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19,168 | import types
import torch
class FusedAdamV1(torch.optim.Optimizer):
"""
Implements Adam algorithm. Currently GPU-only. Requires Apex to be installed via
``python setup.py install --cuda_ext --cpp_ext``.
It has been proposed in `Adam: A Method for Stochastic Optimization`_.
Compared to the original version in Apex, the fairseq version casts grads
and params to FP32 internally to support ``--memory-efficient-fp16``.
Arguments:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups.
lr (float, optional): learning rate. (default: 1e-3)
betas (Tuple[float, float], optional): coefficients used for computing
running averages of gradient and its square. (default: (0.9, 0.999))
eps (float, optional): term added to the denominator to improve
numerical stability. (default: 1e-8)
weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
amsgrad (boolean, optional): whether to use the AMSGrad variant of this
algorithm from the paper `On the Convergence of Adam and Beyond`_
(default: False) NOT SUPPORTED in FusedAdam!
eps_inside_sqrt (boolean, optional): in the 'update parameters' step,
adds eps to the bias-corrected second moment estimate before
evaluating square root instead of adding it to the square root of
second moment estimate as in the original paper. (default: False)
.. _Adam: A Method for Stochastic Optimization:
https://arxiv.org/abs/1412.6980
.. _On the Convergence of Adam and Beyond:
https://openreview.net/forum?id=ryQu7f-RZ
"""
def __init__(self, params,
lr=1e-3, bias_correction=True,
betas=(0.9, 0.999), eps=1e-8, eps_inside_sqrt=False,
weight_decay=0., max_grad_norm=0., amsgrad=False):
global fused_adam_cuda
import importlib
fused_adam_cuda = importlib.import_module("fused_adam_cuda")
if amsgrad:
raise RuntimeError('FusedAdam does not support the AMSGrad variant.')
defaults = {
'lr': lr,
'bias_correction': bias_correction,
'betas': betas,
'eps': eps,
'weight_decay': weight_decay,
'max_grad_norm': max_grad_norm,
}
super().__init__(params, defaults)
self.eps_mode = 0 if eps_inside_sqrt else 1
def supports_memory_efficient_fp16(self):
return True
def supports_flat_params(self):
return True
def step(self, closure=None, grads=None, scale=1., grad_norms=None):
"""Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
grads (list of tensors, optional): weight gradient to use for the
optimizer update. If gradients have type torch.half, parameters
are expected to be in type torch.float. (default: None)
output params (list of tensors, optional): A reduced precision copy
of the updated weights written out in addition to the regular
updated weights. Have to be of same type as gradients. (default: None)
scale (float, optional): factor to divide gradient tensor values
by before applying to weights. (default: 1)
"""
loss = None
if closure is not None:
loss = closure()
if grads is None:
grads_group = [None] * len(self.param_groups)
# backward compatibility
# assuming a list/generator of parameter means single group
elif isinstance(grads, types.GeneratorType):
grads_group = [grads]
elif type(grads[0]) != list:
grads_group = [grads]
else:
grads_group = grads
if grad_norms is None:
grad_norms = [None]*len(self.param_groups)
for group, grads_this_group, grad_norm in zip(self.param_groups, grads_group, grad_norms):
if grads_this_group is None:
grads_this_group = [None]*len(group['params'])
# compute combined scale factor for this group
combined_scale = scale
if group.get('max_grad_norm', 0) > 0:
# norm is in fact norm*scale
clip = ((grad_norm / scale) + 1e-6) / group['max_grad_norm']
