerateInsertionLabel,
"Generate Insertion Label");
}
PyTorch/NLP/new-Transformer/fairseq/clib/libnat_cuda/edit_dist.cu 0 → 100644
View file @ c0f05c10
/**
* Copyright 2017-present, Facebook, Inc.
* All rights reserved.
*
* This source code is licensed under the license found in the
* LICENSE file in the root directory of this source tree.
*/
#include "edit_dist.h"
#include <THC/THC.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <device_launch_parameters.h>
#include <utility> // std::pair
template <typename scalar_t>
__global__ void generate_deletion_label_kernel(
const scalar_t* __restrict__ source,
const size_t source_size,
const size_t operation_size,
int* __restrict__ operations,
int* __restrict__ labels) {
const int index = blockIdx.x;
const int offset = index * operation_size;
const int offset_label = index * source_size;
for (int i = 0; i < source_size; i++) {
labels[offset_label + i] = 0;
}
int k = 0;
for (int i = 0; i < operation_size; i++) {
if (operations[offset + i] == 0) {
break;
} else if (operations[offset + i] == 1) {
continue;
} else {
labels[offset_label + k] = 3 - operations[offset + i];
k++;
}
}
}
template <typename scalar_t>
__global__ void generate_insertion_label_kernel(
const scalar_t* __restrict__ target,
const size_t target_size,
const size_t operation_size,
int* __restrict__ operations,
int* __restrict__ labels,
int* __restrict__ masks) {
const int index = blockIdx.x;
const int offset = index * operation_size;
const int offset_label = index * target_size;
int k = 0;
int u = 0;
int m = 0;
for (int i = 0; i < target_size; i++) {
labels[offset_label + i] = 0;
masks[offset_label + i] = 0;
}
for (int i = 0; i < operation_size - 1; i++) {
if (operations[offset + i] == 0) {
break;
} else if (operations[offset + i] == 2) {
continue;
} else if (operations[offset + i] == 1) {
masks[offset_label + m] = 1;
u++;
m++;
} else {
labels[offset_label + k] = u;
masks[offset_label + m] = 0;
k++;
m++;
u = 0;
}
}
}
template <typename scalar_t>
__global__ void levenshtein_distance_kernel(
const scalar_t* __restrict__ source,
const scalar_t* __restrict__ target,
const int* __restrict__ source_length,
const int* __restrict__ target_length,
const size_t source_size,
const size_t target_size,
int* __restrict__ operations,
int* __restrict__ errors_curr) {
const int index = blockIdx.x;
const int offset = index * (source_size + target_size);
const int d = index * (source_size + 1) * (target_size + 1);
const int t = target_size + 1;
auto err_idx = [d, t](int i, int j) { return d + i * t + j; };
auto opt_idx = [offset](int k) { return offset + k; };
const int hyp_len = source_length[index];
const int ref_len = target_length[index];
const scalar_t* hyp_begin = source + index * source_size;
const scalar_t* ref_begin = target + index * target_size;
// dynamic programming
for (int i = 0; i <= hyp_len; i++) {
errors_curr[err_idx(i, 0)] = i;
}
for (int j = 0; j <= ref_len; j++) {
errors_curr[err_idx(0, j)] = j;
}
for (int i = 1; i <= hyp_len; i++) {
for (int j = 1; j <= ref_len; j++) {
errors_curr[err_idx(i, j)] = min(
min(errors_curr[err_idx(i - 1, j)], errors_curr[err_idx(i, j - 1)]) +
1,
errors_curr[err_idx(i - 1, j - 1)] +
2 * (*(hyp_begin + i - 1) == *(ref_begin + j - 1) ? 0 : 1));
}
}
// back-tracing
int i = hyp_len;
