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CIF-HieraDist | CIF-HieraDist-main/examples/speech_recognition/criterions/ASG_loss.py | #!/usr/bin/env python3
# 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 torch
from examples.speech_recognition.data.replabels import pack_replabels
from fairseq import utils
from fairseq.criterions import FairseqCriterion, register_criterion
@register_criterion("asg_loss")
class ASGCriterion(FairseqCriterion):
@staticmethod
def add_args(parser):
group = parser.add_argument_group("ASG Loss")
group.add_argument(
"--asg-transitions-init",
help="initial diagonal value of transition matrix",
type=float,
default=0.0,
)
group.add_argument(
"--max-replabel", help="maximum # of replabels", type=int, default=2
)
group.add_argument(
"--linseg-updates",
help="# of training updates to use LinSeg initialization",
type=int,
default=0,
)
group.add_argument(
"--hide-linseg-messages",
help="hide messages about LinSeg initialization",
action="store_true",
)
def __init__(
self,
task,
silence_token,
asg_transitions_init,
max_replabel,
linseg_updates,
hide_linseg_messages,
):
from flashlight.lib.sequence.criterion import ASGLoss, CriterionScaleMode
super().__init__(task)
self.tgt_dict = task.target_dictionary
self.eos = self.tgt_dict.eos()
self.silence = (
self.tgt_dict.index(silence_token)
if silence_token in self.tgt_dict
else None
)
self.max_replabel = max_replabel
num_labels = len(self.tgt_dict)
self.asg = ASGLoss(num_labels, scale_mode=CriterionScaleMode.TARGET_SZ_SQRT)
self.asg.trans = torch.nn.Parameter(
asg_transitions_init * torch.eye(num_labels), requires_grad=True
)
self.linseg_progress = torch.nn.Parameter(
torch.tensor([0], dtype=torch.int), requires_grad=False
)
self.linseg_maximum = linseg_updates
self.linseg_message_state = "none" if hide_linseg_messages else "start"
@classmethod
def build_criterion(cls, args, task):
return cls(
task,
args.silence_token,
args.asg_transitions_init,
args.max_replabel,
args.linseg_updates,
args.hide_linseg_messages,
)
def linseg_step(self):
if not self.training:
return False
if self.linseg_progress.item() < self.linseg_maximum:
if self.linseg_message_state == "start":
print("| using LinSeg to initialize ASG")
self.linseg_message_state = "finish"
self.linseg_progress.add_(1)
return True
elif self.linseg_message_state == "finish":
print("| finished LinSeg initialization")
self.linseg_message_state = "none"
return False
def replace_eos_with_silence(self, tgt):
if tgt[-1] != self.eos:
return tgt
elif self.silence is None or (len(tgt) > 1 and tgt[-2] == self.silence):
return tgt[:-1]
else:
return tgt[:-1] + [self.silence]
def forward(self, model, sample, reduce=True):
"""Compute the loss for the given sample.
Returns a tuple with three elements:
1) the loss
2) the sample size, which is used as the denominator for the gradient
3) logging outputs to display while training
"""
net_output = model(**sample["net_input"])
emissions = net_output["encoder_out"].transpose(0, 1).contiguous()
B = emissions.size(0)
T = emissions.size(1)
device = emissions.device
target = torch.IntTensor(B, T)
target_size = torch.IntTensor(B)
using_linseg = self.linseg_step()
for b in range(B):
initial_target_size = sample["target_lengths"][b].item()
if initial_target_size == 0:
raise ValueError("target size cannot be zero")
tgt = sample["target"][b, :initial_target_size].tolist()
tgt = self.replace_eos_with_silence(tgt)
tgt = pack_replabels(tgt, self.tgt_dict, self.max_replabel)
tgt = tgt[:T]
if using_linseg:
tgt = [tgt[t * len(tgt) // T] for t in range(T)]
target[b][: len(tgt)] = torch.IntTensor(tgt)
target_size[b] = len(tgt)
loss = self.asg.forward(emissions, target.to(device), target_size.to(device))
if reduce:
loss = torch.sum(loss)
sample_size = (
sample["target"].size(0) if self.args.sentence_avg else sample["ntokens"]
)
logging_output = {
"loss": utils.item(loss.data) if reduce else loss.data,
"ntokens": sample["ntokens"],
"nsentences": sample["target"].size(0),
"sample_size": sample_size,
}
return loss, sample_size, logging_output
@staticmethod
def aggregate_logging_outputs(logging_outputs):
"""Aggregate logging outputs from data parallel training."""
loss_sum = sum(log.get("loss", 0) for log in logging_outputs)
ntokens = sum(log.get("ntokens", 0) for log in logging_outputs)
nsentences = sum(log.get("nsentences", 0) for log in logging_outputs)
sample_size = sum(log.get("sample_size", 0) for log in logging_outputs)
agg_output = {
"loss": loss_sum / nsentences,
"ntokens": ntokens,
"nsentences": nsentences,
"sample_size": sample_size,
}
return agg_output
| 5,870 | 33.333333 | 85 | py |
CIF-HieraDist | CIF-HieraDist-main/examples/speech_recognition/models/vggtransformer.py | # 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 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,
FairseqIncrementalDecoder,
register_model,
register_model_architecture,
)
from fairseq.modules import (
LinearizedConvolution,
TransformerDecoderLayer,
TransformerEncoderLayer,
VGGBlock,
)
@register_model("asr_vggtransformer")
class VGGTransformerModel(FairseqEncoderDecoderModel):
"""
Transformers with convolutional context for ASR
https://arxiv.org/abs/1904.11660
"""
def __init__(self, encoder, decoder):
super().__init__(encoder, decoder)
@staticmethod
def add_args(parser):
"""Add model-specific arguments to the parser."""
parser.add_argument(
"--input-feat-per-channel",
type=int,
metavar="N",
help="encoder input dimension per input channel",
)
parser.add_argument(
"--vggblock-enc-config",
type=str,
metavar="EXPR",
help="""
an array of tuples each containing the configuration of one vggblock:
[(out_channels,
conv_kernel_size,
pooling_kernel_size,
num_conv_layers,
use_layer_norm), ...])
""",
)
parser.add_argument(
"--transformer-enc-config",
type=str,
metavar="EXPR",
help=""""
a tuple containing the configuration of the encoder transformer layers
configurations:
[(input_dim,
num_heads,
ffn_dim,
normalize_before,
dropout,
attention_dropout,
relu_dropout), ...]')
""",
)
parser.add_argument(
"--enc-output-dim",
type=int,
metavar="N",
help="""
encoder output dimension, can be None. If specified, projecting the
transformer output to the specified dimension""",
)
parser.add_argument(
"--in-channels",
type=int,
metavar="N",
help="number of encoder input channels",
)
parser.add_argument(
"--tgt-embed-dim",
type=int,
metavar="N",
help="embedding dimension of the decoder target tokens",
)
parser.add_argument(
"--transformer-dec-config",
type=str,
metavar="EXPR",
help="""
a tuple containing the configuration of the decoder transformer layers
configurations:
[(input_dim,
num_heads,
ffn_dim,
normalize_before,
dropout,
attention_dropout,
relu_dropout), ...]
""",
)
parser.add_argument(
"--conv-dec-config",
type=str,
metavar="EXPR",
help="""
an array of tuples for the decoder 1-D convolution config
[(out_channels, conv_kernel_size, use_layer_norm), ...]""",
)
@classmethod
def build_encoder(cls, args, task):
return VGGTransformerEncoder(
input_feat_per_channel=args.input_feat_per_channel,
vggblock_config=eval(args.vggblock_enc_config),
transformer_config=eval(args.transformer_enc_config),
encoder_output_dim=args.enc_output_dim,
in_channels=args.in_channels,
)
@classmethod
def build_decoder(cls, args, task):
return TransformerDecoder(
dictionary=task.target_dictionary,
embed_dim=args.tgt_embed_dim,
transformer_config=eval(args.transformer_dec_config),
conv_config=eval(args.conv_dec_config),
encoder_output_dim=args.enc_output_dim,
)
@classmethod
def build_model(cls, args, task):
"""Build a new model instance."""
# make sure that all args are properly defaulted
# (in case there are any new ones)
base_architecture(args)
encoder = cls.build_encoder(args, task)
decoder = cls.build_decoder(args, task)
return cls(encoder, decoder)
def get_normalized_probs(self, net_output, log_probs, sample=None):
# net_output['encoder_out'] is a (B, T, D) tensor
lprobs = super().get_normalized_probs(net_output, log_probs, sample)
lprobs.batch_first = True
return lprobs
DEFAULT_ENC_VGGBLOCK_CONFIG = ((32, 3, 2, 2, False),) * 2
DEFAULT_ENC_TRANSFORMER_CONFIG = ((256, 4, 1024, True, 0.2, 0.2, 0.2),) * 2
# 256: embedding dimension
# 4: number of heads
# 1024: FFN
# True: apply layerNorm before (dropout + resiaul) instead of after
# 0.2 (dropout): dropout after MultiheadAttention and second FC
# 0.2 (attention_dropout): dropout in MultiheadAttention
# 0.2 (relu_dropout): dropout after ReLu
DEFAULT_DEC_TRANSFORMER_CONFIG = ((256, 2, 1024, True, 0.2, 0.2, 0.2),) * 2
DEFAULT_DEC_CONV_CONFIG = ((256, 3, True),) * 2
# TODO: repace transformer encoder config from one liner
# to explicit args to get rid of this transformation
def prepare_transformer_encoder_params(
input_dim,
num_heads,
ffn_dim,
normalize_before,
dropout,
attention_dropout,
relu_dropout,
):
args = argparse.Namespace()
args.encoder_embed_dim = input_dim
args.encoder_attention_heads = num_heads
args.attention_dropout = attention_dropout
args.dropout = dropout
args.activation_dropout = relu_dropout
args.encoder_normalize_before = normalize_before
args.encoder_ffn_embed_dim = ffn_dim
return args
def prepare_transformer_decoder_params(
input_dim,
num_heads,
ffn_dim,
normalize_before,
dropout,
attention_dropout,
relu_dropout,
):
args = argparse.Namespace()
args.encoder_embed_dim = None
args.decoder_embed_dim = input_dim
args.decoder_attention_heads = num_heads
args.attention_dropout = attention_dropout
args.dropout = dropout
args.activation_dropout = relu_dropout
args.decoder_normalize_before = normalize_before
args.decoder_ffn_embed_dim = ffn_dim
return args
class VGGTransformerEncoder(FairseqEncoder):
"""VGG + Transformer encoder"""
def __init__(
self,
input_feat_per_channel,
vggblock_config=DEFAULT_ENC_VGGBLOCK_CONFIG,
transformer_config=DEFAULT_ENC_TRANSFORMER_CONFIG,
encoder_output_dim=512,
in_channels=1,
transformer_context=None,
transformer_sampling=None,
):
"""constructor for VGGTransformerEncoder
Args:
- input_feat_per_channel: feature dim (not including stacked,
just base feature)
- in_channel: # input channels (e.g., if stack 8 feature vector
together, this is 8)
- vggblock_config: configuration of vggblock, see comments on
DEFAULT_ENC_VGGBLOCK_CONFIG
- transformer_config: configuration of transformer layer, see comments
on DEFAULT_ENC_TRANSFORMER_CONFIG
- encoder_output_dim: final transformer output embedding dimension
- transformer_context: (left, right) if set, self-attention will be focused
on (t-left, t+right)
- transformer_sampling: an iterable of int, must match with
len(transformer_config), transformer_sampling[i] indicates sampling
factor for i-th transformer layer, after multihead att and feedfoward
part
"""
super().__init__(None)
self.num_vggblocks = 0
if vggblock_config is not None:
if not isinstance(vggblock_config, Iterable):
raise ValueError("vggblock_config is not iterable")
self.num_vggblocks = len(vggblock_config)
self.conv_layers = nn.ModuleList()
self.in_channels = in_channels
self.input_dim = input_feat_per_channel
self.pooling_kernel_sizes = []
if vggblock_config is not None:
for _, config in enumerate(vggblock_config):
(
out_channels,
conv_kernel_size,
pooling_kernel_size,
num_conv_layers,
layer_norm,
) = config
self.conv_layers.append(
VGGBlock(
in_channels,
out_channels,
conv_kernel_size,
pooling_kernel_size,
num_conv_layers,
input_dim=input_feat_per_channel,
layer_norm=layer_norm,
)
)
self.pooling_kernel_sizes.append(pooling_kernel_size)
in_channels = out_channels
input_feat_per_channel = self.conv_layers[-1].output_dim
transformer_input_dim = self.infer_conv_output_dim(
self.in_channels, self.input_dim
)
# transformer_input_dim is the output dimension of VGG part
self.validate_transformer_config(transformer_config)
self.transformer_context = self.parse_transformer_context(transformer_context)
self.transformer_sampling = self.parse_transformer_sampling(
transformer_sampling, len(transformer_config)
)
self.transformer_layers = nn.ModuleList()
if transformer_input_dim != transformer_config[0][0]:
self.transformer_layers.append(
Linear(transformer_input_dim, transformer_config[0][0])
)
self.transformer_layers.append(
TransformerEncoderLayer(
prepare_transformer_encoder_params(*transformer_config[0])
)
)
for i in range(1, len(transformer_config)):
if transformer_config[i - 1][0] != transformer_config[i][0]:
self.transformer_layers.append(
Linear(transformer_config[i - 1][0], transformer_config[i][0])
)
self.transformer_layers.append(
TransformerEncoderLayer(
prepare_transformer_encoder_params(*transformer_config[i])
)
)
self.encoder_output_dim = encoder_output_dim
self.transformer_layers.extend(
[
Linear(transformer_config[-1][0], encoder_output_dim),
LayerNorm(encoder_output_dim),
]
)
def forward(self, src_tokens, src_lengths, **kwargs):
"""
src_tokens: padded tensor (B, T, C * feat)
src_lengths: tensor of original lengths of input utterances (B,)
"""
bsz, max_seq_len, _ = src_tokens.size()
x = src_tokens.view(bsz, max_seq_len, self.in_channels, self.input_dim)
x = x.transpose(1, 2).contiguous()
# (B, C, T, feat)
for layer_idx in range(len(self.conv_layers)):
x = self.conv_layers[layer_idx](x)
bsz, _, output_seq_len, _ = x.size()
# (B, C, T, feat) -> (B, T, C, feat) -> (T, B, C, feat) -> (T, B, C * feat)
x = x.transpose(1, 2).transpose(0, 1)
x = x.contiguous().view(output_seq_len, bsz, -1)
input_lengths = src_lengths.clone()
for s in self.pooling_kernel_sizes:
input_lengths = (input_lengths.float() / s).ceil().long()
encoder_padding_mask, _ = lengths_to_encoder_padding_mask(
input_lengths, batch_first=True
)
if not encoder_padding_mask.any():
encoder_padding_mask = None
subsampling_factor = int(max_seq_len * 1.0 / output_seq_len + 0.5)
attn_mask = self.lengths_to_attn_mask(input_lengths, subsampling_factor)
transformer_layer_idx = 0
for layer_idx in range(len(self.transformer_layers)):
if isinstance(self.transformer_layers[layer_idx], TransformerEncoderLayer):
x = self.transformer_layers[layer_idx](
x, encoder_padding_mask, attn_mask
)
if self.transformer_sampling[transformer_layer_idx] != 1:
sampling_factor = self.transformer_sampling[transformer_layer_idx]
x, encoder_padding_mask, attn_mask = self.slice(
x, encoder_padding_mask, attn_mask, sampling_factor
)
transformer_layer_idx += 1
else:
x = self.transformer_layers[layer_idx](x)
# encoder_padding_maks is a (T x B) tensor, its [t, b] elements indicate
# whether encoder_output[t, b] is valid or not (valid=0, invalid=1)
return {
"encoder_out": x, # (T, B, C)
"encoder_padding_mask": encoder_padding_mask.t()
if encoder_padding_mask is not None
else None,
# (B, T) --> (T, B)
}
def infer_conv_output_dim(self, in_channels, input_dim):
sample_seq_len = 200
sample_bsz = 10
x = torch.randn(sample_bsz, in_channels, sample_seq_len, input_dim)
for i, _ in enumerate(self.conv_layers):
x = self.conv_layers[i](x)
x = x.transpose(1, 2)
mb, seq = x.size()[:2]
return x.contiguous().view(mb, seq, -1).size(-1)
def validate_transformer_config(self, transformer_config):
for config in transformer_config:
input_dim, num_heads = config[:2]
if input_dim % num_heads != 0:
msg = (
"ERROR in transformer config {}: ".format(config)
+ "input dimension {} ".format(input_dim)
+ "not dividable by number of heads {}".format(num_heads)
)
raise ValueError(msg)
def parse_transformer_context(self, transformer_context):
"""
transformer_context can be the following:
- None; indicates no context is used, i.e.,
transformer can access full context
- a tuple/list of two int; indicates left and right context,
any number <0 indicates infinite context
* e.g., (5, 6) indicates that for query at x_t, transformer can
access [t-5, t+6] (inclusive)
* e.g., (-1, 6) indicates that for query at x_t, transformer can
access [0, t+6] (inclusive)
"""
if transformer_context is None:
return None
if not isinstance(transformer_context, Iterable):
raise ValueError("transformer context must be Iterable if it is not None")
if len(transformer_context) != 2:
raise ValueError("transformer context must have length 2")
left_context = transformer_context[0]
if left_context < 0:
left_context = None
right_context = transformer_context[1]
if right_context < 0:
right_context = None
if left_context is None and right_context is None:
return None
return (left_context, right_context)
def parse_transformer_sampling(self, transformer_sampling, num_layers):
"""
parsing transformer sampling configuration
Args:
- transformer_sampling, accepted input:
* None, indicating no sampling
* an Iterable with int (>0) as element
- num_layers, expected number of transformer layers, must match with
the length of transformer_sampling if it is not None
Returns:
- A tuple with length num_layers
"""
if transformer_sampling is None:
return (1,) * num_layers
if not isinstance(transformer_sampling, Iterable):
raise ValueError(
"transformer_sampling must be an iterable if it is not None"
)
if len(transformer_sampling) != num_layers:
raise ValueError(
"transformer_sampling {} does not match with the number "
"of layers {}".format(transformer_sampling, num_layers)
)
for layer, value in enumerate(transformer_sampling):
if not isinstance(value, int):
raise ValueError("Invalid value in transformer_sampling: ")
if value < 1:
raise ValueError(
"{} layer's subsampling is {}.".format(layer, value)
+ " This is not allowed! "
)
return transformer_sampling
def slice(self, embedding, padding_mask, attn_mask, sampling_factor):
"""
embedding is a (T, B, D) tensor
padding_mask is a (B, T) tensor or None
attn_mask is a (T, T) tensor or None
"""
embedding = embedding[::sampling_factor, :, :]
if padding_mask is not None:
padding_mask = padding_mask[:, ::sampling_factor]
if attn_mask is not None:
attn_mask = attn_mask[::sampling_factor, ::sampling_factor]
return embedding, padding_mask, attn_mask
def lengths_to_attn_mask(self, input_lengths, subsampling_factor=1):
"""
create attention mask according to sequence lengths and transformer
context
Args:
- input_lengths: (B, )-shape Int/Long tensor; input_lengths[b] is
the length of b-th sequence
- subsampling_factor: int
* Note that the left_context and right_context is specified in
the input frame-level while input to transformer may already
go through subsampling (e.g., the use of striding in vggblock)
we use subsampling_factor to scale the left/right context
Return:
- a (T, T) binary tensor or None, where T is max(input_lengths)
* if self.transformer_context is None, None
* if left_context is None,
* attn_mask[t, t + right_context + 1:] = 1
* others = 0
* if right_context is None,
* attn_mask[t, 0:t - left_context] = 1
* others = 0
* elsif
* attn_mask[t, t - left_context: t + right_context + 1] = 0
* others = 1
"""
if self.transformer_context is None:
return None
maxT = torch.max(input_lengths).item()
attn_mask = torch.zeros(maxT, maxT)
left_context = self.transformer_context[0]
right_context = self.transformer_context[1]
if left_context is not None:
left_context = math.ceil(self.transformer_context[0] / subsampling_factor)
if right_context is not None:
right_context = math.ceil(self.transformer_context[1] / subsampling_factor)
for t in range(maxT):
if left_context is not None:
st = 0
en = max(st, t - left_context)
attn_mask[t, st:en] = 1
if right_context is not None:
st = t + right_context + 1
st = min(st, maxT - 1)
attn_mask[t, st:] = 1
return attn_mask.to(input_lengths.device)
def reorder_encoder_out(self, encoder_out, new_order):
encoder_out["encoder_out"] = encoder_out["encoder_out"].index_select(
1, new_order
)
if encoder_out["encoder_padding_mask"] is not None:
encoder_out["encoder_padding_mask"] = encoder_out[
"encoder_padding_mask"
].index_select(1, new_order)
return encoder_out
class TransformerDecoder(FairseqIncrementalDecoder):
"""
Transformer decoder consisting of *args.decoder_layers* layers. Each layer
is a :class:`TransformerDecoderLayer`.
Args:
args (argparse.Namespace): parsed command-line arguments
dictionary (~fairseq.data.Dictionary): decoding dictionary
embed_tokens (torch.nn.Embedding): output embedding
no_encoder_attn (bool, optional): whether to attend to encoder outputs.
Default: ``False``
left_pad (bool, optional): whether the input is left-padded. Default:
``False``
"""
def __init__(
self,
dictionary,
embed_dim=512,
transformer_config=DEFAULT_ENC_TRANSFORMER_CONFIG,
conv_config=DEFAULT_DEC_CONV_CONFIG,
encoder_output_dim=512,
):
super().__init__(dictionary)
vocab_size = len(dictionary)
self.padding_idx = dictionary.pad()
self.embed_tokens = Embedding(vocab_size, embed_dim, self.padding_idx)
self.conv_layers = nn.ModuleList()
for i in range(len(conv_config)):
out_channels, kernel_size, layer_norm = conv_config[i]
if i == 0:
conv_layer = LinearizedConv1d(
embed_dim, out_channels, kernel_size, padding=kernel_size - 1
)
else:
conv_layer = LinearizedConv1d(
conv_config[i - 1][0],
out_channels,
kernel_size,
padding=kernel_size - 1,
)
self.conv_layers.append(conv_layer)
if layer_norm:
self.conv_layers.append(nn.LayerNorm(out_channels))
self.conv_layers.append(nn.ReLU())
self.layers = nn.ModuleList()
if conv_config[-1][0] != transformer_config[0][0]:
self.layers.append(Linear(conv_config[-1][0], transformer_config[0][0]))
self.layers.append(
TransformerDecoderLayer(
prepare_transformer_decoder_params(*transformer_config[0])
)
)
for i in range(1, len(transformer_config)):
if transformer_config[i - 1][0] != transformer_config[i][0]:
self.layers.append(
Linear(transformer_config[i - 1][0], transformer_config[i][0])
)
self.layers.append(
TransformerDecoderLayer(
prepare_transformer_decoder_params(*transformer_config[i])
)
)
self.fc_out = Linear(transformer_config[-1][0], vocab_size)
def forward(self, prev_output_tokens, encoder_out=None, incremental_state=None):
"""
Args:
prev_output_tokens (LongTensor): previous decoder outputs of shape
`(batch, tgt_len)`, for input feeding/teacher forcing
encoder_out (Tensor, optional): output from the encoder, used for
encoder-side attention
incremental_state (dict): dictionary used for storing state during
:ref:`Incremental decoding`
Returns:
tuple:
- the last decoder layer's output of shape `(batch, tgt_len,
vocab)`
- the last decoder layer's attention weights of shape `(batch,
tgt_len, src_len)`
"""
target_padding_mask = (
(prev_output_tokens == self.padding_idx).to(prev_output_tokens.device)
if incremental_state is None
else None
)
if incremental_state is not None:
prev_output_tokens = prev_output_tokens[:, -1:]
# embed tokens
x = self.embed_tokens(prev_output_tokens)
# B x T x C -> T x B x C
x = self._transpose_if_training(x, incremental_state)
for layer in self.conv_layers:
if isinstance(layer, LinearizedConvolution):
x = layer(x, incremental_state)
else:
x = layer(x)
# B x T x C -> T x B x C
x = self._transpose_if_inference(x, incremental_state)
# decoder layers
for layer in self.layers:
if isinstance(layer, TransformerDecoderLayer):
x, *_ = layer(
x,
(encoder_out["encoder_out"] if encoder_out is not None else None),
(
encoder_out["encoder_padding_mask"].t()
if encoder_out["encoder_padding_mask"] is not None
else None
),
incremental_state,
self_attn_mask=(
self.buffered_future_mask(x)
if incremental_state is None
else None
),
self_attn_padding_mask=(
target_padding_mask if incremental_state is None else None
),
)
else:
x = layer(x)
# T x B x C -> B x T x C
x = x.transpose(0, 1)
x = self.fc_out(x)
return x, None
def buffered_future_mask(self, tensor):
dim = tensor.size(0)
if (
not hasattr(self, "_future_mask")
or self._future_mask is None
or self._future_mask.device != tensor.device
):
self._future_mask = torch.triu(
utils.fill_with_neg_inf(tensor.new(dim, dim)), 1
)
if self._future_mask.size(0) < dim:
self._future_mask = torch.triu(
utils.fill_with_neg_inf(self._future_mask.resize_(dim, dim)), 1
)
return self._future_mask[:dim, :dim]
def _transpose_if_training(self, x, incremental_state):
if incremental_state is None:
x = x.transpose(0, 1)
return x
def _transpose_if_inference(self, x, incremental_state):
if incremental_state:
x = x.transpose(0, 1)
return x
@register_model("asr_vggtransformer_encoder")
class VGGTransformerEncoderModel(FairseqEncoderModel):
def __init__(self, encoder):
super().__init__(encoder)
@staticmethod
def add_args(parser):
"""Add model-specific arguments to the parser."""
parser.add_argument(
"--input-feat-per-channel",
type=int,
metavar="N",
help="encoder input dimension per input channel",
)
parser.add_argument(
"--vggblock-enc-config",
type=str,
metavar="EXPR",
help="""
an array of tuples each containing the configuration of one vggblock
[(out_channels, conv_kernel_size, pooling_kernel_size,num_conv_layers), ...]
""",
)
parser.add_argument(
"--transformer-enc-config",
type=str,
metavar="EXPR",
help="""
a tuple containing the configuration of the Transformer layers
configurations:
[(input_dim,
num_heads,
ffn_dim,
normalize_before,
dropout,
attention_dropout,
relu_dropout), ]""",
)
parser.add_argument(
"--enc-output-dim",
type=int,
metavar="N",
help="encoder output dimension, projecting the LSTM output",
)
parser.add_argument(
"--in-channels",
type=int,
metavar="N",
help="number of encoder input channels",
)
parser.add_argument(
"--transformer-context",
type=str,
metavar="EXPR",
help="""
either None or a tuple of two ints, indicating left/right context a
transformer can have access to""",
)
parser.add_argument(
"--transformer-sampling",
type=str,
metavar="EXPR",
help="""
either None or a tuple of ints, indicating sampling factor in each layer""",
)
@classmethod
def build_model(cls, args, task):
"""Build a new model instance."""
base_architecture_enconly(args)
encoder = VGGTransformerEncoderOnly(
vocab_size=len(task.target_dictionary),
input_feat_per_channel=args.input_feat_per_channel,
vggblock_config=eval(args.vggblock_enc_config),
transformer_config=eval(args.transformer_enc_config),
encoder_output_dim=args.enc_output_dim,
in_channels=args.in_channels,
transformer_context=eval(args.transformer_context),
transformer_sampling=eval(args.transformer_sampling),
)
return cls(encoder)
def get_normalized_probs(self, net_output, log_probs, sample=None):
# net_output['encoder_out'] is a (T, B, D) tensor
lprobs = super().get_normalized_probs(net_output, log_probs, sample)
# lprobs is a (T, B, D) tensor
# we need to transoose to get (B, T, D) tensor
lprobs = lprobs.transpose(0, 1).contiguous()
lprobs.batch_first = True
return lprobs
class VGGTransformerEncoderOnly(VGGTransformerEncoder):
def __init__(
self,
vocab_size,
input_feat_per_channel,
vggblock_config=DEFAULT_ENC_VGGBLOCK_CONFIG,
transformer_config=DEFAULT_ENC_TRANSFORMER_CONFIG,
encoder_output_dim=512,
in_channels=1,
transformer_context=None,
transformer_sampling=None,
):
super().__init__(
input_feat_per_channel=input_feat_per_channel,
vggblock_config=vggblock_config,
transformer_config=transformer_config,
encoder_output_dim=encoder_output_dim,
in_channels=in_channels,
transformer_context=transformer_context,
transformer_sampling=transformer_sampling,
)
self.fc_out = Linear(self.encoder_output_dim, vocab_size)
def forward(self, src_tokens, src_lengths, **kwargs):
"""
src_tokens: padded tensor (B, T, C * feat)
src_lengths: tensor of original lengths of input utterances (B,)
"""
enc_out = super().forward(src_tokens, src_lengths)
x = self.fc_out(enc_out["encoder_out"])
# x = F.log_softmax(x, dim=-1)
# Note: no need this line, because model.get_normalized_prob will call
# log_softmax
return {
"encoder_out": x, # (T, B, C)
"encoder_padding_mask": enc_out["encoder_padding_mask"], # (T, B)
}
def max_positions(self):
"""Maximum input length supported by the encoder."""
return (1e6, 1e6) # an arbitrary large number
def Embedding(num_embeddings, embedding_dim, padding_idx):
m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx)
# nn.init.uniform_(m.weight, -0.1, 0.1)
# nn.init.constant_(m.weight[padding_idx], 0)
return m
def Linear(in_features, out_features, bias=True, dropout=0):
"""Linear layer (input: N x T x C)"""
m = nn.Linear(in_features, out_features, bias=bias)
# m.weight.data.uniform_(-0.1, 0.1)
# if bias:
# m.bias.data.uniform_(-0.1, 0.1)
return m
def LinearizedConv1d(in_channels, out_channels, kernel_size, dropout=0, **kwargs):
"""Weight-normalized Conv1d layer optimized for decoding"""
m = LinearizedConvolution(in_channels, out_channels, kernel_size, **kwargs)
std = math.sqrt((4 * (1.0 - dropout)) / (m.kernel_size[0] * in_channels))
nn.init.normal_(m.weight, mean=0, std=std)
nn.init.constant_(m.bias, 0)
return nn.utils.weight_norm(m, dim=2)
def LayerNorm(embedding_dim):
m = nn.LayerNorm(embedding_dim)
return m
# seq2seq models
def base_architecture(args):
args.input_feat_per_channel = getattr(args, "input_feat_per_channel", 40)
args.vggblock_enc_config = getattr(
args, "vggblock_enc_config", DEFAULT_ENC_VGGBLOCK_CONFIG
)
args.transformer_enc_config = getattr(
args, "transformer_enc_config", DEFAULT_ENC_TRANSFORMER_CONFIG
)
args.enc_output_dim = getattr(args, "enc_output_dim", 512)
args.in_channels = getattr(args, "in_channels", 1)
args.tgt_embed_dim = getattr(args, "tgt_embed_dim", 128)
args.transformer_dec_config = getattr(
args, "transformer_dec_config", DEFAULT_ENC_TRANSFORMER_CONFIG
)
args.conv_dec_config = getattr(args, "conv_dec_config", DEFAULT_DEC_CONV_CONFIG)
args.transformer_context = getattr(args, "transformer_context", "None")
@register_model_architecture("asr_vggtransformer", "vggtransformer_1")
def vggtransformer_1(args):
args.input_feat_per_channel = getattr(args, "input_feat_per_channel", 80)
args.vggblock_enc_config = getattr(
args, "vggblock_enc_config", "[(64, 3, 2, 2, True), (128, 3, 2, 2, True)]"
)
args.transformer_enc_config = getattr(
args,
"transformer_enc_config",
"((1024, 16, 4096, True, 0.15, 0.15, 0.15),) * 14",
)
args.enc_output_dim = getattr(args, "enc_output_dim", 1024)
args.tgt_embed_dim = getattr(args, "tgt_embed_dim", 128)
args.conv_dec_config = getattr(args, "conv_dec_config", "((256, 3, True),) * 4")
args.transformer_dec_config = getattr(
args,
"transformer_dec_config",
"((1024, 16, 4096, True, 0.15, 0.15, 0.15),) * 4",
)
@register_model_architecture("asr_vggtransformer", "vggtransformer_2")
def vggtransformer_2(args):
args.input_feat_per_channel = getattr(args, "input_feat_per_channel", 80)
args.vggblock_enc_config = getattr(
args, "vggblock_enc_config", "[(64, 3, 2, 2, True), (128, 3, 2, 2, True)]"
)
args.transformer_enc_config = getattr(
args,
"transformer_enc_config",
"((1024, 16, 4096, True, 0.15, 0.15, 0.15),) * 16",
)
args.enc_output_dim = getattr(args, "enc_output_dim", 1024)
args.tgt_embed_dim = getattr(args, "tgt_embed_dim", 512)
args.conv_dec_config = getattr(args, "conv_dec_config", "((256, 3, True),) * 4")
args.transformer_dec_config = getattr(
args,
"transformer_dec_config",
"((1024, 16, 4096, True, 0.15, 0.15, 0.15),) * 6",
)
@register_model_architecture("asr_vggtransformer", "vggtransformer_base")
def vggtransformer_base(args):
args.input_feat_per_channel = getattr(args, "input_feat_per_channel", 80)
args.vggblock_enc_config = getattr(
args, "vggblock_enc_config", "[(64, 3, 2, 2, True), (128, 3, 2, 2, True)]"
)
args.transformer_enc_config = getattr(
args, "transformer_enc_config", "((512, 8, 2048, True, 0.15, 0.15, 0.15),) * 12"
)
args.enc_output_dim = getattr(args, "enc_output_dim", 512)
args.tgt_embed_dim = getattr(args, "tgt_embed_dim", 512)
args.conv_dec_config = getattr(args, "conv_dec_config", "((256, 3, True),) * 4")
args.transformer_dec_config = getattr(
args, "transformer_dec_config", "((512, 8, 2048, True, 0.15, 0.15, 0.15),) * 6"
)
# Size estimations:
# Encoder:
# - vggblock param: 64*1*3*3 + 64*64*3*3 + 128*64*3*3 + 128*128*3 = 258K
# Transformer:
# - input dimension adapter: 2560 x 512 -> 1.31M
# - transformer_layers (x12) --> 37.74M
# * MultiheadAttention: 512*512*3 (in_proj) + 512*512 (out_proj) = 1.048M
# * FFN weight: 512*2048*2 = 2.097M
# - output dimension adapter: 512 x 512 -> 0.26 M
# Decoder:
# - LinearizedConv1d: 512 * 256 * 3 + 256 * 256 * 3 * 3
# - transformer_layer: (x6) --> 25.16M
# * MultiheadAttention (self-attention): 512*512*3 + 512*512 = 1.048M
# * MultiheadAttention (encoder-attention): 512*512*3 + 512*512 = 1.048M
# * FFN: 512*2048*2 = 2.097M
# Final FC:
# - FC: 512*5000 = 256K (assuming vocab size 5K)
# In total:
# ~65 M
# CTC models
def base_architecture_enconly(args):
args.input_feat_per_channel = getattr(args, "input_feat_per_channel", 40)
args.vggblock_enc_config = getattr(
args, "vggblock_enc_config", "[(32, 3, 2, 2, True)] * 2"
)
args.transformer_enc_config = getattr(
args, "transformer_enc_config", "((256, 4, 1024, True, 0.2, 0.2, 0.2),) * 2"
)
args.enc_output_dim = getattr(args, "enc_output_dim", 512)
args.in_channels = getattr(args, "in_channels", 1)
args.transformer_context = getattr(args, "transformer_context", "None")
args.transformer_sampling = getattr(args, "transformer_sampling", "None")
@register_model_architecture("asr_vggtransformer_encoder", "vggtransformer_enc_1")
def vggtransformer_enc_1(args):
# vggtransformer_1 is the same as vggtransformer_enc_big, except the number
# of layers is increased to 16
# keep it here for backward compatiablity purpose
args.input_feat_per_channel = getattr(args, "input_feat_per_channel", 80)
args.vggblock_enc_config = getattr(
args, "vggblock_enc_config", "[(64, 3, 2, 2, True), (128, 3, 2, 2, True)]"
)
args.transformer_enc_config = getattr(
args,
"transformer_enc_config",
"((1024, 16, 4096, True, 0.15, 0.15, 0.15),) * 16",
)
args.enc_output_dim = getattr(args, "enc_output_dim", 1024)
| 37,258 | 35.564279 | 88 | py |
CIF-HieraDist | CIF-HieraDist-main/examples/speech_recognition/models/w2l_conv_glu_enc.py | #!/usr/bin/env python3
# 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 math
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq.models import (
FairseqEncoder,
FairseqEncoderModel,
register_model,
register_model_architecture,
)
from fairseq.modules.fairseq_dropout import FairseqDropout
default_conv_enc_config = """[
(400, 13, 170, 0.2),
(440, 14, 0, 0.214),
(484, 15, 0, 0.22898),
(532, 16, 0, 0.2450086),
(584, 17, 0, 0.262159202),
(642, 18, 0, 0.28051034614),
(706, 19, 0, 0.30014607037),
(776, 20, 0, 0.321156295296),
(852, 21, 0, 0.343637235966),
(936, 22, 0, 0.367691842484),
(1028, 23, 0, 0.393430271458),
(1130, 24, 0, 0.42097039046),
(1242, 25, 0, 0.450438317792),
(1366, 26, 0, 0.481969000038),
(1502, 27, 0, 0.51570683004),
(1652, 28, 0, 0.551806308143),
(1816, 29, 0, 0.590432749713),
]"""
@register_model("asr_w2l_conv_glu_encoder")
class W2lConvGluEncoderModel(FairseqEncoderModel):
def __init__(self, encoder):
super().__init__(encoder)
@staticmethod
def add_args(parser):
"""Add model-specific arguments to the parser."""
parser.add_argument(
"--input-feat-per-channel",
type=int,
metavar="N",
help="encoder input dimension per input channel",
)
parser.add_argument(
"--in-channels",
type=int,
metavar="N",
help="number of encoder input channels",
)
parser.add_argument(
"--conv-enc-config",
type=str,
metavar="EXPR",
help="""
an array of tuples each containing the configuration of one conv layer
[(out_channels, kernel_size, padding, dropout), ...]
""",
)
@classmethod
def build_model(cls, args, task):
"""Build a new model instance."""
conv_enc_config = getattr(args, "conv_enc_config", default_conv_enc_config)
encoder = W2lConvGluEncoder(
vocab_size=len(task.target_dictionary),
input_feat_per_channel=args.input_feat_per_channel,
in_channels=args.in_channels,
conv_enc_config=eval(conv_enc_config),
)
return cls(encoder)
def get_normalized_probs(self, net_output, log_probs, sample=None):
lprobs = super().get_normalized_probs(net_output, log_probs, sample)
lprobs.batch_first = False
return lprobs
class W2lConvGluEncoder(FairseqEncoder):
def __init__(
self, vocab_size, input_feat_per_channel, in_channels, conv_enc_config
):
super().__init__(None)
self.input_dim = input_feat_per_channel
if in_channels != 1:
raise ValueError("only 1 input channel is currently supported")
self.conv_layers = nn.ModuleList()
self.linear_layers = nn.ModuleList()
self.dropouts = []
cur_channels = input_feat_per_channel
for out_channels, kernel_size, padding, dropout in conv_enc_config:
layer = nn.Conv1d(cur_channels, out_channels, kernel_size, padding=padding)
layer.weight.data.mul_(math.sqrt(3)) # match wav2letter init
self.conv_layers.append(nn.utils.weight_norm(layer))
self.dropouts.append(
FairseqDropout(dropout, module_name=self.__class__.__name__)
)
if out_channels % 2 != 0:
raise ValueError("odd # of out_channels is incompatible with GLU")
cur_channels = out_channels // 2 # halved by GLU
for out_channels in [2 * cur_channels, vocab_size]:
layer = nn.Linear(cur_channels, out_channels)
layer.weight.data.mul_(math.sqrt(3))
self.linear_layers.append(nn.utils.weight_norm(layer))
cur_channels = out_channels // 2
def forward(self, src_tokens, src_lengths, **kwargs):
"""
src_tokens: padded tensor (B, T, C * feat)
src_lengths: tensor of original lengths of input utterances (B,)
"""
B, T, _ = src_tokens.size()
x = src_tokens.transpose(1, 2).contiguous() # (B, feat, T) assuming C == 1
for layer_idx in range(len(self.conv_layers)):
x = self.conv_layers[layer_idx](x)
x = F.glu(x, dim=1)
x = self.dropouts[layer_idx](x)
x = x.transpose(1, 2).contiguous() # (B, T, 908)
x = self.linear_layers[0](x)
x = F.glu(x, dim=2)
x = self.dropouts[-1](x)
x = self.linear_layers[1](x)
assert x.size(0) == B
assert x.size(1) == T
encoder_out = x.transpose(0, 1) # (T, B, vocab_size)
# need to debug this -- find a simpler/elegant way in pytorch APIs
encoder_padding_mask = (
torch.arange(T).view(1, T).expand(B, -1).to(x.device)
>= src_lengths.view(B, 1).expand(-1, T)
).t() # (B x T) -> (T x B)
return {
"encoder_out": encoder_out, # (T, B, vocab_size)
"encoder_padding_mask": encoder_padding_mask, # (T, B)
}
def reorder_encoder_out(self, encoder_out, new_order):
encoder_out["encoder_out"] = encoder_out["encoder_out"].index_select(
1, new_order
)
encoder_out["encoder_padding_mask"] = encoder_out[
"encoder_padding_mask"
].index_select(1, new_order)
return encoder_out
def max_positions(self):
"""Maximum input length supported by the encoder."""
return (1e6, 1e6) # an arbitrary large number
@register_model_architecture("asr_w2l_conv_glu_encoder", "w2l_conv_glu_enc")
def w2l_conv_glu_enc(args):
args.input_feat_per_channel = getattr(args, "input_feat_per_channel", 80)
args.in_channels = getattr(args, "in_channels", 1)
args.conv_enc_config = getattr(args, "conv_enc_config", default_conv_enc_config)
| 6,077 | 33.338983 | 87 | py |
CIF-HieraDist | CIF-HieraDist-main/examples/speech_recognition/datasets/asr_prep_json.py | #!/usr/bin/env python3
# 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.
from __future__ import absolute_import, division, print_function, unicode_literals
import argparse
import concurrent.futures
import json
import multiprocessing
import os
from collections import namedtuple
from itertools import chain
import sentencepiece as spm
from fairseq.data import Dictionary
MILLISECONDS_TO_SECONDS = 0.001
def process_sample(aud_path, lable, utt_id, sp, tgt_dict):
import torchaudio
input = {}
output = {}
si, ei = torchaudio.info(aud_path)
input["length_ms"] = int(
si.length / si.channels / si.rate / MILLISECONDS_TO_SECONDS
)
input["path"] = aud_path
token = " ".join(sp.EncodeAsPieces(lable))
ids = tgt_dict.encode_line(token, append_eos=False)
output["text"] = lable
output["token"] = token
output["tokenid"] = ", ".join(map(str, [t.tolist() for t in ids]))
return {utt_id: {"input": input, "output": output}}
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--audio-dirs",
nargs="+",
default=["-"],
required=True,
help="input directories with audio files",
)
parser.add_argument(
"--labels",
required=True,
help="aggregated input labels with format <ID LABEL> per line",
type=argparse.FileType("r", encoding="UTF-8"),
)
parser.add_argument(
"--spm-model",
required=True,
help="sentencepiece model to use for encoding",
type=argparse.FileType("r", encoding="UTF-8"),
)
parser.add_argument(
"--dictionary",
required=True,
help="file to load fairseq dictionary from",
type=argparse.FileType("r", encoding="UTF-8"),
)
parser.add_argument("--audio-format", choices=["flac", "wav"], default="wav")
parser.add_argument(
"--output",
required=True,
type=argparse.FileType("w"),
help="path to save json output",
)
args = parser.parse_args()
sp = spm.SentencePieceProcessor()
sp.Load(args.spm_model.name)
tgt_dict = Dictionary.load(args.dictionary)
labels = {}
for line in args.labels:
(utt_id, label) = line.split(" ", 1)
labels[utt_id] = label
if len(labels) == 0:
raise Exception("No labels found in ", args.labels_path)
Sample = namedtuple("Sample", "aud_path utt_id")
samples = []
for path, _, files in chain.from_iterable(
os.walk(path) for path in args.audio_dirs
):
for f in files:
if f.endswith(args.audio_format):
if len(os.path.splitext(f)) != 2:
raise Exception("Expect <utt_id.extension> file name. Got: ", f)
utt_id = os.path.splitext(f)[0]
if utt_id not in labels:
continue
samples.append(Sample(os.path.join(path, f), utt_id))
utts = {}
num_cpu = multiprocessing.cpu_count()
with concurrent.futures.ThreadPoolExecutor(max_workers=num_cpu) as executor:
future_to_sample = {
executor.submit(
process_sample, s.aud_path, labels[s.utt_id], s.utt_id, sp, tgt_dict
): s
for s in samples
}
for future in concurrent.futures.as_completed(future_to_sample):
try:
data = future.result()
except Exception as exc:
print("generated an exception: ", exc)
else:
utts.update(data)
json.dump({"utts": utts}, args.output, indent=4)
if __name__ == "__main__":
main()
| 3,775 | 28.968254 | 84 | py |
CIF-HieraDist | CIF-HieraDist-main/examples/speech_recognition/new/infer.py | #!/usr/bin/env python -u
# 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 hashlib
import logging
import os
import shutil
import sys
from dataclasses import dataclass, field, is_dataclass
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple, Union
import editdistance
import torch
import torch.distributed as dist
from examples.speech_recognition.new.decoders.decoder_config import (
DecoderConfig,
FlashlightDecoderConfig,
)
from examples.speech_recognition.new.decoders.decoder import Decoder
from fairseq import checkpoint_utils, distributed_utils, progress_bar, tasks, utils
from fairseq.data.data_utils import post_process
from fairseq.dataclass.configs import (
CheckpointConfig,
CommonConfig,
CommonEvalConfig,
DatasetConfig,
DistributedTrainingConfig,
FairseqDataclass,
)
from fairseq.logging.meters import StopwatchMeter, TimeMeter
from fairseq.logging.progress_bar import BaseProgressBar
from fairseq.models.fairseq_model import FairseqModel
from omegaconf import OmegaConf
import hydra
from hydra.core.config_store import ConfigStore
logging.root.setLevel(logging.INFO)
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
config_path = Path(__file__).resolve().parent / "conf"
@dataclass
class DecodingConfig(DecoderConfig, FlashlightDecoderConfig):
unique_wer_file: bool = field(
default=False,
metadata={"help": "If set, use a unique file for storing WER"},
)
results_path: Optional[str] = field(
default=None,
metadata={
"help": "If set, write hypothesis and reference sentences into this directory"
},
)
@dataclass
class InferConfig(FairseqDataclass):
task: Any = None
decoding: DecodingConfig = DecodingConfig()
common: CommonConfig = CommonConfig()
common_eval: CommonEvalConfig = CommonEvalConfig()
checkpoint: CheckpointConfig = CheckpointConfig()
distributed_training: DistributedTrainingConfig = DistributedTrainingConfig()
dataset: DatasetConfig = DatasetConfig()
is_ax: bool = field(
default=False,
metadata={
"help": "if true, assumes we are using ax for tuning and returns a tuple for ax to consume"
},
)
def reset_logging():
root = logging.getLogger()
for handler in root.handlers:
root.removeHandler(handler)
root.setLevel(os.environ.get("LOGLEVEL", "INFO").upper())
handler = logging.StreamHandler(sys.stdout)
handler.setFormatter(
logging.Formatter(
fmt="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
)
)
root.addHandler(handler)
class InferenceProcessor:
cfg: InferConfig
def __init__(self, cfg: InferConfig) -> None:
self.cfg = cfg
self.task = tasks.setup_task(cfg.task)
models, saved_cfg = self.load_model_ensemble()
self.models = models
self.saved_cfg = saved_cfg
self.tgt_dict = self.task.target_dictionary
self.task.load_dataset(
self.cfg.dataset.gen_subset,
task_cfg=saved_cfg.task,
)
self.generator = Decoder(cfg.decoding, self.tgt_dict)
self.gen_timer = StopwatchMeter()
self.wps_meter = TimeMeter()
self.num_sentences = 0
self.total_errors = 0
self.total_length = 0
self.hypo_words_file = None
self.hypo_units_file = None
self.ref_words_file = None
self.ref_units_file = None
self.progress_bar = self.build_progress_bar()
def __enter__(self) -> "InferenceProcessor":
if self.cfg.decoding.results_path is not None:
self.hypo_words_file = self.get_res_file("hypo.word")
self.hypo_units_file = self.get_res_file("hypo.units")
self.ref_words_file = self.get_res_file("ref.word")
self.ref_units_file = self.get_res_file("ref.units")
return self
def __exit__(self, *exc) -> bool:
if self.cfg.decoding.results_path is not None:
self.hypo_words_file.close()
self.hypo_units_file.close()
self.ref_words_file.close()
self.ref_units_file.close()
return False
def __iter__(self) -> Any:
for sample in self.progress_bar:
if not self.cfg.common.cpu:
sample = utils.move_to_cuda(sample)
# Happens on the last batch.
if "net_input" not in sample:
continue
yield sample
def log(self, *args, **kwargs):
self.progress_bar.log(*args, **kwargs)
def print(self, *args, **kwargs):
self.progress_bar.print(*args, **kwargs)
def get_res_file(self, fname: str) -> None:
fname = os.path.join(self.cfg.decoding.results_path, fname)
if self.data_parallel_world_size > 1:
fname = f"{fname}.{self.data_parallel_rank}"
return open(fname, "w", buffering=1)
def merge_shards(self) -> None:
"""Merges all shard files into shard 0, then removes shard suffix."""
shard_id = self.data_parallel_rank
num_shards = self.data_parallel_world_size
if self.data_parallel_world_size > 1:
def merge_shards_with_root(fname: str) -> None:
fname = os.path.join(self.cfg.decoding.results_path, fname)
logger.info("Merging %s on shard %d", fname, shard_id)
base_fpath = Path(f"{fname}.0")
with open(base_fpath, "a") as out_file:
for s in range(1, num_shards):
shard_fpath = Path(f"{fname}.{s}")
with open(shard_fpath, "r") as in_file:
for line in in_file:
out_file.write(line)
shard_fpath.unlink()
shutil.move(f"{fname}.0", fname)
dist.barrier() # ensure all shards finished writing
if shard_id == (0 % num_shards):
merge_shards_with_root("hypo.word")
if shard_id == (1 % num_shards):
merge_shards_with_root("hypo.units")
if shard_id == (2 % num_shards):
merge_shards_with_root("ref.word")
if shard_id == (3 % num_shards):
merge_shards_with_root("ref.units")
dist.barrier()
def optimize_model(self, model: FairseqModel) -> None:
model.make_generation_fast_()
if self.cfg.common.fp16:
model.half()
if not self.cfg.common.cpu:
model.cuda()
def load_model_ensemble(self) -> Tuple[List[FairseqModel], FairseqDataclass]:
arg_overrides = ast.literal_eval(self.cfg.common_eval.model_overrides)
models, saved_cfg = checkpoint_utils.load_model_ensemble(
utils.split_paths(self.cfg.common_eval.path, separator="\\"),
arg_overrides=arg_overrides,
task=self.task,
suffix=self.cfg.checkpoint.checkpoint_suffix,
strict=(self.cfg.checkpoint.checkpoint_shard_count == 1),
num_shards=self.cfg.checkpoint.checkpoint_shard_count,
)
for model in models:
self.optimize_model(model)
return models, saved_cfg
def get_dataset_itr(self, disable_iterator_cache: bool = False) -> None:
return self.task.get_batch_iterator(
dataset=self.task.dataset(self.cfg.dataset.gen_subset),
max_tokens=self.cfg.dataset.max_tokens,
max_sentences=self.cfg.dataset.batch_size,
max_positions=(sys.maxsize, sys.maxsize),
ignore_invalid_inputs=self.cfg.dataset.skip_invalid_size_inputs_valid_test,
required_batch_size_multiple=self.cfg.dataset.required_batch_size_multiple,
seed=self.cfg.common.seed,
num_shards=self.data_parallel_world_size,
shard_id=self.data_parallel_rank,
num_workers=self.cfg.dataset.num_workers,
data_buffer_size=self.cfg.dataset.data_buffer_size,
disable_iterator_cache=disable_iterator_cache,
).next_epoch_itr(shuffle=False)
def build_progress_bar(
self,
epoch: Optional[int] = None,
prefix: Optional[str] = None,
default_log_format: str = "tqdm",
) -> BaseProgressBar:
return progress_bar.progress_bar(
iterator=self.get_dataset_itr(),
log_format=self.cfg.common.log_format,
log_interval=self.cfg.common.log_interval,
epoch=epoch,
prefix=prefix,
tensorboard_logdir=self.cfg.common.tensorboard_logdir,
default_log_format=default_log_format,
)
@property
def data_parallel_world_size(self):
if self.cfg.distributed_training.distributed_world_size == 1:
return 1
return distributed_utils.get_data_parallel_world_size()
@property
def data_parallel_rank(self):
if self.cfg.distributed_training.distributed_world_size == 1:
return 0
return distributed_utils.get_data_parallel_rank()
def process_sentence(
self,
sample: Dict[str, Any],
hypo: Dict[str, Any],
sid: int,
batch_id: int,
) -> Tuple[int, int]:
speaker = None # Speaker can't be parsed from dataset.
if "target_label" in sample:
toks = sample["target_label"]
else:
toks = sample["target"]
toks = toks[batch_id, :]
# Processes hypothesis.
hyp_pieces = self.tgt_dict.string(hypo["tokens"].int().cpu())
if "words" in hypo:
hyp_words = " ".join(hypo["words"])
else:
hyp_words = post_process(hyp_pieces, self.cfg.common_eval.post_process)
# Processes target.
target_tokens = utils.strip_pad(toks, self.tgt_dict.pad())
tgt_pieces = self.tgt_dict.string(target_tokens.int().cpu())
tgt_words = post_process(tgt_pieces, self.cfg.common_eval.post_process)
if self.cfg.decoding.results_path is not None:
print(f"{hyp_pieces} ({speaker}-{sid})", file=self.hypo_units_file)
print(f"{hyp_words} ({speaker}-{sid})", file=self.hypo_words_file)
print(f"{tgt_pieces} ({speaker}-{sid})", file=self.ref_units_file)
print(f"{tgt_words} ({speaker}-{sid})", file=self.ref_words_file)
if not self.cfg.common_eval.quiet:
logger.info(f"HYPO: {hyp_words}")
logger.info(f"REF: {tgt_words}")
logger.info("---------------------")
hyp_words, tgt_words = hyp_words.split(), tgt_words.split()
return editdistance.eval(hyp_words, tgt_words), len(tgt_words)
def process_sample(self, sample: Dict[str, Any]) -> None:
self.gen_timer.start()
hypos = self.task.inference_step(
generator=self.generator,
models=self.models,
sample=sample,
)
num_generated_tokens = sum(len(h[0]["tokens"]) for h in hypos)
self.gen_timer.stop(num_generated_tokens)
self.wps_meter.update(num_generated_tokens)
for batch_id, sample_id in enumerate(sample["id"].tolist()):
errs, length = self.process_sentence(
sample=sample,
sid=sample_id,
batch_id=batch_id,
hypo=hypos[batch_id][0],
)
self.total_errors += errs
self.total_length += length
self.log({"wps": round(self.wps_meter.avg)})
if "nsentences" in sample:
self.num_sentences += sample["nsentences"]
else:
self.num_sentences += sample["id"].numel()
def log_generation_time(self) -> None:
logger.info(
"Processed %d sentences (%d tokens) in %.1fs %.2f "
"sentences per second, %.2f tokens per second)",
self.num_sentences,
self.gen_timer.n,
self.gen_timer.sum,
self.num_sentences / self.gen_timer.sum,
1.0 / self.gen_timer.avg,
)
def parse_wer(wer_file: Path) -> float:
with open(wer_file, "r") as f:
return float(f.readline().strip().split(" ")[1])
def get_wer_file(cfg: InferConfig) -> Path:
"""Hashes the decoding parameters to a unique file ID."""
base_path = "wer"
if cfg.decoding.results_path is not None:
base_path = os.path.join(cfg.decoding.results_path, base_path)
if cfg.decoding.unique_wer_file:
yaml_str = OmegaConf.to_yaml(cfg.decoding)
fid = int(hashlib.md5(yaml_str.encode("utf-8")).hexdigest(), 16)
return Path(f"{base_path}.{fid % 1000000}")
else:
return Path(base_path)
def main(cfg: InferConfig) -> float:
"""Entry point for main processing logic.
Args:
cfg: The inferance configuration to use.
wer: Optional shared memory pointer for returning the WER. If not None,
the final WER value will be written here instead of being returned.
Returns:
The final WER if `wer` is None, otherwise None.
"""
yaml_str, wer_file = OmegaConf.to_yaml(cfg.decoding), get_wer_file(cfg)
# Validates the provided configuration.
if cfg.dataset.max_tokens is None and cfg.dataset.batch_size is None:
cfg.dataset.max_tokens = 4000000
if not cfg.common.cpu and not torch.cuda.is_available():
raise ValueError("CUDA not found; set `cpu=True` to run without CUDA")
with InferenceProcessor(cfg) as processor:
for sample in processor:
processor.process_sample(sample)
processor.log_generation_time()
if cfg.decoding.results_path is not None:
processor.merge_shards()
errs_t, leng_t = processor.total_errors, processor.total_length
if cfg.common.cpu:
logger.warning("Merging WER requires CUDA.")
elif processor.data_parallel_world_size > 1:
stats = torch.LongTensor([errs_t, leng_t]).cuda()
dist.all_reduce(stats, op=dist.ReduceOp.SUM)
errs_t, leng_t = stats[0].item(), stats[1].item()
wer = errs_t * 100.0 / leng_t
if distributed_utils.is_master(cfg.distributed_training):
with open(wer_file, "w") as f:
f.write(
(
f"WER: {wer}\n"
f"err / num_ref_words = {errs_t} / {leng_t}\n\n"
f"{yaml_str}"
)
)
return wer
@hydra.main(config_path=config_path, config_name="infer")
def hydra_main(cfg: InferConfig) -> Union[float, Tuple[float, Optional[float]]]:
container = OmegaConf.to_container(cfg, resolve=True, enum_to_str=True)
cfg = OmegaConf.create(container)
OmegaConf.set_struct(cfg, True)
if cfg.common.reset_logging:
reset_logging()
# logger.info("Config:\n%s", OmegaConf.to_yaml(cfg))
wer = float("inf")
try:
if cfg.common.profile:
with torch.cuda.profiler.profile():
with torch.autograd.profiler.emit_nvtx():
distributed_utils.call_main(cfg, main)
else:
distributed_utils.call_main(cfg, main)
wer = parse_wer(get_wer_file(cfg))
except BaseException as e: # pylint: disable=broad-except
if not cfg.common.suppress_crashes:
raise
else:
logger.error("Crashed! %s", str(e))
logger.info("Word error rate: %.4f", wer)
if cfg.is_ax:
return wer, None
return wer
def cli_main() -> None:
try:
from hydra._internal.utils import (
get_args,
) # pylint: disable=import-outside-toplevel
cfg_name = get_args().config_name or "infer"
except ImportError:
logger.warning("Failed to get config name from hydra args")
cfg_name = "infer"
cs = ConfigStore.instance()
cs.store(name=cfg_name, node=InferConfig)
for k in InferConfig.__dataclass_fields__:
if is_dataclass(InferConfig.__dataclass_fields__[k].type):
v = InferConfig.__dataclass_fields__[k].default
cs.store(name=k, node=v)
hydra_main() # pylint: disable=no-value-for-parameter
if __name__ == "__main__":
cli_main()
| 16,498 | 33.955508 | 103 | py |
CIF-HieraDist | CIF-HieraDist-main/examples/speech_recognition/new/decoders/base_decoder.py | # 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 itertools as it
from typing import Any, Dict, List
import torch
from fairseq.data.dictionary import Dictionary
from fairseq.models.fairseq_model import FairseqModel
class BaseDecoder:
def __init__(self, tgt_dict: Dictionary) -> None:
self.tgt_dict = tgt_dict
self.vocab_size = len(tgt_dict)
self.blank = (
tgt_dict.index("<ctc_blank>")
if "<ctc_blank>" in tgt_dict.indices
else tgt_dict.bos()
)
if "<sep>" in tgt_dict.indices:
self.silence = tgt_dict.index("<sep>")
elif "|" in tgt_dict.indices:
self.silence = tgt_dict.index("|")
else:
self.silence = tgt_dict.eos()
def generate(
self, models: List[FairseqModel], sample: Dict[str, Any], **unused
) -> List[List[Dict[str, torch.LongTensor]]]:
encoder_input = {
k: v for k, v in sample["net_input"].items() if k != "prev_output_tokens"
}
emissions = self.get_emissions(models, encoder_input)
return self.decode(emissions)
def get_emissions(
self,
models: List[FairseqModel],
encoder_input: Dict[str, Any],
) -> torch.FloatTensor:
model = models[0]
encoder_out = model(**encoder_input)
if hasattr(model, "get_logits"):
emissions = model.get_logits(encoder_out)
else:
emissions = model.get_normalized_probs(encoder_out, log_probs=True)
return emissions.transpose(0, 1).float().cpu().contiguous()
def get_tokens(self, idxs: torch.IntTensor) -> torch.LongTensor:
idxs = (g[0] for g in it.groupby(idxs))
idxs = filter(lambda x: x != self.blank, idxs)
return torch.LongTensor(list(idxs))
def decode(
self,
emissions: torch.FloatTensor,
) -> List[List[Dict[str, torch.LongTensor]]]:
raise NotImplementedError
| 2,093 | 32.238095 | 85 | py |
CIF-HieraDist | CIF-HieraDist-main/examples/speech_recognition/new/decoders/viterbi_decoder.py | #!/usr/bin/env python3
# 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 torch
from typing import List, Dict
from .base_decoder import BaseDecoder
class ViterbiDecoder(BaseDecoder):
def decode(
self,
emissions: torch.FloatTensor,
) -> List[List[Dict[str, torch.LongTensor]]]:
def get_pred(e):
toks = e.argmax(dim=-1).unique_consecutive()
return toks[toks != self.blank]
return [[{"tokens": get_pred(x), "score": 0}] for x in emissions]
| 641 | 24.68 | 73 | py |
CIF-HieraDist | CIF-HieraDist-main/examples/speech_recognition/new/decoders/flashlight_decoder.py | #!/usr/bin/env python3
# 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 gc
import os.path as osp
import warnings
from collections import deque, namedtuple
from typing import Any, Dict, Tuple
import numpy as np
import torch
from fairseq import tasks
from fairseq.data.dictionary import Dictionary
from fairseq.dataclass.utils import convert_namespace_to_omegaconf
from fairseq.models.fairseq_model import FairseqModel
from fairseq.utils import apply_to_sample
from omegaconf import open_dict, OmegaConf
from typing import List
from .decoder_config import FlashlightDecoderConfig
from .base_decoder import BaseDecoder
try:
from flashlight.lib.text.decoder import (
LM,
CriterionType,
DecodeResult,
KenLM,
LexiconDecoder,
LexiconDecoderOptions,
LexiconFreeDecoder,
LexiconFreeDecoderOptions,
LMState,
SmearingMode,
Trie,
)
from flashlight.lib.text.dictionary import create_word_dict, load_words
except ImportError:
warnings.warn(
"flashlight python bindings are required to use this functionality. "
"Please install from "
"https://github.com/facebookresearch/flashlight/tree/master/bindings/python"
)
LM = object
LMState = object
class KenLMDecoder(BaseDecoder):
def __init__(self, cfg: FlashlightDecoderConfig, tgt_dict: Dictionary) -> None:
super().__init__(tgt_dict)
self.nbest = cfg.nbest
self.unitlm = cfg.unitlm
if cfg.lexicon:
self.lexicon = load_words(cfg.lexicon)
self.word_dict = create_word_dict(self.lexicon)
self.unk_word = self.word_dict.get_index("<unk>")
self.lm = KenLM(cfg.lmpath, self.word_dict)
self.trie = Trie(self.vocab_size, self.silence)
start_state = self.lm.start(False)
for word, spellings in self.lexicon.items():
word_idx = self.word_dict.get_index(word)
_, score = self.lm.score(start_state, word_idx)
for spelling in spellings:
spelling_idxs = [tgt_dict.index(token) for token in spelling]
assert (
tgt_dict.unk() not in spelling_idxs
), f"{word} {spelling} {spelling_idxs}"
self.trie.insert(spelling_idxs, word_idx, score)
self.trie.smear(SmearingMode.MAX)
self.decoder_opts = LexiconDecoderOptions(
beam_size=cfg.beam,
beam_size_token=cfg.beamsizetoken or len(tgt_dict),
beam_threshold=cfg.beamthreshold,
lm_weight=cfg.lmweight,
word_score=cfg.wordscore,
unk_score=cfg.unkweight,
sil_score=cfg.silweight,
log_add=False,
criterion_type=CriterionType.CTC,
)
self.decoder = LexiconDecoder(
self.decoder_opts,
self.trie,
self.lm,
self.silence,
self.blank,
self.unk_word,
[],
self.unitlm,
)
else:
assert self.unitlm, "Lexicon-free decoding requires unit LM"
d = {w: [[w]] for w in tgt_dict.symbols}
self.word_dict = create_word_dict(d)
self.lm = KenLM(cfg.lmpath, self.word_dict)
self.decoder_opts = LexiconFreeDecoderOptions(
beam_size=cfg.beam,
beam_size_token=cfg.beamsizetoken or len(tgt_dict),
beam_threshold=cfg.beamthreshold,
lm_weight=cfg.lmweight,
sil_score=cfg.silweight,
log_add=False,
criterion_type=CriterionType.CTC,
)
self.decoder = LexiconFreeDecoder(
self.decoder_opts, self.lm, self.silence, self.blank, []
)
def get_timesteps(self, token_idxs: List[int]) -> List[int]:
"""Returns frame numbers corresponding to every non-blank token.
Parameters
----------
token_idxs : List[int]
IDs of decoded tokens.
Returns
-------
List[int]
Frame numbers corresponding to every non-blank token.
"""
timesteps = []
for i, token_idx in enumerate(token_idxs):
if token_idx == self.blank:
continue
if i == 0 or token_idx != token_idxs[i - 1]:
timesteps.append(i)
return timesteps
def decode(
self,
emissions: torch.FloatTensor,
) -> List[List[Dict[str, torch.LongTensor]]]:
B, T, N = emissions.size()
hypos = []
for b in range(B):
emissions_ptr = emissions.data_ptr() + 4 * b * emissions.stride(0)
results = self.decoder.decode(emissions_ptr, T, N)
nbest_results = results[: self.nbest]
hypos.append(
[
{
"tokens": self.get_tokens(result.tokens),
"score": result.score,
"timesteps": self.get_timesteps(result.tokens),
"words": [
self.word_dict.get_entry(x) for x in result.words if x >= 0
],
}
for result in nbest_results
]
)
return hypos
FairseqLMState = namedtuple(
"FairseqLMState",
[
"prefix",
"incremental_state",
"probs",
],
)
class FairseqLM(LM):
def __init__(self, dictionary: Dictionary, model: FairseqModel) -> None:
super().__init__()
self.dictionary = dictionary
self.model = model
self.unk = self.dictionary.unk()
self.save_incremental = False # this currently does not work properly
self.max_cache = 20_000
if torch.cuda.is_available():
model.cuda()
model.eval()
model.make_generation_fast_()
self.states = {}
self.stateq = deque()
def start(self, start_with_nothing: bool) -> LMState:
state = LMState()
prefix = torch.LongTensor([[self.dictionary.eos()]])
incremental_state = {} if self.save_incremental else None
with torch.no_grad():
res = self.model(prefix.cuda(), incremental_state=incremental_state)
probs = self.model.get_normalized_probs(res, log_probs=True, sample=None)
if incremental_state is not None:
incremental_state = apply_to_sample(lambda x: x.cpu(), incremental_state)
self.states[state] = FairseqLMState(
prefix.numpy(), incremental_state, probs[0, -1].cpu().numpy()
)
self.stateq.append(state)
return state
def score(
self,
state: LMState,
token_index: int,
no_cache: bool = False,
) -> Tuple[LMState, int]:
"""
Evaluate language model based on the current lm state and new word
Parameters:
-----------
state: current lm state
token_index: index of the word
(can be lexicon index then you should store inside LM the
mapping between indices of lexicon and lm, or lm index of a word)
Returns:
--------
(LMState, float): pair of (new state, score for the current word)
"""
curr_state = self.states[state]
def trim_cache(targ_size: int) -> None:
while len(self.stateq) > targ_size:
rem_k = self.stateq.popleft()
rem_st = self.states[rem_k]
rem_st = FairseqLMState(rem_st.prefix, None, None)
self.states[rem_k] = rem_st
if curr_state.probs is None:
new_incremental_state = (
curr_state.incremental_state.copy()
if curr_state.incremental_state is not None
else None
)
with torch.no_grad():
if new_incremental_state is not None:
new_incremental_state = apply_to_sample(
lambda x: x.cuda(), new_incremental_state
)
elif self.save_incremental:
new_incremental_state = {}
res = self.model(
torch.from_numpy(curr_state.prefix).cuda(),
incremental_state=new_incremental_state,
)
probs = self.model.get_normalized_probs(
res, log_probs=True, sample=None
)
if new_incremental_state is not None:
new_incremental_state = apply_to_sample(
lambda x: x.cpu(), new_incremental_state
)
curr_state = FairseqLMState(
curr_state.prefix, new_incremental_state, probs[0, -1].cpu().numpy()
)
if not no_cache:
self.states[state] = curr_state
self.stateq.append(state)
score = curr_state.probs[token_index].item()
trim_cache(self.max_cache)
outstate = state.child(token_index)
if outstate not in self.states and not no_cache:
prefix = np.concatenate(
[curr_state.prefix, torch.LongTensor([[token_index]])], -1
)
incr_state = curr_state.incremental_state
self.states[outstate] = FairseqLMState(prefix, incr_state, None)
if token_index == self.unk:
score = float("-inf")
return outstate, score
def finish(self, state: LMState) -> Tuple[LMState, int]:
"""
Evaluate eos for language model based on the current lm state
Returns:
--------
(LMState, float): pair of (new state, score for the current word)
"""
return self.score(state, self.dictionary.eos())
def empty_cache(self) -> None:
self.states = {}
self.stateq = deque()
gc.collect()
class FairseqLMDecoder(BaseDecoder):
def __init__(self, cfg: FlashlightDecoderConfig, tgt_dict: Dictionary) -> None:
super().__init__(tgt_dict)
self.nbest = cfg.nbest
self.unitlm = cfg.unitlm
self.lexicon = load_words(cfg.lexicon) if cfg.lexicon else None
self.idx_to_wrd = {}
checkpoint = torch.load(cfg.lmpath, map_location="cpu")
if "cfg" in checkpoint and checkpoint["cfg"] is not None:
lm_args = checkpoint["cfg"]
else:
lm_args = convert_namespace_to_omegaconf(checkpoint["args"])
if not OmegaConf.is_dict(lm_args):
lm_args = OmegaConf.create(lm_args)
with open_dict(lm_args.task):
lm_args.task.data = osp.dirname(cfg.lmpath)
task = tasks.setup_task(lm_args.task)
model = task.build_model(lm_args.model)
model.load_state_dict(checkpoint["model"], strict=False)
self.trie = Trie(self.vocab_size, self.silence)
self.word_dict = task.dictionary
self.unk_word = self.word_dict.unk()
self.lm = FairseqLM(self.word_dict, model)
if self.lexicon:
start_state = self.lm.start(False)
for i, (word, spellings) in enumerate(self.lexicon.items()):
if self.unitlm:
word_idx = i
self.idx_to_wrd[i] = word
score = 0
else:
word_idx = self.word_dict.index(word)
_, score = self.lm.score(start_state, word_idx, no_cache=True)
for spelling in spellings:
spelling_idxs = [tgt_dict.index(token) for token in spelling]
assert (
tgt_dict.unk() not in spelling_idxs
), f"{spelling} {spelling_idxs}"
self.trie.insert(spelling_idxs, word_idx, score)
self.trie.smear(SmearingMode.MAX)
self.decoder_opts = LexiconDecoderOptions(
beam_size=cfg.beam,
beam_size_token=cfg.beamsizetoken or len(tgt_dict),
beam_threshold=cfg.beamthreshold,
lm_weight=cfg.lmweight,
word_score=cfg.wordscore,
unk_score=cfg.unkweight,
sil_score=cfg.silweight,
log_add=False,
criterion_type=CriterionType.CTC,
)
self.decoder = LexiconDecoder(
self.decoder_opts,
self.trie,
self.lm,
self.silence,
self.blank,
self.unk_word,
[],
self.unitlm,
)
else:
assert self.unitlm, "Lexicon-free decoding requires unit LM"
d = {w: [[w]] for w in tgt_dict.symbols}
self.word_dict = create_word_dict(d)
self.lm = KenLM(cfg.lmpath, self.word_dict)
self.decoder_opts = LexiconFreeDecoderOptions(
beam_size=cfg.beam,
beam_size_token=cfg.beamsizetoken or len(tgt_dict),
beam_threshold=cfg.beamthreshold,
lm_weight=cfg.lmweight,
sil_score=cfg.silweight,
log_add=False,
criterion_type=CriterionType.CTC,
)
self.decoder = LexiconFreeDecoder(
self.decoder_opts, self.lm, self.silence, self.blank, []
)
def decode(
self,
emissions: torch.FloatTensor,
) -> List[List[Dict[str, torch.LongTensor]]]:
B, T, N = emissions.size()
hypos = []
def make_hypo(result: DecodeResult) -> Dict[str, Any]:
hypo = {
"tokens": self.get_tokens(result.tokens),
"score": result.score,
}
if self.lexicon:
hypo["words"] = [
self.idx_to_wrd[x] if self.unitlm else self.word_dict[x]
for x in result.words
if x >= 0
]
return hypo
for b in range(B):
emissions_ptr = emissions.data_ptr() + 4 * b * emissions.stride(0)
results = self.decoder.decode(emissions_ptr, T, N)
nbest_results = results[: self.nbest]
hypos.append([make_hypo(result) for result in nbest_results])
self.lm.empty_cache()
return hypos
| 14,748 | 33.141204 | 88 | py |
CIF-HieraDist | CIF-HieraDist-main/examples/speech_recognition/kaldi/kaldi_decoder.py | #!/usr/bin/env python3
# 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.
from concurrent.futures import ThreadPoolExecutor
import logging
from omegaconf import MISSING
import os
import torch
from typing import Optional
import warnings
from dataclasses import dataclass
from fairseq.dataclass import FairseqDataclass
from .kaldi_initializer import KaldiInitializerConfig, initalize_kaldi
logger = logging.getLogger(__name__)
@dataclass
class KaldiDecoderConfig(FairseqDataclass):
hlg_graph_path: Optional[str] = None
output_dict: str = MISSING
kaldi_initializer_config: Optional[KaldiInitializerConfig] = None
acoustic_scale: float = 0.5
max_active: int = 10000
beam_delta: float = 0.5
hash_ratio: float = 2.0
is_lattice: bool = False
lattice_beam: float = 10.0
prune_interval: int = 25
determinize_lattice: bool = True
prune_scale: float = 0.1
max_mem: int = 0
phone_determinize: bool = True
word_determinize: bool = True
minimize: bool = True
num_threads: int = 1
class KaldiDecoder(object):
def __init__(
self,
cfg: KaldiDecoderConfig,
beam: int,
nbest: int = 1,
):
try:
from kaldi.asr import FasterRecognizer, LatticeFasterRecognizer
from kaldi.base import set_verbose_level
from kaldi.decoder import (
FasterDecoder,
FasterDecoderOptions,
LatticeFasterDecoder,
LatticeFasterDecoderOptions,
)
from kaldi.lat.functions import DeterminizeLatticePhonePrunedOptions
from kaldi.fstext import read_fst_kaldi, SymbolTable
except:
warnings.warn(
"pykaldi is required for this functionality. Please install from https://github.com/pykaldi/pykaldi"
)
# set_verbose_level(2)
self.acoustic_scale = cfg.acoustic_scale
self.nbest = nbest
if cfg.hlg_graph_path is None:
assert (
cfg.kaldi_initializer_config is not None
), "Must provide hlg graph path or kaldi initializer config"
cfg.hlg_graph_path = initalize_kaldi(cfg.kaldi_initializer_config)
assert os.path.exists(cfg.hlg_graph_path), cfg.hlg_graph_path
if cfg.is_lattice:
self.dec_cls = LatticeFasterDecoder
opt_cls = LatticeFasterDecoderOptions
self.rec_cls = LatticeFasterRecognizer
else:
assert self.nbest == 1, "nbest > 1 requires lattice decoder"
self.dec_cls = FasterDecoder
opt_cls = FasterDecoderOptions
self.rec_cls = FasterRecognizer
self.decoder_options = opt_cls()
self.decoder_options.beam = beam
self.decoder_options.max_active = cfg.max_active
self.decoder_options.beam_delta = cfg.beam_delta
self.decoder_options.hash_ratio = cfg.hash_ratio
if cfg.is_lattice:
self.decoder_options.lattice_beam = cfg.lattice_beam
self.decoder_options.prune_interval = cfg.prune_interval
self.decoder_options.determinize_lattice = cfg.determinize_lattice
self.decoder_options.prune_scale = cfg.prune_scale
det_opts = DeterminizeLatticePhonePrunedOptions()
det_opts.max_mem = cfg.max_mem
det_opts.phone_determinize = cfg.phone_determinize
det_opts.word_determinize = cfg.word_determinize
det_opts.minimize = cfg.minimize
self.decoder_options.det_opts = det_opts
self.output_symbols = {}
with open(cfg.output_dict, "r") as f:
for line in f:
items = line.rstrip().split()
assert len(items) == 2
self.output_symbols[int(items[1])] = items[0]
logger.info(f"Loading FST from {cfg.hlg_graph_path}")
self.fst = read_fst_kaldi(cfg.hlg_graph_path)
self.symbol_table = SymbolTable.read_text(cfg.output_dict)
self.executor = ThreadPoolExecutor(max_workers=cfg.num_threads)
def generate(self, models, sample, **unused):
"""Generate a batch of inferences."""
# model.forward normally channels prev_output_tokens into the decoder
# separately, but SequenceGenerator directly calls model.encoder
encoder_input = {
k: v for k, v in sample["net_input"].items() if k != "prev_output_tokens"
}
emissions, padding = self.get_emissions(models, encoder_input)
return self.decode(emissions, padding)
def get_emissions(self, models, encoder_input):
"""Run encoder and normalize emissions"""
model = models[0]
all_encoder_out = [m(**encoder_input) for m in models]
if len(all_encoder_out) > 1:
if "encoder_out" in all_encoder_out[0]:
encoder_out = {
"encoder_out": sum(e["encoder_out"] for e in all_encoder_out)
/ len(all_encoder_out),
"encoder_padding_mask": all_encoder_out[0]["encoder_padding_mask"],
}
padding = encoder_out["encoder_padding_mask"]
else:
encoder_out = {
"logits": sum(e["logits"] for e in all_encoder_out)
/ len(all_encoder_out),
"padding_mask": all_encoder_out[0]["padding_mask"],
}
padding = encoder_out["padding_mask"]
else:
encoder_out = all_encoder_out[0]
padding = (
encoder_out["padding_mask"]
if "padding_mask" in encoder_out
else encoder_out["encoder_padding_mask"]
)
if hasattr(model, "get_logits"):
emissions = model.get_logits(encoder_out, normalize=True)
else:
emissions = model.get_normalized_probs(encoder_out, log_probs=True)
return (
emissions.cpu().float().transpose(0, 1),
padding.cpu() if padding is not None and padding.any() else None,
)
def decode_one(self, logits, padding):
from kaldi.matrix import Matrix
decoder = self.dec_cls(self.fst, self.decoder_options)
asr = self.rec_cls(
decoder, self.symbol_table, acoustic_scale=self.acoustic_scale
)
if padding is not None:
logits = logits[~padding]
mat = Matrix(logits.numpy())
out = asr.decode(mat)
if self.nbest > 1:
from kaldi.fstext import shortestpath
from kaldi.fstext.utils import (
convert_compact_lattice_to_lattice,
convert_lattice_to_std,
convert_nbest_to_list,
get_linear_symbol_sequence,
)
lat = out["lattice"]
sp = shortestpath(lat, nshortest=self.nbest)
sp = convert_compact_lattice_to_lattice(sp)
sp = convert_lattice_to_std(sp)
seq = convert_nbest_to_list(sp)
results = []
for s in seq:
_, o, w = get_linear_symbol_sequence(s)
words = list(self.output_symbols[z] for z in o)
results.append(
{
"tokens": words,
"words": words,
"score": w.value,
"emissions": logits,
}
)
return results
else:
words = out["text"].split()
return [
{
"tokens": words,
"words": words,
"score": out["likelihood"],
"emissions": logits,
}
]
def decode(self, emissions, padding):
if padding is None:
padding = [None] * len(emissions)
ret = list(
map(
lambda e, p: self.executor.submit(self.decode_one, e, p),
emissions,
padding,
)
)
return ret
| 8,264 | 32.872951 | 116 | py |
CIF-HieraDist | CIF-HieraDist-main/examples/speech_recognition/data/collaters.py | # 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.
"""
This module contains collection of classes which implement
collate functionalities for various tasks.
Collaters should know what data to expect for each sample
and they should pack / collate them into batches
"""
from __future__ import absolute_import, division, print_function, unicode_literals
import numpy as np
import torch
from fairseq.data import data_utils as fairseq_data_utils
class Seq2SeqCollater(object):
"""
Implements collate function mainly for seq2seq tasks
This expects each sample to contain feature (src_tokens) and
targets.
This collator is also used for aligned training task.
"""
def __init__(
self,
feature_index=0,
label_index=1,
pad_index=1,
eos_index=2,
move_eos_to_beginning=True,
):
self.feature_index = feature_index
self.label_index = label_index
self.pad_index = pad_index
self.eos_index = eos_index
self.move_eos_to_beginning = move_eos_to_beginning
def _collate_frames(self, frames):
"""Convert a list of 2d frames into a padded 3d tensor
Args:
frames (list): list of 2d frames of size L[i]*f_dim. Where L[i] is
length of i-th frame and f_dim is static dimension of features
Returns:
3d tensor of size len(frames)*len_max*f_dim where len_max is max of L[i]
"""
len_max = max(frame.size(0) for frame in frames)
f_dim = frames[0].size(1)
res = frames[0].new(len(frames), len_max, f_dim).fill_(0.0)
for i, v in enumerate(frames):
res[i, : v.size(0)] = v
return res
def collate(self, samples):
"""
utility function to collate samples into batch for speech recognition.
"""
if len(samples) == 0:
return {}
# parse samples into torch tensors
parsed_samples = []
for s in samples:
# skip invalid samples
if s["data"][self.feature_index] is None:
continue
source = s["data"][self.feature_index]
if isinstance(source, (np.ndarray, np.generic)):
source = torch.from_numpy(source)
target = s["data"][self.label_index]
if isinstance(target, (np.ndarray, np.generic)):
target = torch.from_numpy(target).long()
elif isinstance(target, list):
target = torch.LongTensor(target)
parsed_sample = {"id": s["id"], "source": source, "target": target}
parsed_samples.append(parsed_sample)
samples = parsed_samples
id = torch.LongTensor([s["id"] for s in samples])
frames = self._collate_frames([s["source"] for s in samples])
# sort samples by descending number of frames
frames_lengths = torch.LongTensor([s["source"].size(0) for s in samples])
frames_lengths, sort_order = frames_lengths.sort(descending=True)
id = id.index_select(0, sort_order)
frames = frames.index_select(0, sort_order)
target = None
target_lengths = None
prev_output_tokens = None
if samples[0].get("target", None) is not None:
ntokens = sum(len(s["target"]) for s in samples)
target = fairseq_data_utils.collate_tokens(
[s["target"] for s in samples],
self.pad_index,
self.eos_index,
left_pad=False,
move_eos_to_beginning=False,
)
target = target.index_select(0, sort_order)
target_lengths = torch.LongTensor(
[s["target"].size(0) for s in samples]
).index_select(0, sort_order)
prev_output_tokens = fairseq_data_utils.collate_tokens(
[s["target"] for s in samples],
self.pad_index,
self.eos_index,
left_pad=False,
move_eos_to_beginning=self.move_eos_to_beginning,
)
prev_output_tokens = prev_output_tokens.index_select(0, sort_order)
else:
ntokens = sum(len(s["source"]) for s in samples)
batch = {
"id": id,
"ntokens": ntokens,
"net_input": {"src_tokens": frames, "src_lengths": frames_lengths},
"target": target,
"target_lengths": target_lengths,
"nsentences": len(samples),
}
if prev_output_tokens is not None:
batch["net_input"]["prev_output_tokens"] = prev_output_tokens
return batch
| 4,796 | 35.340909 | 84 | py |
CIF-HieraDist | CIF-HieraDist-main/examples/speech_recognition/data/data_utils.py | # 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 torch
def calc_mean_invstddev(feature):
if len(feature.size()) != 2:
raise ValueError("We expect the input feature to be 2-D tensor")
mean = feature.mean(0)
var = feature.var(0)
# avoid division by ~zero
eps = 1e-8
if (var < eps).any():
return mean, 1.0 / (torch.sqrt(var) + eps)
return mean, 1.0 / torch.sqrt(var)
def apply_mv_norm(features):
# If there is less than 2 spectrograms, the variance cannot be computed (is NaN)
# and normalization is not possible, so return the item as it is
if features.size(0) < 2:
return features
mean, invstddev = calc_mean_invstddev(features)
res = (features - mean) * invstddev
return res
def lengths_to_encoder_padding_mask(lengths, batch_first=False):
"""
convert lengths (a 1-D Long/Int tensor) to 2-D binary tensor
Args:
lengths: a (B, )-shaped tensor
Return:
max_length: maximum length of B sequences
encoder_padding_mask: a (max_length, B) binary mask, where
[t, b] = 0 for t < lengths[b] and 1 otherwise
TODO:
kernelize this function if benchmarking shows this function is slow
"""
max_lengths = torch.max(lengths).item()
bsz = lengths.size(0)
encoder_padding_mask = torch.arange(
max_lengths
).to( # a (T, ) tensor with [0, ..., T-1]
lengths.device
).view( # move to the right device
1, max_lengths
).expand( # reshape to (1, T)-shaped tensor
bsz, -1
) >= lengths.view( # expand to (B, T)-shaped tensor
bsz, 1
).expand(
-1, max_lengths
)
if not batch_first:
return encoder_padding_mask.t(), max_lengths
else:
return encoder_padding_mask, max_lengths
def encoder_padding_mask_to_lengths(
encoder_padding_mask, max_lengths, batch_size, device
):
"""
convert encoder_padding_mask (2-D binary tensor) to a 1-D tensor
Conventionally, encoder output contains a encoder_padding_mask, which is
a 2-D mask in a shape (T, B), whose (t, b) element indicate whether
encoder_out[t, b] is a valid output (=0) or not (=1). Occasionally, we
need to convert this mask tensor to a 1-D tensor in shape (B, ), where
[b] denotes the valid length of b-th sequence
Args:
encoder_padding_mask: a (T, B)-shaped binary tensor or None; if None,
indicating all are valid
Return:
seq_lengths: a (B,)-shaped tensor, where its (b, )-th element is the
number of valid elements of b-th sequence
max_lengths: maximum length of all sequence, if encoder_padding_mask is
not None, max_lengths must equal to encoder_padding_mask.size(0)
batch_size: batch size; if encoder_padding_mask is
not None, max_lengths must equal to encoder_padding_mask.size(1)
device: which device to put the result on
"""
if encoder_padding_mask is None:
return torch.Tensor([max_lengths] * batch_size).to(torch.int32).to(device)
assert encoder_padding_mask.size(0) == max_lengths, "max_lengths does not match"
assert encoder_padding_mask.size(1) == batch_size, "batch_size does not match"
return max_lengths - torch.sum(encoder_padding_mask, dim=0)
| 3,429 | 32.960396 | 84 | py |
CIF-HieraDist | CIF-HieraDist-main/examples/speech_recognition/data/asr_dataset.py | # 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 os
import numpy as np
from fairseq.data import FairseqDataset
from . import data_utils
from .collaters import Seq2SeqCollater
class AsrDataset(FairseqDataset):
"""
A dataset representing speech and corresponding transcription.