if clip > 1:
combined_scale = clip * scale
bias_correction = 1 if group.get('bias_correction', 1) else 0
for p, grad in zip(group['params'], grads_this_group):
# note: p.grad should not ever be set for correct
# operation of mixed precision optimizer that sometimes
# sends None gradients
if p.grad is None and grad is None:
continue
if grad is None:
grad = p.grad.data
if grad.is_sparse:
raise RuntimeError(
'FusedAdam does not support sparse gradients, '
'please consider SparseAdam instead'
)
p_data_fp32 = p.data.float()
state = self.state[p]
# State initialization
if len(state) == 0:
state['step'] = 0
# Exponential moving average of gradient values
state['exp_avg'] = torch.zeros_like(p_data_fp32)
# Exponential moving average of squared gradient values
state['exp_avg_sq'] = torch.zeros_like(p_data_fp32)
else:
state['exp_avg'] = state['exp_avg'].to(p_data_fp32)
state['exp_avg_sq'] = state['exp_avg_sq'].to(p_data_fp32)
exp_avg = state['exp_avg']
exp_avg_sq = state['exp_avg_sq']
beta1, beta2 = group['betas']
state['step'] += 1
out_p = p.data
with torch.cuda.device(p.device):
fused_adam_cuda.adam(p_data_fp32,
out_p,
exp_avg,
exp_avg_sq,
grad,
group['lr'],
beta1,
beta2,
group['eps'],
combined_scale,
state['step'],
self.eps_mode,
bias_correction,
group['weight_decay'])
return loss
try:
from apex.optimizers import FusedAdam
from apex.multi_tensor_apply import multi_tensor_applier
class FusedAdamV2(FusedAdam):
"""
Compared to the original version in Apex, the fairseq version casts grads
and params to FP32 internally to support ``--memory-efficient-fp16``.
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
if not hasattr(self, 'multi_tensor_adam'):
raise Exception('Apex installation is outdated. Please install an updated version of apex.')
def supports_memory_efficient_fp16(self):
return True
def supports_flat_params(self):
return True
def step(self, closure=None, grads=None, output_params=None, scale=None, grad_norms=None):
"""Performs a single optimization step."""
loss = None
if closure is not None:
loss = closure()
for group in self.param_groups:
bias_correction = 1 if group['bias_correction'] else 0
beta1, beta2 = group['betas']
# assume same step across group now to simplify things
# per parameter step can be easily support by making it tensor, or pass list into kernel
if 'step' in group:
group['step'] += 1
else:
group['step'] = 1
# create lists for multi-tensor apply
g_16, p_16, orig_p_16, m_16, v_16 = [], [], [], [], []
g_32, p_32, m_32, v_32 = [], [], [], []
for p in group['params']:
if p.grad is None:
continue
if p.grad.data.is_sparse:
raise RuntimeError(
'FusedAdam does not support sparse gradients, '
'please consider SparseAdam instead'
)
state = self.state[p]
# State initialization
if len(state) == 0:
# Exponential moving average of gradient values
state['exp_avg'] = torch.zeros_like(p.data, dtype=torch.float)
# Exponential moving average of squared gradient values
state['exp_avg_sq'] = torch.zeros_like(p.data, dtype=torch.float)
else:
state['exp_avg'] = state['exp_avg'].to(device=p.data.device, dtype=torch.float)
state['exp_avg_sq'] = state['exp_avg_sq'].to(device=p.data.device, dtype=torch.float)
if p.dtype == torch.float16:
g_16.append(p.grad.data.float())
p_16.append(p.data.float())
orig_p_16.append(p.data)
m_16.append(state['exp_avg'])
v_16.append(state['exp_avg_sq'])
elif p.dtype == torch.float32:
g_32.append(p.grad.data)
p_32.append(p.data)
m_32.append(state['exp_avg'])
v_32.append(state['exp_avg_sq'])
else:
raise RuntimeError('FusedAdam only support fp16 and fp32.')