int j = ref_len;
int o = hyp_len + ref_len;
for (int k = 0; k < source_size + target_size; k++) {
operations[opt_idx(k)] = 0;
}
while ((i >= 0) && (j >= 0)) {
if ((i == 0) && (j == 0)) {
break;
}
if ((j > 0) &&
(errors_curr[err_idx(i, j - 1)] < errors_curr[err_idx(i, j)])) {
o--;
operations[opt_idx(o)] = 1;
j--; // insertion
} else if (
(i > 0) &&
(errors_curr[err_idx(i - 1, j)] < errors_curr[err_idx(i, j)])) {
o--;
operations[opt_idx(o)] = 2;
i--; // deletion
} else {
o--;
operations[opt_idx(o)] = 3;
i--;
j--; // do nothing
}
}
// moving to the left
for (int k = 0; k < hyp_len + ref_len; k++) {
if (k + o < hyp_len + ref_len) {
operations[opt_idx(k)] = operations[opt_idx(k + o)];
} else {
operations[opt_idx(k)] = 0; // padding
}
}
}
template <typename scalar_t>
__global__ void faster_levenshtein_distance_kernel(
const scalar_t* __restrict__ source,
const scalar_t* __restrict__ target,
const int* __restrict__ source_length,
const int* __restrict__ target_length,
const size_t source_size,
const size_t target_size,
int* __restrict__ operations) {
extern __shared__ short errors[];
auto errors_curr = errors;
const int index = blockIdx.x;
const int offset = index * (source_size + target_size);
const int t = target_size + 1;
auto err_idx = [t](int i, int j) { return i * t + j; };
auto opt_idx = [offset](int k) { return offset + k; };
const int hyp_len = source_length[index];
const int ref_len = target_length[index];
const scalar_t* hyp_begin = source + index * source_size;
const scalar_t* ref_begin = target + index * target_size;
// dynamic programming
for (int i = 0; i <= hyp_len; i++) {
errors_curr[err_idx(i, 0)] = i;
}
for (int j = 0; j <= ref_len; j++) {
errors_curr[err_idx(0, j)] = j;
}
for (int i = 1; i <= hyp_len; i++) {
for (int j = 1; j <= ref_len; j++) {
errors_curr[err_idx(i, j)] = min(
min(errors_curr[err_idx(i - 1, j)], errors_curr[err_idx(i, j - 1)]) +
1,
errors_curr[err_idx(i - 1, j - 1)] +
2 * (*(hyp_begin + i - 1) == *(ref_begin + j - 1) ? 0 : 1));
}
}
// back-tracing
int i = hyp_len;
int j = ref_len;
int o = hyp_len + ref_len;
for (int k = 0; k < source_size + target_size; k++) {
operations[opt_idx(k)] = 0;
}
while ((i >= 0) && (j >= 0)) {
if ((i == 0) && (j == 0)) {
break;
}
if ((j > 0) &&
(errors_curr[err_idx(i, j - 1)] < errors_curr[err_idx(i, j)])) {
o--;
operations[opt_idx(o)] = 1;
j--; // insertion
} else if (
(i > 0) &&
(errors_curr[err_idx(i - 1, j)] < errors_curr[err_idx(i, j)])) {
o--;
operations[opt_idx(o)] = 2;
i--; // deletion
} else {
o--;
operations[opt_idx(o)] = 3;
i--;
j--; // do nothing
}
}
// moving to the left
for (int k = 0; k < hyp_len + ref_len; k++) {
if (k + o < hyp_len + ref_len) {
operations[opt_idx(k)] = operations[opt_idx(k + o)];
} else {
operations[opt_idx(k)] = 0; // padding
}
}
}
torch::Tensor GenerateDeletionLabelCuda(
torch::Tensor source,
torch::Tensor operations) {
const auto batch_size = source.size(0);
at::TensorOptions options(source.device());
options = options.dtype(at::ScalarType::Int);
auto labels = torch::empty({batch_size, source.size(1)}, options);
auto stream = at::cuda::getCurrentCUDAStream(source.device().index());
AT_DISPATCH_ALL_TYPES(source.scalar_type(), "generate_deletion_labels", ([&] {
generate_deletion_label_kernel<scalar_t>
<<<batch_size, 1, 0, stream>>>(