Args:
aud_paths: (List[str]): A list of str with paths to audio files.
aud_durations_ms (List[int]): A list of int containing the durations of
audio files.
tgt (List[torch.LongTensor]): A list of LongTensors containing the indices
of target transcriptions.
tgt_dict (~fairseq.data.Dictionary): target vocabulary.
ids (List[str]): A list of utterance IDs.
speakers (List[str]): A list of speakers corresponding to utterances.
num_mel_bins (int): Number of triangular mel-frequency bins (default: 80)
frame_length (float): Frame length in milliseconds (default: 25.0)
frame_shift (float): Frame shift in milliseconds (default: 10.0)
"""
def __init__(
self,
aud_paths,
aud_durations_ms,
tgt,
tgt_dict,
ids,
speakers,
num_mel_bins=80,
frame_length=25.0,
frame_shift=10.0,
):
assert frame_length > 0
assert frame_shift > 0
assert all(x > frame_length for x in aud_durations_ms)
self.frame_sizes = [
int(1 + (d - frame_length) / frame_shift) for d in aud_durations_ms
]
assert len(aud_paths) > 0
assert len(aud_paths) == len(aud_durations_ms)
assert len(aud_paths) == len(tgt)
assert len(aud_paths) == len(ids)
assert len(aud_paths) == len(speakers)
self.aud_paths = aud_paths
self.tgt_dict = tgt_dict
self.tgt = tgt
self.ids = ids
self.speakers = speakers
self.num_mel_bins = num_mel_bins
self.frame_length = frame_length
self.frame_shift = frame_shift
self.s2s_collater = Seq2SeqCollater(
0,
1,
pad_index=self.tgt_dict.pad(),
eos_index=self.tgt_dict.eos(),
move_eos_to_beginning=True,
)
def __getitem__(self, index):
import torchaudio
import torchaudio.compliance.kaldi as kaldi
tgt_item = self.tgt[index] if self.tgt is not None else None
path = self.aud_paths[index]
if not os.path.exists(path):
raise FileNotFoundError("Audio file not found: {}".format(path))
sound, sample_rate = torchaudio.load_wav(path)
output = kaldi.fbank(
sound,
num_mel_bins=self.num_mel_bins,
frame_length=self.frame_length,
frame_shift=self.frame_shift,
)
output_cmvn = data_utils.apply_mv_norm(output)
return {"id": index, "data": [output_cmvn.detach(), tgt_item]}
def __len__(self):
return len(self.aud_paths)
def collater(self, samples):
"""Merge a list of samples to form a mini-batch.
Args:
samples (List[int]): sample indices to collate
Returns:
dict: a mini-batch suitable for forwarding with a Model
"""
return self.s2s_collater.collate(samples)
def num_tokens(self, index):
return self.frame_sizes[index]
def size(self, index):
"""Return an example's size as a float or tuple. This value is used when
filtering a dataset with ``--max-positions``."""
return (
self.frame_sizes[index],
len(self.tgt[index]) if self.tgt is not None else 0,
)
def ordered_indices(self):
"""Return an ordered list of indices. Batches will be constructed based
on this order."""
return np.arange(len(self))
| 3,955 | 31.162602 | 82 | py |
CIF-HieraDist | CIF-HieraDist-main/examples/speech_recognition/tasks/speech_recognition.py | # 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 json
import os
import re
import sys
import torch
from examples.speech_recognition.data import AsrDataset
from examples.speech_recognition.data.replabels import replabel_symbol
from fairseq.data import Dictionary
from fairseq.tasks import LegacyFairseqTask, register_task
def get_asr_dataset_from_json(data_json_path, tgt_dict):
"""
Parse data json and create dataset.
See scripts/asr_prep_json.py which pack json from raw files
Json example:
{
"utts": {
"4771-29403-0025": {
"input": {
"length_ms": 170,
"path": "/tmp/file1.flac"
},
"output": {
"text": "HELLO \n",
"token": "HE LLO",
"tokenid": "4815, 861"
}
},
"1564-142299-0096": {
...
}
}
"""
if not os.path.isfile(data_json_path):
raise FileNotFoundError("Dataset not found: {}".format(data_json_path))
with open(data_json_path, "rb") as f:
data_samples = json.load(f)["utts"]
assert len(data_samples) != 0
sorted_samples = sorted(
data_samples.items(),
key=lambda sample: int(sample[1]["input"]["length_ms"]),
reverse=True,
)
aud_paths = [s[1]["input"]["path"] for s in sorted_samples]
ids = [s[0] for s in sorted_samples]
speakers = []
for s in sorted_samples:
m = re.search("(.+?)-(.+?)-(.+?)", s[0])
speakers.append(m.group(1) + "_" + m.group(2))
frame_sizes = [s[1]["input"]["length_ms"] for s in sorted_samples]
tgt = [
[int(i) for i in s[1]["output"]["tokenid"].split(", ")]
for s in sorted_samples
]
# append eos
tgt = [[*t, tgt_dict.eos()] for t in tgt]
return AsrDataset(aud_paths, frame_sizes, tgt, tgt_dict, ids, speakers)
@register_task("speech_recognition")
class SpeechRecognitionTask(LegacyFairseqTask):
"""
Task for training speech recognition model.
"""
@staticmethod
def add_args(parser):
"""Add task-specific arguments to the parser."""
parser.add_argument("data", help="path to data directory")
parser.add_argument(
"--silence-token", default="\u2581", help="token for silence (used by w2l)"
)
parser.add_argument(
"--max-source-positions",
default=sys.maxsize,
type=int,
metavar="N",
help="max number of frames in the source sequence",
)
parser.add_argument(
"--max-target-positions",
default=1024,
type=int,
metavar="N",
help="max number of tokens in the target sequence",
)
def __init__(self, args, tgt_dict):
super().__init__(args)
self.tgt_dict = tgt_dict
@classmethod
def setup_task(cls, args, **kwargs):
"""Setup the task (e.g., load dictionaries)."""
dict_path = os.path.join(args.data, "dict.txt")
if not os.path.isfile(dict_path):
raise FileNotFoundError("Dict not found: {}".format(dict_path))
tgt_dict = Dictionary.load(dict_path)
if args.criterion == "ctc_loss":
tgt_dict.add_symbol("<ctc_blank>")
elif args.criterion == "asg_loss":
for i in range(1, args.max_replabel + 1):
tgt_dict.add_symbol(replabel_symbol(i))
print("| dictionary: {} types".format(len(tgt_dict)))
return cls(args, tgt_dict)
def load_dataset(self, split, combine=False, **kwargs):
"""Load a given dataset split.
Args:
split (str): name of the split (e.g., train, valid, test)
"""
data_json_path = os.path.join(self.args.data, "{}.json".format(split))
self.datasets[split] = get_asr_dataset_from_json(data_json_path, self.tgt_dict)
def build_generator(self, models, args, **unused):
w2l_decoder = getattr(args, "w2l_decoder", None)
if w2l_decoder == "viterbi":
from examples.speech_recognition.w2l_decoder import W2lViterbiDecoder
return W2lViterbiDecoder(args, self.target_dictionary)
elif w2l_decoder == "kenlm":
from examples.speech_recognition.w2l_decoder import W2lKenLMDecoder
return W2lKenLMDecoder(args, self.target_dictionary)
elif w2l_decoder == "fairseqlm":
from examples.speech_recognition.w2l_decoder import W2lFairseqLMDecoder
return W2lFairseqLMDecoder(args, self.target_dictionary)
else:
return super().build_generator(models, args)
@property
def target_dictionary(self):
"""Return the :class:`~fairseq.data.Dictionary` for the language
model."""
return self.tgt_dict
@property
def source_dictionary(self):
"""Return the source :class:`~fairseq.data.Dictionary` (if applicable
for this task)."""
return None
def max_positions(self):
"""Return the max speech and sentence length allowed by the task."""
return (self.args.max_source_positions, self.args.max_target_positions)
| 5,397 | 33.164557 | 87 | py |
CIF-HieraDist | CIF-HieraDist-main/scripts/average_checkpoints.py | #!/usr/bin/env python3
# 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 argparse
import collections
import os
import re
import torch
from fairseq.file_io import PathManager
def average_checkpoints(inputs):
"""Loads checkpoints from inputs and returns a model with averaged weights.
Args:
inputs: An iterable of string paths of checkpoints 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()
params_keys = None
new_state = None
num_models = len(inputs)
for fpath in inputs:
with PathManager.open(fpath, "rb") as f:
state = torch.load(
f,
map_location=(
lambda s, _: torch.serialization.default_restore_location(s, "cpu")
),
)
# Copies over the settings from the first checkpoint
if new_state is None:
new_state = state
model_params = state["model"]
model_params_keys = list(model_params.keys())
if params_keys is None:
params_keys = model_params_keys
elif params_keys != model_params_keys:
raise KeyError(
"For checkpoint {}, expected list of params: {}, "
"but found: {}".format(f, params_keys, model_params_keys)
)
for k in params_keys:
p = model_params[k]
if isinstance(p, torch.HalfTensor):
p = p.float()
if k not in params_dict:
params_dict[k] = p.clone()
# NOTE: clone() is needed in case of p is a shared parameter
else:
params_dict[k] += p
averaged_params = collections.OrderedDict()
for k, v in params_dict.items():
averaged_params[k] = v
if averaged_params[k].is_floating_point():
averaged_params[k].div_(num_models)
else:
averaged_params[k] //= num_models
new_state["model"] = averaged_params
return new_state
def last_n_checkpoints(paths, n, update_based, upper_bound=None):
assert len(paths) == 1
path = paths[0]
if update_based:
pt_regexp = re.compile(r"checkpoint_\d+_(\d+)\.pt")
else:
pt_regexp = re.compile(r"checkpoint(\d+)\.pt")
files = PathManager.ls(path)
entries = []
for f in files:
m = pt_regexp.fullmatch(f)
if m is not None:
sort_key = int(m.group(1))
if upper_bound is None or sort_key <= upper_bound:
entries.append((sort_key, m.group(0)))
if len(entries) < n:
raise Exception(
"Found {} checkpoint files but need at least {}", len(entries), n
)
return [os.path.join(path, x[1]) for x in sorted(entries, reverse=True)[:n]]
def main():
parser = argparse.ArgumentParser(
description="Tool to average the params of input checkpoints to "
"produce a new checkpoint",
)
# fmt: off
parser.add_argument('--inputs', required=True, nargs='+',
help='Input checkpoint file paths.')
parser.add_argument('--output', required=True, metavar='FILE',
help='Write the new checkpoint containing the averaged weights to this path.')
num_group = parser.add_mutually_exclusive_group()
num_group.add_argument('--num-epoch-checkpoints', type=int,
help='if set, will try to find checkpoints with names checkpoint_xx.pt in the path specified by input, '
'and average last this many of them.')
num_group.add_argument('--num-update-checkpoints', type=int,
help='if set, will try to find checkpoints with names checkpoint_ee_xx.pt in the path specified by input, '
'and average last this many of them.')
parser.add_argument('--checkpoint-upper-bound', type=int,
help='when using --num-epoch-checkpoints, this will set an upper bound on which epoch to use, '
'when using --num-update-checkpoints, this will set an upper bound on which update to use'
'e.g., with --num-epoch-checkpoints=10 --checkpoint-upper-bound=50, checkpoints 41-50 would be averaged.'
'e.g., with --num-update-checkpoints=10 --checkpoint-upper-bound=50000, checkpoints 40500-50000 would be averaged assuming --save-interval-updates 500'
)
# fmt: on
args = parser.parse_args()
print(args)
num = None
is_update_based = False
if args.num_update_checkpoints is not None:
num = args.num_update_checkpoints
is_update_based = True
elif args.num_epoch_checkpoints is not None:
num = args.num_epoch_checkpoints
assert args.checkpoint_upper_bound is None or (
args.num_epoch_checkpoints is not None
or args.num_update_checkpoints is not None
), "--checkpoint-upper-bound requires --num-epoch-checkpoints or --num-update-checkpoints"
assert (
args.num_epoch_checkpoints is None or args.num_update_checkpoints is None
), "Cannot combine --num-epoch-checkpoints and --num-update-checkpoints"
if num is not None:
args.inputs = last_n_checkpoints(
args.inputs,
num,
is_update_based,
upper_bound=args.checkpoint_upper_bound,
)
print("averaging checkpoints: ", args.inputs)
new_state = average_checkpoints(args.inputs)
with PathManager.open(args.output, "wb") as f:
torch.save(new_state, f)
print("Finished writing averaged checkpoint to {}".format(args.output))
if __name__ == "__main__":
main()
| 6,021 | 36.874214 | 175 | py |
CIF-HieraDist | CIF-HieraDist-main/tests/test_train.py | # 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 contextlib
import logging
import unittest
from io import StringIO
from unittest.mock import MagicMock, patch
import torch
from fairseq import checkpoint_utils, data
from omegaconf import OmegaConf
def mock_trainer(epoch, num_updates, iterations_in_epoch):
trainer = MagicMock()
trainer.load_checkpoint.return_value = {
"train_iterator": {
"epoch": epoch,
"iterations_in_epoch": iterations_in_epoch,
"shuffle": False,
},
}
trainer.get_num_updates.return_value = num_updates
return trainer
def mock_dict():
d = MagicMock()
d.pad.return_value = 1
d.eos.return_value = 2
d.unk.return_value = 3
return d
def get_trainer_and_epoch_itr(epoch, epoch_size, num_updates, iterations_in_epoch):
tokens = torch.LongTensor(list(range(epoch_size))).view(1, -1)
tokens_ds = data.TokenBlockDataset(
tokens,
sizes=[tokens.size(-1)],
block_size=1,
pad=0,
eos=1,
include_targets=False,
)
trainer = mock_trainer(epoch, num_updates, iterations_in_epoch)
dataset = data.LanguagePairDataset(
tokens_ds, tokens_ds.sizes, mock_dict(), shuffle=False
)
epoch_itr = data.EpochBatchIterator(
dataset=dataset,
collate_fn=dataset.collater,
batch_sampler=[[i] for i in range(epoch_size)],
)
return trainer, epoch_itr
def get_mock_cfg(finetune_from_model):
cfg_mock = OmegaConf.create(
{
"checkpoint": {
"save_dir": None,
"optimizer_overrides": "{}",
"reset_dataloader": False,
"reset_meters": False,
"reset_optimizer": False,
"reset_lr_scheduler": False,
"finetune_from_model": finetune_from_model,
"model_parallel_size": 1,
"restore_file": "checkpoint_last.pt",
},
"common": {
"model_parallel_size": 1,
},
}
)
return cfg_mock
class TestLoadCheckpoint(unittest.TestCase):
def setUp(self):
self.cfg_mock = get_mock_cfg(None)
self.patches = {
"os.makedirs": MagicMock(),
"os.path.join": MagicMock(),
"os.path.isfile": MagicMock(return_value=True),
"os.path.isabs": MagicMock(return_value=False),
"fairseq.file_io.PathManager.exists": MagicMock(return_value=False),
}
self.applied_patches = [patch(p, d) for p, d in self.patches.items()]
[p.start() for p in self.applied_patches]
logging.disable(logging.CRITICAL)
def tearDown(self):
patch.stopall()
logging.disable(logging.NOTSET)
def test_load_partial_checkpoint(self):
with contextlib.redirect_stdout(StringIO()):
trainer, epoch_itr = get_trainer_and_epoch_itr(2, 150, 200, 50)
trainer.get_train_iterator = MagicMock(return_value=epoch_itr)
_, epoch_itr = checkpoint_utils.load_checkpoint(
self.cfg_mock.checkpoint, trainer
)
self.assertEqual(epoch_itr.epoch, 2)
self.assertEqual(epoch_itr.iterations_in_epoch, 50)
itr = epoch_itr.next_epoch_itr(shuffle=False)
self.assertEqual(epoch_itr.epoch, 2)
self.assertEqual(epoch_itr.iterations_in_epoch, 50)
self.assertEqual(next(itr)["net_input"]["src_tokens"][0].item(), 50)
self.assertEqual(epoch_itr.iterations_in_epoch, 51)
for _ in range(150 - 52):
next(itr)
self.assertEqual(epoch_itr.iterations_in_epoch, 149)
self.assertTrue(itr.has_next())
next(itr)
self.assertFalse(itr.has_next())
itr = epoch_itr.next_epoch_itr(shuffle=False)
self.assertTrue(itr.has_next())
self.assertEqual(epoch_itr.epoch, 3)
self.assertEqual(epoch_itr.iterations_in_epoch, 0)
def test_load_full_checkpoint(self):
with contextlib.redirect_stdout(StringIO()):
trainer, epoch_itr = get_trainer_and_epoch_itr(2, 150, 300, 150)
trainer.get_train_iterator = MagicMock(return_value=epoch_itr)
_, epoch_itr = checkpoint_utils.load_checkpoint(
self.cfg_mock.checkpoint, trainer
)
itr = epoch_itr.next_epoch_itr(shuffle=False)
self.assertEqual(epoch_itr.epoch, 3)
self.assertEqual(epoch_itr.iterations_in_epoch, 0)
self.assertEqual(next(itr)["net_input"]["src_tokens"][0].item(), 0)
def test_load_no_checkpoint(self):
with contextlib.redirect_stdout(StringIO()):
trainer, epoch_itr = get_trainer_and_epoch_itr(1, 150, 0, 0)
trainer.get_train_iterator = MagicMock(return_value=epoch_itr)
self.patches["os.path.isfile"].return_value = False
_, epoch_itr = checkpoint_utils.load_checkpoint(
self.cfg_mock.checkpoint, trainer
)
itr = epoch_itr.next_epoch_itr(shuffle=False)
self.assertEqual(epoch_itr.epoch, 1)
self.assertEqual(epoch_itr.iterations_in_epoch, 0)
self.assertEqual(next(itr)["net_input"]["src_tokens"][0].item(), 0)
def test_finetune_from_model_args_conflict(self):
with contextlib.redirect_stdout(StringIO()):
trainer, epoch_itr = get_trainer_and_epoch_itr(1, 150, 0, 0)
trainer.get_train_iterator = MagicMock(return_value=epoch_itr)
for arg in [
"reset_optimizer",
"reset_lr_scheduler",
"reset_meters",
"reset_dataloader",
]:
with self.subTest(arg=arg):
cfg_mock = get_mock_cfg("/temp/checkpoint_pretrained.pt")
cfg_mock["checkpoint"][arg] = True
with self.assertRaises(Exception) as context:
_, _ = checkpoint_utils.load_checkpoint(
cfg_mock.checkpoint, trainer
)
self.assertTrue(
"--finetune-from-model can not be set together with either --reset-optimizer"
" or reset_lr_scheduler or reset_meters or reset_dataloader"
in str(context.exception)
)
def test_finetune_from_model(self):
with contextlib.redirect_stdout(StringIO()):
trainer, epoch_itr = get_trainer_and_epoch_itr(1, 150, 0, 0)
trainer.get_train_iterator = MagicMock(return_value=epoch_itr)
from_model_path = "/temp/checkpoint_pretrained.pt"
def mock_finetune_exist(path):
if path == from_model_path:
return True
else:
return False
self.patches[
"fairseq.file_io.PathManager.exists"
].side_effect = mock_finetune_exist
cfg_mock = get_mock_cfg(from_model_path)
cfg_mock.checkpoint.restore_file = "checkpoint_last.pt"
_, _ = checkpoint_utils.load_checkpoint(cfg_mock.checkpoint, trainer)
(
checkpoint_path,
reset_optimizer,
reset_lr_scheduler,
optimizer_overrides,
) = trainer.load_checkpoint.call_args[0]
reset_meters = trainer.load_checkpoint.call_args[1]["reset_meters"]
self.assertTrue(reset_optimizer)
self.assertTrue(reset_lr_scheduler)
self.assertTrue(reset_meters)
def test_finetune_from_model_resume(self):
with contextlib.redirect_stdout(StringIO()):
trainer, epoch_itr = get_trainer_and_epoch_itr(1, 150, 0, 0)
trainer.get_train_iterator = MagicMock(return_value=epoch_itr)
from_model_path = "/temp/checkpoint_pretrained.pt"
# launch second time
# both restore_file=checkpoint_last.pt and finetune_from_model are set
def mock_finetune_exist(path):
if path == from_model_path or path.endsWith("checkpoint_last.pt"):
return True
else:
return False
self.patches[
"fairseq.file_io.PathManager.exists"
].side_effect = mock_finetune_exist
cfg_mock = get_mock_cfg(from_model_path)
cfg_mock.checkpoint.restore_file = "checkpoint_last.pt"
_, _ = checkpoint_utils.load_checkpoint(cfg_mock.checkpoint, trainer)
(
checkpoint_path,
reset_optimizer,
reset_lr_scheduler,
optimizer_overrides,
) = trainer.load_checkpoint.call_args[0]
reset_meters = trainer.load_checkpoint.call_args[1]["reset_meters"]
self.assertFalse(reset_optimizer)
self.assertFalse(reset_lr_scheduler)
self.assertFalse(reset_meters)
if __name__ == "__main__":
unittest.main()
| 9,292 | 36.471774 | 101 | py |
CIF-HieraDist | CIF-HieraDist-main/tests/test_checkpoint_utils.py | # 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 contextlib
import logging
import os
import tempfile
import unittest
from io import StringIO
from unittest.mock import patch
from fairseq import checkpoint_utils
from omegaconf import OmegaConf
from tests.utils import (
create_dummy_data,
preprocess_translation_data,
train_translation_model,
)
class TestCheckpointUtils(unittest.TestCase):
def setUp(self):
logging.disable(logging.CRITICAL)
def tearDown(self):
logging.disable(logging.NOTSET)
@contextlib.contextmanager
def _train_transformer(self, seed, extra_args=None):
if extra_args is None:
extra_args = []
with tempfile.TemporaryDirectory(f"_train_transformer_seed{seed}") as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir)
train_translation_model(
data_dir,
"transformer_iwslt_de_en",
[
"--encoder-layers",
"3",
"--decoder-layers",
"3",
"--encoder-embed-dim",
"8",
"--decoder-embed-dim",
"8",
"--seed",
str(seed),
]
+ extra_args,
)
yield os.path.join(data_dir, "checkpoint_last.pt")
def test_load_model_ensemble_and_task(self):
# with contextlib.redirect_stdout(StringIO()):
with self._train_transformer(seed=123) as model1:
with self._train_transformer(seed=456) as model2:
ensemble, cfg, task = checkpoint_utils.load_model_ensemble_and_task(
filenames=[model1, model2]
)
self.assertEqual(len(ensemble), 2)
# after Transformer has been migrated to Hydra, this will probably
# become cfg.common.seed
self.assertEqual(ensemble[0].args.seed, 123)
self.assertEqual(ensemble[1].args.seed, 456)
# the task from the first model should be returned
self.assertTrue("seed123" in task.cfg.data)
# last cfg is saved
self.assertEqual(cfg.common.seed, 456)
def test_prune_state_dict(self):
with contextlib.redirect_stdout(StringIO()):
extra_args = ["--encoder-layerdrop", "0.01", "--decoder-layerdrop", "0.01"]
with self._train_transformer(seed=1, extra_args=extra_args) as model:
ensemble, cfg, task = checkpoint_utils.load_model_ensemble_and_task(
filenames=[model],
arg_overrides={
"encoder_layers_to_keep": "0,2",
"decoder_layers_to_keep": "1",
},
)
self.assertEqual(len(ensemble), 1)
self.assertEqual(len(ensemble[0].encoder.layers), 2)
self.assertEqual(len(ensemble[0].decoder.layers), 1)
def test_torch_persistent_save_async(self):
state_dict = {}
filename = "async_checkpoint.pt"
with patch(f"{checkpoint_utils.__name__}.PathManager.opena") as mock_opena:
with patch(
f"{checkpoint_utils.__name__}._torch_persistent_save"
) as mock_save:
checkpoint_utils.torch_persistent_save(
state_dict, filename, async_write=True
)
mock_opena.assert_called_with(filename, "wb")
mock_save.assert_called()
if __name__ == "__main__":
unittest.main()
| 3,834 | 34.183486 | 87 | py |
CIF-HieraDist | CIF-HieraDist-main/tests/test_average_checkpoints.py | # 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 collections
import os
import shutil
import tempfile
import unittest
import numpy as np
import torch
from scripts.average_checkpoints import average_checkpoints
from torch import nn
class ModelWithSharedParameter(nn.Module):
def __init__(self):
super(ModelWithSharedParameter, self).__init__()
self.embedding = nn.Embedding(1000, 200)
self.FC1 = nn.Linear(200, 200)
self.FC2 = nn.Linear(200, 200)
# tie weight in FC2 to FC1
self.FC2.weight = nn.Parameter(self.FC1.weight)
self.FC2.bias = nn.Parameter(self.FC1.bias)
self.relu = nn.ReLU()
def forward(self, input):
return self.FC2(self.ReLU(self.FC1(input))) + self.FC1(input)
class TestAverageCheckpoints(unittest.TestCase):
def test_average_checkpoints(self):
params_0 = collections.OrderedDict(
[
("a", torch.DoubleTensor([100.0])),
("b", torch.FloatTensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])),
("c", torch.IntTensor([7, 8, 9])),
]
)
params_1 = collections.OrderedDict(
[
("a", torch.DoubleTensor([1.0])),
("b", torch.FloatTensor([[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]])),
("c", torch.IntTensor([2, 2, 2])),
]
)
params_avg = collections.OrderedDict(
[
("a", torch.DoubleTensor([50.5])),
("b", torch.FloatTensor([[1.0, 1.5, 2.0], [2.5, 3.0, 3.5]])),
# We expect truncation for integer division
("c", torch.IntTensor([4, 5, 5])),
]
)
fd_0, path_0 = tempfile.mkstemp()
fd_1, path_1 = tempfile.mkstemp()
torch.save(collections.OrderedDict([("model", params_0)]), path_0)
torch.save(collections.OrderedDict([("model", params_1)]), path_1)
output = average_checkpoints([path_0, path_1])["model"]
os.close(fd_0)
os.remove(path_0)
os.close(fd_1)
os.remove(path_1)
for (k_expected, v_expected), (k_out, v_out) in zip(
params_avg.items(), output.items()
):
self.assertEqual(
k_expected,
k_out,
"Key mismatch - expected {} but found {}. "
"(Expected list of keys: {} vs actual list of keys: {})".format(
k_expected, k_out, params_avg.keys(), output.keys()
),
)
np.testing.assert_allclose(
v_expected.numpy(),
v_out.numpy(),
err_msg="Tensor value mismatch for key {}".format(k_expected),
)
def test_average_checkpoints_with_shared_parameters(self):
def _construct_model_with_shared_parameters(path, value):
m = ModelWithSharedParameter()
nn.init.constant_(m.FC1.weight, value)
torch.save({"model": m.state_dict()}, path)
return m
tmpdir = tempfile.mkdtemp()
paths = []
path = os.path.join(tmpdir, "m1.pt")
m1 = _construct_model_with_shared_parameters(path, 1.0)
paths.append(path)
path = os.path.join(tmpdir, "m2.pt")
m2 = _construct_model_with_shared_parameters(path, 2.0)
paths.append(path)
path = os.path.join(tmpdir, "m3.pt")
m3 = _construct_model_with_shared_parameters(path, 3.0)
paths.append(path)
new_model = average_checkpoints(paths)
self.assertTrue(
torch.equal(
new_model["model"]["embedding.weight"],
(m1.embedding.weight + m2.embedding.weight + m3.embedding.weight) / 3.0,
)
)
self.assertTrue(
torch.equal(
new_model["model"]["FC1.weight"],
(m1.FC1.weight + m2.FC1.weight + m3.FC1.weight) / 3.0,
)
)
self.assertTrue(
torch.equal(
new_model["model"]["FC2.weight"],
(m1.FC2.weight + m2.FC2.weight + m3.FC2.weight) / 3.0,
)
)
shutil.rmtree(tmpdir)
if __name__ == "__main__":
unittest.main()
| 4,385 | 31.488889 | 88 | py |
CIF-HieraDist | CIF-HieraDist-main/tests/test_reproducibility.py | # 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 contextlib
import json
import os
import tempfile
import unittest
from io import StringIO
import torch
from . import test_binaries
class TestReproducibility(unittest.TestCase):
def _test_reproducibility(
self,
name,
extra_flags=None,
delta=0.0001,
resume_checkpoint="checkpoint1.pt",
max_epoch=3,
):
def get_last_log_stats_containing_string(log_records, search_string):
for log_record in logs.records[::-1]:
if isinstance(log_record.msg, str) and search_string in log_record.msg:
return json.loads(log_record.msg)
if extra_flags is None:
extra_flags = []
with tempfile.TemporaryDirectory(name) as data_dir:
with self.assertLogs() as logs:
test_binaries.create_dummy_data(data_dir)
test_binaries.preprocess_translation_data(data_dir)
# train epochs 1 and 2 together
with self.assertLogs() as logs:
test_binaries.train_translation_model(
data_dir,
"fconv_iwslt_de_en",
[
"--dropout",
"0.0",
"--log-format",
"json",
"--log-interval",
"1",
"--max-epoch",
str(max_epoch),
]
+ extra_flags,
)
train_log = get_last_log_stats_containing_string(logs.records, "train_loss")
valid_log = get_last_log_stats_containing_string(logs.records, "valid_loss")
# train epoch 2, resuming from previous checkpoint 1
os.rename(
os.path.join(data_dir, resume_checkpoint),
os.path.join(data_dir, "checkpoint_last.pt"),
)
with self.assertLogs() as logs:
test_binaries.train_translation_model(
data_dir,
"fconv_iwslt_de_en",
[
"--dropout",
"0.0",
"--log-format",
"json",
"--log-interval",
"1",
"--max-epoch",
str(max_epoch),
]
+ extra_flags,
)
train_res_log = get_last_log_stats_containing_string(
logs.records, "train_loss"
)
valid_res_log = get_last_log_stats_containing_string(
logs.records, "valid_loss"
)
for k in ["train_loss", "train_ppl", "train_num_updates", "train_gnorm"]:
self.assertAlmostEqual(
float(train_log[k]), float(train_res_log[k]), delta=delta
)
for k in [
"valid_loss",
"valid_ppl",
"valid_num_updates",
"valid_best_loss",
]:
self.assertAlmostEqual(
float(valid_log[k]), float(valid_res_log[k]), delta=delta
)
def test_reproducibility(self):
self._test_reproducibility("test_reproducibility")
@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU")
def test_reproducibility_fp16(self):
self._test_reproducibility(
"test_reproducibility_fp16",
[
"--fp16",
"--fp16-init-scale",
"4096",
],
delta=0.011,
)
@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU")
def test_reproducibility_memory_efficient_fp16(self):
self._test_reproducibility(
"test_reproducibility_memory_efficient_fp16",
[
"--memory-efficient-fp16",
"--fp16-init-scale",
"4096",
],
)
@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU")
def test_reproducibility_amp(self):
self._test_reproducibility(
"test_reproducibility_amp",
[
"--amp",
"--fp16-init-scale",
"4096",
],
delta=0.011,
)
def test_mid_epoch_reproducibility(self):
self._test_reproducibility(
"test_mid_epoch_reproducibility",
["--save-interval-updates", "3"],
resume_checkpoint="checkpoint_1_3.pt",
max_epoch=1,
)
if __name__ == "__main__":
unittest.main()
| 4,906 | 31.496689 | 88 | py |
CIF-HieraDist | CIF-HieraDist-main/tests/test_sequence_scorer.py | # 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 argparse
import unittest
import tests.utils as test_utils
import torch
from fairseq.sequence_scorer import SequenceScorer
class TestSequenceScorer(unittest.TestCase):
def test_sequence_scorer(self):
# construct dummy dictionary
d = test_utils.dummy_dictionary(vocab_size=2)
self.assertEqual(d.pad(), 1)
self.assertEqual(d.eos(), 2)
self.assertEqual(d.unk(), 3)
eos = d.eos()
w1 = 4
w2 = 5
# construct dataloader
data = [
{
"source": torch.LongTensor([w1, w2, eos]),
"target": torch.LongTensor([w1, w2, w1, eos]),
},
{
"source": torch.LongTensor([w2, eos]),
"target": torch.LongTensor([w2, w1, eos]),
},
{
"source": torch.LongTensor([w2, eos]),
"target": torch.LongTensor([w2, eos]),
},
]
data_itr = test_utils.dummy_dataloader(data)
# specify expected output probabilities
args = argparse.Namespace()
unk = 0.0
args.beam_probs = [
# step 0:
torch.FloatTensor(
[
# eos w1 w2
[0.0, unk, 0.6, 0.4], # sentence 1
[0.0, unk, 0.4, 0.6], # sentence 2
[0.0, unk, 0.7, 0.3], # sentence 3
]
),
# step 1:
torch.FloatTensor(
[
# eos w1 w2
[0.0, unk, 0.2, 0.7], # sentence 1
[0.0, unk, 0.8, 0.2], # sentence 2
[0.7, unk, 0.1, 0.2], # sentence 3
]
),
# step 2:
torch.FloatTensor(
[
# eos w1 w2
[0.10, unk, 0.50, 0.4], # sentence 1
[0.15, unk, 0.15, 0.7], # sentence 2
[0.00, unk, 0.00, 0.0], # sentence 3
]
),
# step 3:
torch.FloatTensor(
[
# eos w1 w2
[0.9, unk, 0.05, 0.05], # sentence 1
[0.0, unk, 0.00, 0.0], # sentence 2
[0.0, unk, 0.00, 0.0], # sentence 3
]
),
]
expected_scores = [
[0.6, 0.7, 0.5, 0.9], # sentence 1
[0.6, 0.8, 0.15], # sentence 2
[0.3, 0.7], # sentence 3
]
task = test_utils.TestTranslationTask.setup_task(args, d, d)
model = task.build_model(args)
scorer = SequenceScorer(task.target_dictionary)
for sample in data_itr:
hypos = task.inference_step(scorer, [model], sample)
for id, hypos_id in zip(sample["id"].tolist(), hypos):
self.assertHypoTokens(hypos_id[0], data[id]["target"])
self.assertHypoScore(hypos_id[0], expected_scores[id])
def assertHypoTokens(self, hypo, tokens):
self.assertTensorEqual(hypo["tokens"], torch.LongTensor(tokens))
def assertHypoScore(self, hypo, pos_probs, normalized=True, lenpen=1.0):
pos_scores = torch.FloatTensor(pos_probs).log()
self.assertAlmostEqual(hypo["positional_scores"], pos_scores)
self.assertEqual(pos_scores.numel(), hypo["tokens"].numel())
score = pos_scores.sum()
if normalized:
score /= pos_scores.numel() ** lenpen
self.assertLess(abs(score - hypo["score"]), 1e-6)
def assertAlmostEqual(self, t1, t2):
self.assertEqual(t1.size(), t2.size(), "size mismatch")
self.assertLess((t1 - t2).abs().max(), 1e-4)
def assertTensorEqual(self, t1, t2):
self.assertEqual(t1.size(), t2.size(), "size mismatch")
self.assertEqual(t1.ne(t2).long().sum(), 0)
if __name__ == "__main__":
unittest.main()
| 4,150 | 33.305785 | 76 | py |
CIF-HieraDist | CIF-HieraDist-main/tests/test_multi_corpus_dataset.py | # 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 unittest
from collections import OrderedDict
import torch
from fairseq.data import LanguagePairDataset, TokenBlockDataset
from fairseq.data.multi_corpus_dataset import MultiCorpusDataset
from tests.test_train import mock_dict
class TestMultiCorpusDataset(unittest.TestCase):
def setUp(self):
d = mock_dict()
tokens_1 = torch.LongTensor([i for i in range(1, 5000, 2)]).view(1, -1)
tokens_ds1 = TokenBlockDataset(
tokens_1,
sizes=[tokens_1.size(-1)],
block_size=1,
pad=0,
eos=1,
include_targets=False,
)
self.dataset_1 = LanguagePairDataset(
tokens_ds1, tokens_ds1.sizes, d, shuffle=False
)
tokens_2 = torch.LongTensor([i for i in range(0, 5000, 2)]).view(1, -1)
tokens_ds2 = TokenBlockDataset(
tokens_2,
sizes=[tokens_2.size(-1)],
block_size=1,
pad=0,
eos=1,
include_targets=False,
)
self.dataset_2 = LanguagePairDataset(
tokens_ds2, tokens_ds2.sizes, d, shuffle=False
)
def _test_sample_helper(
self,
distribution,
):
m = MultiCorpusDataset(
OrderedDict({0: self.dataset_1, 1: self.dataset_2}),
distribution=distribution,
seed=0,
sort_indices=True,
)
m.set_epoch(1)
indices = m.ordered_indices()
count_sample_from_first_dataset = 0
items = set()
for i in indices:
item = m[i]["source"].item()
if item % 2 == 1:
count_sample_from_first_dataset += 1
items.add(item)
sample_from_first_ds_percentage = (
1.0 * count_sample_from_first_dataset / len(indices)
)
self.assertLess(
abs(sample_from_first_ds_percentage - distribution[0]),
0.01,
)
self.assertEqual(
len(items),
int(
min(len(self.dataset_1), len(indices) * distribution[0])
+ min(len(self.dataset_1), len(indices) * distribution[1])
),
)
print(distribution)
def test_multi_corpus_dataset(self):
for distribution in [[0.5, 0.5], [0.1, 0.9], [0.9, 0.1], [0.0, 1.0]]:
self._test_sample_helper(distribution=distribution)
| 2,586 | 30.54878 | 79 | py |
CIF-HieraDist | CIF-HieraDist-main/tests/test_memory_efficient_fp16.py | # 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 argparse
import logging
import unittest
import torch
from fairseq.optim.adam import FairseqAdam
from fairseq.optim.fp16_optimizer import MemoryEfficientFP16Optimizer
from omegaconf import OmegaConf
@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU")
class TestMemoryEfficientFP16(unittest.TestCase):
def setUp(self):
logging.disable(logging.CRITICAL)
def tearDown(self):
logging.disable(logging.NOTSET)
def test_load_state_dict(self):
# define simple FP16 model
model = torch.nn.Linear(5, 5).cuda().half()
params = list(model.parameters())
# initialize memory efficient FP16 optimizer
# with pseudo DictConfigs
optimizer = FairseqAdam(
cfg=OmegaConf.create(
vars(
argparse.Namespace(
adam_betas="(0.9, 0.999)",
adam_eps=1e-8,
weight_decay=0.0,
lr=[0.00001],
)
)
),
params=params,
)
me_optimizer = MemoryEfficientFP16Optimizer(
cfg=OmegaConf.create(
{
"common": vars(
argparse.Namespace(
fp16_init_scale=1,
fp16_scale_window=1,
fp16_scale_tolerance=1,
threshold_loss_scale=1,
min_loss_scale=1e-4,
)
)
}
),
params=params,
optimizer=optimizer,
)
# optimizer state is created in the first step
loss = model(torch.rand(5).cuda().half()).sum()
me_optimizer.backward(loss)
me_optimizer.step()
# reload state
state = me_optimizer.state_dict()
me_optimizer.load_state_dict(state)
for k, v in me_optimizer.optimizer.state.items():
self.assertTrue(k.dtype == torch.float16)
for v_i in v.values():
if torch.is_tensor(v_i):
self.assertTrue(v_i.dtype == torch.float32)
if __name__ == "__main__":
unittest.main()
| 2,452 | 30.050633 | 70 | py |
CIF-HieraDist | CIF-HieraDist-main/tests/test_ema.py | # 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 unittest
from copy import deepcopy
from dataclasses import dataclass
from typing import Optional
import torch
from fairseq.models.ema import EMA
class DummyModule(torch.nn.Module):
def __init__(self) -> None:
"""LightningModule for testing purposes
Args:
epoch_min_loss_override (int, optional): Pass in an epoch that will be set to the minimum
validation loss for testing purposes (zero based). If None this is ignored. Defaults to None.
"""
super().__init__()
self.layer = torch.nn.Linear(in_features=32, out_features=2)
self.another_layer = torch.nn.Linear(in_features=2, out_features=2)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.layer(x)
return self.another_layer(x)
@dataclass
class EMAConfig(object):
ema_decay: float = 0.99
ema_start_update: int = 0
ema_fp32: bool = False
ema_seed_model: Optional[str] = None
class TestEMAGPU(unittest.TestCase):
def assertTorchAllClose(self, x, y, atol=1e-8, rtol=1e-5, msg=None):
diff = x.float() - y.float()
diff_norm = torch.norm(diff)
other_norm = torch.norm(y.float())
if msg is None:
msg = "|input - other| > {} + {} * |other|".format(atol, rtol)
self.assertLessEqual(
diff_norm,
atol + rtol * other_norm,
msg=msg,
)
def test_ema(self):
model = DummyModule()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
state = deepcopy(model.state_dict())
config = EMAConfig()
ema = EMA(model, config)
# set decay
ema._set_decay(config.ema_decay)
self.assertEqual(ema.get_decay(), config.ema_decay)
# get model
self.assertEqual(ema.get_model(), ema.model)
# Since fp32 params is not used, it should be of size 0
self.assertEqual(len(ema.fp32_params), 0)
# EMA step
x = torch.randn(32)
y = model(x)
loss = y.sum()
loss.backward()
optimizer.step()
ema.step(model)
ema_state_dict = ema.get_model().state_dict()
for key, param in model.state_dict().items():
prev_param = state[key]
ema_param = ema_state_dict[key]
if "version" in key:
# Do not decay a model.version pytorch param
continue
self.assertTorchAllClose(
ema_param,
config.ema_decay * prev_param + (1 - config.ema_decay) * param,
)
# Since fp32 params is not used, it should be of size 0
self.assertEqual(len(ema.fp32_params), 0)
# Load EMA into model
model2 = DummyModule()
ema.reverse(model2)
for key, param in model2.state_dict().items():
ema_param = ema_state_dict[key]
self.assertTrue(torch.allclose(ema_param, param))
def test_ema_fp32(self):
model = DummyModule().half()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
state = deepcopy(model.state_dict())
config = EMAConfig(ema_fp32=True)
ema = EMA(model, config)
x = torch.randn(32)
y = model(x.half())
loss = y.sum()
loss.backward()
optimizer.step()
ema.step(model)
for key, param in model.state_dict().items():
prev_param = state[key]
ema_param = ema.get_model().state_dict()[key]
if "version" in key:
# Do not decay a model.version pytorch param
continue
self.assertIn(key, ema.fp32_params)
# EMA update is done in fp32, and hence the EMA param must be
# closer to the EMA update done in fp32 than in fp16.
self.assertLessEqual(
torch.norm(
ema_param.float()
- (
config.ema_decay * prev_param.float()
+ (1 - config.ema_decay) * param.float()
)
.half()
.float()
),
torch.norm(
ema_param.float()
- (
config.ema_decay * prev_param + (1 - config.ema_decay) * param
).float()
),
)
self.assertTorchAllClose(
ema_param,
(
config.ema_decay * prev_param.float()
+ (1 - config.ema_decay) * param.float()
).half(),
)
def test_ema_fp16(self):
model = DummyModule().half()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
state = deepcopy(model.state_dict())
config = EMAConfig(ema_fp32=False)
ema = EMA(model, config)
# Since fp32 params is not used, it should be of size 0
self.assertEqual(len(ema.fp32_params), 0)
x = torch.randn(32)
y = model(x.half())
loss = y.sum()
loss.backward()
optimizer.step()
ema.step(model)
for key, param in model.state_dict().items():
prev_param = state[key]
ema_param = ema.get_model().state_dict()[key]
if "version" in key:
# Do not decay a model.version pytorch param
continue
# EMA update is done in fp16, and hence the EMA param must be
# closer to the EMA update done in fp16 than in fp32.
self.assertLessEqual(
torch.norm(
ema_param.float()
- (
config.ema_decay * prev_param + (1 - config.ema_decay) * param
).float()
),
torch.norm(
ema_param.float()
- (
config.ema_decay * prev_param.float()
+ (1 - config.ema_decay) * param.float()
)
.half()
.float()
),
)
self.assertTorchAllClose(
ema_param,
config.ema_decay * prev_param + (1 - config.ema_decay) * param,
)
# Since fp32 params is not used, it should be of size 0
self.assertEqual(len(ema.fp32_params), 0)
if __name__ == "__main__":
unittest.main()
| 6,675 | 30.342723 | 109 | py |
CIF-HieraDist | CIF-HieraDist-main/tests/test_lstm_jitable.py | # 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 argparse
import tempfile
import unittest
import torch
from fairseq.data.dictionary import Dictionary
from fairseq.models.lstm import LSTMModel
from fairseq.tasks.fairseq_task import LegacyFairseqTask
DEFAULT_TEST_VOCAB_SIZE = 100
class DummyTask(LegacyFairseqTask):
def __init__(self, args):
super().__init__(args)
self.dictionary = get_dummy_dictionary()
if getattr(self.args, "ctc", False):
self.dictionary.add_symbol("<ctc_blank>")
self.src_dict = self.dictionary
self.tgt_dict = self.dictionary
@property
def source_dictionary(self):
return self.src_dict
@property
def target_dictionary(self):
return self.dictionary
def get_dummy_dictionary(vocab_size=DEFAULT_TEST_VOCAB_SIZE):
dummy_dict = Dictionary()
# add dummy symbol to satisfy vocab size
for id, _ in enumerate(range(vocab_size)):
dummy_dict.add_symbol("{}".format(id), 1000)
return dummy_dict
def get_dummy_task_and_parser():
"""
to build a fariseq model, we need some dummy parse and task. This function
is used to create dummy task and parser to faciliate model/criterion test
Note: we use FbSpeechRecognitionTask as the dummy task. You may want
to use other task by providing another function
"""
parser = argparse.ArgumentParser(
description="test_dummy_s2s_task", argument_default=argparse.SUPPRESS
)
DummyTask.add_args(parser)
args = parser.parse_args([])
task = DummyTask.setup_task(args)
return task, parser
class TestJitLSTMModel(unittest.TestCase):
def _test_save_and_load(self, scripted_module):
with tempfile.NamedTemporaryFile() as f:
scripted_module.save(f.name)
torch.jit.load(f.name)
def assertTensorEqual(self, t1, t2):
t1 = t1[~torch.isnan(t1)] # can cause size mismatch errors if there are NaNs
t2 = t2[~torch.isnan(t2)]
self.assertEqual(t1.size(), t2.size(), "size mismatch")
self.assertEqual(t1.ne(t2).long().sum(), 0)
def test_jit_and_export_lstm(self):
task, parser = get_dummy_task_and_parser()
LSTMModel.add_args(parser)
args = parser.parse_args([])
args.criterion = ""
model = LSTMModel.build_model(args, task)
scripted_model = torch.jit.script(model)
self._test_save_and_load(scripted_model)
def test_assert_jit_vs_nonjit_(self):
task, parser = get_dummy_task_and_parser()
LSTMModel.add_args(parser)
args = parser.parse_args([])
args.criterion = ""
model = LSTMModel.build_model(args, task)
model.eval()
scripted_model = torch.jit.script(model)
scripted_model.eval()
idx = len(task.source_dictionary)
iter = 100
# Inject random input and check output
seq_len_tensor = torch.randint(1, 10, (iter,))
num_samples_tensor = torch.randint(1, 10, (iter,))
for i in range(iter):
seq_len = seq_len_tensor[i]
num_samples = num_samples_tensor[i]
src_token = (torch.randint(0, idx, (num_samples, seq_len)),)
src_lengths = torch.randint(1, seq_len + 1, (num_samples,))
src_lengths, _ = torch.sort(src_lengths, descending=True)
# Force the first sample to have seq_len
src_lengths[0] = seq_len
prev_output_token = (torch.randint(0, idx, (num_samples, 1)),)
result = model(src_token[0], src_lengths, prev_output_token[0], None)
scripted_result = scripted_model(
src_token[0], src_lengths, prev_output_token[0], None
)
self.assertTensorEqual(result[0], scripted_result[0])
self.assertTensorEqual(result[1], scripted_result[1])
if __name__ == "__main__":
unittest.main()
| 4,041 | 33.844828 | 85 | py |
CIF-HieraDist | CIF-HieraDist-main/tests/test_multihead_attention.py | # 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 unittest
import torch
from fairseq.modules.multihead_attention import MultiheadAttention
class TestMultiheadAttention(unittest.TestCase):
def test_append_prev_key_padding_mask(self):
bsz = 1
src_len = 4
cases = [
# no padding mask
(None, None, None),
# current padding mask only
(
torch.tensor([[1]]).bool(),
None,
torch.tensor([[0, 0, 0, 1]]).bool(),
),
# previous padding mask only
(
None,
torch.tensor([[0, 1, 0]]).bool(),
torch.tensor([[0, 1, 0, 0]]).bool(),
),
# both padding masks
(
torch.tensor([[1]]).bool(),
torch.tensor([[0, 1, 0]]).bool(),
torch.tensor([[0, 1, 0, 1]]).bool(),
),
# prev_key_padding_mask already full
(
torch.tensor([[0, 1, 0, 1]]).bool(),
None,
torch.tensor([[0, 1, 0, 1]]).bool(),
),
# key_padding_mask already full
(
None,
torch.tensor([[0, 1, 0, 1]]).bool(),
torch.tensor([[0, 1, 0, 1]]).bool(),
),
]
for c in cases:
key_padding_mask = MultiheadAttention._append_prev_key_padding_mask(
c[0],
c[1],
batch_size=bsz,
src_len=src_len,
static_kv=False,
)
if key_padding_mask is not None:
self.assertTrue(
torch.all(torch.eq(key_padding_mask, c[2])),
f"Unexpected resultant key padding mask: {key_padding_mask}"
f" given current: {c[0]} and previous: {c[1]}",
)
self.assertEqual(key_padding_mask.size(0), bsz)
self.assertEqual(key_padding_mask.size(1), src_len)
else:
self.assertIsNone(c[2])
if __name__ == "__main__":
unittest.main()
| 2,312 | 30.256757 | 80 | py |
CIF-HieraDist | CIF-HieraDist-main/tests/utils.py | # 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 argparse
import json
import os
import random
import sys
from io import StringIO
import torch
import torch.nn.functional as F
from fairseq import options, utils
from fairseq.data import Dictionary
from fairseq.data.language_pair_dataset import collate
from fairseq.models import (
FairseqEncoder,
FairseqEncoderDecoderModel,
FairseqIncrementalDecoder,
)
from fairseq.models.fairseq_encoder import EncoderOut
from fairseq.tasks import LegacyFairseqTask
from fairseq_cli import generate, interactive, preprocess, train, validate
import fairseq.distributed.utils as distributed_utils
from fairseq.dataclass.utils import convert_namespace_to_omegaconf
def dummy_dictionary(vocab_size, prefix="token_"):
d = Dictionary()
for i in range(vocab_size):
token = prefix + str(i)
d.add_symbol(token)
d.finalize(padding_factor=1) # don't add extra padding symbols
return d
def dummy_dataloader(
samples,
padding_idx=1,
eos_idx=2,
batch_size=None,
):
if batch_size is None:
batch_size = len(samples)
# add any missing data to samples
for i, sample in enumerate(samples):
if "id" not in sample:
sample["id"] = i
# create dataloader
dataset = TestDataset(samples)
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=batch_size,
collate_fn=(lambda samples: collate(samples, padding_idx, eos_idx)),
)
return iter(dataloader)
def sequence_generator_setup():
# construct dummy dictionary
d = dummy_dictionary(vocab_size=2)
eos = d.eos()
w1 = 4
w2 = 5
# construct source data
src_tokens = torch.LongTensor([[w1, w2, eos], [w1, w2, eos]])
src_lengths = torch.LongTensor([2, 2])
args = argparse.Namespace()
unk = 0.0
args.beam_probs = [
# step 0:
torch.FloatTensor(
[
# eos w1 w2
# sentence 1:
[0.0, unk, 0.9, 0.1], # beam 1
[0.0, unk, 0.9, 0.1], # beam 2
# sentence 2:
[0.0, unk, 0.7, 0.3],
[0.0, unk, 0.7, 0.3],
]
),
# step 1:
torch.FloatTensor(
[
# eos w1 w2 prefix
# sentence 1:
[1.0, unk, 0.0, 0.0], # w1: 0.9 (emit: w1 <eos>: 0.9*1.0)
[0.0, unk, 0.9, 0.1], # w2: 0.1
# sentence 2:
[0.25, unk, 0.35, 0.4], # w1: 0.7 (don't emit: w1 <eos>: 0.7*0.25)
[0.00, unk, 0.10, 0.9], # w2: 0.3
]
),
# step 2:
torch.FloatTensor(
[
# eos w1 w2 prefix
# sentence 1:
[0.0, unk, 0.1, 0.9], # w2 w1: 0.1*0.9
[
0.6,
unk,
0.2,
0.2,
], # w2 w2: 0.1*0.1 (emit: w2 w2 <eos>: 0.1*0.1*0.6)
# sentence 2:
[
0.60,
unk,
0.4,
0.00,
], # w1 w2: 0.7*0.4 (emit: w1 w2 <eos>: 0.7*0.4*0.6)
[0.01, unk, 0.0, 0.99], # w2 w2: 0.3*0.9
]
),
# step 3:
torch.FloatTensor(
[
# eos w1 w2 prefix
# sentence 1:
[
1.0,
unk,
0.0,
0.0,
], # w2 w1 w2: 0.1*0.9*0.9 (emit: w2 w1 w2 <eos>: 0.1*0.9*0.9*1.0)
[
1.0,
unk,
0.0,
0.0,
], # w2 w1 w1: 0.1*0.9*0.1 (emit: w2 w1 w1 <eos>: 0.1*0.9*0.1*1.0)
# sentence 2:
[
0.1,
unk,
0.5,
0.4,
], # w2 w2 w2: 0.3*0.9*0.99 (emit: w2 w2 w2 <eos>: 0.3*0.9*0.99*0.1)
[
1.0,
unk,
0.0,
0.0,
], # w1 w2 w1: 0.7*0.4*0.4 (emit: w1 w2 w1 <eos>: 0.7*0.4*0.4*1.0)
]
),
]
task = TestTranslationTask.setup_task(args, d, d)
model = task.build_model(args)
tgt_dict = task.target_dictionary
return tgt_dict, w1, w2, src_tokens, src_lengths, model
def create_dummy_data(data_dir, num_examples=100, maxlen=20, alignment=False):
def _create_dummy_data(filename):
data = torch.rand(num_examples * maxlen)
data = 97 + torch.floor(26 * data).int()
with open(os.path.join(data_dir, filename), "w") as h:
offset = 0
for _ in range(num_examples):
ex_len = random.randint(1, maxlen)
ex_str = " ".join(map(chr, data[offset : offset + ex_len]))
print(ex_str, file=h)
offset += ex_len
def _create_dummy_alignment_data(filename_src, filename_tgt, filename):
with open(os.path.join(data_dir, filename_src), "r") as src_f, open(
os.path.join(data_dir, filename_tgt), "r"
) as tgt_f, open(os.path.join(data_dir, filename), "w") as h:
for src, tgt in zip(src_f, tgt_f):
src_len = len(src.split())
tgt_len = len(tgt.split())
avg_len = (src_len + tgt_len) // 2
num_alignments = random.randint(avg_len // 2, 2 * avg_len)
src_indices = torch.floor(torch.rand(num_alignments) * src_len).int()
tgt_indices = torch.floor(torch.rand(num_alignments) * tgt_len).int()
ex_str = " ".join(
[
"{}-{}".format(src, tgt)
for src, tgt in zip(src_indices, tgt_indices)
]
)
print(ex_str, file=h)
_create_dummy_data("train.in")
_create_dummy_data("train.out")
_create_dummy_data("valid.in")
_create_dummy_data("valid.out")
_create_dummy_data("test.in")
_create_dummy_data("test.out")
if alignment:
_create_dummy_alignment_data("train.in", "train.out", "train.align")
_create_dummy_alignment_data("valid.in", "valid.out", "valid.align")
_create_dummy_alignment_data("test.in", "test.out", "test.align")
def preprocess_lm_data(data_dir):
preprocess_parser = options.get_preprocessing_parser()
preprocess_args = preprocess_parser.parse_args(
[
"--only-source",
"--trainpref",
os.path.join(data_dir, "train.out"),
"--validpref",
os.path.join(data_dir, "valid.out"),
"--testpref",
os.path.join(data_dir, "test.out"),
"--destdir",
data_dir,
]
)
preprocess.main(preprocess_args)
def preprocess_translation_data(data_dir, extra_flags=None):
preprocess_parser = options.get_preprocessing_parser()
preprocess_args = preprocess_parser.parse_args(
[
"--source-lang",
"in",
"--target-lang",
"out",
"--trainpref",
os.path.join(data_dir, "train"),
"--validpref",
os.path.join(data_dir, "valid"),
"--testpref",
os.path.join(data_dir, "test"),
"--thresholdtgt",
"0",
"--thresholdsrc",
"0",
"--destdir",
data_dir,
]
+ (extra_flags or []),
)
preprocess.main(preprocess_args)
def preprocess_summarization_data(data_dir, extra_flags=None):
preprocess_parser = options.get_preprocessing_parser()
preprocess_args = preprocess_parser.parse_args(
[
"--source-lang",
"in",
"--target-lang",
"out",
"--trainpref",
os.path.join(data_dir, "train"),
"--validpref",
os.path.join(data_dir, "valid"),
"--testpref",
os.path.join(data_dir, "test"),
"--thresholdtgt",
"0",
"--thresholdsrc",
"0",
"--joined-dictionary",
"--destdir",
data_dir,
]
+ (extra_flags or []),
)
preprocess.main(preprocess_args)
def create_laser_data_and_config_json(data_dir):
src_langs = ["de", "fr", "ru", "tr", "zh"]
tgt_langs = ["en", "es"]
config_json = {}
config_train_json = []
src_vocab = None
tgt_vocab = None
for src_lang in src_langs:
for tgt_lang in tgt_langs:
langpair_folder = f"{src_lang}-{tgt_lang}"
langpair_path = os.path.join(data_dir, langpair_folder)
os.mkdir(langpair_path)
create_dummy_data(langpair_path)
preprocess_translation_data(langpair_path, ["--dataset-impl", "cached"])
src_vocab = os.path.join(langpair_path, "dict.in.txt")
tgt_vocab = os.path.join(langpair_path, "dict.out.txt")
config_train_json.append(
{
"id": 0 if tgt_lang == "en" else 1,
"src": os.path.join(langpair_path, "train.in-out.in"),
"tgt": os.path.join(langpair_path, "train.in-out.out"),
}
)
config_json["src_vocab"] = src_vocab
config_json["tgt_vocab"] = tgt_vocab
config_json["train"] = config_train_json
with open(os.path.join(data_dir, "laserconfig.json"), "w") as config_file:
json.dump(config_json, config_file)
return config_file
def train_translation_model(
data_dir,
arch,
extra_flags=None,
task="translation",
run_validation=False,
lang_flags=None,
extra_valid_flags=None,
world_size=1,
):
if lang_flags is None:
lang_flags = [
"--source-lang",
"in",
"--target-lang",
"out",
]
train_parser = options.get_training_parser()
train_args = options.parse_args_and_arch(
train_parser,
[
"--task",
task,
data_dir,
"--save-dir",
data_dir,
"--arch",
arch,
"--optimizer",
"nag",
"--lr",
"0.05",
"--max-tokens",
"500",
"--max-epoch",
"1",
"--no-progress-bar",
"--distributed-world-size",
str(world_size),
"--num-workers",
"0",
]
+ lang_flags
+ (extra_flags or []),
)
cfg = convert_namespace_to_omegaconf(train_args)
distributed_utils.call_main(cfg, train.main)
if run_validation:
# test validation
validate_parser = options.get_validation_parser()
validate_args = options.parse_args_and_arch(
validate_parser,
[
"--task",
task,
data_dir,
"--path",
os.path.join(data_dir, "checkpoint_last.pt"),
"--valid-subset",
"valid",
"--max-tokens",
"500",
"--no-progress-bar",
"--num-workers",
"0",
]
+ lang_flags
+ (extra_valid_flags or []),
)
validate.main(validate_args)
def generate_main(data_dir, extra_flags=None, path=None):
if extra_flags is None:
extra_flags = [
"--print-alignment",
]
if path is None:
path = os.path.join(data_dir, "checkpoint_last.pt")
generate_parser = options.get_generation_parser()
generate_args = options.parse_args_and_arch(
generate_parser,
[
data_dir,
"--path",
path,
"--beam",
"3",
"--batch-size",
"64",
"--max-len-b",
"5",
"--gen-subset",
"valid",
"--no-progress-bar",
"--num-workers",
"0",
]
+ (extra_flags or []),
)
# evaluate model in batch mode
generate.main(generate_args)
# evaluate model interactively
generate_args.buffer_size = 0
generate_args.input = "-"
generate_args.batch_size = None
orig_stdin = sys.stdin
sys.stdin = StringIO("h e l l o\n")
interactive.main(generate_args)
sys.stdin = orig_stdin
class TestDataset(torch.utils.data.Dataset):
def __init__(self, data):
super().__init__()
self.data = data
self.sizes = None
def __getitem__(self, index):
return self.data[index]
def __len__(self):
return len(self.data)
class TestTranslationTask(LegacyFairseqTask):
def __init__(self, args, src_dict, tgt_dict, model):
super().__init__(args)
self.src_dict = src_dict
self.tgt_dict = tgt_dict
self.model = model
@classmethod
def setup_task(cls, args, src_dict=None, tgt_dict=None, model=None):
return cls(args, src_dict, tgt_dict, model)
def build_model(self, args):
return TestModel.build_model(args, self)
@property
def source_dictionary(self):
return self.src_dict
@property
def target_dictionary(self):
return self.tgt_dict
class TestModel(FairseqEncoderDecoderModel):
def __init__(self, encoder, decoder):
super().__init__(encoder, decoder)
@classmethod
def build_model(cls, args, task):
encoder = TestEncoder(args, task.source_dictionary)
decoder = TestIncrementalDecoder(args, task.target_dictionary)
return cls(encoder, decoder)
class TestEncoder(FairseqEncoder):
def __init__(self, args, dictionary):
super().__init__(dictionary)
self.args = args
def forward(self, src_tokens, src_lengths=None, **kwargs):
return EncoderOut(
encoder_out=src_tokens,
encoder_padding_mask=None,
encoder_embedding=None,
encoder_states=None,
src_tokens=None,
src_lengths=None,
)
def reorder_encoder_out(self, encoder_out, new_order):
return EncoderOut(
encoder_out=encoder_out.encoder_out.index_select(0, new_order),
encoder_padding_mask=None,
encoder_embedding=None,
encoder_states=None,
src_tokens=None,
src_lengths=None,
)
class TestIncrementalDecoder(FairseqIncrementalDecoder):
def __init__(self, args, dictionary):
super().__init__(dictionary)
assert hasattr(args, "beam_probs") or hasattr(args, "probs")
args.max_decoder_positions = getattr(args, "max_decoder_positions", 100)
self.args = args
def forward(self, prev_output_tokens, encoder_out=None, incremental_state=None):
if incremental_state is not None:
prev_output_tokens = prev_output_tokens[:, -1:]
bbsz = prev_output_tokens.size(0)
vocab = len(self.dictionary)
src_len = encoder_out.encoder_out.size(1)
tgt_len = prev_output_tokens.size(1)
# determine number of steps
if incremental_state is not None:
# cache step number
step = utils.get_incremental_state(self, incremental_state, "step")
if step is None:
step = 0
utils.set_incremental_state(self, incremental_state, "step", step + 1)
steps = [step]
else:
steps = list(range(tgt_len))
# define output in terms of raw probs
if hasattr(self.args, "probs"):
assert (
self.args.probs.dim() == 3
), "expected probs to have size bsz*steps*vocab"
probs = self.args.probs.index_select(1, torch.LongTensor(steps))
else:
probs = torch.FloatTensor(bbsz, len(steps), vocab).zero_()
for i, step in enumerate(steps):
# args.beam_probs gives the probability for every vocab element,
# starting with eos, then unknown, and then the rest of the vocab
if step < len(self.args.beam_probs):
probs[:, i, self.dictionary.eos() :] = self.args.beam_probs[step]
else:
probs[:, i, self.dictionary.eos()] = 1.0
# random attention
attn = torch.rand(bbsz, tgt_len, src_len)
dev = prev_output_tokens.device
return probs.to(dev), {"attn": [attn.to(dev)]}
def get_normalized_probs(self, net_output, log_probs, _):
# the decoder returns probabilities directly
probs = net_output[0]
if log_probs:
return probs.log()
else:
return probs
def max_positions(self):
return self.args.max_decoder_positions
class TestReshapingEncoder(FairseqEncoder):
def __init__(self, args, dictionary):
super().__init__(dictionary)
self.args = args
def forward(self, src_tokens, src_lengths=None, **kwargs):
b_sz, t_sz = src_tokens.shape
padding_needed = t_sz % 2
x = src_tokens
if padding_needed > 0:
padding_needed = 2 - padding_needed
x = F.pad(x, (0, padding_needed))
return EncoderOut(
encoder_out=x.view(b_sz, -1, 2),
encoder_padding_mask=None,
encoder_embedding=None,
encoder_states=None,
src_tokens=None,
src_lengths=None,
)
def reorder_encoder_out(self, encoder_out, new_order):
return EncoderOut(
encoder_out=encoder_out.encoder_out.index_select(0, new_order),
encoder_padding_mask=None,
encoder_embedding=None,
encoder_states=None,
src_tokens=None,
src_lengths=None,
)
class TestReshapingModel(FairseqEncoderDecoderModel):
def __init__(self, encoder, decoder):
super().__init__(encoder, decoder)
@classmethod
def build_model(cls, args, task):
encoder = TestReshapingEncoder(args, task.source_dictionary)
decoder = TestIncrementalDecoder(args, task.target_dictionary)
return cls(encoder, decoder)
class TestAdditionalInputEncoder(FairseqEncoder):
def __init__(self, args, dictionary):
super().__init__(dictionary)
self.args = args
def forward(self, src_tokens, src_lengths=None, **kwargs):
assert "fancy_other_input" in kwargs
assert kwargs["fancy_other_input"] is not None
return EncoderOut(
encoder_out=src_tokens,
encoder_padding_mask=None,
encoder_embedding=None,
encoder_states=None,
src_tokens=None,
src_lengths=None,
)
def reorder_encoder_out(self, encoder_out, new_order):
return EncoderOut(
encoder_out=encoder_out.encoder_out.index_select(0, new_order),
encoder_padding_mask=None,
encoder_embedding=None,
encoder_states=None,
src_tokens=None,
src_lengths=None,
)
class TestAdditionalInputModel(FairseqEncoderDecoderModel):
def __init__(self, encoder, decoder):
super().__init__(encoder, decoder)
@classmethod
def build_model(cls, args, task):
encoder = TestAdditionalInputEncoder(args, task.source_dictionary)
decoder = TestIncrementalDecoder(args, task.target_dictionary)
return cls(encoder, decoder)
def forward(self, src_tokens, src_lengths, prev_output_tokens, **kwargs):
encoder_out = self.encoder(src_tokens, src_lengths=src_lengths, **kwargs)
decoder_out = self.decoder(
prev_output_tokens, encoder_out=encoder_out, **kwargs
)
return decoder_out
def train_language_model(
data_dir,
arch,
extra_flags=None,
run_validation=False,
extra_valid_flags=None,
task="language_modeling",
world_size=1,
):
train_parser = options.get_training_parser()
train_args = options.parse_args_and_arch(
train_parser,
[
"--task",
task,
data_dir,
"--arch",
arch,
"--optimizer",
"adam",
"--lr",
"0.0001",
"--max-tokens",
"500",
"--tokens-per-sample",
"500",
"--save-dir",
data_dir,
"--max-epoch",
"1",
"--no-progress-bar",
"--distributed-world-size",
str(world_size),
"--ddp-backend",
"no_c10d",
"--num-workers",
"0",
]
+ (extra_flags or []),
)
cfg = convert_namespace_to_omegaconf(train_args)
distributed_utils.call_main(cfg, train.main)
if run_validation:
# test validation
validate_parser = options.get_validation_parser()
validate_args = options.parse_args_and_arch(
validate_parser,
[
"--task",
task,
data_dir,
"--path",
os.path.join(data_dir, "checkpoint_last.pt"),
"--valid-subset",
"valid",
"--max-tokens",
"500",
"--no-progress-bar",
"--num-workers",
"0",
]
+ (extra_valid_flags or []),
)
validate.main(validate_args)
| 21,929 | 29.416089 | 86 | py |
CIF-HieraDist | CIF-HieraDist-main/tests/test_binaries.py | # 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 contextlib
import logging
import json
import os
import random
import sys
import tempfile
import unittest
from io import StringIO
from typing import List, Dict
import torch
from fairseq import options
from fairseq_cli import eval_lm, train
from tests.utils import (
create_dummy_data,
generate_main,
preprocess_lm_data,
preprocess_summarization_data,
preprocess_translation_data,
create_laser_data_and_config_json,
train_translation_model,
train_language_model,
)
try:
import transformers # noqa
has_hf_transformers = True
except ImportError:
has_hf_transformers = False
class TestTranslation(unittest.TestCase):
def setUp(self):
logging.disable(logging.CRITICAL)
def tearDown(self):
logging.disable(logging.NOTSET)
def test_fconv(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_fconv") as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir)
train_translation_model(data_dir, "fconv_iwslt_de_en")
generate_main(data_dir)
def test_raw(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_fconv_raw") as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir, ["--dataset-impl", "raw"])
train_translation_model(
data_dir, "fconv_iwslt_de_en", ["--dataset-impl", "raw"]
)
generate_main(data_dir, ["--dataset-impl", "raw"])
def test_update_freq(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_update_freq") as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir)
train_translation_model(
data_dir, "fconv_iwslt_de_en", ["--update-freq", "3"]
)
generate_main(data_dir)
def test_max_positions(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_max_positions") as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir)
with self.assertRaises(Exception) as context:
train_translation_model(
data_dir,
"fconv_iwslt_de_en",
["--max-target-positions", "5"],
)
self.assertTrue(
"skip this example with --skip-invalid-size-inputs-valid-test"
in str(context.exception)
)
train_translation_model(
data_dir,
"fconv_iwslt_de_en",
[
"--max-target-positions",
"5",
"--skip-invalid-size-inputs-valid-test",
],
)
with self.assertRaises(Exception) as context:
generate_main(data_dir)
generate_main(data_dir, ["--skip-invalid-size-inputs-valid-test"])
def test_generation(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_sampling") as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir)
train_translation_model(data_dir, "fconv_iwslt_de_en")
generate_main(
data_dir,
[
"--sampling",
"--temperature",
"2",
"--beam",
"2",
"--nbest",
"2",
],
)
generate_main(
data_dir,
[
"--sampling",
"--sampling-topk",
"3",
"--beam",
"2",
"--nbest",
"2",
],
)
generate_main(
data_dir,
[
"--sampling",
"--sampling-topp",
"0.2",
"--beam",
"2",
"--nbest",
"2",
],
)
generate_main(
data_dir,
[
"--diversity-rate",
"0.5",
"--beam",
"6",
],
)
with self.assertRaises(ValueError):
generate_main(
data_dir,
[
"--diverse-beam-groups",
"4",
"--match-source-len",
],
)
generate_main(data_dir, ["--prefix-size", "2"])
generate_main(data_dir, ["--retain-dropout"])
def test_eval_bleu(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_eval_bleu") as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir)
train_translation_model(
data_dir,
"fconv_iwslt_de_en",
[
"--eval-bleu",
"--eval-bleu-print-samples",
"--eval-bleu-remove-bpe",
"--eval-bleu-detok",
"space",
"--eval-bleu-args",
'{"beam": 4, "min_len": 10}',
],
)
def test_lstm(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_lstm") as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir)
train_translation_model(
data_dir,
"lstm_wiseman_iwslt_de_en",
[
"--encoder-layers",
"2",
"--decoder-layers",
"2",
"--encoder-embed-dim",
"8",
"--decoder-embed-dim",
"8",
"--decoder-out-embed-dim",
"8",
],
)
generate_main(data_dir)
def test_lstm_bidirectional(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_lstm_bidirectional") as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir)
train_translation_model(
data_dir,
"lstm",
[
"--encoder-layers",
"2",
"--encoder-bidirectional",
"--encoder-hidden-size",
"16",
"--encoder-embed-dim",
"8",
"--decoder-embed-dim",
"8",
"--decoder-out-embed-dim",
"8",
"--decoder-layers",
"2",
],
)
generate_main(data_dir)
def test_transformer(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_transformer") as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir)
train_translation_model(
data_dir,
"transformer_iwslt_de_en",
[
"--encoder-layers",
"2",
"--decoder-layers",
"2",
"--encoder-embed-dim",
"8",
"--decoder-embed-dim",
"8",
],
run_validation=True,
)
generate_main(data_dir)
def test_multilingual_transformer(self):
# test with all combinations of encoder/decoder lang tokens
encoder_langtok_flags = [
[],
["--encoder-langtok", "src"],
["--encoder-langtok", "tgt"],
]
decoder_langtok_flags = [[], ["--decoder-langtok"]]
with contextlib.redirect_stdout(StringIO()):
for i in range(len(encoder_langtok_flags)):
for j in range(len(decoder_langtok_flags)):
enc_ltok_flag = encoder_langtok_flags[i]
dec_ltok_flag = decoder_langtok_flags[j]
with tempfile.TemporaryDirectory(
f"test_multilingual_transformer_{i}_{j}"
) as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir)
train_translation_model(
data_dir,
arch="multilingual_transformer",
task="multilingual_translation",
extra_flags=[
"--encoder-layers",
"2",
"--decoder-layers",
"2",
"--encoder-embed-dim",
"8",
"--decoder-embed-dim",
"8",
]
+ enc_ltok_flag
+ dec_ltok_flag,
lang_flags=["--lang-pairs", "in-out,out-in"],
run_validation=True,
extra_valid_flags=enc_ltok_flag + dec_ltok_flag,
)
generate_main(
data_dir,
extra_flags=[
"--task",
"multilingual_translation",
"--lang-pairs",
"in-out,out-in",
"--source-lang",
"in",
"--target-lang",
"out",
]
+ enc_ltok_flag
+ dec_ltok_flag,
)
@unittest.skipIf(
sys.platform.lower() == "darwin", "skip latent depth test on MacOS"
)
def test_multilingual_translation_latent_depth(self):
# test with latent depth in encoder, decoder, or both
encoder_latent_layer = [[], ["--encoder-latent-layer"]]
decoder_latent_layer = [[], ["--decoder-latent-layer"]]
with contextlib.redirect_stdout(StringIO()):
for i in range(len(encoder_latent_layer)):
for j in range(len(decoder_latent_layer)):
if i == 0 and j == 0:
continue
enc_ll_flag = encoder_latent_layer[i]
dec_ll_flag = decoder_latent_layer[j]
with tempfile.TemporaryDirectory(
f"test_multilingual_translation_latent_depth_{i}_{j}"
) as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(
data_dir, extra_flags=["--joined-dictionary"]
)
train_translation_model(
data_dir,
arch="latent_multilingual_transformer",
task="multilingual_translation_latent_depth",
extra_flags=[
"--user-dir",
"examples/latent_depth/latent_depth_src",
"--encoder-layers",
"2",
"--decoder-layers",
"2",
"--encoder-embed-dim",
"8",
"--decoder-embed-dim",
"8",
"--share-encoders",
"--share-decoders",
"--sparsity-weight",
"0.1",
]
+ enc_ll_flag
+ dec_ll_flag,
lang_flags=["--lang-pairs", "in-out,out-in"],
run_validation=True,
extra_valid_flags=[
"--user-dir",
"examples/latent_depth/latent_depth_src",
]
+ enc_ll_flag
+ dec_ll_flag,
)
generate_main(
data_dir,
extra_flags=[
"--user-dir",
"examples/latent_depth/latent_depth_src",
"--task",
"multilingual_translation_latent_depth",
"--lang-pairs",
"in-out,out-in",
"--source-lang",
"in",
"--target-lang",
"out",
]
+ enc_ll_flag
+ dec_ll_flag,
)
def test_translation_multi_simple_epoch(self):
# test with all combinations of encoder/decoder lang tokens
encoder_langtok_flags = [
[],
["--encoder-langtok", "src"],
["--encoder-langtok", "tgt"],
]
decoder_langtok_flags = [[], ["--decoder-langtok"]]
with contextlib.redirect_stdout(StringIO()):
for i in range(len(encoder_langtok_flags)):
for j in range(len(decoder_langtok_flags)):
enc_ltok_flag = encoder_langtok_flags[i]
dec_ltok_flag = decoder_langtok_flags[j]
with tempfile.TemporaryDirectory(
f"test_translation_multi_simple_epoch_{i}_{j}"
) as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(
data_dir, extra_flags=["--joined-dictionary"]
)
train_translation_model(
data_dir,
arch="transformer",
task="translation_multi_simple_epoch",
extra_flags=[
"--encoder-layers",
"2",
"--decoder-layers",
"2",
"--encoder-embed-dim",
"8",
"--decoder-embed-dim",
"8",
"--sampling-method",
"temperature",
"--sampling-temperature",
"1.5",
"--virtual-epoch-size",
"1000",
]
+ enc_ltok_flag
+ dec_ltok_flag,
lang_flags=["--lang-pairs", "in-out,out-in"],
run_validation=True,
extra_valid_flags=enc_ltok_flag + dec_ltok_flag,
)
generate_main(
data_dir,
extra_flags=[
"--task",
"translation_multi_simple_epoch",
"--lang-pairs",
"in-out,out-in",
"--source-lang",
"in",
"--target-lang",
"out",
]
+ enc_ltok_flag
+ dec_ltok_flag,
)
def test_translation_multi_simple_epoch_no_vepoch(self):
# test with all combinations of encoder/decoder lang tokens
with contextlib.redirect_stdout(StringIO()):
enc_ltok_flag = ["--encoder-langtok", "src"]
dec_ltok_flag = ["--decoder-langtok"]
with tempfile.TemporaryDirectory(
"test_translation_multi_simple_epoch_dict"
) as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir, extra_flags=[])
train_translation_model(
data_dir,
arch="transformer",
task="translation_multi_simple_epoch",
extra_flags=[
"--encoder-layers",
"2",
"--decoder-layers",
"2",
"--encoder-embed-dim",
"8",
"--decoder-embed-dim",
"8",
"--sampling-method",
"temperature",
"--sampling-temperature",
"1.5",
]
+ enc_ltok_flag
+ dec_ltok_flag,
lang_flags=["--lang-pairs", "in-out"],
run_validation=True,
extra_valid_flags=enc_ltok_flag + dec_ltok_flag,
)
generate_main(
data_dir,
extra_flags=[
"--task",
"translation_multi_simple_epoch",
"--lang-pairs",
"in-out",
"--source-lang",
"in",
"--target-lang",
"out",
]
+ enc_ltok_flag
+ dec_ltok_flag,
)
def test_translation_multi_simple_epoch_dicts(self):
# test with all combinations of encoder/decoder lang tokens
with contextlib.redirect_stdout(StringIO()):
enc_ltok_flag = ["--encoder-langtok", "src"]
dec_ltok_flag = ["--decoder-langtok"]
with tempfile.TemporaryDirectory(
"test_translation_multi_simple_epoch_dict"
) as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir, extra_flags=[])
train_translation_model(
data_dir,
arch="transformer",
task="translation_multi_simple_epoch",
extra_flags=[
"--encoder-layers",
"2",
"--decoder-layers",
"2",
"--encoder-embed-dim",
"8",
"--decoder-embed-dim",
"8",
"--sampling-method",
"temperature",
"--sampling-temperature",
"1.5",
"--virtual-epoch-size",
"1000",
]
+ enc_ltok_flag
+ dec_ltok_flag,
lang_flags=["--lang-pairs", "in-out"],
run_validation=True,
extra_valid_flags=enc_ltok_flag + dec_ltok_flag,
)
generate_main(
data_dir,
extra_flags=[
"--task",
"translation_multi_simple_epoch",
"--lang-pairs",
"in-out",
"--source-lang",
"in",
"--target-lang",
"out",
]
+ enc_ltok_flag
+ dec_ltok_flag,
)
def test_translation_multi_simple_epoch_src_tgt_dict_spec(self):
# test the specification of explicit --src-dict and --tgt-dict
with contextlib.redirect_stdout(StringIO()):
enc_ltok_flag = ["--encoder-langtok", "src"]
dec_ltok_flag = ["--decoder-langtok"]
with tempfile.TemporaryDirectory(
"test_translation_multi_simple_epoch_dict"
) as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir, extra_flags=[])
train_translation_model(
data_dir,
arch="transformer",
task="translation_multi_simple_epoch",
extra_flags=[
"--source-dict",
f"{data_dir}/dict.in.txt",
"--target-dict",
f"{data_dir}/dict.out.txt",
"--encoder-layers",
"2",
"--decoder-layers",
"2",
"--encoder-embed-dim",
"8",
"--decoder-embed-dim",
"8",
"--sampling-method",
"temperature",
"--sampling-temperature",
"1.5",
"--virtual-epoch-size",
"1000",
]
+ enc_ltok_flag
+ dec_ltok_flag,
lang_flags=["--lang-pairs", "in-out"],
run_validation=True,
extra_valid_flags=enc_ltok_flag + dec_ltok_flag,
)
generate_main(
data_dir,
extra_flags=[
"--task",
"translation_multi_simple_epoch",
"--lang-pairs",
"in-out",
"--source-lang",
"in",
"--target-lang",
"out",
]
+ enc_ltok_flag
+ dec_ltok_flag,
)
def test_transformer_cross_self_attention(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory(
"test_transformer_cross_self_attention"
) as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir)
train_translation_model(
data_dir,
"transformer_iwslt_de_en",
[
"--encoder-layers",
"2",
"--decoder-layers",
"2",
"--encoder-embed-dim",
"8",
"--decoder-embed-dim",
"8",
"--decoder-embed-dim",
"8",
"--no-cross-attention",
"--cross-self-attention",
],
run_validation=True,
)
generate_main(data_dir, extra_flags=[])
def test_transformer_pointer_generator(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory(
"test_transformer_pointer_generator"
) as data_dir:
create_dummy_data(data_dir)
preprocess_summarization_data(data_dir)
train_translation_model(
data_dir,
"transformer_pointer_generator",
extra_flags=[
"--user-dir",
"examples/pointer_generator/pointer_generator_src",
"--encoder-layers",
"2",
"--decoder-layers",
"2",
"--encoder-embed-dim",
"8",
"--decoder-embed-dim",
"8",
"--alignment-layer",
"-1",
"--alignment-heads",
"1",
"--source-position-markers",
"0",
],
run_validation=True,
extra_valid_flags=[
"--user-dir",
"examples/pointer_generator/pointer_generator_src",
],
)
generate_main(
data_dir,
extra_flags=[
"--user-dir",
"examples/pointer_generator/pointer_generator_src",
],
)
def test_lightconv(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_lightconv") as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir)
train_translation_model(
data_dir,
"lightconv_iwslt_de_en",
[
"--encoder-conv-type",
"lightweight",
"--decoder-conv-type",
"lightweight",
"--encoder-embed-dim",
"8",
"--decoder-embed-dim",
"8",
],
)
generate_main(data_dir)
def test_dynamicconv(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_dynamicconv") as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir)
train_translation_model(
data_dir,
"lightconv_iwslt_de_en",
[
"--encoder-conv-type",
"dynamic",
"--decoder-conv-type",
"dynamic",
"--encoder-embed-dim",
"8",
"--decoder-embed-dim",
"8",
],
)
generate_main(data_dir)
def test_cmlm_transformer(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_cmlm_transformer") as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir, ["--joined-dictionary"])
train_translation_model(
data_dir,
"cmlm_transformer",
[
"--apply-bert-init",
"--criterion",
"nat_loss",
"--noise",
"full_mask",
"--pred-length-offset",
"--length-loss-factor",
"0.1",
],
task="translation_lev",
)
generate_main(
data_dir,
[
"--task",
"translation_lev",
"--iter-decode-max-iter",
"9",
"--iter-decode-eos-penalty",
"0",
"--print-step",
],
)
def test_nonautoregressive_transformer(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory(
"test_nonautoregressive_transformer"
) as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir, ["--joined-dictionary"])
train_translation_model(
data_dir,
"nonautoregressive_transformer",
[
"--apply-bert-init",
"--src-embedding-copy",
"--criterion",
"nat_loss",
"--noise",
"full_mask",
"--pred-length-offset",
"--length-loss-factor",
"0.1",
],
task="translation_lev",
)
generate_main(
data_dir,
[
"--task",
"translation_lev",
"--iter-decode-max-iter",
"0",
"--iter-decode-eos-penalty",
"0",
"--print-step",
],
)
# def test_nat_crf_transformer(self):
# with contextlib.redirect_stdout(StringIO()):
# with tempfile.TemporaryDirectory('test_nat_crf_transformer') as data_dir:
# create_dummy_data(data_dir)
# preprocess_translation_data(data_dir, ['--joined-dictionary'])
# train_translation_model(data_dir, 'nacrf_transformer', [
# '--apply-bert-init', '--criterion',
# 'nat_loss', '--noise', 'full_mask', '--pred-length-offset',
# '--length-loss-factor', '0.1',
# '--word-ins-loss-factor', '0.5',
# '--crf-lowrank-approx', '1',
# '--crf-beam-approx', '1'
# ], task='translation_lev')
# generate_main(data_dir, [
# '--task', 'translation_lev',
# '--iter-decode-max-iter', '0',
# '--iter-decode-eos-penalty', '0',
# '--print-step',
# ])
def test_iterative_nonautoregressive_transformer(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory(
"test_iterative_nonautoregressive_transformer"
) as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir, ["--joined-dictionary"])
train_translation_model(
data_dir,
"iterative_nonautoregressive_transformer",
[
"--apply-bert-init",
"--src-embedding-copy",
"--criterion",
"nat_loss",
"--noise",
"full_mask",
"--stochastic-approx",
"--dae-ratio",
"0.5",
"--train-step",
"3",
],
task="translation_lev",
)
generate_main(
data_dir,
[
"--task",
"translation_lev",
"--iter-decode-max-iter",
"9",
"--iter-decode-eos-penalty",
"0",
"--print-step",
],
)
def test_insertion_transformer(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_insertion_transformer") as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir, ["--joined-dictionary"])
train_translation_model(
data_dir,
"insertion_transformer",
[
"--apply-bert-init",
"--criterion",
"nat_loss",
"--noise",
"random_mask",
],
task="translation_lev",
)
generate_main(
data_dir,
[
"--task",
"translation_lev",
"--iter-decode-max-iter",
"9",
"--iter-decode-eos-penalty",
"0",
"--print-step",
],
)
def test_mixture_of_experts(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_moe") as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir)
train_translation_model(
data_dir,
"transformer_iwslt_de_en",
[
"--task",
"translation_moe",
"--user-dir",
"examples/translation_moe/translation_moe_src",
"--method",
"hMoElp",
"--mean-pool-gating-network",
"--num-experts",
"3",
"--encoder-layers",
"2",
"--decoder-layers",
"2",
"--encoder-embed-dim",
"8",
"--decoder-embed-dim",
"8",
],
)
generate_main(
data_dir,
[
"--task",
"translation_moe",
"--user-dir",
"examples/translation_moe/translation_moe_src",
"--method",
"hMoElp",
"--mean-pool-gating-network",
"--num-experts",
"3",
"--gen-expert",
"0",
],
)
def test_alignment(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_alignment") as data_dir:
create_dummy_data(data_dir, alignment=True)
preprocess_translation_data(data_dir, ["--align-suffix", "align"])
train_translation_model(
data_dir,
"transformer_align",
[
"--encoder-layers",
"2",
"--decoder-layers",
"2",
"--encoder-embed-dim",
"8",
"--decoder-embed-dim",
"8",
"--load-alignments",
"--alignment-layer",
"1",
"--criterion",
"label_smoothed_cross_entropy_with_alignment",
],
run_validation=True,
)
generate_main(data_dir)
def test_laser_lstm(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_laser_lstm") as data_dir:
laser_config_file = create_laser_data_and_config_json(data_dir)
train_translation_model(
laser_config_file.name,
"laser_lstm",
[
"--user-dir",
"examples/laser/laser_src",
"--weighting-alpha",
"0.3",
"--encoder-bidirectional",
"--encoder-hidden-size",
"512",
"--encoder-layers",
"5",
"--decoder-layers",
"1",
"--encoder-embed-dim",
"320",
"--decoder-embed-dim",
"320",
"--decoder-lang-embed-dim",
"32",
"--save-dir",
data_dir,
"--disable-validation",
],
task="laser",
lang_flags=[],
)
def test_laser_transformer(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_laser_transformer") as data_dir:
laser_config_file = create_laser_data_and_config_json(data_dir)
train_translation_model(
laser_config_file.name,
"laser_transformer",
[
"--user-dir",
"examples/laser/laser_src",
"--weighting-alpha",
"0.3",
"--encoder-embed-dim",
"320",
"--decoder-embed-dim",
"320",
"--decoder-lang-embed-dim",
"32",
"--save-dir",
data_dir,
"--disable-validation",
],
task="laser",
lang_flags=[],
)
def test_alignment_full_context(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_alignment") as data_dir:
create_dummy_data(data_dir, alignment=True)
preprocess_translation_data(data_dir, ["--align-suffix", "align"])
train_translation_model(
data_dir,
"transformer_align",
[
"--encoder-layers",
"2",
"--decoder-layers",
"2",
"--encoder-embed-dim",
"8",
"--decoder-embed-dim",
"8",
"--load-alignments",
"--alignment-layer",
"1",
"--criterion",
"label_smoothed_cross_entropy_with_alignment",
"--full-context-alignment",
],
run_validation=True,
)
generate_main(data_dir)
def test_transformer_layerdrop(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_transformer_layerdrop") as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir)
train_translation_model(
data_dir,
"transformer_iwslt_de_en",
[
"--encoder-layers",
"3",
"--decoder-layers",
"3",
"--encoder-embed-dim",
"8",
"--decoder-embed-dim",
"8",
"--encoder-layerdrop",
"0.01",
"--decoder-layerdrop",
"0.01",
],
)
generate_main(data_dir)
generate_main(
data_dir,
[
"--model-overrides",
"{'encoder_layers_to_keep':'0,2','decoder_layers_to_keep':'1'}",
],
)
class TestStories(unittest.TestCase):
def setUp(self):
logging.disable(logging.CRITICAL)
def tearDown(self):
logging.disable(logging.NOTSET)
def test_fconv_self_att_wp(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_fconv_self_att_wp") as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir)
config = [
"--encoder-layers",
"[(128, 3)] * 2",
"--decoder-layers",
"[(128, 3)] * 2",
"--decoder-attention",
"True",
"--encoder-attention",
"False",
"--gated-attention",
"True",
"--self-attention",
"True",
"--project-input",
"True",
"--encoder-embed-dim",
"8",
"--decoder-embed-dim",
"8",
"--decoder-out-embed-dim",
"8",
"--multihead-self-attention-nheads",
"2",
]
train_translation_model(data_dir, "fconv_self_att_wp", config)
generate_main(data_dir)
# fusion model
os.rename(
os.path.join(data_dir, "checkpoint_last.pt"),
os.path.join(data_dir, "pretrained.pt"),
)
config.extend(
[
"--pretrained",
"True",
"--pretrained-checkpoint",
os.path.join(data_dir, "pretrained.pt"),
"--save-dir",
os.path.join(data_dir, "fusion_model"),
]
)
train_translation_model(data_dir, "fconv_self_att_wp", config)
class TestLanguageModeling(unittest.TestCase):
def setUp(self):
logging.disable(logging.CRITICAL)
def tearDown(self):
logging.disable(logging.NOTSET)
def test_fconv_lm(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_fconv_lm") as data_dir:
create_dummy_data(data_dir)
preprocess_lm_data(data_dir)
train_language_model(
data_dir,
"fconv_lm",
[
"--decoder-layers",
"[(850, 3)] * 2 + [(1024,4)]",
"--decoder-embed-dim",
"280",
"--optimizer",
"nag",
"--lr",
"0.1",
],
)
eval_lm_main(data_dir)
generate_main(
data_dir,
[
"--task",
"language_modeling",
"--sample-break-mode",
"eos",
"--tokens-per-sample",
"500",
],
)
def test_transformer_lm(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_transformer_lm") as data_dir:
create_dummy_data(data_dir)
preprocess_lm_data(data_dir)
train_language_model(
data_dir,
"transformer_lm",
["--add-bos-token", "--nval", "1"],
run_validation=True,
)
eval_lm_main(data_dir)
eval_lm_main(data_dir, extra_flags=["--context-window", "25"])
generate_main(
data_dir,
[
"--task",
"language_modeling",
"--sample-break-mode",
"eos",
"--tokens-per-sample",
"500",
],
)
def test_normformer_lm(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_transformer_lm") as data_dir:
create_dummy_data(data_dir)
preprocess_lm_data(data_dir)
train_language_model(
data_dir,
"transformer_lm",
[
"--add-bos-token",
"--nval",
"1",
"--scale-fc",
"--scale-heads",
"--scale-attn",
"--scale-fc",
],
run_validation=True,
)
eval_lm_main(data_dir)
eval_lm_main(data_dir, extra_flags=["--context-window", "25"])
generate_main(
data_dir,
[
"--task",
"language_modeling",
"--sample-break-mode",
"eos",
"--tokens-per-sample",
"500",
],
)
def test_transformer_lm_with_adaptive_softmax(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory(
"test_transformer_lm_with_adaptive_softmax"
) as data_dir:
create_dummy_data(data_dir)
preprocess_lm_data(data_dir)
train_language_model(
data_dir,
"transformer_lm",
[
"--add-bos-token",
"--criterion",
"adaptive_loss",
"--adaptive-softmax-cutoff",
"5,10,15",
],
run_validation=True,
)
eval_lm_main(data_dir)
generate_main(
data_dir,
[
"--task",
"language_modeling",
"--sample-break-mode",
"eos",
"--tokens-per-sample",
"500",
],
)
def test_lightconv_lm(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_lightconv_lm") as data_dir:
create_dummy_data(data_dir)
preprocess_lm_data(data_dir)
train_language_model(
data_dir,
"lightconv_lm",
["--add-bos-token"],
run_validation=True,
)
eval_lm_main(data_dir)
generate_main(
data_dir,
[
"--task",
"language_modeling",
"--sample-break-mode",
"eos",
"--tokens-per-sample",
"500",
],
)
def test_lstm_lm(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_lstm_lm") as data_dir:
create_dummy_data(data_dir)
preprocess_lm_data(data_dir)
train_language_model(
data_dir,
"lstm_lm",
["--add-bos-token"],
run_validation=True,
)
eval_lm_main(data_dir)
generate_main(
data_dir,
[
"--task",
"language_modeling",
"--sample-break-mode",
"eos",
"--tokens-per-sample",
"500",
],
)
def test_lstm_lm_residuals(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_lstm_lm_residuals") as data_dir:
create_dummy_data(data_dir)
preprocess_lm_data(data_dir)
train_language_model(
data_dir,
"lstm_lm",
["--add-bos-token", "--residuals"],
run_validation=True,
)
eval_lm_main(data_dir)
generate_main(
data_dir,
[
"--task",
"language_modeling",
"--sample-break-mode",
"eos",
"--tokens-per-sample",
"500",
],
)
@unittest.skipIf(not has_hf_transformers, "skip test if transformers is missing")
def test_transformer_xl_bptt_lm(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_transformer_xl_bptt_lm") as data_dir:
create_dummy_data(data_dir)
preprocess_lm_data(data_dir)
task_flags = [
"--user-dir",
"examples/truncated_bptt",
"--task",
"truncated_bptt_lm",
"--batch-size",
"2",
"--tokens-per-sample",
"50",
]
train_language_model(
data_dir=data_dir,
arch="transformer_xl",
extra_flags=task_flags
+ [
"--n-layer",
"2",
],
task="truncated_bptt_lm",
run_validation=True,
extra_valid_flags=task_flags,
)
eval_lm_main(data_dir, extra_flags=task_flags)
# Train with activation offloading
train_language_model(
data_dir=data_dir,
arch="transformer_xl",
extra_flags=task_flags
+ [
"--n-layer",
"2",
"--offload-activations",
],
task="truncated_bptt_lm",
run_validation=True,
extra_valid_flags=task_flags,
)
class TestMaskedLanguageModel(unittest.TestCase):
def setUp(self):
logging.disable(logging.CRITICAL)
def tearDown(self):
logging.disable(logging.NOTSET)
def test_legacy_masked_lm(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_legacy_mlm") as data_dir:
create_dummy_data(data_dir)
preprocess_lm_data(data_dir)
train_legacy_masked_language_model(data_dir, "masked_lm")
def test_roberta_masked_lm(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_roberta_mlm") as data_dir:
create_dummy_data(data_dir)
preprocess_lm_data(data_dir)
train_masked_lm(
data_dir, "roberta_base", extra_flags=["--encoder-layers", "2"]
)
def test_roberta_sentence_prediction(self):
num_classes = 3
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_roberta_head") as data_dir:
create_dummy_roberta_head_data(data_dir, num_classes=num_classes)
preprocess_lm_data(os.path.join(data_dir, "input0"))
preprocess_lm_data(os.path.join(data_dir, "label"))
train_roberta_head(data_dir, "roberta_base", num_classes=num_classes)
def test_roberta_regression_single(self):
num_classes = 1
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory(
"test_roberta_regression_single"
) as data_dir:
create_dummy_roberta_head_data(
data_dir, num_classes=num_classes, regression=True
)
preprocess_lm_data(os.path.join(data_dir, "input0"))
train_roberta_head(
data_dir,
"roberta_base",
num_classes=num_classes,
extra_flags=["--regression-target"],
)
def test_roberta_regression_multiple(self):
num_classes = 3
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory(
"test_roberta_regression_multiple"
) as data_dir:
create_dummy_roberta_head_data(
data_dir, num_classes=num_classes, regression=True
)
preprocess_lm_data(os.path.join(data_dir, "input0"))
train_roberta_head(
data_dir,
"roberta_base",
num_classes=num_classes,
extra_flags=["--regression-target"],
)
def test_linformer_roberta_masked_lm(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_linformer_roberta_mlm") as data_dir:
create_dummy_data(data_dir)
preprocess_lm_data(data_dir)
train_masked_lm(
data_dir,
"linformer_roberta_base",
extra_flags=[
"--user-dir",
"examples/linformer/linformer_src",
"--encoder-layers",
"2",
],
)
def test_linformer_roberta_sentence_prediction(self):
num_classes = 3
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_linformer_roberta_head") as data_dir:
create_dummy_roberta_head_data(data_dir, num_classes=num_classes)
preprocess_lm_data(os.path.join(data_dir, "input0"))
preprocess_lm_data(os.path.join(data_dir, "label"))
train_roberta_head(
data_dir,
"linformer_roberta_base",
num_classes=num_classes,
extra_flags=["--user-dir", "examples/linformer/linformer_src"],
)
def test_linformer_roberta_regression_single(self):
num_classes = 1
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory(
"test_linformer_roberta_regression_single"
) as data_dir:
create_dummy_roberta_head_data(
data_dir, num_classes=num_classes, regression=True
)
preprocess_lm_data(os.path.join(data_dir, "input0"))
train_roberta_head(
data_dir,
"linformer_roberta_base",
num_classes=num_classes,
extra_flags=[
"--regression-target",
"--user-dir",
"examples/linformer/linformer_src",
],
)
def test_linformer_roberta_regression_multiple(self):
num_classes = 3
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory(
"test_linformer_roberta_regression_multiple"
) as data_dir:
create_dummy_roberta_head_data(
data_dir, num_classes=num_classes, regression=True
)
preprocess_lm_data(os.path.join(data_dir, "input0"))
train_roberta_head(
data_dir,
"linformer_roberta_base",
num_classes=num_classes,
extra_flags=[
"--regression-target",
"--user-dir",
"examples/linformer/linformer_src",
],
)
def _test_pretrained_masked_lm_for_translation(self, learned_pos_emb, encoder_only):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_mlm") as data_dir:
create_dummy_data(data_dir)
preprocess_lm_data(data_dir)
train_legacy_masked_language_model(
data_dir,
arch="masked_lm",
extra_args=("--encoder-learned-pos",) if learned_pos_emb else (),
)
with tempfile.TemporaryDirectory(
"test_mlm_translation"
) as translation_dir:
create_dummy_data(translation_dir)
preprocess_translation_data(
translation_dir, extra_flags=["--joined-dictionary"]
)
# Train transformer with data_dir/checkpoint_last.pt
train_translation_model(
translation_dir,
arch="transformer_from_pretrained_xlm",
extra_flags=[
"--decoder-layers",
"1",
"--decoder-embed-dim",
"32",
"--decoder-attention-heads",
"1",
"--decoder-ffn-embed-dim",
"32",
"--encoder-layers",
"1",
"--encoder-embed-dim",
"32",
"--encoder-attention-heads",
"1",
"--encoder-ffn-embed-dim",
"32",
"--pretrained-xlm-checkpoint",
"{}/checkpoint_last.pt".format(data_dir),
"--activation-fn",
"gelu",
"--max-source-positions",
"500",
"--max-target-positions",
"500",
]
+ (
["--encoder-learned-pos", "--decoder-learned-pos"]
if learned_pos_emb
else []
)
+ (["--init-encoder-only"] if encoder_only else []),
task="translation_from_pretrained_xlm",
)
def test_pretrained_masked_lm_for_translation_learned_pos_emb(self):
self._test_pretrained_masked_lm_for_translation(True, False)
def test_pretrained_masked_lm_for_translation_sinusoidal_pos_emb(self):
self._test_pretrained_masked_lm_for_translation(False, False)
def test_pretrained_masked_lm_for_translation_encoder_only(self):
self._test_pretrained_masked_lm_for_translation(True, True)
def test_r4f_roberta(self):
num_classes = 3
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_r4f_roberta_head") as data_dir:
create_dummy_roberta_head_data(data_dir, num_classes=num_classes)
preprocess_lm_data(os.path.join(data_dir, "input0"))
preprocess_lm_data(os.path.join(data_dir, "label"))
train_roberta_head(
data_dir,
"roberta_base",
num_classes=num_classes,
extra_flags=[
"--user-dir",
"examples/rxf/rxf_src",
"--criterion",
"sentence_prediction_r3f",
"--spectral-norm-classification-head",
],
)
def train_legacy_masked_language_model(data_dir, arch, extra_args=()):
train_parser = options.get_training_parser()
# TODO: langs should be in and out right?
train_args = options.parse_args_and_arch(
train_parser,
[
"--task",
"cross_lingual_lm",
data_dir,
"--arch",
arch,
# Optimizer args
"--optimizer",
"adam",
"--lr-scheduler",
"reduce_lr_on_plateau",
"--lr-shrink",
"0.5",
"--lr",
"0.0001",
"--stop-min-lr",
"1e-09",
# dropout, attention args
"--dropout",
"0.1",
"--attention-dropout",
"0.1",
# MLM args
"--criterion",
"legacy_masked_lm_loss",
"--masked-lm-only",
"--monolingual-langs",
"in,out",
"--num-segment",
"5",
# Transformer args: use a small transformer model for fast training
"--encoder-layers",
"1",
"--encoder-embed-dim",
"32",
"--encoder-attention-heads",
"1",
"--encoder-ffn-embed-dim",
"32",
# Other training args
"--max-tokens",
"500",
"--tokens-per-sample",
"500",
"--save-dir",
data_dir,
"--max-epoch",
"1",
"--no-progress-bar",
"--distributed-world-size",
"1",
"--dataset-impl",
"raw",
"--num-workers",
"0",
]
+ list(extra_args),
)
train.main(train_args)
class TestOptimizers(unittest.TestCase):
def setUp(self):
logging.disable(logging.CRITICAL)
def tearDown(self):
logging.disable(logging.NOTSET)
def test_optimizers(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_optimizers") as data_dir:
# Use just a bit of data and tiny model to keep this test runtime reasonable
create_dummy_data(data_dir, num_examples=10, maxlen=5)
preprocess_translation_data(data_dir)
optimizers = ["adafactor", "adam", "nag", "adagrad", "sgd", "adadelta"]
last_checkpoint = os.path.join(data_dir, "checkpoint_last.pt")
for optimizer in optimizers:
if os.path.exists(last_checkpoint):
os.remove(last_checkpoint)
train_translation_model(
data_dir,
"lstm",
[
"--required-batch-size-multiple",
"1",
"--encoder-layers",
"1",
"--encoder-hidden-size",
"32",
"--decoder-layers",
"1",
"--optimizer",
optimizer,
],
)
generate_main(data_dir)
def read_last_log_entry(
logs: List[logging.LogRecord], logger_name: str
) -> Dict[str, float]:
for x in reversed(logs):
if x.name == logger_name:
return json.loads(x.message)
raise ValueError(f"No entries from {logger_name} found in captured logs")
class TestActivationCheckpointing(unittest.TestCase):
base_flags = [
"--encoder-layers",
"2",
"--decoder-layers",
"2",
"--encoder-embed-dim",
"8",
"--decoder-embed-dim",
"8",
"--restore-file",
"x.pt",
"--log-format",
"json",
"--log-interval",
"1",
"--max-update",
"2",
]
def _train(self, data_dir, extra_flags):
with self.assertLogs() as logs:
train_translation_model(
data_dir,
"transformer_iwslt_de_en",
self.base_flags + extra_flags,
run_validation=True,
extra_valid_flags=["--log-format", "json"],
)
return logs.records
def test_activation_offloading_does_not_change_metrics(self):
"""Neither ----checkpoint-activations nor --offload-activations should change loss"""
with tempfile.TemporaryDirectory("test_transformer_with_act_cpt") as data_dir:
with self.assertLogs():
create_dummy_data(data_dir, num_examples=20)
preprocess_translation_data(data_dir)
offload_logs = self._train(data_dir, ["--offload-activations"])
baseline_logs = self._train(data_dir, [])
assert len(baseline_logs) == len(offload_logs)
baseline_valid_stats = read_last_log_entry(baseline_logs, "valid")
offload_valid_stats = read_last_log_entry(offload_logs, "valid")
baseline_train_stats = read_last_log_entry(baseline_logs, "train")
offload_train_stats = read_last_log_entry(offload_logs, "train")
assert (
baseline_train_stats["train_loss"] == offload_train_stats["train_loss"]
)
assert (
baseline_valid_stats["valid_loss"] == offload_valid_stats["valid_loss"]
)
def test_activation_checkpointing_does_not_change_metrics(self):
"""--checkpoint-activations should not change loss"""
with tempfile.TemporaryDirectory("test_transformer_with_act_cpt") as data_dir:
with self.assertLogs():
create_dummy_data(data_dir, num_examples=20)
preprocess_translation_data(data_dir)
ckpt_logs = self._train(data_dir, ["--checkpoint-activations"])
baseline_logs = self._train(data_dir, [])
assert len(baseline_logs) == len(ckpt_logs)
baseline_train_stats = read_last_log_entry(baseline_logs, "train")
ckpt_train_stats = read_last_log_entry(ckpt_logs, "train")
assert baseline_train_stats["train_loss"] == ckpt_train_stats["train_loss"]
baseline_valid_stats = read_last_log_entry(baseline_logs, "valid")
ckpt_valid_stats = read_last_log_entry(ckpt_logs, "valid")
assert baseline_valid_stats["valid_loss"] == ckpt_valid_stats["valid_loss"]
def create_dummy_roberta_head_data(
data_dir, num_examples=100, maxlen=10, num_classes=2, regression=False
):
input_dir = "input0"
def _create_dummy_data(filename):
random_data = torch.rand(num_examples * maxlen)
input_data = 97 + torch.floor(26 * random_data).int()
if regression:
output_data = torch.rand((num_examples, num_classes))
else:
output_data = 1 + torch.floor(num_classes * torch.rand(num_examples)).int()
with open(os.path.join(data_dir, input_dir, filename + ".out"), "w") as f_in:
label_filename = filename + ".label" if regression else filename + ".out"
with open(os.path.join(data_dir, "label", label_filename), "w") as f_out:
offset = 0
for i in range(num_examples):
# write example input
ex_len = random.randint(1, maxlen)
ex_str = " ".join(map(chr, input_data[offset : offset + ex_len]))
print(ex_str, file=f_in)
# write example label
if regression:
class_str = " ".join(map(str, output_data[i].numpy()))
print(class_str, file=f_out)
else:
class_str = "class{}".format(output_data[i])
print(class_str, file=f_out)
offset += ex_len
os.mkdir(os.path.join(data_dir, input_dir))
os.mkdir(os.path.join(data_dir, "label"))
_create_dummy_data("train")
_create_dummy_data("valid")
_create_dummy_data("test")
def train_masked_lm(data_dir, arch, extra_flags=None):
train_parser = options.get_training_parser()
train_args = options.parse_args_and_arch(
train_parser,
[
"--task",
"masked_lm",
data_dir,
"--arch",
arch,
"--optimizer",
"adam",
"--lr",
"0.0001",
"--criterion",
"masked_lm",
"--batch-size",
"500",
"--save-dir",
data_dir,
"--max-epoch",
"1",
"--no-progress-bar",
"--distributed-world-size",
"1",
"--ddp-backend",
"no_c10d",
"--num-workers",
"0",
]
+ (extra_flags or []),
)
train.main(train_args)
def train_roberta_head(data_dir, arch, num_classes=2, extra_flags=None):
train_parser = options.get_training_parser()
train_args = options.parse_args_and_arch(
train_parser,
[
"--task",
"sentence_prediction",
data_dir,
"--arch",
arch,
"--encoder-layers",
"2",
"--num-classes",
str(num_classes),
"--optimizer",
"adam",
"--lr",
"0.0001",
"--criterion",
"sentence_prediction",
"--max-tokens",
"500",
"--max-positions",
"500",
"--batch-size",
"500",
"--save-dir",
data_dir,
"--max-epoch",
"1",
"--no-progress-bar",
"--distributed-world-size",
"1",
"--ddp-backend",
"no_c10d",
"--num-workers",
"0",
]
+ (extra_flags or []),
)
train.main(train_args)
def eval_lm_main(data_dir, extra_flags=None):
eval_lm_parser = options.get_eval_lm_parser()
eval_lm_args = options.parse_args_and_arch(
eval_lm_parser,
[
data_dir,
"--path",
os.path.join(data_dir, "checkpoint_last.pt"),
"--no-progress-bar",
"--num-workers",
"0",
]
+ (extra_flags or []),
)
eval_lm.main(eval_lm_args)
if __name__ == "__main__":
unittest.main()
| 73,337 | 37.437107 | 93 | py |
CIF-HieraDist | CIF-HieraDist-main/tests/test_concat_dataset.py | # 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 unittest
import torch
from fairseq.data import LanguagePairDataset, TokenBlockDataset
from fairseq.data.concat_dataset import ConcatDataset
from tests.test_train import mock_dict
class TestConcatDataset(unittest.TestCase):
def setUp(self):
d = mock_dict()
tokens_1 = torch.LongTensor([1]).view(1, -1)
tokens_ds1 = TokenBlockDataset(
tokens_1,
sizes=[tokens_1.size(-1)],
block_size=1,
pad=0,
eos=1,
include_targets=False,
)
self.dataset_1 = LanguagePairDataset(
tokens_ds1, tokens_ds1.sizes, d, shuffle=False
)
tokens_2 = torch.LongTensor([2]).view(1, -1)
tokens_ds2 = TokenBlockDataset(
tokens_2,
sizes=[tokens_2.size(-1)],
block_size=1,
pad=0,
eos=1,
include_targets=False,
)
self.dataset_2 = LanguagePairDataset(
tokens_ds2, tokens_ds2.sizes, d, shuffle=False
)
def test_concat_dataset_basics(self):
d = ConcatDataset([self.dataset_1, self.dataset_2])
assert len(d) == 2
assert d[0]["source"][0] == 1
assert d[1]["source"][0] == 2
d = ConcatDataset([self.dataset_1, self.dataset_2], sample_ratios=[1, 2])
assert len(d) == 3
assert d[0]["source"][0] == 1
assert d[1]["source"][0] == 2
assert d[2]["source"][0] == 2
d = ConcatDataset([self.dataset_1, self.dataset_2], sample_ratios=[2, 1])
assert len(d) == 3
assert d[0]["source"][0] == 1
assert d[1]["source"][0] == 1
assert d[2]["source"][0] == 2
| 1,866 | 30.644068 | 81 | py |
CIF-HieraDist | CIF-HieraDist-main/tests/test_activation_checkpointing.py | # 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 unittest
import torch
import torch.nn as nn
from fairseq.modules.checkpoint_activations import checkpoint_wrapper
from torch.utils.checkpoint import checkpoint
class Model(nn.Module):
def __init__(
self, use_pytorch_checkpoint=False, use_fairseq_checkpoint=False, **kwargs
):
super().__init__()
torch.manual_seed(0)
self.use_pytorch_checkpoint = use_pytorch_checkpoint
self.ffn = nn.Sequential(
nn.Linear(32, 128),
# add a Dropout layer to test RNG save/restore
nn.Dropout(p=0.5),
nn.Linear(128, 32),
)
if use_fairseq_checkpoint:
self.ffn = checkpoint_wrapper(self.ffn, **kwargs)
self.out = nn.Linear(32, 1)
def forward(self, x):
if self.use_pytorch_checkpoint:
x = checkpoint(self.ffn, x)
else:
x = self.ffn(x)
return self.out(x)
class TestActivationCheckpointing(unittest.TestCase):
def _test_checkpoint_wrapper(self, device, log_memory_usage=False):
def get_loss_and_gnorm(model):
torch.manual_seed(1)
input = torch.rand(2, 16, 32).requires_grad_(True).to(device)
model.zero_grad()
loss = model(input).sum()
loss.backward()
gnorm = torch.norm(
torch.stack([torch.norm(p.grad.detach()) for p in model.parameters()])
)
return {"loss": loss, "gnorm": gnorm}
model = Model().to(device)
no_cpt = get_loss_and_gnorm(model)
model = Model(use_pytorch_checkpoint=True).to(device)
pyt_cpt = get_loss_and_gnorm(model)
torch.testing.assert_allclose(no_cpt["loss"], pyt_cpt["loss"])
torch.testing.assert_allclose(no_cpt["gnorm"], pyt_cpt["gnorm"])
model = Model(use_fairseq_checkpoint=True).to(device)
fairseq_cpt = get_loss_and_gnorm(model)
torch.testing.assert_allclose(no_cpt["loss"], fairseq_cpt["loss"])
torch.testing.assert_allclose(no_cpt["gnorm"], fairseq_cpt["gnorm"])
model = Model(use_fairseq_checkpoint=True, offload_to_cpu=True).to(device)
fairseq_cpt_offload = get_loss_and_gnorm(model)
torch.testing.assert_allclose(no_cpt["loss"], fairseq_cpt_offload["loss"])
torch.testing.assert_allclose(no_cpt["gnorm"], fairseq_cpt_offload["gnorm"])
def test_checkpoint_wrapper_cpu(self):
self._test_checkpoint_wrapper(device=torch.device("cpu"))
@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU")
def test_checkpoint_wrapper_cuda(self):
self._test_checkpoint_wrapper(device=torch.device("cuda"))
if __name__ == "__main__":
unittest.main()
| 2,904 | 35.3125 | 86 | py |
CIF-HieraDist | CIF-HieraDist-main/tests/test_noising.py | # 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 unittest
from typing import Dict, List
import tests.utils as test_utils
import torch
from fairseq import utils
from fairseq.data import (
Dictionary,
LanguagePairDataset,
TransformEosDataset,
data_utils,
noising,
)
class TestDataNoising(unittest.TestCase):
def _get_test_data_with_bpe_cont_marker(self, append_eos=True):
"""
Args:
append_eos: if True, each input sentence in the source tokens tensor
will have an EOS appended to the end.
Returns:
vocabs: BPE vocab with continuation markers as suffixes to denote
non-end of word tokens. This is the standard BPE format used in
fairseq's preprocessing.
x: input tensor containing numberized source tokens, with EOS at the
end if append_eos is true
src_lengths: and source lengths.
"""
vocab = Dictionary()
vocab.add_symbol("he@@")
vocab.add_symbol("llo")
vocab.add_symbol("how")
vocab.add_symbol("are")
vocab.add_symbol("y@@")
vocab.add_symbol("ou")
vocab.add_symbol("n@@")
vocab.add_symbol("ew")
vocab.add_symbol("or@@")
vocab.add_symbol("k")
src_tokens = [
["he@@", "llo", "n@@", "ew", "y@@", "or@@", "k"],
["how", "are", "y@@", "ou"],
]
x, src_lengths = x, src_lengths = self._convert_src_tokens_to_tensor(
vocab=vocab, src_tokens=src_tokens, append_eos=append_eos
)
return vocab, x, src_lengths
def _get_test_data_with_bpe_end_marker(self, append_eos=True):
"""
Args:
append_eos: if True, each input sentence in the source tokens tensor
will have an EOS appended to the end.
Returns:
vocabs: BPE vocab with end-of-word markers as suffixes to denote
tokens at the end of a word. This is an alternative to fairseq's
standard preprocessing framework and is not generally supported
within fairseq.
x: input tensor containing numberized source tokens, with EOS at the
end if append_eos is true
src_lengths: and source lengths.
"""
vocab = Dictionary()
vocab.add_symbol("he")
vocab.add_symbol("llo_EOW")
vocab.add_symbol("how_EOW")
vocab.add_symbol("are_EOW")
vocab.add_symbol("y")
vocab.add_symbol("ou_EOW")
vocab.add_symbol("n")
vocab.add_symbol("ew_EOW")
vocab.add_symbol("or")
vocab.add_symbol("k_EOW")
src_tokens = [
["he", "llo_EOW", "n", "ew_EOW", "y", "or", "k_EOW"],
["how_EOW", "are_EOW", "y", "ou_EOW"],
]
x, src_lengths = x, src_lengths = self._convert_src_tokens_to_tensor(
vocab=vocab, src_tokens=src_tokens, append_eos=append_eos
)
return vocab, x, src_lengths
def _get_test_data_with_word_vocab(self, append_eos=True):
"""
Args:
append_eos: if True, each input sentence in the source tokens tensor
will have an EOS appended to the end.
Returns:
vocabs: word vocab
x: input tensor containing numberized source tokens, with EOS at the
end if append_eos is true
src_lengths: and source lengths.
"""
vocab = Dictionary()
vocab.add_symbol("hello")
vocab.add_symbol("how")
vocab.add_symbol("are")
vocab.add_symbol("you")
vocab.add_symbol("new")
vocab.add_symbol("york")
src_tokens = [
["hello", "new", "york", "you"],
["how", "are", "you", "new", "york"],
]
x, src_lengths = self._convert_src_tokens_to_tensor(
vocab=vocab, src_tokens=src_tokens, append_eos=append_eos
)
return vocab, x, src_lengths
def _convert_src_tokens_to_tensor(
self, vocab: Dictionary, src_tokens: List[List[str]], append_eos: bool
):
src_len = [len(x) for x in src_tokens]
# If we have to append EOS, we include EOS in counting src length
if append_eos:
src_len = [length + 1 for length in src_len]
x = torch.LongTensor(len(src_tokens), max(src_len)).fill_(vocab.pad())
for i in range(len(src_tokens)):
for j in range(len(src_tokens[i])):
x[i][j] = vocab.index(src_tokens[i][j])
if append_eos:
x[i][j + 1] = vocab.eos()
x = x.transpose(1, 0)
return x, torch.LongTensor(src_len)
def assert_eos_at_end(self, x, x_len, eos):
"""Asserts last token of every sentence in x is EOS"""
for i in range(len(x_len)):
self.assertEqual(
x[x_len[i] - 1][i],
eos,
(
"Expected eos (token id {eos}) at the end of sentence {i} "
"but got {other} instead"
).format(i=i, eos=eos, other=x[i][-1]),
)
def assert_word_dropout_correct(self, x, x_noised, x_len, l_noised):
# Expect only the first word (2 bpe tokens) of the first example
# was dropped out
self.assertEqual(x_len[0] - 2, l_noised[0])
for i in range(l_noised[0]):
self.assertEqual(x_noised[i][0], x[i + 2][0])
def test_word_dropout_with_eos(self):
vocab, x, x_len = self._get_test_data_with_bpe_cont_marker(append_eos=True)
with data_utils.numpy_seed(1234):
noising_gen = noising.WordDropout(vocab)
x_noised, l_noised = noising_gen.noising(x, x_len, 0.2)
self.assert_word_dropout_correct(
x=x, x_noised=x_noised, x_len=x_len, l_noised=l_noised
)
self.assert_eos_at_end(x=x_noised, x_len=l_noised, eos=vocab.eos())
def assert_word_blanking_correct(self, x, x_noised, x_len, l_noised, unk):
# Expect only the first word (2 bpe tokens) of the first example
# was blanked out
self.assertEqual(x_len[0], l_noised[0])
for i in range(l_noised[0]):
if i < 2:
self.assertEqual(x_noised[i][0], unk)
else:
self.assertEqual(x_noised[i][0], x[i][0])
def test_word_blank_with_eos(self):
vocab, x, x_len = self._get_test_data_with_bpe_cont_marker(append_eos=True)
with data_utils.numpy_seed(1234):
noising_gen = noising.WordDropout(vocab)
x_noised, l_noised = noising_gen.noising(x, x_len, 0.2, vocab.unk())
self.assert_word_blanking_correct(
x=x, x_noised=x_noised, x_len=x_len, l_noised=l_noised, unk=vocab.unk()
)
self.assert_eos_at_end(x=x_noised, x_len=l_noised, eos=vocab.eos())
def generate_unchanged_shuffle_map(self, length):
return {i: i for i in range(length)}
def assert_word_shuffle_matches_expected(
self,
x,
x_len,
max_shuffle_distance: int,
vocab: Dictionary,
expected_shufle_maps: List[Dict[int, int]],
expect_eos_at_end: bool,
bpe_end_marker=None,
):
"""
This verifies that with a given x, x_len, max_shuffle_distance, and
vocab, we get the expected shuffle result.
Args:
x: Tensor of shape (T x B) = (sequence_length, batch_size)
x_len: Tensor of length B = batch_size
max_shuffle_distance: arg to pass to noising
expected_shuffle_maps: List[mapping] where mapping is a
Dict[old_index, new_index], mapping x's elements from their
old positions in x to their new positions in x.
expect_eos_at_end: if True, check the output to make sure there is
an EOS at the end.
bpe_end_marker: str denoting the BPE end token. If this is not None, we
set the BPE cont token to None in the noising classes.
"""
bpe_cont_marker = None
if bpe_end_marker is None:
bpe_cont_marker = "@@"
with data_utils.numpy_seed(1234):
word_shuffle = noising.WordShuffle(
vocab, bpe_cont_marker=bpe_cont_marker, bpe_end_marker=bpe_end_marker
)
x_noised, l_noised = word_shuffle.noising(
x, x_len, max_shuffle_distance=max_shuffle_distance
)
# For every example, we have a different expected shuffle map. We check
# that each example is shuffled as expected according to each
# corresponding shuffle map.
for i in range(len(expected_shufle_maps)):
shuffle_map = expected_shufle_maps[i]
for k, v in shuffle_map.items():
self.assertEqual(x[k][i], x_noised[v][i])
# Shuffling should not affect the length of each example
for pre_shuffle_length, post_shuffle_length in zip(x_len, l_noised):
self.assertEqual(pre_shuffle_length, post_shuffle_length)
if expect_eos_at_end:
self.assert_eos_at_end(x=x_noised, x_len=l_noised, eos=vocab.eos())
def test_word_shuffle_with_eos(self):
vocab, x, x_len = self._get_test_data_with_bpe_cont_marker(append_eos=True)
# Assert word shuffle with max shuffle distance 0 causes input to be
# unchanged
self.assert_word_shuffle_matches_expected(
x=x,
x_len=x_len,
max_shuffle_distance=0,
vocab=vocab,
expected_shufle_maps=[
self.generate_unchanged_shuffle_map(example_len)
for example_len in x_len
],
expect_eos_at_end=True,
)
# Assert word shuffle with max shuffle distance 3 matches our expected
# shuffle order
self.assert_word_shuffle_matches_expected(
x=x,
x_len=x_len,
vocab=vocab,
max_shuffle_distance=3,
expected_shufle_maps=[
self.generate_unchanged_shuffle_map(x_len[0]),
{0: 0, 1: 3, 2: 1, 3: 2},
],
expect_eos_at_end=True,
)
def test_word_shuffle_with_eos_nonbpe(self):
"""The purpose of this is to test shuffling logic with word vocabs"""
vocab, x, x_len = self._get_test_data_with_word_vocab(append_eos=True)
# Assert word shuffle with max shuffle distance 0 causes input to be
# unchanged
self.assert_word_shuffle_matches_expected(
x=x,
x_len=x_len,
max_shuffle_distance=0,
vocab=vocab,
expected_shufle_maps=[
self.generate_unchanged_shuffle_map(example_len)
for example_len in x_len
],
expect_eos_at_end=True,
)
# Assert word shuffle with max shuffle distance 3 matches our expected
# shuffle order
self.assert_word_shuffle_matches_expected(
x=x,
x_len=x_len,
vocab=vocab,
max_shuffle_distance=3,
expected_shufle_maps=[
{0: 0, 1: 1, 2: 3, 3: 2},
{0: 0, 1: 2, 2: 1, 3: 3, 4: 4},
],
expect_eos_at_end=True,
)
def test_word_shuffle_without_eos(self):
"""Same result as word shuffle with eos except no EOS at end"""
vocab, x, x_len = self._get_test_data_with_bpe_cont_marker(append_eos=False)
# Assert word shuffle with max shuffle distance 0 causes input to be
# unchanged
self.assert_word_shuffle_matches_expected(
x=x,
x_len=x_len,
max_shuffle_distance=0,
vocab=vocab,
expected_shufle_maps=[
self.generate_unchanged_shuffle_map(example_len)
for example_len in x_len
],
expect_eos_at_end=False,
)
# Assert word shuffle with max shuffle distance 3 matches our expected
# shuffle order
self.assert_word_shuffle_matches_expected(
x=x,
x_len=x_len,
vocab=vocab,
max_shuffle_distance=3,
expected_shufle_maps=[
self.generate_unchanged_shuffle_map(x_len[0]),
{0: 0, 1: 3, 2: 1, 3: 2},
],
expect_eos_at_end=False,
)
def test_word_shuffle_without_eos_with_bpe_end_marker(self):
"""Same result as word shuffle without eos except using BPE end token"""
vocab, x, x_len = self._get_test_data_with_bpe_end_marker(append_eos=False)
# Assert word shuffle with max shuffle distance 0 causes input to be
# unchanged
self.assert_word_shuffle_matches_expected(
x=x,
x_len=x_len,
max_shuffle_distance=0,
vocab=vocab,
expected_shufle_maps=[
self.generate_unchanged_shuffle_map(example_len)
for example_len in x_len
],
expect_eos_at_end=False,
bpe_end_marker="_EOW",
)
# Assert word shuffle with max shuffle distance 3 matches our expected
# shuffle order
self.assert_word_shuffle_matches_expected(
x=x,
x_len=x_len,
vocab=vocab,
max_shuffle_distance=3,
expected_shufle_maps=[
self.generate_unchanged_shuffle_map(x_len[0]),
{0: 0, 1: 3, 2: 1, 3: 2},
],
expect_eos_at_end=False,
bpe_end_marker="_EOW",
)
def assert_no_eos_at_end(self, x, x_len, eos):
"""Asserts that the last token of each sentence in x is not EOS"""
for i in range(len(x_len)):
self.assertNotEqual(
x[x_len[i] - 1][i],
eos,
"Expected no eos (token id {eos}) at the end of sentence {i}.".format(
eos=eos, i=i
),
)
def test_word_dropout_without_eos(self):
"""Same result as word dropout with eos except no EOS at end"""
vocab, x, x_len = self._get_test_data_with_bpe_cont_marker(append_eos=False)
with data_utils.numpy_seed(1234):
noising_gen = noising.WordDropout(vocab)
x_noised, l_noised = noising_gen.noising(x, x_len, 0.2)
self.assert_word_dropout_correct(
x=x, x_noised=x_noised, x_len=x_len, l_noised=l_noised
)
self.assert_no_eos_at_end(x=x_noised, x_len=l_noised, eos=vocab.eos())
def test_word_blank_without_eos(self):
"""Same result as word blank with eos except no EOS at end"""
vocab, x, x_len = self._get_test_data_with_bpe_cont_marker(append_eos=False)
with data_utils.numpy_seed(1234):
noising_gen = noising.WordDropout(vocab)
x_noised, l_noised = noising_gen.noising(x, x_len, 0.2, vocab.unk())
self.assert_word_blanking_correct(
x=x, x_noised=x_noised, x_len=x_len, l_noised=l_noised, unk=vocab.unk()
)
self.assert_no_eos_at_end(x=x_noised, x_len=l_noised, eos=vocab.eos())
def _get_noising_dataset_batch(
self,
src_tokens_no_pad,
src_dict,
append_eos_to_tgt=False,
):
"""
Constructs a NoisingDataset and the corresponding
``LanguagePairDataset(NoisingDataset(src), src)``. If
*append_eos_to_tgt* is True, wrap the source dataset in
:class:`TransformEosDataset` to append EOS to the clean source when
using it as the target.
"""
src_dataset = test_utils.TestDataset(data=src_tokens_no_pad)
noising_dataset = noising.NoisingDataset(
src_dataset=src_dataset,
src_dict=src_dict,
seed=1234,
max_word_shuffle_distance=3,
word_dropout_prob=0.2,
word_blanking_prob=0.2,
noising_class=noising.UnsupervisedMTNoising,
)
tgt = src_dataset
language_pair_dataset = LanguagePairDataset(
src=noising_dataset, tgt=tgt, src_sizes=None, src_dict=src_dict
)
language_pair_dataset = TransformEosDataset(
language_pair_dataset,
src_dict.eos(),
append_eos_to_tgt=append_eos_to_tgt,
)
dataloader = torch.utils.data.DataLoader(
dataset=language_pair_dataset,
batch_size=2,
collate_fn=language_pair_dataset.collater,
)
denoising_batch_result = next(iter(dataloader))
return denoising_batch_result
def test_noising_dataset_with_eos(self):
src_dict, src_tokens, _ = self._get_test_data_with_bpe_cont_marker(
append_eos=True
)
# Format data for src_dataset
src_tokens = torch.t(src_tokens)
src_tokens_no_pad = []
for src_sentence in src_tokens:
src_tokens_no_pad.append(
utils.strip_pad(tensor=src_sentence, pad=src_dict.pad())
)
denoising_batch_result = self._get_noising_dataset_batch(
src_tokens_no_pad=src_tokens_no_pad, src_dict=src_dict
)
eos, pad = src_dict.eos(), src_dict.pad()
# Generated noisy source as source
expected_src = torch.LongTensor(
[[4, 5, 10, 11, 8, 12, 13, eos], [pad, pad, pad, 6, 8, 9, 7, eos]]
)
# Original clean source as target (right-padded)
expected_tgt = torch.LongTensor(
[[4, 5, 10, 11, 8, 12, 13, eos], [6, 7, 8, 9, eos, pad, pad, pad]]
)
generated_src = denoising_batch_result["net_input"]["src_tokens"]
tgt_tokens = denoising_batch_result["target"]
self.assertTensorEqual(expected_src, generated_src)
self.assertTensorEqual(expected_tgt, tgt_tokens)
def test_noising_dataset_without_eos(self):
"""
Similar to test noising dataset with eos except that we have to set
*append_eos_to_tgt* to ``True``.
"""
src_dict, src_tokens, _ = self._get_test_data_with_bpe_cont_marker(
append_eos=False
)
# Format data for src_dataset
src_tokens = torch.t(src_tokens)
src_tokens_no_pad = []
for src_sentence in src_tokens:
src_tokens_no_pad.append(
utils.strip_pad(tensor=src_sentence, pad=src_dict.pad())
)
denoising_batch_result = self._get_noising_dataset_batch(
src_tokens_no_pad=src_tokens_no_pad,
src_dict=src_dict,
append_eos_to_tgt=True,
)
eos, pad = src_dict.eos(), src_dict.pad()
# Generated noisy source as source
expected_src = torch.LongTensor(
[[4, 5, 10, 11, 8, 12, 13], [pad, pad, pad, 6, 8, 9, 7]]
)
# Original clean source as target (right-padded)
expected_tgt = torch.LongTensor(
[[4, 5, 10, 11, 8, 12, 13, eos], [6, 7, 8, 9, eos, pad, pad, pad]]
)
generated_src = denoising_batch_result["net_input"]["src_tokens"]
tgt_tokens = denoising_batch_result["target"]
self.assertTensorEqual(expected_src, generated_src)
self.assertTensorEqual(expected_tgt, tgt_tokens)
def assertTensorEqual(self, t1, t2):
self.assertEqual(t1.size(), t2.size(), "size mismatch")
self.assertEqual(t1.ne(t2).long().sum(), 0)
if __name__ == "__main__":
unittest.main()
| 19,813 | 36.314501 | 87 | py |
CIF-HieraDist | CIF-HieraDist-main/tests/test_constraints.py | # 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 sys
import unittest
import torch
from fairseq.token_generation_constraints import *
def tensorize(constraints: List[List[int]]) -> torch.Tensor:
return [torch.tensor(x) for x in constraints]
class TestHelperRoutines(unittest.TestCase):
def setUp(self):
self.examples = [
([[]], torch.tensor([[0]])),
([[], []], torch.tensor([[0], [0]])),
([[torch.tensor([1, 2])], []], torch.tensor([[1, 1, 2, 0], [0, 0, 0, 0]])),
(
[
[
torch.tensor([3, 1, 2]),
torch.tensor([3]),
torch.tensor([4, 5, 6, 7]),
],
[],
[torch.tensor([1, 8, 9, 10, 1, 4, 11, 12])],
],
torch.tensor(
[
[3, 3, 1, 2, 0, 3, 0, 4, 5, 6, 7, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 8, 9, 10, 1, 4, 11, 12, 0, 0, 0],
]
),
),
]
def test_packing(self):
"""Ensures the list of lists of tensors gets packed correctly."""
for batch_constraints, expected_tensor in self.examples:
packed = pack_constraints(batch_constraints)
assert torch.equal(packed, expected_tensor)
class TestUnorderedConstraintState(unittest.TestCase):
def setUp(self):
# Tuples of (contraint set, expected printed graph, token counts per node)
self.examples = [
(
tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]),
"([None].False#6 ([1].True#4 ([2].False#1 [3].True#1) [3].True#1 [4].True#1) ([4].False#2 ([5].True#2 ([6].False#1 [7].True#1))))",
{1: 4, 2: 1, 3: 2, 4: 3, 5: 2, 6: 1, 7: 1},
),
([], "[None].False#0", {}),
(tensorize([[0]]), "([None].False#1 [0].True#1)", {0: 1}),
(
tensorize([[100000, 1, 2, 3, 4, 5]]),
"([None].False#1 ([100000].False#1 ([1].False#1 ([2].False#1 ([3].False#1 ([4].False#1 [5].True#1))))))",
{100000: 1, 1: 1, 2: 1, 3: 1, 4: 1, 5: 1},
),
(
tensorize([[1, 2], [1, 2]]),
"([None].False#2 ([1].False#2 [2].True#2))",
{1: 2, 2: 2},
),
(
tensorize([[1, 2], [3, 4]]),
"([None].False#2 ([1].False#1 [2].True#1) ([3].False#1 [4].True#1))",
{1: 1, 2: 1, 3: 1, 4: 1},
),
]
self.sequences = [
(
self.examples[0][0],
[],
{"bank": 0, "num_completed": 0, "finished": False, "is_root": True},
),
(
self.examples[0][0],
[1, 2],
{"bank": 2, "num_completed": 0, "finished": False, "is_root": False},
),
(
self.examples[0][0],
[1, 2, 94],
{"bank": 1, "num_completed": 1, "finished": False, "is_root": True},
),
(
self.examples[0][0],
[1, 3, 999, 1, 4],
{"bank": 4, "num_completed": 2, "finished": False, "is_root": False},
),
(
self.examples[0][0],
[1, 3, 999, 1, 4, 999],
{"bank": 4, "num_completed": 2, "finished": False, "is_root": True},
),
(
self.examples[0][0],
[4, 5, 6, 8],
{"bank": 2, "num_completed": 1, "finished": False, "is_root": True},
),
(
self.examples[0][0],
# Tricky, because in last three, goes down [1->4] branch, could miss [1] and [4->5]
# [[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]],
[1, 2, 3, 1, 3, 1, 4, 4, 5, 6, 7, 1, 4, 5],
{"bank": 14, "num_completed": 6, "finished": True, "is_root": False},
),
(
self.examples[0][0],
[1, 2, 3, 999, 1, 3, 1, 4, 4, 5, 6, 7, 1, 4, 5, 117],
{"bank": 14, "num_completed": 6, "finished": True, "is_root": True},
),
(
tensorize([[1], [2, 3]]),
# Should not be able to get credit for entering 1 a second time
[1, 1],
{"bank": 1, "num_completed": 1, "finished": False, "is_root": True},
),
(
self.examples[4][0],
[1, 2, 1, 2],
{"bank": 4, "num_completed": 2, "finished": True, "is_root": False},
),
(
self.examples[4][0],
[1, 2, 1, 2, 1],
{"bank": 4, "num_completed": 2, "finished": True, "is_root": True},
),
(
self.examples[5][0],
[1, 2, 3, 4, 5],
{"bank": 4, "num_completed": 2, "finished": True, "is_root": True},
),
]
def test_graphs(self):
"""
Test whether unordered graph systems are created correctly.
"""
for example in self.examples:
constraints, expected, gold_counts = example
c = ConstraintNode.create(constraints)
assert (
ConstraintNode.print_graph(c) == expected
), f"got {ConstraintNode.print_graph(c)}, expected {expected}"
assert (
c.token_counts() == gold_counts
), f"{c} got {c.token_counts()} wanted {gold_counts}"
def test_next_tokens(self):
"""
Tests that the set of next tokens is correct.
"""
for example in self.examples:
constraints, expected, gold_counts = example
root = ConstraintNode.create(constraints)
root_tokens = set(root.children.keys())
for sequence in constraints:
state = UnorderedConstraintState(root)
for token in sequence:
all_tokens = root_tokens.union(state.node.children.keys())
assert (
all_tokens == state.next_tokens()
), f"ALL {all_tokens} NEXT {state.next_tokens()}"
state = state.advance(token)
def test_sequences(self):
for constraints, tokens, expected in self.sequences:
state = UnorderedConstraintState.create(pack_constraints([constraints])[0])
for token in tokens:
state = state.advance(token)
result = {}
for attr in expected.keys():
result[attr] = getattr(state, attr)
assert (
result == expected
), f"TEST({tokens}) GOT: {result} WANTED: {expected}"
class TestOrderedConstraintState(unittest.TestCase):
def setUp(self):
self.sequences = [
(
tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]),
[],
{"bank": 0, "num_completed": 0, "finished": False, "is_root": True},
),
(
tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]),
[1, 2],
{"bank": 2, "num_completed": 0, "finished": False, "is_root": False},
),
(
tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]),
[1, 2, 94],
{"bank": 0, "num_completed": 0, "finished": False, "is_root": True},
),
(
tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]),
[1, 3, 999, 1, 4],
{"bank": 0, "num_completed": 0, "finished": False, "is_root": True},
),
(
tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]),
[1, 2, 3, 999, 999],
{"bank": 3, "num_completed": 1, "finished": False, "is_root": False},
),
(
tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]),
[1, 2, 3, 77, 1, 3, 1],
{"bank": 6, "num_completed": 2, "finished": False, "is_root": False},
),
(
tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]),
[1, 2, 3, 1, 3, 1, 4, 4, 5, 6, 7, 1, 4, 5],
{"bank": 14, "num_completed": 6, "finished": True, "is_root": False},
),
(
tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]),
[1, 2, 999, 1, 2, 3, 999, 1, 3, 1, 4, 4, 5, 6, 7, 1, 4, 5, 117],
{"bank": 14, "num_completed": 6, "finished": True, "is_root": False},
),
(
tensorize([[1], [2, 3]]),
[1, 1],
{"bank": 1, "num_completed": 1, "finished": False, "is_root": False},
),
(
tensorize([[1, 2], [1, 2]]),
[1, 2, 1, 2],
{"bank": 4, "num_completed": 2, "finished": True, "is_root": False},
),
(
tensorize([[1, 2], [1, 2]]),
[1, 2, 1, 2, 1],
{"bank": 4, "num_completed": 2, "finished": True, "is_root": False},
),
(
tensorize([[1, 2], [3, 4]]),
[1, 2, 3, 4, 5],
{"bank": 4, "num_completed": 2, "finished": True, "is_root": False},
),
]
def test_sequences(self):
for i, (constraints, tokens, expected) in enumerate(self.sequences):
state = OrderedConstraintState.create(pack_constraints([constraints])[0])
for token in tokens:
state = state.advance(token)
result = {}
for attr in expected.keys():
result[attr] = getattr(state, attr)
assert (
result == expected
), f"TEST({tokens}) GOT: {result} WANTED: {expected}"
if __name__ == "__main__":
unittest.main()
| 10,488 | 37.848148 | 147 | py |
CIF-HieraDist | CIF-HieraDist-main/tests/test_sparse_multihead_attention.py | # 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 unittest
import torch
from fairseq.modules.sparse_multihead_attention import SparseMultiheadAttention
class TestSparseMultiheadAttention(unittest.TestCase):
def test_sparse_multihead_attention(self):
attn_weights = torch.randn(1, 8, 8)
bidirectional_sparse_mask = torch.tensor(
[
[0, 0, 0, 0, 0, float("-inf"), float("-inf"), 0],
[0, 0, 0, 0, 0, float("-inf"), float("-inf"), 0],
[0, 0, 0, 0, 0, float("-inf"), float("-inf"), 0],
[0, 0, 0, 0, 0, float("-inf"), float("-inf"), 0],
[float("-inf"), float("-inf"), float("-inf"), 0, 0, 0, 0, 0],
[float("-inf"), float("-inf"), float("-inf"), 0, 0, 0, 0, 0],
[float("-inf"), float("-inf"), float("-inf"), 0, 0, 0, 0, 0],
[float("-inf"), float("-inf"), float("-inf"), 0, 0, 0, 0, 0],
]
)
bidirectional_attention = SparseMultiheadAttention(
16, 1, stride=4, expressivity=1, is_bidirectional=True
)
bidirectional_attention_sparse_mask = (
bidirectional_attention.buffered_sparse_mask(attn_weights, 8, 8)
)
torch.all(
torch.eq(bidirectional_attention_sparse_mask, bidirectional_sparse_mask)
)
sparse_mask = torch.tensor(
[
[
0,
float("-inf"),
float("-inf"),
float("-inf"),
float("-inf"),
float("-inf"),
float("-inf"),
float("-inf"),
],
[
0,
0,
float("-inf"),
float("-inf"),
float("-inf"),
float("-inf"),
float("-inf"),
float("-inf"),
],
[
0,
0,
0,
float("-inf"),
float("-inf"),
float("-inf"),
float("-inf"),
float("-inf"),
],
[
0,
0,
0,
0,
float("-inf"),
float("-inf"),
float("-inf"),
float("-inf"),
],
[0, 0, 0, 0, 0, float("-inf"), float("-inf"), float("-inf")],
[
float("-inf"),
float("-inf"),
float("-inf"),
0,
0,
0,
float("-inf"),
float("-inf"),
],
[
float("-inf"),
float("-inf"),
float("-inf"),
0,
0,
0,
0,
float("-inf"),
],
[float("-inf"), float("-inf"), float("-inf"), 0, 0, 0, 0, 0],
]
)
attention = SparseMultiheadAttention(
16, 1, stride=4, expressivity=1, is_bidirectional=False
)
attention_sparse_mask = attention.buffered_sparse_mask(attn_weights, 8, 8)
torch.all(torch.eq(attention_sparse_mask, sparse_mask))
if __name__ == "__main__":
unittest.main()
| 3,738 | 31.513043 | 84 | py |
CIF-HieraDist | CIF-HieraDist-main/tests/test_export.py | #!/usr/bin/env python3
# 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 argparse
import tempfile
import unittest
import torch
from fairseq.data.dictionary import Dictionary
from fairseq.models.transformer import TransformerModel
from fairseq.modules import multihead_attention, sinusoidal_positional_embedding
from fairseq.tasks.fairseq_task import LegacyFairseqTask
DEFAULT_TEST_VOCAB_SIZE = 100
class DummyTask(LegacyFairseqTask):
def __init__(self, args):
super().__init__(args)
self.dictionary = get_dummy_dictionary()
if getattr(self.args, "ctc", False):
self.dictionary.add_symbol("<ctc_blank>")
self.src_dict = self.dictionary
self.tgt_dict = self.dictionary
@property
def source_dictionary(self):
return self.src_dict
@property
def target_dictionary(self):
return self.dictionary
def get_dummy_dictionary(vocab_size=DEFAULT_TEST_VOCAB_SIZE):
dummy_dict = Dictionary()
# add dummy symbol to satisfy vocab size
for id, _ in enumerate(range(vocab_size)):
dummy_dict.add_symbol("{}".format(id), 1000)
return dummy_dict
def get_dummy_task_and_parser():
"""
Return a dummy task and argument parser, which can be used to
create a model/criterion.
"""
parser = argparse.ArgumentParser(
description="test_dummy_s2s_task", argument_default=argparse.SUPPRESS
)
DummyTask.add_args(parser)
args = parser.parse_args([])
task = DummyTask.setup_task(args)
return task, parser
def _test_save_and_load(scripted_module):
with tempfile.NamedTemporaryFile() as f:
scripted_module.save(f.name)
torch.jit.load(f.name)
class TestExportModels(unittest.TestCase):
def test_export_multihead_attention(self):
module = multihead_attention.MultiheadAttention(embed_dim=8, num_heads=2)
scripted = torch.jit.script(module)
_test_save_and_load(scripted)
def test_incremental_state_multihead_attention(self):
module1 = multihead_attention.MultiheadAttention(embed_dim=8, num_heads=2)
module1 = torch.jit.script(module1)
module2 = multihead_attention.MultiheadAttention(embed_dim=8, num_heads=2)
module2 = torch.jit.script(module2)
state = {}
state = module1.set_incremental_state(state, "key", {"a": torch.tensor([1])})
state = module2.set_incremental_state(state, "key", {"a": torch.tensor([2])})
v1 = module1.get_incremental_state(state, "key")["a"]
v2 = module2.get_incremental_state(state, "key")["a"]
self.assertEqual(v1, 1)
self.assertEqual(v2, 2)
def test_positional_embedding(self):
module = sinusoidal_positional_embedding.SinusoidalPositionalEmbedding(
embedding_dim=8, padding_idx=1
)
scripted = torch.jit.script(module)
_test_save_and_load(scripted)
@unittest.skipIf(
torch.__version__ < "1.6.0", "Targeting OSS scriptability for the 1.6 release"
)
def test_export_transformer(self):
task, parser = get_dummy_task_and_parser()
TransformerModel.add_args(parser)
args = parser.parse_args([])
model = TransformerModel.build_model(args, task)
scripted = torch.jit.script(model)
_test_save_and_load(scripted)
@unittest.skipIf(
torch.__version__ < "1.6.0", "Targeting OSS scriptability for the 1.6 release"
)
def test_export_transformer_no_token_pos_emb(self):
task, parser = get_dummy_task_and_parser()
TransformerModel.add_args(parser)
args = parser.parse_args([])
args.no_token_positional_embeddings = True
model = TransformerModel.build_model(args, task)
scripted = torch.jit.script(model)
_test_save_and_load(scripted)
if __name__ == "__main__":
unittest.main()
| 4,002 | 32.082645 | 86 | py |
CIF-HieraDist | CIF-HieraDist-main/tests/test_roberta.py | # 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 functools
import unittest
from typing import Any, Dict, Sequence
import fairseq
import fairseq.options
import fairseq.tasks
import torch
from tests.utils import dummy_dictionary
VOCAB_SIZE = 100
@fairseq.tasks.register_task("fake_task")
class FakeTask(fairseq.tasks.LegacyFairseqTask):
def __init__(self, args):
super().__init__(args)
self.dictionary = dummy_dictionary(VOCAB_SIZE - 4)
assert len(self.dictionary) == VOCAB_SIZE
@property
def source_dictionary(self):
return self.dictionary
@property
def target_dictionary(self):
return self.dictionary
@functools.lru_cache()
def get_toy_model(
device: str,
architecture: str = "roberta_enc_dec",
**extra_args: Any,
):
assert device in ("gpu", "cpu")
kwargs = {
"arch": architecture,
# Use characteristics dimensions
"encoder_layers": 3,
"encoder_embed_dim": 12,
"encoder_ffn_embed_dim": 14,
"encoder_attention_heads": 4,
"decoder_layers": 3,
"decoder_embed_dim": 12,
"decoder_ffn_embed_dim": 14,
"decoder_attention_heads": 4,
# Disable dropout so we have comparable tests.
"dropout": 0,
"attention_dropout": 0,
"activation_dropout": 0,
"encoder_layerdrop": 0,
# required args
"tokens_per_sample": 256,
"data": "/tmp/test_roberta",
}
kwargs.update(extra_args)
fake_task = FakeTask(kwargs)
args = fairseq.options.get_args(
task="online_backtranslation",
mono_langs="en,ro",
valid_lang_pairs="en-ro",
**kwargs,
)
torch.manual_seed(0)
model = fake_task.build_model(args)
if device == "gpu":
model.cuda()
return fake_task, model
def mk_sample(
lang: str, device: str, tok: Sequence[int] = None, batch_size: int = 2
) -> Dict[str, Any]:
assert device in ("gpu", "cpu")
if not tok:
if lang == "en":
tok = [10, 11, 12, 13, 14, 15, 2]
else:
tok = [20, 21, 22, 23, 24, 25, 26, 27, 2]
batch = torch.stack([torch.tensor(tok, dtype=torch.long)] * batch_size)
if device == "gpu":
batch = batch.cuda()
sample = {
"net_input": {
"src_tokens": batch,
"prev_output_tokens": batch,
"src_lengths": torch.tensor(
[len(tok)] * batch_size, dtype=torch.long, device=batch.device
),
},
"target": batch[:, 1:],
}
return sample
def cpu_gpu(fn):
def helper(self):
fn(self, "cpu")
if torch.cuda.is_available():
fn(self, "gpu")
return helper
def architectures(fn):
def helper(self):
for arch in ["roberta_enc_dec", "transformer"]:
fn(self, arch)
return helper
class RobertaTest(unittest.TestCase):
def assertTensorEqual(self, t1, t2, delta: float = 1e-6):
self.assertEqual(t1.size(), t2.size(), "size mismatch")
if delta == 0.0:
self.assertEqual(t1.ne(t2).long().sum(), 0)
else:
self.assertEqual(((t2 - t1).abs() > delta).long().sum(), 0)
def assertSharing(self, model, link_groups: Sequence[Sequence[str]]):
ids = {}
for group in link_groups:
group_ids = {name: id(params(model, name)) for name in group}
shared_id = group_ids[group[0]]
self.assertEqual(group_ids, {name: shared_id for name in group})
self.assertNotIn(shared_id, ids)
ids[shared_id] = group
def test_roberta_shared_params(self):
_, roberta = get_toy_model("cpu", architecture="roberta")
self.assertSharing(
roberta,
[
[
"encoder.sentence_encoder.embed_tokens.weight",
"encoder.lm_head.weight",
]
],
)
_, roberta = get_toy_model(
"cpu", architecture="roberta", untie_weights_roberta=True
)
self.assertSharing(
roberta,
[
["encoder.sentence_encoder.embed_tokens.weight"],
["encoder.lm_head.weight"],
],
)
def test_roberta_enc_dec_shared_params(self):
# 3 distinct embeddings
_, enc_dec = get_toy_model("cpu", architecture="roberta_enc_dec")
self.assertSharing(
enc_dec,
[
["encoder.embed_tokens.weight"],
["decoder.embed_tokens.weight"],
["decoder.output_projection.weight"],
],
)
# 2 distinct embeddings, one for encoder, one for decoder
_, enc_dec = get_toy_model(
"cpu", architecture="roberta_enc_dec", share_decoder_input_output_embed=True
)
self.assertSharing(
enc_dec,
[
["encoder.embed_tokens.weight"],
[
"decoder.embed_tokens.weight",
"decoder.output_projection.weight",
],
],
)
# shared embeddings
_, enc_dec = get_toy_model(
"cpu", architecture="roberta_enc_dec", share_all_embeddings=True
)
self.assertSharing(
enc_dec,
[
[
"encoder.embed_tokens.weight",
"decoder.embed_tokens.weight",
"decoder.output_projection.weight",
]
],
)
def test_roberta_max_positions_is_correctly_set(self):
device = "cpu"
task, model = get_toy_model(device)
max_pos = model.max_decoder_positions()
self.assertEqual(max_pos, 256)
self.assertEqual(max_pos, model.decoder.max_positions())
self.assertEqual(max_pos, model.encoder.max_positions())
self.assertEqual(max_pos, model.encoder.embed_positions.max_positions)
sentence = [31 for _ in range(max_pos)]
sample = mk_sample("en", device, sentence, batch_size=1)
self.assertEqual(list(sample["net_input"]["src_lengths"]), [max_pos])
self.assertEqual(len(sample["net_input"]["src_tokens"][0]), max_pos)
x, _ = model.forward(**sample["net_input"])
self.assertEqual(x.shape, (1, max_pos, VOCAB_SIZE))
@cpu_gpu
def test_roberta_forward_backward(self, device: str):
_, model = get_toy_model(device)
sample = mk_sample("en", device)
en_tokens = sample["net_input"]["src_tokens"]
(bs, l) = en_tokens.shape
# Forward
logits, _ = model(**sample["net_input"])
self.assertEqual(logits.shape, (bs, l, VOCAB_SIZE))
# Backward
loss = logits.sum()
loss.backward()
@cpu_gpu
def test_roberta_forward_backward_bs1(self, device: str):
_, model = get_toy_model(device)
sample = mk_sample("en", device, batch_size=1)
o, _ = model.forward(**sample["net_input"])
loss = o.sum()
sample2 = mk_sample("ro", device, batch_size=1)
o, _ = model.forward(**sample2["net_input"])
loss += o.sum()
loss.backward()
@cpu_gpu
def test_roberta_batching(self, device: str):
"""
Checks that the batch of size 2 give twice the same results than the batch of size 1.
"""
_, model = get_toy_model(device)
sample = mk_sample("en", device, batch_size=1)
slen = sample["net_input"]["src_lengths"][0]
sample2 = mk_sample("en", device, batch_size=2)
with torch.no_grad():
z = model.encoder.forward(
sample["net_input"]["src_tokens"], sample["net_input"]["src_lengths"]
)
z = z["encoder_out"][-1]
logits, _ = model.forward(**sample["net_input"])
z2 = model.encoder.forward(
sample2["net_input"]["src_tokens"], sample["net_input"]["src_lengths"]
)
z2 = z2["encoder_out"][-1]
logits2, _ = model.forward(**sample2["net_input"])
self.assertEqual(z.shape, (slen, 1, 12))
self.assertEqual(z2.shape, (slen, 2, 12))
self.assertTensorEqual(logits2[0], logits2[1])
self.assertTensorEqual(logits[0], logits2[0])
@cpu_gpu
def test_roberta_incremental_decoder(self, device: str):
"""
Checks that incremental decoding yields the same result than non incremental one.
"""
task, model = get_toy_model(device)
en_sample = mk_sample("en", device)
en_tokens = en_sample["net_input"]["src_tokens"]
ro_sample = mk_sample("ro", device)
ro_tokens = ro_sample["net_input"]["src_tokens"]
en_enc = model.encoder.forward(
en_tokens, src_lengths=en_sample["net_input"]["src_lengths"]
)
(bs, tgt_len) = ro_tokens.shape
# Decode without incremental state
ro_dec, _ = model.decoder.forward(ro_tokens, encoder_out=en_enc)
self.assertEqual(ro_dec.shape, (bs, tgt_len, VOCAB_SIZE))
self.assertTensorEqual(ro_dec[0], ro_dec[1])
# Decode with incremental state
inc_state = {}
ro_dec_inc = []
for l in range(tgt_len):
ro, _ = model.decoder.forward(
ro_tokens[:, : l + 1], encoder_out=en_enc, incremental_state=inc_state
)
self.assertEqual(ro.shape, (bs, 1, VOCAB_SIZE))
ro_dec_inc.append(ro)
for l in range(tgt_len):
# Intra-batch
self.assertTensorEqual(ro_dec_inc[l][0], ro_dec_inc[l][1])
# Incremental vs non-incremental
self.assertTensorEqual(ro_dec_inc[l][:, 0], ro_dec[:, l])
def params(model, name):
if "." not in name:
return getattr(model, name)
prefix, name = name.split(".", 1)
return params(getattr(model, prefix), name)
| 10,095 | 31.050794 | 93 | py |
CIF-HieraDist | CIF-HieraDist-main/tests/test_online_backtranslation.py | # 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 tempfile
import unittest
from pathlib import Path
from typing import Any, Dict, Sequence
import fairseq.data.indexed_dataset as indexed_dataset
import fairseq.options
import fairseq.tasks.online_backtranslation as obt
import torch
from tests import utils
def mk_sample(tokens: Sequence[int], batch_size: int = 2) -> Dict[str, Any]:
batch = torch.stack([torch.tensor(tokens, dtype=torch.long)] * batch_size)
sample = {
"net_input": {
"src_tokens": batch,
"prev_output_tokens": batch,
"src_lengths": torch.tensor([len(tokens)] * batch_size, dtype=torch.long),
},
"target": batch[:, 1:],
}
return sample
def mk_dataset(num_samples: int, max_len: int, output: Path):
output.parent.mkdir(exist_ok=True)
idx = indexed_dataset.IndexedDatasetBuilder(str(output))
data = torch.randint(5, 100, (num_samples, max_len))
lengths = torch.randint(3, max_len, (num_samples,))
for d, l in zip(data, lengths):
d[0] = 0
idx.add_item(d[:l])
idx.finalize(output.with_suffix(".idx"))
assert output.exists()
assert output.with_suffix(".idx").exists()
class OnlineBacktranslationTest(unittest.TestCase):
tmp_dir = Path(tempfile.mkdtemp(suffix="OnlineBacktranslationTest"))
@classmethod
def obt_task(
cls, languages: Sequence[str], data: Path = None, language_mapping: str = None
):
dict_path = cls.tmp_dir / "dict.txt"
if not dict_path.exists():
dictionary = utils.dummy_dictionary(100)
dictionary.save(str(dict_path))
if data is not None:
(data / "dict.txt").write_text(dict_path.read_text())
else:
data = cls.tmp_dir
assert len(languages) >= 2
kwargs = {
"arch": "transformer",
# --max-sentences=1 for better predictability of batches
"max_sentences": 1,
# Use characteristics dimensions
"encoder_layers": 3,
"encoder_embed_dim": 12,
"encoder_ffn_embed_dim": 14,
"encoder_attention_heads": 4,
"decoder_layers": 3,
"decoder_embed_dim": 12,
"decoder_output_dim": 12,
"decoder_ffn_embed_dim": 14,
"decoder_attention_heads": 4,
# Disable dropout so we have comparable tests.
"dropout": 0,
"attention_dropout": 0,
"activation_dropout": 0,
"encoder_layerdrop": 0,
}
args = fairseq.options.get_args(
data,
task="online_backtranslation",
mono_langs=",".join(languages),
valid_lang_pairs=f"{languages[0]}-{languages[1]}",
tokens_per_sample=256,
language_mapping=language_mapping,
**kwargs,
)
task = obt.OnlineBackTranslationTask.setup_task(args)
# we need to build the model to have the correct dictionary
model = task.build_model(task.args)
return task, model
def tmp_path(self, test_case: str) -> Path:
return Path(tempfile.mkdtemp(test_case, dir=self.tmp_dir))
def test_lang_tokens(self):
task, model = self.obt_task(["en", "ro", "zh"])
assert obt._lang_token("en") in task.dictionary
assert obt._lang_token("ro") in task.dictionary
assert obt._lang_token("zh") in task.dictionary
en_bos = obt._lang_token_index(task.common_dict, "en")
assert "en" == task.common_dict[en_bos].strip("_")
zh_bos = obt._lang_token_index(task.common_dict, "zh")
assert "zh" == task.common_dict[zh_bos].strip("_")
zh_sample = mk_sample([zh_bos, 16, 14, 12, 10])
# we expect to receive the bos token for translation
assert task.get_bos_token_from_sample(zh_sample) == en_bos
def test_backtranslate_sample(self):
task, model = self.obt_task(["en", "ro", "zh"])
en_bos = obt._lang_token_index(task.common_dict, "en")
zh_bos = obt._lang_token_index(task.common_dict, "zh")
sample = mk_sample([zh_bos, 16, 14, 12, 10])
task.backtranslate_sample(sample, "zh", "en")
target_zh = list(sample["target"][0])
assert target_zh == [16, 14, 12, 10] # original zh sentence
generated_en = sample["net_input"]["src_tokens"][0]
assert generated_en[0] == en_bos
def test_train_dataset(self):
data = self.tmp_path("test_train_dataset")
mk_dataset(20, 10, data / "en" / "train.bin")
mk_dataset(10, 10, data / "zh" / "train.bin")
task, model = self.obt_task(["en", "zh"], data)
task.load_dataset("train")
en_bos = obt._lang_token_index(task.common_dict, "en")
zh_bos = obt._lang_token_index(task.common_dict, "zh")
train = task.datasets["train"]
train.ordered_indices()
train.prefetch([0, 19])
sample_0 = train[0]
sample_19 = train[19]
self.assertEqual(
set(sample_0.keys()), {"en-BT", "en-DENOISE", "zh-BT", "zh-DENOISE"}
)
for sample in (sample_0, sample_19):
self.assertEqual(sample["en-BT"]["source"][0], en_bos)
# bt target isn't ready to look at.
self.assertEqual(sample["en-DENOISE"]["source"][0], en_bos)
# TODO What could we check on the target side ?
for i in range(10):
# Zh dataset is shorter, and is wrapped around En dataset.
train.prefetch([i, i + 10])
self.assertEqual(
list(train[i]["zh-DENOISE"]["source"]),
list(train[i + 10]["zh-DENOISE"]["source"]),
)
self.assertEqual(train[i]["zh-DENOISE"]["source"][0].item(), zh_bos)
# Sorted by increasing len
self.assertLess(
len(sample_0["en-BT"]["source"]), len(sample_19["en-BT"]["source"])
)
def test_valid_dataset(self):
data = self.tmp_path("test_valid_dataset")
mk_dataset(10, 21, data / "valid.en-zh.en.bin")
mk_dataset(10, 21, data / "valid.en-zh.zh.bin")
task, model = self.obt_task(["en", "zh"], data)
valid = task.load_dataset("valid")
en_bos = obt._lang_token_index(task.common_dict, "en")
assert valid is not None
valid.prefetch(range(10))
sample_0 = valid[0]
sample_9 = valid[9]
self.assertEqual(sample_0["id"], 0)
self.assertEqual(sample_9["id"], 9)
self.assertEqual(sample_0["source"][0], en_bos)
self.assertEqual(sample_9["source"][0], en_bos)