source.data_ptr<scalar_t>(),
source.size(1),
operations.size(1),
operations.data_ptr<int>(),
labels.data_ptr<int>());
}));
return labels;
}
std::pair<torch::Tensor, torch::Tensor> GenerateInsertionLabelCuda(
torch::Tensor target,
torch::Tensor operations) {
const auto batch_size = target.size(0);
at::TensorOptions options(target.device());
options = options.dtype(at::ScalarType::Int);
auto labels = torch::empty({batch_size, target.size(1)}, options);
auto masks = torch::empty({batch_size, target.size(1)}, options);
auto stream = at::cuda::getCurrentCUDAStream(target.device().index());
AT_DISPATCH_ALL_TYPES(
target.scalar_type(), "generate_insertion_labels", ([&] {
generate_insertion_label_kernel<scalar_t><<<batch_size, 1, 0, stream>>>(
target.data_ptr<scalar_t>(),
target.size(1),
operations.size(1),
operations.data_ptr<int>(),
labels.data_ptr<int>(),
masks.data_ptr<int>());
}));
return std::make_pair(labels, masks);
}
torch::Tensor LevenshteinDistanceCuda(
torch::Tensor source,
torch::Tensor target,
torch::Tensor source_length,
torch::Tensor target_length) {
const auto batch_size = source.size(0);
const auto shared_size =
(source.size(1) + 1) * (target.size(1) + 1) * sizeof(short);
at::TensorOptions options(source.device());
options = options.dtype(at::ScalarType::Int);
auto operations =
torch::empty({batch_size, source.size(1) + target.size(1)}, options);
auto stream = at::cuda::getCurrentCUDAStream(source.device().index());
if (shared_size > 40000) {
auto distances = torch::empty(
{batch_size, (source.size(1) + 1) * (target.size(1) + 1)}, options);
AT_DISPATCH_ALL_TYPES(source.scalar_type(), "levenshtein_distance", ([&] {
levenshtein_distance_kernel<scalar_t>
<<<batch_size, 1, 0, stream>>>(
source.data_ptr<scalar_t>(),
target.data_ptr<scalar_t>(),
source_length.data_ptr<int>(),
target_length.data_ptr<int>(),
source.size(1),
target.size(1),
operations.data_ptr<int>(),
distances.data_ptr<int>());
}));
} else {
AT_DISPATCH_ALL_TYPES(
source.scalar_type(), "faster_levenshtein_distance", ([&] {
faster_levenshtein_distance_kernel<scalar_t>
<<<batch_size, 1, shared_size, stream>>>(
source.data_ptr<scalar_t>(),
target.data_ptr<scalar_t>(),
source_length.data_ptr<int>(),
target_length.data_ptr<int>(),
source.size(1),
target.size(1),
operations.data_ptr<int>());
}));
}
return operations;
}
PyTorch/NLP/new-Transformer/fairseq/clib/libnat_cuda/edit_dist.h 0 → 100644
View file @ c0f05c10
/**
* Copyright 2017-present, Facebook, Inc.
* All rights reserved.
*
* This source code is licensed under the license found in the
* LICENSE file in the root directory of this source tree.
*/
#pragma once
#include <torch/extension.h>
torch::Tensor LevenshteinDistanceCuda(
torch::Tensor source,
torch::Tensor target,
torch::Tensor source_length,
torch::Tensor target_length);
torch::Tensor GenerateDeletionLabelCuda(
torch::Tensor source,
torch::Tensor operations);
std::pair<torch::Tensor, torch::Tensor> GenerateInsertionLabelCuda(
torch::Tensor source,
torch::Tensor operations);
PyTorch/NLP/new-Transformer/fairseq/config/__init__.py 0 → 100644
View file @ c0f05c10
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
PyTorch/NLP/new-Transformer/fairseq/config/config.yaml 0 → 100644
View file @ c0f05c10
# @package _group_
hydra:
run:
dir: .