# TODO: could we test the target side ?
def assertFnMatch(self, fn, values):
for x, y in values.items():
fn_x = fn(x)
self.assertEqual(fn_x, y, f"Fn has wrong value: fn({x}) = {fn_x} != {y}")
def test_piecewise_linear_fn(self):
self.assertFnMatch(
obt.PiecewiseLinearFn.from_string("1.0"), {0: 1, 100: 1, 500: 1, 1000: 1}
)
self.assertFnMatch(
obt.PiecewiseLinearFn.from_string("0:1,1000:0"),
{0: 1, 500: 0.5, 1000: 0, 2000: 0},
)
self.assertFnMatch(
obt.PiecewiseLinearFn.from_string("0:0,1000:1"),
{0: 0, 500: 0.5, 1000: 1, 2000: 1},
)
self.assertFnMatch(
obt.PiecewiseLinearFn.from_string("0:0,1000:1,2000:0"),
{0: 0, 500: 0.5, 1000: 1, 1500: 0.5, 2000: 0, 3000: 0},
)
| 7,650 | 35.961353 | 86 | py |
CIF-HieraDist | CIF-HieraDist-main/tests/test_backtranslation_dataset.py | # 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 unittest
import tests.utils as test_utils
import torch
from fairseq.data import (
BacktranslationDataset,
LanguagePairDataset,
TransformEosDataset,
)
from fairseq.sequence_generator import SequenceGenerator
class TestBacktranslationDataset(unittest.TestCase):
def setUp(self):
(
self.tgt_dict,
self.w1,
self.w2,
self.src_tokens,
self.src_lengths,
self.model,
) = test_utils.sequence_generator_setup()
dummy_src_samples = self.src_tokens
self.tgt_dataset = test_utils.TestDataset(data=dummy_src_samples)
self.cuda = torch.cuda.is_available()
def _backtranslation_dataset_helper(
self,
remove_eos_from_input_src,
remove_eos_from_output_src,
):
tgt_dataset = LanguagePairDataset(
src=self.tgt_dataset,
src_sizes=self.tgt_dataset.sizes,
src_dict=self.tgt_dict,
tgt=None,
tgt_sizes=None,
tgt_dict=None,
)
generator = SequenceGenerator(
[self.model],
tgt_dict=self.tgt_dict,
max_len_a=0,
max_len_b=200,
beam_size=2,
unk_penalty=0,
)
backtranslation_dataset = BacktranslationDataset(
tgt_dataset=TransformEosDataset(
dataset=tgt_dataset,
eos=self.tgt_dict.eos(),
# remove eos from the input src
remove_eos_from_src=remove_eos_from_input_src,
),
src_dict=self.tgt_dict,
backtranslation_fn=(
lambda sample: generator.generate([self.model], sample)
),
output_collater=TransformEosDataset(
dataset=tgt_dataset,
eos=self.tgt_dict.eos(),
# if we remove eos from the input src, then we need to add it
# back to the output tgt
append_eos_to_tgt=remove_eos_from_input_src,
remove_eos_from_src=remove_eos_from_output_src,
).collater,
cuda=self.cuda,
)
dataloader = torch.utils.data.DataLoader(
backtranslation_dataset,
batch_size=2,
collate_fn=backtranslation_dataset.collater,
)
backtranslation_batch_result = next(iter(dataloader))
eos, pad, w1, w2 = self.tgt_dict.eos(), self.tgt_dict.pad(), self.w1, self.w2
# Note that we sort by src_lengths and add left padding, so actually
# ids will look like: [1, 0]
expected_src = torch.LongTensor([[w1, w2, w1, eos], [pad, pad, w1, eos]])
if remove_eos_from_output_src:
expected_src = expected_src[:, :-1]
expected_tgt = torch.LongTensor([[w1, w2, eos], [w1, w2, eos]])
generated_src = backtranslation_batch_result["net_input"]["src_tokens"]
tgt_tokens = backtranslation_batch_result["target"]
self.assertTensorEqual(expected_src, generated_src)
self.assertTensorEqual(expected_tgt, tgt_tokens)
def test_backtranslation_dataset_no_eos_in_output_src(self):
self._backtranslation_dataset_helper(
remove_eos_from_input_src=False,
remove_eos_from_output_src=True,
)
def test_backtranslation_dataset_with_eos_in_output_src(self):
self._backtranslation_dataset_helper(
remove_eos_from_input_src=False,
remove_eos_from_output_src=False,
)
def test_backtranslation_dataset_no_eos_in_input_src(self):
self._backtranslation_dataset_helper(
remove_eos_from_input_src=True,
remove_eos_from_output_src=False,
)
def assertTensorEqual(self, t1, t2):
self.assertEqual(t1.size(), t2.size(), "size mismatch")
self.assertEqual(t1.ne(t2).long().sum(), 0)
if __name__ == "__main__":
unittest.main()
| 4,140 | 32.395161 | 85 | py |
CIF-HieraDist | CIF-HieraDist-main/tests/test_fp16_optimizer.py | # 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 argparse
import copy
import logging
import unittest
import torch
from fairseq.optim.fp16_optimizer import FP16Optimizer, MemoryEfficientFP16Optimizer
from omegaconf import OmegaConf
@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU")
class TestGradientScaling(unittest.TestCase):
def setUp(self):
self.x = torch.tensor([2.0]).cuda().half()
weight = 3.0
bias = 5.0
self.error = 1.0
self.target = torch.tensor([self.x * weight + bias + self.error]).cuda().half()
self.loss_fn = torch.nn.L1Loss()
self.model = torch.nn.Linear(1, 1)
self.model.weight.data = torch.tensor([[weight]])
self.model.bias.data = torch.tensor([bias])
self.model.cuda().half()
self.params = list(self.model.parameters())
self.cfg_dls = OmegaConf.create(
{
"optimization": {
"lr": [0.1],
},
"optimizer": {
"_name": "adam",
"lr": [0.1],
"adam_betas": "(0.9, 0.999)",
"adam_eps": 1e-8,
"weight_decay": 0.0,
},
"common": {
"fp16_init_scale": 1,
"fp16_scale_window": 1,
"fp16_scale_tolerance": 1,
"threshold_loss_scale": 1,
"min_loss_scale": 1e-4,
"tpu": False,
},
}
)
logging.disable(logging.CRITICAL)
def tearDown(self):
logging.disable(logging.NOTSET)
def run_iter(self, model, params, optimizer):
optimizer.zero_grad()
y = model(self.x)
loss = self.loss_fn(y, self.target)
optimizer.backward(loss)
self.assertEqual(loss, torch.tensor(1.0, device="cuda:0", dtype=torch.float16))
grad_norm = optimizer.clip_grad_norm(0)
self.assertAlmostEqual(grad_norm.item(), 2.2361, 4)
optimizer.step()
self.assertEqual(
model.weight,
torch.tensor(
[[3.0996]], device="cuda:0", dtype=torch.float16, requires_grad=True
),
)
self.assertEqual(
model.bias,
torch.tensor(
[5.1016], device="cuda:0", dtype=torch.float16, requires_grad=True
),
)
self.assertEqual(optimizer.scaler.loss_scale, 2.0)
def test_mixed_precision(self):
model = copy.deepcopy(self.model)
params = list(model.parameters())
optimizer = FP16Optimizer.build_optimizer(self.cfg_dls, params)
self.run_iter(model, params, optimizer)
self.assertTrue(
all(
torch.all(
fp32_params.eq(
torch.tensor(
[3.1000, 5.1000], device="cuda:0", requires_grad=True
)
)
)
for fp32_params in optimizer.fp32_params.values()
)
)
def test_memory_efficient(self):
model = copy.deepcopy(self.model)
params = list(model.parameters())
optimizer = MemoryEfficientFP16Optimizer.build_optimizer(self.cfg_dls, params)
self.run_iter(model, params, optimizer)
if __name__ == "__main__":
unittest.main()
| 3,587 | 30.752212 | 87 | py |
CIF-HieraDist | CIF-HieraDist-main/tests/test_sequence_generator.py | # 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 argparse
import tempfile
import unittest
import math
import numpy as np
import tests.utils as test_utils
import torch
from fairseq import search
from fairseq.data.dictionary import Dictionary
from fairseq.models.transformer import TransformerModel
from fairseq.sequence_generator import EnsembleModel, SequenceGenerator
from fairseq.ngram_repeat_block import NGramRepeatBlock
from fairseq.tasks.fairseq_task import LegacyFairseqTask
DEFAULT_TEST_VOCAB_SIZE = 100
class DummyTask(LegacyFairseqTask):
def __init__(self, args):
super().__init__(args)
self.dictionary = get_dummy_dictionary()
if getattr(self.args, "ctc", False):
self.dictionary.add_symbol("<ctc_blank>")
self.src_dict = self.dictionary
self.tgt_dict = self.dictionary
@property
def source_dictionary(self):
return self.src_dict
@property
def target_dictionary(self):
return self.dictionary
def get_dummy_dictionary(vocab_size=DEFAULT_TEST_VOCAB_SIZE):
dummy_dict = Dictionary()
# add dummy symbol to satisfy vocab size
for id, _ in enumerate(range(vocab_size)):
dummy_dict.add_symbol("{}".format(id), n=1000)
return dummy_dict
def get_dummy_task_and_parser():
"""
to build a fariseq model, we need some dummy parse and task. This function
is used to create dummy task and parser to faciliate model/criterion test
Note: we use FbSpeechRecognitionTask as the dummy task. You may want
to use other task by providing another function
"""
parser = argparse.ArgumentParser(
description="test_dummy_s2s_task", argument_default=argparse.SUPPRESS
)
DummyTask.add_args(parser)
args = parser.parse_args([])
task = DummyTask.setup_task(args)
return task, parser
class TestJitSequenceGeneratorBase(unittest.TestCase):
def setUp(self):
self.task, self.parser = get_dummy_task_and_parser()
eos = self.task.tgt_dict.eos()
src_tokens = torch.randint(3, 50, (2, 10)).long()
src_tokens = torch.cat((src_tokens, torch.LongTensor([[eos], [eos]])), -1)
src_lengths = torch.LongTensor([2, 10])
self.sample = {
"net_input": {"src_tokens": src_tokens, "src_lengths": src_lengths}
}
TransformerModel.add_args(self.parser)
args = self.parser.parse_args([])
args.encoder_layers = 2
args.decoder_layers = 1
self.transformer_model = TransformerModel.build_model(args, self.task)
def assertOutputEqual(self, hypo, pos_probs):
pos_scores = torch.FloatTensor(pos_probs).log()
self.assertTensorSizeEqual(hypo["positional_scores"], pos_scores)
self.assertTensorSizeEqual(pos_scores.numel(), hypo["tokens"].numel())
def assertTensorSizeEqual(self, t1, t2):
self.assertEqual(t1.size(), t2.size(), "size mismatch")
def assertAlmostEqual(self, t1, t2):
self.assertEqual(t1.size(), t2.size(), "size mismatch")
self.assertLess((t1 - t2).abs().max(), 1e-4)
def assertTensorEqual(self, t1, t2):
self.assertEqual(t1.size(), t2.size(), "size mismatch")
self.assertEqual(t1.ne(t2).long().sum(), 0)
def assertHypoEqual(self, h1, h2):
"Check two hypos are equal"
self.assertTensorEqual(h1["tokens"], h2["tokens"])
self.assertAlmostEqual(h1["positional_scores"], h2["positional_scores"])
self.assertLess(abs(h1["score"] - h2["score"]), 1e-6)
self.assertAlmostEqual(h1["attention"], h2["attention"])
def _test_save_and_load(self, scripted_module):
with tempfile.NamedTemporaryFile() as f:
scripted_module.save(f.name)
torch.jit.load(f.name)
JIT_MSG = "Targeting OSS scriptability for the 1.6 release"
@unittest.skipIf(torch.__version__ < "1.6.0", JIT_MSG)
class TestJitSequenceGenerator(TestJitSequenceGeneratorBase):
def test_export_transformer(self):
model = self.transformer_model
torch.jit.script(model)
def test_ensemble_sequence_generator(self):
model = self.transformer_model
generator = SequenceGenerator(
[model],
self.task.tgt_dict,
beam_size=2,
no_repeat_ngram_size=2,
max_len_b=10,
)
scripted_model = torch.jit.script(generator)
self._test_save_and_load(scripted_model)
def test_export_ensemble_model(self):
model = self.transformer_model
ensemble_models = EnsembleModel([model])
torch.jit.script(ensemble_models)
class TestExportSearch(unittest.TestCase):
def setUp(self):
task, _ = get_dummy_task_and_parser()
self.tgt_dict = task.tgt_dict
self.min_top1_prob = 0.4
def test_export_diverse_bs(self):
search_strategy = search.DiverseBeamSearch(
self.tgt_dict, num_groups=2, diversity_strength=0.0
)
torch.jit.script(search_strategy)
def test_export_sampling(self):
low_sampling_topp = self.min_top1_prob / 2.0
search_strategy = search.Sampling(
self.tgt_dict, sampling_topp=low_sampling_topp
)
torch.jit.script(search_strategy)
def test_export_diverse_siblings_search(self):
search_strategy = search.DiverseSiblingsSearch(
self.tgt_dict, diversity_rate=0.5
)
torch.jit.script(search_strategy)
class TestSequenceGeneratorBase(unittest.TestCase):
def assertHypoTokens(self, hypo, tokens):
self.assertTensorEqual(hypo["tokens"], torch.LongTensor(tokens))
def assertHypoScore(self, hypo, pos_probs, normalized=True, lenpen=1.0):
pos_scores = torch.FloatTensor(pos_probs).log()
self.assertAlmostEqual(hypo["positional_scores"], pos_scores)
self.assertEqual(pos_scores.numel(), hypo["tokens"].numel())
score = pos_scores.sum()
if normalized:
score /= pos_scores.numel() ** lenpen
self.assertLess(abs(score - hypo["score"]), 1e-6)
def assertAlmostEqual(self, t1, t2):
self.assertEqual(t1.size(), t2.size(), "size mismatch")
self.assertLess((t1 - t2).abs().max(), 1e-4)
def assertTensorEqual(self, t1, t2):
self.assertEqual(t1.size(), t2.size(), "size mismatch")
self.assertEqual(t1.ne(t2).long().sum(), 0)
class TestSequenceGenerator(TestSequenceGeneratorBase):
def setUp(self):
(
self.tgt_dict,
self.w1,
self.w2,
src_tokens,
src_lengths,
self.model,
) = test_utils.sequence_generator_setup()
self.sample = {
"net_input": {"src_tokens": src_tokens, "src_lengths": src_lengths}
}
def test_with_normalization(self):
generator = SequenceGenerator([self.model], self.tgt_dict, beam_size=2)
hypos = generator.forward(self.sample)
eos, w1, w2 = self.tgt_dict.eos(), self.w1, self.w2
# sentence 1, beam 1
self.assertHypoTokens(hypos[0][0], [w1, eos])
self.assertHypoScore(hypos[0][0], [0.9, 1.0])
# sentence 1, beam 2
self.assertHypoTokens(hypos[0][1], [w2, w1, w2, eos])
self.assertHypoScore(hypos[0][1], [0.1, 0.9, 0.9, 1.0])
# sentence 2, beam 1
self.assertHypoTokens(hypos[1][0], [w1, w2, w1, eos])
self.assertHypoScore(hypos[1][0], [0.7, 0.4, 0.4, 1.0])
# sentence 2, beam 2
self.assertHypoTokens(hypos[1][1], [w1, w2, eos])
self.assertHypoScore(hypos[1][1], [0.7, 0.4, 0.6])
def test_without_normalization(self):
# Sentence 1: unchanged from the normalized case
# Sentence 2: beams swap order
generator = SequenceGenerator(
[self.model], self.tgt_dict, beam_size=2, normalize_scores=False
)
hypos = generator.forward(self.sample)
eos, w1, w2 = self.tgt_dict.eos(), self.w1, self.w2
# sentence 1, beam 1
self.assertHypoTokens(hypos[0][0], [w1, eos])
self.assertHypoScore(hypos[0][0], [0.9, 1.0], normalized=False)
# sentence 1, beam 2
self.assertHypoTokens(hypos[0][1], [w2, w1, w2, eos])
self.assertHypoScore(hypos[0][1], [0.1, 0.9, 0.9, 1.0], normalized=False)
# sentence 2, beam 1
self.assertHypoTokens(hypos[1][0], [w1, w2, eos])
self.assertHypoScore(hypos[1][0], [0.7, 0.4, 0.6], normalized=False)
# sentence 2, beam 2
self.assertHypoTokens(hypos[1][1], [w1, w2, w1, eos])
self.assertHypoScore(hypos[1][1], [0.7, 0.4, 0.4, 1.0], normalized=False)
def test_with_lenpen_favoring_short_hypos(self):
lenpen = 0.6
generator = SequenceGenerator(
[self.model], self.tgt_dict, beam_size=2, len_penalty=lenpen
)
hypos = generator.forward(self.sample)
eos, w1, w2 = self.tgt_dict.eos(), self.w1, self.w2
# sentence 1, beam 1
self.assertHypoTokens(hypos[0][0], [w1, eos])
self.assertHypoScore(hypos[0][0], [0.9, 1.0], lenpen=lenpen)
# sentence 1, beam 2
self.assertHypoTokens(hypos[0][1], [w2, w1, w2, eos])
self.assertHypoScore(hypos[0][1], [0.1, 0.9, 0.9, 1.0], lenpen=lenpen)
# sentence 2, beam 1
self.assertHypoTokens(hypos[1][0], [w1, w2, eos])
self.assertHypoScore(hypos[1][0], [0.7, 0.4, 0.6], lenpen=lenpen)
# sentence 2, beam 2
self.assertHypoTokens(hypos[1][1], [w1, w2, w1, eos])
self.assertHypoScore(hypos[1][1], [0.7, 0.4, 0.4, 1.0], lenpen=lenpen)
def test_with_lenpen_favoring_long_hypos(self):
lenpen = 5.0
generator = SequenceGenerator(
[self.model], self.tgt_dict, beam_size=2, len_penalty=lenpen
)
hypos = generator.forward(self.sample)
eos, w1, w2 = self.tgt_dict.eos(), self.w1, self.w2
# sentence 1, beam 1
self.assertHypoTokens(hypos[0][0], [w2, w1, w2, eos])
self.assertHypoScore(hypos[0][0], [0.1, 0.9, 0.9, 1.0], lenpen=lenpen)
# sentence 1, beam 2
self.assertHypoTokens(hypos[0][1], [w1, eos])
self.assertHypoScore(hypos[0][1], [0.9, 1.0], lenpen=lenpen)
# sentence 2, beam 1
self.assertHypoTokens(hypos[1][0], [w1, w2, w1, eos])
self.assertHypoScore(hypos[1][0], [0.7, 0.4, 0.4, 1.0], lenpen=lenpen)
# sentence 2, beam 2
self.assertHypoTokens(hypos[1][1], [w1, w2, eos])
self.assertHypoScore(hypos[1][1], [0.7, 0.4, 0.6], lenpen=lenpen)
def test_maxlen(self):
generator = SequenceGenerator(
[self.model], self.tgt_dict, beam_size=2, max_len_b=2
)
hypos = generator.forward(self.sample)
eos, w1, w2 = self.tgt_dict.eos(), self.w1, self.w2
# sentence 1, beam 1
self.assertHypoTokens(hypos[0][0], [w1, eos])
self.assertHypoScore(hypos[0][0], [0.9, 1.0])
# sentence 1, beam 2
self.assertHypoTokens(hypos[0][1], [w2, w2, eos])
self.assertHypoScore(hypos[0][1], [0.1, 0.1, 0.6])
# sentence 2, beam 1
self.assertHypoTokens(hypos[1][0], [w1, w2, eos])
self.assertHypoScore(hypos[1][0], [0.7, 0.4, 0.6])
# sentence 2, beam 2
self.assertHypoTokens(hypos[1][1], [w2, w2, eos])
self.assertHypoScore(hypos[1][1], [0.3, 0.9, 0.01])
def test_encoder_with_different_output_len(self):
args = self.model.encoder.args
task = test_utils.TestTranslationTask.setup_task(
args, self.tgt_dict, self.tgt_dict
)
reshaping_model = test_utils.TestReshapingModel.build_model(args, task)
generator = SequenceGenerator(
[reshaping_model], self.tgt_dict, beam_size=2, max_len_b=2
)
hypos = generator.forward(self.sample)
for sent in [0, 1]:
for beam in [0, 1]:
assert hypos[sent][beam]["attention"] is not None
def test_generation_with_additional_input(self):
args = self.model.encoder.args
task = test_utils.TestTranslationTask.setup_task(
args, self.tgt_dict, self.tgt_dict
)
add_input_model = test_utils.TestAdditionalInputModel.build_model(args, task)
generator = SequenceGenerator([add_input_model], self.tgt_dict, beam_size=2)
sample = self.sample.copy()
sample["net_input"]["fancy_other_input"] = sample["net_input"]["src_tokens"]
hypos = generator.forward(self.sample)
eos, w1, w2 = self.tgt_dict.eos(), self.w1, self.w2
# sentence 1, beam 1
self.assertHypoTokens(hypos[0][0], [w1, eos])
self.assertHypoScore(hypos[0][0], [0.9, 1.0])
@unittest.skipUnless(torch.cuda.is_available(), "")
class TestRepeatNgramBlocking(TestSequenceGeneratorBase):
@classmethod
def setUpClass(cls):
(
cls.tgt_dict,
cls.w1,
cls.w2,
src_tokens,
src_lengths,
cls.model,
) = test_utils.sequence_generator_setup()
return cls
def test_finds_repetitive_tokens(self):
bsz, vocab_size, beam_size, step = 2, 4, 1, 3
generated_tok = torch.tensor(
[[2, 2, 2, 2], [3, 3, 3, 3]], dtype=torch.long, device="cuda"
)
lprobs = torch.zeros((beam_size * bsz, vocab_size), device="cuda")
desired_result = lprobs.new_tensor(
[[0.0, 0.0, -math.inf, 0.0], [0.0, 0.0, 0.0, -math.inf]]
)
cuda_ext_result, baseline_result = self._compare_cuda_ext_to_default_implem(
bsz, beam_size, generated_tok, lprobs, step, 2
)
self.assertTensorEqual(cuda_ext_result, desired_result)
self.assertTensorEqual(baseline_result, desired_result)
@unittest.skipIf(torch.__version__ < "1.6.0", JIT_MSG)
def test_jit_no_extension(self):
bsz, vocab_size, beam_size, step = 2, 4, 1, 3
generated_tok = torch.tensor(
[[2, 2, 2, 2], [3, 3, 3, 3]], dtype=torch.long, device="cuda"
)
lprobs = torch.zeros((beam_size * bsz, vocab_size), device="cuda")
blocker = NGramRepeatBlock(2, use_extension=False)
base_result = blocker(generated_tok, lprobs.clone(), bsz, beam_size, step)
scripted_blocker = torch.jit.script(blocker)
jit_result = scripted_blocker(
generated_tok, lprobs.clone(), bsz, beam_size, step
)
self.assertTensorEqual(base_result, jit_result)
def test_ngram_blocking_same_as_default_implem(self):
"""Test that cuda extension returns same things as default impl in many settings."""
vocab_size = 4
step = 6
for _ in range(2):
block_param = np.random.choice([1, 2, 3, 4])
batch_size = np.random.randint(1, 8)
beam_size = np.random.choice([1, 2, 4, 8])
lprobs = torch.zeros((beam_size * batch_size, vocab_size), device="cuda")
generated_tok = torch.tensor(
np.random.randint(
0, vocab_size, size=(batch_size * beam_size, step + 1)
),
device="cuda",
dtype=torch.long,
)
self._compare_cuda_ext_to_default_implem(
batch_size,
beam_size,
generated_tok,
lprobs,
step,
block_param,
)
def _compare_cuda_ext_to_default_implem(
self, bsz, beam_size, generated_tok, lprobs, step, block_param
):
"""Assert that cuda extension and default implem return the same thing."""
blocker = NGramRepeatBlock(block_param)
assert blocker.use_extension, "Extension not compiled"
cuda_ext_result = blocker(
generated_tok,
lprobs.clone(),
bsz,
beam_size,
step,
)
blocker.use_extension = False
baseline_result = blocker(
generated_tok,
lprobs.clone(),
bsz,
beam_size,
step,
)
self.assertTensorEqual(cuda_ext_result, baseline_result)
blocker.use_extension = True
return cuda_ext_result, baseline_result
class TestDiverseBeamSearch(TestSequenceGeneratorBase):
def setUp(self):
# construct dummy dictionary
d = test_utils.dummy_dictionary(vocab_size=2)
self.assertEqual(d.pad(), 1)
self.assertEqual(d.eos(), 2)
self.assertEqual(d.unk(), 3)
self.eos = d.eos()
self.w1 = 4
self.w2 = 5
# construct source data
self.src_tokens = torch.LongTensor(
[
[self.w1, self.w2, self.eos],
[self.w1, self.w2, self.eos],
]
)
self.src_lengths = torch.LongTensor([2, 2])
args = argparse.Namespace()
unk = 0.0
args.beam_probs = [
# step 0:
torch.FloatTensor(
[
# eos w1 w2
# sentence 1:
[0.0, unk, 0.9, 0.1], # beam 1
[0.0, unk, 0.9, 0.1], # beam 2
# sentence 2:
[0.0, unk, 0.7, 0.3],
[0.0, unk, 0.7, 0.3],
]
),
# step 1:
torch.FloatTensor(
[
# eos w1 w2
# sentence 1:
[0.0, unk, 0.6, 0.4],
[0.0, unk, 0.6, 0.4],
# sentence 2:
[0.25, unk, 0.35, 0.4],
[0.25, unk, 0.35, 0.4],
]
),
# step 2:
torch.FloatTensor(
[
# eos w1 w2
# sentence 1:
[1.0, unk, 0.0, 0.0],
[1.0, unk, 0.0, 0.0],
# sentence 2:
[0.9, unk, 0.1, 0.0],
[0.9, unk, 0.1, 0.0],
]
),
]
task = test_utils.TestTranslationTask.setup_task(args, d, d)
self.model = task.build_model(args)
self.tgt_dict = task.target_dictionary
def test_diverse_beam_search(self):
search_strategy = search.DiverseBeamSearch(
self.tgt_dict, num_groups=2, diversity_strength=0.0
)
generator = SequenceGenerator(
[self.model],
self.tgt_dict,
beam_size=2,
search_strategy=search_strategy,
)
sample = {
"net_input": {
"src_tokens": self.src_tokens,
"src_lengths": self.src_lengths,
}
}
hypos = generator.forward(sample)
eos, w1, w2 = self.eos, self.w1, self.w2
# sentence 1, beam 1
self.assertHypoTokens(hypos[0][0], [w1, w1, eos])
self.assertHypoScore(hypos[0][0], [0.9, 0.6, 1.0])
# sentence 1, beam 2
self.assertHypoTokens(hypos[0][1], [w1, w1, eos])
self.assertHypoScore(hypos[0][1], [0.9, 0.6, 1.0])
# sentence 2, beam 1
self.assertHypoTokens(hypos[1][0], [w1, w2, eos])
self.assertHypoScore(hypos[1][0], [0.7, 0.4, 0.9])
# sentence 2, beam 2
self.assertHypoTokens(hypos[1][1], [w1, w2, eos])
self.assertHypoScore(hypos[1][1], [0.7, 0.4, 0.9])
class TestDiverseSiblingsSearch(TestDiverseBeamSearch):
def assertHypoScore(
self, hypo, pos_probs, sibling_rank, diversity_rate, normalized=True, lenpen=1.0
):
pos_scores = torch.FloatTensor(pos_probs).log()
pos_scores.sub_(torch.Tensor(sibling_rank) * diversity_rate)
self.assertAlmostEqual(hypo["positional_scores"], pos_scores)
self.assertEqual(pos_scores.numel(), hypo["tokens"].numel())
score = pos_scores.sum()
if normalized:
score /= pos_scores.numel() ** lenpen
self.assertLess(abs(score - hypo["score"]), 1e-6)
def test_diverse_beam_search(self):
search_strategy = search.DiverseSiblingsSearch(
self.tgt_dict, diversity_rate=0.5
)
generator = SequenceGenerator(
[self.model], self.tgt_dict, beam_size=2, search_strategy=search_strategy
)
sample = {
"net_input": {
"src_tokens": self.src_tokens,
"src_lengths": self.src_lengths,
}
}
hypos = generator.forward(sample)
eos, w1, w2 = self.eos, self.w1, self.w2
# sentence 1, beam 1
self.assertHypoTokens(hypos[0][0], [w1, w1, eos])
self.assertHypoScore(hypos[0][0], [0.9, 0.6, 1.0], [0, 1, 1], 0.5)
# sentence 1, beam 2
self.assertHypoTokens(hypos[0][1], [w1, w2, eos])
self.assertHypoScore(hypos[0][1], [0.9, 0.4, 1.0], [0, 2, 1], 0.5)
# sentence 2, beam 1
self.assertHypoTokens(hypos[1][0], [w1, w2, eos])
self.assertHypoScore(hypos[1][0], [0.7, 0.4, 0.9], [0, 1, 1], 0.5)
# sentence 2, beam 2
self.assertHypoTokens(hypos[1][1], [w1, w1, eos])
self.assertHypoScore(hypos[1][1], [0.7, 0.35, 0.9], [0, 2, 1], 0.5)
class TestPrefixBeamSearch(TestSequenceGeneratorBase):
def setUp(self):
# construct dummy dictionary
vocab_size = 10
d = test_utils.dummy_dictionary(vocab_size=vocab_size)
self.assertEqual(d.pad(), 1)
self.assertEqual(d.eos(), 2)
self.assertEqual(d.unk(), 3)
self.eos = d.eos()
self.w1 = 4
self.w2 = 5
self.beam_size = 3
# construct prefix data
self.tokens = torch.LongTensor(
[
[self.w1, self.w2, self.eos],
]
)
self.token_lengths = torch.LongTensor([2])
args = argparse.Namespace()
unk = 0.0
args.beam_probs = [
# prefix step 0:
torch.FloatTensor(
[
# eos
[0.0, unk]
+ [1.0 / vocab_size] * vocab_size # beam 1
]
* self.beam_size
),
] * vocab_size
task = test_utils.TestTranslationTask.setup_task(args, d, d)
self.model = task.build_model(args)
self.tgt_dict = task.target_dictionary
def test_prefix_beam_search(self):
search_strategy = search.BeamSearch(self.tgt_dict)
generator = SequenceGenerator(
[self.model],
self.tgt_dict,
beam_size=self.beam_size,
search_strategy=search_strategy,
)
sample = {
"net_input": {
"src_tokens": self.tokens,
"src_lengths": self.token_lengths,
}
}
# make sure test sample doesn't break any assertion
generator.forward(sample, prefix_tokens=self.tokens[:, :-1])
class TestTopPSamplingSearch(TestSequenceGeneratorBase):
def setUp(self):
# construct dummy dictionary
d = test_utils.dummy_dictionary(vocab_size=2)
self.assertEqual(d.pad(), 1)
self.assertEqual(d.eos(), 2)
self.assertEqual(d.unk(), 3)
self.eos = d.eos()
self.w1 = 4
self.w2 = 5
# construct source data
self.src_tokens = torch.LongTensor(
[
[self.w1, self.w2, self.eos],
[self.w1, self.w2, self.eos],
]
)
self.src_lengths = torch.LongTensor([2, 2])
args = argparse.Namespace()
unk = 0.0
# The minimal probability of top 2 tokens.
self.min_top2_prob = 0.75
# The minimal probability of the top 1 token.
self.min_top1_prob = 0.4
w1_prob = self.min_top1_prob
w2_prob = self.min_top2_prob - self.min_top1_prob
eos_prob = 1 - self.min_top2_prob
args.beam_probs = [
# step 0:
torch.FloatTensor(
[
# eos w1 w2
[0.0, unk, 1.0, 0.0],
[0.0, unk, 1.0, 0.0],
[0.0, unk, 1.0, 0.0],
[0.0, unk, 1.0, 0.0],
]
),
# step 1:
torch.FloatTensor(
[
# eos w1 w2
[eos_prob, unk, w1_prob, w2_prob],
[eos_prob, unk, w1_prob, w2_prob],
[eos_prob, unk, w1_prob, w2_prob],
[eos_prob, unk, w1_prob, w2_prob],
]
),
# step 2:
torch.FloatTensor(
[
# eos w1 w2
[1.0, unk, 0.0, 0.0],
[1.0, unk, 0.0, 0.0],
[1.0, unk, 0.0, 0.0],
[1.0, unk, 0.0, 0.0],
]
),
]
task = test_utils.TestTranslationTask.setup_task(args, d, d)
self.model = task.build_model(args)
self.tgt_dict = task.target_dictionary
def test_topp_sampling_search_low_prob(self):
# Given a prob low enough to top-P sampling, we expect only the top
# 1 token to be sampled, which always results in the same output.
low_sampling_topp = self.min_top1_prob / 2.0
search_strategy = search.Sampling(
self.tgt_dict, sampling_topp=low_sampling_topp
)
generator = SequenceGenerator(
[self.model], self.tgt_dict, beam_size=2, search_strategy=search_strategy
)
sample = {
"net_input": {
"src_tokens": self.src_tokens,
"src_lengths": self.src_lengths,
}
}
hypos = generator.forward(sample)
eos, w1 = self.eos, self.w1
# sentence 1, beam 1
self.assertHypoTokens(hypos[0][0], [w1, w1, eos])
self.assertHypoScore(hypos[0][0], [1.0, 0.4, 1.0])
# sentence 1, beam 2
self.assertHypoTokens(hypos[0][1], [w1, w1, eos])
self.assertHypoScore(hypos[0][1], [1.0, 0.4, 1.0])
# sentence 2, beam 1
self.assertHypoTokens(hypos[1][0], [w1, w1, eos])
self.assertHypoScore(hypos[1][0], [1.0, 0.4, 1.0])
# sentence 2, beam 2
self.assertHypoTokens(hypos[1][1], [w1, w1, eos])
self.assertHypoScore(hypos[1][1], [1.0, 0.4, 1.0])
def test_topp_sampling_search_high_prob(self):
# Given a prob high enough to top-P sampling, any of the top 2
# tokens could be sampled. This can cause different outputs.
high_sampling_topp = (self.min_top1_prob + self.min_top2_prob) / 2.0
search_strategy = search.Sampling(
self.tgt_dict, sampling_topp=high_sampling_topp
)
generator = SequenceGenerator(
[self.model], self.tgt_dict, beam_size=2, search_strategy=search_strategy
)
sample = {
"net_input": {
"src_tokens": self.src_tokens,
"src_lengths": self.src_lengths,
}
}
hypos = generator.forward(sample)
eos, w1, w2 = self.eos, self.w1, self.w2
# sentence 1, beam 1
self.assertTrue(
self.hypoTokens(hypos[0][0], [w1, w1, eos])
or self.hypoTokens(hypos[0][0], [w1, w2, eos])
)
self.assertTrue(
self.hypoScore(hypos[0][0], [1.0, 0.4, 1.0])
or self.hypoScore(hypos[0][0], [1.0, 0.35, 1.0])
)
# sentence 1, beam 2
self.assertTrue(
self.hypoTokens(hypos[0][1], [w1, w1, eos])
or self.hypoTokens(hypos[0][1], [w1, w2, eos])
)
self.assertTrue(
self.hypoScore(hypos[0][1], [1.0, 0.4, 1.0])
or self.hypoScore(hypos[0][1], [1.0, 0.35, 1.0])
)
# sentence 2, beam 1
self.assertTrue(
self.hypoTokens(hypos[1][0], [w1, w1, eos])
or self.hypoTokens(hypos[1][0], [w1, w2, eos])
)
self.assertTrue(
self.hypoScore(hypos[1][0], [1.0, 0.4, 1.0])
or self.hypoScore(hypos[1][0], [1.0, 0.35, 1.0])
)
# sentence 2, beam 2
self.assertTrue(
self.hypoTokens(hypos[1][1], [w1, w1, eos])
or self.hypoTokens(hypos[1][1], [w1, w2, eos])
)
self.assertTrue(
self.hypoScore(hypos[1][1], [1.0, 0.4, 1.0])
or self.hypoScore(hypos[1][1], [1.0, 0.35, 1.0])
)
def hypoTokens(self, hypo, tokens):
return self.tensorEqual(hypo["tokens"], torch.LongTensor(tokens))
def hypoScore(self, hypo, pos_probs, normalized=True, lenpen=1.0):
pos_scores = torch.FloatTensor(pos_probs).log()
if not self.almostEqual(hypo["positional_scores"], pos_scores):
return False
if pos_scores.numel() != hypo["tokens"].numel():
return False
score = pos_scores.sum()
if normalized:
score /= pos_scores.numel() ** lenpen
return abs(score - hypo["score"]) < 1e-6
def almostEqual(self, t1, t2):
return t1.size() == t2.size() and (t1 - t2).abs().max() < 1e-4
def tensorEqual(self, t1, t2):
return t1.size() == t2.size() and t1.ne(t2).long().sum() == 0
if __name__ == "__main__":
unittest.main()
| 29,548 | 35.798257 | 92 | py |
CIF-HieraDist | CIF-HieraDist-main/tests/test_label_smoothing.py | # 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 argparse
import copy
import unittest
import tests.utils as test_utils
import torch
from fairseq.criterions.cross_entropy import CrossEntropyCriterion
from fairseq.criterions.label_smoothed_cross_entropy import (
LabelSmoothedCrossEntropyCriterion,
)
class TestLabelSmoothing(unittest.TestCase):
def setUp(self):
# build dictionary
self.d = test_utils.dummy_dictionary(3)
vocab = len(self.d)
self.assertEqual(vocab, 4 + 3) # 4 special + 3 tokens
self.assertEqual(self.d.pad(), 1)
self.assertEqual(self.d.eos(), 2)
self.assertEqual(self.d.unk(), 3)
pad, eos, unk, w1, w2, w3 = 1, 2, 3, 4, 5, 6 # noqa: F841
# build dataset
self.data = [
# the first batch item has padding
{
"source": torch.LongTensor([w1, eos]),
"target": torch.LongTensor([w1, eos]),
},
{
"source": torch.LongTensor([w1, eos]),
"target": torch.LongTensor([w1, w1, eos]),
},
]
self.sample = next(test_utils.dummy_dataloader(self.data))
# build model
self.args = argparse.Namespace()
self.args.sentence_avg = False
self.args.report_accuracy = False
self.args.probs = (
torch.FloatTensor(
[
# pad eos unk w1 w2 w3
[0.05, 0.05, 0.1, 0.05, 0.3, 0.4, 0.05],
[0.05, 0.10, 0.2, 0.05, 0.2, 0.3, 0.10],
[0.05, 0.15, 0.3, 0.05, 0.1, 0.2, 0.15],
]
)
.unsqueeze(0)
.expand(2, 3, 7)
) # add batch dimension
self.task = test_utils.TestTranslationTask.setup_task(self.args, self.d, self.d)
self.model = self.task.build_model(self.args)
def test_nll_loss(self):
self.args.label_smoothing = 0.1
nll_crit = CrossEntropyCriterion.build_criterion(self.args, self.task)
smooth_crit = LabelSmoothedCrossEntropyCriterion.build_criterion(
self.args, self.task
)
nll_loss, nll_sample_size, nll_logging_output = nll_crit(
self.model, self.sample
)
smooth_loss, smooth_sample_size, smooth_logging_output = smooth_crit(
self.model, self.sample
)
self.assertLess(abs(nll_loss - nll_logging_output["loss"]), 1e-6)
self.assertLess(abs(nll_loss - smooth_logging_output["nll_loss"]), 1e-6)
def test_padding(self):
self.args.label_smoothing = 0.1
crit = LabelSmoothedCrossEntropyCriterion.build_criterion(self.args, self.task)
loss, _, logging_output = crit(self.model, self.sample)
def get_one_no_padding(idx):
# create a new sample with just a single batch item so that there's
# no padding
sample1 = next(test_utils.dummy_dataloader([self.data[idx]]))
args1 = copy.copy(self.args)
args1.probs = args1.probs[idx, :, :].unsqueeze(0)
model1 = self.task.build_model(args1)
loss1, _, _ = crit(model1, sample1)
return loss1
loss1 = get_one_no_padding(0)
loss2 = get_one_no_padding(1)
self.assertAlmostEqual(loss, loss1 + loss2)
def test_reduction(self):
self.args.label_smoothing = 0.1
crit = LabelSmoothedCrossEntropyCriterion.build_criterion(self.args, self.task)
loss, _, logging_output = crit(self.model, self.sample, reduce=True)
unreduced_loss, _, _ = crit(self.model, self.sample, reduce=False)
self.assertAlmostEqual(loss, unreduced_loss.sum())
def test_zero_eps(self):
self.args.label_smoothing = 0.0
nll_crit = CrossEntropyCriterion.build_criterion(self.args, self.task)
smooth_crit = LabelSmoothedCrossEntropyCriterion.build_criterion(
self.args, self.task
)
nll_loss, nll_sample_size, nll_logging_output = nll_crit(
self.model, self.sample
)
smooth_loss, smooth_sample_size, smooth_logging_output = smooth_crit(
self.model, self.sample
)
self.assertAlmostEqual(nll_loss, smooth_loss)
def assertAlmostEqual(self, t1, t2):
self.assertEqual(t1.size(), t2.size(), "size mismatch")
self.assertLess((t1 - t2).abs().max(), 1e-6)
if __name__ == "__main__":
unittest.main()
| 4,629 | 36.33871 | 88 | py |
CIF-HieraDist | CIF-HieraDist-main/tests/test_convtbc.py | # 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 unittest
import torch
import torch.nn as nn
from fairseq.modules import ConvTBC
class TestConvTBC(unittest.TestCase):
def test_convtbc(self):
# ksz, in_channels, out_channels
conv_tbc = ConvTBC(4, 5, kernel_size=3, padding=1)
# out_channels, in_channels, ksz
conv1d = nn.Conv1d(4, 5, kernel_size=3, padding=1)
conv_tbc.weight.data.copy_(conv1d.weight.data.transpose(0, 2))
conv_tbc.bias.data.copy_(conv1d.bias.data)
input_tbc = torch.randn(7, 2, 4, requires_grad=True)
input1d = input_tbc.data.transpose(0, 1).transpose(1, 2)
input1d.requires_grad = True
output_tbc = conv_tbc(input_tbc)
output1d = conv1d(input1d)
self.assertAlmostEqual(
output_tbc.data.transpose(0, 1).transpose(1, 2), output1d.data
)
grad_tbc = torch.randn(output_tbc.size())
grad1d = grad_tbc.transpose(0, 1).transpose(1, 2).contiguous()
output_tbc.backward(grad_tbc)
output1d.backward(grad1d)
self.assertAlmostEqual(
conv_tbc.weight.grad.data.transpose(0, 2), conv1d.weight.grad.data
)
self.assertAlmostEqual(conv_tbc.bias.grad.data, conv1d.bias.grad.data)
self.assertAlmostEqual(
input_tbc.grad.data.transpose(0, 1).transpose(1, 2), input1d.grad.data
)
def assertAlmostEqual(self, t1, t2):
self.assertEqual(t1.size(), t2.size(), "size mismatch")
self.assertLess((t1 - t2).abs().max(), 1e-4)
if __name__ == "__main__":
unittest.main()
| 1,745 | 30.745455 | 82 | py |
CIF-HieraDist | CIF-HieraDist-main/tests/test_lm_context_window.py | # 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 unittest
import torch
from fairseq.data import MonolingualDataset
from fairseq.tasks.language_modeling import LanguageModelingTask, LanguageModelingConfig
from tests import utils as test_utils
class TestLMContextWindow(unittest.TestCase):
def test_eval_dataloader(self):
dictionary = test_utils.dummy_dictionary(10)
assert len(dictionary) == 14 # 4 extra special symbols
assert dictionary.pad() == 1
dataset = test_utils.TestDataset(
[
torch.tensor([4, 5, 6, 7], dtype=torch.long),
torch.tensor([8, 9, 10, 11], dtype=torch.long),
torch.tensor([12, 13], dtype=torch.long),
]
)
dataset = MonolingualDataset(dataset, sizes=[4, 4, 2], src_vocab=dictionary)
config = LanguageModelingConfig(tokens_per_sample=4)
task = LanguageModelingTask(config, dictionary)
eval_dataloader = task.eval_lm_dataloader(
dataset=dataset,
batch_size=1,
context_window=2,
)
batch = next(eval_dataloader)
assert batch["net_input"]["src_tokens"][0].tolist() == [4, 5, 6, 7, 1, 1]
assert batch["target"][0].tolist() == [4, 5, 6, 7, 1, 1]
batch = next(eval_dataloader)
assert batch["net_input"]["src_tokens"][0].tolist() == [6, 7, 8, 9, 10, 11]
assert batch["target"][0].tolist() == [1, 1, 8, 9, 10, 11]
batch = next(eval_dataloader)
assert batch["net_input"]["src_tokens"][0].tolist() == [10, 11, 12, 13]
assert batch["target"][0].tolist() == [1, 1, 12, 13]
if __name__ == "__main__":
unittest.main()
| 1,837 | 33.679245 | 88 | py |
CIF-HieraDist | CIF-HieraDist-main/tests/test_amp_optimizer.py | # 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 argparse
import copy
import unittest
import torch
from torch.cuda.amp import autocast, GradScaler
from fairseq.optim import build_optimizer
@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU")
class TestGradientScalingAMP(unittest.TestCase):
def setUp(self):
self.x = torch.tensor([2.0]).cuda().half()
weight = 3.0
bias = 5.0
self.error = 1.0
self.target = torch.tensor([self.x * weight + bias + self.error]).cuda()
self.loss_fn = torch.nn.L1Loss()
self.model = torch.nn.Linear(1, 1)
self.model.weight.data = torch.tensor([[weight]])
self.model.bias.data = torch.tensor([bias])
self.model.cuda()
self.params = list(self.model.parameters())
self.namespace_dls = argparse.Namespace(
optimizer="adam",
lr=[0.1],
adam_betas="(0.9, 0.999)",
adam_eps=1e-8,
weight_decay=0.0,
threshold_loss_scale=1,
min_loss_scale=1e-4,
)
self.scaler = GradScaler(
init_scale=1,
growth_interval=1,
)
def run_iter(self, model, params, optimizer):
optimizer.zero_grad()
with autocast():
y = model(self.x)
loss = self.loss_fn(y, self.target)
self.scaler.scale(loss).backward()
self.assertEqual(loss, torch.tensor(1.0, device="cuda:0", dtype=torch.float16))
self.scaler.unscale_(optimizer)
grad_norm = optimizer.clip_grad_norm(0)
self.assertAlmostEqual(grad_norm.item(), 2.2361, 4)
self.scaler.step(optimizer)
self.scaler.update()
self.assertEqual(
model.weight,
torch.tensor([[3.1]], device="cuda:0", requires_grad=True),
)
self.assertEqual(
model.bias,
torch.tensor([5.1], device="cuda:0", requires_grad=True),
)
self.assertEqual(self.scaler.get_scale(), 2.0)
def test_automatic_mixed_precision(self):
model = copy.deepcopy(self.model)
params = list(model.parameters())
optimizer = build_optimizer(self.namespace_dls, params)
self.run_iter(model, params, optimizer)
| 2,410 | 31.146667 | 87 | py |
CIF-HieraDist | CIF-HieraDist-main/tests/test_token_block_dataset.py | # 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 unittest
import tests.utils as test_utils
import torch
from fairseq.data import TokenBlockDataset
class TestTokenBlockDataset(unittest.TestCase):
def _build_dataset(self, data, **kwargs):
sizes = [len(x) for x in data]
underlying_ds = test_utils.TestDataset(data)
return TokenBlockDataset(underlying_ds, sizes, **kwargs)
def test_eos_break_mode(self):
data = [
torch.tensor([5, 4, 3, 2, 1], dtype=torch.long),
torch.tensor([1], dtype=torch.long),
torch.tensor([8, 7, 6, 1], dtype=torch.long),
]
ds = self._build_dataset(data, block_size=None, pad=0, eos=1, break_mode="eos")
self.assertEqual(ds[0].tolist(), [5, 4, 3, 2, 1])
self.assertEqual(ds[1].tolist(), [1])
self.assertEqual(ds[2].tolist(), [8, 7, 6, 1])
data = [
torch.tensor([5, 4, 3, 2, 1], dtype=torch.long),
torch.tensor([8, 7, 6, 1], dtype=torch.long),
torch.tensor([1], dtype=torch.long),
]
ds = self._build_dataset(data, block_size=None, pad=0, eos=1, break_mode="eos")
self.assertEqual(ds[0].tolist(), [5, 4, 3, 2, 1])
self.assertEqual(ds[1].tolist(), [8, 7, 6, 1])
self.assertEqual(ds[2].tolist(), [1])
def test_block_break_mode(self):
data = [
torch.tensor([5, 4, 3, 2, 1], dtype=torch.long),
torch.tensor([8, 7, 6, 1], dtype=torch.long),
torch.tensor([9, 1], dtype=torch.long),
]
ds = self._build_dataset(data, block_size=3, pad=0, eos=1, break_mode="none")
self.assertEqual(ds[0].tolist(), [5, 4, 3])
self.assertEqual(ds[1].tolist(), [2, 1, 8])
self.assertEqual(ds[2].tolist(), [7, 6, 1])
self.assertEqual(ds[3].tolist(), [9, 1])
def test_complete_break_mode(self):
data = [
torch.tensor([5, 4, 3, 2, 1], dtype=torch.long),
torch.tensor([8, 7, 6, 1], dtype=torch.long),
torch.tensor([9, 1], dtype=torch.long),
]
ds = self._build_dataset(
data, block_size=6, pad=0, eos=1, break_mode="complete"
)
self.assertEqual(ds[0].tolist(), [5, 4, 3, 2, 1])
self.assertEqual(ds[1].tolist(), [8, 7, 6, 1, 9, 1])
data = [
torch.tensor([4, 3, 2, 1], dtype=torch.long),
torch.tensor([5, 1], dtype=torch.long),
torch.tensor([1], dtype=torch.long),
torch.tensor([6, 1], dtype=torch.long),
]
ds = self._build_dataset(
data, block_size=3, pad=0, eos=1, break_mode="complete"
)
self.assertEqual(ds[0].tolist(), [4, 3, 2, 1])
self.assertEqual(ds[1].tolist(), [5, 1, 1])
self.assertEqual(ds[2].tolist(), [6, 1])
def test_4billion_tokens(self):
"""Regression test for numpy type promotion issue https://github.com/numpy/numpy/issues/5745"""
data = [torch.tensor(list(range(10000)), dtype=torch.long)] * 430000
ds = self._build_dataset(
data, block_size=6, pad=0, eos=1, break_mode="complete"
)
ds[-1] # __getitem__ works
start, end = ds.slice_indices[-1]
assert end > 4294967295 # data must be sufficiently large to overflow uint32
assert not isinstance(
end + 1, float
) # this would also raise, since np.uint64(1) + 1 => 2.0
if __name__ == "__main__":
unittest.main()
| 3,629 | 38.032258 | 103 | py |
CIF-HieraDist | CIF-HieraDist-main/tests/test_transformer.py | import argparse
import unittest
from typing import Any, Dict, Sequence
import torch
from fairseq.models import transformer
from tests.test_roberta import FakeTask
def mk_sample(tok: Sequence[int] = None, batch_size: int = 2) -> Dict[str, Any]:
if not tok:
tok = [10, 11, 12, 13, 14, 15, 2]
batch = torch.stack([torch.tensor(tok, dtype=torch.long)] * batch_size)
sample = {
"net_input": {
"src_tokens": batch,
"prev_output_tokens": batch,
"src_lengths": torch.tensor(
[len(tok)] * batch_size, dtype=torch.long, device=batch.device
),
},
"target": batch[:, 1:],
}
return sample
def mk_transformer(**extra_args: Any):
overrides = {
# Use characteristics dimensions
"encoder_embed_dim": 12,
"encoder_ffn_embed_dim": 14,
"decoder_embed_dim": 12,
"decoder_ffn_embed_dim": 14,
# Disable dropout so we have comparable tests.
"dropout": 0,
"attention_dropout": 0,
"activation_dropout": 0,
"encoder_layerdrop": 0,
}
overrides.update(extra_args)
# Overrides the defaults from the parser
args = argparse.Namespace(**overrides)
transformer.tiny_architecture(args)
torch.manual_seed(0)
task = FakeTask(args)
return transformer.TransformerModel.build_model(args, task)
class TransformerTestCase(unittest.TestCase):
def test_forward_backward(self):
model = mk_transformer(encoder_embed_dim=12, decoder_embed_dim=12)
sample = mk_sample()
o, _ = model.forward(**sample["net_input"])
loss = o.sum()
loss.backward()
def test_different_encoder_decoder_embed_dim(self):
model = mk_transformer(encoder_embed_dim=12, decoder_embed_dim=16)
sample = mk_sample()
o, _ = model.forward(**sample["net_input"])
loss = o.sum()
loss.backward()
| 1,942 | 28.439394 | 80 | py |
CIF-HieraDist | CIF-HieraDist-main/tests/test_multi_corpus_sampled_dataset.py | # 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 unittest
from collections import OrderedDict
import numpy as np
import torch
from fairseq.data import LanguagePairDataset, TokenBlockDataset
from fairseq.data.multi_corpus_sampled_dataset import MultiCorpusSampledDataset
from tests.test_train import mock_dict
class TestMultiCorpusSampledDataset(unittest.TestCase):
def setUp(self):
d = mock_dict()
tokens_1 = torch.LongTensor([1]).view(1, -1)
tokens_ds1 = TokenBlockDataset(
tokens_1,
sizes=[tokens_1.size(-1)],
block_size=1,
pad=0,
eos=1,
include_targets=False,
)
self.dataset_1 = LanguagePairDataset(
tokens_ds1, tokens_ds1.sizes, d, shuffle=False
)
tokens_2 = torch.LongTensor([2]).view(1, -1)
tokens_ds2 = TokenBlockDataset(
tokens_2,
sizes=[tokens_2.size(-1)],
block_size=1,
pad=0,
eos=1,
include_targets=False,
)
self.dataset_2 = LanguagePairDataset(
tokens_ds2, tokens_ds2.sizes, d, shuffle=False
)
def _test_sample_helper(
self,
expected_sample_from_first_ds_percentage,
num_samples=1000,
sampling_func=None,
):
# To make sure test is not flaky
np.random.seed(0)
if sampling_func is None:
m = MultiCorpusSampledDataset(
OrderedDict({0: self.dataset_1, 1: self.dataset_2}),
)
else:
m = MultiCorpusSampledDataset(
OrderedDict({0: self.dataset_1, 1: self.dataset_2}),
sampling_func=sampling_func,
)
m.ordered_indices()
count_sample_from_first_dataset = 0
for _ in range(num_samples):
if m.collater([m[0], m[1]])["net_input"]["src_tokens"][0] == 1:
count_sample_from_first_dataset += 1
sample_from_first_ds_percentage = (
1.0 * count_sample_from_first_dataset / num_samples
)
self.assertLess(
abs(
sample_from_first_ds_percentage
- expected_sample_from_first_ds_percentage
),
0.01,
)
def test_multi_corpus_sampled_dataset_uniform_sample(self):
self._test_sample_helper(expected_sample_from_first_ds_percentage=0.5)
def test_multi_corpus_sampled_dataset_weighted_sample(self):
def naive_weighted_sample(weights):
def f(l):
v = np.random.random()
agg = 0
for i, weight in enumerate(weights):
agg += weight
if agg > v:
return i
return f
self._test_sample_helper(
expected_sample_from_first_ds_percentage=0.9,
sampling_func=naive_weighted_sample(weights=[0.9, 0.1]),
)
| 3,105 | 31.354167 | 79 | py |
CIF-HieraDist | CIF-HieraDist-main/tests/test_dictionary.py | # 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 io
import os
import string
import tempfile
import unittest
import torch
from fairseq import tokenizer
from fairseq.data import Dictionary
class TestDictionary(unittest.TestCase):
def test_finalize(self):
txt = [
"A B C D",
"B C D",
"C D",
"D",
]
ref_ids1 = list(
map(
torch.IntTensor,
[
[4, 5, 6, 7, 2],
[5, 6, 7, 2],
[6, 7, 2],
[7, 2],
],
)
)
ref_ids2 = list(
map(
torch.IntTensor,
[
[7, 6, 5, 4, 2],
[6, 5, 4, 2],
[5, 4, 2],
[4, 2],
],
)
)
# build dictionary
d = Dictionary()
for line in txt:
d.encode_line(line, add_if_not_exist=True)
def get_ids(dictionary):
ids = []
for line in txt:
ids.append(dictionary.encode_line(line, add_if_not_exist=False))
return ids
def assertMatch(ids, ref_ids):
for toks, ref_toks in zip(ids, ref_ids):
self.assertEqual(toks.size(), ref_toks.size())
self.assertEqual(0, (toks != ref_toks).sum().item())
ids = get_ids(d)
assertMatch(ids, ref_ids1)
# check finalized dictionary
d.finalize()
finalized_ids = get_ids(d)
assertMatch(finalized_ids, ref_ids2)
# write to disk and reload
with tempfile.NamedTemporaryFile(mode="w") as tmp_dict:
d.save(tmp_dict.name)
d = Dictionary.load(tmp_dict.name)
reload_ids = get_ids(d)
assertMatch(reload_ids, ref_ids2)
assertMatch(finalized_ids, reload_ids)
def test_overwrite(self):
# for example, Camembert overwrites <unk>, <s> and </s>
dict_file = io.StringIO(
"<unk> 999 #fairseq:overwrite\n"
"<s> 999 #fairseq:overwrite\n"
"</s> 999 #fairseq:overwrite\n"
", 999\n"
"▁de 999\n"
)
d = Dictionary()
d.add_from_file(dict_file)
self.assertEqual(d.index("<pad>"), 1)
self.assertEqual(d.index("foo"), 3)
self.assertEqual(d.index("<unk>"), 4)
self.assertEqual(d.index("<s>"), 5)
self.assertEqual(d.index("</s>"), 6)
self.assertEqual(d.index(","), 7)
self.assertEqual(d.index("▁de"), 8)
def test_no_overwrite(self):
# for example, Camembert overwrites <unk>, <s> and </s>
dict_file = io.StringIO(
"<unk> 999\n" "<s> 999\n" "</s> 999\n" ", 999\n" "▁de 999\n"
)
d = Dictionary()
with self.assertRaisesRegex(RuntimeError, "Duplicate"):
d.add_from_file(dict_file)
def test_space(self):
# for example, character models treat space as a symbol
dict_file = io.StringIO(" 999\n" "a 999\n" "b 999\n")
d = Dictionary()
d.add_from_file(dict_file)
self.assertEqual(d.index(" "), 4)
self.assertEqual(d.index("a"), 5)
self.assertEqual(d.index("b"), 6)
def test_add_file_to_dict(self):
counts = {}
num_lines = 100
per_line = 10
with tempfile.TemporaryDirectory("test_sampling") as data_dir:
filename = os.path.join(data_dir, "dummy.txt")
with open(filename, "w", encoding="utf-8") as data:
for c in string.ascii_letters:
line = f"{c} " * per_line
for _ in range(num_lines):
data.write(f"{line}\n")
counts[c] = per_line * num_lines
per_line += 5
dict = Dictionary()
Dictionary.add_file_to_dictionary(
filename, dict, tokenizer.tokenize_line, 10
)
dict.finalize(threshold=0, nwords=-1, padding_factor=8)
for c in string.ascii_letters:
count = dict.get_count(dict.index(c))
self.assertEqual(
counts[c], count, f"{c} count is {count} but should be {counts[c]}"
)
if __name__ == "__main__":
unittest.main()
| 4,545 | 30.136986 | 87 | py |
CIF-HieraDist | CIF-HieraDist-main/tests/test_utils.py | # 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 unittest
import torch
from fairseq import utils
class TestUtils(unittest.TestCase):
def test_convert_padding_direction(self):
pad = 1
left_pad = torch.LongTensor(
[
[2, 3, 4, 5, 6],
[1, 7, 8, 9, 10],
[1, 1, 1, 11, 12],
]
)
right_pad = torch.LongTensor(
[
[2, 3, 4, 5, 6],
[7, 8, 9, 10, 1],
[11, 12, 1, 1, 1],
]
)
self.assertAlmostEqual(
right_pad,
utils.convert_padding_direction(
left_pad,
pad,
left_to_right=True,
),
)
self.assertAlmostEqual(
left_pad,
utils.convert_padding_direction(
right_pad,
pad,
right_to_left=True,
),
)
def test_make_positions(self):
pad = 1
left_pad_input = torch.LongTensor(
[
[9, 9, 9, 9, 9],
[1, 9, 9, 9, 9],
[1, 1, 1, 9, 9],
]
)
left_pad_output = torch.LongTensor(
[
[2, 3, 4, 5, 6],
[1, 2, 3, 4, 5],
[1, 1, 1, 2, 3],
]
)
right_pad_input = torch.LongTensor(
[
[9, 9, 9, 9, 9],
[9, 9, 9, 9, 1],
[9, 9, 1, 1, 1],
]
)
right_pad_output = torch.LongTensor(
[
[2, 3, 4, 5, 6],
[2, 3, 4, 5, 1],
[2, 3, 1, 1, 1],
]
)
self.assertAlmostEqual(
left_pad_output,
utils.make_positions(left_pad_input, pad),
)
self.assertAlmostEqual(
right_pad_output,
utils.make_positions(right_pad_input, pad),
)
def test_clip_grad_norm_(self):
params = torch.nn.Parameter(torch.zeros(5)).requires_grad_(False)
grad_norm = utils.clip_grad_norm_(params, 1.0)
self.assertTrue(torch.is_tensor(grad_norm))
self.assertEqual(grad_norm, 0.0)
params = [torch.nn.Parameter(torch.zeros(5)) for i in range(3)]
for p in params:
p.grad = torch.full((5,), fill_value=2.0)
grad_norm = utils.clip_grad_norm_(params, 1.0)
exp_grad_norm = torch.full((15,), fill_value=2.0).norm()
self.assertTrue(torch.is_tensor(grad_norm))
self.assertEqual(grad_norm, exp_grad_norm)
grad_norm = utils.clip_grad_norm_(params, 1.0)
self.assertAlmostEqual(grad_norm, torch.tensor(1.0))
def test_resolve_max_positions_with_tuple(self):
resolved = utils.resolve_max_positions(None, (2000, 100, 2000), 12000)
self.assertEqual(resolved, (2000, 100, 2000))
def assertAlmostEqual(self, t1, t2):
self.assertEqual(t1.size(), t2.size(), "size mismatch")
self.assertLess(utils.item((t1 - t2).abs().max()), 1e-4)
if __name__ == "__main__":
unittest.main()
| 3,295 | 27.66087 | 78 | py |
CIF-HieraDist | CIF-HieraDist-main/tests/test_character_token_embedder.py | # 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 unittest
import torch
from fairseq.data import Dictionary
from fairseq.modules import CharacterTokenEmbedder
class TestCharacterTokenEmbedder(unittest.TestCase):
def test_character_token_embedder(self):
vocab = Dictionary()
vocab.add_symbol("hello")
vocab.add_symbol("there")
embedder = CharacterTokenEmbedder(
vocab, [(2, 16), (4, 32), (8, 64), (16, 2)], 64, 5, 2
)
test_sents = [["hello", "unk", "there"], ["there"], ["hello", "there"]]
max_len = max(len(s) for s in test_sents)
input = torch.LongTensor(len(test_sents), max_len + 2).fill_(vocab.pad())
for i in range(len(test_sents)):
input[i][0] = vocab.eos()
for j in range(len(test_sents[i])):
input[i][j + 1] = vocab.index(test_sents[i][j])
input[i][j + 2] = vocab.eos()
embs = embedder(input)
assert embs.size() == (len(test_sents), max_len + 2, 5)
self.assertAlmostEqual(embs[0][0], embs[1][0])
self.assertAlmostEqual(embs[0][0], embs[0][-1])
self.assertAlmostEqual(embs[0][1], embs[2][1])
self.assertAlmostEqual(embs[0][3], embs[1][1])
embs.sum().backward()
assert embedder.char_embeddings.weight.grad is not None
def assertAlmostEqual(self, t1, t2):
self.assertEqual(t1.size(), t2.size(), "size mismatch")
self.assertLess((t1 - t2).abs().max(), 1e-6)
if __name__ == "__main__":
unittest.main()
| 1,678 | 33.265306 | 81 | py |
CIF-HieraDist | CIF-HieraDist-main/tests/gpu/test_ema_gpu.py | # 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 unittest
from copy import deepcopy
from dataclasses import dataclass
from typing import Optional
import torch
from fairseq.models.ema import EMA
class DummyModule(torch.nn.Module):
def __init__(self) -> None:
"""LightningModule for testing purposes
Args:
epoch_min_loss_override (int, optional): Pass in an epoch that will be set to the minimum
validation loss for testing purposes (zero based). If None this is ignored. Defaults to None.
"""
super().__init__()
self.layer = torch.nn.Linear(in_features=32, out_features=2)
self.another_layer = torch.nn.Linear(in_features=2, out_features=2)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.layer(x)
return self.another_layer(x)
@dataclass
class EMAConfig(object):
ema_decay: float = 0.99
ema_start_update: int = 0
ema_fp32: bool = False
ema_seed_model: Optional[str] = None
@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU")
class TestEMAGPU(unittest.TestCase):
def assertTorchAllClose(self, x, y, atol=1e-8, rtol=1e-5, msg=None):
diff = x.float() - y.float()
diff_norm = torch.norm(diff)
other_norm = torch.norm(y.float())
if msg is None:
msg = "|input - other| > {} + {} * |other|".format(atol, rtol)
self.assertLessEqual(
diff_norm,
atol + rtol * other_norm,
msg=msg,
)
def test_ema(self):
model = DummyModule().cuda()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
state = deepcopy(model.state_dict())
config = EMAConfig()
ema = EMA(model, config)
# set decay
ema._set_decay(config.ema_decay)
self.assertEqual(ema.get_decay(), config.ema_decay)
# get model
self.assertEqual(ema.get_model(), ema.model)
# Since fp32 params is not used, it should be of size 0
self.assertEqual(len(ema.fp32_params), 0)
# EMA step
x = torch.randn(32).cuda()
y = model(x)
loss = y.sum()
loss.backward()
optimizer.step()
ema.step(model)
ema_state_dict = ema.get_model().state_dict()
for key, param in model.state_dict().items():
prev_param = state[key]
ema_param = ema_state_dict[key]
if "version" in key:
# Do not decay a model.version pytorch param
continue
self.assertTorchAllClose(
ema_param,
config.ema_decay * prev_param + (1 - config.ema_decay) * param,
)
# Since fp32 params is not used, it should be of size 0
self.assertEqual(len(ema.fp32_params), 0)
# Load EMA into model
model2 = DummyModule().cuda()
ema.reverse(model2)
for key, param in model2.state_dict().items():
ema_param = ema_state_dict[key]
self.assertTrue(torch.allclose(ema_param, param))
def test_ema_fp32(self):
model = DummyModule().cuda().half()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
state = deepcopy(model.state_dict())
config = EMAConfig(ema_fp32=True)
ema = EMA(model, config)
x = torch.randn(32).cuda()
y = model(x.half())
loss = y.sum()
loss.backward()
optimizer.step()
ema.step(model)
for key, param in model.state_dict().items():
prev_param = state[key]
ema_param = ema.get_model().state_dict()[key]
if "version" in key:
# Do not decay a model.version pytorch param
continue
self.assertIn(key, ema.fp32_params)
# EMA update is done in fp32, and hence the EMA param must be
# closer to the EMA update done in fp32 than in fp16.
self.assertLessEqual(
torch.norm(
ema_param.float()
- (
config.ema_decay * prev_param.float()
+ (1 - config.ema_decay) * param.float()
)
.half()
.float()
),
torch.norm(
ema_param.float()
- (
config.ema_decay * prev_param + (1 - config.ema_decay) * param
).float()
),
)
self.assertTorchAllClose(
ema_param,
(
config.ema_decay * prev_param.float()
+ (1 - config.ema_decay) * param.float()
).half(),
)
def test_ema_fp16(self):
model = DummyModule().cuda().half()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
state = deepcopy(model.state_dict())
config = EMAConfig(ema_fp32=False)
ema = EMA(model, config)
# Since fp32 params is not used, it should be of size 0
self.assertEqual(len(ema.fp32_params), 0)
x = torch.randn(32).cuda()
y = model(x.half())
loss = y.sum()
loss.backward()
optimizer.step()
ema.step(model)
for key, param in model.state_dict().items():
prev_param = state[key]
ema_param = ema.get_model().state_dict()[key]
if "version" in key:
# Do not decay a model.version pytorch param
continue
# EMA update is done in fp16, and hence the EMA param must be
# closer to the EMA update done in fp16 than in fp32.
self.assertLessEqual(
torch.norm(
ema_param.float()
- (
config.ema_decay * prev_param + (1 - config.ema_decay) * param
).float()
),
torch.norm(
ema_param.float()
- (
config.ema_decay * prev_param.float()
+ (1 - config.ema_decay) * param.float()
)
.half()
.float()
),
)
self.assertTorchAllClose(
ema_param,
config.ema_decay * prev_param + (1 - config.ema_decay) * param,
)
# Since fp32 params is not used, it should be of size 0
self.assertEqual(len(ema.fp32_params), 0)
if __name__ == "__main__":
unittest.main()
| 6,795 | 30.757009 | 109 | py |
CIF-HieraDist | CIF-HieraDist-main/tests/gpu/test_binaries_gpu.py | # 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 contextlib
import logging
import json
import os
import tempfile
import unittest
from io import StringIO
import torch
from fairseq import options
from fairseq_cli import train
from tests.utils import (
create_dummy_data,
generate_main,
preprocess_lm_data,
preprocess_translation_data,
train_translation_model,
)
@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU")
class TestTranslationGPU(unittest.TestCase):
def setUp(self):
logging.disable(logging.CRITICAL)
def tearDown(self):
logging.disable(logging.NOTSET)
def test_fp16_multigpu(self):
self._test_multigpu("test_fp16", ["--fp16"])
def test_slowmo_multigpu(self):
self._test_multigpu(
"test_slowmo", ["--ddp-backend", "slowmo", "--nprocs-per-node", "1"]
)
def test_slowmo_single_node_multigpu(self):
self._test_multigpu(
"test_slowmo_single_node",
["--ddp-backend", "slowmo", "--nprocs-per-node", "2"],
)
def _test_multigpu(self, test_name, test_args):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory(test_name) as data_dir:
log = os.path.join(data_dir, "train.log")
create_dummy_data(data_dir)
preprocess_translation_data(data_dir)
train_translation_model(
data_dir,
"fconv_iwslt_de_en",
test_args + ["--log-file", log],
world_size=min(torch.cuda.device_count(), 2),
)
generate_main(data_dir)
assert os.path.exists(log)
@staticmethod
def parse_logs(logfile):
logs = []
for ln in open(logfile, "r").readlines():
try:
logs.append(json.loads(ln))
except json.JSONDecodeError:
continue
return logs
def test_resume_training_fsdp(self):
self._test_resume_training(["--ddp-backend", "fully_sharded"])
def test_resume_training_fsdp_sharded_state(self):
self._test_resume_training(
["--ddp-backend", "fully_sharded", "--use-sharded-state"]
)
def test_resume_training_noc10d(self):
self._test_resume_training([])
def _test_resume_training(self, extra_clargs, arch="fconv_iwslt_de_en"):
flags = [
"--fp16",
"--log-format",
"json",
"--max-update",
"10",
"--save-interval-updates",
"2",
"--log-interval",
"1",
] + extra_clargs
world_size = min(torch.cuda.device_count(), 2)
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_fp16") as data_dir:
log = os.path.join(data_dir, "train.log")
create_dummy_data(data_dir)
preprocess_translation_data(data_dir)
train_translation_model(
data_dir,
arch,
flags + ["--log-file", log],
world_size=world_size,
)
log2 = os.path.join(data_dir, "resume.log")
restore_file = os.path.join(data_dir, "checkpoint_1_2.pt")
train_translation_model(
data_dir,
arch,
flags + ["--log-file", log2, "--restore-file", restore_file],
world_size=world_size,
)
l1 = self.parse_logs(log)
l2 = self.parse_logs(log2)
assert int(l2[0]["num_updates"]) == 3, f"{l1}\n\n {l2}"
for k in [
"train_loss",
"train_num_updates",
"train_ppl",
"train_gnorm",
]:
from_scratch, resumed = l1[-1][k], l2[-1][k]
assert (
from_scratch == resumed
), f"difference at {k} {from_scratch} != {resumed}"
def test_memory_efficient_fp16(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_memory_efficient_fp16") as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir)
train_translation_model(
data_dir, "fconv_iwslt_de_en", ["--memory-efficient-fp16"]
)
generate_main(data_dir)
def test_transformer_fp16(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_transformer") as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir)
train_translation_model(
data_dir,
"transformer_iwslt_de_en",
[
"--encoder-layers",
"2",
"--decoder-layers",
"2",
"--encoder-embed-dim",
"64",
"--decoder-embed-dim",
"64",
"--fp16",
],
run_validation=True,
)
generate_main(data_dir)
@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU")
def test_amp(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_amp") as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir)
train_translation_model(data_dir, "fconv_iwslt_de_en", ["--amp"])
generate_main(data_dir)
@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU")
def test_transformer_amp(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_transformer") as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir)
train_translation_model(
data_dir,
"transformer_iwslt_de_en",
[
"--encoder-layers",
"2",
"--decoder-layers",
"2",
"--encoder-embed-dim",
"64",
"--decoder-embed-dim",
"64",
"--amp",
],
run_validation=True,
)
generate_main(data_dir)
@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU")
def test_levenshtein_transformer(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory(
"test_levenshtein_transformer"
) as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir, ["--joined-dictionary"])
train_translation_model(
data_dir,
"levenshtein_transformer",
[
"--apply-bert-init",
"--early-exit",
"6,6,6",
"--criterion",
"nat_loss",
],
task="translation_lev",
)
gen_config = [
"--task",
"translation_lev",
"--iter-decode-max-iter",
"9",
"--iter-decode-eos-penalty",
"0",
"--print-step",
]
# non-ensemble generation
generate_main(data_dir, gen_config)
# ensemble generation
generate_main(
data_dir,
gen_config,
path=os.pathsep.join(
[
os.path.join(data_dir, "checkpoint_last.pt"),
os.path.join(data_dir, "checkpoint_last.pt"),
]
),
)
def test_fsdp_checkpoint_generate(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_fsdp_sharded") as data_dir:
log = os.path.join(data_dir, "train.log")
create_dummy_data(data_dir)
preprocess_translation_data(data_dir)
world_size = min(torch.cuda.device_count(), 2)
train_translation_model(
data_dir,
"fconv_iwslt_de_en",
["--log-file", log, "--ddp-backend", "fully_sharded"],
world_size=world_size,
)
generate_main(data_dir)
assert os.path.exists(log)
def test_fsdp_sharded_checkpoint_generate(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_fsdp_sharded") as data_dir:
log = os.path.join(data_dir, "train.log")
create_dummy_data(data_dir)
preprocess_translation_data(data_dir)
world_size = min(torch.cuda.device_count(), 2)
train_translation_model(
data_dir,
"fconv_iwslt_de_en",
[
"--log-file",
log,
"--ddp-backend",
"fully_sharded",
"--use-sharded-state",
],
world_size=world_size,
)
generate_main(data_dir, ["--checkpoint-shard-count", str(world_size)])
assert os.path.exists(log)
def _quantize_language_model(data_dir, arch, extra_flags=None, run_validation=False):
train_parser = options.get_training_parser()
train_args = options.parse_args_and_arch(
train_parser,
[
"--task",
"language_modeling",
data_dir,
"--arch",
arch,
"--optimizer",
"adam",
"--lr",
"0.0001",
"--criterion",
"adaptive_loss",
"--adaptive-softmax-cutoff",
"5,10,15",
"--max-tokens",
"500",
"--tokens-per-sample",
"500",
"--save-dir",
data_dir,
"--max-epoch",
"1",
"--no-progress-bar",
"--distributed-world-size",
"1",
"--ddp-backend",
"no_c10d",
"--num-workers",
"0",
]
+ (extra_flags or []),
)
train.main(train_args)
# try scalar quantization
scalar_quant_train_parser = options.get_training_parser()
scalar_quant_train_args = options.parse_args_and_arch(
scalar_quant_train_parser,
[
"--task",
"language_modeling",
data_dir,
"--arch",
arch,
"--optimizer",
"adam",
"--lr",
"0.0001",
"--criterion",
"adaptive_loss",
"--adaptive-softmax-cutoff",
"5,10,15",
"--max-tokens",
"500",
"--tokens-per-sample",
"500",
"--save-dir",
data_dir,
"--max-update",
"3",
"--no-progress-bar",
"--distributed-world-size",
"1",
"--ddp-backend",
"no_c10d",
"--num-workers",
"0",
"--quant-noise-scalar",
"0.5",
]
+ (extra_flags or []),
)
train.main(scalar_quant_train_args)
# try iterative PQ quantization
quantize_parser = options.get_training_parser()
quantize_args = options.parse_args_and_arch(
quantize_parser,
[
"--task",
"language_modeling",
data_dir,
"--arch",
arch,
"--optimizer",
"adam",
"--lr",
"0.0001",
"--criterion",
"adaptive_loss",
"--adaptive-softmax-cutoff",
"5,10,15",
"--max-tokens",
"50",
"--tokens-per-sample",
"50",
"--max-update",
"6",
"--no-progress-bar",
"--distributed-world-size",
"1",
"--ddp-backend",
"no_c10d",
"--num-workers",
"0",
"--restore-file",
os.path.join(data_dir, "checkpoint_last.pt"),
"--reset-optimizer",
"--quantization-config-path",
os.path.join(
os.path.dirname(__file__), "transformer_quantization_config.yaml"
),
]
+ (extra_flags or []),
)
train.main(quantize_args)
@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU")
class TestQuantization(unittest.TestCase):
def setUp(self):
logging.disable(logging.CRITICAL)
def tearDown(self):
logging.disable(logging.NOTSET)
def test_quantization(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_quantization") as data_dir:
create_dummy_data(data_dir)
preprocess_lm_data(data_dir)
# tests both scalar and iterative PQ quantization
_quantize_language_model(data_dir, "transformer_lm")
@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU")
class TestOptimizersGPU(unittest.TestCase):
def setUp(self):
logging.disable(logging.CRITICAL)
def tearDown(self):
logging.disable(logging.NOTSET)
def test_flat_grads(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_flat_grads") as data_dir:
# Use just a bit of data and tiny model to keep this test runtime reasonable
create_dummy_data(data_dir, num_examples=10, maxlen=5)
preprocess_translation_data(data_dir)
with self.assertRaises(RuntimeError):
# adafactor isn't compatible with flat grads, which
# are used by default with --fp16
train_translation_model(
data_dir,
"lstm",
[
"--required-batch-size-multiple",
"1",
"--encoder-layers",
"1",
"--encoder-hidden-size",
"32",
"--decoder-layers",
"1",
"--optimizer",
"adafactor",
"--fp16",
],
)
# but it should pass once we set --fp16-no-flatten-grads
train_translation_model(
data_dir,
"lstm",
[
"--required-batch-size-multiple",
"1",
"--encoder-layers",
"1",
"--encoder-hidden-size",
"32",
"--decoder-layers",
"1",
"--optimizer",
"adafactor",
"--fp16",
"--fp16-no-flatten-grads",
],
)
if __name__ == "__main__":
unittest.main()
| 16,419 | 33.568421 | 92 | py |
CIF-HieraDist | CIF-HieraDist-main/tests/distributed/test_distributed_timeout_wrapper.py | # 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 logging
import signal
import time
import unittest
import torch
from torch import nn
from fairseq.distributed import DistributedTimeoutWrapper
class ModuleWithDelay(nn.Module):
def __init__(self, delay):
super().__init__()
self.delay = delay
def forward(self, x):
time.sleep(self.delay)
return x
class TestDistributedTimeoutWrapper(unittest.TestCase):
def setUp(self):
logging.disable(logging.CRITICAL)
def tearDown(self):
logging.disable(logging.NOTSET)
def test_no_timeout(self):
module = DistributedTimeoutWrapper(ModuleWithDelay(1), 0, signal.SIGINT)
module(torch.rand(5))
module.stop_timeout()
def test_timeout_safe(self):
module = DistributedTimeoutWrapper(ModuleWithDelay(1), 10, signal.SIGINT)
module(torch.rand(5))
module.stop_timeout()
def test_timeout_killed(self):
with self.assertRaises(KeyboardInterrupt):
module = DistributedTimeoutWrapper(ModuleWithDelay(5), 1, signal.SIGINT)
module(torch.rand(5))
module.stop_timeout()
if __name__ == "__main__":
unittest.main()
| 1,349 | 24.471698 | 84 | py |
CIF-HieraDist | CIF-HieraDist-main/tests/distributed/test_module_proxy_wrapper.py | # 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 unittest
import torch
from torch import nn
from fairseq.distributed import ModuleProxyWrapper
from .utils import objects_are_equal
class MockDDPWrapper(nn.Module):
"""A simple wrapper with an interface similar to DistributedDataParallel."""
def __init__(self, module):
super().__init__()
self.module = module
def forward(self, x):
return self.module(x)
class Model(nn.Module):
def __init__(self):
super().__init__()
self.linear = nn.Linear(5, 10)
self.xyz = "hello"
def forward(self, x):
return self.linear(x)
def get_xyz(self):
return self.xyz
class TestModuleProxyWrapper(unittest.TestCase):
def _get_module(self):
module = Model()
wrapped_module = MockDDPWrapper(module)
wrapped_module = ModuleProxyWrapper(wrapped_module)
return wrapped_module, module
def test_getattr_forwarding(self):
wrapped_module, module = self._get_module()
assert module.xyz == "hello"
assert module.get_xyz() == "hello"
assert wrapped_module.xyz == "hello"
wrapped_module.xyz = "world"
assert wrapped_module.xyz == "world"
assert module.get_xyz() == "hello"
def test_state_dict(self):
wrapped_module, module = self._get_module()
assert objects_are_equal(wrapped_module.state_dict(), module.state_dict())
def test_load_state_dict(self):
wrapped_module, module = self._get_module()
wrapped_module.load_state_dict(module.state_dict())
input = torch.rand(4, 5)
torch.testing.assert_allclose(wrapped_module(input), module(input))
def test_forward(self):
wrapped_module, module = self._get_module()
input = torch.rand(4, 5)
torch.testing.assert_allclose(wrapped_module(input), module(input))
if __name__ == "__main__":
unittest.main()
| 2,085 | 26.813333 | 82 | py |
CIF-HieraDist | CIF-HieraDist-main/tests/distributed/utils.py | # 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 functools
import tempfile
import torch
def spawn_and_init(fn, world_size, args=None):
if args is None:
args = ()
with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
torch.multiprocessing.spawn(
fn=functools.partial(init_and_run, fn, args),
args=(
world_size,
tmp_file.name,
),
nprocs=world_size,
join=True,
)
def distributed_init(rank, world_size, tmp_file):
torch.distributed.init_process_group(
backend="nccl",
init_method="file://{}".format(tmp_file),
world_size=world_size,
rank=rank,
)
torch.cuda.set_device(rank)
def init_and_run(fn, args, rank, world_size, tmp_file):
distributed_init(rank, world_size, tmp_file)
group = torch.distributed.new_group()
fn(rank, group, *args)
def objects_are_equal(a, b) -> bool:
if type(a) is not type(b):
return False
if isinstance(a, dict):
if set(a.keys()) != set(b.keys()):
return False
for k in a.keys():
if not objects_are_equal(a[k], b[k]):
return False
return True
elif isinstance(a, (list, tuple, set)):
if len(a) != len(b):
return False
return all(objects_are_equal(x, y) for x, y in zip(a, b))
elif torch.is_tensor(a):
return (
a.size() == b.size()
and a.dtype == b.dtype
and a.device == b.device
and torch.all(a == b)
)
else:
return a == b
| 1,765 | 25.757576 | 65 | py |
CIF-HieraDist | CIF-HieraDist-main/tests/distributed/test_bmuf.py | # 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 argparse
import functools
import random
import unittest
from multiprocessing import Manager
import torch
import torch.nn as nn
from fairseq import optim
from fairseq.distributed import utils as distributed_utils
from omegaconf import OmegaConf
class Model(nn.Module):
def __init__(self, input_size, output_size):
super(Model, self).__init__()
self.fc = nn.Linear(input_size, output_size)
def forward(self, input):
output = self.fc(input)
return output
def setup_model_loss_criterion(cfg, args, rank, is_cuda):
"""
setup model, criterion and optimizer based on input args
"""
args.distributed_rank = rank
cfg.distributed_training.distributed_rank = args.distributed_rank
if cfg.distributed_training.distributed_world_size > 1:
distributed_utils.distributed_init(cfg)
torch.manual_seed(1)
model = Model(args.input_size, args.nb_classes)
loss_fn = nn.CrossEntropyLoss()
if is_cuda:
model = model.cuda()
loss_fn = loss_fn.cuda()
optimizer = optim.sgd.SGD(args, model.parameters())
optimizer = optim.FairseqBMUF(cfg=cfg.bmuf, optimizer=optimizer)
return model, loss_fn, optimizer
def train_step(input, target, model, loss_fn, optimizer, **unused):
"""Do forward, backward and parameter update."""
model.train()
output = model(input)
loss = loss_fn(output, target)
optimizer.backward(loss)
optimizer.step()
def single_gpu_training(cfg, args, rank, iterations, shared_results):
is_cuda = torch.cuda.is_available()
if is_cuda:
torch.cuda.set_device(rank)
model, loss_fn, optimizer = setup_model_loss_criterion(cfg, args, rank, is_cuda)
for _ in range(iterations):
input = torch.randn(1, args.input_size)
target = torch.empty(args.batch_size, dtype=torch.long).random_(args.nb_classes)
if is_cuda:
input = input.cuda()
target = target.cuda()
train_step(input, target, model, loss_fn, optimizer)
results = []
for param in model.parameters():
if len(results) == 0:
results = param.flatten().cpu().data
else:
results = torch.cat((results, param.flatten().cpu().data), 0)
shared_results[rank] = results
def setup_args():
args = argparse.Namespace()
args.global_sync_iter = 20
args.block_momentum = 0.875
args.block_lr = 0.5
args.input_size = 5
args.nb_classes = 2
args.batch_size = 1
args.lr = [1e-3]
args.momentum = 0
args.weight_decay = 0
args.warmup_iterations = 0
args.use_nbm = True
args.average_sync = True
args.global_sync_iter = 1
args.model_parallel_size = 1
args.distributed_backend = "gloo"
args.distributed_world_size = 2
port = random.randint(10000, 20000)
args.distributed_init_method = "tcp://localhost:{port}".format(port=port)
args.distributed_init_host = "localhost"
args.distributed_port = port + 1
args.local_world_size = args.distributed_world_size
cfg = OmegaConf.create()
cfg.optimization = OmegaConf.create()
cfg.common = OmegaConf.create()
cfg.distributed_training = OmegaConf.create()
cfg.dataset = OmegaConf.create()
cfg.bmuf = OmegaConf.create()
cfg.optimizer = OmegaConf.create()
cfg.bmuf.global_sync_iter = args.global_sync_iter
cfg.bmuf.block_momentum = args.block_momentum
cfg.bmuf.block_lr = args.block_lr
cfg.dataset.batch_size = args.batch_size
cfg.optimization.lr = args.lr
cfg.optimizer.momentum = args.momentum
cfg.optimizer.weight_decay = args.weight_decay
cfg.bmuf.warmup_iterations = args.warmup_iterations
cfg.bmuf.use_nbm = args.use_nbm
cfg.bmuf.average_sync = args.average_sync
cfg.common.model_parallel_size = args.model_parallel_size
cfg.distributed_training.distributed_backend = args.distributed_backend
cfg.distributed_training.distributed_world_size = args.distributed_world_size
cfg.bmuf.distributed_world_size = args.distributed_world_size
cfg.distributed_training.distributed_init_method = args.distributed_init_method
cfg.distributed_training.distributed_port = args.distributed_port
return cfg, args
@unittest.skipIf(torch.cuda.device_count() < 2, "test requires 2 GPUs")
class TestBMUF(unittest.TestCase):
def bmuf_process(self, cfg, args, iterations):
processes = []
results = Manager().dict()
torch.multiprocessing.spawn(
fn=functools.partial(single_gpu_training, cfg, args),
args=(iterations, results),
nprocs=args.distributed_world_size,
join=True,
)
return results
def test_bmuf_sync(self):
# Train model for 1 iteration and do bmuf sync without doing warmup
cfg, args = setup_args()
iterations = 1
results = self.bmuf_process(cfg, args, iterations)
# Make sure params in both machines are same
assert len(results) == 2
self.assertAlmostEqual(results[0], results[1])
def test_warmup_sync(self):
# Train model for 20 iteration and do warmup sync without doing bmuf sync
cfg, args = setup_args()
args.warmup_iterations = 20
cfg.bmuf.warmup_iterations = args.warmup_iterations
iterations = 20
results = self.bmuf_process(cfg, args, iterations)
# Make sure params in both machines are same
assert len(results) == 2
self.assertAlmostEqual(results[0], results[1])
def test_warmup_sync_bmuf_sync(self):
# Train model for 25 iteration and do warmup sync after 20 iteration
# and bmuf sync after 25 iteration
cfg, args = setup_args()
args.warmup_iterations = 20
args.global_sync_iter = 5
cfg.bmuf.warmup_iterations = args.warmup_iterations
cfg.bmuf.global_sync_iter = args.global_sync_iter
iterations = 25
results = self.bmuf_process(cfg, args, iterations)
# Make sure params in both machines are same
assert len(results) == 2
self.assertAlmostEqual(results[0], results[1])
def test_single_gpu_bmuf(self):
# Train model for 5 iterations and use GPU 1
cfg, args = setup_args()
args.distributed_world_size = 1
args.warmup_iterations = 5
cfg.distributed_training.distributed_world_size = args.distributed_world_size
cfg.bmuf.distributed_world_size = args.distributed_world_size
cfg.bmuf.warmup_iterations = args.warmup_iterations
iterations = 20
results = self.bmuf_process(cfg, args, iterations)
assert len(results) == 1
def assertAlmostEqual(self, t1, t2):
self.assertEqual(t1.size(), t2.size(), "size mismatch")
self.assertLess((t1 - t2).abs().max(), 1e-4)
if __name__ == "__main__":
unittest.main()
| 7,049 | 33.390244 | 88 | py |
CIF-HieraDist | CIF-HieraDist-main/tests/distributed/test_utils.py | # 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 functools
import sys
import unittest
import torch
from fairseq.distributed import utils as dist_utils
from .utils import objects_are_equal, spawn_and_init
class DistributedTest(unittest.TestCase):
def setUp(self):
if not torch.cuda.is_available():
raise unittest.SkipTest("CUDA not available, skipping test")
if sys.platform == "win32":
raise unittest.SkipTest("NCCL doesn't support Windows, skipping test")
if torch.cuda.device_count() < 2:
raise unittest.SkipTest("distributed tests require 2+ GPUs, skipping")
class TestBroadcastObject(DistributedTest):
def test_str(self):
spawn_and_init(
functools.partial(
TestBroadcastObject._test_broadcast_object, "hello world"
),
world_size=2,
)
def test_tensor(self):
spawn_and_init(
functools.partial(
TestBroadcastObject._test_broadcast_object,
torch.rand(5),
),
world_size=2,
)
def test_complex(self):
spawn_and_init(
functools.partial(
TestBroadcastObject._test_broadcast_object,
{
"a": "1",
"b": [2, torch.rand(2, 3), 3],
"c": (torch.rand(2, 3), 4),
"d": {5, torch.rand(5)},
"e": torch.rand(5),
"f": torch.rand(5).int().cuda(),
},
),
world_size=2,
)
@staticmethod
def _test_broadcast_object(ref_obj, rank, group):
obj = dist_utils.broadcast_object(
ref_obj if rank == 0 else None, src_rank=0, group=group
)
assert objects_are_equal(ref_obj, obj)
class TestAllGatherList(DistributedTest):
def test_str_equality(self):
spawn_and_init(
functools.partial(
TestAllGatherList._test_all_gather_list_equality,
"hello world",
),
world_size=2,
)
def test_tensor_equality(self):
spawn_and_init(
functools.partial(
TestAllGatherList._test_all_gather_list_equality,
torch.rand(5),
),
world_size=2,
)
def test_complex_equality(self):
spawn_and_init(
functools.partial(
TestAllGatherList._test_all_gather_list_equality,
{
"a": "1",
"b": [2, torch.rand(2, 3), 3],
"c": (torch.rand(2, 3), 4),
"d": {5, torch.rand(5)},
"e": torch.rand(5),
"f": torch.rand(5).int(),
},
),
world_size=2,
)
@staticmethod
def _test_all_gather_list_equality(ref_obj, rank, group):
objs = dist_utils.all_gather_list(ref_obj, group)
for obj in objs:
assert objects_are_equal(ref_obj, obj)
def test_rank_tensor(self):
spawn_and_init(
TestAllGatherList._test_all_gather_list_rank_tensor, world_size=2
)
@staticmethod
def _test_all_gather_list_rank_tensor(rank, group):
obj = torch.tensor([rank])
objs = dist_utils.all_gather_list(obj, group)
for i, obj in enumerate(objs):
assert obj.item() == i
if __name__ == "__main__":
unittest.main()
| 3,656 | 28.256 | 82 | py |
CIF-HieraDist | CIF-HieraDist-main/tests/speech_recognition/test_data_utils.py | #!/usr/bin/env python3
# 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 unittest
import torch
from examples.speech_recognition.data import data_utils
class DataUtilsTest(unittest.TestCase):
def test_normalization(self):
sample_len1 = torch.tensor(
[
[
-0.7661,
-1.3889,
-2.0972,
-0.9134,
-0.7071,
-0.9765,
-0.8700,
-0.8283,
0.7512,
1.3211,
2.1532,
2.1174,
1.2800,
1.2633,
1.6147,
1.6322,
2.0723,
3.1522,
3.2852,
2.2309,
2.5569,
2.2183,
2.2862,
1.5886,
0.8773,
0.8725,
1.2662,
0.9899,
1.1069,
1.3926,
1.2795,
1.1199,
1.1477,
1.2687,
1.3843,
1.1903,
0.8355,
1.1367,
1.2639,
1.4707,
]
]
)
out = data_utils.apply_mv_norm(sample_len1)
assert not torch.isnan(out).any()
assert (out == sample_len1).all()
| 1,736 | 26.571429 | 65 | py |
CIF-HieraDist | CIF-HieraDist-main/tests/speech_recognition/asr_test_base.py | #!/usr/bin/env python3
import argparse
import os
import unittest
from inspect import currentframe, getframeinfo
import numpy as np
import torch
from examples.speech_recognition.data.data_utils import lengths_to_encoder_padding_mask
from fairseq.data import data_utils as fairseq_data_utils
from fairseq.data.dictionary import Dictionary
from fairseq.models import (
BaseFairseqModel,
FairseqDecoder,
FairseqEncoder,
FairseqEncoderDecoderModel,
FairseqEncoderModel,
FairseqModel,
)
from fairseq.tasks.fairseq_task import LegacyFairseqTask
DEFAULT_TEST_VOCAB_SIZE = 100
# ///////////////////////////////////////////////////////////////////////////
# utility function to setup dummy dict/task/input
# ///////////////////////////////////////////////////////////////////////////
def get_dummy_dictionary(vocab_size=DEFAULT_TEST_VOCAB_SIZE):
dummy_dict = Dictionary()
# add dummy symbol to satisfy vocab size
for id, _ in enumerate(range(vocab_size)):
dummy_dict.add_symbol("{}".format(id), 1000)
return dummy_dict
class DummyTask(LegacyFairseqTask):
def __init__(self, args):
super().__init__(args)
self.dictionary = get_dummy_dictionary()
if getattr(self.args, "ctc", False):
self.dictionary.add_symbol("<ctc_blank>")
self.tgt_dict = self.dictionary
@property
def target_dictionary(self):
return self.dictionary
def get_dummy_task_and_parser():
"""
to build a fariseq model, we need some dummy parse and task. This function
is used to create dummy task and parser to faciliate model/criterion test
Note: we use FbSpeechRecognitionTask as the dummy task. You may want
to use other task by providing another function
"""
parser = argparse.ArgumentParser(
description="test_dummy_s2s_task", argument_default=argparse.SUPPRESS
)
DummyTask.add_args(parser)
args = parser.parse_args([])
task = DummyTask.setup_task(args)
return task, parser
def get_dummy_input(T=100, D=80, B=5, K=100):
forward_input = {}
# T max sequence length
# D feature vector dimension
# B batch size
# K target dimension size
feature = torch.randn(B, T, D)
# this (B, T, D) layout is just a convention, you can override it by
# write your own _prepare_forward_input function
src_lengths = torch.from_numpy(
np.random.randint(low=1, high=T, size=B, dtype=np.int64)
)
src_lengths[0] = T # make sure the maximum length matches
prev_output_tokens = []
for b in range(B):
token_length = np.random.randint(low=1, high=src_lengths[b].item() + 1)
tokens = np.random.randint(low=0, high=K, size=token_length, dtype=np.int64)
prev_output_tokens.append(torch.from_numpy(tokens))
prev_output_tokens = fairseq_data_utils.collate_tokens(
prev_output_tokens,
pad_idx=1,
eos_idx=2,
left_pad=False,
move_eos_to_beginning=False,
)
src_lengths, sorted_order = src_lengths.sort(descending=True)
forward_input["src_tokens"] = feature.index_select(0, sorted_order)
forward_input["src_lengths"] = src_lengths
forward_input["prev_output_tokens"] = prev_output_tokens
return forward_input
def get_dummy_encoder_output(encoder_out_shape=(100, 80, 5)):
"""
This only provides an example to generate dummy encoder output
"""
(T, B, D) = encoder_out_shape
encoder_out = {}
encoder_out["encoder_out"] = torch.from_numpy(
np.random.randn(*encoder_out_shape).astype(np.float32)
)
seq_lengths = torch.from_numpy(np.random.randint(low=1, high=T, size=B))
# some dummy mask
encoder_out["encoder_padding_mask"] = torch.arange(T).view(1, T).expand(
B, -1
) >= seq_lengths.view(B, 1).expand(-1, T)
encoder_out["encoder_padding_mask"].t_()
# encoer_padding_mask is (T, B) tensor, with (t, b)-th element indicate
# whether encoder_out[t, b] is valid (=0) or not (=1)
return encoder_out
def _current_postion_info():
cf = currentframe()
frameinfo = " (at {}:{})".format(
os.path.basename(getframeinfo(cf).filename), cf.f_back.f_lineno
)
return frameinfo
def check_encoder_output(encoder_output, batch_size=None):
"""we expect encoder_output to be a dict with the following
key/value pairs:
- encoder_out: a Torch.Tensor
- encoder_padding_mask: a binary Torch.Tensor
"""
if not isinstance(encoder_output, dict):
msg = (
"FairseqEncoderModel.forward(...) must be a dict" + _current_postion_info()
)
return False, msg
if "encoder_out" not in encoder_output:
msg = (
"FairseqEncoderModel.forward(...) must contain encoder_out"
+ _current_postion_info()
)
return False, msg
if "encoder_padding_mask" not in encoder_output:
msg = (
"FairseqEncoderModel.forward(...) must contain encoder_padding_mask"
+ _current_postion_info()
)
return False, msg
if not isinstance(encoder_output["encoder_out"], torch.Tensor):
msg = "encoder_out must be a torch.Tensor" + _current_postion_info()
return False, msg
if encoder_output["encoder_out"].dtype != torch.float32:
msg = "encoder_out must have float32 dtype" + _current_postion_info()
return False, msg
mask = encoder_output["encoder_padding_mask"]
if mask is not None:
if not isinstance(mask, torch.Tensor):
msg = (
"encoder_padding_mask must be a torch.Tensor" + _current_postion_info()
)
return False, msg
if mask.dtype != torch.uint8 and (
not hasattr(torch, "bool") or mask.dtype != torch.bool
):
msg = (
"encoder_padding_mask must have dtype of uint8"
+ _current_postion_info()
)
return False, msg
if mask.dim() != 2:
msg = (
"we expect encoder_padding_mask to be a 2-d tensor, in shape (T, B)"
+ _current_postion_info()
)
return False, msg
if batch_size is not None and mask.size(1) != batch_size:
msg = (
"we expect encoder_padding_mask to be a 2-d tensor, with size(1)"
+ " being the batch size"
+ _current_postion_info()
)
return False, msg
return True, None
def check_decoder_output(decoder_output):
"""we expect output from a decoder is a tuple with the following constraint:
- the first element is a torch.Tensor
- the second element can be anything (reserved for future use)
"""
if not isinstance(decoder_output, tuple):
msg = "FariseqDecoder output must be a tuple" + _current_postion_info()
return False, msg
if len(decoder_output) != 2:
msg = "FairseqDecoder output must be 2-elem tuple" + _current_postion_info()
return False, msg
if not isinstance(decoder_output[0], torch.Tensor):
msg = (
"FariseqDecoder output[0] must be a torch.Tensor" + _current_postion_info()
)
return False, msg
return True, None
# ///////////////////////////////////////////////////////////////////////////
# Base Test class
# ///////////////////////////////////////////////////////////////////////////
class TestBaseFairseqModelBase(unittest.TestCase):
"""
This class is used to facilitate writing unittest for any class derived from
`BaseFairseqModel`.
"""
@classmethod
def setUpClass(cls):
if cls is TestBaseFairseqModelBase:
raise unittest.SkipTest("Skipping test case in base")
super().setUpClass()
def setUpModel(self, model):
self.assertTrue(isinstance(model, BaseFairseqModel))
self.model = model
def setupInput(self):
pass
def setUp(self):
self.model = None
self.forward_input = None
pass
class TestFairseqEncoderDecoderModelBase(TestBaseFairseqModelBase):
"""
base code to test FairseqEncoderDecoderModel (formally known as
`FairseqModel`) must be derived from this base class
"""
@classmethod
def setUpClass(cls):
if cls is TestFairseqEncoderDecoderModelBase:
raise unittest.SkipTest("Skipping test case in base")
super().setUpClass()
def setUpModel(self, model_cls, extra_args_setters=None):
self.assertTrue(
issubclass(model_cls, (FairseqEncoderDecoderModel, FairseqModel)),
msg="This class only tests for FairseqModel subclasses",
)
task, parser = get_dummy_task_and_parser()
model_cls.add_args(parser)
args = parser.parse_args([])
if extra_args_setters is not None:
for args_setter in extra_args_setters:
args_setter(args)
model = model_cls.build_model(args, task)
self.model = model
def setUpInput(self, input=None):
self.forward_input = get_dummy_input() if input is None else input
def setUp(self):
super().setUp()
def test_forward(self):
if self.model and self.forward_input:
forward_output = self.model.forward(**self.forward_input)
# for FairseqEncoderDecoderModel, forward returns a tuple of two
# elements, the first one is a Torch.Tensor
succ, msg = check_decoder_output(forward_output)
if not succ:
self.assertTrue(succ, msg=msg)
self.forward_output = forward_output
def test_get_normalized_probs(self):
if self.model and self.forward_input:
forward_output = self.model.forward(**self.forward_input)
logprob = self.model.get_normalized_probs(forward_output, log_probs=True)
prob = self.model.get_normalized_probs(forward_output, log_probs=False)
# in order for different models/criterion to play with each other
# we need to know whether the logprob or prob output is batch_first
# or not. We assume an additional attribute will be attached to logprob
# or prob. If you find your code failed here, simply override
# FairseqModel.get_normalized_probs, see example at
# https://fburl.com/batch_first_example
self.assertTrue(hasattr(logprob, "batch_first"))
self.assertTrue(hasattr(prob, "batch_first"))
self.assertTrue(torch.is_tensor(logprob))
self.assertTrue(torch.is_tensor(prob))
class TestFairseqEncoderModelBase(TestBaseFairseqModelBase):
"""
base class to test FairseqEncoderModel
"""
@classmethod
def setUpClass(cls):
if cls is TestFairseqEncoderModelBase:
raise unittest.SkipTest("Skipping test case in base")
super().setUpClass()
def setUpModel(self, model_cls, extra_args_setters=None):
self.assertTrue(
issubclass(model_cls, FairseqEncoderModel),
msg="This class is only used for testing FairseqEncoderModel",
)
task, parser = get_dummy_task_and_parser()
model_cls.add_args(parser)
args = parser.parse_args([])
if extra_args_setters is not None:
for args_setter in extra_args_setters:
args_setter(args)
model = model_cls.build_model(args, task)
self.model = model
def setUpInput(self, input=None):
self.forward_input = get_dummy_input() if input is None else input
# get_dummy_input() is originally for s2s, here we delete extra dict
# items, so it can be used for EncoderModel / Encoder as well
self.forward_input.pop("prev_output_tokens", None)
def setUp(self):
super().setUp()
def test_forward(self):
if self.forward_input and self.model:
bsz = self.forward_input["src_tokens"].size(0)
forward_output = self.model.forward(**self.forward_input)
# we expect forward_output to be a dict with the following
# key/value pairs:
# - encoder_out: a Torch.Tensor
# - encoder_padding_mask: a binary Torch.Tensor
succ, msg = check_encoder_output(forward_output, batch_size=bsz)
if not succ:
self.assertTrue(succ, msg=msg)
self.forward_output = forward_output
def test_get_normalized_probs(self):
if self.model and self.forward_input:
forward_output = self.model.forward(**self.forward_input)
logprob = self.model.get_normalized_probs(forward_output, log_probs=True)
prob = self.model.get_normalized_probs(forward_output, log_probs=False)
# in order for different models/criterion to play with each other
# we need to know whether the logprob or prob output is batch_first
# or not. We assume an additional attribute will be attached to logprob
# or prob. If you find your code failed here, simply override
# FairseqModel.get_normalized_probs, see example at
# https://fburl.com/batch_first_example
self.assertTrue(hasattr(logprob, "batch_first"))
self.assertTrue(hasattr(prob, "batch_first"))
self.assertTrue(torch.is_tensor(logprob))
self.assertTrue(torch.is_tensor(prob))
class TestFairseqEncoderBase(unittest.TestCase):
"""
base class to test FairseqEncoder
"""
@classmethod
def setUpClass(cls):
if cls is TestFairseqEncoderBase:
raise unittest.SkipTest("Skipping test case in base")
super().setUpClass()
def setUpEncoder(self, encoder):
self.assertTrue(
isinstance(encoder, FairseqEncoder),
msg="This class is only used for test FairseqEncoder",
)
self.encoder = encoder
def setUpInput(self, input=None):
self.forward_input = get_dummy_input() if input is None else input
# get_dummy_input() is originally for s2s, here we delete extra dict
# items, so it can be used for EncoderModel / Encoder as well
self.forward_input.pop("prev_output_tokens", None)
def setUp(self):
self.encoder = None
self.forward_input = None
def test_forward(self):
if self.encoder and self.forward_input:
bsz = self.forward_input["src_tokens"].size(0)
forward_output = self.encoder.forward(**self.forward_input)
succ, msg = check_encoder_output(forward_output, batch_size=bsz)
if not succ:
self.assertTrue(succ, msg=msg)
self.forward_output = forward_output
class TestFairseqDecoderBase(unittest.TestCase):
"""
base class to test FairseqDecoder
"""
@classmethod
def setUpClass(cls):
if cls is TestFairseqDecoderBase:
raise unittest.SkipTest("Skipping test case in base")
super().setUpClass()
def setUpDecoder(self, decoder):
self.assertTrue(
isinstance(decoder, FairseqDecoder),
msg="This class is only used for test FairseqDecoder",
)
self.decoder = decoder
def setUpInput(self, input=None):
self.forward_input = get_dummy_encoder_output() if input is None else input
def setUpPrevOutputTokens(self, tokens=None):
if tokens is None:
self.encoder_input = get_dummy_input()
self.prev_output_tokens = self.encoder_input["prev_output_tokens"]
else:
self.prev_output_tokens = tokens
def setUp(self):
self.decoder = None
self.forward_input = None
self.prev_output_tokens = None
def test_forward(self):
if (
self.decoder is not None
and self.forward_input is not None
and self.prev_output_tokens is not None
):
forward_output = self.decoder.forward(
prev_output_tokens=self.prev_output_tokens,
encoder_out=self.forward_input,
)
succ, msg = check_decoder_output(forward_output)
if not succ:
self.assertTrue(succ, msg=msg)
self.forward_input = forward_output
class DummyEncoderModel(FairseqEncoderModel):
def __init__(self, encoder):
super().__init__(encoder)
@classmethod
def build_model(cls, args, task):
return cls(DummyEncoder())
def get_logits(self, net_output):
# Inverse of sigmoid to use with BinaryCrossEntropyWithLogitsCriterion as
# F.binary_cross_entropy_with_logits combines sigmoid and CE
return torch.log(
torch.div(net_output["encoder_out"], 1 - net_output["encoder_out"])
)
def get_normalized_probs(self, net_output, log_probs, sample=None):
lprobs = super().get_normalized_probs(net_output, log_probs, sample=sample)
lprobs.batch_first = True
return lprobs
class DummyEncoder(FairseqEncoder):
def __init__(self):
super().__init__(None)
def forward(self, src_tokens, src_lengths):
mask, max_len = lengths_to_encoder_padding_mask(src_lengths)
return {"encoder_out": src_tokens, "encoder_padding_mask": mask}
class CrossEntropyCriterionTestBase(unittest.TestCase):
@classmethod
def setUpClass(cls):
if cls is CrossEntropyCriterionTestBase:
raise unittest.SkipTest("Skipping base class test case")
super().setUpClass()
def setUpArgs(self):
args = argparse.Namespace()
args.sentence_avg = False
args.threshold = 0.1 # to use with BinaryCrossEntropyWithLogitsCriterion
return args
def setUp(self):
args = self.setUpArgs()
self.model = DummyEncoderModel(encoder=DummyEncoder())
self.criterion = self.criterion_cls.build_criterion(args, task=DummyTask(args))
def get_src_tokens(self, correct_prediction, aggregate):
"""
correct_prediction: True if the net_output (src_tokens) should
predict the correct target
aggregate: True if the criterion expects net_output (src_tokens)
aggregated across time axis
"""
predicted_idx = 0 if correct_prediction else 1
if aggregate:
src_tokens = torch.zeros((2, 2), dtype=torch.float)
for b in range(2):
src_tokens[b][predicted_idx] = 1.0
else:
src_tokens = torch.zeros((2, 10, 2), dtype=torch.float)
for b in range(2):
for t in range(10):
src_tokens[b][t][predicted_idx] = 1.0
return src_tokens
def get_target(self, soft_target):
if soft_target:
target = torch.zeros((2, 2), dtype=torch.float)
for b in range(2):
target[b][0] = 1.0
else:
target = torch.zeros((2, 10), dtype=torch.long)
return target
def get_test_sample(self, correct, soft_target, aggregate):
src_tokens = self.get_src_tokens(correct, aggregate)
target = self.get_target(soft_target)
L = src_tokens.size(1)
return {
"net_input": {"src_tokens": src_tokens, "src_lengths": torch.tensor([L])},
"target": target,
"ntokens": src_tokens.size(0) * src_tokens.size(1),
}
| 19,469 | 33.892473 | 87 | py |
CIF-HieraDist | CIF-HieraDist-main/tests/speech_recognition/test_collaters.py | #!/usr/bin/env python3
# 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 unittest
import numpy as np
import torch
from examples.speech_recognition.data.collaters import Seq2SeqCollater
class TestSeq2SeqCollator(unittest.TestCase):
def test_collate(self):
eos_idx = 1
pad_idx = 0
collater = Seq2SeqCollater(
feature_index=0, label_index=1, pad_index=pad_idx, eos_index=eos_idx
)
# 2 frames in the first sample and 3 frames in the second one
frames1 = np.array([[7, 8], [9, 10]])
frames2 = np.array([[1, 2], [3, 4], [5, 6]])
target1 = np.array([4, 2, 3, eos_idx])
target2 = np.array([3, 2, eos_idx])
sample1 = {"id": 0, "data": [frames1, target1]}
sample2 = {"id": 1, "data": [frames2, target2]}
batch = collater.collate([sample1, sample2])
# collate sort inputs by frame's length before creating the batch
self.assertTensorEqual(batch["id"], torch.tensor([1, 0]))
self.assertEqual(batch["ntokens"], 7)
self.assertTensorEqual(
batch["net_input"]["src_tokens"],
torch.tensor(
[[[1, 2], [3, 4], [5, 6]], [[7, 8], [9, 10], [pad_idx, pad_idx]]]
),
)
self.assertTensorEqual(
batch["net_input"]["prev_output_tokens"],
torch.tensor([[eos_idx, 3, 2, pad_idx], [eos_idx, 4, 2, 3]]),
)
self.assertTensorEqual(batch["net_input"]["src_lengths"], torch.tensor([3, 2]))
self.assertTensorEqual(
batch["target"],
torch.tensor([[3, 2, eos_idx, pad_idx], [4, 2, 3, eos_idx]]),
)
self.assertEqual(batch["nsentences"], 2)
def assertTensorEqual(self, t1, t2):
self.assertEqual(t1.size(), t2.size(), "size mismatch")
self.assertEqual(t1.ne(t2).long().sum(), 0)
if __name__ == "__main__":
unittest.main()
| 2,048 | 33.728814 | 87 | py |
CIF-HieraDist | CIF-HieraDist-main/fairseq/checkpoint_utils.py | # 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 sys
import ast
import collections
import contextlib
import logging
import numpy as np
import os
import re
import time
import traceback
from collections import OrderedDict
from typing import Any, Dict, Optional, Union
import argparse
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, open_dict, OmegaConf
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:
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 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"--funetune-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
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
# TODO: Revised by Minglun Han, for the convience of speech generation with speech chain
def load_speech_chain_to_tts(
filename,
datapath,
arg_overrides: Optional[Dict[str, Any]] = None,
task=None,
suffix="",
num_shards=1,
):
from fairseq import tasks
cfg = None
model = None
orig_filename = filename
for shard_idx in range(num_shards):
filename = get_maybe_sharded_checkpoint_filename(
orig_filename, suffix, shard_idx, num_shards
)
# Load model to cpu
state = load_checkpoint_to_cpu(filename, arg_overrides)
# TODO: Change the model-related hyper-parameters
state_sc = load_checkpoint_to_cpu(
path=arg_overrides["path_to_speech_chain_ckpt"]
)
sc_model_cfg_dict = vars(state_sc["cfg"].model)
sc_task_cfg_dict = vars(state_sc["cfg"].task)
model_cfg_dict = vars(state["cfg"].model)
task_cfg_dict = vars(state["cfg"].task)
for key, value in sc_task_cfg_dict.items():
if "speaker" in key:
task_cfg_dict[key] = value
task_cfg_dict["data"] = datapath
for key, value in sc_model_cfg_dict.items():
if key.startswith("tts"):
model_cfg_dict[key.replace("tts_", "")] = value
task_cfg = argparse.Namespace(**task_cfg_dict)
model_cfg = argparse.Namespace(**model_cfg_dict)
task = tasks.setup_task(task_cfg)
cfg = state["cfg"]
model = task.build_model(model_cfg)
return model, cfg, task
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
# old model checkpoints may not have separate source/target positions
if (
"args" in state
and 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
# use stateful training data iterator
if "train_iterator" not in state["extra_state"]:
state["extra_state"]["train_iterator"] = {
"epoch": state["extra_state"]["epoch"],
"iterations_in_epoch": state["extra_state"].get("batch_offset", 0),
}
# backward compatibility, cfg updates
if "args" in state and state["args"] is not None:
# 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]
# remove keys in state["args"] related to teacher-student learning
for key in [
"static_teachers",
"static_teacher_weights",
"dynamic_teachers",
"dynamic_teacher_weights",
]:
if key in state["args"]:
delattr(state["args"], key)
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
):
"""
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=True)
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 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
| 35,232 | 37.296739 | 114 | py |
CIF-HieraDist | CIF-HieraDist-main/fairseq/utils.py | # 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 argparse
import contextlib
import copy
import importlib
import logging
import os
import sys
import warnings
from itertools import accumulate
from typing import Callable, Dict, List, Optional, TYPE_CHECKING
import torch
import torch.nn.functional as F
from torch import Tensor
import collections
if TYPE_CHECKING:
from fairseq.modules.multihead_attention import MultiheadAttention
try:
from amp_C import multi_tensor_l2norm
multi_tensor_l2norm_available = True
except ImportError:
multi_tensor_l2norm_available = False
try:
import torch_xla.core.xla_model as xm
except ImportError:
xm = None
logger = logging.getLogger(__name__)
MANIFOLD_PATH_SEP = "|"
class FileContentsAction(argparse.Action):
def __init__(self, option_strings, dest, nargs=None, **kwargs):
if nargs is not None:
raise ValueError("nargs not allowed")
super(FileContentsAction, self).__init__(option_strings, dest, **kwargs)
def __call__(self, parser, namespace, values, option_string=None):
from fairseq.file_io import PathManager
if PathManager.isfile(values):
with PathManager.open(values) as f:
argument = f.read().strip()
else:
argument = values
setattr(namespace, self.dest, argument)
def split_paths(paths: str, separator=os.pathsep) -> List[str]:
return (
paths.split(separator) if "://" not in paths else paths.split(MANIFOLD_PATH_SEP)
)
def load_ensemble_for_inference(filenames, task, model_arg_overrides=None):
from fairseq import checkpoint_utils
deprecation_warning(
"utils.load_ensemble_for_inference is deprecated. "
"Please use checkpoint_utils.load_model_ensemble instead."
)
return checkpoint_utils.load_model_ensemble(
filenames, arg_overrides=model_arg_overrides, task=task
)
def apply_to_sample(f, sample):
if hasattr(sample, "__len__") and len(sample) == 0:
return {}
def _apply(x):
if torch.is_tensor(x):
return f(x)
elif isinstance(x, collections.OrderedDict):
# OrderedDict has attributes that needs to be preserved
od = collections.OrderedDict(
(key, _apply(value)) for key, value in x.items()
)
od.__dict__ = x.__dict__
return od
elif isinstance(x, dict):
return {key: _apply(value) for key, value in x.items()}
elif isinstance(x, list):
return [_apply(x) for x in x]
elif isinstance(x, tuple):
return tuple(_apply(x) for x in x)
elif isinstance(x, set):
return {_apply(x) for x in x}
else:
return x
return _apply(sample)
def move_to_cuda(sample, device=None):
device = device or torch.cuda.current_device()
def _move_to_cuda(tensor):
# non_blocking is ignored if tensor is not pinned, so we can always set
# to True (see github.com/PyTorchLightning/pytorch-lightning/issues/620)
return tensor.to(device=device, non_blocking=True)
return apply_to_sample(_move_to_cuda, sample)
def move_to_cpu(sample):
def _move_to_cpu(tensor):
# PyTorch has poor support for half tensors (float16) on CPU.
# Move any such tensors to float32.
if tensor.dtype in {torch.bfloat16, torch.float16}:
tensor = tensor.to(dtype=torch.float32)
return tensor.cpu()
return apply_to_sample(_move_to_cpu, sample)
def move_to_tpu(sample):
import torch_xla.core.xla_model as xm
device = xm.xla_device()
def _move_to_tpu(tensor):
return tensor.to(device)
return apply_to_sample(_move_to_tpu, sample)
def get_incremental_state(
module: "MultiheadAttention",
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]],
key: str,
) -> Optional[Dict[str, Optional[Tensor]]]:
"""Helper for getting incremental state for an nn.Module."""
return module.get_incremental_state(incremental_state, key)
def set_incremental_state(
module: "MultiheadAttention",
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]],
key: str,
value: Dict[str, Optional[Tensor]],
) -> Optional[Dict[str, Dict[str, Optional[Tensor]]]]:
"""Helper for setting incremental state for an nn.Module."""
if incremental_state is not None:
result = module.set_incremental_state(incremental_state, key, value)
if result is not None:
incremental_state = result
return incremental_state
def load_align_dict(replace_unk):
if replace_unk is None:
align_dict = None
elif isinstance(replace_unk, str) and len(replace_unk) > 0:
# Load alignment dictionary for unknown word replacement if it was passed as an argument.
align_dict = {}
with open(replace_unk, "r") as f:
for line in f:
cols = line.split()
align_dict[cols[0]] = cols[1]
else:
# No alignment dictionary provided but we still want to perform unknown word replacement by copying the
# original source word.
align_dict = {}
return align_dict
def print_embed_overlap(embed_dict, vocab_dict):
embed_keys = set(embed_dict.keys())
vocab_keys = set(vocab_dict.symbols)
overlap = len(embed_keys & vocab_keys)
logger.info("found {}/{} types in embedding file".format(overlap, len(vocab_dict)))
def parse_embedding(embed_path):
"""Parse embedding text file into a dictionary of word and embedding tensors.
The first line can have vocabulary size and dimension. The following lines
should contain word and embedding separated by spaces.
Example:
2 5
the -0.0230 -0.0264 0.0287 0.0171 0.1403
at -0.0395 -0.1286 0.0275 0.0254 -0.0932
"""
embed_dict = {}
with open(embed_path) as f_embed:
next(f_embed) # skip header
for line in f_embed:
pieces = line.rstrip().split(" ")
embed_dict[pieces[0]] = torch.Tensor(
[float(weight) for weight in pieces[1:]]
)
return embed_dict
def load_embedding(embed_dict, vocab, embedding):
for idx in range(len(vocab)):
token = vocab[idx]
if token in embed_dict:
embedding.weight.data[idx] = embed_dict[token]
return embedding
def replace_unk(hypo_str, src_str, alignment, align_dict, unk):
from fairseq import tokenizer
# Tokens are strings here
hypo_tokens = tokenizer.tokenize_line(hypo_str)
# TODO: Very rare cases where the replacement is '<eos>' should be handled gracefully
src_tokens = tokenizer.tokenize_line(src_str) + ["<eos>"]
for i, ht in enumerate(hypo_tokens):
if ht == unk:
src_token = src_tokens[alignment[i]]
# Either take the corresponding value in the aligned dictionary or just copy the original value.
hypo_tokens[i] = align_dict.get(src_token, src_token)
return " ".join(hypo_tokens)
def post_process_prediction(
hypo_tokens,
src_str,
alignment,
align_dict,
tgt_dict,
remove_bpe=None,
extra_symbols_to_ignore=None,
):
hypo_str = tgt_dict.string(
hypo_tokens, remove_bpe, extra_symbols_to_ignore=extra_symbols_to_ignore
)
if align_dict is not None:
hypo_str = replace_unk(
hypo_str, src_str, alignment, align_dict, tgt_dict.unk_string()
)
if align_dict is not None or remove_bpe is not None:
# Convert back to tokens for evaluating with unk replacement or without BPE
# Note that the dictionary can be modified inside the method.
hypo_tokens = tgt_dict.encode_line(hypo_str, add_if_not_exist=True)
return hypo_tokens, hypo_str, alignment
def make_positions(tensor, padding_idx: int, onnx_trace: bool = False):
"""Replace non-padding symbols with their position numbers.
Position numbers begin at padding_idx+1. Padding symbols are ignored.
"""
# The series of casts and type-conversions here are carefully
# balanced to both work with ONNX export and XLA. In particular XLA
# prefers ints, cumsum defaults to output longs, and ONNX doesn't know
# how to handle the dtype kwarg in cumsum.
mask = tensor.ne(padding_idx).int()
return (torch.cumsum(mask, dim=1).type_as(mask) * mask).long() + padding_idx
def strip_pad(tensor, pad):
return tensor[tensor.ne(pad)]
def buffered_arange(max):
if not hasattr(buffered_arange, "buf"):
buffered_arange.buf = torch.LongTensor()
if max > buffered_arange.buf.numel():
buffered_arange.buf.resize_(max)
torch.arange(max, out=buffered_arange.buf)
return buffered_arange.buf[:max]
def convert_padding_direction(
src_tokens, padding_idx, right_to_left: bool = False, left_to_right: bool = False
):
assert right_to_left ^ left_to_right
pad_mask = src_tokens.eq(padding_idx)
if not pad_mask.any():
# no padding, return early
return src_tokens
if left_to_right and not pad_mask[:, 0].any():
# already right padded
return src_tokens
if right_to_left and not pad_mask[:, -1].any():
# already left padded
return src_tokens
max_len = src_tokens.size(1)
buffered = torch.empty(0).long()
if max_len > 0:
torch.arange(max_len, out=buffered)
range = buffered.type_as(src_tokens).expand_as(src_tokens)
num_pads = pad_mask.long().sum(dim=1, keepdim=True)
if right_to_left:
index = torch.remainder(range - num_pads, max_len)
else:
index = torch.remainder(range + num_pads, max_len)
return src_tokens.gather(1, index)
def item(tensor):
# tpu-comment: making this a no-op for xla devices.
if torch.is_tensor(tensor) and tensor.device.type == "xla":
return tensor.detach()
if hasattr(tensor, "item"):
return tensor.item()
if hasattr(tensor, "__getitem__"):
return tensor[0]
return tensor
def multi_tensor_total_norm(grads, chunk_size=2048 * 32) -> torch.Tensor:
per_device_grads = {}
norms = []
for grad in grads:
device = grad.device
cur_device_grads = per_device_grads.get(device)
if cur_device_grads is None:
cur_device_grads = []
per_device_grads[device] = cur_device_grads
cur_device_grads.append(grad)
for device in per_device_grads.keys():
cur_device_grads = per_device_grads[device]
if device.type == "cuda":
# TODO(msb) return has_inf
has_inf = torch.zeros((1, 1), dtype=torch.int, device=device)
with torch.cuda.device(device):
norm = multi_tensor_l2norm(
chunk_size, has_inf, [cur_device_grads], False
)
norms.append(norm[0].to(torch.cuda.current_device()))
else:
norms += [torch.norm(g, p=2, dtype=torch.float32) for g in cur_device_grads]
total_norm = torch.norm(torch.stack(norms))
return total_norm
@torch.no_grad()
def clip_grad_norm_(params, max_norm, aggregate_norm_fn=None) -> torch.Tensor:
def grad_exists(p):
return p is not None and getattr(p, "grad", None) is not None
if isinstance(params, torch.Tensor):
params = [params]
params = list(params)
grads = [
p.grad.detach() for p in params if grad_exists(p) and not hasattr(p, "expert")
]
expert_grads = [
p.grad.detach() for p in params if grad_exists(p) and hasattr(p, "expert")
]
if len(grads) == 0:
if len(params) > 0:
return params[0].new_tensor(0.0)
else:
return torch.tensor(0.0)
if len(grads) == 1:
total_norm = torch.norm(grads[0], p=2, dtype=torch.float32)
else:
if multi_tensor_l2norm_available:
total_norm = multi_tensor_total_norm(grads)
else:
if torch.cuda.is_available():
warnings.warn(
"amp_C fused kernels unavailable, disabling multi_tensor_l2norm; "
"you may get better performance by installing NVIDIA's apex library"
)
device = torch.cuda.current_device()
elif grads[0].device.type == "xla":
device = grads[0].device
else:
device = torch.device("cpu")
total_norm = torch.norm(
torch.stack(
[torch.norm(g, p=2, dtype=torch.float32).to(device) for g in grads]
)
)
if aggregate_norm_fn is not None:
total_norm = aggregate_norm_fn(total_norm)
if max_norm > 0:
max_norm = float(max_norm)
clip_coef = (max_norm / (total_norm + 1e-6)).clamp_(max=1)
for g in grads + expert_grads:
g.mul_(clip_coef)
return total_norm
def fill_with_neg_inf(t):
"""FP16-compatible function that fills a tensor with -inf."""
return t.float().fill_(float("-inf")).type_as(t)
def _match_types(arg1, arg2):
"""Convert the numerical argument to the same type as the other argument"""
def upgrade(arg_number, arg_structure):
if isinstance(arg_structure, tuple):
return tuple([arg_number] * len(arg_structure))
elif isinstance(arg_structure, dict):
arg = copy.deepcopy(arg_structure)
for k in arg:
arg[k] = upgrade(arg_number, arg_structure[k])
return arg
else:
return arg_number
if isinstance(arg1, float) or isinstance(arg1, int):
return upgrade(arg1, arg2), arg2
elif isinstance(arg2, float) or isinstance(arg2, int):
return arg1, upgrade(arg2, arg1)
return arg1, arg2
def resolve_max_positions(*args):
"""Resolve max position constraints from multiple sources."""
def map_value_update(d1, d2):
updated_value = copy.deepcopy(d1)
for key in d2:
if key not in updated_value:
updated_value[key] = d2[key]
else:
updated_value[key] = min(d1[key], d2[key])
return updated_value
def nullsafe_min(l):
minim = None
for item in l:
if minim is None:
minim = item
elif item is not None and item < minim:
minim = item
return minim
max_positions = None
for arg in args:
if max_positions is None:
max_positions = arg
elif arg is not None:
max_positions, arg = _match_types(max_positions, arg)
if isinstance(arg, float) or isinstance(arg, int):
max_positions = min(max_positions, arg)
elif isinstance(arg, dict):
max_positions = map_value_update(max_positions, arg)
else:
max_positions = tuple(map(nullsafe_min, zip(max_positions, arg)))
return max_positions
def import_user_module(args):
module_path = getattr(args, "user_dir", None)
if module_path is not None:
module_path = os.path.abspath(args.user_dir)
if not os.path.exists(module_path) and not os.path.isfile(
os.path.dirname(module_path)
):
fairseq_rel_path = os.path.join(os.path.dirname(__file__), args.user_dir)
if os.path.exists(fairseq_rel_path):
module_path = fairseq_rel_path
else:
fairseq_rel_path = os.path.join(
os.path.dirname(__file__), "..", args.user_dir
)
if os.path.exists(fairseq_rel_path):
module_path = fairseq_rel_path
else:
raise FileNotFoundError(module_path)
# ensure that user modules are only imported once
import_user_module.memo = getattr(import_user_module, "memo", set())
if module_path not in import_user_module.memo:
import_user_module.memo.add(module_path)
module_parent, module_name = os.path.split(module_path)
if module_name not in sys.modules:
sys.path.insert(0, module_parent)
importlib.import_module(module_name)
tasks_path = os.path.join(module_path, "tasks")
if os.path.exists(tasks_path):
from fairseq.tasks import import_tasks
import_tasks(tasks_path, f"{module_name}.tasks")
models_path = os.path.join(module_path, "models")
if os.path.exists(models_path):
from fairseq.models import import_models
import_models(models_path, f"{module_name}.models")
else:
raise ImportError(
"Failed to import --user-dir={} because the corresponding module name "
"({}) is not globally unique. Please rename the directory to "
"something unique and try again.".format(module_path, module_name)
)
def softmax(x, dim: int, onnx_trace: bool = False):
if onnx_trace:
return F.softmax(x.float(), dim=dim)
else:
return F.softmax(x, dim=dim, dtype=torch.float32)
def log_softmax(x, dim: int, onnx_trace: bool = False):
if onnx_trace:
return F.log_softmax(x.float(), dim=dim)
else:
return F.log_softmax(x, dim=dim, dtype=torch.float32)
def get_perplexity(loss, round=2, base=2):
from fairseq.logging.meters import safe_round
if loss is None:
return 0.0
try:
return safe_round(base**loss, round)
except OverflowError:
return float("inf")
def deprecation_warning(message, stacklevel=3):
# don't use DeprecationWarning, since it's ignored by default
warnings.warn(message, stacklevel=stacklevel)
def relu_squared(x: torch.Tensor):
return F.relu(x).pow(2)
def get_activation_fn(activation: str) -> Callable:
"""Returns the activation function corresponding to `activation`"""
from fairseq.modules import gelu, gelu_accurate
if activation == "relu":
return F.relu
elif activation == "relu_squared":
return relu_squared
elif activation == "gelu":
return gelu
elif activation == "gelu_fast":
deprecation_warning(
"--activation-fn=gelu_fast has been renamed to gelu_accurate"
)
return gelu_accurate
elif activation == "gelu_accurate":
return gelu_accurate
elif activation == "tanh":
return torch.tanh
elif activation == "linear":
return lambda x: x
elif activation == "swish":
# from fairseq.modules.swish import Swish
# return Swish
return torch.nn.SiLU
else:
raise RuntimeError("--activation-fn {} not supported".format(activation))
def get_available_activation_fns() -> List:
return [
"relu",
"gelu",
"gelu_fast", # deprecated
"gelu_accurate",
"tanh",
"linear",
]
@contextlib.contextmanager
def model_eval(model):
is_training = model.training
model.eval()
yield
model.train(is_training)
def has_parameters(module):
try:
next(module.parameters())
return True
except StopIteration:
return False
def get_rng_state():
state = {"torch_rng_state": torch.get_rng_state()}
if xm is not None:
state["xla_rng_state"] = xm.get_rng_state()
if torch.cuda.is_available():
state["cuda_rng_state"] = torch.cuda.get_rng_state()
return state
def set_rng_state(state):
torch.set_rng_state(state["torch_rng_state"])
if xm is not None:
xm.set_rng_state(state["xla_rng_state"])
if torch.cuda.is_available():
torch.cuda.set_rng_state(state["cuda_rng_state"])
class set_torch_seed(object):
def __init__(self, seed):
assert isinstance(seed, int)
self.rng_state = get_rng_state()
torch.manual_seed(seed)
if xm is not None:
xm.set_rng_state(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(seed)
def __enter__(self):
return self
def __exit__(self, *exc):
set_rng_state(self.rng_state)
def parse_alignment(line):
"""
Parses a single line from the alingment file.
Args:
line (str): String containing the alignment of the format:
<src_idx_1>-<tgt_idx_1> <src_idx_2>-<tgt_idx_2> ..
<src_idx_m>-<tgt_idx_m>. All indices are 0 indexed.
Returns:
torch.IntTensor: packed alignments of shape (2 * m).
"""
alignments = line.strip().split()
parsed_alignment = torch.IntTensor(2 * len(alignments))
for idx, alignment in enumerate(alignments):
src_idx, tgt_idx = alignment.split("-")
parsed_alignment[2 * idx] = int(src_idx)
parsed_alignment[2 * idx + 1] = int(tgt_idx)
return parsed_alignment
def get_token_to_word_mapping(tokens, exclude_list):
n = len(tokens)
word_start = [int(token not in exclude_list) for token in tokens]
word_idx = list(accumulate(word_start))
token_to_word = {i: word_idx[i] for i in range(n)}
return token_to_word
def extract_hard_alignment(attn, src_sent, tgt_sent, pad, eos):
tgt_valid = (
((tgt_sent != pad) & (tgt_sent != eos)).nonzero(as_tuple=False).squeeze(dim=-1)
)
src_invalid = (
((src_sent == pad) | (src_sent == eos)).nonzero(as_tuple=False).squeeze(dim=-1)
)
src_token_to_word = get_token_to_word_mapping(src_sent, [eos, pad])
tgt_token_to_word = get_token_to_word_mapping(tgt_sent, [eos, pad])
alignment = []
if len(tgt_valid) != 0 and len(src_invalid) < len(src_sent):
attn_valid = attn[tgt_valid]
attn_valid[:, src_invalid] = float("-inf")
_, src_indices = attn_valid.max(dim=1)
for tgt_idx, src_idx in zip(tgt_valid, src_indices):
alignment.append(
(
src_token_to_word[src_idx.item()] - 1,
tgt_token_to_word[tgt_idx.item()] - 1,
)
)
return alignment
def extract_soft_alignment(attn, src_sent, tgt_sent, pad, eos):
tgt_valid = ((tgt_sent != pad)).nonzero(as_tuple=False)
src_valid = ((src_sent != pad)).nonzero(as_tuple=False).squeeze(dim=-1)
alignment = []
if len(tgt_valid) != 0 and len(src_valid) != 0:
attn_valid = attn[tgt_valid, src_valid]
alignment = [
["{:.6f}".format(p) for p in src_probs.tolist()] for src_probs in attn_valid
]
return alignment
def new_arange(x, *size):
"""
Return a Tensor of `size` filled with a range function on the device of x.
If size is empty, using the size of the variable x.
"""
if len(size) == 0:
size = x.size()
return torch.arange(size[-1], device=x.device).expand(*size).contiguous()
def get_tpu_device():
return xm.xla_device()
def tpu_data_loader(itr):
import torch_xla.core.xla_model as xm
import torch_xla.distributed.parallel_loader as pl
from fairseq.data import iterators
xm.rendezvous("tpu_data_loader") # wait for all workers
xm.mark_step()
device = xm.xla_device()
return iterators.CountingIterator(
pl.ParallelLoader(itr, [device]).per_device_loader(device),
start=getattr(itr, "n", 0),
total=len(itr),
)
def is_xla_tensor(tensor):
return torch.is_tensor(tensor) and tensor.device.type == "xla"
def index_put(tensor, indices, value):
if is_xla_tensor(tensor):
for _ in range(indices.dim(), tensor.dim()):
indices = indices.unsqueeze(-1)
if indices.size(-1) < tensor.size(-1):
indices = indices.expand_as(tensor)
tensor = torch.mul(tensor, ~indices) + torch.mul(value, indices)
else:
tensor[indices] = value
return tensor
def xla_device_to_cpu(dat):
import torch_xla.core.xla_model as xm
return xm._maybe_convert_to_cpu(dat)
class CudaEnvironment(object):
def __init__(self):
cur_device = torch.cuda.current_device()
prop = torch.cuda.get_device_properties("cuda:{}".format(cur_device))
self.name = prop.name
self.major = prop.major
self.minor = prop.minor
self.total_memory_in_GB = prop.total_memory / 1024 / 1024 / 1024
@staticmethod
def pretty_print_cuda_env_list(cuda_env_list):
"""
Given a list of CudaEnviorments, pretty print them
"""
num_workers = len(cuda_env_list)
center = "CUDA enviroments for all {} workers".format(num_workers)
banner_len = 40 - len(center) // 2
first_line = "*" * banner_len + center + "*" * banner_len
logger.info(first_line)
for r, env in enumerate(cuda_env_list):
logger.info(
"rank {:3d}: ".format(r)
+ "capabilities = {:2d}.{:<2d} ; ".format(env.major, env.minor)
+ "total memory = {:.3f} GB ; ".format(env.total_memory_in_GB)
+ "name = {:40s}".format(env.name)
)
logger.info(first_line)
def csv_str_list(x):
return x.split(",")
def eval_str_list(x, type=float):
if x is None:
return None
if isinstance(x, str):
x = eval(x)
try:
return list(map(type, x))
except TypeError:
return [type(x)]
def eval_str_dict(x, type=dict):
if x is None:
return None
if isinstance(x, str):
x = eval(x)
return x
def eval_bool(x, default=False):
if x is None:
return default
try:
return bool(eval(x))
except TypeError:
return default
def reset_logging():
root = logging.getLogger()
for handler in root.handlers:
root.removeHandler(handler)
root.setLevel(os.environ.get("LOGLEVEL", "INFO").upper())
handler = logging.StreamHandler(sys.stdout)
handler.setFormatter(
logging.Formatter(
fmt="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
)
)
root.addHandler(handler)
def safe_getattr(obj, k, default=None):
"""Returns obj[k] if it exists and is not None, otherwise returns default."""
from omegaconf import OmegaConf
if OmegaConf.is_config(obj):
return obj[k] if k in obj and obj[k] is not None else default
return getattr(obj, k, default)
def safe_hasattr(obj, k):
"""Returns True if the given key exists and is not None."""
return getattr(obj, k, None) is not None
| 26,794 | 30.82304 | 111 | py |
CIF-HieraDist | CIF-HieraDist-main/fairseq/hub_utils.py | #!/usr/bin/env python3 -u
# 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 argparse
import copy
import logging
import os
from typing import Any, Dict, Iterator, List
import torch
from fairseq import utils
from fairseq.data import encoders
from omegaconf import open_dict
from torch import nn
logger = logging.getLogger(__name__)
def from_pretrained(
model_name_or_path,
checkpoint_file="model.pt",
data_name_or_path=".",
archive_map=None,
**kwargs
):
from fairseq import checkpoint_utils, file_utils
if archive_map is not None:
if model_name_or_path in archive_map:
model_name_or_path = archive_map[model_name_or_path]
if data_name_or_path is not None and data_name_or_path in archive_map:
data_name_or_path = archive_map[data_name_or_path]
# allow archive_map to set default arg_overrides (e.g., tokenizer, bpe)
# for each model
if isinstance(model_name_or_path, dict):
for k, v in model_name_or_path.items():
if k == "checkpoint_file":
checkpoint_file = v
elif (
k != "path"
# only set kwargs that don't already have overrides
and k not in kwargs
):
kwargs[k] = v
model_name_or_path = model_name_or_path["path"]
model_path = file_utils.load_archive_file(model_name_or_path)
# convenience hack for loading data and BPE codes from model archive
if data_name_or_path.startswith("."):
kwargs["data"] = os.path.abspath(os.path.join(model_path, data_name_or_path))
else:
kwargs["data"] = file_utils.load_archive_file(data_name_or_path)
for file, arg in {
"code": "bpe_codes",
"bpecodes": "bpe_codes",
"sentencepiece.bpe.model": "sentencepiece_model",
"merges.txt": "bpe_merges",
"vocab.json": "bpe_vocab",
}.items():
path = os.path.join(model_path, file)
if os.path.exists(path):
kwargs[arg] = path
if "user_dir" in kwargs:
utils.import_user_module(argparse.Namespace(user_dir=kwargs["user_dir"]))
models, args, task = checkpoint_utils.load_model_ensemble_and_task(
[os.path.join(model_path, cpt) for cpt in checkpoint_file.split(os.pathsep)],
arg_overrides=kwargs,
)
return {
"args": args,
"task": task,
"models": models,
}
class GeneratorHubInterface(nn.Module):
"""
PyTorch Hub interface for generating sequences from a pre-trained
translation or language model.
"""
def __init__(self, cfg, task, models):
super().__init__()
self.cfg = cfg
self.task = task
self.models = nn.ModuleList(models)
self.src_dict = task.source_dictionary
self.tgt_dict = task.target_dictionary
# optimize model for generation
for model in self.models:
model.prepare_for_inference_(cfg)
# Load alignment dictionary for unknown word replacement
# (None if no unknown word replacement, empty if no path to align dictionary)
self.align_dict = utils.load_align_dict(cfg.generation.replace_unk)
self.tokenizer = encoders.build_tokenizer(cfg.tokenizer)
self.bpe = encoders.build_bpe(cfg.bpe)
self.max_positions = utils.resolve_max_positions(
self.task.max_positions(), *[model.max_positions() for model in models]
)
# this is useful for determining the device
self.register_buffer("_float_tensor", torch.tensor([0], dtype=torch.float))
@property
def device(self):
return self._float_tensor.device
def translate(
self, sentences: List[str], beam: int = 5, verbose: bool = False, **kwargs
) -> List[str]:
return self.sample(sentences, beam, verbose, **kwargs)
def sample(
self, sentences: List[str], beam: int = 1, verbose: bool = False, **kwargs
) -> List[str]:
if isinstance(sentences, str):
return self.sample([sentences], beam=beam, verbose=verbose, **kwargs)[0]
tokenized_sentences = [self.encode(sentence) for sentence in sentences]
batched_hypos = self.generate(tokenized_sentences, beam, verbose, **kwargs)
return [self.decode(hypos[0]["tokens"]) for hypos in batched_hypos]
def score(self, sentences: List[str], **kwargs):
if isinstance(sentences, str):
return self.score([sentences], **kwargs)[0]
# NOTE: this doesn't support translation tasks currently
tokenized_sentences = [self.encode(sentence) for sentence in sentences]
return [
hypos[0]
for hypos in self.generate(
tokenized_sentences, score_reference=True, **kwargs
)
]
def generate(
self,
tokenized_sentences: List[torch.LongTensor],
beam: int = 5,
verbose: bool = False,
skip_invalid_size_inputs=False,
inference_step_args=None,
prefix_allowed_tokens_fn=None,
**kwargs
) -> List[List[Dict[str, torch.Tensor]]]:
if torch.is_tensor(tokenized_sentences) and tokenized_sentences.dim() == 1:
return self.generate(
tokenized_sentences.unsqueeze(0), beam=beam, verbose=verbose, **kwargs
)[0]
# build generator using current args as well as any kwargs
gen_args = copy.deepcopy(self.cfg.generation)
with open_dict(gen_args):
gen_args.beam = beam
for k, v in kwargs.items():
setattr(gen_args, k, v)
generator = self.task.build_generator(
self.models,
gen_args,
prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
)
inference_step_args = inference_step_args or {}
results = []
for batch in self._build_batches(tokenized_sentences, skip_invalid_size_inputs):
batch = utils.apply_to_sample(lambda t: t.to(self.device), batch)
translations = self.task.inference_step(
generator, self.models, batch, **inference_step_args
)
for id, hypos in zip(batch["id"].tolist(), translations):
results.append((id, hypos))
# sort output to match input order
outputs = [hypos for _, hypos in sorted(results, key=lambda x: x[0])]
if verbose:
def getarg(name, default):
return getattr(gen_args, name, getattr(self.cfg, name, default))
for source_tokens, target_hypotheses in zip(tokenized_sentences, outputs):
src_str_with_unk = self.string(source_tokens)
logger.info("S\t{}".format(src_str_with_unk))
for hypo in target_hypotheses:
hypo_str = self.decode(hypo["tokens"])
logger.info("H\t{}\t{}".format(hypo["score"], hypo_str))
logger.info(
"P\t{}".format(
" ".join(
map(
lambda x: "{:.4f}".format(x),
hypo["positional_scores"].tolist(),
)
)
)
)
if hypo["alignment"] is not None and getarg(
"print_alignment", False
):
logger.info(
"A\t{}".format(
" ".join(
[
"{}-{}".format(src_idx, tgt_idx)
for src_idx, tgt_idx in hypo["alignment"]
]
)
)
)
return outputs
def encode(self, sentence: str) -> torch.LongTensor:
sentence = self.tokenize(sentence)
sentence = self.apply_bpe(sentence)
return self.binarize(sentence)
def decode(self, tokens: torch.LongTensor) -> str:
sentence = self.string(tokens)
sentence = self.remove_bpe(sentence)
return self.detokenize(sentence)
def tokenize(self, sentence: str) -> str:
if self.tokenizer is not None:
sentence = self.tokenizer.encode(sentence)
return sentence
def detokenize(self, sentence: str) -> str:
if self.tokenizer is not None:
sentence = self.tokenizer.decode(sentence)
return sentence
def apply_bpe(self, sentence: str) -> str:
if self.bpe is not None:
sentence = self.bpe.encode(sentence)
return sentence
def remove_bpe(self, sentence: str) -> str:
if self.bpe is not None:
sentence = self.bpe.decode(sentence)
return sentence
def binarize(self, sentence: str) -> torch.LongTensor:
return self.src_dict.encode_line(sentence, add_if_not_exist=False).long()
def string(self, tokens: torch.LongTensor) -> str:
return self.tgt_dict.string(tokens)
def _build_batches(
self, tokens: List[List[int]], skip_invalid_size_inputs: bool
) -> Iterator[Dict[str, Any]]:
lengths = torch.LongTensor([t.numel() for t in tokens])
batch_iterator = self.task.get_batch_iterator(
dataset=self.task.build_dataset_for_inference(tokens, lengths),
max_tokens=self.cfg.dataset.max_tokens,
max_sentences=self.cfg.dataset.batch_size,
max_positions=self.max_positions,
ignore_invalid_inputs=skip_invalid_size_inputs,
disable_iterator_cache=True,
).next_epoch_itr(shuffle=False)
return batch_iterator
class BPEHubInterface(object):
"""PyTorch Hub interface for Byte-Pair Encoding (BPE)."""
def __init__(self, bpe, **kwargs):
super().__init__()
args = argparse.Namespace(bpe=bpe, **kwargs)
self.bpe = encoders.build_bpe(args)
assert self.bpe is not None
def encode(self, sentence: str) -> str:
return self.bpe.encode(sentence)
def decode(self, sentence: str) -> str:
return self.bpe.decode(sentence)
class TokenizerHubInterface(object):
"""PyTorch Hub interface for tokenization."""
def __init__(self, tokenizer, **kwargs):
super().__init__()
args = argparse.Namespace(tokenizer=tokenizer, **kwargs)
self.tokenizer = encoders.build_tokenizer(args)
assert self.tokenizer is not None
def encode(self, sentence: str) -> str:
return self.tokenizer.encode(sentence)
def decode(self, sentence: str) -> str:
return self.tokenizer.decode(sentence)
| 10,996 | 35.174342 | 88 | py |
CIF-HieraDist | CIF-HieraDist-main/fairseq/sequence_scorer.py | # 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 sys
import os
import numpy as np
import torch
from fairseq import utils
class SequenceScorer(object):
"""Scores the target for a given source sentence."""
def __init__(
self,
tgt_dict,
softmax_batch=None,
compute_alignment=False,
eos=None,
symbols_to_strip_from_output=None,
):
self.pad = tgt_dict.pad()
self.eos = tgt_dict.eos() if eos is None else eos
self.softmax_batch = softmax_batch or sys.maxsize
assert self.softmax_batch > 0
self.compute_alignment = compute_alignment
self.symbols_to_strip_from_output = (
symbols_to_strip_from_output.union({self.eos})
if symbols_to_strip_from_output is not None
else {self.eos}
)
@torch.no_grad()
def generate(self, models, sample, **kwargs):
"""Score a batch of translations."""
net_input = sample["net_input"]
def batch_for_softmax(dec_out, target):
# assumes decoder_out[0] is the only thing needed (may not be correct for future models!)
first, rest = dec_out[0], dec_out[1:]
bsz, tsz, dim = first.shape
if bsz * tsz < self.softmax_batch:
yield dec_out, target, True
else:
flat = first.contiguous().view(1, -1, dim)
flat_tgt = target.contiguous().view(flat.shape[:-1])
s = 0
while s < flat.size(1):
e = s + self.softmax_batch
yield (flat[:, s:e],) + rest, flat_tgt[:, s:e], False
s = e
def gather_target_probs(probs, target):
probs = probs.gather(
dim=2,
index=target.unsqueeze(-1),
)
return probs
orig_target = sample["target"]
# compute scores for each model in the ensemble
avg_probs = None
avg_attn = None
for model in models:
model.eval()
decoder_out = model(**net_input)
attn = decoder_out[1] if len(decoder_out) > 1 else None
if type(attn) is dict:
attn = attn.get("attn", None)
batched = batch_for_softmax(decoder_out, orig_target)
probs, idx = None, 0
for bd, tgt, is_single in batched:
sample["target"] = tgt
curr_prob = model.get_normalized_probs(
bd, log_probs=len(models) == 1, sample=sample
).data
if is_single:
probs = gather_target_probs(curr_prob, orig_target)
else:
if probs is None:
probs = curr_prob.new(orig_target.numel())
step = curr_prob.size(0) * curr_prob.size(1)
end = step + idx
tgt_probs = gather_target_probs(
curr_prob.view(tgt.shape + (curr_prob.size(-1),)), tgt
)
probs[idx:end] = tgt_probs.view(-1)
idx = end
sample["target"] = orig_target
probs = probs.view(sample["target"].shape)
if avg_probs is None:
avg_probs = probs
else:
avg_probs.add_(probs)
if attn is not None:
if torch.is_tensor(attn):
attn = attn.data
else:
attn = attn[0]
if avg_attn is None:
avg_attn = attn
else:
avg_attn.add_(attn)
if len(models) > 1:
avg_probs.div_(len(models))
avg_probs.log_()
if avg_attn is not None:
avg_attn.div_(len(models))
bsz = avg_probs.size(0)
hypos = []
start_idxs = sample["start_indices"] if "start_indices" in sample else [0] * bsz
for i in range(bsz):
# remove padding from ref
ref = (
utils.strip_pad(sample["target"][i, start_idxs[i] :], self.pad)
if sample["target"] is not None
else None
)
tgt_len = ref.numel()
avg_probs_i = avg_probs[i][start_idxs[i] : start_idxs[i] + tgt_len]
score_i = avg_probs_i.sum() / tgt_len
if avg_attn is not None:
avg_attn_i = avg_attn[i]
if self.compute_alignment:
alignment = utils.extract_hard_alignment(
avg_attn_i,
sample["net_input"]["src_tokens"][i],
sample["target"][i],
self.pad,
self.eos,
)
else:
alignment = None
else:
avg_attn_i = alignment = None
hypos.append(
[
{
"tokens": ref,
"score": score_i,
"attention": avg_attn_i,
"alignment": alignment,
"positional_scores": avg_probs_i,
}
]
)
return hypos
| 5,479 | 34.128205 | 101 | py |
CIF-HieraDist | CIF-HieraDist-main/fairseq/binarizer.py | # 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.
from collections import Counter
from typing import Dict
import torch
from fairseq.file_chunker_utils import Chunker
from fairseq.file_io import PathManager
from fairseq.tokenizer import tokenize_line
class Binarizer:
@staticmethod
def binarize(
filename,
dict,
consumer,
tokenize=tokenize_line,
append_eos=True,
reverse_order=False,
offset=0,
end=-1,
already_numberized=False,
) -> Dict[str, int]:
nseq, ntok = 0, 0
replaced = Counter()
def replaced_consumer(word, idx):
if idx == dict.unk_index and word != dict.unk_word:
replaced.update([word])
with Chunker(
PathManager.get_local_path(filename), offset, end
) as line_iterator:
for line in line_iterator:
if already_numberized:
id_strings = line.strip().split()
id_list = [int(id_string) for id_string in id_strings]
if reverse_order:
id_list.reverse()
if append_eos:
id_list.append(dict.eos())
ids = torch.IntTensor(id_list)
else:
ids = dict.encode_line(
line=line,
line_tokenizer=tokenize,
add_if_not_exist=False,
consumer=replaced_consumer,
append_eos=append_eos,
reverse_order=reverse_order,
)
nseq += 1
ntok += len(ids)
consumer(ids)
return {
"nseq": nseq,
"nunk": sum(replaced.values()),
"ntok": ntok,
"replaced": replaced,
}
@staticmethod
def binarize_alignments(
filename, alignment_parser, consumer, offset=0, end=-1
) -> Dict[str, int]:
nseq = 0
with Chunker(
PathManager.get_local_path(filename), offset, end
) as line_iterator:
for line in line_iterator:
ids = alignment_parser(line)
nseq += 1
consumer(ids)
return {"nseq": nseq}
| 2,457 | 29.345679 | 74 | py |
CIF-HieraDist | CIF-HieraDist-main/fairseq/sequence_generator.py | # 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 math
from typing import Dict, List, Optional
import sys
import torch
import torch.nn as nn
from fairseq import search, utils
from fairseq.data import data_utils
from fairseq.models import FairseqIncrementalDecoder
from torch import Tensor
from fairseq.ngram_repeat_block import NGramRepeatBlock
class SequenceGenerator(nn.Module):
def __init__(
self,
models,
tgt_dict,
beam_size=1,
max_len_a=0,
max_len_b=200,
max_len=0,
min_len=1,
normalize_scores=True,
len_penalty=1.0,
unk_penalty=0.0,
temperature=1.0,
match_source_len=False,
no_repeat_ngram_size=0,
search_strategy=None,
eos=None,
symbols_to_strip_from_output=None,
lm_model=None,
lm_weight=1.0,
):
"""Generates translations of a given source sentence.
Args:
models (List[~fairseq.models.FairseqModel]): ensemble of models,
currently support fairseq.models.TransformerModel for scripting
beam_size (int, optional): beam width (default: 1)
max_len_a/b (int, optional): generate sequences of maximum length
ax + b, where x is the source length
max_len (int, optional): the maximum length of the generated output
(not including end-of-sentence)
min_len (int, optional): the minimum length of the generated output
(not including end-of-sentence)
normalize_scores (bool, optional): normalize scores by the length
of the output (default: True)
len_penalty (float, optional): length penalty, where <1.0 favors
shorter, >1.0 favors longer sentences (default: 1.0)
unk_penalty (float, optional): unknown word penalty, where <0
produces more unks, >0 produces fewer (default: 0.0)
temperature (float, optional): temperature, where values
>1.0 produce more uniform samples and values <1.0 produce
sharper samples (default: 1.0)
match_source_len (bool, optional): outputs should match the source
length (default: False)
"""
super().__init__()
if isinstance(models, EnsembleModel):
self.model = models
else:
self.model = EnsembleModel(models)
self.tgt_dict = tgt_dict
self.pad = tgt_dict.pad()
self.unk = tgt_dict.unk()
self.eos = tgt_dict.eos() if eos is None else eos
self.symbols_to_strip_from_output = (
symbols_to_strip_from_output.union({self.eos})
if symbols_to_strip_from_output is not None
else {self.eos}
)
self.vocab_size = len(tgt_dict)
self.beam_size = beam_size
# the max beam size is the dictionary size - 1, since we never select pad
self.beam_size = min(beam_size, self.vocab_size - 1)
self.max_len_a = max_len_a
self.max_len_b = max_len_b
self.min_len = min_len
self.max_len = max_len or self.model.max_decoder_positions()
self.normalize_scores = normalize_scores
self.len_penalty = len_penalty
self.unk_penalty = unk_penalty
self.temperature = temperature
self.match_source_len = match_source_len
if no_repeat_ngram_size > 0:
self.repeat_ngram_blocker = NGramRepeatBlock(no_repeat_ngram_size)
else:
self.repeat_ngram_blocker = None
assert temperature > 0, "--temperature must be greater than 0"
self.search = (
search.BeamSearch(tgt_dict) if search_strategy is None else search_strategy
)
# We only need to set src_lengths in LengthConstrainedBeamSearch.
# As a module attribute, setting it would break in multithread
# settings when the model is shared.
self.should_set_src_lengths = (
hasattr(self.search, "needs_src_lengths") and self.search.needs_src_lengths
)
self.model.eval()
self.lm_model = lm_model
self.lm_weight = lm_weight
if self.lm_model is not None:
self.lm_model.eval()
def cuda(self):
self.model.cuda()
return self
@torch.no_grad()
def forward(
self,
sample: Dict[str, Dict[str, Tensor]],
prefix_tokens: Optional[Tensor] = None,
bos_token: Optional[int] = None,
):
"""Generate a batch of translations.
Args:
sample (dict): batch
prefix_tokens (torch.LongTensor, optional): force decoder to begin
with these tokens
bos_token (int, optional): beginning of sentence token
(default: self.eos)
"""
return self._generate(sample, prefix_tokens, bos_token=bos_token)
# TODO(myleott): unused, deprecate after pytorch-translate migration
def generate_batched_itr(self, data_itr, beam_size=None, cuda=False, timer=None):
"""Iterate over a batched dataset and yield individual translations.
Args:
cuda (bool, optional): use GPU for generation
timer (StopwatchMeter, optional): time generations
"""
for sample in data_itr:
s = utils.move_to_cuda(sample) if cuda else sample
if "net_input" not in s:
continue
input = s["net_input"]
# model.forward normally channels prev_output_tokens into the decoder
# separately, but SequenceGenerator directly calls model.encoder
encoder_input = {
k: v for k, v in input.items() if k != "prev_output_tokens"
}
if timer is not None:
timer.start()
with torch.no_grad():
hypos = self.generate(encoder_input)
if timer is not None:
timer.stop(sum(len(h[0]["tokens"]) for h in hypos))
for i, id in enumerate(s["id"].data):
# remove padding
src = utils.strip_pad(input["src_tokens"].data[i, :], self.pad)
ref = (
utils.strip_pad(s["target"].data[i, :], self.pad)
if s["target"] is not None
else None
)
yield id, src, ref, hypos[i]
@torch.no_grad()
def generate(
self, models, sample: Dict[str, Dict[str, Tensor]], **kwargs
) -> List[List[Dict[str, Tensor]]]:
"""Generate translations. Match the api of other fairseq generators.
Args:
models (List[~fairseq.models.FairseqModel]): ensemble of models
sample (dict): batch
prefix_tokens (torch.LongTensor, optional): force decoder to begin
with these tokens
constraints (torch.LongTensor, optional): force decoder to include
the list of constraints
bos_token (int, optional): beginning of sentence token
(default: self.eos)
"""
return self._generate(sample, **kwargs)
def _generate(
self,
sample: Dict[str, Dict[str, Tensor]],
prefix_tokens: Optional[Tensor] = None,
constraints: Optional[Tensor] = None,
bos_token: Optional[int] = None,
):
incremental_states = torch.jit.annotate(
List[Dict[str, Dict[str, Optional[Tensor]]]],
[
torch.jit.annotate(Dict[str, Dict[str, Optional[Tensor]]], {})
for i in range(self.model.models_size)
],
)
net_input = sample["net_input"]
if "src_tokens" in net_input:
src_tokens = net_input["src_tokens"]
# length of the source text being the character length except EndOfSentence and pad
src_lengths = (
(src_tokens.ne(self.eos) & src_tokens.ne(self.pad)).long().sum(dim=1)
)
elif "source" in net_input:
src_tokens = net_input["source"]
src_lengths = (
net_input["padding_mask"].size(-1) - net_input["padding_mask"].sum(-1)
if net_input["padding_mask"] is not None
else torch.tensor(src_tokens.size(-1)).to(src_tokens)
)
elif "features" in net_input:
src_tokens = net_input["features"]
src_lengths = (
net_input["padding_mask"].size(-1) - net_input["padding_mask"].sum(-1)
if net_input["padding_mask"] is not None
else torch.tensor(src_tokens.size(-1)).to(src_tokens)
)
else:
raise Exception(
"expected src_tokens or source in net input. input keys: "
+ str(net_input.keys())
)
# bsz: total number of sentences in beam
# Note that src_tokens may have more than 2 dimensions (i.e. audio features)
bsz, src_len = src_tokens.size()[:2]
beam_size = self.beam_size
if constraints is not None and not self.search.supports_constraints:
raise NotImplementedError(
"Target-side constraints were provided, but search method doesn't support them"
)
# Initialize constraints, when active
self.search.init_constraints(constraints, beam_size)
max_len: int = -1
if self.match_source_len:
max_len = src_lengths.max().item()
else:
max_len = min(
int(self.max_len_a * src_len + self.max_len_b),
self.max_len - 1,
)
assert (
self.min_len <= max_len
), "min_len cannot be larger than max_len, please adjust these!"
# compute the encoder output for each beam
with torch.autograd.profiler.record_function("EnsembleModel: forward_encoder"):
encoder_outs = self.model.forward_encoder(net_input)
# placeholder of indices for bsz * beam_size to hold tokens and accumulative scores
new_order = (
torch.arange(bsz).view(-1, 1).repeat(1, beam_size).view(-1)
) # bsz * beam_size
new_order = new_order.to(
src_tokens.device
).long() # cast new_order to new data type
encoder_outs = self.model.reorder_encoder_out(encoder_outs, new_order)
# ensure encoder_outs is a List.
assert encoder_outs is not None
# initialize buffers
scores = (
torch.zeros(bsz * beam_size, max_len + 1).to(src_tokens).float()
) # +1 for eos; pad is never chosen for scoring
tokens = (
torch.zeros(bsz * beam_size, max_len + 2)
.to(src_tokens)
.long()
.fill_(self.pad)
) # +2 for eos and pad
tokens[:, 0] = self.eos if bos_token is None else bos_token
attn: Optional[Tensor] = None
# A list that indicates candidates that should be ignored.
# For example, suppose we're sampling and have already finalized 2/5
# samples. Then cands_to_ignore would mark 2 positions as being ignored,
# so that we only finalize the remaining 3 samples.
cands_to_ignore = (
torch.zeros(bsz, beam_size).to(src_tokens).eq(-1)
) # forward and backward-compatible False mask
# list of completed sentences
finalized = torch.jit.annotate(
List[List[Dict[str, Tensor]]],
[torch.jit.annotate(List[Dict[str, Tensor]], []) for i in range(bsz)],
) # contains lists of dictionaries of infomation about the hypothesis being finalized at each step
# a boolean array indicating if the sentence at the index is finished or not
finished = [False for i in range(bsz)]
num_remaining_sent = bsz # number of sentences remaining
# number of candidate hypos per step
cand_size = 2 * beam_size # 2 x beam size in case half are EOS
# offset arrays for converting between different indexing schemes
bbsz_offsets = (
(torch.arange(0, bsz) * beam_size)
.unsqueeze(1)
.type_as(tokens)
.to(src_tokens.device)
)
cand_offsets = torch.arange(0, cand_size).type_as(tokens).to(src_tokens.device)
reorder_state: Optional[Tensor] = None
batch_idxs: Optional[Tensor] = None
original_batch_idxs: Optional[Tensor] = None
if "id" in sample and isinstance(sample["id"], Tensor):
original_batch_idxs = sample["id"]
else:
original_batch_idxs = torch.arange(0, bsz).type_as(tokens)
for step in range(max_len + 1): # one extra step for EOS marker
# reorder decoder internal states based on the prev choice of beams
if reorder_state is not None:
if batch_idxs is not None:
# update beam indices to take into account removed sentences
corr = batch_idxs - torch.arange(batch_idxs.numel()).type_as(
batch_idxs
)
reorder_state.view(-1, beam_size).add_(
corr.unsqueeze(-1) * beam_size
)
original_batch_idxs = original_batch_idxs[batch_idxs]
self.model.reorder_incremental_state(incremental_states, reorder_state)
encoder_outs = self.model.reorder_encoder_out(
encoder_outs, reorder_state
)
with torch.autograd.profiler.record_function(
"EnsembleModel: forward_decoder"
):
# print(tokens.size())
lprobs, avg_attn_scores = self.model.forward_decoder(
tokens[:, : step + 1],
encoder_outs,
incremental_states,
self.temperature,
)
if self.lm_model is not None:
lm_out = self.lm_model(tokens[:, : step + 1])
probs = self.lm_model.get_normalized_probs(
lm_out, log_probs=True, sample=None
)
probs = probs[:, -1, :] * self.lm_weight
lprobs += probs
# handle prefix tokens (possibly with different lengths)
if (
prefix_tokens is not None
and step < prefix_tokens.size(1)
and step < max_len
):
lprobs, tokens, scores = self._prefix_tokens(
step, lprobs, scores, tokens, prefix_tokens, beam_size
)
elif step < self.min_len:
# minimum length constraint (does not apply if using prefix_tokens)
lprobs[:, self.eos] = -math.inf
lprobs[lprobs != lprobs] = torch.tensor(-math.inf).to(lprobs)
lprobs[:, self.pad] = -math.inf # never select pad
lprobs[:, self.unk] -= self.unk_penalty # apply unk penalty
# handle max length constraint
if step >= max_len:
lprobs[:, : self.eos] = -math.inf
lprobs[:, self.eos + 1 :] = -math.inf
# Record attention scores, only support avg_attn_scores is a Tensor
if avg_attn_scores is not None:
if attn is None:
attn = torch.empty(
bsz * beam_size, avg_attn_scores.size(1), max_len + 2
).to(scores)
attn[:, :, step + 1].copy_(avg_attn_scores)
scores = scores.type_as(lprobs)
eos_bbsz_idx = torch.empty(0).to(
tokens
) # indices of hypothesis ending with eos (finished sentences)
eos_scores = torch.empty(0).to(
scores
) # scores of hypothesis ending with eos (finished sentences)
if self.should_set_src_lengths:
self.search.set_src_lengths(src_lengths)
if self.repeat_ngram_blocker is not None:
lprobs = self.repeat_ngram_blocker(tokens, lprobs, bsz, beam_size, step)
# Shape: (batch, cand_size)
cand_scores, cand_indices, cand_beams = self.search.step(
step,
lprobs.view(bsz, -1, self.vocab_size),
scores.view(bsz, beam_size, -1)[:, :, :step],
tokens[:, : step + 1],
original_batch_idxs,
)
# cand_bbsz_idx contains beam indices for the top candidate
# hypotheses, with a range of values: [0, bsz*beam_size),
# and dimensions: [bsz, cand_size]
cand_bbsz_idx = cand_beams.add(bbsz_offsets)
# finalize hypotheses that end in eos
# Shape of eos_mask: (batch size, beam size)
eos_mask = cand_indices.eq(self.eos) & cand_scores.ne(-math.inf)
eos_mask[:, :beam_size][cands_to_ignore] = torch.tensor(0).to(eos_mask)
# only consider eos when it's among the top beam_size indices
# Now we know what beam item(s) to finish
# Shape: 1d list of absolute-numbered
eos_bbsz_idx = torch.masked_select(
cand_bbsz_idx[:, :beam_size], mask=eos_mask[:, :beam_size]
)
finalized_sents: List[int] = []
if eos_bbsz_idx.numel() > 0:
eos_scores = torch.masked_select(
cand_scores[:, :beam_size], mask=eos_mask[:, :beam_size]
)
finalized_sents = self.finalize_hypos(
step,
eos_bbsz_idx,
eos_scores,
tokens,
scores,
finalized,
finished,
beam_size,
attn,
src_lengths,
max_len,
)
num_remaining_sent -= len(finalized_sents)
assert num_remaining_sent >= 0
if num_remaining_sent == 0:
break
if self.search.stop_on_max_len and step >= max_len:
break
assert step < max_len, f"{step} < {max_len}"
# Remove finalized sentences (ones for which {beam_size}
# finished hypotheses have been generated) from the batch.
if len(finalized_sents) > 0:
new_bsz = bsz - len(finalized_sents)
# construct batch_idxs which holds indices of batches to keep for the next pass
batch_mask = torch.ones(
bsz, dtype=torch.bool, device=cand_indices.device
)
batch_mask[finalized_sents] = False
# TODO replace `nonzero(as_tuple=False)` after TorchScript supports it
batch_idxs = torch.arange(
bsz, device=cand_indices.device
).masked_select(batch_mask)
# Choose the subset of the hypothesized constraints that will continue
self.search.prune_sentences(batch_idxs)
eos_mask = eos_mask[batch_idxs]
cand_beams = cand_beams[batch_idxs]
bbsz_offsets.resize_(new_bsz, 1)
cand_bbsz_idx = cand_beams.add(bbsz_offsets)
cand_scores = cand_scores[batch_idxs]
cand_indices = cand_indices[batch_idxs]
if prefix_tokens is not None:
prefix_tokens = prefix_tokens[batch_idxs]
src_lengths = src_lengths[batch_idxs]
cands_to_ignore = cands_to_ignore[batch_idxs]
scores = scores.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, -1)
tokens = tokens.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, -1)
if attn is not None:
attn = attn.view(bsz, -1)[batch_idxs].view(
new_bsz * beam_size, attn.size(1), -1
)
bsz = new_bsz
else:
batch_idxs = None
# Set active_mask so that values > cand_size indicate eos hypos
# and values < cand_size indicate candidate active hypos.
# After, the min values per row are the top candidate active hypos
# Rewrite the operator since the element wise or is not supported in torchscript.
eos_mask[:, :beam_size] = ~((~cands_to_ignore) & (~eos_mask[:, :beam_size]))
active_mask = torch.add(
eos_mask.type_as(cand_offsets) * cand_size,
cand_offsets[: eos_mask.size(1)],
)
# get the top beam_size active hypotheses, which are just
# the hypos with the smallest values in active_mask.
# {active_hypos} indicates which {beam_size} hypotheses
# from the list of {2 * beam_size} candidates were
# selected. Shapes: (batch size, beam size)
new_cands_to_ignore, active_hypos = torch.topk(
active_mask, k=beam_size, dim=1, largest=False
)
# update cands_to_ignore to ignore any finalized hypos.
cands_to_ignore = new_cands_to_ignore.ge(cand_size)[:, :beam_size]
# Make sure there is at least one active item for each sentence in the batch.
assert (~cands_to_ignore).any(dim=1).all()
# update cands_to_ignore to ignore any finalized hypos
# {active_bbsz_idx} denotes which beam number is continued for each new hypothesis (a beam
# can be selected more than once).
active_bbsz_idx = torch.gather(cand_bbsz_idx, dim=1, index=active_hypos)
active_scores = torch.gather(cand_scores, dim=1, index=active_hypos)
active_bbsz_idx = active_bbsz_idx.view(-1)
active_scores = active_scores.view(-1)
# print("active_bbsz_idx", active_bbsz_idx)
# copy tokens and scores for active hypotheses
# Set the tokens for each beam (can select the same row more than once)
tokens[:, : step + 1] = torch.index_select(
tokens[:, : step + 1], dim=0, index=active_bbsz_idx
)
# Select the next token for each of them
tokens.view(bsz, beam_size, -1)[:, :, step + 1] = torch.gather(
cand_indices, dim=1, index=active_hypos
)
if step > 0:
scores[:, :step] = torch.index_select(
scores[:, :step], dim=0, index=active_bbsz_idx
)
scores.view(bsz, beam_size, -1)[:, :, step] = torch.gather(
cand_scores, dim=1, index=active_hypos
)
# Update constraints based on which candidates were selected for the next beam
self.search.update_constraints(active_hypos)
# copy attention for active hypotheses
if attn is not None:
attn[:, :, : step + 2] = torch.index_select(
attn[:, :, : step + 2], dim=0, index=active_bbsz_idx
)
# reorder incremental state in decoder
reorder_state = active_bbsz_idx
# sort by score descending
for sent in range(len(finalized)):
scores = torch.tensor(
[float(elem["score"].item()) for elem in finalized[sent]]
)
_, sorted_scores_indices = torch.sort(scores, descending=True)
finalized[sent] = [finalized[sent][ssi] for ssi in sorted_scores_indices]
finalized[sent] = torch.jit.annotate(
List[Dict[str, Tensor]], finalized[sent]
)
return finalized
def _prefix_tokens(
self, step: int, lprobs, scores, tokens, prefix_tokens, beam_size: int
):
"""Handle prefix tokens"""
prefix_toks = prefix_tokens[:, step].unsqueeze(-1).repeat(1, beam_size).view(-1)
prefix_lprobs = lprobs.gather(-1, prefix_toks.unsqueeze(-1))
prefix_mask = prefix_toks.ne(self.pad)
lprobs[prefix_mask] = torch.min(prefix_lprobs) - 1
lprobs[prefix_mask] = lprobs[prefix_mask].scatter(
-1, prefix_toks[prefix_mask].unsqueeze(-1), prefix_lprobs[prefix_mask]
)
# if prefix includes eos, then we should make sure tokens and
# scores are the same across all beams
eos_mask = prefix_toks.eq(self.eos)
if eos_mask.any():
# validate that the first beam matches the prefix
first_beam = tokens[eos_mask].view(-1, beam_size, tokens.size(-1))[
:, 0, 1 : step + 1
]
eos_mask_batch_dim = eos_mask.view(-1, beam_size)[:, 0]
target_prefix = prefix_tokens[eos_mask_batch_dim][:, :step]
assert (first_beam == target_prefix).all()
# copy tokens, scores and lprobs from the first beam to all beams
tokens = self.replicate_first_beam(tokens, eos_mask_batch_dim, beam_size)
scores = self.replicate_first_beam(scores, eos_mask_batch_dim, beam_size)
lprobs = self.replicate_first_beam(lprobs, eos_mask_batch_dim, beam_size)
return lprobs, tokens, scores
def replicate_first_beam(self, tensor, mask, beam_size: int):
tensor = tensor.view(-1, beam_size, tensor.size(-1))
tensor[mask] = tensor[mask][:, :1, :]
return tensor.view(-1, tensor.size(-1))
def finalize_hypos(
self,
step: int,
bbsz_idx,
eos_scores,
tokens,
scores,
finalized: List[List[Dict[str, Tensor]]],
finished: List[bool],
beam_size: int,
attn: Optional[Tensor],
src_lengths,
max_len: int,
):
"""Finalize hypothesis, store finalized information in `finalized`, and change `finished` accordingly.
A sentence is finalized when {beam_size} finished items have been collected for it.
Returns number of sentences (not beam items) being finalized.
These will be removed from the batch and not processed further.
Args:
bbsz_idx (Tensor):
"""
assert bbsz_idx.numel() == eos_scores.numel()
# clone relevant token and attention tensors.
# tokens is (batch * beam, max_len). So the index_select
# gets the newly EOS rows, then selects cols 1..{step + 2}
tokens_clone = tokens.index_select(0, bbsz_idx)[
:, 1 : step + 2
] # skip the first index, which is EOS
tokens_clone[:, step] = self.eos
attn_clone = (
attn.index_select(0, bbsz_idx)[:, :, 1 : step + 2]
if attn is not None
else None
)
# compute scores per token position
pos_scores = scores.index_select(0, bbsz_idx)[:, : step + 1]
pos_scores[:, step] = eos_scores
# convert from cumulative to per-position scores
pos_scores[:, 1:] = pos_scores[:, 1:] - pos_scores[:, :-1]
# normalize sentence-level scores
if self.normalize_scores:
eos_scores /= (step + 1) ** self.len_penalty
# cum_unfin records which sentences in the batch are finished.
# It helps match indexing between (a) the original sentences
# in the batch and (b) the current, possibly-reduced set of
# sentences.
cum_unfin: List[int] = []
prev = 0
for f in finished:
if f:
prev += 1
else:
cum_unfin.append(prev)
cum_fin_tensor = torch.tensor(cum_unfin, dtype=torch.int).to(bbsz_idx)
unfin_idx = bbsz_idx // beam_size
sent = unfin_idx + torch.index_select(cum_fin_tensor, 0, unfin_idx)
# Create a set of "{sent}{unfin_idx}", where
# "unfin_idx" is the index in the current (possibly reduced)
# list of sentences, and "sent" is the index in the original,
# unreduced batch
# For every finished beam item
# sentence index in the current (possibly reduced) batch
seen = (sent << 32) + unfin_idx
unique_seen: List[int] = torch.unique(seen).tolist()
if self.match_source_len:
condition = step > torch.index_select(src_lengths, 0, unfin_idx)
eos_scores = torch.where(condition, torch.tensor(-math.inf), eos_scores)
sent_list: List[int] = sent.tolist()
for i in range(bbsz_idx.size()[0]):
# An input sentence (among those in a batch) is finished when
# beam_size hypotheses have been collected for it
if len(finalized[sent_list[i]]) < beam_size:
if attn_clone is not None:
# remove padding tokens from attn scores
hypo_attn = attn_clone[i]
else:
hypo_attn = torch.empty(0)
finalized[sent_list[i]].append(
{
"tokens": tokens_clone[i],
"score": eos_scores[i],
"attention": hypo_attn, # src_len x tgt_len
"alignment": torch.empty(0),
"positional_scores": pos_scores[i],
}
)
newly_finished: List[int] = []
for unique_s in unique_seen:
# check termination conditions for this sentence
unique_sent: int = unique_s >> 32
unique_unfin_idx: int = unique_s - (unique_sent << 32)
if not finished[unique_sent] and self.is_finished(
step, unique_unfin_idx, max_len, len(finalized[unique_sent]), beam_size
):
finished[unique_sent] = True
newly_finished.append(unique_unfin_idx)
return newly_finished
def is_finished(
self,
step: int,
unfin_idx: int,
max_len: int,
finalized_sent_len: int,
beam_size: int,
):
"""
Check whether decoding for a sentence is finished, which
occurs when the list of finalized sentences has reached the
beam size, or when we reach the maximum length.
"""
assert finalized_sent_len <= beam_size
if finalized_sent_len == beam_size or step == max_len:
return True
return False
class EnsembleModel(nn.Module):
"""A wrapper around an ensemble of models."""
def __init__(self, models):
super().__init__()
self.models_size = len(models)
# method '__len__' is not supported in ModuleList for torch script
self.single_model = models[0]
self.models = nn.ModuleList(models)
self.has_incremental: bool = False
if all(
hasattr(m, "decoder") and isinstance(m.decoder, FairseqIncrementalDecoder)
for m in models
):
self.has_incremental = True
def forward(self):
pass
def has_encoder(self):
return hasattr(self.single_model, "encoder")
def has_incremental_states(self):
return self.has_incremental
def max_decoder_positions(self):
return min(
[
m.max_decoder_positions()
for m in self.models
if hasattr(m, "max_decoder_positions")
]
+ [sys.maxsize]
)
@torch.jit.export
def forward_encoder(self, net_input: Dict[str, Tensor]):
if not self.has_encoder():
return None
return [model.encoder.forward_torchscript(net_input) for model in self.models]
@torch.jit.export
def forward_decoder(
self,
tokens,
encoder_outs: List[Dict[str, List[Tensor]]],
incremental_states: List[Dict[str, Dict[str, Optional[Tensor]]]],
temperature: float = 1.0,
):
log_probs = []
avg_attn: Optional[Tensor] = None
encoder_out: Optional[Dict[str, List[Tensor]]] = None
for i, model in enumerate(self.models):
if self.has_encoder():
encoder_out = encoder_outs[i]
# decode each model
if self.has_incremental_states():
decoder_out = model.decoder.forward(
tokens,
encoder_out=encoder_out,
incremental_state=incremental_states[i],
)
else:
if hasattr(model, "decoder"):
decoder_out = model.decoder.forward(tokens, encoder_out=encoder_out)
else:
decoder_out = model.forward(tokens)
attn: Optional[Tensor] = None
decoder_len = len(decoder_out)
if decoder_len > 1 and decoder_out[1] is not None:
if isinstance(decoder_out[1], Tensor):
attn = decoder_out[1]
else:
attn_holder = decoder_out[1]["attn"]
if isinstance(attn_holder, Tensor):
attn = attn_holder
elif attn_holder is not None:
attn = attn_holder[0]
if attn is not None:
attn = attn[:, -1, :]
decoder_out_tuple = (
decoder_out[0][:, -1:, :].div_(temperature),
None if decoder_len <= 1 else decoder_out[1],
)
probs = model.get_normalized_probs(
decoder_out_tuple, log_probs=True, sample=None
)
probs = probs[:, -1, :]
if self.models_size == 1:
return probs, attn
log_probs.append(probs)
if attn is not None:
if avg_attn is None:
avg_attn = attn
else:
avg_attn.add_(attn)
avg_probs = torch.logsumexp(torch.stack(log_probs, dim=0), dim=0) - math.log(
self.models_size
)
if avg_attn is not None:
avg_attn.div_(self.models_size)
return avg_probs, avg_attn
@torch.jit.export
def reorder_encoder_out(
self, encoder_outs: Optional[List[Dict[str, List[Tensor]]]], new_order
):
"""
Reorder encoder output according to *new_order*.
Args:
encoder_out: output from the ``forward()`` method
new_order (LongTensor): desired order
Returns:
*encoder_out* rearranged according to *new_order*
"""
new_outs: List[Dict[str, List[Tensor]]] = []
if not self.has_encoder():
return new_outs
for i, model in enumerate(self.models):
assert encoder_outs is not None
new_outs.append(
model.encoder.reorder_encoder_out(encoder_outs[i], new_order)
)
return new_outs
@torch.jit.export
def reorder_incremental_state(
self,
incremental_states: List[Dict[str, Dict[str, Optional[Tensor]]]],
new_order,
):
if not self.has_incremental_states():
return
for i, model in enumerate(self.models):
model.decoder.reorder_incremental_state_scripting(
incremental_states[i], new_order
)
class SequenceGeneratorWithAlignment(SequenceGenerator):
def __init__(
self, models, tgt_dict, left_pad_target=False, print_alignment="hard", **kwargs
):
"""Generates translations of a given source sentence.
Produces alignments following "Jointly Learning to Align and
Translate with Transformer Models" (Garg et al., EMNLP 2019).
Args:
left_pad_target (bool, optional): Whether or not the
hypothesis should be left padded or not when they are
teacher forced for generating alignments.