defaults:
- _self_
- task: null
- model: null
- criterion: cross_entropy
- optimizer: null
- lr_scheduler: fixed
- bpe: null
- tokenizer: null
- scoring: null
- generation: null
- common_eval: null
- eval_lm: null
PyTorch/NLP/new-Transformer/fairseq/config/model/transformer_lm/transformer_lm_baevski_gbw.yaml 0 → 100644
View file @ c0f05c10
# @package _group_
activation_fn: "relu"
dropout: 0.1
attention_dropout: 0.1
activation_dropout: 0.0
relu_dropout: 0.0
decoder_embed_dim: 512
decoder_output_dim: 512
decoder_input_dim: 512
decoder_ffn_embed_dim: 4096
decoder_layers: 12
decoder_attention_heads: 16
decoder_normalize_before: true
no_decoder_final_norm: true
adaptive_softmax_cutoff: null
adaptive_softmax_dropout: 0
adaptive_softmax_factor: 4
no_token_positional_embeddings: false
share_decoder_input_output_embed: false
character_embeddings: false
character_filters: "[(1, 64), (2, 128), (3, 192), (4, 256), (5, 256), (6, 256), (7, 256)]"
character_embedding_dim: 4
char_embedder_highway_layers: 2
adaptive_input: false
adaptive_input_factor: 4
adaptive_input_cutoff: null
tie_adaptive_weights: false
tie_adaptive_proj: false
decoder_learned_pos: false
decoder_layerdrop: 0
decoder_layers_to_keep: null
layernorm_embedding: false
no_scale_embedding: false
quant_noise_pq: 0
quant_noise_pq_block_size: 8
quant_noise_scalar: 0
PyTorch/NLP/new-Transformer/fairseq/config/model/transformer_lm/transformer_lm_baevski_wiki103.yaml 0 → 100644
View file @ c0f05c10
# @package _group_
activation_fn: "relu"
dropout: 0.3
attention_dropout: 0.1
activation_dropout: 0.1
relu_dropout: 0.1
decoder_embed_dim: 1024
decoder_output_dim: 1024
decoder_input_dim: 1024
decoder_ffn_embed_dim: 4096
decoder_layers: 16
decoder_attention_heads: 8
decoder_normalize_before: true
no_decoder_final_norm: true
adaptive_softmax_cutoff: "20000,60000"
adaptive_softmax_dropout: 0.2
adaptive_softmax_factor: 4
no_token_positional_embeddings: false
share_decoder_input_output_embed: false
character_embeddings: false
character_filters: "[(1, 64), (2, 128), (3, 192), (4, 256), (5, 256), (6, 256), (7, 256)]"
character_embedding_dim: 4
char_embedder_highway_layers: 2
adaptive_input: true
adaptive_input_factor: 4
adaptive_input_cutoff: "20000,60000"
tie_adaptive_weights: true
tie_adaptive_proj: true
decoder_learned_pos: false
decoder_layerdrop: 0
decoder_layers_to_keep: null
layernorm_embedding: false
no_scale_embedding: false
quant_noise_pq: 0
quant_noise_pq_block_size: 8
quant_noise_scalar: 0
PyTorch/NLP/new-Transformer/fairseq/config/model/transformer_lm/transformer_lm_big.yaml 0 → 100644
View file @ c0f05c10
# @package _group_
activation_fn: "relu"
dropout: 0.1
attention_dropout: 0.0
activation_dropout: 0.0
relu_dropout: 0.0
decoder_embed_dim: 1024
decoder_output_dim: 1024
decoder_input_dim: 1024
decoder_ffn_embed_dim: 4096
decoder_layers: 12
decoder_attention_heads: 16
decoder_normalize_before: true
no_decoder_final_norm: false
adaptive_softmax_cutoff: null
adaptive_softmax_dropout: 0
adaptive_softmax_factor: 4
no_token_positional_embeddings: false
share_decoder_input_output_embed: false