"""
super().__init__(EnsembleModelWithAlignment(models), tgt_dict, **kwargs)
self.left_pad_target = left_pad_target
if print_alignment == "hard":
self.extract_alignment = utils.extract_hard_alignment
elif print_alignment == "soft":
self.extract_alignment = utils.extract_soft_alignment
@torch.no_grad()
def generate(self, models, sample, **kwargs):
finalized = super()._generate(sample, **kwargs)
src_tokens = sample["net_input"]["src_tokens"]
bsz = src_tokens.shape[0]
beam_size = self.beam_size
(
src_tokens,
src_lengths,
prev_output_tokens,
tgt_tokens,
) = self._prepare_batch_for_alignment(sample, finalized)
if any(getattr(m, "full_context_alignment", False) for m in self.model.models):
attn = self.model.forward_align(src_tokens, src_lengths, prev_output_tokens)
else:
attn = [
finalized[i // beam_size][i % beam_size]["attention"].transpose(1, 0)
for i in range(bsz * beam_size)
]
if src_tokens.device != "cpu":
src_tokens = src_tokens.to("cpu")
tgt_tokens = tgt_tokens.to("cpu")
attn = [i.to("cpu") for i in attn]
# Process the attn matrix to extract hard alignments.
for i in range(bsz * beam_size):
alignment = self.extract_alignment(
attn[i], src_tokens[i], tgt_tokens[i], self.pad, self.eos
)
finalized[i // beam_size][i % beam_size]["alignment"] = alignment
return finalized
def _prepare_batch_for_alignment(self, sample, hypothesis):
src_tokens = sample["net_input"]["src_tokens"]
bsz = src_tokens.shape[0]
src_tokens = (
src_tokens[:, None, :]
.expand(-1, self.beam_size, -1)
.contiguous()
.view(bsz * self.beam_size, -1)
)
src_lengths = sample["net_input"]["src_lengths"]
src_lengths = (
src_lengths[:, None]
.expand(-1, self.beam_size)
.contiguous()
.view(bsz * self.beam_size)
)
prev_output_tokens = data_utils.collate_tokens(
[beam["tokens"] for example in hypothesis for beam in example],
self.pad,
self.eos,
self.left_pad_target,
move_eos_to_beginning=True,
)
tgt_tokens = data_utils.collate_tokens(
[beam["tokens"] for example in hypothesis for beam in example],
self.pad,
self.eos,
self.left_pad_target,
move_eos_to_beginning=False,
)
return src_tokens, src_lengths, prev_output_tokens, tgt_tokens
class EnsembleModelWithAlignment(EnsembleModel):
"""A wrapper around an ensemble of models."""
def __init__(self, models):
super().__init__(models)
def forward_align(self, src_tokens, src_lengths, prev_output_tokens):
avg_attn = None
for model in self.models:
decoder_out = model(src_tokens, src_lengths, prev_output_tokens)
attn = decoder_out[1]["attn"][0]
if avg_attn is None:
avg_attn = attn
else:
avg_attn.add_(attn)
if len(self.models) > 1:
avg_attn.div_(len(self.models))
return avg_attn
| 39,594 | 38.754016 | 110 | py |
CIF-HieraDist | CIF-HieraDist-main/fairseq/ngram_repeat_block.py | # Originally from Microsoft Corporation.
# Licensed under the MIT License.
""" Wrapper for ngram_repeat_block cuda extension """
import torch
from torch import nn
import math
from typing import Dict, List, Optional
import warnings
try:
from fairseq import ngram_repeat_block_cuda
EXTENSION_BUILT = True
except ImportError:
EXTENSION_BUILT = False
def is_cuda_extension_usable() -> bool:
"""Check whether ngram_repeat_block_cuda is built properly"""
if not EXTENSION_BUILT or not torch.cuda.is_available():
return False
bsz = 2
tokens = torch.tensor([[4, 4, 3, 2], [1, 2, 3, 4]], dtype=torch.long, device="cuda")
lprobs = torch.rand((8, 12), device="cuda")
try:
outputs = ngram_repeat_block_cuda.forward(tokens, lprobs, bsz, 3, 4, 3)
outputs = outputs + 4 # This line breaks if the extension is built incorrectly.
return True
except RuntimeError:
warnings.warn(
"NGramRepeatBlock extension must be rebuilt."
'Run TORCH_CUDA_ARCH_LIST="6.0;6.1;7.0" python setup.py build_ext --inplace'
)
return False
class NGramRepeatBlock(nn.Module):
"""Wrapper class for calling ngram_repeat_block cuda extension"""
def __init__(self, no_repeat_ngram_size: int, use_extension: bool = True):
super().__init__()
self.use_extension = is_cuda_extension_usable() if use_extension else False
self.no_repeat_ngram_size = no_repeat_ngram_size
def reset_parameters(self):
pass
@torch.jit.unused
def call_cuda_extension(
self,
tokens,
lprobs,
bsz: int,
beam_size: int,
step: int,
):
return ngram_repeat_block_cuda.forward(
tokens, lprobs, bsz, step, beam_size, self.no_repeat_ngram_size
)
def forward(
self,
tokens,
lprobs,
bsz: int,
beam_size: int,
step: int,
):
"""
Args:
tokens(Tensor): Input tokens(Bsz*beam, seq_len)
lprobs(Tensor): likelihood probability,
Expected to be updated in place.(Bsz*beam, vocab_size)
bsz(int): batch size
step(int): current step
beam_size(int): beam size
no_repeat_ngram_size(int): Ngram size
"""
msg = f"expected {bsz *beam_size} got"
assert tokens.size(0) == bsz * beam_size, f"{msg} {tokens.size(0)}"
assert lprobs.size(0) == bsz * beam_size, f"{msg} {lprobs.size(0)}"
if self.use_extension:
return self.call_cuda_extension(tokens, lprobs, bsz, beam_size, step)
else:
return self._no_repeat_ngram(
tokens,
lprobs,
bsz,
beam_size,
step,
)
def _no_repeat_ngram(self, tokens, lprobs, bsz: int, beam_size: int, step: int):
"""For each hypothesis generate a list of previous ngrams and set associated lprobs to -inf"""
gen_ngrams: List[Dict[str, List[int]]] = [
torch.jit.annotate(Dict[str, List[int]], {})
for bbsz_idx in range(bsz * beam_size)
]
cpu_tokens = tokens.cpu()
for bbsz_idx in range(bsz * beam_size):
gen_tokens: List[int] = cpu_tokens[bbsz_idx].tolist()
for ngram in self.transpose_list(
[gen_tokens[i:] for i in range(self.no_repeat_ngram_size)]
):
key = ",".join([str(x) for x in ngram[:-1]])
gen_ngrams[bbsz_idx][key] = gen_ngrams[bbsz_idx].get(
key, torch.jit.annotate(List[int], [])
) + [ngram[-1]]
if step + 2 - self.no_repeat_ngram_size >= 0:
# no banned tokens if we haven't generated no_repeat_ngram_size tokens yet
banned_tokens = [
self.calculate_banned_tokens(
tokens, step, gen_ngrams, self.no_repeat_ngram_size, bbsz_idx
)
for bbsz_idx in range(bsz * beam_size)
]
else:
banned_tokens = [
torch.jit.annotate(List[int], []) for bbsz_idx in range(bsz * beam_size)
]
for bbsz_idx in range(bsz * beam_size):
lprobs[bbsz_idx][
torch.tensor(banned_tokens[bbsz_idx], dtype=torch.int64)
] = torch.tensor(-math.inf).to(lprobs)
return lprobs
@staticmethod
def calculate_banned_tokens(
tokens,
step: int,
gen_ngrams: List[Dict[str, List[int]]],
no_repeat_ngram_size: int,
bbsz_idx: int,
):
tokens_list: List[int] = tokens[
bbsz_idx, step + 2 - no_repeat_ngram_size : step + 1
].tolist()
# before decoding the next token, prevent decoding of ngrams that have already appeared
ngram_index = ",".join([str(x) for x in tokens_list])
return gen_ngrams[bbsz_idx].get(ngram_index, torch.jit.annotate(List[int], []))
@staticmethod
def transpose_list(l: List[List[int]]):
# GeneratorExp aren't supported in TS so ignoring the lint
min_len = min([len(x) for x in l]) # noqa
l2 = [[row[i] for row in l] for i in range(min_len)]
return l2
| 5,286 | 34.013245 | 102 | py |
CIF-HieraDist | CIF-HieraDist-main/fairseq/options.py | # 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 argparse
from pathlib import Path
from typing import Callable, List, Optional, Union
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,
EvalLMConfig,
GenerationConfig,
InteractiveConfig,
OptimizationConfig,
EMAConfig,
)
from fairseq.dataclass.utils import gen_parser_from_dataclass
# this import is for backward compatibility
from fairseq.utils import csv_str_list, eval_bool, eval_str_dict, eval_str_list # noqa
def get_preprocessing_parser(default_task="translation"):
parser = get_parser("Preprocessing", default_task)
add_preprocess_args(parser)
return parser
def get_training_parser(default_task="translation"):
parser = get_parser("Trainer", default_task)
add_dataset_args(parser, train=True)
add_distributed_training_args(parser)
add_model_args(parser)
add_optimization_args(parser)
add_checkpoint_args(parser)
add_ema_args(parser)
return parser
def get_generation_parser(interactive=False, default_task="translation"):
parser = get_parser("Generation", default_task)
add_dataset_args(parser, gen=True)
add_distributed_training_args(parser, default_world_size=1)
add_generation_args(parser)
add_checkpoint_args(parser)
if interactive:
add_interactive_args(parser)
return parser
def get_speech_generation_parser(default_task="text_to_speech"):
parser = get_parser("Speech Generation", default_task)
add_dataset_args(parser, gen=True)
add_distributed_training_args(parser, default_world_size=1)
add_speech_generation_args(parser)
return parser
def get_interactive_generation_parser(default_task="translation"):
return get_generation_parser(interactive=True, default_task=default_task)
def get_eval_lm_parser(default_task="language_modeling"):
parser = get_parser("Evaluate Language Model", default_task)
add_dataset_args(parser, gen=True)
add_distributed_training_args(parser, default_world_size=1)
add_eval_lm_args(parser)
return parser
def get_validation_parser(default_task=None):
parser = get_parser("Validation", default_task)
add_dataset_args(parser, train=True)
add_distributed_training_args(parser, default_world_size=1)
group = parser.add_argument_group("Evaluation")
gen_parser_from_dataclass(group, CommonEvalConfig())
return parser
def parse_args_and_arch(
parser: argparse.ArgumentParser,
input_args: List[str] = None,
parse_known: bool = False,
suppress_defaults: bool = False,
modify_parser: Optional[Callable[[argparse.ArgumentParser], None]] = None,
):
"""
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]]):
function to modify the parser, e.g., to set default values
"""
if suppress_defaults:
# Parse args without any default values. This requires us to parse
# twice, once to identify all the necessary task/model args, and a second
# time with all defaults set to None.
args = parse_args_and_arch(
parser,
input_args=input_args,
parse_known=parse_known,
suppress_defaults=False,
)
suppressed_parser = argparse.ArgumentParser(add_help=False, parents=[parser])
suppressed_parser.set_defaults(**{k: None for k, v in vars(args).items()})
args = suppressed_parser.parse_args(input_args)
return argparse.Namespace(
**{k: v for k, v in vars(args).items() if v is not None}
)
from fairseq.models import ARCH_MODEL_REGISTRY, ARCH_CONFIG_REGISTRY, MODEL_REGISTRY
# Before creating the true parser, we need to import optional user module
# in order to eagerly import custom tasks, optimizers, architectures, etc.
usr_parser = argparse.ArgumentParser(add_help=False, allow_abbrev=False)
usr_parser.add_argument("--user-dir", default=None)
usr_args, _ = usr_parser.parse_known_args(input_args)
utils.import_user_module(usr_args)
if modify_parser is not None:
modify_parser(parser)
# The parser doesn't know about model/criterion/optimizer-specific args, so
# we parse twice. First we parse the model/criterion/optimizer, then we
# parse a second time after adding the *-specific arguments.
# If input_args is given, we will parse those args instead of sys.argv.
args, _ = parser.parse_known_args(input_args)
# Add model-specific args to parser.
if hasattr(args, "arch"):
model_specific_group = parser.add_argument_group(
"Model-specific configuration",
# Only include attributes which are explicitly given as command-line
# arguments or which have default values.
argument_default=argparse.SUPPRESS,
)
if args.arch in ARCH_MODEL_REGISTRY:
ARCH_MODEL_REGISTRY[args.arch].add_args(model_specific_group)
elif args.arch in MODEL_REGISTRY:
MODEL_REGISTRY[args.arch].add_args(model_specific_group)
else:
raise RuntimeError()
if hasattr(args, "task"):
from fairseq.tasks import TASK_REGISTRY
TASK_REGISTRY[args.task].add_args(parser)
if getattr(args, "use_bmuf", False):
# hack to support extra args for block distributed data parallelism
from fairseq.optim.bmuf import FairseqBMUF
FairseqBMUF.add_args(parser)
# Add *-specific args to parser.
from fairseq.registry import REGISTRIES
for registry_name, REGISTRY in REGISTRIES.items():
choice = getattr(args, registry_name, None)
if choice is not None:
cls = REGISTRY["registry"][choice]
if hasattr(cls, "add_args"):
cls.add_args(parser)
elif hasattr(cls, "__dataclass"):
gen_parser_from_dataclass(parser, cls.__dataclass())
# Modify the parser a second time, since defaults may have been reset
if modify_parser is not None:
modify_parser(parser)
# Parse a second time.
if parse_known:
args, extra = parser.parse_known_args(input_args)
else:
args = parser.parse_args(input_args)
extra = None
# Post-process args.
if (
hasattr(args, "batch_size_valid") and args.batch_size_valid is None
) or not hasattr(args, "batch_size_valid"):
args.batch_size_valid = args.batch_size
if hasattr(args, "max_tokens_valid") and args.max_tokens_valid is None:
args.max_tokens_valid = args.max_tokens
if getattr(args, "memory_efficient_fp16", False):
args.fp16 = True
if getattr(args, "memory_efficient_bf16", False):
args.bf16 = True
args.tpu = getattr(args, "tpu", False)
args.bf16 = getattr(args, "bf16", False)
if args.bf16:
args.tpu = True
if args.tpu and args.fp16:
raise ValueError("Cannot combine --fp16 and --tpu, use --bf16 on TPUs")
if getattr(args, "seed", None) is None:
args.seed = 1 # default seed for training
args.no_seed_provided = True
else:
args.no_seed_provided = False
if getattr(args, "update_epoch_batch_itr", None) is None:
if hasattr(args, "grouped_shuffling"):
args.update_epoch_batch_itr = args.grouped_shuffling
else:
args.grouped_shuffling = False
args.update_epoch_batch_itr = False
# Apply architecture configuration.
if hasattr(args, "arch") and args.arch in ARCH_CONFIG_REGISTRY:
ARCH_CONFIG_REGISTRY[args.arch](args)
if parse_known:
return args, extra
else:
return args
def get_parser(desc, default_task="translation"):
# Before creating the true parser, we need to import optional user module
# in order to eagerly import custom tasks, optimizers, architectures, etc.
usr_parser = argparse.ArgumentParser(add_help=False, allow_abbrev=False)
usr_parser.add_argument("--user-dir", default=None)
usr_args, _ = usr_parser.parse_known_args()
utils.import_user_module(usr_args)
parser = argparse.ArgumentParser(allow_abbrev=False)
gen_parser_from_dataclass(parser, CommonConfig())
from fairseq.registry import REGISTRIES
for registry_name, REGISTRY in REGISTRIES.items():
parser.add_argument(
"--" + registry_name.replace("_", "-"),
default=REGISTRY["default"],
choices=REGISTRY["registry"].keys(),
)
# Task definitions can be found under fairseq/tasks/
from fairseq.tasks import TASK_REGISTRY
parser.add_argument(
"--task",
metavar="TASK",
default=default_task,
choices=TASK_REGISTRY.keys(),
help="task",
)
# fmt: on
return parser
def add_preprocess_args(parser):
group = parser.add_argument_group("Preprocessing")
# fmt: off
group.add_argument("-s", "--source-lang", default=None, metavar="SRC",
help="source language")
group.add_argument("-t", "--target-lang", default=None, metavar="TARGET",
help="target language")
group.add_argument("--trainpref", metavar="FP", default=None,
help="train file prefix (also used to build dictionaries)")
group.add_argument("--validpref", metavar="FP", default=None,
help="comma separated, valid file prefixes "
"(words missing from train set are replaced with <unk>)")
group.add_argument("--testpref", metavar="FP", default=None,
help="comma separated, test file prefixes "
"(words missing from train set are replaced with <unk>)")
group.add_argument("--align-suffix", metavar="FP", default=None,
help="alignment file suffix")
group.add_argument("--destdir", metavar="DIR", default="data-bin",
help="destination dir")
group.add_argument("--thresholdtgt", metavar="N", default=0, type=int,
help="map words appearing less than threshold times to unknown")
group.add_argument("--thresholdsrc", metavar="N", default=0, type=int,
help="map words appearing less than threshold times to unknown")
group.add_argument("--tgtdict", metavar="FP",
help="reuse given target dictionary")
group.add_argument("--srcdict", metavar="FP",
help="reuse given source dictionary")
group.add_argument("--nwordstgt", metavar="N", default=-1, type=int,
help="number of target words to retain")
group.add_argument("--nwordssrc", metavar="N", default=-1, type=int,
help="number of source words to retain")
group.add_argument("--alignfile", metavar="ALIGN", default=None,
help="an alignment file (optional)")
parser.add_argument('--dataset-impl', metavar='FORMAT', default='mmap',
choices=get_available_dataset_impl(),
help='output dataset implementation')
group.add_argument("--joined-dictionary", action="store_true",
help="Generate joined dictionary")
group.add_argument("--only-source", action="store_true",
help="Only process the source language")
group.add_argument("--padding-factor", metavar="N", default=8, type=int,
help="Pad dictionary size to be multiple of N")
group.add_argument("--workers", metavar="N", default=1, type=int,
help="number of parallel workers")
group.add_argument("--dict-only", action='store_true',
help="if true, only builds a dictionary and then exits")
# fmt: on
return parser
def add_dataset_args(parser, train=False, gen=False):
group = parser.add_argument_group("dataset_data_loading")
gen_parser_from_dataclass(group, DatasetConfig())
# fmt: on
return group
def add_distributed_training_args(parser, default_world_size=None):
group = parser.add_argument_group("distributed_training")
if default_world_size is None:
default_world_size = max(1, torch.cuda.device_count())
gen_parser_from_dataclass(
group, DistributedTrainingConfig(distributed_world_size=default_world_size)
)
return group
def add_optimization_args(parser):
group = parser.add_argument_group("optimization")
# fmt: off
gen_parser_from_dataclass(group, OptimizationConfig())
# fmt: on
return group
def add_checkpoint_args(parser):
group = parser.add_argument_group("checkpoint")
# fmt: off
gen_parser_from_dataclass(group, CheckpointConfig())
# fmt: on
return group
def add_common_eval_args(group):
gen_parser_from_dataclass(group, CommonEvalConfig())
def add_eval_lm_args(parser):
group = parser.add_argument_group("LM Evaluation")
add_common_eval_args(group)
gen_parser_from_dataclass(group, EvalLMConfig())
def add_generation_args(parser):
group = parser.add_argument_group("Generation")
add_common_eval_args(group)
gen_parser_from_dataclass(group, GenerationConfig())
return group
def add_speech_generation_args(parser):
group = parser.add_argument_group("Speech Generation")
add_common_eval_args(group) # NOTE: remove_bpe is not needed
# fmt: off
group.add_argument('--eos_prob_threshold', default=0.5, type=float,
help='terminate when eos probability exceeds this')
# fmt: on
return group
def add_interactive_args(parser):
group = parser.add_argument_group("Interactive")
gen_parser_from_dataclass(group, InteractiveConfig())
def add_model_args(parser):
group = parser.add_argument_group("Model configuration")
# fmt: off
# Model definitions can be found under fairseq/models/
#
# The model architecture can be specified in several ways.
# In increasing order of priority:
# 1) model defaults (lowest priority)
# 2) --arch argument
# 3) --encoder/decoder-* arguments (highest priority)
from fairseq.models import ARCH_MODEL_REGISTRY
group.add_argument('--arch', '-a', metavar='ARCH',
choices=ARCH_MODEL_REGISTRY.keys(),
help='model architecture')
# fmt: on
return group
def get_args(
data: Union[str, Path],
task: str = "translation",
arch: str = "transformer",
**overrides
):
parser = get_training_parser(task)
args = parse_args_and_arch(parser, [str(data), "--task", task, "--arch", arch])
for k, v in overrides.items():
setattr(args, k, v)
return args
def add_ema_args(parser):
group = parser.add_argument_group("EMA configuration")
gen_parser_from_dataclass(group, EMAConfig())
| 15,440 | 36.297101 | 88 | py |
CIF-HieraDist | CIF-HieraDist-main/fairseq/token_generation_constraints.py | # 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.
"""Implements tracking of constraints for a beam item.
A list of constraints is given as a list of one or more token
sequences, each of length at least one token. For example, for an input sentence
> Die maschinelle Übersetzung ist schwer zu kontrollieren.
We could have the constraints:
* to influence
* hard
There are two implementations:
* OrderedConstraintState: Tracks progress through an ordered list of multitoken constraints.
* UnorderedConstraintState: Tracks progress through an unordered list of multitoken constraints.
The difference is that in the first, the constraints are assumed to be
in order; the algorithm will permit zero or more tokens between them.
In the second, the constraints are not ordered, so many orderings will
be explored.
The same sequence can be present any number of times, and will appear
that many times in the output.
"""
from collections import Counter
from typing import List, Optional, Set, Tuple
import torch
class ConstraintState:
def __init__(self):
pass
def pack_constraints(batch_constraints: List[List[torch.Tensor]]) -> torch.Tensor:
"""Takes a list of list of constraints in tensor form (a list of
tensor constraints for each sentence) and transforms it into a
packed Tensor. For example, here is a batch of size 3 with 3, 0,
and 1 constraints:
[ [ [3 1 2], [3], [4 5 6 7], ]
[],
[ [1 8 9 10 1 4 11 12], ]
]
Its corresponding packed structure is:
[ [ 3 3 1 2 0 3 0 4 5 6 7 0],
[ 0 0 0 0 0 0 0 0 0 0 0 0],
[ 1 1 8 9 10 1 4 11 12 0 0 0] ]
The packed tensor has shape (batch size, maxlen), where
maxlen is defined below. Each row contains concatenated
constraint tokens for that sentence, with 0 appended after
each constraint. The first item in each row is the number
of constraints for that sentence. So maxlen is the maximum
of
(number of constraints) + (sum length of constraints) + 1.
across all sentences in the batch.
"""
# The maximum word length of concatenated constraints for any sentence
max_constraints_len = 1
for sentence_constraints in batch_constraints:
if len(sentence_constraints):
# number of constraints, plus sum of constrain lens, plus a zero after each
constraints_len = (
1
+ sum([c.size(0) for c in sentence_constraints])
+ len(sentence_constraints)
)
max_constraints_len = max(max_constraints_len, constraints_len)
batch_size = len(batch_constraints)
constraints_tensor = torch.zeros((batch_size, max_constraints_len)).long()
for i, sentence_constraints in enumerate(batch_constraints):
constraints_tensor[i, 0] = len(sentence_constraints)
offset = 1
for j, constraint in enumerate(sentence_constraints):
this_len = constraint.size(0)
constraints_tensor[i, offset : offset + this_len] = constraint
offset += this_len + 1
return constraints_tensor.long()
def unpack_constraints(constraint_tensor: torch.Tensor) -> List[torch.Tensor]:
"""
Transforms *one row* of a packed constraint tensor (e.g., for one
sentence in the batch) into a list of constraint tensors.
"""
constraint_list = []
num_constraints = constraint_tensor[0]
constraints = constraint_tensor.tolist()
offset = 1
for i in range(num_constraints):
where = constraints.index(0, offset)
constraint_list.append(constraint_tensor[offset:where])
offset = where + 1
return constraint_list
class ConstraintNode:
"""
Represents a node in a trie managing unordered constraints.
"""
def __init__(self, token: int = None, parent=None):
# The token associate with this node (None for the root)
self.token = int(token) if token is not None else None
# The parent (None at the root)
self.parent = parent
# Whether this node is a completed constraint
self.terminal = 0
# List of child nodes
self.children = {}
# The cumulative number of constraints from this point in the
# trie forward
self.num_constraints = 0
@property
def id(self):
return self.token
def __str__(self):
term = self.terminal != 0
return f"[{self.token}].{term}#{self.num_constraints}"
def __getitem__(self, key: int):
return self.children.get(key, None)
def next_tokens(self) -> Set[int]:
"""The set of child labels."""
return set(self.children.keys())
@staticmethod
def create(constraints: List[List[int]]):
root = ConstraintNode()
for sequence in constraints:
root.add_sequence(sequence)
return root
@staticmethod
def print_graph(node: "ConstraintNode"):
if len(node.children) == 0:
return str(node)
else:
s = f"({node}"
for child in node.children.values():
s += " " + ConstraintNode.print_graph(child)
s += ")"
return s
def token_counts(self) -> Counter:
"""Returns a counter of the number of times each token is used
in a constraint.
"""
token_counts = Counter()
kids = list(self.children.values())
while len(kids) > 0:
kid = kids.pop()
token_counts[kid.id] += kid.num_constraints
kids += list(kid.children.values())
return token_counts
def tokens(self) -> Set[int]:
"""Returns the set of tokens in constraints."""
return set(self.token_counts().keys())
def add_sequence(self, sequence: List[int]):
"""Adds a constraint, represented as a list of integers, to
the trie."""
assert len(sequence) > 0
token = int(sequence[0])
if token not in self.children:
self.children[token] = ConstraintNode(token, parent=self)
node = self.children[token]
if len(sequence) == 1:
node.terminal += 1
node.num_constraints += 1
parent = node.parent
while parent is not None:
parent.num_constraints += 1
parent = parent.parent
else:
node.add_sequence(sequence[1:])
class UnorderedConstraintState(ConstraintState):
"""
Records progress through the set of constraints for each item in the beam
using a trie.
"""
def __init__(self, node: ConstraintNode, copy_from: "ConstraintState" = None):
self.node = node
if copy_from is None:
# The root node
self.root = node
# The set of states in the graph that have been completed
self.completed = Counter()
# The...
self.generated = Counter()
# The list of tokens we need to generate
self.needed_tokens = self.root.tokens()
else:
self.completed = Counter(copy_from.completed)
self.generated = Counter(copy_from.generated)
self.root = copy_from.root
# Mark the node as generated
if self.node != self.root:
self.generated[node] += 1
@staticmethod
def create(constraint_tensor: torch.Tensor):
constraint_list = unpack_constraints(constraint_tensor)
constraint_trie_root = ConstraintNode.create(constraint_list)
return UnorderedConstraintState(constraint_trie_root)
def __str__(self):
gen_str = ",".join([str(node) for node in self.generated])
return f"{self.name}/{self.bank}({gen_str})x{self.num_completed}"
def __copy__(self):
copied_state = UnorderedConstraintState(self.node, copy_from=self)
return copied_state
def copy(self):
return self.__copy__()
@property
def name(self):
if self.node.id is None:
return "ROOT"
else:
return str(self.node.id)
@property
def is_root(self):
return self.node == self.root
@property
def bank(self):
return sum(self.generated.values())
@property
def num_completed(self):
"""The number of constraints (not constraint tokens) that are completed.
In addition to the already-completed states, we need to account for the
current state, which might get marked as completed when another token
is generated.
"""
in_final = self.node.terminal and self.completed[self.node] < self.node.terminal
return sum(self.completed.values()) + in_final
@property
def finished(self):
return self.root.num_constraints - self.num_completed == 0
@property
def token_counts(self):
return self.root.token_counts()
@property
def tokens(self):
return self.root.tokens()
@property
def num_constraint_tokens(self):
return sum(self.token_counts.values())
def next_tokens(self) -> Set[int]:
"""Returns the list of tokens that could come next.
These are (a) all tokens extending the root state and, for
non-root states, additionally all tokens extending the current
state."""
if self.node != self.root:
return self.root.next_tokens().union(self.node.next_tokens())
else:
return self.root.next_tokens()
def advance(self, token: int):
"""Reads in a token and advances the state. Here's how it works.
We can advance to the next state if:
- there is a matching child
- its path isn't blocked
A path is blocked when all constraints that are descendants of
that node have already been generated, in the current state.
If we are not able to advance from the current state, we "fall
off the graph" and return to the root state. There, we again
try to advance, checking the same criteria.
In any case, when falling off the graph, we need to do some
bookkeeping. We:
- check whether any constraints were met (all prefixes of
current state)
- if one is found, mark it as completed
- adjust visited nodes accordingly
"""
token = int(token)
next_state = None
child = self.node[token]
if child is not None and self.generated[child] < child.num_constraints:
next_state = UnorderedConstraintState(child, copy_from=self)
def rewind():
"""If we're mid-trie and an "illegal" token is chosen next, we need
to reset our state to the root state. However, along the way, we need
to check whether a prefix of the current trie state represents a state
we could mark as completed.
"""
node = self.node
while node != self.root:
if node.terminal and self.completed[node] < node.terminal:
next_state.completed[node] += 1
return
next_state.generated[node] -= 1
node = node.parent
# Fall off the graph, check the root
if next_state is None and token in self.root.next_tokens():
child = self.root[token]
# We can only traverse this edge if it's not saturated
if self.generated[child] < child.num_constraints:
next_state = UnorderedConstraintState(child, copy_from=self)
else:
next_state = UnorderedConstraintState(self.root, copy_from=self)
# Rewind
rewind()
elif next_state is None:
next_state = UnorderedConstraintState(self.root, copy_from=self)
# Rewind
rewind()
return next_state
class ConstraintSequence:
def __init__(self, sequences: List[List[int]]):
"""Represents a set of possibly multitoken constraints by
concatenating them and internally recording the end points.
"""
self.sequences = []
self.endpoints = []
self.num_tokens = 0
self.tokens = set()
for sequence in sequences:
for token in sequence:
self.tokens.add(token)
self.num_tokens += len(sequence)
self.endpoints += [False for x in range(len(sequence) - 1)] + [True]
self.sequences += sequence
def __getitem__(self, key: int):
return self.sequences[key]
def __len__(self):
return len(self.sequences)
def __str__(self):
return str(self.sequences)
class OrderedConstraintState(ConstraintState):
"""
Records progress through the set of linear nonbranching constraints with gaps.
"""
def __init__(self, sequence: ConstraintSequence, state: int = -1):
self.sequence = sequence
self.state = state
@staticmethod
def create(constraint_tensor: torch.Tensor):
constraint_list = unpack_constraints(constraint_tensor)
return OrderedConstraintState(ConstraintSequence(constraint_list), -1)
def __str__(self):
return f"{self.state}/{self.bank}x{self.num_completed}"
def __copy__(self):
return OrderedConstraintState(self.sequence, self.state)
def copy(self):
return self.__copy__()
@property
def num_completed(self):
if self.state == -1:
return 0
count = len(
list(filter(lambda x: x, self.sequence.endpoints[0 : self.state + 1]))
)
return count
@property
def is_root(self):
return self.state == -1
@property
def name(self):
if self.state == -1:
return "ROOT"
else:
return str(self.sequence[self.state])
@property
def bank(self) -> int:
return self.state + 1
@property
def finished(self):
return self.state + 1 == len(self.sequence)
@property
def token_counts(self):
return self.sequence.token_counts()
@property
def tokens(self):
return self.sequence.tokens
@property
def num_constraint_tokens(self):
return sum(self.token_counts.values())
def next_tokens(self) -> Set[int]:
"""Returns the list of tokens that could come next.
These are (a) all tokens extending the root state and, for
non-root states, additionally all tokens extending the current
state."""
tokens = set()
if self.state > 0:
tokens.add(self.sequence[0])
if not self.finished:
tokens.add(self.sequence[self.state + 1])
return tokens
def advance(self, token: int):
"""Reads in a token and advances the state. Here's how it works.
We can advance to the next state if:
- there is a matching child
- its path isn't blocked
A path is blocked when all constraints that are descendants of
that node have already been generated, in the current state.
If we are not able to advance from the current state, we "fall
off the graph" and return to the root state. There, we again
try to advance, checking the same criteria.
In any case, when falling off the graph, we need to do some
bookkeeping. We:
- check whether any constraints were met (all prefixes of
current state)
- if one is found, mark it as completed
- adjust visited nodes accordingly
"""
token = int(token)
# print(f"{self} ADVANCE({token}) {self.sequence} -> ", end="")
if self.finished:
# Accept anything
next_state = self.copy()
elif self.sequence[self.state + 1] == token:
# Advance to the next token
next_state = OrderedConstraintState(self.sequence, self.state + 1)
elif self.sequence.endpoints[self.state]:
# Accept anything between constraints (*)
next_state = self.copy()
elif token == self.sequence[0]:
# Start over having generated the first token
next_state = OrderedConstraintState(self.sequence, 0)
else:
# Start over from the root
next_state = OrderedConstraintState(self.sequence, -1)
return next_state
| 16,555 | 31.654832 | 96 | py |
CIF-HieraDist | CIF-HieraDist-main/fairseq/file_utils.py | # 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.
"""
Utilities for working with the local dataset cache.
This file is adapted from `AllenNLP <https://github.com/allenai/allennlp>`_.
and `huggingface <https://github.com/huggingface>`_.
"""
import fnmatch
import json
import logging
import os
import shutil
import tarfile
import tempfile
from functools import partial, wraps
from hashlib import sha256
from io import open
try:
from torch.hub import _get_torch_home
torch_cache_home = _get_torch_home()
except ImportError:
torch_cache_home = os.path.expanduser(
os.getenv(
"TORCH_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "torch")
)
)
default_cache_path = os.path.join(torch_cache_home, "pytorch_fairseq")
try:
from urllib.parse import urlparse
except ImportError:
from urlparse import urlparse
try:
from pathlib import Path
PYTORCH_FAIRSEQ_CACHE = Path(os.getenv("PYTORCH_FAIRSEQ_CACHE", default_cache_path))
except (AttributeError, ImportError):
PYTORCH_FAIRSEQ_CACHE = os.getenv("PYTORCH_FAIRSEQ_CACHE", default_cache_path)
CONFIG_NAME = "config.json"
WEIGHTS_NAME = "pytorch_model.bin"
logger = logging.getLogger(__name__) # pylint: disable=invalid-name
def load_archive_file(archive_file):
# redirect to the cache, if necessary
try:
resolved_archive_file = cached_path(archive_file, cache_dir=None)
except EnvironmentError:
logger.info(
"Archive name '{}' was not found in archive name list. "
"We assumed '{}' was a path or URL but couldn't find any file "
"associated to this path or URL.".format(
archive_file,
archive_file,
)
)
return None
if resolved_archive_file == archive_file:
logger.info("loading archive file {}".format(archive_file))
else:
logger.info(
"loading archive file {} from cache at {}".format(
archive_file, resolved_archive_file
)
)
# Extract archive to temp dir and replace .tar.bz2 if necessary
tempdir = None
if not os.path.isdir(resolved_archive_file):
tempdir = tempfile.mkdtemp()
logger.info(
"extracting archive file {} to temp dir {}".format(
resolved_archive_file, tempdir
)
)
ext = os.path.splitext(archive_file)[1][1:]
with tarfile.open(resolved_archive_file, "r:" + ext) as archive:
top_dir = os.path.commonprefix(archive.getnames())
archive.extractall(tempdir)
os.remove(resolved_archive_file)
shutil.move(os.path.join(tempdir, top_dir), resolved_archive_file)
shutil.rmtree(tempdir)
return resolved_archive_file
def url_to_filename(url, etag=None):
"""
Convert `url` into a hashed filename in a repeatable way.
If `etag` is specified, append its hash to the URL's, delimited
by a period.
"""
url_bytes = url.encode("utf-8")
url_hash = sha256(url_bytes)
filename = url_hash.hexdigest()
if etag:
etag_bytes = etag.encode("utf-8")
etag_hash = sha256(etag_bytes)
filename += "." + etag_hash.hexdigest()
return filename
def filename_to_url(filename, cache_dir=None):
"""
Return the url and etag (which may be ``None``) stored for `filename`.
Raise ``EnvironmentError`` if `filename` or its stored metadata do not exist.
"""
if cache_dir is None:
cache_dir = PYTORCH_FAIRSEQ_CACHE
if isinstance(cache_dir, Path):
cache_dir = str(cache_dir)
cache_path = os.path.join(cache_dir, filename)
if not os.path.exists(cache_path):
raise EnvironmentError("file {} not found".format(cache_path))
meta_path = cache_path + ".json"
if not os.path.exists(meta_path):
raise EnvironmentError("file {} not found".format(meta_path))
with open(meta_path, encoding="utf-8") as meta_file:
metadata = json.load(meta_file)
url = metadata["url"]
etag = metadata["etag"]
return url, etag
def cached_path_from_pm(url_or_filename):
"""
Tries to cache the specified URL using PathManager class.
Returns the cached path if success otherwise failure.
"""
try:
from fairseq.file_io import PathManager
local_path = PathManager.get_local_path(url_or_filename)
return local_path
except Exception:
return None
def cached_path(url_or_filename, cache_dir=None):
"""
Given something that might be a URL (or might be a local path),
determine which. If it's a URL, download the file and cache it, and
return the path to the cached file. If it's already a local path,
make sure the file exists and then return the path.
"""
if cache_dir is None:
cache_dir = PYTORCH_FAIRSEQ_CACHE
if isinstance(url_or_filename, Path):
url_or_filename = str(url_or_filename)
if isinstance(cache_dir, Path):
cache_dir = str(cache_dir)
parsed = urlparse(url_or_filename)
if parsed.scheme in ("http", "https", "s3"):
# URL, so get it from the cache (downloading if necessary)
return get_from_cache(url_or_filename, cache_dir)
elif os.path.exists(url_or_filename):
# File, and it exists.
return url_or_filename
elif parsed.scheme == "":
# File, but it doesn't exist.
raise EnvironmentError("file {} not found".format(url_or_filename))
else:
cached_path = cached_path_from_pm(url_or_filename)
if cached_path:
return cached_path
# Something unknown
raise ValueError(
"unable to parse {} as a URL or as a local path".format(url_or_filename)
)
def split_s3_path(url):
"""Split a full s3 path into the bucket name and path."""
parsed = urlparse(url)
if not parsed.netloc or not parsed.path:
raise ValueError("bad s3 path {}".format(url))
bucket_name = parsed.netloc
s3_path = parsed.path
# Remove '/' at beginning of path.
if s3_path.startswith("/"):
s3_path = s3_path[1:]
return bucket_name, s3_path
def s3_request(func):
"""
Wrapper function for s3 requests in order to create more helpful error
messages.
"""
@wraps(func)
def wrapper(url, *args, **kwargs):
from botocore.exceptions import ClientError
try:
return func(url, *args, **kwargs)
except ClientError as exc:
if int(exc.response["Error"]["Code"]) == 404:
raise EnvironmentError("file {} not found".format(url))
else:
raise
return wrapper
@s3_request
def s3_etag(url):
"""Check ETag on S3 object."""
import boto3
s3_resource = boto3.resource("s3")
bucket_name, s3_path = split_s3_path(url)
s3_object = s3_resource.Object(bucket_name, s3_path)
return s3_object.e_tag
@s3_request
def s3_get(url, temp_file):
"""Pull a file directly from S3."""
import boto3
s3_resource = boto3.resource("s3")
bucket_name, s3_path = split_s3_path(url)
s3_resource.Bucket(bucket_name).download_fileobj(s3_path, temp_file)
def request_wrap_timeout(func, url):
import requests
for attempt, timeout in enumerate([10, 20, 40, 60, 60]):
try:
return func(timeout=timeout)
except requests.exceptions.Timeout as e:
logger.warning(
"Request for %s timed-out (attempt %d). Retrying with a timeout of %d secs",
url,
attempt,
timeout,
exc_info=e,
)
continue
raise RuntimeError(f"Unable to fetch file {url}")
def http_get(url, temp_file):
import requests
from tqdm import tqdm
req = request_wrap_timeout(partial(requests.get, url, stream=True), url)
content_length = req.headers.get("Content-Length")
total = int(content_length) if content_length is not None else None
progress = tqdm(unit="B", total=total)
for chunk in req.iter_content(chunk_size=1024):
if chunk: # filter out keep-alive new chunks
progress.update(len(chunk))
temp_file.write(chunk)
progress.close()
def get_from_cache(url, cache_dir=None):
"""
Given a URL, look for the corresponding dataset in the local cache.
If it's not there, download it. Then return the path to the cached file.
"""
if cache_dir is None:
cache_dir = PYTORCH_FAIRSEQ_CACHE
if isinstance(cache_dir, Path):
cache_dir = str(cache_dir)
if not os.path.exists(cache_dir):
os.makedirs(cache_dir)
# Get eTag to add to filename, if it exists.
if url.startswith("s3://"):
etag = s3_etag(url)
else:
try:
import requests
response = request_wrap_timeout(
partial(requests.head, url, allow_redirects=True), url
)
if response.status_code != 200:
etag = None
else:
etag = response.headers.get("ETag")
except RuntimeError:
etag = None
filename = url_to_filename(url, etag)
# get cache path to put the file
cache_path = os.path.join(cache_dir, filename)
# If we don't have a connection (etag is None) and can't identify the file
# try to get the last downloaded one
if not os.path.exists(cache_path) and etag is None:
matching_files = fnmatch.filter(os.listdir(cache_dir), filename + ".*")
matching_files = list(filter(lambda s: not s.endswith(".json"), matching_files))
if matching_files:
cache_path = os.path.join(cache_dir, matching_files[-1])
if not os.path.exists(cache_path):
# Download to temporary file, then copy to cache dir once finished.
# Otherwise you get corrupt cache entries if the download gets interrupted.
with tempfile.NamedTemporaryFile() as temp_file:
logger.info("%s not found in cache, downloading to %s", url, temp_file.name)
# GET file object
if url.startswith("s3://"):
s3_get(url, temp_file)
else:
http_get(url, temp_file)
# we are copying the file before closing it, so flush to avoid truncation
temp_file.flush()
# shutil.copyfileobj() starts at the current position, so go to the start
temp_file.seek(0)
logger.info("copying %s to cache at %s", temp_file.name, cache_path)
with open(cache_path, "wb") as cache_file:
shutil.copyfileobj(temp_file, cache_file)
logger.info("creating metadata file for %s", cache_path)
meta = {"url": url, "etag": etag}
meta_path = cache_path + ".json"
with open(meta_path, "w") as meta_file:
output_string = json.dumps(meta)
meta_file.write(output_string)
logger.info("removing temp file %s", temp_file.name)
return cache_path
def read_set_from_file(filename):
"""
Extract a de-duped collection (set) of text from a file.
Expected file format is one item per line.
"""
collection = set()
with open(filename, "r", encoding="utf-8") as file_:
for line in file_:
collection.add(line.rstrip())
return collection
def get_file_extension(path, dot=True, lower=True):
ext = os.path.splitext(path)[1]
ext = ext if dot else ext[1:]
return ext.lower() if lower else ext
| 11,718 | 30.587601 | 92 | py |
CIF-HieraDist | CIF-HieraDist-main/fairseq/incremental_decoding_utils.py | # 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 uuid
from typing import Dict, Optional
from torch import Tensor
class FairseqIncrementalState(object):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.init_incremental_state()
def init_incremental_state(self):
self._incremental_state_id = str(uuid.uuid4())
def _get_full_incremental_state_key(self, key: str) -> str:
return "{}.{}".format(self._incremental_state_id, key)
def get_incremental_state(
self,
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]],
key: str,
) -> Optional[Dict[str, Optional[Tensor]]]:
"""Helper for getting incremental state for an nn.Module."""
full_key = self._get_full_incremental_state_key(key)
if incremental_state is None or full_key not in incremental_state:
return None
return incremental_state[full_key]
def set_incremental_state(
self,
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]],
key: str,
value: Dict[str, Optional[Tensor]],
) -> Optional[Dict[str, Dict[str, Optional[Tensor]]]]:
"""Helper for setting incremental state for an nn.Module."""
if incremental_state is not None:
full_key = self._get_full_incremental_state_key(key)
incremental_state[full_key] = value
return incremental_state
def with_incremental_state(cls):
cls.__bases__ = (FairseqIncrementalState,) + tuple(
b for b in cls.__bases__ if b != FairseqIncrementalState
)
return cls
| 1,773 | 33.115385 | 76 | py |
CIF-HieraDist | CIF-HieraDist-main/fairseq/search.py | # 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 math
from typing import List, Optional
import torch
import torch.nn as nn
from fairseq.token_generation_constraints import (
ConstraintState,
OrderedConstraintState,
UnorderedConstraintState,
)
from torch import Tensor
class Search(nn.Module):
def __init__(self, tgt_dict):
super().__init__()
self.pad = tgt_dict.pad()
self.unk = tgt_dict.unk()
self.eos = tgt_dict.eos()
self.vocab_size = len(tgt_dict)
self.src_lengths = torch.tensor(-1)
self.supports_constraints = False
self.stop_on_max_len = False
def step(
self, step, lprobs, scores, prev_output_tokens=None, original_batch_idxs=None
):
"""Take a single search step.
Args:
step: the current search step, starting at 0
lprobs: (bsz x input_beam_size x vocab_size)
the model's log-probabilities over the vocabulary at the current step
scores: (bsz x input_beam_size x step)
the historical model scores of each hypothesis up to this point
prev_output_tokens: (bsz x step)
the previously generated oputput tokens
original_batch_idxs: (bsz)
the tensor with the batch indices, in the range [0, bsz)
this is useful in case there has been applied a re-ordering
and we need to know the orignal indices
Return: A tuple of (scores, indices, beams) where:
scores: (bsz x output_beam_size)
the scores of the chosen elements; output_beam_size can be
larger than input_beam_size, e.g., we may return
2*input_beam_size to account for EOS
indices: (bsz x output_beam_size)
the indices of the chosen elements
beams: (bsz x output_beam_size)
the hypothesis ids of the chosen elements, in the range [0, input_beam_size)
"""
raise NotImplementedError
@torch.jit.export
def set_src_lengths(self, src_lengths):
self.src_lengths = src_lengths
@torch.jit.export
def init_constraints(self, batch_constraints: Optional[Tensor], beam_size: int):
"""Initialize constraint states for constrained decoding (if supported).
Args:
batch_constraints: (torch.Tensor, optional)
the list of constraints, in packed form
beam_size: (int)
the beam size
Returns:
*encoder_out* rearranged according to *new_order*
"""
pass
def prune_sentences(self, batch_idxs: Tensor):
"""
Removes constraint states for completed sentences (if supported).
This is called from sequence_generator._generate() when sentences are
deleted from the batch.
Args:
batch_idxs: Indices of *sentences* whose constraint state should be *kept*.
"""
pass
def update_constraints(self, active_hypos: Tensor):
"""
Updates the constraint states by selecting the beam items that are retained.
This is called at each time step of sequence_generator._generate() when
the set of 2 * {beam_size} candidate hypotheses are reduced to the beam size.
Args:
active_hypos: (batch size, beam size)
list of integers denoting, for each sentence, which beam candidate items
should be kept.
"""
pass
class BeamSearch(Search):
def __init__(self, tgt_dict):
super().__init__(tgt_dict)
self.constraint_states = None
@torch.jit.export
def step(
self,
step: int,
lprobs,
scores: Optional[Tensor],
prev_output_tokens: Optional[Tensor] = None,
original_batch_idxs: Optional[Tensor] = None,
):
bsz, beam_size, vocab_size = lprobs.size()
if step == 0:
# at the first step all hypotheses are equally likely, so use
# only the first beam
lprobs = lprobs[:, ::beam_size, :].contiguous()
else:
# make probs contain cumulative scores for each hypothesis
assert scores is not None
lprobs = lprobs + scores[:, :, step - 1].unsqueeze(-1)
top_prediction = torch.topk(
lprobs.view(bsz, -1),
k=min(
# Take the best 2 x beam_size predictions. We'll choose the first
# beam_size of these which don't predict eos to continue with.
beam_size * 2,
lprobs.view(bsz, -1).size(1) - 1, # -1 so we never select pad
),
)
scores_buf = top_prediction[0]
indices_buf = top_prediction[1]
# Project back into relative indices and beams
beams_buf = indices_buf // vocab_size
indices_buf = indices_buf.fmod(vocab_size)
# At this point, beams_buf and indices_buf are single-dim and contain relative indices
return scores_buf, indices_buf, beams_buf
class PrefixConstrainedBeamSearch(Search):
def __init__(self, tgt_dict, prefix_allowed_tokens_fn):
super().__init__(tgt_dict)
self.prefix_allowed_tokens_fn = prefix_allowed_tokens_fn
self.stop_on_max_len = True
@torch.jit.export
def apply_mask(self, x, prev_output_tokens, original_batch_idxs):
beam_size = x.shape[0] // original_batch_idxs.shape[0]
original_batch_idxs = (
original_batch_idxs.unsqueeze(-1).repeat((1, beam_size)).flatten().tolist()
)
mask = torch.full_like(x, -math.inf)
for sent_i, (sent, batch_i) in enumerate(
zip(prev_output_tokens, original_batch_idxs)
):
mask[sent_i, :, self.prefix_allowed_tokens_fn(batch_i, sent)] = 0
return mask
@torch.jit.export
def step(
self,
step: int,
lprobs: Tensor,
scores: Tensor,
prev_output_tokens: Tensor,
original_batch_idxs: Tensor,
):
bsz, beam_size, vocab_size = lprobs.size()
lprobs += self.apply_mask(
lprobs.view(bsz * beam_size, 1, vocab_size),
prev_output_tokens,
original_batch_idxs,
).view(bsz, beam_size, vocab_size)
if step == 0:
# at the first step all hypotheses are equally likely, so use
# only the first beam
lprobs = lprobs[:, ::beam_size, :].contiguous()
else:
# make probs contain cumulative scores for each hypothesis
assert scores is not None
lprobs = lprobs + scores[:, :, step - 1].unsqueeze(-1)
top_prediction = torch.topk(
lprobs.view(bsz, -1),
k=min(
# Take the best beam_size predictions. We'll choose the first
# beam_size of these which don't predict eos to continue with.
beam_size,
lprobs.view(bsz, -1).size(1) - 1, # -1 so we never select pad
),
)
scores_buf = top_prediction[0]
indices_buf = top_prediction[1]
beams_buf = indices_buf // vocab_size
indices_buf = indices_buf.fmod(vocab_size)
return scores_buf, indices_buf, beams_buf
class LexicallyConstrainedBeamSearch(Search):
"""Implements lexically constrained beam search as described in
Fast Lexically Constrained Decoding with Dynamic Beam
Allocation for Neural Machine Translation. Post & Vilar,
NAACL 2018. https://www.aclweb.org/anthology/N18-1119/
and
Improved Lexically Constrained Decoding for Translation and
Monolingual Rewriting. Hu et al, NAACL
2019. https://www.aclweb.org/anthology/N19-1090/
This is accomplished by maintaining, for each beam hypothesis, a
ConstraintState object (see constraints.py) that tracks which
constraints have been generated and using this information to
shape the beam for each input sentence.
"""
def __init__(self, tgt_dict, representation):
super().__init__(tgt_dict)
self.representation = representation
self.vocab_size = len(tgt_dict)
self.num_cands = 0
self.supports_constraints = True
@torch.jit.export
def init_constraints(self, batch_constraints: Optional[Tensor], beam_size: int):
self.constraint_states = []
for constraint_tensor in batch_constraints:
if self.representation == "ordered":
constraint_state = OrderedConstraintState.create(constraint_tensor)
elif self.representation == "unordered":
constraint_state = UnorderedConstraintState.create(constraint_tensor)
self.constraint_states.append([constraint_state for i in range(beam_size)])
@torch.jit.export
def prune_sentences(self, batch_idxs: Tensor):
self.constraint_states = [
self.constraint_states[i] for i in batch_idxs.tolist()
]
@torch.jit.export
def update_constraints(self, active_hypos: Tensor):
if self.constraint_states:
batch_size = active_hypos.size(0)
for sentid in range(batch_size):
self.constraint_states[sentid] = [
self.constraint_states[sentid][i] for i in active_hypos[sentid]
]
@torch.jit.export
def step(
self,
step: int,
lprobs: Tensor,
scores: Optional[Tensor],
prev_output_tokens: Optional[Tensor] = None,
original_batch_idxs: Optional[Tensor] = None,
):
"""
A constrained step builds a large candidates list from the following:
- the top 2 * {beam_size} items over the whole beam
- for each item in the beam
- the top {each_k} (default 1)
- all next constraints
We then compute the constrained state of each beam item, and assign
stripe codes: 0 to the best in each bank, 1 to the 2nd-best, and so
on. We then sort by (stripe, score), and truncate the list at
2 * beam size.
Args:
step: the decoder step
lprobs: (batch size, beam size, target vocab)
the target-vocab distributions for each item in the beam.
Retrun: A tuple of (scores, indices, beams, constraints) where:
scores: (batch, output beam size)
the scores of the chosen elements
indices: (batch, output beam size)
the target vocab indices of the chosen elements
beams: (batch, output beam size)
the 0-indexed hypothesis ids of the chosen elements
constraints: (batch, output beam size)
the new constraint states
"""
each_k = 1
device = lprobs.device
batch_size, beam_size, vocab_size = lprobs.size()
self.num_cands = min(
# Just take the k-best. We'll get another k from the 1-best from each
# row, plus more from the constraints
beam_size * 2,
lprobs.view(batch_size, -1).size(1) - 1, # -1 so we never select pad
)
# STEP 0: Preliminary. Prevent EOS for unfinished hyps across all batch items
constraint_states = self.constraint_states
if constraint_states and step > 0:
not_finished_indices = []
for sentno, sent_constraints in enumerate(constraint_states):
for beamno, state in enumerate(sent_constraints):
index = sentno * beam_size + beamno
if not state.finished:
not_finished_indices.append(index)
not_finished_indices = torch.tensor(not_finished_indices)
if not_finished_indices.numel() > 0:
lprobs.view(batch_size * beam_size, -1)[
not_finished_indices, self.eos
] = -math.inf
if step == 0:
# at the first step all hypotheses are equally likely, so use
# only the first beam entry for each batch item
lprobs = lprobs[:, ::beam_size, :].contiguous()
else:
# make probs contain cumulative scores for each hypothesis
assert scores is not None
lprobs = lprobs + scores[:, :, step - 1].unsqueeze(-1)
top_prediction = torch.topk(
lprobs.view(batch_size, -1),
self.num_cands,
)
scores_buf, indices_buf = top_prediction
# Project back into relative indices and beams
beams_buf = indices_buf // vocab_size
indices_buf = indices_buf.fmod(vocab_size)
# Short circuit if there are no constraints in this batch
if not constraint_states:
return scores_buf, indices_buf, beams_buf
# STEP 1: get top-1 from each hypothesis across all sentences in the batch
if step > 0:
top_scores, top_indices = torch.topk(
lprobs.view(batch_size * beam_size, -1),
k=each_k,
dim=1,
)
top_scores = top_scores.view(batch_size, -1)
top_indices = top_indices.view(batch_size, -1)
scores_buf = torch.cat((scores_buf, top_scores), dim=1)
indices_buf = torch.cat((indices_buf, top_indices), dim=1)
new_beams = torch.arange(0, beam_size, device=device).repeat(batch_size, 1)
beams_buf = torch.cat((beams_buf, new_beams), dim=1)
# Now, process sentences in the batch one by one.
new_scores_buf = torch.zeros((batch_size, 2 * beam_size), device=device)
new_indices_buf = torch.zeros((batch_size, 2 * beam_size), device=device).long()
new_beams_buf = torch.zeros((batch_size, 2 * beam_size), device=device).long()
for sentno, states in enumerate(constraint_states):
scores, indices, beams, new_states = self.step_sentence(
step,
sentno,
lprobs[sentno],
constraint_states[sentno],
beams_buf[sentno].clone(),
indices_buf[sentno].clone(),
scores_buf[sentno].clone(),
)
new_scores_buf[sentno] = scores
new_indices_buf[sentno] = indices
new_beams_buf[sentno] = beams
self.constraint_states[sentno] = new_states
return new_scores_buf, new_indices_buf, new_beams_buf
@torch.jit.export
def step_sentence(
self,
step: int,
sentno: int,
lprobs: Tensor,
constraint_states: List[List[ConstraintState]],
beams_buf: Tensor,
indices_buf: Tensor,
scores_buf: Tensor,
):
"""Does per-sentence processing. Adds all constraints for each
hypothesis to the list of candidates; then removes duplicates,
sorts, and dynamically stripes across the banks. All tensor inputs
are collapsed to those pertaining to a single input sentence.
"""
device = lprobs.device
# STEP 2: Add all constraints for each beam item
for beamno, state in enumerate(constraint_states):
next_tokens = torch.tensor(list(state.next_tokens()), device=device).long()
if next_tokens.numel() != 0:
indices_buf = torch.cat((indices_buf, next_tokens))
next_beams = (
torch.tensor(beamno, device=device)
.repeat(next_tokens.size(0))
.long()
)
beams_buf = torch.cat((beams_buf, next_beams))
next_values = lprobs[beamno].take(next_tokens.view(-1))
scores_buf = torch.cat((scores_buf, next_values))
# At the 0th time step, there is just one beam item
if step == 0:
break
# STEP 3: Compute the "bank" for each candidate. This is the
# number of constraints it's generated. We need this so that
# we can do round-robin allocation of the beam across these
# banks. If C is the number of constraints, we select the best
# item in bank C, then the best in bank C-1, etc, followed by
# the 2nd-best in bank C, the 2nd-best in bank C-1, etc, and so
# on, until the maximum beam size. We accomplish this by
# creating a sort key and striping across the banks.
# Compute the new states for all candidates
cands_size = indices_buf.size(0)
constraint_states = [
constraint_states[beams_buf[i]].advance(indices_buf[i])
for i in range(cands_size)
]
banks = torch.tensor([state.bank for state in constraint_states], device=device)
# STEP 4: Sort
num_constraint_tokens = len(state.tokens)
# Sort by keys (bank, score) (i.e., sort banks together, and scores
# within banks). AFAIK pytorch doesn't support either stable sort or
# multi-key sorting, so we have to hack this.
MAX_SCORE = -100
sort_key = (num_constraint_tokens - banks) * MAX_SCORE + scores_buf
sort_values, sort_indices = sort_key.sort(dim=0, descending=True)
scores_buf = scores_buf[sort_indices]
indices_buf = indices_buf[sort_indices]
beams_buf = beams_buf[sort_indices]
banks = banks[sort_indices]
# Sort the constraints to follow suit
constraint_states = [constraint_states[i] for i in sort_indices]
# STEP 5: Remove duplicates. The topk calls (overall and
# per-row) plus the per-row generation of constraints will
# produce duplicates. Here we remove them.
def roll(t):
"""Rolls a 1d tensor left by 1.
[0, 1, 2, 3, 4] becomes [4, 0, 1, 2, 3]
"""
return torch.cat((t[-1].unsqueeze(0), t[0:-1]), dim=0)
# We map candidates (beam, token_id) to a single dimension.
# This is then shifted by 1. We can then easily identify
# duplicates and create a mask that identifies unique
# extensions.
uniques_mask = beams_buf * (self.vocab_size + 1) + indices_buf
uniques_mask = roll(uniques_mask) != uniques_mask
# Use the mask to pare down the data structures
scores_buf = torch.masked_select(scores_buf, uniques_mask)
indices_buf = torch.masked_select(indices_buf, uniques_mask)
beams_buf = torch.masked_select(beams_buf, uniques_mask)
banks = torch.masked_select(banks, uniques_mask)
i = 1
for mask in uniques_mask[1:]:
if not mask:
constraint_states.pop(i)
i += mask
# STEP 6: Assign IDs round-robin across banks, sort, and
# truncate. Now that the candidates are sorted by (bank,
# score) and uniqed, we dynamically allocate the {beam_size}
# beam by striping across the candidates. These stripes will
# be used as sort keys to do round-robin selection. This is
# accomplished in a single pass with offsets. Sorting by
# highest-banks (furthest-along hypotheses) first ensures
# progress through the constraints.
#
# e.g., BANKS: 3 3 3 2 2 2 2 1 1 1 0 0
# OLD STRIPES: 0 1 2 0 1 2 3 0 1 2 0 1
# NEW STRIPES: 0 1+4 2+8 0+1 1+5 2+9 3+11 0+2 1+6 2+10 0+3 1+7
# = 0 5 10 1 6 11 13 2 7 12 3 8
#
# Sorting by this then gives the following banks:
#
# 3 2 1 0 3 2 1 0 3 2 1 2
#
# We'll take the top {beam_size} of these.
stripe_offsets = [offset * (len(banks) + 1) for offset in range(len(banks) + 1)]
stripes = torch.zeros_like(banks)
cur_bank_count = -1
cur_bank = banks[0]
for i, bank in enumerate(banks):
if bank != cur_bank:
cur_bank_count = 0
cur_bank = bank
else:
cur_bank_count += 1
stripes[i] = num_constraint_tokens - bank + stripe_offsets[cur_bank_count]
# STEP 7: Sort by the stripes values
sort_values, sort_indices = stripes.sort(dim=0)
scores_buf = scores_buf[sort_indices]
indices_buf = indices_buf[sort_indices]
beams_buf = beams_buf[sort_indices]
constraint_states = [constraint_states[i] for i in sort_indices]
# STEP 8: Truncate to the candidates size!
scores_buf = scores_buf[: self.num_cands]
indices_buf = indices_buf[: self.num_cands]
beams_buf = beams_buf[: self.num_cands]
return scores_buf, indices_buf, beams_buf, constraint_states
class LengthConstrainedBeamSearch(Search):
def __init__(self, tgt_dict, min_len_a, min_len_b, max_len_a, max_len_b):
super().__init__(tgt_dict)
self.min_len_a = min_len_a
self.min_len_b = min_len_b
self.max_len_a = max_len_a
self.max_len_b = max_len_b
self.beam = BeamSearch(tgt_dict)
self.needs_src_lengths = True
def step(
self,
step: int,
lprobs,
scores,
prev_output_tokens: Optional[Tensor] = None,
original_batch_idxs: Optional[Tensor] = None,
):
min_lens = self.min_len_a * self.src_lengths + self.min_len_b
max_lens = self.max_len_a * self.src_lengths + self.max_len_b
lprobs[step < min_lens, :, self.eos] = -math.inf
lprobs[step >= max_lens, :, self.eos] = 0
return self.beam.step(step, lprobs, scores)
class DiverseBeamSearch(Search):
"""Diverse Beam Search.
See "Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence
Models" for details.
We only implement the Hamming Diversity penalty here, which performed best
in the original paper.
"""
def __init__(self, tgt_dict, num_groups, diversity_strength):
super().__init__(tgt_dict)
self.num_groups = num_groups
self.diversity_strength = -diversity_strength
self.beam = BeamSearch(tgt_dict)
@torch.jit.export
def step(
self,
step: int,
lprobs,
scores,
prev_output_tokens: Optional[Tensor] = None,
original_batch_idxs: Optional[Tensor] = None,
):
bsz, beam_size, vocab_size = lprobs.size()
if beam_size % self.num_groups != 0:
raise ValueError(
"DiverseBeamSearch requires --beam to be divisible by the number of groups"
)
# initialize diversity penalty
diversity_buf = torch.zeros(lprobs[:, 0, :].size()).to(lprobs)
scores_G, indices_G, beams_G = [], [], []
for g in range(self.num_groups):
lprobs_g = lprobs[:, g :: self.num_groups, :]
scores_g = scores[:, g :: self.num_groups, :] if step > 0 else None
# apply diversity penalty
if g > 0:
lprobs_g = torch.add(
lprobs_g,
other=diversity_buf.unsqueeze(1),
alpha=self.diversity_strength,
)
else:
lprobs_g = lprobs_g.contiguous()
scores_buf, indices_buf, beams_buf = self.beam.step(
step, lprobs_g, scores_g
)
beams_buf.mul_(self.num_groups).add_(g)
scores_G.append(scores_buf.clone())
indices_G.append(indices_buf.clone())
beams_G.append(beams_buf.clone())
# update diversity penalty
diversity_buf.scatter_add_(
1, indices_buf, torch.ones(indices_buf.size()).to(diversity_buf)
)
# interleave results from different groups
scores_buf = torch.stack(scores_G, dim=2).view(bsz, -1)
indices_buf = torch.stack(indices_G, dim=2).view(bsz, -1)
beams_buf = torch.stack(beams_G, dim=2).view(bsz, -1)
return scores_buf, indices_buf, beams_buf
class Sampling(Search):
sampling_topk: int
sampling_topp: float
def __init__(self, tgt_dict, sampling_topk=-1, sampling_topp=-1.0):
super().__init__(tgt_dict)
self.sampling_topk = sampling_topk
self.sampling_topp = sampling_topp
def _sample_topp(self, lprobs):
"""Sample among the smallest set of elements whose cumulative probability mass exceeds p.
See `"The Curious Case of Neural Text Degeneration"
(Holtzman et al., 2019) <https://arxiv.org/abs/1904.09751>`_.
Args:
lprobs: (bsz x input_beam_size x vocab_size)
the model's log-probabilities over the vocabulary at the current step
Return: A tuple of (trimed_probs, truncated_indices) where:
trimed_probs: (bsz x input_beam_size x ?)
the model's probabilities over the elements selected to sample from. The
width of the third dimension is determined by top-P.
truncated_indices: (bsz x input_beam_size x ?)
the indices of the chosen elements.
"""
probs = lprobs.exp_()
# sort the last dimension (vocab dimension) in descending order
sorted_probs, sorted_indices = probs.sort(descending=True)
# compute a mask to indicate the words to be included in the top-P set.
cumsum_probs = sorted_probs.cumsum(dim=2)
mask = cumsum_probs.lt(self.sampling_topp)
# note that mask was computed by 'lt'. One more word needs to be included
# so that the cumulative probability mass can exceed p.
cumsum_mask = mask.cumsum(dim=2)
last_included = cumsum_mask[:, :, -1:]
last_included.clamp_(0, mask.size()[2] - 1)
mask = mask.scatter_(2, last_included, 1)
# truncate unnecessary dims.
max_dim = last_included.max()
truncated_mask = mask[:, :, : max_dim + 1]
truncated_probs = sorted_probs[:, :, : max_dim + 1]
truncated_indices = sorted_indices[:, :, : max_dim + 1]
# trim the words that are not in top-P by setting their probabilities
# to 0, so that they would not be sampled later.
trim_mask = ~truncated_mask
trimed_probs = truncated_probs.masked_fill_(trim_mask, 0)
return trimed_probs, truncated_indices
@torch.jit.export
def step(
self,
step: int,
lprobs,
scores,
prev_output_tokens: Optional[Tensor] = None,
original_batch_idxs: Optional[Tensor] = None,
):
bsz, beam_size, vocab_size = lprobs.size()
if step == 0:
# at the first step all hypotheses are equally likely, so use
# only the first beam
lprobs = lprobs[:, ::beam_size, :].contiguous()
if self.sampling_topp > 0:
# only sample from the smallest set of words whose cumulative probability mass exceeds p
probs, top_indices = self._sample_topp(lprobs)
elif self.sampling_topk > 0:
# only sample from top-k candidates
lprobs, top_indices = lprobs.topk(self.sampling_topk)
probs = lprobs.exp_()
else:
probs = lprobs.exp_()
# dummy data to be consistent with true branch for type check
top_indices = torch.empty(0).to(probs)
# sample
if step == 0:
indices_buf = torch.multinomial(
probs.view(bsz, -1),
beam_size,
replacement=True,
).view(bsz, beam_size)
else:
indices_buf = torch.multinomial(
probs.view(bsz * beam_size, -1),
1,
replacement=True,
).view(bsz, beam_size)
if step == 0:
# expand to beam size
probs = probs.expand(bsz, beam_size, -1)
# gather scores
scores_buf = torch.gather(probs, dim=2, index=indices_buf.unsqueeze(-1))
scores_buf = scores_buf.log_().view(bsz, -1)
# remap indices if using top-k or top-P sampling
if self.sampling_topk > 0 or self.sampling_topp > 0:
indices_buf = torch.gather(
top_indices.expand(bsz, beam_size, -1),
dim=2,
index=indices_buf.unsqueeze(-1),
).squeeze(2)
if step == 0:
beams_buf = indices_buf.new_zeros(bsz, beam_size)
else:
beams_buf = torch.arange(0, beam_size).to(indices_buf).repeat(bsz, 1)
# make scores cumulative
scores_buf.add_(
torch.gather(scores[:, :, step - 1], dim=1, index=beams_buf)
)
return scores_buf, indices_buf, beams_buf
class DiverseSiblingsSearch(Search):
"""
Beam search with diverse siblings.
See "A Simple, Fast Diverse Decoding Algorithm for Neural Generation" for details.
https://arxiv.org/abs/1611.08562
1/ Calculate hypotheses for each beam
2/ Intra-sibling ordering
3/ Rewrite scores
4/ Choose top K hypotheses
if diversity_rate == 0 is equivalent to BeamSearch
"""
def __init__(self, tgt_dict, diversity_rate):
super().__init__(tgt_dict)
self.diversity_rate = diversity_rate
self.beam = BeamSearch(tgt_dict)
def step(
self,
step: int,
lprobs,
scores,
prev_output_tokens: Optional[Tensor] = None,
original_batch_idxs: Optional[Tensor] = None,
):
bsz, beam_size, vocab_size = lprobs.size()
k = min(
# Take the best 2 x beam_size predictions. We'll choose the first
# beam_size of these which don't predict eos to continue with.
beam_size * 2,
lprobs.view(bsz, -1).size(1) - 1, # -1 so we never select pad
)
s_list: List[Tensor]
i_list: List[Tensor]
s_list = [torch.empty(0).to(lprobs) for i in range(beam_size)]
i_list = [torch.LongTensor().to(device=lprobs.device) for i in range(beam_size)]
sibling_score = torch.arange(1, k + 1).to(lprobs) * self.diversity_rate
if step == 0:
return self.beam.step(step, lprobs, scores)
lprobs.add_(scores[:, :, step - 1].unsqueeze(-1))
# 1/ Calculate hypotheses for each beam
for i in range(beam_size):
torch.topk(lprobs[:, i, :].view(bsz, -1), k, out=(s_list[i], i_list[i]))
i_list[i].fmod_(vocab_size)
# 2/ Intra-sibling ordering by default from topk + 3/ Rewrite scores
s_list[i].sub_(sibling_score)
# 4/ Choose top K hypotheses
indices = torch.stack(i_list, dim=1).view(bsz, -1)
final_scores = torch.empty(0).to(lprobs)
final_indices = torch.LongTensor().to(device=lprobs.device)
final_beams = torch.LongTensor().to(device=lprobs.device)
(final_scores, final_indices) = torch.topk(
torch.stack(s_list, dim=1).view(bsz, -1),
k,
)
final_beams = final_indices // k
for i in range(bsz):
final_indices[i] = indices[i][final_indices[i]]
return final_scores, final_indices, final_beams
| 31,337 | 37.451534 | 100 | py |
CIF-HieraDist | CIF-HieraDist-main/fairseq/nan_detector.py | # 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 logging
import torch
logger = logging.getLogger(__name__)
class NanDetector:
"""
Detects the first NaN or Inf in forward and/or backward pass and logs, together with the module name
"""
def __init__(self, model, forward=True, backward=True):
self.bhooks = []
self.fhooks = []
self.forward = forward
self.backward = backward
self.named_parameters = list(model.named_parameters())
self.reset()
for name, mod in model.named_modules():
mod.__module_name = name
self.add_hooks(mod)
def __enter__(self):
return self
def __exit__(self, exc_type, exc_value, exc_traceback):
# Dump out all model gnorms to enable better debugging
norm = {}
gradients = {}
for name, param in self.named_parameters:
if param.grad is not None:
grad_norm = torch.norm(param.grad.data, p=2, dtype=torch.float32)
norm[name] = grad_norm.item()
if torch.isnan(grad_norm).any() or torch.isinf(grad_norm).any():
gradients[name] = param.grad.data
if len(gradients) > 0:
logger.info("Detected nan/inf grad norm, dumping norms...")
logger.info(f"norms: {norm}")
logger.info(f"gradients: {gradients}")
self.close()
def add_hooks(self, module):
if self.forward:
self.fhooks.append(module.register_forward_hook(self.fhook_fn))
if self.backward:
self.bhooks.append(module.register_backward_hook(self.bhook_fn))
def reset(self):
self.has_printed_f = False
self.has_printed_b = False
def _detect(self, tensor, name, backward):
err = None
if (
torch.is_floating_point(tensor)
# single value tensors (like the loss) will not provide much info
and tensor.numel() >= 2
):
with torch.no_grad():
if torch.isnan(tensor).any():
err = "NaN"
elif torch.isinf(tensor).any():
err = "Inf"
if err is not None:
err = f"{err} detected in output of {name}, shape: {tensor.shape}, {'backward' if backward else 'forward'}"
return err
def _apply(self, module, inp, x, backward):
if torch.is_tensor(x):
if isinstance(inp, tuple) and len(inp) > 0:
inp = inp[0]
err = self._detect(x, module.__module_name, backward)
if err is not None:
if torch.is_tensor(inp) and not backward:
err += (
f" input max: {inp.max().item()}, input min: {inp.min().item()}"
)
has_printed_attr = "has_printed_b" if backward else "has_printed_f"
logger.warning(err)
setattr(self, has_printed_attr, True)
elif isinstance(x, dict):
for v in x.values():
self._apply(module, inp, v, backward)
elif isinstance(x, list) or isinstance(x, tuple):
for v in x:
self._apply(module, inp, v, backward)
def fhook_fn(self, module, inp, output):
if not self.has_printed_f:
self._apply(module, inp, output, backward=False)
def bhook_fn(self, module, inp, output):
if not self.has_printed_b:
self._apply(module, inp, output, backward=True)
def close(self):
for hook in self.fhooks + self.bhooks:
hook.remove()
| 3,755 | 33.458716 | 119 | py |
CIF-HieraDist | CIF-HieraDist-main/fairseq/speech_generator.py | # 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 torch
import numpy as np
from fairseq.data.audio.speech_to_text_dataset import S2TDataConfig
class SpeechGenerator(object):
def __init__(self, model, vocoder, data_cfg: S2TDataConfig):
self.model = model
self.vocoder = vocoder
stats_npz_path = data_cfg.global_cmvn_stats_npz
self.gcmvn_stats = None
if stats_npz_path is not None:
self.gcmvn_stats = np.load(stats_npz_path)
def gcmvn_denormalize(self, x):
# x: B x T x C
if self.gcmvn_stats is None:
return x
mean = torch.from_numpy(self.gcmvn_stats["mean"]).to(x)
std = torch.from_numpy(self.gcmvn_stats["std"]).to(x)
assert len(x.shape) == 3 and mean.shape[0] == std.shape[0] == x.shape[2]
x = x * std.view(1, 1, -1).expand_as(x)
return x + mean.view(1, 1, -1).expand_as(x)
def get_waveform(self, feat):
# T x C -> T
return None if self.vocoder is None else self.vocoder(feat).squeeze(0)
class AutoRegressiveSpeechGenerator(SpeechGenerator):
def __init__(
self,
model,
vocoder,
data_cfg,
max_iter: int = 6000,
eos_prob_threshold: float = 0.5,
):
super().__init__(model, vocoder, data_cfg)
self.max_iter = max_iter
self.eos_prob_threshold = eos_prob_threshold
@torch.no_grad()
def generate(self, model, sample, has_targ=False, **kwargs):
model.eval()
src_tokens = sample["net_input"]["src_tokens"]
src_lengths = sample["net_input"]["src_lengths"]
bsz, src_len = src_tokens.size()
n_frames_per_step = model.decoder.n_frames_per_step
out_dim = model.decoder.out_dim
raw_dim = out_dim // n_frames_per_step
# initialize
encoder_out = model.forward_encoder(
src_tokens, src_lengths, speaker=sample["speaker"]
)
incremental_state = {}
feat, attn, eos_prob = [], [], []
finished = src_tokens.new_zeros((bsz,)).bool()
out_lens = src_lengths.new_zeros((bsz,)).long().fill_(self.max_iter)
prev_feat_out = encoder_out["encoder_out"][0].new_zeros(bsz, 1, out_dim)
for step in range(self.max_iter):
cur_out_lens = out_lens.clone()
cur_out_lens.masked_fill_(cur_out_lens.eq(self.max_iter), step + 1)
_, cur_eos_out, cur_extra = model.forward_decoder(
prev_feat_out,
encoder_out=encoder_out,
incremental_state=incremental_state,
target_lengths=cur_out_lens,
speaker=sample["speaker"],
**kwargs
)
cur_eos_prob = torch.sigmoid(cur_eos_out).squeeze(2)
feat.append(cur_extra["feature_out"])
attn.append(cur_extra["attn"])
eos_prob.append(cur_eos_prob)
cur_finished = cur_eos_prob.squeeze(1) > self.eos_prob_threshold
out_lens.masked_fill_((~finished) & cur_finished, step + 1)
finished = finished | cur_finished
if finished.sum().item() == bsz:
break
prev_feat_out = cur_extra["feature_out"]
feat = torch.cat(feat, dim=1)
feat = model.decoder.postnet(feat) + feat
eos_prob = torch.cat(eos_prob, dim=1)
attn = torch.cat(attn, dim=2)
alignment = attn.max(dim=1)[1]
feat = feat.reshape(bsz, -1, raw_dim)
feat = self.gcmvn_denormalize(feat)
eos_prob = eos_prob.repeat_interleave(n_frames_per_step, dim=1)
attn = attn.repeat_interleave(n_frames_per_step, dim=2)
alignment = alignment.repeat_interleave(n_frames_per_step, dim=1)
out_lens = out_lens * n_frames_per_step
finalized = [
{
"feature": feat[b, :out_len],
"eos_prob": eos_prob[b, :out_len],
"attn": attn[b, :, :out_len],
"alignment": alignment[b, :out_len],
"waveform": self.get_waveform(feat[b, :out_len]),
}
for b, out_len in zip(range(bsz), out_lens)
]
if has_targ:
assert sample["target"].size(-1) == out_dim
tgt_feats = sample["target"].view(bsz, -1, raw_dim)
tgt_feats = self.gcmvn_denormalize(tgt_feats)
tgt_lens = sample["target_lengths"] * n_frames_per_step
for b, (f, l) in enumerate(zip(tgt_feats, tgt_lens)):
finalized[b]["targ_feature"] = f[:l]
finalized[b]["targ_waveform"] = self.get_waveform(f[:l])
return finalized
class NonAutoregressiveSpeechGenerator(SpeechGenerator):
@torch.no_grad()
def generate(self, model, sample, has_targ=False, **kwargs):
model.eval()
bsz, max_src_len = sample["net_input"]["src_tokens"].size()
n_frames_per_step = model.encoder.n_frames_per_step
out_dim = model.encoder.out_dim
raw_dim = out_dim // n_frames_per_step
feat, feat_post, out_lens, log_dur_out, _, _ = model(
src_tokens=sample["net_input"]["src_tokens"],
src_lengths=sample["net_input"]["src_lengths"],
prev_output_tokens=sample["net_input"]["prev_output_tokens"],
incremental_state=None,
target_lengths=sample["target_lengths"],
speaker=sample["speaker"],
)
if feat_post is not None:
feat = feat_post
feat = feat.view(bsz, -1, raw_dim)
feat = self.gcmvn_denormalize(feat)
dur_out = torch.clamp(torch.round(torch.exp(log_dur_out) - 1).long(), min=0)
def get_dur_plot_data(d):
r = []
for i, dd in enumerate(d):
r += [i + 1] * dd.item()
return r
out_lens = out_lens * n_frames_per_step
finalized = [
{
"feature": feat[b, :l] if l > 0 else feat.new_zeros([1, raw_dim]),
"waveform": self.get_waveform(
feat[b, :l] if l > 0 else feat.new_zeros([1, raw_dim])
),
"attn": feat.new_tensor(get_dur_plot_data(dur_out[b])),
}
for b, l in zip(range(bsz), out_lens)
]
if has_targ:
tgt_feats = sample["target"].view(bsz, -1, raw_dim)
tgt_feats = self.gcmvn_denormalize(tgt_feats)
tgt_lens = sample["target_lengths"] * n_frames_per_step
for b, (f, l) in enumerate(zip(tgt_feats, tgt_lens)):
finalized[b]["targ_feature"] = f[:l]
finalized[b]["targ_waveform"] = self.get_waveform(f[:l])
return finalized
class TeacherForcingAutoRegressiveSpeechGenerator(AutoRegressiveSpeechGenerator):
@torch.no_grad()
def generate(self, model, sample, has_targ=False, **kwargs):
model.eval()
src_tokens = sample["net_input"]["src_tokens"]
src_lens = sample["net_input"]["src_lengths"]
prev_out_tokens = sample["net_input"]["prev_output_tokens"]
tgt_lens = sample["target_lengths"]
n_frames_per_step = model.decoder.n_frames_per_step
raw_dim = model.decoder.out_dim // n_frames_per_step
bsz = src_tokens.shape[0]
feat, eos_prob, extra = model(
src_tokens,
src_lens,
prev_out_tokens,
incremental_state=None,
target_lengths=tgt_lens,
speaker=sample["speaker"],
)
attn = extra["attn"] # B x T_s x T_t
alignment = attn.max(dim=1)[1]
feat = feat.reshape(bsz, -1, raw_dim)
feat = self.gcmvn_denormalize(feat)
eos_prob = eos_prob.repeat_interleave(n_frames_per_step, dim=1)
attn = attn.repeat_interleave(n_frames_per_step, dim=2)
alignment = alignment.repeat_interleave(n_frames_per_step, dim=1)
tgt_lens = sample["target_lengths"] * n_frames_per_step
finalized = [
{
"feature": feat[b, :tgt_len],
"eos_prob": eos_prob[b, :tgt_len],
"attn": attn[b, :, :tgt_len],
"alignment": alignment[b, :tgt_len],
"waveform": self.get_waveform(feat[b, :tgt_len]),
}
for b, tgt_len in zip(range(bsz), tgt_lens)
]
if has_targ:
tgt_feats = sample["target"].view(bsz, -1, raw_dim)
tgt_feats = self.gcmvn_denormalize(tgt_feats)
for b, (f, l) in enumerate(zip(tgt_feats, tgt_lens)):
finalized[b]["targ_feature"] = f[:l]
finalized[b]["targ_waveform"] = self.get_waveform(f[:l])
return finalized
| 8,836 | 37.090517 | 84 | py |
CIF-HieraDist | CIF-HieraDist-main/fairseq/iterative_refinement_generator.py | # 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.
from collections import namedtuple
import numpy as np
import torch
from fairseq import utils
DecoderOut = namedtuple(
"IterativeRefinementDecoderOut",
["output_tokens", "output_scores", "attn", "step", "max_step", "history"],
)
class IterativeRefinementGenerator(object):
def __init__(
self,
tgt_dict,
models=None,
eos_penalty=0.0,
max_iter=10,
max_ratio=2,
beam_size=1,
decoding_format=None,
retain_dropout=False,
adaptive=True,
retain_history=False,
reranking=False,
):
"""
Generates translations based on iterative refinement.
Args:
tgt_dict: target dictionary
eos_penalty: if > 0.0, it penalized early-stopping in decoding
max_iter: maximum number of refinement iterations
max_ratio: generate sequences of maximum length ax, where x is the source length
decoding_format: decoding mode in {'unigram', 'ensemble', 'vote', 'dp', 'bs'}
retain_dropout: retaining dropout in the inference
adaptive: decoding with early stop
"""
self.bos = tgt_dict.bos()
self.pad = tgt_dict.pad()
self.unk = tgt_dict.unk()
self.eos = tgt_dict.eos()
self.vocab_size = len(tgt_dict)
self.eos_penalty = eos_penalty
self.max_iter = max_iter
self.max_ratio = max_ratio
self.beam_size = beam_size
self.reranking = reranking
self.decoding_format = decoding_format
self.retain_dropout = retain_dropout
self.retain_history = retain_history
self.adaptive = adaptive
self.models = models
def generate_batched_itr(
self,
data_itr,
maxlen_a=None,
maxlen_b=None,
cuda=False,
timer=None,
prefix_size=0,
):
"""Iterate over a batched dataset and yield individual translations.
Args:
maxlen_a/b: generate sequences of maximum length ax + b,
where x is the source sentence length.
cuda: use GPU for generation
timer: StopwatchMeter for timing generations.