character_embeddings: false
character_filters: "[(1, 64), (2, 128), (3, 192), (4, 256), (5, 256), (6, 256), (7, 256)]"
character_embedding_dim: 4
char_embedder_highway_layers: 2
adaptive_input: false
adaptive_input_factor: 4
adaptive_input_cutoff: null
tie_adaptive_weights: false
tie_adaptive_proj: false
decoder_learned_pos: false
decoder_layerdrop: 0
decoder_layers_to_keep: null
layernorm_embedding: false
no_scale_embedding: false
quant_noise_pq: 0
quant_noise_pq_block_size: 8
quant_noise_scalar: 0
PyTorch/NLP/new-Transformer/fairseq/config/model/transformer_lm/transformer_lm_gbw.yaml 0 → 100644
View file @ c0f05c10
# @package _group_
activation_fn: "relu"
dropout: 0.1
attention_dropout: 0.1
activation_dropout: 0.0
relu_dropout: 0.0
decoder_embed_dim: 512
decoder_output_dim: 512
decoder_input_dim: 512
decoder_ffn_embed_dim: 4096
decoder_layers: 12
decoder_attention_heads: 16
decoder_normalize_before: true
no_decoder_final_norm: true
adaptive_softmax_cutoff: null
adaptive_softmax_dropout: 0
adaptive_softmax_factor: 4
no_token_positional_embeddings: false
share_decoder_input_output_embed: false
character_embeddings: false
character_filters: "[(1, 64), (2, 128), (3, 192), (4, 256), (5, 256), (6, 256), (7, 256)]"
character_embedding_dim: 4
char_embedder_highway_layers: 2
adaptive_input: false
adaptive_input_factor: 4
adaptive_input_cutoff: null
tie_adaptive_weights: false
tie_adaptive_proj: false
decoder_learned_pos: false
decoder_layerdrop: 0
decoder_layers_to_keep: null
layernorm_embedding: false
no_scale_embedding: false
quant_noise_pq: 0
quant_noise_pq_block_size: 8
quant_noise_scalar: 0
PyTorch/NLP/new-Transformer/fairseq/config/model/transformer_lm/transformer_lm_gpt.yaml 0 → 100644
View file @ c0f05c10
# @package _group_
activation_fn: "gelu"
dropout: 0.1
attention_dropout: 0.1
activation_dropout: 0.0
relu_dropout: 0.0
decoder_embed_dim: 768
decoder_output_dim: 768
decoder_input_dim: 768
decoder_ffn_embed_dim: 3072
decoder_layers: 12
decoder_attention_heads: 12
decoder_normalize_before: true
no_decoder_final_norm: false
adaptive_softmax_cutoff: null
adaptive_softmax_dropout: 0
adaptive_softmax_factor: 4
no_token_positional_embeddings: false
share_decoder_input_output_embed: false
character_embeddings: false
character_filters: "[(1, 64), (2, 128), (3, 192), (4, 256), (5, 256), (6, 256), (7, 256)]"
character_embedding_dim: 4
char_embedder_highway_layers: 2
adaptive_input: false
adaptive_input_factor: 4
adaptive_input_cutoff: null
tie_adaptive_weights: false
tie_adaptive_proj: false
decoder_learned_pos: false
decoder_layerdrop: 0
decoder_layers_to_keep: null
layernorm_embedding: false
no_scale_embedding: false
quant_noise_pq: 0
quant_noise_pq_block_size: 8
quant_noise_scalar: 0
PyTorch/NLP/new-Transformer/fairseq/config/model/transformer_lm/transformer_lm_gpt2_big.yaml 0 → 100644
View file @ c0f05c10
# @package _group_
activation_fn: "gelu"
dropout: 0.1
attention_dropout: 0.1
activation_dropout: 0.0
relu_dropout: 0.0
decoder_embed_dim: 1600
decoder_output_dim: 1600
decoder_input_dim: 1600
decoder_ffn_embed_dim: 6400