"""
for sample in data_itr:
if "net_input" not in sample:
continue
if timer is not None:
timer.start()
with torch.no_grad():
hypos = self.generate(
self.models,
sample,
prefix_tokens=sample["target"][:, :prefix_size]
if prefix_size > 0
else None,
)
if timer is not None:
timer.stop(sample["ntokens"])
for i, id in enumerate(sample["id"]):
# remove padding
src = utils.strip_pad(sample["net_input"]["src_tokens"][i, :], self.pad)
ref = utils.strip_pad(sample["target"][i, :], self.pad)
yield id, src, ref, hypos[i]
@torch.no_grad()
def generate(self, models, sample, prefix_tokens=None, constraints=None):
if constraints is not None:
raise NotImplementedError(
"Constrained decoding with the IterativeRefinementGenerator is not supported"
)
# TODO: iterative refinement generator does not support ensemble for now.
if not self.retain_dropout:
for model in models:
model.eval()
model, reranker = models[0], None
if self.reranking:
assert len(models) > 1, "Assuming the last checkpoint is the reranker"
assert (
self.beam_size > 1
), "Reranking requires multiple translation for each example"
reranker = models[-1]
models = models[:-1]
if len(models) > 1 and hasattr(model, "enable_ensemble"):
assert model.allow_ensemble, "{} does not support ensembling".format(
model.__class__.__name__
)
model.enable_ensemble(models)
# TODO: better encoder inputs?
src_tokens = sample["net_input"]["src_tokens"]
src_lengths = sample["net_input"]["src_lengths"]
bsz, src_len = src_tokens.size()
# initialize
encoder_out = model.forward_encoder([src_tokens, src_lengths])
prev_decoder_out = model.initialize_output_tokens(encoder_out, src_tokens)
if self.beam_size > 1:
assert (
model.allow_length_beam
), "{} does not support decoding with length beam.".format(
model.__class__.__name__
)
# regenerate data based on length-beam
length_beam_order = (
utils.new_arange(src_tokens, self.beam_size, bsz).t().reshape(-1)
)
encoder_out = model.encoder.reorder_encoder_out(
encoder_out, length_beam_order
)
prev_decoder_out = model.regenerate_length_beam(
prev_decoder_out, self.beam_size
)
bsz = bsz * self.beam_size
sent_idxs = torch.arange(bsz)
prev_output_tokens = prev_decoder_out.output_tokens.clone()
if self.retain_history:
prev_decoder_out = prev_decoder_out._replace(history=[prev_output_tokens])
finalized = [[] for _ in range(bsz)]
def is_a_loop(x, y, s, a):
b, l_x, l_y = x.size(0), x.size(1), y.size(1)
if l_x > l_y:
y = torch.cat([y, x.new_zeros(b, l_x - l_y).fill_(self.pad)], 1)
s = torch.cat([s, s.new_zeros(b, l_x - l_y)], 1)
if a is not None:
a = torch.cat([a, a.new_zeros(b, l_x - l_y, a.size(2))], 1)
elif l_x < l_y:
x = torch.cat([x, y.new_zeros(b, l_y - l_x).fill_(self.pad)], 1)
return (x == y).all(1), y, s, a
def finalized_hypos(step, prev_out_token, prev_out_score, prev_out_attn):
cutoff = prev_out_token.ne(self.pad)
tokens = prev_out_token[cutoff]
if prev_out_score is None:
scores, score = None, None
else:
scores = prev_out_score[cutoff]
score = scores.mean()
if prev_out_attn is None:
hypo_attn, alignment = None, None
else:
hypo_attn = prev_out_attn[cutoff]
alignment = hypo_attn.max(dim=1)[1]
return {
"steps": step,
"tokens": tokens,
"positional_scores": scores,
"score": score,
"hypo_attn": hypo_attn,
"alignment": alignment,
}
for step in range(self.max_iter + 1):
decoder_options = {
"eos_penalty": self.eos_penalty,
"max_ratio": self.max_ratio,
"decoding_format": self.decoding_format,
}
prev_decoder_out = prev_decoder_out._replace(
step=step,
max_step=self.max_iter + 1,
)
decoder_out = model.forward_decoder(
prev_decoder_out, encoder_out, **decoder_options
)
if self.adaptive:
# terminate if there is a loop
terminated, out_tokens, out_scores, out_attn = is_a_loop(
prev_output_tokens,
decoder_out.output_tokens,
decoder_out.output_scores,
decoder_out.attn,
)
decoder_out = decoder_out._replace(
output_tokens=out_tokens,
output_scores=out_scores,
attn=out_attn,
)
else:
terminated = decoder_out.output_tokens.new_zeros(
decoder_out.output_tokens.size(0)
).bool()
if step == self.max_iter: # reach last iteration, terminate
terminated.fill_(1)
# collect finalized sentences
finalized_idxs = sent_idxs[terminated]
finalized_tokens = decoder_out.output_tokens[terminated]
finalized_scores = decoder_out.output_scores[terminated]
finalized_attn = (
None
if (decoder_out.attn is None or decoder_out.attn.size(0) == 0)
else decoder_out.attn[terminated]
)
if self.retain_history:
finalized_history_tokens = [h[terminated] for h in decoder_out.history]
for i in range(finalized_idxs.size(0)):
finalized[finalized_idxs[i]] = [
finalized_hypos(
step,
finalized_tokens[i],
finalized_scores[i],
None if finalized_attn is None else finalized_attn[i],
)
]
if self.retain_history:
finalized[finalized_idxs[i]][0]["history"] = []
for j in range(len(finalized_history_tokens)):
finalized[finalized_idxs[i]][0]["history"].append(
finalized_hypos(
step, finalized_history_tokens[j][i], None, None
)
)
# check if all terminated
if terminated.sum() == terminated.size(0):
break
# for next step
not_terminated = ~terminated
prev_decoder_out = decoder_out._replace(
output_tokens=decoder_out.output_tokens[not_terminated],
output_scores=decoder_out.output_scores[not_terminated],
attn=decoder_out.attn[not_terminated]
if (decoder_out.attn is not None and decoder_out.attn.size(0) > 0)
else None,
history=[h[not_terminated] for h in decoder_out.history]
if decoder_out.history is not None
else None,
)
encoder_out = model.encoder.reorder_encoder_out(
encoder_out, not_terminated.nonzero(as_tuple=False).squeeze()
)
sent_idxs = sent_idxs[not_terminated]
prev_output_tokens = prev_decoder_out.output_tokens.clone()
if self.beam_size > 1:
if reranker is not None:
finalized = self.rerank(
reranker, finalized, [src_tokens, src_lengths], self.beam_size
)
# aggregate information from length beam
finalized = [
finalized[
np.argmax(
[
finalized[self.beam_size * i + j][0]["score"]
for j in range(self.beam_size)
]
)
+ self.beam_size * i
]
for i in range(len(finalized) // self.beam_size)
]
return finalized
def rerank(self, reranker, finalized, encoder_input, beam_size):
def rebuild_batch(finalized):
finalized_tokens = [f[0]["tokens"] for f in finalized]
finalized_maxlen = max(f.size(0) for f in finalized_tokens)
final_output_tokens = (
finalized_tokens[0]
.new_zeros(len(finalized_tokens), finalized_maxlen)
.fill_(self.pad)
)
for i, f in enumerate(finalized_tokens):
final_output_tokens[i, : f.size(0)] = f
return final_output_tokens
final_output_tokens = rebuild_batch(finalized)
final_output_tokens[
:, 0
] = self.eos # autoregressive model assumes starting with EOS
reranker_encoder_out = reranker.encoder(*encoder_input)
length_beam_order = (
utils.new_arange(
final_output_tokens, beam_size, reranker_encoder_out.encoder_out.size(1)
)
.t()
.reshape(-1)
)
reranker_encoder_out = reranker.encoder.reorder_encoder_out(
reranker_encoder_out, length_beam_order
)
reranking_scores = reranker.get_normalized_probs(
reranker.decoder(final_output_tokens[:, :-1], reranker_encoder_out),
True,
None,
)
reranking_scores = reranking_scores.gather(2, final_output_tokens[:, 1:, None])
reranking_masks = final_output_tokens[:, 1:].ne(self.pad)
reranking_scores = (
reranking_scores[:, :, 0].masked_fill_(~reranking_masks, 0).sum(1)
)
reranking_scores = reranking_scores / reranking_masks.sum(1).type_as(
reranking_scores
)
for i in range(len(finalized)):
finalized[i][0]["score"] = reranking_scores[i]
return finalized
| 13,238 | 35.775 | 93 | py |
CIF-HieraDist | CIF-HieraDist-main/fairseq/trainer.py | # 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.
"""
Train a network across multiple GPUs.
"""
import contextlib
import logging
import sys
import time
from argparse import Namespace
from itertools import chain
from typing import Any, Dict, List
import torch
from fairseq import checkpoint_utils, models, optim, utils
from fairseq.dataclass.configs import FairseqConfig
from fairseq.dataclass.utils import convert_namespace_to_omegaconf
from fairseq.distributed import utils as distributed_utils
from fairseq.file_io import PathManager
from fairseq.logging import meters, metrics
from fairseq.models.ema import build_ema
from fairseq.nan_detector import NanDetector
from fairseq.optim import lr_scheduler
from omegaconf import OmegaConf
logger = logging.getLogger(__name__)
class Trainer(object):
"""Main class for data parallel training.
This class supports synchronous distributed data parallel training,
where multiple workers each have a full model replica and gradients
are accumulated across workers before each update. We use
:class:`~torch.nn.parallel.DistributedDataParallel` to handle
communication of the gradients across workers.
"""
def __init__(self, cfg: FairseqConfig, task, model, criterion, quantizer=None):
if isinstance(cfg, Namespace):
logger.warning(
"argparse.Namespace configuration is deprecated! Automatically converting to OmegaConf"
)
cfg = convert_namespace_to_omegaconf(cfg)
self.cfg = cfg
self.task = task
# catalog shared parameters
shared_params = _catalog_shared_params(model)
self.tpu = cfg.common.tpu
self.cuda = torch.cuda.is_available() and not cfg.common.cpu and not self.tpu
if self.cuda:
self.device = torch.device("cuda")
elif self.tpu:
self.device = utils.get_tpu_device()
else:
self.device = torch.device("cpu")
if self.is_fsdp:
import fairscale
if self.cfg.common.bf16:
raise ValueError(
"FullyShardedDataParallel is not compatible with --bf16 or "
"--memory-efficient-bf16"
)
if self.cfg.distributed_training.zero_sharding != "none":
raise ValueError(
"FullyShardedDataParallel is not compatible with --zero-sharding "
"option (it's already built in)"
)
if (
max(self.cfg.optimization.update_freq) > 1
and fairscale.__version__ < "0.4.0"
):
raise RuntimeError(
"Please update to fairscale 0.4.0 or newer when combining "
"--update-freq with FullyShardedDataParallel"
)
else:
if (
hasattr(self.cfg.distributed_training, "cpu_offload")
and self.cfg.distributed_training.cpu_offload
):
raise ValueError("--cpu-offload requires --ddp-backend=fully_sharded")
# copy model and criterion to current device/dtype
self._criterion = criterion
self._model = model
if not self.is_fsdp:
if cfg.common.fp16:
assert not cfg.common.amp, "Cannot use fp16 and AMP together"
self._criterion = self._criterion.half()
self._model = self._model.half()
elif cfg.common.bf16:
self._criterion = self._criterion.to(dtype=torch.bfloat16)
self._model = self._model.to(dtype=torch.bfloat16)
elif cfg.common.amp:
self._amp_retries = 0
if (
not cfg.distributed_training.pipeline_model_parallel
# the DistributedFairseqModel wrapper will handle moving to device,
# so only handle cases which don't use the wrapper
and not self.use_distributed_wrapper
):
self._criterion = self._criterion.to(device=self.device)
self._model = self._model.to(device=self.device)
self.pipeline_model_parallel = cfg.distributed_training.pipeline_model_parallel
self.last_device = None
if self.cuda and self.pipeline_model_parallel:
self.last_device = torch.device(
cfg.distributed_training.pipeline_devices[-1]
)
# check that shared parameters are preserved after device transfer
for shared_param in shared_params:
ref = _get_module_by_path(self._model, shared_param[0])
for path in shared_param[1:]:
logger.info(
"detected shared parameter: {} <- {}".format(shared_param[0], path)
)
_set_module_by_path(self._model, path, ref)
self._dummy_batch = None # indicates we don't have a dummy batch at first
self._lr_scheduler = None
self._num_updates = 0
self._num_xla_compiles = 0 # for TPUs
self._optim_history = None
self._optimizer = None
self._warn_once = set()
self._wrapped_criterion = None
self._wrapped_model = None
self._ema = None
# TODO(myleott): support tpu
if self.cuda and self.data_parallel_world_size > 1:
self._grad_norm_buf = torch.cuda.DoubleTensor(self.data_parallel_world_size)
else:
self._grad_norm_buf = None
self.quantizer = quantizer
if self.quantizer is not None:
self.quantizer.set_trainer(self)
# get detailed cuda environment
if self.cuda:
self.cuda_env = utils.CudaEnvironment()
if self.data_parallel_world_size > 1:
self.cuda_env_arr = distributed_utils.all_gather_list(
self.cuda_env, group=distributed_utils.get_global_group()
)
else:
self.cuda_env_arr = [self.cuda_env]
if self.data_parallel_rank == 0:
utils.CudaEnvironment.pretty_print_cuda_env_list(self.cuda_env_arr)
else:
self.cuda_env = None
self.cuda_env_arr = None
metrics.log_start_time("wall", priority=790, round=0)
self._start_time = time.time()
self._previous_training_time = 0
self._cumulative_training_time = None
def reinitialize(self):
"""Reinitialize the Trainer, typically after model params change."""
self._lr_scheduler = None
self._optimizer = None
self._wrapped_criterion = None
self._wrapped_model = None
@property
def data_parallel_world_size(self):
if self.cfg.distributed_training.distributed_world_size == 1:
return 1
return distributed_utils.get_data_parallel_world_size()
@property
def data_parallel_process_group(self):
return distributed_utils.get_data_parallel_group()
@property
def data_parallel_rank(self):
if self.cfg.distributed_training.distributed_world_size == 1:
return 0
return distributed_utils.get_data_parallel_rank()
@property
def is_data_parallel_master(self):
# NOTE: this returns true for all model parallel replicas with data
# parallel rank 0
return self.data_parallel_rank == 0
@property
def use_distributed_wrapper(self) -> bool:
return (
self.data_parallel_world_size > 1 and not self.cfg.optimization.use_bmuf
) or (self.is_fsdp and self.cfg.distributed_training.cpu_offload)
@property
def should_save_checkpoint_on_current_rank(self) -> bool:
"""Indicates whether to save checkpoints on the current DDP rank."""
if (
self.is_fsdp and self.cfg.distributed_training.use_sharded_state
) or getattr(self.cfg.model, "base_layers", 0) > 0:
return True
else:
return self.is_data_parallel_master
@property
def always_call_state_dict_during_save_checkpoint(self) -> bool:
if self.is_fsdp and not self.cfg.distributed_training.use_sharded_state:
# FSDP calls communication collective when consolidating checkpoints
return True
else:
return False
@property
def checkpoint_suffix(self) -> str:
"""Suffix to add to the checkpoint file name."""
if self.is_fsdp and self.cfg.distributed_training.use_sharded_state:
return self.cfg.checkpoint.checkpoint_suffix + "-shard{0}".format(
self.data_parallel_rank
)
else:
return self.cfg.checkpoint.checkpoint_suffix or ""
@property
def criterion(self):
if self._wrapped_criterion is None:
if utils.has_parameters(self._criterion) and self.use_distributed_wrapper:
self._wrapped_criterion = models.DistributedFairseqModel(
self.cfg.distributed_training,
self._criterion,
process_group=self.data_parallel_process_group,
device=self.device,
)
else:
self._wrapped_criterion = self._criterion
return self._wrapped_criterion
@property
def model(self):
if self._wrapped_model is None:
if self.use_distributed_wrapper:
self._wrapped_model = models.DistributedFairseqModel(
self.cfg.distributed_training,
self._model,
process_group=self.data_parallel_process_group,
device=self.device,
)
else:
self._wrapped_model = self._model
return self._wrapped_model
@property
def ema(self):
if self._ema is None:
self._build_ema()
return self._ema
def _build_ema(self):
if self.cfg.ema.store_ema:
self._ema = build_ema(self._model, self.cfg.ema, self.device)
logger.info("Exponential Moving Average Shadow Model is initialized.")
@property
def optimizer(self):
if self._optimizer is None:
self._build_optimizer()
return self._optimizer
@property
def lr_scheduler(self):
if self._lr_scheduler is None:
self._build_optimizer() # this will initialize self._lr_scheduler
return self._lr_scheduler
def _build_optimizer(self):
params = list(
filter(
lambda p: p.requires_grad,
chain(self.model.parameters(), self.criterion.parameters()),
)
)
if self.is_fsdp and self.cfg.common.fp16:
# FullyShardedDataParallel always uses MemoryEfficientFP16 wrapper,
# mostly for the grad scaling. But if we don't have the
# --memory-efficient-fp16 flag set, then we're effectively doing
# regular --fp16 and can allow the use of optimizers that would
# otherwise be unsupported by MemoryEfficientFP16Optimizer.
allow_unsupported = not self.cfg.common.memory_efficient_fp16
self._optimizer = optim.MemoryEfficientFP16Optimizer.build_optimizer(
self.cfg, params, allow_unsupported=allow_unsupported
)
elif self.cfg.common.fp16 or self.cfg.common.bf16 or self.cfg.common.amp:
if self.cuda and torch.cuda.get_device_capability(0)[0] < 7:
logger.info(
"NOTE: your device does NOT support faster training with --fp16 or --amp, "
"please switch to FP32 which is likely to be faster"
)
if (
self.cfg.common.memory_efficient_fp16
or self.cfg.common.memory_efficient_bf16
):
self._optimizer = optim.MemoryEfficientFP16Optimizer.build_optimizer(
self.cfg, params
)
elif self.cfg.common.amp:
self._optimizer = optim.AMPOptimizer.build_optimizer(self.cfg, params)
else:
self._optimizer = optim.FP16Optimizer.build_optimizer(self.cfg, params)
else:
if self.cuda and torch.cuda.get_device_capability(0)[0] >= 7:
logger.info(
"NOTE: your device may support faster training with --fp16 or --amp"
)
self._optimizer = optim.build_optimizer(self.cfg.optimizer, params)
if self.is_fsdp:
assert (
not self.cfg.optimization.use_bmuf
), "--ddp-backend=fully_sharded is not compatible with BMUF"
assert self._optimizer.supports_flat_params, (
"--ddp-backend=fully_sharded is only compatible with pointwise "
"optimizers (e.g., Adam, AdamW, Adadelta, Adamax, SGD, etc.). "
"However, the sharding will result in slightly different results when "
"using non-pointwise optimizers (e.g., Adagrad, Adafactor, LAMB)"
)
if self.cfg.optimization.use_bmuf:
self._optimizer = optim.FairseqBMUF(
self.cfg.bmuf,
self._optimizer,
)
if self.cfg.distributed_training.zero_sharding == "os":
if (
self.cfg.common.fp16
and not self.cfg.common.memory_efficient_fp16
and not self.cfg.common.memory_efficient_bf16
) and not self.cfg.common.fp16_no_flatten_grads:
raise ValueError(
"ZeRO is incomptabile with fp16 and flattened grads. "
"Please use --fp16-no-flatten-grads"
)
else:
optim.shard_(self._optimizer, self.data_parallel_process_group)
# We should initialize the learning rate scheduler immediately after
# building the optimizer, so that the initial learning rate is set.
self._lr_scheduler = lr_scheduler.build_lr_scheduler(
self.cfg.lr_scheduler,
self.optimizer,
)
self._lr_scheduler.step_update(0)
@property
def is_fsdp(self):
return self.cfg.distributed_training.ddp_backend == "fully_sharded"
def consolidate_optimizer(self):
"""For OSS, we need to consolidate the state dict."""
if self.cfg.checkpoint.no_save_optimizer_state:
return
self._gathered_optim_state = None
if hasattr(self.optimizer.optimizer, "consolidate_state_dict"):
self.optimizer.optimizer.consolidate_state_dict()
elif self.is_fsdp and not self.model.use_sharded_state:
st = self.model.gather_full_optim_state_dict(
self.optimizer
) # only returns on rank 0
self._gathered_optim_state = st
def state_dict(self):
state_dict = {
"args": None, # legacy
"cfg": (
OmegaConf.to_container(self.cfg, resolve=True, enum_to_str=True)
if OmegaConf.is_config(self.cfg)
else self.cfg
),
"model": self.model.state_dict(),
"criterion": (
self.criterion.state_dict()
if utils.has_parameters(self.criterion)
else None
),
"optimizer_history": (self._optim_history or [])
+ [
{
"criterion_name": self.get_criterion().__class__.__name__,
"optimizer_name": self.optimizer.__class__.__name__,
"lr_scheduler_state": self.lr_scheduler.state_dict(),
"num_updates": self.get_num_updates(),
}
],
"task_state": self.task.state_dict() if self.task is not None else {},
"extra_state": {
"metrics": metrics.state_dict(),
"previous_training_time": self.cumulative_training_time(),
},
}
if self.cfg.ema.store_ema:
# Save EMA model state as extra state
state_dict["extra_state"]["ema"] = self.ema.get_model().state_dict()
if self.cfg.ema.ema_fp32:
# Save EMA params in fp32
state_dict["extra_state"]["ema_fp32_params"] = self.ema.fp32_params
if not self.cfg.checkpoint.no_save_optimizer_state:
if self._gathered_optim_state is not None:
state_dict["last_optimizer_state"] = self._gathered_optim_state
self._gathered_optim_state = None
else:
state_dict["last_optimizer_state"] = self.optimizer.state_dict()
if self.is_fsdp:
# save meta data for recombining checkpoint upon loading
state_dict["fsdp_metadata"] = self.model.local_metadata_dict()
return state_dict
def save_checkpoint(self, filename, extra_state):
"""Save all training state in a checkpoint file."""
logger.info(f"Saving checkpoint to {filename}")
# call state_dict on all ranks in case it needs internal communication
state_dict = utils.move_to_cpu(self.state_dict())
state_dict["extra_state"].update(extra_state)
if self.should_save_checkpoint_on_current_rank:
checkpoint_utils.torch_persistent_save(
state_dict,
filename,
async_write=self.cfg.checkpoint.write_checkpoints_asynchronously,
)
logger.info(f"Finished saving checkpoint to {filename}")
def load_checkpoint(
self,
filename,
reset_optimizer=False,
reset_lr_scheduler=False,
optimizer_overrides=None,
reset_meters=False,
):
"""
Load all training state from a checkpoint file.
rank = 0 will load the checkpoint, and then broadcast it to all
other ranks.
"""
extra_state, self._optim_history, last_optim_state = None, [], None
logger.info(f"Preparing to load checkpoint {filename}")
is_distributed = self.data_parallel_world_size > 1
bexists = PathManager.isfile(filename)
if bexists:
load_on_all_ranks = (
self.cfg.checkpoint.load_checkpoint_on_all_dp_ranks
# TPUs don't support broadcast yet, so load checkpoints
# on every worker for now
or self.tpu
# FSDP requires loading checkpoint shards on all ranks
or (self.is_fsdp and self.cfg.distributed_training.use_sharded_state)
or getattr(self.cfg.model, "base_layers", 0) > 0
)
if load_on_all_ranks or self.data_parallel_rank == 0:
state = checkpoint_utils.load_checkpoint_to_cpu(
filename, load_on_all_ranks=load_on_all_ranks
)
last_optim_state = state.get("last_optimizer_state", None)
# If doing zero_sharding, do not broadcast global optimizer
# state. Later we will broadcast sharded states to each rank
# to avoid memory from exploding.
if (
not load_on_all_ranks
and self.cfg.distributed_training.zero_sharding == "os"
and "last_optimizer_state" in state
and is_distributed
):
state["last_optimizer_state"] = "SHARDED"
else:
last_optim_state = None
state = None
if is_distributed and not load_on_all_ranks:
state = distributed_utils.broadcast_object(
state,
src_rank=0,
group=self.data_parallel_process_group,
dist_device=self.device,
)
if self.data_parallel_rank > 0:
last_optim_state = state.get("last_optimizer_state", None)
# load model parameters
try:
self.model.load_state_dict(
state["model"], strict=True, model_cfg=self.cfg.model
)
# save memory for later steps
del state["model"]
if utils.has_parameters(self.get_criterion()):
self.get_criterion().load_state_dict(
state["criterion"], strict=True
)
del state["criterion"]
except Exception:
raise Exception(
"Cannot load model parameters from checkpoint {}; "
"please ensure that the architectures match.".format(filename)
)
extra_state = state["extra_state"]
self._optim_history = state["optimizer_history"]
if last_optim_state is not None and not reset_optimizer:
# rebuild optimizer after loading model, since params may have changed
self._build_optimizer()
# only reload optimizer and lr_scheduler if they match
last_optim = self._optim_history[-1]
assert (
last_optim["criterion_name"] == self.get_criterion().__class__.__name__
), f"Criterion does not match; please reset the optimizer (--reset-optimizer). {last_optim['criterion_name']} vs {self.get_criterion().__class__.__name__}"
assert (
last_optim["optimizer_name"] == self.optimizer.__class__.__name__
), f"Optimizer does not match; please reset the optimizer (--reset-optimizer). {last_optim['optimizer_name']} vs {self.optimizer.__class__.__name__}"
if not reset_lr_scheduler:
self.lr_scheduler.load_state_dict(last_optim["lr_scheduler_state"])
if self.is_fsdp and not self.model.use_sharded_state:
# if use_sharded_state, the last_optim_state is already sharded, skip this
last_optim_state = self.model.get_shard_from_optim_state_dict(
last_optim_state
)
elif not load_on_all_ranks and is_distributed:
last_optim_state = self.optimizer.broadcast_global_state_dict(
last_optim_state
)
self.optimizer.load_state_dict(last_optim_state, optimizer_overrides)
self.set_num_updates(last_optim["num_updates"])
if extra_state is not None:
itr_state = extra_state["train_iterator"]
epoch = itr_state["epoch"]
if "previous_training_time" in extra_state:
self._previous_training_time = extra_state["previous_training_time"]
self._start_time = time.time()
self.lr_step(epoch)
if (
itr_state.get("version", 1) >= 2
and itr_state["iterations_in_epoch"] == 0
):
# reset meters at start of epoch
reset_meters = True
if "metrics" in extra_state and not reset_meters:
metrics.load_state_dict(extra_state["metrics"])
# reset TimeMeters, since their start times don't make sense anymore
for meter in metrics.get_meters("default"):
if isinstance(meter, meters.TimeMeter):
meter.reset()
if self.cfg.ema.store_ema:
if "ema" not in extra_state:
logger.warn(
"EMA not found in checkpoint. But store_ema is True. "
"EMA is re-initialized from checkpoint."
)
self.ema.restore(
state["model"], build_fp32_params=self.cfg.ema.ema_fp32
)
else:
logger.info("Loading EMA from checkpoint")
self.ema.restore(extra_state["ema"], build_fp32_params=False)
if self.cfg.ema.ema_fp32:
if "ema_fp32_params" in extra_state:
logger.info("Loading EMA fp32 params from checkpoint")
self.ema.build_fp32_params(extra_state["ema_fp32_params"])
else:
logger.info(
"Building EMA fp32 params from EMA model in checkpoint"
)
self.ema.build_fp32_params()
logger.info(
"Loaded checkpoint {} (epoch {} @ {} updates)".format(
filename, epoch, self.get_num_updates()
)
)
else:
logger.info("No existing checkpoint found {}".format(filename))
return extra_state
def get_train_iterator(
self,
epoch,
combine=True,
load_dataset=True,
data_selector=None,
shard_batch_itr=True,
disable_iterator_cache=False,
):
"""Return an EpochBatchIterator over the training set for a given epoch."""
if load_dataset:
logger.info("loading train data for epoch {}".format(epoch))
self.task.load_dataset(
self.cfg.dataset.train_subset,
epoch=epoch,
combine=combine,
data_selector=data_selector,
tpu=self.tpu,
)
batch_iterator = self.task.get_batch_iterator(
dataset=self.task.dataset(self.cfg.dataset.train_subset),
max_tokens=self.cfg.dataset.max_tokens,
max_sentences=self.cfg.dataset.batch_size,
max_positions=utils.resolve_max_positions(
self.task.max_positions(),
self.model.max_positions(),
self.cfg.dataset.max_tokens,
),
ignore_invalid_inputs=True,
required_batch_size_multiple=self.cfg.dataset.required_batch_size_multiple,
seed=(self.cfg.common.seed + epoch)
if self.cfg.dataset.update_ordered_indices_seed
else self.cfg.common.seed,
num_shards=self.data_parallel_world_size if shard_batch_itr else 1,
shard_id=self.data_parallel_rank if shard_batch_itr else 0,
num_workers=self.cfg.dataset.num_workers,
epoch=epoch,
data_buffer_size=self.cfg.dataset.data_buffer_size,
disable_iterator_cache=disable_iterator_cache,
grouped_shuffling=self.cfg.dataset.grouped_shuffling,
update_epoch_batch_itr=self.cfg.dataset.update_epoch_batch_itr,
)
self.reset_dummy_batch(batch_iterator.first_batch)
return batch_iterator
def get_valid_iterator(
self,
subset,
disable_iterator_cache=False,
):
"""Return an EpochBatchIterator over given validation subset for a given epoch."""
batch_iterator = self.task.get_batch_iterator(
dataset=self.task.dataset(subset),
max_tokens=self.cfg.dataset.max_tokens_valid,
max_sentences=self.cfg.dataset.batch_size_valid,
max_positions=utils.resolve_max_positions(
self.task.max_positions(),
self.model.max_positions(),
),
ignore_invalid_inputs=self.cfg.dataset.skip_invalid_size_inputs_valid_test,
required_batch_size_multiple=self.cfg.dataset.required_batch_size_multiple,
seed=self.cfg.common.seed,
num_shards=self.data_parallel_world_size,
shard_id=self.data_parallel_rank,
num_workers=self.cfg.dataset.num_workers,
# always pass a fixed "epoch" to keep validation data consistent
# across training epochs
epoch=1,
data_buffer_size=self.cfg.dataset.data_buffer_size,
disable_iterator_cache=disable_iterator_cache,
)
self.reset_dummy_batch(batch_iterator.first_batch)
return batch_iterator
def begin_epoch(self, epoch):
"""Called at the beginning of each epoch."""
logger.info("begin training epoch {}".format(epoch))
self.lr_step_begin_epoch(epoch)
if self.quantizer is not None:
self.quantizer.begin_epoch(epoch)
# task specific setup per epoch
self.task.begin_epoch(epoch, self.get_model())
if self.tpu:
import torch_xla.core.xla_model as xm
xm.rendezvous("begin_epoch") # wait for all workers
xm.mark_step()
def begin_valid_epoch(self, epoch):
"""Called at the beginning of each validation epoch."""
# task specific setup per validation epoch
self.task.begin_valid_epoch(epoch, self.get_model())
def reset_dummy_batch(self, batch):
self._dummy_batch = batch
@metrics.aggregate("train")
def train_step(self, samples, raise_oom=False):
"""Do forward, backward and parameter update."""
self._set_seed()
self.model.train()
self.criterion.train()
self.zero_grad()
metrics.log_start_time("train_wall", priority=800, round=0)
# If EMA is enabled through store_ema=True
# and task.uses_ema is True, pass the EMA model as a keyword
# argument to the task.
extra_kwargs = {}
if self.cfg.ema.store_ema and getattr(self.task, "uses_ema", False):
extra_kwargs["ema_model"] = self.ema.get_model()
# forward and backward pass
logging_outputs, sample_size, ooms = [], 0, 0
for i, sample in enumerate(samples): # delayed update loop
sample, is_dummy_batch = self._prepare_sample(sample)
def maybe_no_sync():
"""
Whenever *samples* contains more than one mini-batch, we
want to accumulate gradients locally and only call
all-reduce in the last backwards pass.
"""
if (
self.data_parallel_world_size > 1
and hasattr(self.model, "no_sync")
and i < len(samples) - 1
# The no_sync context manager results in increased memory
# usage with FSDP, since full-size gradients will be
# accumulated on each GPU. It's typically a better tradeoff
# to do the extra communication with FSDP.
and not self.is_fsdp
):
return self.model.no_sync()
else:
return contextlib.ExitStack() # dummy contextmanager
try:
with maybe_no_sync():
# forward and backward
loss, sample_size_i, logging_output = self.task.train_step(
sample=sample,
model=self.model,
criterion=self.criterion,
optimizer=self.optimizer,
update_num=self.get_num_updates(),
ignore_grad=is_dummy_batch,
**extra_kwargs,
)
del loss
logging_outputs.append(logging_output)
sample_size += sample_size_i
# emptying the CUDA cache after the first step can
# reduce the chance of OOM
if self.cuda and self.get_num_updates() == 0:
torch.cuda.empty_cache()
except RuntimeError as e:
if "out of memory" in str(e):
self._log_oom(e)
if raise_oom:
raise e
logger.warning(
"attempting to recover from OOM in forward/backward pass"
)
ooms += 1
self.zero_grad()
if self.cuda:
torch.cuda.empty_cache()
if self.cfg.distributed_training.distributed_world_size == 1:
return None
else:
raise e
if self.tpu and i < len(samples) - 1:
# tpu-comment: every XLA operation before marking step is
# appended to the IR graph, and processing too many batches
# before marking step can lead to OOM errors.
# To handle gradient accumulation use case, we explicitly
# mark step here for every forward pass without a backward pass
self._xla_markstep_and_send_to_cpu()
if is_dummy_batch:
if torch.is_tensor(sample_size):
sample_size.zero_()
else:
sample_size *= 0.0
if torch.is_tensor(sample_size):
sample_size = sample_size.float()
else:
sample_size = float(sample_size)
# gather logging outputs from all replicas
if self._sync_stats():
train_time = self._local_cumulative_training_time()
logging_outputs, (
sample_size,
ooms,
total_train_time,
) = self._aggregate_logging_outputs(
logging_outputs, sample_size, ooms, train_time, ignore=is_dummy_batch
)
self._cumulative_training_time = (
total_train_time / self.data_parallel_world_size
)
overflow = False
try:
with torch.autograd.profiler.record_function("reduce-grads"):
# reduce gradients across workers
self.optimizer.all_reduce_grads(self.model)
if utils.has_parameters(self.criterion):
self.optimizer.all_reduce_grads(self.criterion)
with torch.autograd.profiler.record_function("multiply-grads"):
# multiply gradients by (data_parallel_size / sample_size) since
# DDP normalizes by the number of data parallel workers for
# improved fp16 precision.
# Thus we get (sum_of_gradients / sample_size) at the end.
# In case of fp16, this step also undoes loss scaling.
# (Debugging note: Some optimizers perform this scaling on the
# fly, so inspecting model.parameters() or optimizer.params may
# still show the original, unscaled gradients.)
numer = (
self.data_parallel_world_size
if not self.cfg.optimization.use_bmuf or self._sync_stats()
else 1
)
self.optimizer.multiply_grads(numer / (sample_size or 1.0))
# Note: (sample_size or 1.0) handles the case of a zero gradient, in a
# way that avoids CPU/device transfers in case sample_size is a GPU or
# TPU object. The assumption is that the gradient itself is also 0.
with torch.autograd.profiler.record_function("clip-grads"):
# clip grads
grad_norm = self.clip_grad_norm(self.cfg.optimization.clip_norm)
# check that grad norms are consistent across workers
# on tpu check tensor is slow
if not self.tpu:
if (
not self.cfg.optimization.use_bmuf
and self.cfg.distributed_training.ddp_backend != "slowmo"
):
self._check_grad_norms(grad_norm)
if not torch.isfinite(grad_norm).all():
# in case of AMP, if gradients are Nan/Inf then
# optimizer step is still required
if self.cfg.common.amp:
overflow = True
else:
# check local gradnorm single GPU case, trigger NanDetector
raise FloatingPointError("gradients are Nan/Inf")
with torch.autograd.profiler.record_function("optimizer"):
# take an optimization step
self.task.optimizer_step(
self.optimizer, model=self.model, update_num=self.get_num_updates()
)
if self.cfg.common.amp and overflow:
if self._amp_retries == self.cfg.common.amp_batch_retries:
logger.info("AMP: skipping this batch.")
self._amp_retries = 0
else:
self._amp_retries += 1
return self.train_step(
samples, raise_oom
) # recursion to feed in same batch
except FloatingPointError:
# re-run the forward and backward pass with hooks attached to print
# out where it fails
self.zero_grad()
with NanDetector(self.get_model()):
for _, sample in enumerate(samples):
sample, _ = self._prepare_sample(sample)
self.task.train_step(
sample,
self.model,
self.criterion,
self.optimizer,
self.get_num_updates(),
ignore_grad=False,
**extra_kwargs,
)
raise
except OverflowError as e:
overflow = True
logger.info(
f"NOTE: gradient overflow detected, ignoring gradient, {str(e)}"
)
grad_norm = torch.tensor(0.0).cuda()
self.zero_grad()
except RuntimeError as e:
if "out of memory" in str(e):
self._log_oom(e)
logger.error("OOM during optimization, irrecoverable")
raise e
# Some distributed wrappers (e.g., SlowMo) need access to the optimizer
# after the step
if hasattr(self.model, "perform_slowmo"):
self.model.perform_slowmo(
self.optimizer.optimizer, getattr(self.optimizer, "fp32_params", None)
)
logging_output = None
if not overflow or self.cfg.distributed_training.ddp_backend == "slowmo":
self.set_num_updates(self.get_num_updates() + 1)
# FIXME: Added by Minglun Han
# self.model.set_num_updates(self.get_num_updates() + 1)
if self.cfg.ema.store_ema:
# Step EMA forward with new model.
self.ema.step(
self.get_model(),
self.get_num_updates(),
)
metrics.log_scalar(
"ema_decay",
self.ema.get_decay(),
priority=10000,
round=5,
weight=0,
)
if self.tpu:
import torch_xla.core.xla_model as xm
# mark step on TPUs
self._xla_markstep_and_send_to_cpu()
# only log stats every log_interval steps
# this causes wps to be misreported when log_interval > 1
logging_output = {}
if self.get_num_updates() % self.cfg.common.log_interval == 0:
# log memory usage
mem_info = xm.get_memory_info(self.device)
gb_free = mem_info["kb_free"] / 1024 / 1024
gb_total = mem_info["kb_total"] / 1024 / 1024
metrics.log_scalar(
"gb_free", gb_free, priority=1500, round=1, weight=0
)
metrics.log_scalar(
"gb_total", gb_total, priority=1600, round=1, weight=0
)
logging_outputs = self._xla_markstep_and_send_to_cpu(
logging_outputs
)
logging_output = self._reduce_and_log_stats(
logging_outputs, sample_size, grad_norm
)
# log whenever there's an XLA compilation, since these
# slow down training and may indicate opportunities for
# optimization
self._check_xla_compilation()
else:
if self.cuda and self.cuda_env is not None:
# log minimum free memory over the iteration
gb_used = torch.cuda.max_memory_allocated() / 1024 / 1024 / 1024
torch.cuda.reset_peak_memory_stats()
gb_free = self.cuda_env.total_memory_in_GB - gb_used
metrics.log_scalar(
"gb_free", gb_free, priority=1500, round=1, weight=0
)
# log stats
logging_output = self._reduce_and_log_stats(
logging_outputs, sample_size, grad_norm
)
# clear CUDA cache to reduce memory fragmentation
if (
self.cuda
and self.cfg.common.empty_cache_freq > 0
and (
(self.get_num_updates() + self.cfg.common.empty_cache_freq - 1)
% self.cfg.common.empty_cache_freq
)
== 0
):
torch.cuda.empty_cache()
if self.cfg.common.fp16 or self.cfg.common.amp:
metrics.log_scalar(
"loss_scale",
(
self.optimizer.scaler.loss_scale
if self.cfg.common.fp16
else self.optimizer.scaler.get_scale()
),
priority=700,
round=4,
weight=0,
)
metrics.log_stop_time("train_wall")
return logging_output
@metrics.aggregate("valid")
def valid_step(self, sample, raise_oom=False):
"""Do forward pass in evaluation mode."""
if self.tpu:
import torch_xla.core.xla_model as xm
xm.rendezvous("valid_step") # wait for all workers
# If EMA is enabled through store_ema=True
# and task.uses_ema is True, pass the EMA model as a keyword
# argument to the task.
extra_kwargs = {}
if self.cfg.ema.store_ema and getattr(self.task, "uses_ema", False):
extra_kwargs["ema_model"] = self.ema.get_model()
with torch.no_grad():
self.model.eval()
self.criterion.eval()
sample, is_dummy_batch = self._prepare_sample(sample)
try:
_loss, sample_size, logging_output = self.task.valid_step(
sample, self.model, self.criterion, **extra_kwargs
)
except RuntimeError as e:
if "out of memory" in str(e):
self._log_oom(e)
if not raise_oom:
logger.warning(
"ran out of memory in validation step, retrying batch"
)
for p in self.model.parameters():
if p.grad is not None:
p.grad = None # free some memory
if self.cuda:
torch.cuda.empty_cache()
return self.valid_step(sample, raise_oom=True)
raise e
logging_outputs = [logging_output]
if is_dummy_batch:
if torch.is_tensor(sample_size):
sample_size.zero_()
else:
sample_size *= 0.0
# gather logging outputs from all replicas
if self.data_parallel_world_size > 1:
logging_outputs, (sample_size,) = self._aggregate_logging_outputs(
logging_outputs,
sample_size,
ignore=is_dummy_batch,
)
# log validation stats
if self.tpu:
logging_outputs = self._xla_markstep_and_send_to_cpu(logging_outputs)
logging_output = self._reduce_and_log_stats(logging_outputs, sample_size)
return logging_output
def zero_grad(self):
self.optimizer.zero_grad()
def lr_step_begin_epoch(self, epoch):
"""Adjust the learning rate at the beginning of the epoch."""
self.lr_scheduler.step_begin_epoch(epoch)
# prefer updating the LR based on the number of steps
return self.lr_step_update()
def lr_step(self, epoch, val_loss=None):
"""Adjust the learning rate at the end of the epoch."""
self.lr_scheduler.step(epoch, val_loss)
# prefer updating the LR based on the number of steps
return self.lr_step_update()
def lr_step_update(self):
"""Update the learning rate after each update."""
new_lr = self.lr_scheduler.step_update(self.get_num_updates())
if isinstance(new_lr, dict):
for k, v in new_lr.items():
metrics.log_scalar(f"lr_{k}", v, weight=0, priority=300)
new_lr = new_lr.get("default", next(iter(new_lr.values())))
else:
metrics.log_scalar("lr", new_lr, weight=0, priority=300)
return new_lr
def get_lr(self):
"""Get the current learning rate."""
return self.optimizer.get_lr()
def get_model(self):
"""Get the (non-wrapped) model instance."""
return self._model
def get_criterion(self):
"""Get the (non-wrapped) criterion instance."""
return self._criterion
def get_meter(self, name):
"""[deprecated] Get a specific meter by name."""
from fairseq import meters
if "get_meter" not in self._warn_once:
self._warn_once.add("get_meter")
utils.deprecation_warning(
"Trainer.get_meter is deprecated. Please use fairseq.metrics instead."
)
train_meters = metrics.get_meters("train")
if train_meters is None:
train_meters = {}
if name == "train_loss" and "loss" in train_meters:
return train_meters["loss"]
elif name == "train_nll_loss":
# support for legacy train.py, which assumed this meter is
# always initialized
m = train_meters.get("nll_loss", None)
return m or meters.AverageMeter()
elif name == "wall":
# support for legacy train.py, which assumed this meter is
# always initialized
m = metrics.get_meter("default", "wall")
return m or meters.TimeMeter()
elif name == "wps":
m = metrics.get_meter("train", "wps")
return m or meters.TimeMeter()
elif name in {"valid_loss", "valid_nll_loss"}:
# support for legacy train.py, which assumed these meters
# are always initialized
k = name[len("valid_") :]
m = metrics.get_meter("valid", k)
return m or meters.AverageMeter()
elif name == "oom":
return meters.AverageMeter()
elif name in train_meters:
return train_meters[name]
return None
def get_num_updates(self):
"""Get the number of parameters updates."""
return self._num_updates
def set_num_updates(self, num_updates):
"""Set the number of parameters updates."""
self._num_updates = num_updates
self.lr_step_update()
if self.quantizer:
self.quantizer.step_update(self._num_updates)
metrics.log_scalar("num_updates", self._num_updates, weight=0, priority=200)
def clip_grad_norm(self, clip_norm):
def agg_norm_fn(total_norm):
total_norm = total_norm.cuda().float() ** 2
total_norm = distributed_utils.all_reduce(
total_norm, group=self.data_parallel_process_group
)
return total_norm**0.5
should_agg_norm = self.is_fsdp and (
self.data_parallel_process_group is not None
or torch.distributed.is_initialized()
)
return self.optimizer.clip_grad_norm(
clip_norm, aggregate_norm_fn=agg_norm_fn if should_agg_norm else None
)
def cumulative_training_time(self):
if self._cumulative_training_time is None:
# single GPU
return self._local_cumulative_training_time()
else:
return self._cumulative_training_time
def _local_cumulative_training_time(self):
"""Aggregate training time in seconds."""
return time.time() - self._start_time + self._previous_training_time
def _fp_convert_sample(self, sample):
def apply_half(t):
if t.dtype is torch.float32:
return t.to(dtype=torch.half)
return t
def apply_bfloat16(t):
if t.dtype is torch.float32:
return t.to(dtype=torch.bfloat16)
return t
if self.cfg.common.fp16:
sample = utils.apply_to_sample(apply_half, sample)
if self.cfg.common.bf16:
sample = utils.apply_to_sample(apply_bfloat16, sample)
return sample
def _prepare_sample(self, sample, is_dummy=False):
if sample == "DUMMY":
raise Exception(
"Trying to use an uninitialized 'dummy' batch. This usually indicates "
"that the total number of batches is smaller than the number of "
"participating GPUs. Try reducing the batch size or using fewer GPUs."
)
if sample is None or len(sample) == 0:
assert (
self._dummy_batch is not None and len(self._dummy_batch) > 0
), "Invalid dummy batch: {}".format(self._dummy_batch)
sample, _ = self._prepare_sample(self._dummy_batch, is_dummy=True)
return sample, True
# Given that PCIe/NVLink bandwidth is significantly smaller than DRAM bandwidth
# it makes sense to do the format conversion on the CPU and then transfer
# a smaller buffer to the device. This also saves GPU memory capacity.
if self.cfg.common.on_cpu_convert_precision:
sample = self._fp_convert_sample(sample)
if self.cuda:
if self.pipeline_model_parallel:
if "target" in sample:
sample["target"] = utils.move_to_cuda(
sample["target"], device=self.last_device
)
else:
sample = utils.move_to_cuda(sample)
elif self.tpu and is_dummy:
# the dummy batch may not be on the appropriate device
sample = utils.move_to_cuda(sample, device=self.device)
if not self.cfg.common.on_cpu_convert_precision:
sample = self._fp_convert_sample(sample)
if self._dummy_batch == "DUMMY":
self._dummy_batch = sample
return sample, False
def _set_seed(self):
# Set seed based on args.seed and the update number so that we get
# reproducible results when resuming from checkpoints
seed = self.cfg.common.seed + self.get_num_updates()
utils.set_torch_seed(seed)
def _sync_stats(self):
# Return True if it's using multiple GPUs and DDP or multiple GPUs with
# BMUF and it's a bmuf sync with warmup iterations completed before.
if self.data_parallel_world_size == 1:
return False
elif self.cfg.optimization.use_bmuf:
return (
self.get_num_updates() + 1
) % self.cfg.bmuf.global_sync_iter == 0 and (
self.get_num_updates() + 1
) > self.cfg.bmuf.warmup_iterations
else:
return True
def _log_oom(self, exc):
msg = "OOM: Ran out of memory with exception: {}".format(exc)
logger.warning(msg)
if torch.cuda.is_available() and hasattr(torch.cuda, "memory_summary"):
for device_idx in range(torch.cuda.device_count()):
logger.warning(torch.cuda.memory_summary(device=device_idx))
sys.stderr.flush()
def _aggregate_logging_outputs(
self,
logging_outputs: List[Dict[str, Any]],
*extra_stats_to_sum,
ignore=False,
):
if self.task.__class__.logging_outputs_can_be_summed(self.get_criterion()):
return self._fast_stat_sync_sum(
logging_outputs, *extra_stats_to_sum, ignore=ignore
)
else:
return self._all_gather_list_sync(
logging_outputs, *extra_stats_to_sum, ignore=ignore
)
def _all_gather_list_sync(
self,
logging_outputs: List[Dict[str, Any]],
*extra_stats_to_sum,
ignore=False,
):
"""
Sync logging outputs across workers. all_gather_list_sync is
suitable when logging outputs are complex types.
"""
if self.tpu:
raise NotImplementedError
if ignore:
logging_outputs = []
results = list(
zip(
*distributed_utils.all_gather_list(
[logging_outputs] + list(extra_stats_to_sum),
max_size=getattr(self.cfg.common, "all_gather_list_size", 16384),
group=self.data_parallel_process_group,
)
)
)
logging_outputs, extra_stats_to_sum = results[0], results[1:]
logging_outputs = list(chain.from_iterable(logging_outputs))
extra_stats_to_sum = [sum(s) for s in extra_stats_to_sum]
return logging_outputs, extra_stats_to_sum
def _fast_stat_sync_sum(
self,
logging_outputs: List[Dict[str, Any]],
*extra_stats_to_sum,
ignore=False,
):
"""
Sync logging outputs across workers. fast_stat_sync_sum is
faster than all_gather_list_sync, but is only suitable when
logging outputs are scalars and can be summed. Note that
*logging_outputs* cannot contain any nested dicts/lists.
"""
data = {}
for i, stat in enumerate(extra_stats_to_sum):
data["extra_stats_" + str(i)] = stat
if len(logging_outputs) > 0:
log_keys = list(logging_outputs[0].keys())
for k in log_keys:
if not ignore:
v = sum(log[k] for log in logging_outputs if k in log)
else:
v = logging_outputs[0][k]
v = torch.zeros_like(v) if torch.is_tensor(v) else 0
data["logging_outputs_" + k] = v
else:
log_keys = None
data = distributed_utils.all_reduce_dict(
data, device=self.device, group=self.data_parallel_process_group
)
extra_stats_to_sum = [
data["extra_stats_" + str(i)] for i in range(len(extra_stats_to_sum))
]
if log_keys is not None:
logging_outputs = [{k: data["logging_outputs_" + k] for k in log_keys}]
else:
logging_outputs = []
return logging_outputs, extra_stats_to_sum
def _check_grad_norms(self, grad_norm):
"""Check that grad norms are consistent across workers."""
if self._grad_norm_buf is not None:
self._grad_norm_buf.zero_()
# print(self._grad_norm_buf)
self._grad_norm_buf[self.data_parallel_rank] = grad_norm
# print(self._grad_norm_buf)
distributed_utils.all_reduce(
self._grad_norm_buf, group=self.data_parallel_process_group
)
# print(self._grad_norm_buf)
def is_consistent(tensor):
max_abs_diff = torch.max(torch.abs(tensor - tensor[0]))
return (
(
torch.isfinite(tensor).all()
and (max_abs_diff / (tensor[0] + 1e-6) < 1e-6).all()
)
or (self.cfg.common.amp and not torch.isfinite(tensor).all())
# in case of amp non-finite grads are fine
)
if not is_consistent(self._grad_norm_buf):
pretty_detail = "\n".join(
"rank {:3d} = {:.8f}".format(r, n)
for r, n in enumerate(self._grad_norm_buf.tolist())
)
error_detail = "grad_norm across the workers:\n{}\n".format(
pretty_detail
)
# use FloatingPointError to trigger NanDetector
raise FloatingPointError(
"Fatal error: gradients are inconsistent between workers. "
"Try --ddp-backend=legacy_ddp. "
"Or are you mixing up different generation of GPUs in training?"
+ "\n"
+ "-" * 80
+ "\n{}\n".format(error_detail)
+ "-" * 80
)
def _reduce_and_log_stats(self, logging_outputs, sample_size, grad_norm=None):
if grad_norm is not None and (
not torch.is_tensor(grad_norm) or torch.isfinite(grad_norm)
):
metrics.log_speed("ups", 1.0, priority=100, round=2)
metrics.log_scalar("gnorm", grad_norm, priority=400, round=3)
if self.cfg.optimization.clip_norm > 0:
metrics.log_scalar(
"clip",
torch.where(
grad_norm > self.cfg.optimization.clip_norm,
grad_norm.new_tensor(100),
grad_norm.new_tensor(0),
),
priority=500,
round=1,
)
with metrics.aggregate() as agg:
if logging_outputs is not None:
self.task.reduce_metrics(logging_outputs, self.get_criterion())
del logging_outputs
# extra warning for criterions that don't properly log a loss value
if "loss" not in agg:
if "loss" not in self._warn_once:
self._warn_once.add("loss")
logger.warning(
"Criterion.reduce_metrics did not log a 'loss' value, "
"which may break some functionality"
)
metrics.log_scalar("loss", -1)
# support legacy interface
if self.tpu:
logging_output = {}
else:
logging_output = agg.get_smoothed_values()
logging_output["sample_size"] = sample_size
for key_to_delete in ["ppl", "wps", "wpb", "bsz"]:
if key_to_delete in logging_output:
del logging_output[key_to_delete]
return logging_output
def _check_xla_compilation(self):
import torch_xla.debug.metrics as met
compile_stats = met.metric_data("CompileTime")
if compile_stats is None:
return
num_xla_compiles = compile_stats[0]
if num_xla_compiles > self._num_xla_compiles:
logger.warning(
"XLA compilation detected on device #{}; too many of these can lead "
"to slow training, but we expect a few in the beginning".format(
self.cfg.distributed_training.distributed_rank
)
)
self._num_xla_compiles = num_xla_compiles
def _xla_markstep_and_send_to_cpu(self, data=None):
import torch_xla.core.xla_model as xm
xm.mark_step()
if data is not None:
from fairseq.utils import xla_device_to_cpu
return xla_device_to_cpu(data)
def _catalog_shared_params(module, memo=None, prefix=""):
if memo is None:
first_call = True
memo = {}
else:
first_call = False
for name, param in module._parameters.items():
param_prefix = prefix + ("." if prefix else "") + name
if param not in memo:
memo[param] = []
memo[param].append(param_prefix)
for name, m in module._modules.items():
if m is None:
continue
submodule_prefix = prefix + ("." if prefix else "") + name
_catalog_shared_params(m, memo, submodule_prefix)
if first_call:
return [x for x in memo.values() if len(x) > 1]
def _get_module_by_path(module, path):
path = path.split(".")
for name in path:
module = getattr(module, name)
return module
def _set_module_by_path(module, path, value):
path = path.split(".")
for name in path[:-1]:
module = getattr(module, name)
setattr(module, path[-1], value)
| 61,234 | 39.339262 | 167 | py |
CIF-HieraDist | CIF-HieraDist-main/fairseq/modules/transformer_sentence_encoder_layer.py | # 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.
from typing import Callable, Optional
import torch
import torch.nn as nn
from fairseq import utils
from fairseq.modules import LayerNorm, MultiheadAttention
from fairseq.modules.fairseq_dropout import FairseqDropout
from fairseq.modules.quant_noise import quant_noise
class TransformerSentenceEncoderLayer(nn.Module):
"""
Implements a Transformer Encoder Layer used in BERT/XLM style pre-trained
models.
"""
def __init__(
self,
embedding_dim: int = 768,
ffn_embedding_dim: int = 3072,
num_attention_heads: int = 8,
dropout: float = 0.1,
attention_dropout: float = 0.1,
activation_dropout: float = 0.1,
activation_fn: str = "relu",
export: bool = False,
q_noise: float = 0.0,
qn_block_size: int = 8,
init_fn: Callable = None,
) -> None:
super().__init__()
if init_fn is not None:
init_fn()
# Initialize parameters
self.embedding_dim = embedding_dim
self.num_attention_heads = num_attention_heads
self.attention_dropout = attention_dropout
self.q_noise = q_noise
self.qn_block_size = qn_block_size
self.dropout_module = FairseqDropout(
dropout, module_name=self.__class__.__name__
)
self.activation_dropout_module = FairseqDropout(
activation_dropout, module_name=self.__class__.__name__
)
# Initialize blocks
self.activation_fn = utils.get_activation_fn(activation_fn)
self.self_attn = self.build_self_attention(
self.embedding_dim,
num_attention_heads,
dropout=attention_dropout,
self_attention=True,
q_noise=q_noise,
qn_block_size=qn_block_size,
)
# layer norm associated with the self attention layer
self.self_attn_layer_norm = LayerNorm(self.embedding_dim, export=export)
self.fc1 = self.build_fc1(
self.embedding_dim,
ffn_embedding_dim,
q_noise=q_noise,
qn_block_size=qn_block_size,
)
self.fc2 = self.build_fc2(
ffn_embedding_dim,
self.embedding_dim,
q_noise=q_noise,
qn_block_size=qn_block_size,
)
# layer norm associated with the position wise feed-forward NN
self.final_layer_norm = LayerNorm(self.embedding_dim, export=export)
def build_fc1(self, input_dim, output_dim, q_noise, qn_block_size):
return quant_noise(nn.Linear(input_dim, output_dim), q_noise, qn_block_size)
def build_fc2(self, input_dim, output_dim, q_noise, qn_block_size):
return quant_noise(nn.Linear(input_dim, output_dim), q_noise, qn_block_size)
def build_self_attention(
self,
embed_dim,
num_attention_heads,
dropout,
self_attention,
q_noise,
qn_block_size,
):
return MultiheadAttention(
embed_dim,
num_attention_heads,
dropout=dropout,
self_attention=True,
q_noise=q_noise,
qn_block_size=qn_block_size,
)
def forward(
self,
x: torch.Tensor,
self_attn_mask: Optional[torch.Tensor] = None,
self_attn_padding_mask: Optional[torch.Tensor] = None,
):
"""
LayerNorm is applied either before or after the self-attention/ffn
modules similar to the original Transformer implementation.
"""
residual = x
x, attn = self.self_attn(
query=x,
key=x,
value=x,
key_padding_mask=self_attn_padding_mask,
need_weights=False,
attn_mask=self_attn_mask,
)
x = self.dropout_module(x)
x = residual + x
x = self.self_attn_layer_norm(x)
residual = x
x = self.activation_fn(self.fc1(x))
x = self.activation_dropout_module(x)
x = self.fc2(x)
x = self.dropout_module(x)
x = residual + x
x = self.final_layer_norm(x)
return x, attn
| 4,326 | 29.907143 | 84 | py |
CIF-HieraDist | CIF-HieraDist-main/fairseq/modules/learned_positional_embedding.py | # 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.
from typing import Dict, Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from torch import Tensor
class LearnedPositionalEmbedding(nn.Embedding):
"""
This module learns positional embeddings up to a fixed maximum size.
Padding ids are ignored by either offsetting based on padding_idx
or by setting padding_idx to None and ensuring that the appropriate
position ids are passed to the forward function.
"""
def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int):
super().__init__(num_embeddings, embedding_dim, padding_idx)
self.onnx_trace = False
if self.padding_idx is not None:
self.max_positions = self.num_embeddings - self.padding_idx - 1
else:
self.max_positions = self.num_embeddings
def forward(
self,
input: Tensor,
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
positions: Optional[Tensor] = None,
):
"""Input is expected to be of size [bsz x seqlen]."""
assert (positions is None) or (
self.padding_idx is None
), "If positions is pre-computed then padding_idx should not be set."
if positions is None:
if incremental_state is not None:
# positions is the same for every token when decoding a single step
# Without the int() cast, it doesn't work in some cases when exporting to ONNX
positions = torch.zeros(
(1, 1), device=input.device, dtype=input.dtype
).fill_(int(self.padding_idx + input.size(1)))
else:
positions = utils.make_positions(
input, self.padding_idx, onnx_trace=self.onnx_trace
)
return F.embedding(
positions,
self.weight,
self.padding_idx,
self.max_norm,
self.norm_type,
self.scale_grad_by_freq,
self.sparse,
)
| 2,259 | 35.451613 | 94 | py |
CIF-HieraDist | CIF-HieraDist-main/fairseq/modules/sparse_multihead_attention.py | # 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 math
import torch
from .multihead_attention import MultiheadAttention
class SparseMultiheadAttention(MultiheadAttention):
"""Sparse Multi-Headed Attention.
"Generating Long Sequences with Sparse Transformers". Implements
fixed factorized self attention, where l=stride and c=expressivity.
A(1) includes all words in the stride window and A(2) takes a summary of c
words from the end of each stride window.
If is_bidirectional=False, we do not include any words past the current word,
as in the paper.
"""
def __init__(
self,
embed_dim,
num_heads,
kdim=None,
vdim=None,
dropout=0.0,
bias=True,
add_bias_kv=False,
add_zero_attn=False,
self_attention=False,
encoder_decoder_attention=False,
stride=32,
expressivity=8,
is_bidirectional=True,
):
super().__init__(
embed_dim,
num_heads,
kdim,
vdim,
dropout,
bias,
add_bias_kv,
add_zero_attn,
self_attention,
encoder_decoder_attention,
)
self.is_bidirectional = is_bidirectional
self.stride = stride
self.expressivity = expressivity
assert self.stride > 0 and self.stride >= self.expressivity
# Used for Ai(2) calculations - beginning of [l-c, l] range
def compute_checkpoint(self, word_index):
if word_index % self.stride == 0 and word_index != 0:
checkpoint_index = word_index - self.expressivity
else:
checkpoint_index = (
math.floor(word_index / self.stride) * self.stride
+ self.stride
- self.expressivity
)
return checkpoint_index
# Computes Ai(2)
def compute_subset_summaries(self, absolute_max):
checkpoint_index = self.compute_checkpoint(0)
subset_two = set()
while checkpoint_index <= absolute_max - 1:
summary = set(
range(
checkpoint_index,
min(checkpoint_index + self.expressivity + 1, absolute_max),
)
)
subset_two = subset_two.union(summary)
checkpoint_index = self.compute_checkpoint(checkpoint_index + self.stride)
return subset_two
# Sparse Transformer Fixed Attention Pattern: https://arxiv.org/pdf/1904.10509.pdf
def compute_fixed_attention_subset(self, word_index, tgt_len):
# +1s account for range function; [min, max) -> [min, max]
if not self.is_bidirectional:
absolute_max = word_index + 1
else:
absolute_max = tgt_len
# Subset 1 - whole window
rounded_index = (
math.floor((word_index + self.stride) / self.stride) * self.stride
)
if word_index % self.stride == 0 and word_index != 0:
subset_one = set(
range(word_index - self.stride, min(absolute_max, word_index + 1))
)
else:
subset_one = set(
range(
max(0, rounded_index - self.stride),
min(absolute_max, rounded_index + 1),
)
)
# Subset 2 - summary per window
# If bidirectional, subset 2 is the same for every index
subset_two = set()
if not self.is_bidirectional:
subset_two = self.compute_subset_summaries(absolute_max)
return subset_one.union(subset_two)
# Compute sparse mask - if bidirectional, can pre-compute and store
def buffered_sparse_mask(self, tensor, tgt_len, src_len):
assert tgt_len > self.stride
sparse_mask = torch.empty((tgt_len, src_len)).float().fill_(float("-inf"))
# If bidirectional, subset 2 is the same for every index
subset_summaries = set()
if self.is_bidirectional:
subset_summaries = self.compute_subset_summaries(tgt_len)
for i in range(tgt_len):
fixed_attention_subset = self.compute_fixed_attention_subset(i, tgt_len)
fixed_attention_subset = fixed_attention_subset.union(subset_summaries)
included_word_indices = torch.LongTensor(list(fixed_attention_subset))
sparse_mask[i].index_fill_(0, included_word_indices, 0)
return sparse_mask.type_as(tensor)
def apply_sparse_mask(self, attn_weights, tgt_len, src_len, bsz):
sparse_mask = self.buffered_sparse_mask(attn_weights, tgt_len, src_len)
sparse_mask = sparse_mask.unsqueeze(0).expand(
bsz * self.num_heads, tgt_len, src_len
)
attn_weights += sparse_mask
| 4,931 | 33.978723 | 86 | py |
CIF-HieraDist | CIF-HieraDist-main/fairseq/modules/multihead_attention.py | # 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 math
from typing import Dict, Optional, Tuple
import torch
import torch.nn.functional as F
from fairseq import utils
from fairseq.incremental_decoding_utils import with_incremental_state
from fairseq.modules.fairseq_dropout import FairseqDropout
from fairseq.modules.quant_noise import quant_noise
from torch import Tensor, nn
from torch.nn import Parameter
@with_incremental_state
class MultiheadAttention(nn.Module):
"""Multi-headed attention.
See "Attention Is All You Need" for more details.
"""
def __init__(
self,
embed_dim,
num_heads,
kdim=None,
vdim=None,
dropout=0.0,
bias=True,
add_bias_kv=False,
add_zero_attn=False,
self_attention=False,
encoder_decoder_attention=False,
q_noise=0.0,
qn_block_size=8,
):
super().__init__()
self.embed_dim = embed_dim
self.kdim = kdim if kdim is not None else embed_dim
self.vdim = vdim if vdim is not None else embed_dim
self.qkv_same_dim = self.kdim == embed_dim and self.vdim == embed_dim
self.num_heads = num_heads
self.dropout_module = FairseqDropout(
dropout, module_name=self.__class__.__name__
)
self.head_dim = embed_dim // num_heads
assert (
self.head_dim * num_heads == self.embed_dim
), "embed_dim must be divisible by num_heads"
self.scaling = self.head_dim**-0.5
self.self_attention = self_attention
self.encoder_decoder_attention = encoder_decoder_attention
assert not self.self_attention or self.qkv_same_dim, (
"Self-attention requires query, key and " "value to be of the same size"
)
self.k_proj = quant_noise(
nn.Linear(self.kdim, embed_dim, bias=bias), q_noise, qn_block_size
)
self.v_proj = quant_noise(
nn.Linear(self.vdim, embed_dim, bias=bias), q_noise, qn_block_size
)
self.q_proj = quant_noise(
nn.Linear(embed_dim, embed_dim, bias=bias), q_noise, qn_block_size
)
self.out_proj = quant_noise(
nn.Linear(embed_dim, embed_dim, bias=bias), q_noise, qn_block_size
)
if add_bias_kv:
self.bias_k = Parameter(torch.Tensor(1, 1, embed_dim))
self.bias_v = Parameter(torch.Tensor(1, 1, embed_dim))
else:
self.bias_k = self.bias_v = None
self.add_zero_attn = add_zero_attn
self.reset_parameters()
self.onnx_trace = False
def prepare_for_onnx_export_(self):
self.onnx_trace = True
def reset_parameters(self):
if self.qkv_same_dim:
# Empirically observed the convergence to be much better with
# the scaled initialization
nn.init.xavier_uniform_(self.k_proj.weight, gain=1 / math.sqrt(2))
nn.init.xavier_uniform_(self.v_proj.weight, gain=1 / math.sqrt(2))
nn.init.xavier_uniform_(self.q_proj.weight, gain=1 / math.sqrt(2))
else:
nn.init.xavier_uniform_(self.k_proj.weight)
nn.init.xavier_uniform_(self.v_proj.weight)
nn.init.xavier_uniform_(self.q_proj.weight)
nn.init.xavier_uniform_(self.out_proj.weight)
if self.out_proj.bias is not None:
nn.init.constant_(self.out_proj.bias, 0.0)
if self.bias_k is not None:
nn.init.xavier_normal_(self.bias_k)
if self.bias_v is not None:
nn.init.xavier_normal_(self.bias_v)
def forward(
self,
query,
key: Optional[Tensor],
value: Optional[Tensor],
key_padding_mask: Optional[Tensor] = None,
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
need_weights: bool = True,
static_kv: bool = False,
attn_mask: Optional[Tensor] = None,
before_softmax: bool = False,
need_head_weights: bool = False,
) -> Tuple[Tensor, Optional[Tensor]]:
"""Input shape: Time x Batch x Channel
Args:
key_padding_mask (ByteTensor, optional): mask to exclude
keys that are pads, of shape `(batch, src_len)`, where
padding elements are indicated by 1s.
need_weights (bool, optional): return the attention weights,
averaged over heads (default: False).
attn_mask (ByteTensor, optional): typically used to
implement causal attention, where the mask prevents the
attention from looking forward in time (default: None).
before_softmax (bool, optional): return the raw attention
weights and values before the attention softmax.
need_head_weights (bool, optional): return the attention
weights for each head. Implies *need_weights*. Default:
return the average attention weights over all heads.
"""
if need_head_weights:
need_weights = True
is_tpu = query.device.type == "xla"
tgt_len, bsz, embed_dim = query.size()
src_len = tgt_len
assert embed_dim == self.embed_dim, f"query dim {embed_dim} != {self.embed_dim}"
assert list(query.size()) == [tgt_len, bsz, embed_dim]
if key is not None:
src_len, key_bsz, _ = key.size()
if not torch.jit.is_scripting():
assert key_bsz == bsz
assert value is not None
assert src_len, bsz == value.shape[:2]
if (
not self.onnx_trace
and not is_tpu # don't use PyTorch version on TPUs
and incremental_state is None
and not static_kv
# A workaround for quantization to work. Otherwise JIT compilation
# treats bias in linear module as method.
and not torch.jit.is_scripting()
):
assert key is not None and value is not None
return F.multi_head_attention_forward(
query,
key,
value,
self.embed_dim,
self.num_heads,
torch.empty([0]),
torch.cat((self.q_proj.bias, self.k_proj.bias, self.v_proj.bias)),
self.bias_k,
self.bias_v,
self.add_zero_attn,
self.dropout_module.p,
self.out_proj.weight,
self.out_proj.bias,
self.training or self.dropout_module.apply_during_inference,
key_padding_mask,
need_weights,
attn_mask,
use_separate_proj_weight=True,
q_proj_weight=self.q_proj.weight,
k_proj_weight=self.k_proj.weight,
v_proj_weight=self.v_proj.weight,
)
if incremental_state is not None:
saved_state = self._get_input_buffer(incremental_state)
if saved_state is not None and "prev_key" in saved_state:
# previous time steps are cached - no need to recompute
# key and value if they are static
if static_kv:
assert self.encoder_decoder_attention and not self.self_attention
key = value = None
else:
saved_state = None
if self.self_attention:
q = self.q_proj(query)
k = self.k_proj(query)
v = self.v_proj(query)
elif self.encoder_decoder_attention:
# encoder-decoder attention
q = self.q_proj(query)
if key is None:
assert value is None
k = v = None
else:
k = self.k_proj(key)
v = self.v_proj(key)
else:
assert key is not None and value is not None
q = self.q_proj(query)
k = self.k_proj(key)
v = self.v_proj(value)
q *= self.scaling
if self.bias_k is not None:
assert self.bias_v is not None
k = torch.cat([k, self.bias_k.repeat(1, bsz, 1)])
v = torch.cat([v, self.bias_v.repeat(1, bsz, 1)])
if attn_mask is not None:
attn_mask = torch.cat(
[attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1
)
if key_padding_mask is not None:
key_padding_mask = torch.cat(
[
key_padding_mask,
key_padding_mask.new_zeros(key_padding_mask.size(0), 1),
],
dim=1,
)
q = (
q.contiguous()
.view(tgt_len, bsz * self.num_heads, self.head_dim)
.transpose(0, 1)
) # (B x num_heads) x T x (C / num_heads)
if k is not None:
k = (
k.contiguous()
.view(-1, bsz * self.num_heads, self.head_dim)
.transpose(0, 1)
)
if v is not None:
v = (
v.contiguous()
.view(-1, bsz * self.num_heads, self.head_dim)
.transpose(0, 1)
)
if saved_state is not None:
# saved states are stored with shape (bsz, num_heads, seq_len, head_dim)
if "prev_key" in saved_state:
_prev_key = saved_state["prev_key"]
assert _prev_key is not None
prev_key = _prev_key.view(bsz * self.num_heads, -1, self.head_dim)
if static_kv:
k = prev_key
else:
assert k is not None
k = torch.cat([prev_key, k], dim=1)
src_len = k.size(1)
if "prev_value" in saved_state:
_prev_value = saved_state["prev_value"]
assert _prev_value is not None
prev_value = _prev_value.view(bsz * self.num_heads, -1, self.head_dim)
if static_kv:
v = prev_value
else:
assert v is not None
v = torch.cat([prev_value, v], dim=1)
prev_key_padding_mask: Optional[Tensor] = None
if "prev_key_padding_mask" in saved_state:
prev_key_padding_mask = saved_state["prev_key_padding_mask"]
assert k is not None and v is not None
key_padding_mask = MultiheadAttention._append_prev_key_padding_mask(
key_padding_mask=key_padding_mask,
prev_key_padding_mask=prev_key_padding_mask,
batch_size=bsz,
src_len=k.size(1),
static_kv=static_kv,
)
saved_state["prev_key"] = k.view(bsz, self.num_heads, -1, self.head_dim)
saved_state["prev_value"] = v.view(bsz, self.num_heads, -1, self.head_dim)
saved_state["prev_key_padding_mask"] = key_padding_mask
# In this branch incremental_state is never None
assert incremental_state is not None
incremental_state = self._set_input_buffer(incremental_state, saved_state)
assert k is not None
assert k.size(1) == src_len
# This is part of a workaround to get around fork/join parallelism
# not supporting Optional types.
if key_padding_mask is not None and key_padding_mask.dim() == 0:
key_padding_mask = None
# Ensure that key_padding_mask has shape B x T
if key_padding_mask is not None:
assert key_padding_mask.size(0) == bsz
assert key_padding_mask.size(1) == src_len
if self.add_zero_attn:
assert v is not None
src_len += 1
k = torch.cat([k, k.new_zeros((k.size(0), 1) + k.size()[2:])], dim=1)
v = torch.cat([v, v.new_zeros((v.size(0), 1) + v.size()[2:])], dim=1)
if attn_mask is not None:
attn_mask = torch.cat(
[attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1
)
if key_padding_mask is not None:
key_padding_mask = torch.cat(
[
key_padding_mask,
torch.zeros(key_padding_mask.size(0), 1).type_as(
key_padding_mask
),
],
dim=1,
)
attn_weights = torch.bmm(q, k.transpose(1, 2))
attn_weights = self.apply_sparse_mask(attn_weights, tgt_len, src_len, bsz)
assert list(attn_weights.size()) == [bsz * self.num_heads, tgt_len, src_len]
if attn_mask is not None:
attn_mask = attn_mask.unsqueeze(0)
if self.onnx_trace:
attn_mask = attn_mask.repeat(attn_weights.size(0), 1, 1)
attn_weights += attn_mask
if key_padding_mask is not None:
# don't attend to padding symbols
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
if not is_tpu:
attn_weights = attn_weights.masked_fill(
key_padding_mask.unsqueeze(1).unsqueeze(2).to(torch.bool),
float("-inf"),
)
else:
attn_weights = attn_weights.transpose(0, 2)
attn_weights = attn_weights.masked_fill(key_padding_mask, float("-inf"))
attn_weights = attn_weights.transpose(0, 2)
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
if before_softmax:
return attn_weights, v
attn_weights_float = utils.softmax(
attn_weights, dim=-1, onnx_trace=self.onnx_trace
)
attn_weights = attn_weights_float.type_as(attn_weights)
attn_probs = self.dropout_module(attn_weights)
assert v is not None
attn = torch.bmm(attn_probs, v)
assert list(attn.size()) == [bsz * self.num_heads, tgt_len, self.head_dim]
if self.onnx_trace and attn.size(1) == 1:
# when ONNX tracing a single decoder step (sequence length == 1)
# the transpose is a no-op copy before view, thus unnecessary
attn = attn.contiguous().view(tgt_len, bsz, embed_dim)
else:
attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim)
attn = self.out_proj(attn)
attn_weights: Optional[Tensor] = None
if need_weights:
attn_weights = attn_weights_float.view(
bsz, self.num_heads, tgt_len, src_len
).transpose(1, 0)
if not need_head_weights:
# average attention weights over heads
attn_weights = attn_weights.mean(dim=0)
return attn, attn_weights
@staticmethod
def _append_prev_key_padding_mask(
key_padding_mask: Optional[Tensor],
prev_key_padding_mask: Optional[Tensor],
batch_size: int,
src_len: int,
static_kv: bool,
) -> Optional[Tensor]:
# saved key padding masks have shape (bsz, seq_len)
if prev_key_padding_mask is not None and static_kv:
new_key_padding_mask = prev_key_padding_mask
elif prev_key_padding_mask is not None and key_padding_mask is not None:
new_key_padding_mask = torch.cat(
[prev_key_padding_mask.float(), key_padding_mask.float()], dim=1
)
# During incremental decoding, as the padding token enters and
# leaves the frame, there will be a time when prev or current
# is None
elif prev_key_padding_mask is not None:
if src_len > prev_key_padding_mask.size(1):
filler = torch.zeros(
(batch_size, src_len - prev_key_padding_mask.size(1)),
device=prev_key_padding_mask.device,
)
new_key_padding_mask = torch.cat(
[prev_key_padding_mask.float(), filler.float()], dim=1
)
else:
new_key_padding_mask = prev_key_padding_mask.float()
elif key_padding_mask is not None:
if src_len > key_padding_mask.size(1):
filler = torch.zeros(
(batch_size, src_len - key_padding_mask.size(1)),
device=key_padding_mask.device,
)
new_key_padding_mask = torch.cat(
[filler.float(), key_padding_mask.float()], dim=1
)
else:
new_key_padding_mask = key_padding_mask.float()
else:
new_key_padding_mask = prev_key_padding_mask
return new_key_padding_mask
@torch.jit.export
def reorder_incremental_state(
self,
incremental_state: Dict[str, Dict[str, Optional[Tensor]]],
new_order: Tensor,
):
"""Reorder buffered internal state (for incremental generation)."""
input_buffer = self._get_input_buffer(incremental_state)
if input_buffer is not None:
for k in input_buffer.keys():
input_buffer_k = input_buffer[k]
if input_buffer_k is not None:
if self.encoder_decoder_attention and input_buffer_k.size(
0
) == new_order.size(0):
break
input_buffer[k] = input_buffer_k.index_select(0, new_order)
incremental_state = self._set_input_buffer(incremental_state, input_buffer)
return incremental_state
def _get_input_buffer(
self, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]]
) -> Dict[str, Optional[Tensor]]:
result = self.get_incremental_state(incremental_state, "attn_state")
if result is not None:
return result
else:
empty_result: Dict[str, Optional[Tensor]] = {}
return empty_result
def _set_input_buffer(
self,
incremental_state: Dict[str, Dict[str, Optional[Tensor]]],
buffer: Dict[str, Optional[Tensor]],
):
return self.set_incremental_state(incremental_state, "attn_state", buffer)
def apply_sparse_mask(self, attn_weights, tgt_len: int, src_len: int, bsz: int):
return attn_weights
def upgrade_state_dict_named(self, state_dict, name):
prefix = name + "." if name != "" else ""
items_to_add = {}
keys_to_remove = []
for k in state_dict.keys():
if k.endswith(prefix + "in_proj_weight"):
# in_proj_weight used to be q + k + v with same dimensions
dim = int(state_dict[k].shape[0] / 3)
items_to_add[prefix + "q_proj.weight"] = state_dict[k][:dim]
items_to_add[prefix + "k_proj.weight"] = state_dict[k][dim : 2 * dim]
items_to_add[prefix + "v_proj.weight"] = state_dict[k][2 * dim :]
keys_to_remove.append(k)
k_bias = prefix + "in_proj_bias"
if k_bias in state_dict.keys():
dim = int(state_dict[k].shape[0] / 3)
items_to_add[prefix + "q_proj.bias"] = state_dict[k_bias][:dim]
items_to_add[prefix + "k_proj.bias"] = state_dict[k_bias][
dim : 2 * dim
]
items_to_add[prefix + "v_proj.bias"] = state_dict[k_bias][2 * dim :]
keys_to_remove.append(prefix + "in_proj_bias")
for k in keys_to_remove:
del state_dict[k]
for key, value in items_to_add.items():
state_dict[key] = value
| 20,017 | 38.876494 | 88 | py |
CIF-HieraDist | CIF-HieraDist-main/fairseq/modules/transpose_last.py | # 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.
"""
transpose last 2 dimensions of the input
"""
import torch.nn as nn
class TransposeLast(nn.Module):
def __init__(self, deconstruct_idx=None):
super().__init__()
self.deconstruct_idx = deconstruct_idx
def forward(self, x):
if self.deconstruct_idx is not None:
x = x[self.deconstruct_idx]
return x.transpose(-2, -1)
| 550 | 25.238095 | 65 | py |
CIF-HieraDist | CIF-HieraDist-main/fairseq/modules/same_pad.py | # 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.
from torch import nn
class SamePad(nn.Module):
def __init__(self, kernel_size, causal=False):
super().__init__()
if causal:
self.remove = kernel_size - 1
else:
self.remove = 1 if kernel_size % 2 == 0 else 0
def forward(self, x):
if self.remove > 0:
x = x[:, :, : -self.remove]
return x
| 552 | 24.136364 | 65 | py |
CIF-HieraDist | CIF-HieraDist-main/fairseq/modules/linearized_convolution.py | # 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 torch
import torch.nn.functional as F
from fairseq import utils
from fairseq.incremental_decoding_utils import with_incremental_state
from .conv_tbc import ConvTBC
from typing import Dict, Optional
from torch import Tensor
@with_incremental_state
class LinearizedConvolution(ConvTBC):
"""An optimized version of nn.Conv1d.
At training time, this module uses ConvTBC, which is an optimized version
of Conv1d. At inference time, it optimizes incremental generation (i.e.,
one time step at a time) by replacing the convolutions with linear layers.
Note that the input order changes from training to inference.
"""
def __init__(self, in_channels, out_channels, kernel_size, **kwargs):
super().__init__(in_channels, out_channels, kernel_size, **kwargs)
self._linearized_weight = None
self.register_backward_hook(self._clear_linearized_weight)
def state_dict(self, destination=None, prefix="", keep_vars=False):
state = ConvTBC.state_dict(self, destination, prefix, keep_vars=keep_vars)
# don't store redundant _linearized_weight in checkpoints
if prefix + "_linearized_weight" in state:
del state[prefix + "_linearized_weight"]
return state
def upgrade_state_dict_named(self, state_dict, name):
prefix = name + "." if name != "" else ""
if prefix + "_linearized_weight" in state_dict:
del state_dict[prefix + "_linearized_weight"]
@torch.jit.export
def forward(
self,
input,
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
):
"""
Args:
incremental_state: Used to buffer signal; if not None, then input is
expected to contain a single frame. If the input order changes
between time steps, call reorder_incremental_state.
Input:
Time x Batch x Channel during training
Batch x Time x Channel during inference
"""
if incremental_state is None:
output = self.conv_tbc(input)
if self.kernel_size[0] > 1 and self.padding[0] > 0:
# remove future timesteps added by padding
output = output[: -self.padding[0], :, :]
return output
# reshape weight
weight = self._get_linearized_weight()
kw = self.kernel_size[0]
bsz = input.size(0) # input: bsz x len x dim
if kw > 1:
input = input.data
input_buffer = self._get_input_buffer(incremental_state)
if input_buffer is None:
input_buffer = input.new(bsz, kw, input.size(2)).zero_()
self._set_input_buffer(incremental_state, input_buffer)
else:
# shift buffer
input_buffer[:, :-1, :] = input_buffer[:, 1:, :].clone()
# append next input
input_buffer[:, -1, :] = input[:, -1, :]
input = input_buffer
with torch.no_grad():
output = F.linear(input.view(bsz, -1), weight, self.bias)
return output.view(bsz, 1, -1)
@torch.jit.unused
def reorder_incremental_state(
self,
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]],
new_order,
):
input_buffer = self._get_input_buffer(incremental_state)
if input_buffer is not None:
input_buffer = input_buffer.index_select(0, new_order)
self._set_input_buffer(incremental_state, input_buffer)
@torch.jit.unused
def _get_input_buffer(
self, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]]
):
return utils.get_incremental_state(self, incremental_state, "input_buffer")
@torch.jit.unused
def _set_input_buffer(
self,
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]],
new_buffer,
):
return utils.set_incremental_state(
self, incremental_state, "input_buffer", new_buffer
)
@torch.jit.unused
def _get_linearized_weight(self):
if self._linearized_weight is None:
kw = self.kernel_size[0]
weight = self.weight.transpose(2, 1).transpose(1, 0).contiguous()
assert weight.size() == (self.out_channels, kw, self.in_channels)
return weight.view(self.out_channels, -1)
return self._linearized_weight
@torch.jit.unused
def _clear_linearized_weight(self, *args):
self._linearized_weight = None
| 4,744 | 36.65873 | 83 | py |
CIF-HieraDist | CIF-HieraDist-main/fairseq/modules/downsampled_multihead_attention.py | # 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 math
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq.modules.fairseq_dropout import FairseqDropout
from fairseq.modules.scalar_bias import scalar_bias
class SingleHeadAttention(nn.Module):
"""
Single-head attention that supports Gating and Downsampling
"""
def __init__(
self,
out_channels,
embed_dim,
head_dim,
head_index,
dropout=0.0,
bias=True,
project_input=True,
gated=False,
downsample=False,
num_heads=1,
):
super().__init__()
self.embed_dim = embed_dim
self.dropout_module = FairseqDropout(
dropout, module_name=self.__class__.__name__
)
self.head_index = head_index
self.head_dim = head_dim
self.project_input = project_input
self.gated = gated
self.downsample = downsample
self.num_heads = num_heads
self.projection = None
k_layers = []
v_layers = []
if self.downsample:
k_layers.append(Downsample(self.head_index))
v_layers.append(Downsample(self.head_index))
out_proj_size = self.head_dim
else:
out_proj_size = self.head_dim * self.num_heads
if self.gated:
k_layers.append(GatedLinear(self.embed_dim, out_proj_size, bias=bias))
self.in_proj_q = GatedLinear(self.embed_dim, out_proj_size, bias=bias)
v_layers.append(GatedLinear(self.embed_dim, out_proj_size, bias=bias))
else:
k_layers.append(Linear(self.embed_dim, out_proj_size, bias=bias))
self.in_proj_q = Linear(self.embed_dim, out_proj_size, bias=bias)
v_layers.append(Linear(self.embed_dim, out_proj_size, bias=bias))
self.in_proj_k = nn.Sequential(*k_layers)
self.in_proj_v = nn.Sequential(*v_layers)
if self.downsample:
self.out_proj = Linear(out_proj_size, self.head_dim, bias=bias)
else:
self.out_proj = Linear(out_proj_size, out_channels, bias=bias)
self.scaling = self.head_dim**-0.5
def forward(
self,
query,
key,
value,
mask_future_timesteps=False,
key_padding_mask=None,
use_scalar_bias=False,
):
"""Input shape: Time x Batch x Channel
Self-attention can be implemented by passing in the same arguments for
query, key and value. Future timesteps can be masked with the
`mask_future_timesteps` argument. Padding elements can be excluded from
the key by passing a binary ByteTensor (`key_padding_mask`) with shape:
batch x src_len, where padding elements are indicated by 1s.