decoder_layers: 48
decoder_attention_heads: 25
decoder_normalize_before: true
no_decoder_final_norm: false
adaptive_softmax_cutoff: null
adaptive_softmax_dropout: 0
adaptive_softmax_factor: 4
no_token_positional_embeddings: false
share_decoder_input_output_embed: false
character_embeddings: false
character_filters: "[(1, 64), (2, 128), (3, 192), (4, 256), (5, 256), (6, 256), (7, 256)]"
character_embedding_dim: 4
char_embedder_highway_layers: 2
adaptive_input: false
adaptive_input_factor: 4
adaptive_input_cutoff: null
tie_adaptive_weights: false
tie_adaptive_proj: false
decoder_learned_pos: false
decoder_layerdrop: 0
decoder_layers_to_keep: null
layernorm_embedding: false
no_scale_embedding: false
quant_noise_pq: 0
quant_noise_pq_block_size: 8
quant_noise_scalar: 0
PyTorch/NLP/new-Transformer/fairseq/config/model/transformer_lm/transformer_lm_gpt2_medium.yaml 0 → 100644
View file @ c0f05c10
# @package _group_
activation_fn: "gelu"
dropout: 0.1
attention_dropout: 0.1
activation_dropout: 0.0
relu_dropout: 0.0
decoder_embed_dim: 1280
decoder_output_dim: 1280
decoder_input_dim: 1280
decoder_ffn_embed_dim: 5120
decoder_layers: 36
decoder_attention_heads: 20
decoder_normalize_before: true
no_decoder_final_norm: false
adaptive_softmax_cutoff: null
adaptive_softmax_dropout: 0
adaptive_softmax_factor: 4
no_token_positional_embeddings: false
share_decoder_input_output_embed: false
character_embeddings: false
character_filters: "[(1, 64), (2, 128), (3, 192), (4, 256), (5, 256), (6, 256), (7, 256)]"
character_embedding_dim: 4
char_embedder_highway_layers: 2
adaptive_input: false
adaptive_input_factor: 4
adaptive_input_cutoff: null
tie_adaptive_weights: false
tie_adaptive_proj: false
decoder_learned_pos: false
decoder_layerdrop: 0
decoder_layers_to_keep: null
layernorm_embedding: false
no_scale_embedding: false
quant_noise_pq: 0
quant_noise_pq_block_size: 8
quant_noise_scalar: 0
PyTorch/NLP/new-Transformer/fairseq/config/model/transformer_lm/transformer_lm_gpt2_small.yaml 0 → 100644
View file @ c0f05c10
# @package _group_
activation_fn: "gelu"
dropout: 0.1
attention_dropout: 0.1
activation_dropout: 0.0
relu_dropout: 0.0
decoder_embed_dim: 1024
decoder_output_dim: 1024
decoder_input_dim: 1024
decoder_ffn_embed_dim: 4096
decoder_layers: 24
decoder_attention_heads: 16
decoder_normalize_before: true
no_decoder_final_norm: false
adaptive_softmax_cutoff: null
adaptive_softmax_dropout: 0
adaptive_softmax_factor: 4
no_token_positional_embeddings: false
share_decoder_input_output_embed: false
character_embeddings: false
character_filters: "[(1, 64), (2, 128), (3, 192), (4, 256), (5, 256), (6, 256), (7, 256)]"
character_embedding_dim: 4
char_embedder_highway_layers: 2
adaptive_input: false
adaptive_input_factor: 4
adaptive_input_cutoff: null
tie_adaptive_weights: false
tie_adaptive_proj: false
decoder_learned_pos: false
decoder_layerdrop: 0
decoder_layers_to_keep: null
layernorm_embedding: false
no_scale_embedding: false
quant_noise_pq: 0
quant_noise_pq_block_size: 8
quant_noise_scalar: 0
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