"""
src_len, bsz, out_channels = key.size()
tgt_len = query.size(0)
assert list(query.size()) == [tgt_len, bsz, out_channels]
assert key.size() == value.size()
if key_padding_mask is not None:
assert key_padding_mask.size(0) == bsz
assert key_padding_mask.size(1) == src_len
if self.downsample:
size = bsz
else:
size = bsz * self.num_heads
k = key
v = value
q = query
if self.project_input:
q = self.in_proj_q(q)
k = self.in_proj_k(k)
v = self.in_proj_v(v)
src_len = k.size()[0]
q *= self.scaling
if not self.downsample:
q = q.view(tgt_len, size, self.head_dim)
k = k.view(src_len, size, self.head_dim)
v = v.view(src_len, size, self.head_dim)
q = q.transpose(0, 1)
k = k.transpose(0, 1)
v = v.transpose(0, 1)
attn_weights = torch.bmm(q, k.transpose(1, 2))
if mask_future_timesteps:
assert (
query.size() == key.size()
), "mask_future_timesteps only applies to self-attention"
attn_weights *= torch.tril(
attn_weights.data.new([1]).expand(tgt_len, tgt_len).clone(),
diagonal=-1,
)[:, :: self.head_index + 1 if self.downsample else 1].unsqueeze(0)
attn_weights += torch.triu(
attn_weights.data.new([-math.inf]).expand(tgt_len, tgt_len).clone(),
diagonal=0,
)[:, :: self.head_index + 1 if self.downsample else 1].unsqueeze(0)
tgt_size = tgt_len
if use_scalar_bias:
attn_weights = scalar_bias(attn_weights, 2)
v = scalar_bias(v, 1)
tgt_size += 1
if key_padding_mask is not None:
# don't attend to padding symbols
if key_padding_mask.max() > 0:
if self.downsample:
attn_weights = attn_weights.view(bsz, 1, tgt_len, src_len)
else:
attn_weights = attn_weights.view(
size, self.num_heads, tgt_len, src_len
)
attn_weights = attn_weights.masked_fill(
key_padding_mask.unsqueeze(1).unsqueeze(2),
-math.inf,
)
attn_weights = attn_weights.view(size, tgt_len, src_len)
attn_weights = F.softmax(attn_weights, dim=-1)
attn_weights = self.dropout_module(attn_weights)
attn = torch.bmm(attn_weights, v)
if self.downsample:
attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, self.head_dim)
else:
attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, self.embed_dim)
attn = self.out_proj(attn)
return attn, attn_weights
class DownsampledMultiHeadAttention(nn.ModuleList):
"""
Multi-headed attention with Gating and Downsampling
"""
def __init__(
self,
out_channels,
embed_dim,
num_heads,
dropout=0.0,
bias=True,
project_input=True,
gated=False,
downsample=False,
):
self.embed_dim = embed_dim
self.num_heads = num_heads
self.head_dim = embed_dim // num_heads
self.downsample = downsample
self.gated = gated
self.project_input = project_input
assert self.head_dim * num_heads == embed_dim
if self.downsample:
attention_heads = []
for index in range(self.num_heads):
attention_heads.append(
SingleHeadAttention(
out_channels,
self.embed_dim,
self.head_dim,
index,
dropout,
bias,
self.project_input,
self.gated,
self.downsample,
self.num_heads,
)
)
super().__init__(modules=attention_heads)
self.out_proj = Linear(embed_dim, out_channels, bias=bias)
else:
# either we have a list of attention heads, or just one attention head
# if not being downsampled, we can do the heads with one linear layer instead of separate ones
super().__init__()
self.attention_module = SingleHeadAttention(
out_channels,
self.embed_dim,
self.head_dim,
1,
dropout,
bias,
self.project_input,
self.gated,
self.downsample,
self.num_heads,
)
def forward(
self,
query,
key,
value,
mask_future_timesteps=False,
key_padding_mask=None,
use_scalar_bias=False,
):
src_len, bsz, embed_dim = key.size()
tgt_len = query.size(0)
assert embed_dim == self.embed_dim
assert list(query.size()) == [tgt_len, bsz, embed_dim]
assert key.size() == value.size()
tgt_size = tgt_len
if use_scalar_bias:
tgt_size += 1
attn = []
attn_weights = []
if self.downsample:
for attention_head_number in range(self.num_heads):
# call the forward of each attention head
_attn, _attn_weight = self[attention_head_number](
query,
key,
value,
mask_future_timesteps,
key_padding_mask,
use_scalar_bias,
)
attn.append(_attn)
attn_weights.append(_attn_weight)
full_attn = torch.cat(attn, dim=2)
full_attn = self.out_proj(full_attn)
return full_attn, attn_weights[0].clone()
else:
_attn, _attn_weight = self.attention_module(
query,
key,
value,
mask_future_timesteps,
key_padding_mask,
use_scalar_bias,
)
attn.append(_attn)
attn_weights.append(_attn_weight)
full_attn = torch.cat(attn, dim=2)
full_attn_weights = torch.cat(attn_weights)
full_attn_weights = full_attn_weights.view(
bsz, self.num_heads, tgt_size, src_len
)
full_attn_weights = full_attn_weights.sum(dim=1) / self.num_heads
return full_attn, full_attn_weights
class Downsample(nn.Module):
"""
Selects every nth element, where n is the index
"""
def __init__(self, index):
super().__init__()
self.index = index
def forward(self, x):
return x[:: self.index + 1]
def Linear(in_features, out_features, dropout=0.0, bias=True):
"""Weight-normalized Linear layer (input: B x T x C)"""
m = nn.Linear(in_features, out_features, bias=bias)
m.weight.data.normal_(mean=0, std=math.sqrt((1 - dropout) / in_features))
m.bias.data.zero_()
return nn.utils.weight_norm(m)
def GatedLinear(in_features, out_features, dropout=0.0, bias=True):
"""Weight-normalized Linear layer (input: B x T x C) with interspersed GLU units"""
return nn.Sequential(
Linear(in_features, out_features * 4, dropout, bias),
nn.GLU(),
Linear(out_features * 2, out_features * 2, dropout, bias),
nn.GLU(),
Linear(out_features, out_features, dropout, bias),
)
| 10,671 | 32.665615 | 106 | py |
CIF-HieraDist | CIF-HieraDist-main/fairseq/modules/base_layer.py | # 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 torch.nn as nn
import torch
import sys
from fairseq import utils
from fairseq.distributed import utils as distributed_utils
from fairseq.modules.layer_norm import LayerNorm
class BaseLayer(nn.Module):
def __init__(self, args):
super().__init__()
self.num_workers = distributed_utils.get_data_parallel_world_size()
expert_centroids = torch.empty(self.num_workers, args.decoder_embed_dim)
torch.nn.init.orthogonal_(expert_centroids, gain=0.1)
self.register_parameter(
"expert_centroids", torch.nn.Parameter(expert_centroids)
)
self.expert_network = nn.Sequential(
*([BaseSublayer(args) for _ in range(args.base_sublayers)])
)
self.expert_id = distributed_utils.get_data_parallel_rank()
self.shuffle = args.base_shuffle
self.cpp = self.load_assignment()
# Add a special attribute to the expert parameters, so we know not to sync their gradients
for param in self.expert_network.parameters():
param.expert = True
def forward(self, input_features, *args, **kwargs):
features = input_features.reshape(-1, input_features.size(-1))
is_training = input_features.requires_grad
if self.shuffle and is_training:
# Send each token to a random worker, to break correlations within the batch
shuffle_sort = torch.randperm(features.size(0), device=features.device)
features = All2All.apply(features[shuffle_sort])
with torch.no_grad():
# Compute similarity of each token to each expert, for routing
token_expert_affinities = features.matmul(
self.expert_centroids.transpose(0, 1)
)
# Compute which token goes to which expert
sort_by_expert, input_splits, output_splits = (
self.balanced_assignment(token_expert_affinities)
if is_training
else self.greedy_assignment(token_expert_affinities)
)
# Swap these tokens for the right ones for our expert
routed_features = All2All.apply(
features[sort_by_expert], output_splits, input_splits
)
if routed_features.size(0) > 0:
# Mix in the expert network based on how appropriate it is for these tokens
alpha = torch.sigmoid(
routed_features.mv(self.expert_centroids[self.expert_id])
).unsqueeze(1)
routed_features = (
alpha * self.expert_network(routed_features)
+ (1 - alpha) * routed_features
)
# Return to original worker and ordering
result = All2All.apply(routed_features, input_splits, output_splits)[
self.inverse_sort(sort_by_expert)
]
if self.shuffle and is_training:
# Undo shuffling
result = All2All.apply(result)[self.inverse_sort(shuffle_sort)]
# Return additional Nones for compatibility with TransformerDecoderLayer
return result.view(input_features.size()), None, None
def inverse_sort(self, order):
# Creates an index that undoes a sort: xs==xs[order][inverse_sort(order)]
return torch.empty_like(order).scatter_(
0, order, torch.arange(0, order.size(0), device=order.device)
)
def balanced_assignment(self, scores):
ok = scores.isfinite()
if not ok.all():
# NaNs here can break the assignment algorithm
scores[~ok] = scores[ok].min()
return self.cpp.balanced_assignment(scores), None, None
# Assigns each token to the top k experts
def greedy_assignment(self, scores, k=1):
token_to_workers = torch.topk(scores, dim=1, k=k, largest=True).indices.view(-1)
token_to_workers, sort_ordering = torch.sort(token_to_workers)
worker2token = sort_ordering // k
# Find how many tokens we're sending to each other worker (being careful for sending 0 tokens to some workers)
output_splits = torch.zeros(
(self.num_workers,), dtype=torch.long, device=scores.device
)
workers, counts = torch.unique_consecutive(token_to_workers, return_counts=True)
output_splits[workers] = counts
# Tell other workers how many tokens to expect from us
input_splits = All2All.apply(output_splits)
return worker2token, input_splits.tolist(), output_splits.tolist()
def load_assignment(self):
try:
from fairseq import libbase
return libbase
except ImportError as e:
sys.stderr.write(
"ERROR: missing libbase. run `python setup.py build_ext --inplace`\n"
)
raise e
class BaseSublayer(nn.Module):
def __init__(self, args):
super().__init__()
self.activation_fn = utils.get_activation_fn(
activation=getattr(args, "activation_fn", "relu") or "relu"
)
self.norm = LayerNorm(args.decoder_embed_dim, export=False)
self.ff1 = torch.nn.Linear(args.decoder_embed_dim, args.decoder_ffn_embed_dim)
self.ff2 = torch.nn.Linear(args.decoder_ffn_embed_dim, args.decoder_embed_dim)
self.ff2.weight.data.zero_()
def forward(self, xs):
return xs + self.ff2(self.activation_fn(self.ff1(self.norm(xs))))
# Wraps torch.distributed.all_to_all_single as a function that supports autograd
class All2All(torch.autograd.Function):
@staticmethod
def forward(ctx, xs, input_splits=None, output_splits=None):
ctx.input_splits = input_splits
ctx.output_splits = output_splits
ys = (
torch.empty_like(xs)
if output_splits is None
else xs.new_empty(size=[sum(output_splits)] + list(xs.size()[1:]))
)
torch.distributed.all_to_all_single(
ys, xs, output_split_sizes=output_splits, input_split_sizes=input_splits
)
return ys
@staticmethod
def backward(ctx, grad_output):
result = (
torch.empty_like(grad_output)
if ctx.input_splits is None
else grad_output.new_empty(
size=[sum(ctx.input_splits)] + list(grad_output.size()[1:])
)
)
torch.distributed.all_to_all_single(
result,
grad_output,
output_split_sizes=ctx.input_splits,
input_split_sizes=ctx.output_splits,
)
return result, None, None
| 6,693 | 38.146199 | 118 | py |
CIF-HieraDist | CIF-HieraDist-main/fairseq/modules/kmeans_attention.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from inspect import isfunction
from operator import mul
from functools import reduce, wraps
from aml.multimodal_video.utils.einops.lib import rearrange, repeat
from aml.multimodal_video.utils.einops.lib.layers.torch import Rearrange
from fairseq.modules.local_attention import LocalAttention
# constants
TOKEN_SELF_ATTN_VALUE = -5e4
KMEAN_INIT_ITERS = 10
# helper functions
def exists(val):
return val is not None
def identity(x, *args, **kwargs):
return x
def default(x, d):
if not exists(x):
return d if not isfunction(d) else d()
return x
def cast_tuple(x):
return x if isinstance(x, tuple) else (x,)
def cache_fn(f):
cache = None
@wraps(f)
def cached_fn(*args, **kwargs):
nonlocal cache
if exists(cache):
return cache
cache = f(*args, **kwargs)
return cache
return cached_fn
def to(t):
return {"device": t.device, "dtype": t.dtype}
def find_modules(nn_module, type):
return [module for module in nn_module.modules() if isinstance(module, type)]
def is_empty(t):
return t.nelement() == 0
def max_neg_value(tensor):
return -torch.finfo(tensor.dtype).max
def batched_index_select(values, indices):
last_dim = values.shape[-1]
return values.gather(2, expand_dim(indices, -1, last_dim))
def merge_dims(ind_from, ind_to, tensor):
shape = list(tensor.shape)
arr_slice = slice(ind_from, ind_to + 1)
shape[arr_slice] = [reduce(mul, shape[arr_slice])]
return tensor.reshape(*shape)
def expand_dim(t, dim, k):
t = t.unsqueeze(dim)
expand_shape = [-1] * len(t.shape)
expand_shape[dim] = k
return t.expand(*expand_shape)
def scatter_mean(src, t, index, dim, eps=1e-5):
numer = src.scatter_add(dim, index, t)
denom = src.scatter_add(dim, index, torch.ones_like(t))
return numer / (denom + eps)
def split_at_index(dim, index, t):
pre_slices = (slice(None),) * dim
l = (*pre_slices, slice(None, index))
r = (*pre_slices, slice(index, None))
return t[l], t[r]
def reshape_dim(t, dim, split_dims):
shape = list(t.shape)
num_dims = len(shape)
dim = (dim + num_dims) % num_dims
shape[dim : dim + 1] = split_dims
return t.reshape(shape)
def ema(old, new, decay):
if not exists(old):
return new
return old * decay + new * (1 - decay)
def ema_inplace(moving_avg, new, decay):
if is_empty(moving_avg):
moving_avg.data.copy_(new)
return
moving_avg.data.mul_(decay).add_(new, alpha=(1 - decay))
# helper classes
def map_first_tuple_or_el(x, fn):
if isinstance(x, tuple):
return (fn(x[0]),) + x[1:]
return fn(x)
class Chunk(nn.Module):
def __init__(self, chunks, fn, along_dim=-1):
super().__init__()
self.dim = along_dim
self.chunks = chunks
self.fn = fn
def forward(self, x, **kwargs):
if self.chunks <= 1:
return self.fn(x, **kwargs)
chunks = x.chunk(self.chunks, dim=self.dim)
return torch.cat([self.fn(c, **kwargs) for c in chunks], dim=self.dim)
class PreNorm(nn.ModuleList):
def __init__(self, norm_class, dim, fn):
super().__init__()
self.norm = norm_class(dim)
self.fn = fn
def forward(self, x, **kwargs):
x = self.norm(x)
return self.fn(x, **kwargs)
class ReZero(nn.Module):
def __init__(self, fn):
super().__init__()
self.residual_weight = nn.Parameter(torch.zeros(1))
self.fn = fn
def forward(self, x, **kwargs):
x = self.fn(x, **kwargs)
return map_first_tuple_or_el(x, lambda t: t * self.residual_weight)
class ScaleNorm(nn.Module):
def __init__(self, dim, eps=1e-5):
super().__init__()
self.g = nn.Parameter(torch.ones(1))
self.eps = eps
def forward(self, x):
def norm(t):
n = torch.norm(t, dim=-1, keepdim=True).clamp(min=self.eps)
return t / n * self.g
return map_first_tuple_or_el(x, norm)
class ProjectInOut(nn.Module):
def __init__(self, fn, dim_in, dim_out, project_out=True):
super().__init__()
self.fn = fn
self.project_in = nn.Linear(dim_in, dim_out)
self.project_out = nn.Linear(dim_out, dim_in) if project_out else identity
def forward(self, x, **kwargs):
x = self.project_in(x)
x, loss = self.fn(x, **kwargs)
x = self.project_out(x)
return x, loss
class MatrixMultiply(nn.Module):
def __init__(self, tensor, transpose=False):
super().__init__()
self.tensor = tensor
self.transpose = transpose
def forward(self, x):
tensor = self.tensor
if self.transpose:
tensor = tensor.t()
return x @ tensor
# positional embeddings
class DepthWiseConv1d(nn.Module):
def __init__(self, dim_in, dim_out, kernel_size, stride=1, bias=True, causal=False):
super().__init__()
self.padding = (
((kernel_size - 1), 0) if causal else (kernel_size // 2, kernel_size // 2)
)
self.net = nn.Sequential(
nn.Conv1d(
dim_in,
dim_in,
kernel_size=kernel_size,
groups=dim_in,
stride=stride,
bias=bias,
),
nn.Conv1d(dim_in, dim_out, 1, bias=bias),
)
def forward(self, x):
x = F.pad(x, self.padding, value=0.0)
return self.net(x)
class FixedPositionalEmbedding(nn.Module):
def __init__(self, dim, max_seq_len):
super().__init__()
inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2).float() / dim))
position = torch.arange(0, max_seq_len, dtype=torch.float)
sinusoid_inp = torch.einsum("i,j->ij", position, inv_freq)
emb = torch.cat((sinusoid_inp.sin(), sinusoid_inp.cos()), dim=-1)
self.register_buffer("emb", emb)
def forward(self, x):
return self.emb[None, : x.shape[1], :].to(x)
def rotate_every_two(x):
x = rearrange(x, "... (d j) -> ... d j", j=2)
x1, x2 = x.unbind(dim=-1)
x = torch.stack((-x2, x1), dim=-1)
return rearrange(x, "... d j -> ... (d j)")
def apply_rotary_pos_emb(q, k, sinu_pos):
sinu_pos = rearrange(sinu_pos, "() n (j d) -> n j d", j=2)
sin, cos = sinu_pos.unbind(dim=-2)
sin, cos = map(lambda t: repeat(t, "b n -> b (n j)", j=2), (sin, cos))
q, k = map(lambda t: (t * cos) + (rotate_every_two(t) * sin), (q, k))
return q, k
# kmeans related function and class
def update_kmeans_on_backwards(module):
module.kmean_modules = find_modules(module, Kmeans)
def hook(_, grad_in, grad_out):
for m in module.kmean_modules:
m.update()
return module.register_backward_hook(hook)
def similarity(x, means):
return torch.einsum("bhld,hcd->bhlc", x, means)
def dists_and_buckets(x, means):
dists = similarity(x, means)
_, buckets = torch.max(dists, dim=-1)
return dists, buckets
def batched_bincount(index, num_classes, dim=-1):
shape = list(index.shape)
shape[dim] = num_classes
out = index.new_zeros(shape)
out.scatter_add_(dim, index, torch.ones_like(index, dtype=index.dtype))
return out
def kmeans_iter(x, means, buckets=None):
b, h, _, d, dtype, num_clusters = *x.shape, x.dtype, means.shape[1]
if not exists(buckets):
_, buckets = dists_and_buckets(x, means)
bins = batched_bincount(buckets, num_clusters).sum(0, keepdim=True)
zero_mask = bins.long() == 0
means_ = buckets.new_zeros(b, h, num_clusters, d, dtype=dtype)
means_.scatter_add_(-2, expand_dim(buckets, -1, d), x)
means_ = F.normalize(means_.sum(0, keepdim=True), dim=-1).type(dtype)
means = torch.where(zero_mask.unsqueeze(-1), means, means_)
means = means.squeeze(0)
return means
def distribution(dists, window_size):
_, topk_indices = dists.topk(k=window_size, dim=-2)
indices = topk_indices.transpose(-2, -1)
return indices.reshape(*indices.size()[:2], -1)
class Kmeans(nn.Module):
def __init__(
self, num_heads, head_dim, num_clusters, ema_decay=0.999, commitment=1e-4
):
super().__init__()
self.commitment = commitment
self.ema_decay = ema_decay
self.register_buffer("means", torch.randn(num_heads, num_clusters, head_dim))
self.register_buffer("initted", torch.tensor(False))
self.num_new_means = 0
self.new_means = None
@torch.no_grad()
def init(self, x):
if self.initted:
return
_, h, _, d, device, _ = *x.shape, x.device, x.dtype
num_clusters = self.means.shape[1]
means = x.transpose(0, 1).contiguous().view(h, -1, d)
num_samples = means.shape[1]
if num_samples >= num_clusters:
indices = torch.randperm(num_samples, device=device)[:num_clusters]
else:
indices = torch.randint(0, num_samples, (num_clusters,), device=device)
means = means[:, indices]
for _ in range(KMEAN_INIT_ITERS):
means = kmeans_iter(x, means)
self.num_new_means = 0
self.means.data.copy_(means)
self.initted.data.copy_(torch.tensor(True))
@torch.no_grad()
def update(self, new_means=None):
new_means = default(new_means, self.new_means)
assert exists(new_means), "new kmeans has not been supplied"
ema_inplace(self.means, new_means, self.ema_decay)
del self.new_means
self.new_means = None
self.num_new_means = 0
def forward(self, x, update_means=False):
self.init(x)
b, dtype = x.shape[0], x.dtype
means = self.means.type(dtype)
x = F.normalize(x, 2, dim=-1).type(dtype)
with torch.no_grad():
dists, buckets = dists_and_buckets(x, means)
routed_means = batched_index_select(expand_dim(means, 0, b), buckets)
loss = F.mse_loss(x, routed_means) * self.commitment
if update_means:
with torch.no_grad():
means = kmeans_iter(x, means, buckets)
self.new_means = ema(
self.new_means, means, self.num_new_means / (self.num_new_means + 1)
)
self.num_new_means += 1
return dists, loss
# kmeans attention class
class KmeansAttention(nn.Module):
def __init__(
self,
num_clusters,
window_size,
num_heads,
head_dim,
causal=False,
dropout=0.0,
ema_decay=0.999,
commitment=1e-4,
context_window_size=None,
receives_context=False,
num_mem_kv=0,
shared_qk=False,
):
super().__init__()
self.num_heads = num_heads
self.num_clusters = num_clusters
self.head_dim = head_dim
self.window_size = window_size
self.context_window_size = default(context_window_size, window_size)
self.causal = causal
self.shared_qk = shared_qk
self.receives_context = receives_context
self.kmeans = Kmeans(num_heads, head_dim, num_clusters, ema_decay, commitment)
self.dropout = nn.Dropout(dropout)
self.num_mem_kv = max(num_mem_kv, 1 if causal and not shared_qk else 0)
self.mem_key = nn.Parameter(
torch.randn(num_heads, num_clusters, self.num_mem_kv, head_dim)
)
self.mem_value = nn.Parameter(
torch.randn(num_heads, num_clusters, self.num_mem_kv, head_dim)
)
def forward(self, q, k, v, query_mask=None, key_mask=None, **kwargs):
b, h, t, d, kv_t, wsz, c_wsz, nc, device, dtype = (
*q.shape,
k.shape[2],
self.window_size,
self.context_window_size,
self.num_clusters,
q.device,
q.dtype,
)
is_reverse = kwargs.pop("_reverse", False)
out = torch.zeros_like(q, dtype=dtype)
update_kmeans = self.training and not is_reverse
key_mask = (
default(key_mask, query_mask) if not self.receives_context else key_mask
)
kv_wsz = wsz if not self.receives_context else c_wsz
wsz = min(wsz, t)
kv_wsz = min(kv_wsz, kv_t)
if not self.shared_qk or self.receives_context:
dists, aux_loss = self.kmeans(torch.cat((q, k), dim=2), update_kmeans)
q_dists, k_dists = split_at_index(2, t, dists)
indices = distribution(q_dists, wsz)
kv_indices = distribution(k_dists, kv_wsz)
else:
dists, aux_loss = self.kmeans(q, update_kmeans)
k = F.normalize(k, dim=-1).to(q)
indices = distribution(dists, wsz)
kv_indices = indices
q = batched_index_select(q, indices)
k = batched_index_select(k, kv_indices)
v = batched_index_select(v, kv_indices)
reshape_with_window = lambda x: x.reshape(b, h, nc, -1, d)
q, k, v = map(reshape_with_window, (q, k, v))
m_k, m_v = map(
lambda x: expand_dim(x, 0, b).to(q), (self.mem_key, self.mem_value)
)
k, v = map(lambda x: torch.cat(x, dim=3), ((m_k, k), (m_v, v)))
dots = torch.einsum("bhnid,bhnjd->bhnij", q, k) * (d**-0.5)
mask_value = max_neg_value(dots)
if exists(query_mask) or exists(key_mask):
query_mask = default(
query_mask, lambda: torch.ones((b, t), device=device).bool()
)
key_mask = default(
key_mask, lambda: torch.ones((b, kv_t), device=device).bool()
)
q_mask = expand_dim(query_mask, 1, h).gather(2, indices)
kv_mask = expand_dim(key_mask, 1, h).gather(2, kv_indices)
q_mask, kv_mask = map(lambda t: t.reshape(b, h, nc, -1), (q_mask, kv_mask))
mask = q_mask[:, :, :, :, None] * kv_mask[:, :, :, None, :]
mask = F.pad(mask, (self.num_mem_kv, 0), value=1)
dots.masked_fill_(~mask, mask_value)
del mask
if self.causal:
q_mask, kv_mask = map(
lambda t: t.reshape(b, h, nc, -1), (indices, kv_indices)
)
mask = q_mask[:, :, :, :, None] >= kv_mask[:, :, :, None, :]
mask = F.pad(mask, (self.num_mem_kv, 0), value=1)
dots.masked_fill_(~mask, mask_value)
del mask
if self.shared_qk:
q_mask, kv_mask = map(
lambda t: t.reshape(b, h, nc, -1), (indices, kv_indices)
)
mask = q_mask[:, :, :, :, None] == kv_mask[:, :, :, None, :]
mask = F.pad(mask, (self.num_mem_kv, 0), value=0)
dots.masked_fill_(mask, TOKEN_SELF_ATTN_VALUE)
del mask
dots = dots.softmax(dim=-1)
dots = self.dropout(dots)
bo = torch.einsum("bhcij,bhcjd->bhcid", dots, v)
so = torch.reshape(bo, (b, h, -1, bo.shape[-1])).type(dtype)
out = scatter_mean(out, so, indices.unsqueeze(-1).expand_as(so), -2)
return out, aux_loss
# feedforward
class GELU_(nn.Module):
def forward(self, x):
return (
0.5
* x
* (
1
+ torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3)))
)
)
GELU = nn.GELU if hasattr(nn, "GELU") else GELU_
class FeedForward(nn.Module):
def __init__(self, dim, mult=4, dropout=0.0, activation=None, glu=False):
super().__init__()
activation = default(activation, GELU)
self.glu = glu
self.w1 = nn.Linear(dim, dim * mult * (2 if glu else 1))
self.act = activation()
self.dropout = nn.Dropout(dropout)
self.w2 = nn.Linear(dim * mult, dim)
def forward(self, x, **kwargs):
if not self.glu:
x = self.w1(x)
x = self.act(x)
else:
x, v = self.w1(x).chunk(2, dim=-1)
x = self.act(x) * v
x = self.dropout(x)
x = self.w2(x)
return x
# self attention
class SelfAttention(nn.Module):
def __init__(
self,
dim,
max_seq_len,
heads,
local_attn_heads,
window_size,
dim_head=None,
local_attn_window_size=None,
local_attn_radius_blocks=1,
causal=False,
attn_dropout=0.0,
dropout=0.0,
kmeans_ema_decay=0.999,
commitment_factor=1e-4,
receives_context=False,
context_window_size=None,
rel_pos_emb=True,
num_mem_kv=0,
shared_qk=False,
conv_query_kernel=9,
):
super().__init__()
assert (
dim_head or (dim % heads) == 0
), "hidden dimension must be divisible by number of heads"
assert (
max_seq_len % window_size
) == 0, "maximum sequence length must be divisible by the target window size"
assert (
local_attn_heads <= heads
), "number of local attention heads must be less than total heads"
assert not (
receives_context and local_attn_heads > 0
), "local attention cannot be used for self attention with context"
assert not (
receives_context and causal
), "contextual attention layer cannot be causal"
local_attn_window_size = default(local_attn_window_size, window_size)
context_window_size = default(context_window_size, window_size)
self.shared_qk = shared_qk
self.receives_context = receives_context
self.heads = heads
self.local_attn_heads = local_attn_heads
self.global_attn_heads = heads - local_attn_heads
self.causal = causal
self.window_size = window_size
dim_head = default(dim_head, dim // heads)
dim_heads = dim_head * heads
self.dim_head = dim_head
num_clusters = max_seq_len // window_size
# local
local_dim_heads = dim_head * self.local_attn_heads
if self.local_attn_heads > 0:
rel_pos_emb_config = (dim_head, local_attn_heads) if rel_pos_emb else None
self.local_attn = LocalAttention(
dim=dim_head,
window_size=local_attn_window_size,
causal=causal,
dropout=attn_dropout,
rel_pos_emb_config=rel_pos_emb_config,
look_backward=local_attn_radius_blocks,
look_forward=0 if causal else local_attn_radius_blocks,
)
self.local_to_qkv = nn.Linear(dim, 3 * local_dim_heads)
# global
global_dim_heads = dim_head * self.global_attn_heads
if self.global_attn_heads > 0:
self.global_attn = KmeansAttention(
num_clusters,
window_size,
self.global_attn_heads,
dim_head,
causal=causal,
dropout=attn_dropout,
ema_decay=kmeans_ema_decay,
commitment=commitment_factor,
receives_context=receives_context,
num_mem_kv=num_mem_kv,
shared_qk=shared_qk,
)
self.to_q = nn.Sequential(
Rearrange("b n c -> b c n"),
DepthWiseConv1d(dim, global_dim_heads, conv_query_kernel, causal=causal),
Rearrange("b c n -> b n c"),
)
self.to_v = nn.Linear(dim, global_dim_heads, bias=False)
if not self.shared_qk:
self.to_k = nn.Linear(dim, global_dim_heads, bias=False)
# out
self.to_out = nn.Linear(dim_heads, dim, bias=False)
self.dropout = nn.Dropout(dropout)
def forward(
self,
query,
key,
value,
context=None,
key_padding_mask=None,
context_mask=None,
pos_emb=None,
**kwargs
):
assert not (
self.receives_context and not exists(context)
), "context must be passed if self attention is set to receive context"
input_mask = key_padding_mask
x = query.transpose(0, 1)
b, t, _, h, dh = *x.shape, self.heads, self.dim_head
has_local, has_global = map(
lambda x: x > 0, (self.local_attn_heads, self.global_attn_heads)
)
split_heads = (
lambda v: reshape_dim(v, -1, (-1, dh)).transpose(1, 2).contiguous()
)
if has_local:
local_qkv = self.local_to_qkv(x).chunk(3, dim=-1)
lq, lk, lv = map(split_heads, local_qkv)
if has_global:
kv_input = x if not self.receives_context else context
q, v = self.to_q(x), self.to_v(kv_input)
if not self.shared_qk:
k = self.to_k(kv_input)
else:
k = self.to_q(kv_input) if self.receives_context else q
q, k, v = map(split_heads, (q, k, v))
out = []
total_loss = torch.tensor(0.0, requires_grad=True, **to(x))
if has_local:
local_out = self.local_attn(lq, lk, lv, input_mask=input_mask)
out.append(local_out)
if has_global:
if not self.receives_context and exists(pos_emb):
q, k = apply_rotary_pos_emb(q, k, pos_emb)
global_out, loss = self.global_attn(
q, k, v, query_mask=input_mask, key_mask=context_mask
)
total_loss = total_loss + loss
out.append(global_out)
out = torch.cat(out, dim=1)
out = out.reshape(b, h, t, -1).transpose(1, 2).reshape(b, t, -1)
out = self.dropout(out.transpose(0, 1))
# out = self.to_out(out)
return out, total_loss
| 21,840 | 28.316779 | 88 | py |
CIF-HieraDist | CIF-HieraDist-main/fairseq/modules/quant_noise.py | # 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 torch
import torch.nn as nn
def quant_noise(module, p, block_size):
"""
Wraps modules and applies quantization noise to the weights for
subsequent quantization with Iterative Product Quantization as
described in "Training with Quantization Noise for Extreme Model Compression"
Args:
- module: nn.Module
- p: amount of Quantization Noise
- block_size: size of the blocks for subsequent quantization with iPQ
Remarks:
- Module weights must have the right sizes wrt the block size
- Only Linear, Embedding and Conv2d modules are supported for the moment
- For more detail on how to quantize by blocks with convolutional weights,
see "And the Bit Goes Down: Revisiting the Quantization of Neural Networks"
- We implement the simplest form of noise here as stated in the paper
which consists in randomly dropping blocks
"""
# if no quantization noise, don't register hook
if p <= 0:
return module
# supported modules
assert isinstance(module, (nn.Linear, nn.Embedding, nn.Conv2d))
# test whether module.weight has the right sizes wrt block_size
is_conv = module.weight.ndim == 4
# 2D matrix
if not is_conv:
assert (
module.weight.size(1) % block_size == 0
), "Input features must be a multiple of block sizes"
# 4D matrix
else:
# 1x1 convolutions
if module.kernel_size == (1, 1):
assert (
module.in_channels % block_size == 0
), "Input channels must be a multiple of block sizes"
# regular convolutions
else:
k = module.kernel_size[0] * module.kernel_size[1]
assert k % block_size == 0, "Kernel size must be a multiple of block size"
def _forward_pre_hook(mod, input):
# no noise for evaluation
if mod.training:
if not is_conv:
# gather weight and sizes
weight = mod.weight
in_features = weight.size(1)
out_features = weight.size(0)
# split weight matrix into blocks and randomly drop selected blocks
mask = torch.zeros(
in_features // block_size * out_features, device=weight.device
)
mask.bernoulli_(p)
mask = mask.repeat_interleave(block_size, -1).view(-1, in_features)
else:
# gather weight and sizes
weight = mod.weight
in_channels = mod.in_channels
out_channels = mod.out_channels
# split weight matrix into blocks and randomly drop selected blocks
if mod.kernel_size == (1, 1):
mask = torch.zeros(
int(in_channels // block_size * out_channels),
device=weight.device,
)
mask.bernoulli_(p)
mask = mask.repeat_interleave(block_size, -1).view(-1, in_channels)
else:
mask = torch.zeros(
weight.size(0), weight.size(1), device=weight.device
)
mask.bernoulli_(p)
mask = (
mask.unsqueeze(2)
.unsqueeze(3)
.repeat(1, 1, mod.kernel_size[0], mod.kernel_size[1])
)
# scale weights and apply mask
mask = mask.to(
torch.bool
) # x.bool() is not currently supported in TorchScript
s = 1 / (1 - p)
mod.weight.data = s * weight.masked_fill(mask, 0)
module.register_forward_pre_hook(_forward_pre_hook)
return module
| 4,005 | 36.092593 | 87 | py |
CIF-HieraDist | CIF-HieraDist-main/fairseq/modules/gelu.py | # 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.
"""
See "Gaussian Error Linear Units (GELUs)" by Dan Hendrycks and Kevin Gimpel with
the corresponding GitHub repo: https://github.com/hendrycks/GELUs
"""
import math
import torch
import torch.nn as nn
def gelu_accurate(x):
if not hasattr(gelu_accurate, "_a"):
gelu_accurate._a = math.sqrt(2 / math.pi)
return (
0.5 * x * (1 + torch.tanh(gelu_accurate._a * (x + 0.044715 * torch.pow(x, 3))))
)
def gelu(x: torch.Tensor) -> torch.Tensor:
return torch.nn.functional.gelu(x.float()).type_as(x)
| 706 | 26.192308 | 87 | py |
CIF-HieraDist | CIF-HieraDist-main/fairseq/modules/lstm_cell_with_zoneout.py | # 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 torch.nn as nn
class LSTMCellWithZoneOut(nn.Module):
"""
Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations
https://arxiv.org/abs/1606.01305
"""
def __init__(
self, prob: float, input_size: int, hidden_size: int, bias: bool = True
):
super(LSTMCellWithZoneOut, self).__init__()
self.lstm_cell = nn.LSTMCell(input_size, hidden_size, bias=bias)
self.prob = prob
if prob > 1.0 or prob < 0.0:
raise ValueError(
"zoneout probability must be in the range from " "0.0 to 1.0."
)
def zoneout(self, h, next_h, prob):
if isinstance(h, tuple):
return tuple([self.zoneout(h[i], next_h[i], prob) for i in range(len(h))])
if self.training:
mask = h.new_zeros(*h.size()).bernoulli_(prob)
return mask * h + (1 - mask) * next_h
return prob * h + (1 - prob) * next_h
def forward(self, x, h):
return self.zoneout(h, self.lstm_cell(x, h), self.prob)
| 1,220 | 31.131579 | 86 | py |
CIF-HieraDist | CIF-HieraDist-main/fairseq/modules/positional_embedding.py | # 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 torch.nn as nn
from .learned_positional_embedding import LearnedPositionalEmbedding
from .sinusoidal_positional_embedding import SinusoidalPositionalEmbedding
def PositionalEmbedding(
num_embeddings: int,
embedding_dim: int,
padding_idx: int,
learned: bool = False,
):
if learned:
# if padding_idx is specified then offset the embedding ids by
# this index and adjust num_embeddings appropriately
# TODO: The right place for this offset would be inside
# LearnedPositionalEmbedding. Move this there for a cleaner implementation.
if padding_idx is not None:
num_embeddings = num_embeddings + padding_idx + 1
m = LearnedPositionalEmbedding(num_embeddings, embedding_dim, padding_idx)
nn.init.normal_(m.weight, mean=0, std=embedding_dim**-0.5)
if padding_idx is not None:
nn.init.constant_(m.weight[padding_idx], 0)
else:
m = SinusoidalPositionalEmbedding(
embedding_dim,
padding_idx,
init_size=num_embeddings + padding_idx + 1,
)
return m
| 1,293 | 34.944444 | 83 | py |
CIF-HieraDist | CIF-HieraDist-main/fairseq/modules/fairseq_dropout.py | # 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 logging
from typing import List, Optional
import torch.nn as nn
import torch.nn.functional as F
logger = logging.getLogger(__name__)
class FairseqDropout(nn.Module):
def __init__(self, p, module_name=None):
super().__init__()
self.p = p
self.module_name = module_name
self.apply_during_inference = False
def forward(self, x, inplace: bool = False):
if self.p > 0 and (self.training or self.apply_during_inference):
return F.dropout(x, p=self.p, training=True, inplace=inplace)
else:
return x
def make_generation_fast_(
self,
name: str,
retain_dropout: bool = False,
retain_dropout_modules: Optional[List[str]] = None,
**kwargs
):
if retain_dropout:
if retain_dropout_modules is not None and self.module_name is None:
logger.warning(
"Cannot enable dropout during inference for module {} "
"because module_name was not set".format(name)
)
elif (
retain_dropout_modules is None # if None, apply to all modules
or self.module_name in retain_dropout_modules
):
logger.info(
"Enabling dropout during inference for module: {}".format(name)
)
self.apply_during_inference = True
else:
logger.info("Disabling dropout for module: {}".format(name))
| 1,703 | 31.769231 | 83 | py |
CIF-HieraDist | CIF-HieraDist-main/fairseq/modules/cross_entropy.py | # 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 logging
import torch
import torch.nn.functional as F
logger = logging.getLogger(__name__)
def _cross_entropy_pytorch(logits, target, ignore_index=None, reduction="mean"):
lprobs = F.log_softmax(logits, dim=-1, dtype=torch.float32)
return F.nll_loss(
lprobs,
target,
ignore_index=ignore_index,
reduction=reduction,
)
try:
import xentropy_cuda
from apex.contrib import xentropy
def cross_entropy(logits, target, ignore_index=-100, reduction="mean"):
if logits.device == torch.device("cpu"):
return _cross_entropy_pytorch(logits, target, ignore_index, reduction)
else:
if not getattr(cross_entropy, "_has_logged_once", False):
logger.info("using fused cross entropy")
cross_entropy._has_logged_once = True
half_to_float = logits.dtype == torch.half
losses = xentropy.SoftmaxCrossEntropyLoss.apply(
logits,
target,
0.0,
ignore_index,
half_to_float,
)
if reduction == "sum":
return losses.sum()
elif reduction == "mean":
if ignore_index >= 0:
return losses.sum() / target.ne(ignore_index).sum()
else:
return losses.mean()
elif reduction == "none":
return losses
else:
raise NotImplementedError
except ImportError:
def cross_entropy(logits, target, ignore_index=-100, reduction="mean"):
return _cross_entropy_pytorch(logits, target, ignore_index, reduction)
| 1,872 | 29.704918 | 82 | py |
CIF-HieraDist | CIF-HieraDist-main/fairseq/modules/adaptive_input.py | # 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.
from typing import List
import torch
from fairseq.modules.quant_noise import quant_noise
from torch import nn
class AdaptiveInput(nn.Module):
def __init__(
self,
vocab_size: int,
padding_idx: int,
initial_dim: int,
factor: float,
output_dim: int,
cutoff: List[int],
q_noise: float = 0,
qn_block_size: int = 8,
):
super().__init__()
if vocab_size > cutoff[-1]:
cutoff = cutoff + [vocab_size]
else:
assert (
vocab_size == cutoff[-1]
), "cannot specify cutoff larger than vocab size"
self.cutoff = cutoff
self.embedding_dim = output_dim
self.padding_idx = padding_idx
self.embeddings = nn.ModuleList()
for i in range(len(self.cutoff)):
prev = self.cutoff[i - 1] if i > 0 else 0
size = self.cutoff[i] - prev
dim = int(initial_dim // (factor**i))
seq = nn.Sequential(
nn.Embedding(size, dim, self.padding_idx),
quant_noise(
nn.Linear(dim, output_dim, bias=False), q_noise, qn_block_size
),
)
self.embeddings.append(seq)
self.padding_idx = None
self.padding_idx = padding_idx
def init_weights(m):
if isinstance(m, nn.Embedding):
nn.init.normal_(m.weight, mean=0, std=m.weight.shape[1] ** -0.5)
nn.init.constant_(m.weight[padding_idx], 0)
elif hasattr(m, "weight"):
nn.init.xavier_uniform_(m.weight)
self.apply(init_weights)
self.register_buffer("_float_tensor", torch.FloatTensor(1))
def weights_for_band(self, band: int):
return self.embeddings[band][0].weight, self.embeddings[band][1].weight
def forward(self, input: torch.Tensor):
result = self._float_tensor.new(input.shape + (self.embedding_dim,))
for i in range(len(self.cutoff)):
mask = input.lt(self.cutoff[i])
if i > 0:
mask.mul_(input.ge(self.cutoff[i - 1]))
chunk_input = input[mask] - self.cutoff[i - 1]
else:
chunk_input = input[mask]
if mask.any():
result[mask] = self.embeddings[i](chunk_input)
return result
| 2,564 | 30.666667 | 82 | py |
CIF-HieraDist | CIF-HieraDist-main/fairseq/modules/gumbel_vector_quantizer.py | # 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 torch
import torch.nn as nn
import torch.nn.functional as F
class GumbelVectorQuantizer(nn.Module):
def __init__(
self,
dim,
num_vars,
temp,
groups,
combine_groups,
vq_dim,
time_first,
activation=nn.GELU(),
weight_proj_depth=1,
weight_proj_factor=1,
):
"""Vector quantization using gumbel softmax
Args:
dim: input dimension (channels)
num_vars: number of quantized vectors per group 每个组中的量化矢量个数
temp: temperature for training. this should be a tuple of 3 elements: (start, stop, decay factor)
groups: number of groups for vector quantization 量化模块中的组的个数
combine_groups: whether to use the vectors for all groups
vq_dim: dimensionality of the resulting quantized vector
time_first: if true, expect input in BxTxC format, otherwise in BxCxT
activation: what activation to use (should be a module). this is only used if weight_proj_depth is > 1
weight_proj_depth: number of layers (with activation in between) to project input before computing logits
weight_proj_factor: this is used only if weight_proj_depth is > 1. scales the inner dimensionality of
projections by this factor
"""
super().__init__()
self.groups = groups
self.combine_groups = combine_groups
self.input_dim = dim
self.num_vars = num_vars
self.time_first = time_first
assert (
vq_dim % groups == 0
), f"dim {vq_dim} must be divisible by groups {groups} for concatenation"
var_dim = vq_dim // groups
num_groups = groups if not combine_groups else 1
# build codebooks
self.vars = nn.Parameter(torch.FloatTensor(1, num_groups * num_vars, var_dim))
nn.init.uniform_(self.vars) # random initialization
if weight_proj_depth > 1:
def block(input_dim, output_dim):
return nn.Sequential(nn.Linear(input_dim, output_dim), activation)
inner_dim = self.input_dim * weight_proj_factor
self.weight_proj = nn.Sequential(
*[
block(self.input_dim if i == 0 else inner_dim, inner_dim)
for i in range(weight_proj_depth - 1)
],
nn.Linear(inner_dim, groups * num_vars),
)
else:
self.weight_proj = nn.Linear(self.input_dim, groups * num_vars)
nn.init.normal_(self.weight_proj.weight, mean=0, std=1)
nn.init.zeros_(self.weight_proj.bias)
if isinstance(temp, str):
import ast
temp = ast.literal_eval(temp)
assert len(temp) == 3, f"{temp}, {len(temp)}"
self.max_temp, self.min_temp, self.temp_decay = temp
self.curr_temp = self.max_temp
self.codebook_indices = None
def set_num_updates(self, num_updates):
self.curr_temp = max(
self.max_temp * self.temp_decay**num_updates, self.min_temp
)
def get_codebook_indices(self):
if self.codebook_indices is None:
from itertools import product
# Get the indices of codebooks
p = [range(self.num_vars)] * self.groups
inds = list(product(*p))
self.codebook_indices = torch.tensor(
inds, dtype=torch.long, device=self.vars.device
).flatten()
if not self.combine_groups:
self.codebook_indices = self.codebook_indices.view(
self.num_vars**self.groups, -1
)
for b in range(1, self.groups):
self.codebook_indices[:, b] += self.num_vars * b
self.codebook_indices = self.codebook_indices.flatten()
return self.codebook_indices
def codebook(self):
indices = self.get_codebook_indices()
return (
self.vars.squeeze(0)
.index_select(0, indices)
.view(self.num_vars**self.groups, -1)
)
def sample_from_codebook(self, b, n):
indices = self.get_codebook_indices()
indices = indices.view(-1, self.groups)
cb_size = indices.size(0)
assert (
n < cb_size
), f"sample size {n} is greater than size of codebook {cb_size}"
sample_idx = torch.randint(low=0, high=cb_size, size=(b * n,))
indices = indices[sample_idx]
z = self.vars.squeeze(0).index_select(0, indices.flatten()).view(b, n, -1)
return z
def to_codebook_index(self, indices):
res = indices.new_full(indices.shape[:-1], 0)
for i in range(self.groups):
exponent = self.groups - i - 1
res += indices[..., i] * (self.num_vars**exponent)
return res
def forward_idx(self, x):
res = self.forward(x, produce_targets=True)
return res["x"], res["targets"]
def forward(self, x, produce_targets=False):
result = {"num_vars": self.num_vars * self.groups}
if not self.time_first:
x = x.transpose(1, 2)
bsz, tsz, fsz = x.shape # B x T x C
x = x.reshape(-1, fsz)
x = self.weight_proj(x) # B x T x (G x V)
x = x.view(
bsz * tsz * self.groups, -1
) # Split to (B x T x G) x (C / G) or (B x T x G) x (V), which is actually z in paper
_, k = x.max(-1)
hard_x = (
x.new_zeros(*x.shape)
.scatter_(-1, k.view(-1, 1), 1.0)
.view(bsz * tsz, self.groups, -1)
)
hard_probs = torch.mean(hard_x.float(), dim=0)
result["code_perplexity"] = torch.exp(
-torch.sum(hard_probs * torch.log(hard_probs + 1e-7), dim=-1)
).sum()
avg_probs = torch.softmax(
x.view(bsz * tsz, self.groups, -1).float(), dim=-1
).mean(dim=0)
result["prob_perplexity"] = torch.exp(
-torch.sum(avg_probs * torch.log(avg_probs + 1e-7), dim=-1)
).sum()
result["temp"] = self.curr_temp
if self.training:
x = F.gumbel_softmax(x.float(), tau=self.curr_temp, hard=True).type_as(
x
) # (B x T x G) x V
else:
x = hard_x # hard labels
x = x.view(bsz * tsz, -1) # (B x T) x (G x V)
vars = self.vars # Get codebooks， 1 x (G x V) x (C / G)
if self.combine_groups:
vars = vars.repeat(1, self.groups, 1)
if produce_targets:
result["targets"] = (
x.view(bsz * tsz * self.groups, -1)
.argmax(dim=-1)
.view(bsz, tsz, self.groups)
.detach()
) # final targets have shape B x T x G
x = x.unsqueeze(-1) * vars
# Fetch quantized vectors from codebooks
x = x.view(bsz * tsz, self.groups, self.num_vars, -1)
# (B x T) x G x V x (C / G)
x = x.sum(-2)
# (B x T) x G x (C / G), here, mainly to retain the only one quantized vector that is not zero vector
x = x.view(bsz, tsz, -1)
# B x T x C
if not self.time_first:
x = x.transpose(1, 2) # BTC -> BCT
result["x"] = x # B x T x C
return result
| 7,540 | 34.403756 | 117 | py |
CIF-HieraDist | CIF-HieraDist-main/fairseq/modules/vggblock.py | # 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.
from __future__ import absolute_import, division, print_function, unicode_literals
from collections.abc import Iterable
from itertools import repeat
import torch
import torch.nn as nn
def _pair(v):
if isinstance(v, Iterable):
assert len(v) == 2, "len(v) != 2"
return v
return tuple(repeat(v, 2))
def infer_conv_output_dim(conv_op, input_dim, sample_inchannel):
sample_seq_len = 200
sample_bsz = 10
x = torch.randn(sample_bsz, sample_inchannel, sample_seq_len, input_dim)
# N x C x H x W
# N: sample_bsz, C: sample_inchannel, H: sample_seq_len, W: input_dim
x = conv_op(x)
# N x C x H x W
x = x.transpose(1, 2)
# N x H x C x W
bsz, seq = x.size()[:2]
per_channel_dim = x.size()[3]
# bsz: N, seq: H, CxW the rest
return x.contiguous().view(bsz, seq, -1).size(-1), per_channel_dim
class VGGBlock(torch.nn.Module):
"""
VGG motibated cnn module https://arxiv.org/pdf/1409.1556.pdf
Args:
in_channels: (int) number of input channels (typically 1)
out_channels: (int) number of output channels
conv_kernel_size: convolution channels
pooling_kernel_size: the size of the pooling window to take a max over
num_conv_layers: (int) number of convolution layers
input_dim: (int) input dimension
conv_stride: the stride of the convolving kernel.
Can be a single number or a tuple (sH, sW) Default: 1
padding: implicit paddings on both sides of the input.
Can be a single number or a tuple (padH, padW). Default: None
layer_norm: (bool) if layer norm is going to be applied. Default: False
Shape:
Input: BxCxTxfeat, i.e. (batch_size, input_size, timesteps, features)
Output: BxCxTxfeat, i.e. (batch_size, input_size, timesteps, features)
"""
def __init__(
self,
in_channels,
out_channels,
conv_kernel_size,
pooling_kernel_size,
num_conv_layers,
input_dim,
conv_stride=1,
padding=None,
layer_norm=False,
):
assert (
input_dim is not None
), "Need input_dim for LayerNorm and infer_conv_output_dim"
super(VGGBlock, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.conv_kernel_size = _pair(conv_kernel_size)
self.pooling_kernel_size = _pair(pooling_kernel_size)
self.num_conv_layers = num_conv_layers
self.padding = (
tuple(e // 2 for e in self.conv_kernel_size)
if padding is None
else _pair(padding)
)
self.conv_stride = _pair(conv_stride)
self.layers = nn.ModuleList()
for layer in range(num_conv_layers):
conv_op = nn.Conv2d(
in_channels if layer == 0 else out_channels,
out_channels,
self.conv_kernel_size,
stride=self.conv_stride,
padding=self.padding,
)
self.layers.append(conv_op)
if layer_norm:
conv_output_dim, per_channel_dim = infer_conv_output_dim(
conv_op, input_dim, in_channels if layer == 0 else out_channels
)
self.layers.append(nn.LayerNorm(per_channel_dim))
input_dim = per_channel_dim
self.layers.append(nn.ReLU())
if self.pooling_kernel_size is not None:
pool_op = nn.MaxPool2d(kernel_size=self.pooling_kernel_size, ceil_mode=True)
self.layers.append(pool_op)
self.total_output_dim, self.output_dim = infer_conv_output_dim(
pool_op, input_dim, out_channels
)
def forward(self, x):
for i, _ in enumerate(self.layers):
x = self.layers[i](x)
return x
| 4,057 | 33.683761 | 88 | py |
CIF-HieraDist | CIF-HieraDist-main/fairseq/modules/character_token_embedder.py | # 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 logging
from typing import List, Tuple
import torch
import torch.nn.functional as F
from fairseq.data import Dictionary
from torch import nn
CHAR_PAD_IDX = 0
CHAR_EOS_IDX = 257
logger = logging.getLogger(__name__)
class CharacterTokenEmbedder(torch.nn.Module):
def __init__(
self,
vocab: Dictionary,
filters: List[Tuple[int, int]],
char_embed_dim: int,
word_embed_dim: int,
highway_layers: int,
max_char_len: int = 50,
char_inputs: bool = False,
):
super(CharacterTokenEmbedder, self).__init__()
self.onnx_trace = False
self.embedding_dim = word_embed_dim
self.max_char_len = max_char_len
self.char_embeddings = nn.Embedding(257, char_embed_dim, padding_idx=0)
self.symbol_embeddings = nn.Parameter(torch.FloatTensor(2, word_embed_dim))
self.eos_idx, self.unk_idx = 0, 1
self.char_inputs = char_inputs
self.convolutions = nn.ModuleList()
for width, out_c in filters:
self.convolutions.append(
nn.Conv1d(char_embed_dim, out_c, kernel_size=width)
)
last_dim = sum(f[1] for f in filters)
self.highway = Highway(last_dim, highway_layers) if highway_layers > 0 else None
self.projection = nn.Linear(last_dim, word_embed_dim)
assert (
vocab is not None or char_inputs
), "vocab must be set if not using char inputs"
self.vocab = None
if vocab is not None:
self.set_vocab(vocab, max_char_len)
self.reset_parameters()
def prepare_for_onnx_export_(self):
self.onnx_trace = True
def set_vocab(self, vocab, max_char_len):
word_to_char = torch.LongTensor(len(vocab), max_char_len)
truncated = 0
for i in range(len(vocab)):
if i < vocab.nspecial:
char_idxs = [0] * max_char_len
else:
chars = vocab[i].encode()
# +1 for padding
char_idxs = [c + 1 for c in chars] + [0] * (max_char_len - len(chars))
if len(char_idxs) > max_char_len:
truncated += 1
char_idxs = char_idxs[:max_char_len]
word_to_char[i] = torch.LongTensor(char_idxs)
if truncated > 0:
logger.info(
"truncated {} words longer than {} characters".format(
truncated, max_char_len
)
)
self.vocab = vocab
self.word_to_char = word_to_char
@property
def padding_idx(self):
return Dictionary().pad() if self.vocab is None else self.vocab.pad()
def reset_parameters(self):
nn.init.xavier_normal_(self.char_embeddings.weight)
nn.init.xavier_normal_(self.symbol_embeddings)
nn.init.xavier_uniform_(self.projection.weight)
nn.init.constant_(
self.char_embeddings.weight[self.char_embeddings.padding_idx], 0.0
)
nn.init.constant_(self.projection.bias, 0.0)
def forward(
self,
input: torch.Tensor,
):
if self.char_inputs:
chars = input.view(-1, self.max_char_len)
pads = chars[:, 0].eq(CHAR_PAD_IDX)
eos = chars[:, 0].eq(CHAR_EOS_IDX)
if eos.any():
if self.onnx_trace:
chars = torch.where(eos.unsqueeze(1), chars.new_zeros(1), chars)
else:
chars[eos] = 0
unk = None
else:
flat_words = input.view(-1)
chars = self.word_to_char[flat_words.type_as(self.word_to_char)].type_as(
input
)
pads = flat_words.eq(self.vocab.pad())
eos = flat_words.eq(self.vocab.eos())
unk = flat_words.eq(self.vocab.unk())
word_embs = self._convolve(chars)
if self.onnx_trace:
if pads.any():
word_embs = torch.where(
pads.unsqueeze(1), word_embs.new_zeros(1), word_embs
)
if eos.any():
word_embs = torch.where(
eos.unsqueeze(1), self.symbol_embeddings[self.eos_idx], word_embs
)
if unk is not None and unk.any():
word_embs = torch.where(
unk.unsqueeze(1), self.symbol_embeddings[self.unk_idx], word_embs
)
else:
if pads.any():
word_embs[pads] = 0
if eos.any():
word_embs[eos] = self.symbol_embeddings[self.eos_idx]
if unk is not None and unk.any():
word_embs[unk] = self.symbol_embeddings[self.unk_idx]
return word_embs.view(input.size()[:2] + (-1,))
def _convolve(
self,
char_idxs: torch.Tensor,
):
char_embs = self.char_embeddings(char_idxs)
char_embs = char_embs.transpose(1, 2) # BTC -> BCT
conv_result = []
for conv in self.convolutions:
x = conv(char_embs)
x, _ = torch.max(x, -1)
x = F.relu(x)
conv_result.append(x)
x = torch.cat(conv_result, dim=-1)
if self.highway is not None:
x = self.highway(x)
x = self.projection(x)
return x
class Highway(torch.nn.Module):
"""
A `Highway layer <https://arxiv.org/abs/1505.00387>`_.
Adopted from the AllenNLP implementation.
"""
def __init__(self, input_dim: int, num_layers: int = 1):
super(Highway, self).__init__()
self.input_dim = input_dim
self.layers = nn.ModuleList(
[nn.Linear(input_dim, input_dim * 2) for _ in range(num_layers)]
)
self.activation = nn.ReLU()
self.reset_parameters()
def reset_parameters(self):
for layer in self.layers:
# As per comment in AllenNLP:
# We should bias the highway layer to just carry its input forward. We do that by
# setting the bias on `B(x)` to be positive, because that means `g` will be biased to
# be high, so we will carry the input forward. The bias on `B(x)` is the second half
# of the bias vector in each Linear layer.
nn.init.constant_(layer.bias[self.input_dim :], 1)
nn.init.constant_(layer.bias[: self.input_dim], 0)
nn.init.xavier_normal_(layer.weight)
def forward(self, x: torch.Tensor):
for layer in self.layers:
projection = layer(x)
proj_x, gate = projection.chunk(2, dim=-1)
proj_x = self.activation(proj_x)
gate = torch.sigmoid(gate)
x = gate * x + (gate.new_tensor([1]) - gate) * proj_x
return x
| 6,974 | 31.44186 | 97 | py |
CIF-HieraDist | CIF-HieraDist-main/fairseq/modules/unfold.py | # 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 torch.nn.functional as F
def unfold1d(x, kernel_size, padding_l, pad_value=0):
"""unfold T x B x C to T x B x C x K"""
if kernel_size > 1:
T, B, C = x.size()
x = F.pad(
x, (0, 0, 0, 0, padding_l, kernel_size - 1 - padding_l), value=pad_value
)
x = x.as_strided((T, B, C, kernel_size), (B * C, C, 1, B * C))
else:
x = x.unsqueeze(3)
return x
| 596 | 28.85 | 84 | py |
CIF-HieraDist | CIF-HieraDist-main/fairseq/modules/fp32_group_norm.py | # 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.
"""
Layer norm done in fp32 (for fp16 training)
"""
import torch.nn as nn
import torch.nn.functional as F
class Fp32GroupNorm(nn.GroupNorm):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def forward(self, input):
output = F.group_norm(
input.float(),
self.num_groups,
self.weight.float() if self.weight is not None else None,
self.bias.float() if self.bias is not None else None,
self.eps,
)
return output.type_as(input)
| 727 | 27 | 69 | py |
CIF-HieraDist | CIF-HieraDist-main/fairseq/modules/adaptive_softmax.py | # 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 functools
import operator
import torch
import torch.nn.functional as F
from fairseq.modules.fairseq_dropout import FairseqDropout
from fairseq.modules.quant_noise import quant_noise
from torch import nn
class TiedLinear(nn.Module):
def __init__(self, weight, transpose):
super().__init__()
self.weight = weight
self.transpose = transpose
def forward(self, input):
return F.linear(input, self.weight.t() if self.transpose else self.weight)
class TiedHeadModule(nn.Module):
def __init__(self, weights, input_dim, num_classes, q_noise, qn_block_size):
super().__init__()
tied_emb, _ = weights
self.num_words, emb_dim = tied_emb.size()
self.word_proj = quant_noise(
TiedLinear(tied_emb, transpose=False), q_noise, qn_block_size
)
if input_dim != emb_dim:
self.word_proj = nn.Sequential(
quant_noise(
nn.Linear(input_dim, emb_dim, bias=False), q_noise, qn_block_size
),
self.word_proj,
)
self.class_proj = quant_noise(
nn.Linear(input_dim, num_classes, bias=False), q_noise, qn_block_size
)
self.out_dim = self.num_words + num_classes
self.register_buffer("_float_tensor", torch.FloatTensor(1))
def forward(self, input):
inp_sz = functools.reduce(operator.mul, input.shape[:-1], 1)
out = self._float_tensor.new(inp_sz, self.out_dim)
out[:, : self.num_words] = self.word_proj(input.view(inp_sz, -1))
out[:, self.num_words :] = self.class_proj(input.view(inp_sz, -1))
return out
class AdaptiveSoftmax(nn.Module):
"""
This is an implementation of the efficient softmax approximation for
graphical processing units (GPU), described in the paper "Efficient softmax
approximation for GPUs" (http://arxiv.org/abs/1609.04309).
"""
def __init__(
self,
vocab_size,
input_dim,
cutoff,
dropout,
factor=4.0,
adaptive_inputs=None,
tie_proj=False,
q_noise=0,
qn_block_size=8,
):
super().__init__()
if vocab_size > cutoff[-1]:
cutoff = cutoff + [vocab_size]
else:
assert (
vocab_size == cutoff[-1]
), "cannot specify cutoff larger than vocab size"
output_dim = cutoff[0] + len(cutoff) - 1
self.vocab_size = vocab_size
self.cutoff = cutoff
self.dropout_module = FairseqDropout(
dropout, module_name=self.__class__.__name__
)
self.input_dim = input_dim
self.factor = factor
self.q_noise = q_noise
self.qn_block_size = qn_block_size
self.lsm = nn.LogSoftmax(dim=1)
if adaptive_inputs is not None:
self.head = TiedHeadModule(
adaptive_inputs.weights_for_band(0),
input_dim,
len(cutoff) - 1,
self.q_noise,
self.qn_block_size,
)
else:
self.head = quant_noise(
nn.Linear(input_dim, output_dim, bias=False),
self.q_noise,
self.qn_block_size,
)
self._make_tail(adaptive_inputs, tie_proj)
def init_weights(m):
if (
hasattr(m, "weight")
and not isinstance(m, TiedLinear)
and not isinstance(m, TiedHeadModule)
):
nn.init.xavier_uniform_(m.weight)
self.apply(init_weights)
self.register_buffer("version", torch.LongTensor([1]))
def _make_tail(self, adaptive_inputs=None, tie_proj=False):
self.tail = nn.ModuleList()
for i in range(len(self.cutoff) - 1):
dim = int(self.input_dim // self.factor ** (i + 1))
tied_emb, tied_proj = (
adaptive_inputs.weights_for_band(i + 1)
if adaptive_inputs is not None
else (None, None)
)
if tied_proj is not None:
if tie_proj:
proj = quant_noise(
TiedLinear(tied_proj, transpose=True),
self.q_noise,
self.qn_block_size,
)
else:
proj = quant_noise(
nn.Linear(tied_proj.size(0), tied_proj.size(1), bias=False),
self.q_noise,
self.qn_block_size,
)
else:
proj = quant_noise(
nn.Linear(self.input_dim, dim, bias=False),
self.q_noise,
self.qn_block_size,
)
if tied_emb is None:
out_proj = nn.Linear(
dim, self.cutoff[i + 1] - self.cutoff[i], bias=False
)
else:
out_proj = TiedLinear(tied_emb, transpose=False)
m = nn.Sequential(
proj,
nn.Dropout(self.dropout_module.p),
quant_noise(out_proj, self.q_noise, self.qn_block_size),
)
self.tail.append(m)
def upgrade_state_dict_named(self, state_dict, name):
version_name = name + ".version"
if version_name not in state_dict:
raise Exception("This version of the model is no longer supported")
def adapt_target(self, target):
"""
In order to be efficient, the AdaptiveSoftMax does not compute the
scores for all the word of the vocabulary for all the examples. It is
thus necessary to call the method adapt_target of the AdaptiveSoftMax
layer inside each forward pass.
"""
target = target.view(-1)
new_target = [target.clone()]
target_idxs = []
for i in range(len(self.cutoff) - 1):
mask = target.ge(self.cutoff[i]).mul(target.lt(self.cutoff[i + 1]))
new_target[0][mask] = self.cutoff[0] + i
if mask.any():
target_idxs.append(mask.nonzero(as_tuple=False).squeeze(1))
new_target.append(target[mask].add(-self.cutoff[i]))
else:
target_idxs.append(None)
new_target.append(None)
return new_target, target_idxs
def forward(self, input, target):
"""
Args:
input: (b x t x d)
target: (b x t)
Returns:
2 lists: output for each cutoff section and new targets by cut off
"""
input = input.contiguous().view(-1, input.size(-1))
input = self.dropout_module(input)
new_target, target_idxs = self.adapt_target(target)
output = [self.head(input)]
for i in range(len(target_idxs)):
if target_idxs[i] is not None:
output.append(self.tail[i](input.index_select(0, target_idxs[i])))
else:
output.append(None)
return output, new_target
def get_log_prob(self, input, target):
"""
Computes the log probabilities for all the words of the vocabulary,
given a 2D tensor of hidden vectors.
"""
bsz, length, dim = input.size()
input = input.contiguous().view(-1, dim)
if target is not None:
_, target_idxs = self.adapt_target(target)
else:
target_idxs = None
head_y = self.head(input)
log_probs = head_y.new_zeros(input.size(0), self.vocab_size)
head_sz = self.cutoff[0] + len(self.tail)
log_probs[:, :head_sz] = self.lsm(head_y)
tail_priors = log_probs[:, self.cutoff[0] : head_sz].clone()
for i in range(len(self.tail)):
start = self.cutoff[i]
end = self.cutoff[i + 1]
if target_idxs is None:
tail_out = log_probs[:, start:end]
tail_out.copy_(self.tail[i](input))
log_probs[:, start:end] = self.lsm(tail_out).add_(
tail_priors[:, i, None]
)
elif target_idxs[i] is not None:
idxs = target_idxs[i]
tail_out = log_probs[idxs, start:end]
tail_out.copy_(self.tail[i](input[idxs]))
log_probs[idxs, start:end] = self.lsm(tail_out).add_(
tail_priors[idxs, i, None]
)
log_probs = log_probs.view(bsz, length, -1)
return log_probs
| 8,789 | 31.67658 | 85 | py |
CIF-HieraDist | CIF-HieraDist-main/fairseq/modules/conformer_layer.py | # 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 torch
from typing import Optional
from fairseq.modules import (
LayerNorm,
MultiheadAttention,
ESPNETMultiHeadedAttention,
RelPositionMultiHeadedAttention,
RotaryPositionMultiHeadedAttention,
)
from fairseq.utils import get_activation_fn
class ConvolutionModule(torch.nn.Module):
"""Convolution block used in the conformer block"""
def __init__(
self,
embed_dim,
channels,
depthwise_kernel_size,
dropout,
activation_fn="swish",
bias=False,
export=False,
):
"""
Args:
embed_dim: Embedding dimension
channels: Number of channels in depthwise conv layers
depthwise_kernel_size: Depthwise conv layer kernel size
dropout: dropout value
activation_fn: Activation function to use after depthwise convolution kernel
bias: If bias should be added to conv layers
export: If layernorm should be exported to jit
"""
super(ConvolutionModule, self).__init__()
assert (
depthwise_kernel_size - 1
) % 2 == 0, "kernel_size should be a odd number for 'SAME' padding"
self.layer_norm = LayerNorm(embed_dim, export=export)
self.pointwise_conv1 = torch.nn.Conv1d(
embed_dim,
2 * channels,
kernel_size=1,
stride=1,
padding=0,
bias=bias,
)
self.glu = torch.nn.GLU(dim=1)
self.depthwise_conv = torch.nn.Conv1d(
channels,
channels,
depthwise_kernel_size,
stride=1,
padding=(depthwise_kernel_size - 1) // 2,
groups=channels,
bias=bias,
)
self.batch_norm = torch.nn.BatchNorm1d(channels)
self.activation = get_activation_fn(activation_fn)(channels)
self.pointwise_conv2 = torch.nn.Conv1d(
channels,
embed_dim,
kernel_size=1,
stride=1,
padding=0,
bias=bias,
)
self.dropout = torch.nn.Dropout(dropout)
def forward(self, x):
"""
Args:
x: Input of shape B X T X C
Returns:
Tensor of shape B X T X C
"""
x = self.layer_norm(x)
# exchange the temporal dimension and the feature dimension
x = x.transpose(1, 2)
# GLU mechanism
x = self.pointwise_conv1(x) # (batch, 2*channel, dim)
x = self.glu(x) # (batch, channel, dim)
# 1D Depthwise Conv
x = self.depthwise_conv(x)
x = self.batch_norm(x)
x = self.activation(x)
x = self.pointwise_conv2(x)
x = self.dropout(x)
return x.transpose(1, 2)
class FeedForwardModule(torch.nn.Module):
"""Positionwise feed forward layer used in conformer"""
def __init__(
self,
input_feat,
hidden_units,
dropout1,
dropout2,
activation_fn="swish",
bias=True,
):
"""
Args:
input_feat: Input feature dimension
hidden_units: Hidden unit dimension
dropout1: dropout value for layer1
dropout2: dropout value for layer2
activation_fn: Name of activation function
bias: If linear layers should have bias
"""
super(FeedForwardModule, self).__init__()
self.layer_norm = LayerNorm(input_feat)
self.w_1 = torch.nn.Linear(input_feat, hidden_units, bias=bias)
self.w_2 = torch.nn.Linear(hidden_units, input_feat, bias=bias)
self.dropout1 = torch.nn.Dropout(dropout1)
self.dropout2 = torch.nn.Dropout(dropout2)
self.activation = get_activation_fn(activation_fn)(hidden_units)
def forward(self, x):
"""
Args:
x: Input Tensor of shape T X B X C
Returns:
Tensor of shape T X B X C
"""
x = self.layer_norm(x)
x = self.w_1(x)
x = self.activation(x)
x = self.dropout1(x)
x = self.w_2(x)
return self.dropout2(x)
class ConformerEncoderLayer(torch.nn.Module):
"""Conformer block based on https://arxiv.org/abs/2005.08100.
We currently don't support relative positional encoding in MHA"""
def __init__(
self,
embed_dim,
ffn_embed_dim,
attention_heads,
dropout,
use_fp16,
depthwise_conv_kernel_size=31,
activation_fn="swish",
attn_type=None,
pos_enc_type="abs",
):
"""
Args:
embed_dim: Input embedding dimension
ffn_embed_dim: FFN layer dimension
attention_heads: Number of attention heads in MHA
dropout: dropout value
depthwise_conv_kernel_size: Size of kernel in depthwise conv layer in convolution module
activation_fn: Activation function name to use in convulation block and feed forward block
attn_type: MHA implementation from ESPNET vs fairseq
pos_enc_type: Positional encoding type - abs, rope, rel_pos
"""
super(ConformerEncoderLayer, self).__init__()
self.ffn1 = FeedForwardModule(
embed_dim,
ffn_embed_dim,
dropout,
dropout,
)
self.pos_enc_type = pos_enc_type
self.self_attn_layer_norm = LayerNorm(embed_dim, export=False)
self.self_attn_dropout = torch.nn.Dropout(dropout)
if attn_type == "espnet":
if self.pos_enc_type == "rel_pos":
self.self_attn = RelPositionMultiHeadedAttention(
embed_dim,
attention_heads,
dropout=dropout,
)
elif self.pos_enc_type == "rope":
self.self_attn = RotaryPositionMultiHeadedAttention(
embed_dim, attention_heads, dropout=dropout, precision=use_fp16
)
elif self.pos_enc_type == "abs":
self.self_attn = ESPNETMultiHeadedAttention(
embed_dim,
attention_heads,
dropout=dropout,
)
else:
raise Exception(f"Unsupported attention type {self.pos_enc_type}")
else:
# Default to fairseq MHA
self.self_attn = MultiheadAttention(
embed_dim,
attention_heads,
dropout=dropout,
)
self.conv_module = ConvolutionModule(
embed_dim=embed_dim,
channels=embed_dim,
depthwise_kernel_size=depthwise_conv_kernel_size,
dropout=dropout,
activation_fn=activation_fn,
)
self.ffn2 = FeedForwardModule(
embed_dim,
ffn_embed_dim,
dropout,
dropout,
activation_fn=activation_fn,
)
self.final_layer_norm = LayerNorm(embed_dim, export=False)
def forward(
self,
x,
encoder_padding_mask: Optional[torch.Tensor],
position_emb: Optional[torch.Tensor] = None,
):
"""
Args:
x: Tensor of shape T X B X C
encoder_padding_mask: Optional mask tensor
positions:
Returns:
Tensor of shape T X B X C
"""
if torch.isnan(x).sum() != 0:
print("x 0 fails")
print(self)
# FFN
residual = x
x = self.ffn1(x)
x = x * 0.5 + residual
if torch.isnan(x).sum() != 0:
print("x 1 fails")
# Multi-head self-attention module
residual = x
x = self.self_attn_layer_norm(x)
if torch.isnan(x).sum() != 0:
print("x 2 fails")
if self.pos_enc_type == "rel_pos":
x, attn = self.self_attn(
query=x,
key=x,
value=x,
key_padding_mask=encoder_padding_mask,
pos_emb=position_emb,
need_weights=False,
)
else:
x, attn = self.self_attn(
query=x,
key=x,
value=x,
key_padding_mask=encoder_padding_mask,
need_weights=False,
)
x = self.self_attn_dropout(x)
x = x + residual
# Convolution module
residual = x
# TBC to BTC
x = x.transpose(0, 1)
x = self.conv_module(x)
# BTC to TBC
x = x.transpose(0, 1)
x = residual + x
# FFN
residual = x
x = self.ffn2(x)
layer_result = x
x = x * 0.5 + residual
x = self.final_layer_norm(x)
return x, (attn, layer_result)
class ConformerWav2Vec2EncoderLayer(ConformerEncoderLayer):
"""Encoder layer for Wav2vec2 encoder"""
def forward(
self,
x: torch.Tensor,
self_attn_mask: torch.Tensor = None,
self_attn_padding_mask: torch.Tensor = None,
need_weights: bool = False,
att_args=None,
position_emb=None,
):
return super().forward(x, self_attn_padding_mask, position_emb)
| 9,471 | 28.786164 | 102 | py |
CIF-HieraDist | CIF-HieraDist-main/fairseq/modules/espnet_multihead_attention.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright 2019 Shigeki Karita
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Multi-Head Attention layer definition."""
import math
import torch
from torch import nn
from fairseq.modules.rotary_positional_embedding import (
RotaryPositionalEmbedding,
apply_rotary_pos_emb,
)
class ESPNETMultiHeadedAttention(nn.Module):
"""Multi-Head Attention layer.
Args:
n_head: The number of heads.
n_feat: The number of features.
dropout: Dropout rate.
"""
def __init__(self, n_feat, n_head, dropout):
"""Construct an MultiHeadedAttention object."""
super(ESPNETMultiHeadedAttention, self).__init__()
assert n_feat % n_head == 0
# We assume d_v always equals d_k
self.d_k = n_feat // n_head
self.h = n_head
self.linear_q = nn.Linear(n_feat, n_feat)
self.linear_k = nn.Linear(n_feat, n_feat)
self.linear_v = nn.Linear(n_feat, n_feat)
self.linear_out = nn.Linear(n_feat, n_feat)
self.attn = None
self.dropout = nn.Dropout(p=dropout)
def forward_qkv(self, query, key, value, **kwargs):
"""Transform query, key and value.
Args:
query: Query tensor B X T1 X C
key: Key tensor B X T2 X C
value: Value tensor B X T2 X C
Returns:
torch.Tensor: Transformed query tensor B X n_head X T1 X d_k
torch.Tensor: Transformed key tensor B X n_head X T2 X d_k
torch.Tensor: Transformed value tensor B X n_head X T2 X d_k
"""
n_batch = query.size(0)
q = self.linear_q(query).view(n_batch, -1, self.h, self.d_k)
k = self.linear_k(key).view(n_batch, -1, self.h, self.d_k)
v = self.linear_v(value).view(n_batch, -1, self.h, self.d_k)
q = q.transpose(1, 2) # (batch, head, time1, d_k)
k = k.transpose(1, 2) # (batch, head, time2, d_k)
v = v.transpose(1, 2) # (batch, head, time2, d_k)
return q, k, v
def forward_attention(self, value, scores, mask):
"""Compute attention context vector.
Args:
value: Transformed value B X n_head X T2 X d_k.
scores: Attention score B X n_head X T1 X T2
mask: Mask B x T2
Returns:
torch.Tensor: Transformed value B X T1 X d_model
weighted by the attention score B X T1 X T2
"""
n_batch = value.size(0)
if mask is not None:
# scores = scores.masked_fill(
# mask.unsqueeze(1).unsqueeze(2).to(bool),
# float("-inf"), # (batch, head, time1, time2)
# )
mask = mask.unsqueeze(1).unsqueeze(2).to(bool)
scores = scores.masked_fill(
mask, float("-inf") # (batch, head, time1, time2)
)
self.attn = torch.softmax(
scores.float(), dim=-1, dtype=torch.float32
).masked_fill(
mask, 0.0
) # (batch, head, time1, time2)
else:
self.attn = torch.softmax(
scores.float(), dim=-1, dtype=torch.float32
) # (batch, head, time1, time2)
if torch.isnan(self.attn).sum() != 0:
print("attention failure!!")
print(scores.max(), scores.min())
self.attn = self.attn.type_as(scores)
p_attn = self.dropout(self.attn)
x = torch.matmul(p_attn, value) # (batch, head, time1, d_k)
x = (
x.transpose(1, 2).contiguous().view(n_batch, -1, self.h * self.d_k)
) # (batch, time1, d_model)
return self.linear_out(x) # (batch, time1, d_model)
def forward(self, query, key, value, key_padding_mask=None, **kwargs):
"""Compute scaled dot product attention.
Args:
query (torch.Tensor): Query tensor T X B X C
key (torch.Tensor): Key tensor T X B X C
value (torch.Tensor): Value tensor T X B X C
mask (torch.Tensor): Mask tensor T X B
Returns:
torch.Tensor: Output tensor T X B X D.
"""
query = query.transpose(0, 1)
key = key.transpose(0, 1)
value = value.transpose(0, 1)
q, k, v = self.forward_qkv(query, key, value)
scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.d_k)
scores = self.forward_attention(v, scores, key_padding_mask)
scores = scores.transpose(0, 1)
return scores, None
class RelPositionMultiHeadedAttention(ESPNETMultiHeadedAttention):
"""Multi-Head Attention layer with relative position encoding.
Paper: https://arxiv.org/abs/1901.02860
Args:
n_head: The number of heads.
n_feat: The number of features.
dropout: Dropout rate.
zero_triu: Whether to zero the upper triangular part of attention matrix.
"""
def __init__(self, n_feat, n_head, dropout, zero_triu=False):
"""Construct an RelPositionMultiHeadedAttention object."""
super().__init__(n_feat, n_head, dropout)
self.zero_triu = zero_triu
# linear transformation for positional encoding
self.linear_pos = nn.Linear(n_feat, n_feat, bias=False)
# these two learnable bias are used in matrix c and matrix d
# as described in https://arxiv.org/abs/1901.02860 Section 3.3
self.pos_bias_u = nn.Parameter(torch.Tensor(self.h, self.d_k))
self.pos_bias_v = nn.Parameter(torch.Tensor(self.h, self.d_k))
torch.nn.init.xavier_uniform_(self.pos_bias_u)
torch.nn.init.xavier_uniform_(self.pos_bias_v)
def rel_shift(self, x):
"""Compute relative positional encoding.
Args:
x: Input tensor B X n_head X T X 2T-1
Returns:
torch.Tensor: Output tensor.
"""
zero_pad = torch.zeros((*x.size()[:3], 1), device=x.device, dtype=x.dtype)
x_padded = torch.cat([zero_pad, x], dim=-1)
x_padded = x_padded.view(*x.size()[:2], x.size(3) + 1, x.size(2))
x = x_padded[:, :, 1:].view_as(x)[
:, :, :, : x.size(-1) // 2 + 1
] # only keep the positions from 0 to time2
if self.zero_triu:
ones = torch.ones((x.size(2), x.size(3)), device=x.device)
x = x * torch.tril(ones, x.size(3) - x.size(2))[None, None, :, :]
return x
def forward(self, query, key, value, pos_emb, key_padding_mask=None, **kwargs):
"""Compute scaled dot product attention.
Args:
query: Query tensor T X B X C
key: Key tensor T X B X C
value: Value tensor T X B X C
pos_emb: Positional embedding tensor B X 2T-1 X C
key_padding_mask: Mask tensor T X B
Returns:
torch.Tensor: Output tensor T X B X C.
"""
query = query.transpose(0, 1)
key = key.transpose(0, 1)
value = value.transpose(0, 1)
pos_emb = pos_emb.transpose(0, 1)
if torch.isnan(query).sum() != 0:
print("query fails")
if torch.isnan(key).sum() != 0:
print("key fails")
q, k, v = self.forward_qkv(query, key, value)
q = q.transpose(1, 2) # (batch, time1, head, d_k)
n_batch_pos = pos_emb.size(0)
p = self.linear_pos(pos_emb).view(n_batch_pos, -1, self.h, self.d_k)
p = p.transpose(1, 2) # (batch, head, 2*time1-1, d_k)
if torch.isnan(q).sum() != 0:
print("q fails")
if torch.isnan(k).sum() != 0:
print("k fails")
if torch.isnan(self.pos_bias_u).sum() != 0:
print("self.pos_bias_u fails")
if torch.isnan(self.pos_bias_v).sum() != 0:
print("self.pos_bias_v fails")
# (batch, head, time1, d_k)
q_with_bias_u = (q + self.pos_bias_u).transpose(1, 2)
# (batch, head, time1, d_k)
q_with_bias_v = (q + self.pos_bias_v).transpose(1, 2)
# compute attention score
# first compute matrix a and matrix c
# as described in https://arxiv.org/abs/1901.02860 Section 3.3
# (batch, head, time1, time2)
matrix_ac = torch.matmul(q_with_bias_u, k.transpose(-2, -1))
# compute matrix b and matrix d
# (batch, head, time1, 2*time1-1)
matrix_bd = torch.matmul(q_with_bias_v, p.transpose(-2, -1))
if torch.isnan(matrix_bd).sum() != 0:
print("matrix_bd 1 fails")
matrix_bd = self.rel_shift(matrix_bd)
if torch.isnan(matrix_bd).sum() != 0:
print("matrix_bd 2 fails")
if torch.isnan(matrix_ac).sum() != 0:
print("matrix_ac fails")
if torch.isnan(matrix_ac + matrix_bd).sum() != 0:
print("matrix_ac + matrix_bd fails")
scores = (matrix_ac + matrix_bd) / math.sqrt(
self.d_k
) # (batch, head, time1, time2)
if torch.isnan(scores).sum() != 0:
if torch.isnan(q_with_bias_u).sum() != 0:
print("q_with_bias_u fails")
if torch.isnan(q_with_bias_v).sum() != 0:
print("q_with_bias_v fails")
if torch.isnan(k).sum() != 0:
print("k fails")
if torch.isnan(p).sum() != 0:
print("p fails")
scores = self.forward_attention(v, scores, key_padding_mask)
scores = scores.transpose(0, 1)
return scores, None
class RotaryPositionMultiHeadedAttention(ESPNETMultiHeadedAttention):
def __init__(
self,
n_feat,
n_head,
dropout,
precision,
rotary_emd_base=10000,
):
"""Construct an RotaryPositionMultiHeadedAttention object."""
super().__init__(n_feat, n_head, dropout)
precision = torch.float
self.rotary_ndims = self.d_k # also try self.d_k//2
if precision == "fp16":
precision = torch.half
self.rotary_emb = RotaryPositionalEmbedding(
self.rotary_ndims, base=rotary_emd_base, precision=precision
)
def forward(self, query, key, value, key_padding_mask=None, **kwargs):
"""Compute rotary position attention.
Args:
query: Query tensor T X B X C
key: Key tensor T X B X C
value: Value tensor T X B X C
key_padding_mask: Mask tensor T X B
Returns:
torch.Tensor: Output tensor T X B X D.
Notes:
Assumes self attn
"""
T, B, C = value.size()
query = query.view(T, B, self.h, self.d_k)
key = key.view(T, B, self.h, self.d_k)
value = value.view(T, B, self.h, self.d_k)
cos, sin = self.rotary_emb(value, seq_len=T)
query, key = apply_rotary_pos_emb(
query, key, cos, sin, offset=0
) # offset is based on layer_past
query = query.view(T, B, self.h * self.d_k)
key = key.view(T, B, self.h * self.d_k)
value = value.view(T, B, self.h * self.d_k)
# TBD to BTD
query = query.transpose(0, 1)
key = key.transpose(0, 1)
value = value.transpose(0, 1)
q, k, v = self.forward_qkv(query, key, value)
scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.d_k)
scores = self.forward_attention(v, scores, key_padding_mask)
scores = scores.transpose(0, 1)
return scores, None
| 11,440 | 36.388889 | 83 | py |
CIF-HieraDist | CIF-HieraDist-main/fairseq/modules/rotary_positional_embedding.py | import torch
class RotaryPositionalEmbedding(torch.nn.Module):
def __init__(self, dim, base=10000, precision=torch.half):
"""Rotary positional embedding
Reference : https://blog.eleuther.ai/rotary-embeddings/
Paper: https://arxiv.org/pdf/2104.09864.pdf
Args:
dim: Dimension of embedding
base: Base value for exponential
precision: precision to use for numerical values
"""
super().__init__()
inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim))
self.register_buffer("inv_freq", inv_freq)
self.seq_len_cached = None
self.cos_cached = None
self.sin_cached = None
self.precision = precision
def forward(self, x, seq_len=None):
"""
Args:
x: Input x with T X B X C
seq_len: Sequence length of input x
"""
if seq_len != self.seq_len_cached:
self.seq_len_cached = seq_len
t = torch.arange(seq_len, device=x.device).type_as(self.inv_freq)
freqs = torch.einsum("i,j->ij", t, self.inv_freq)
emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
self.cos_cached = emb.cos()[:, None, None, :]
self.sin_cached = emb.sin()[:, None, None, :]
return self.cos_cached, self.sin_cached
# rotary pos emb helpers:
def rotate_half(x):
x1, x2 = x[..., : x.shape[-1] // 2], x[..., x.shape[-1] // 2 :]
return torch.cat(
(-x2, x1), dim=x1.ndim - 1
) # dim=-1 triggers a bug in earlier torch versions
def apply_rotary_pos_emb(q, k, cos, sin, offset: int = 0):
cos, sin = (
cos[offset : q.shape[0] + offset, ...],
sin[offset : q.shape[0] + offset, ...],
)
return (q * cos) + (rotate_half(q) * sin), (k * cos) + (rotate_half(k) * sin)
| 1,851 | 34.615385 | 81 | py |
CIF-HieraDist | CIF-HieraDist-main/fairseq/modules/location_attention.py | # 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 torch.nn as nn
import torch
import torch.nn.functional as F
class LocationAttention(nn.Module):
"""
Attention-Based Models for Speech Recognition
https://arxiv.org/pdf/1506.07503.pdf
:param int encoder_dim: # projection-units of encoder
:param int decoder_dim: # units of decoder
:param int attn_dim: attention dimension
:param int conv_dim: # channels of attention convolution
:param int conv_kernel_size: filter size of attention convolution
"""
def __init__(
self,
attn_dim,
encoder_dim,
decoder_dim,
attn_state_kernel_size,
conv_dim,
conv_kernel_size,
scaling=2.0,
):
super(LocationAttention, self).__init__()
self.attn_dim = attn_dim
self.decoder_dim = decoder_dim
self.scaling = scaling
self.proj_enc = nn.Linear(encoder_dim, attn_dim)
self.proj_dec = nn.Linear(decoder_dim, attn_dim, bias=False)
self.proj_attn = nn.Linear(conv_dim, attn_dim, bias=False)
self.conv = nn.Conv1d(
attn_state_kernel_size,
conv_dim,
2 * conv_kernel_size + 1,
padding=conv_kernel_size,
bias=False,
)
self.proj_out = nn.Sequential(nn.Tanh(), nn.Linear(attn_dim, 1))
self.proj_enc_out = None # cache
def clear_cache(self):
self.proj_enc_out = None
def forward(self, encoder_out, encoder_padding_mask, decoder_h, attn_state):
"""
:param torch.Tensor encoder_out: padded encoder hidden state B x T x D
:param torch.Tensor encoder_padding_mask: encoder padding mask
:param torch.Tensor decoder_h: decoder hidden state B x D
:param torch.Tensor attn_prev: previous attention weight B x K x T
:return: attention weighted encoder state (B, D)
:rtype: torch.Tensor
:return: previous attention weights (B x T)
:rtype: torch.Tensor
"""
bsz, seq_len, _ = encoder_out.size()
if self.proj_enc_out is None:
self.proj_enc_out = self.proj_enc(encoder_out)
# B x K x T -> B x C x T
attn = self.conv(attn_state)
# B x C x T -> B x T x C -> B x T x D
attn = self.proj_attn(attn.transpose(1, 2))
if decoder_h is None:
decoder_h = encoder_out.new_zeros(bsz, self.decoder_dim)
dec_h = self.proj_dec(decoder_h).view(bsz, 1, self.attn_dim)
out = self.proj_out(attn + self.proj_enc_out + dec_h).squeeze(2)
out.masked_fill_(encoder_padding_mask, -float("inf"))
w = F.softmax(self.scaling * out, dim=1)
c = torch.sum(encoder_out * w.view(bsz, seq_len, 1), dim=1)
return c, w
| 2,909 | 33.642857 | 80 | py |
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