with torch.cuda.device(p.device):
if(len(g_16) > 0):
multi_tensor_applier(self.multi_tensor_adam,
self._dummy_overflow_buf,
[g_16, p_16, m_16, v_16],
group['lr'],
beta1,
beta2,
group['eps'],
group['step'],
self.adam_w_mode,
bias_correction,
group['weight_decay'])
for orig_p, p in zip(orig_p_16, p_16):
orig_p.copy_(p.data)
if(len(g_32) > 0):
multi_tensor_applier(self.multi_tensor_adam,
self._dummy_overflow_buf,
[g_32, p_32, m_32, v_32],
group['lr'],
beta1,
beta2,
group['eps'],
group['step'],
self.adam_w_mode,
bias_correction,
group['weight_decay'])
return loss
except ImportError:
pass
The provided code snippet includes necessary dependencies for implementing the `get_fused_adam_class` function. Write a Python function `def get_fused_adam_class()` to solve the following problem:
Look for the FusedAdam optimizer from apex. We first try to load the "contrib" interface, which is a bit faster than the main interface, but is technically deprecated.
Here is the function:
def get_fused_adam_class():
"""
Look for the FusedAdam optimizer from apex. We first try to load the
"contrib" interface, which is a bit faster than the main interface,
but is technically deprecated.
"""
try:
# The "deprecated" interface in recent versions of apex is a bit
# faster than the main interface, since we don't use the apex
# optimizer. This can be installed by passing the
# `--deprecated_fused_adam` option when building apex.
global fused_adam_cuda
import importlib
fused_adam_cuda = importlib.import_module("fused_adam_cuda")
return FusedAdamV1
except ImportError:
try:
# fallback to the newer interface
from apex.optimizers import FusedAdam as _FusedAdam # noqa
from apex.multi_tensor_apply import multi_tensor_applier
if multi_tensor_applier.available:
return FusedAdamV2
except ImportError:
pass
return None | Look for the FusedAdam optimizer from apex. We first try to load the "contrib" interface, which is a bit faster than the main interface, but is technically deprecated. |
19,169 | from collections import OrderedDict
from fairseq import utils
from fairseq.models import (
FairseqMultiModel,
register_model,
register_model_architecture,
)
from fairseq.models.transformer import (
base_architecture,
Embedding,
TransformerModel,
TransformerEncoder,
TransformerDecoder,
)
def base_multilingual_architecture(args):
base_architecture(args)
args.share_encoder_embeddings = getattr(args, 'share_encoder_embeddings', False)
args.share_decoder_embeddings = getattr(args, 'share_decoder_embeddings', False)
args.share_encoders = getattr(args, 'share_encoders', False)
args.share_decoders = getattr(args, 'share_decoders', False)
def multilingual_transformer_iwslt_de_en(args):
args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 512)
args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 1024)
args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 4)
args.encoder_layers = getattr(args, 'encoder_layers', 6)
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 512)
args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 1024)
args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 4)
args.decoder_layers = getattr(args, 'decoder_layers', 6)
base_multilingual_architecture(args) | null |
19,170 | import os
from typing import Any, Dict
from fairseq import checkpoint_utils
from fairseq.data.legacy.masked_lm_dictionary import MaskedLMDictionary
from fairseq.models import register_model, register_model_architecture
from fairseq.models.transformer import (
TransformerDecoder,
TransformerEncoder,
TransformerModel,
base_architecture as transformer_base_architecture,
)
The provided code snippet includes necessary dependencies for implementing the `upgrade_state_dict_with_xlm_weights` function. Write a Python function `def upgrade_state_dict_with_xlm_weights( state_dict: Dict[str, Any], pretrained_xlm_checkpoint: str ) -> Dict[str, Any]` to solve the following problem:
Load XLM weights into a Transformer encoder or decoder model. Args: state_dict: state dict for either TransformerEncoder or TransformerDecoder pretrained_xlm_checkpoint: checkpoint to load XLM weights from Raises: AssertionError: If architecture (num layers, attention heads, etc.) does not match between the current Transformer encoder or decoder and the pretrained_xlm_checkpoint
Here is the function:
def upgrade_state_dict_with_xlm_weights(
state_dict: Dict[str, Any], pretrained_xlm_checkpoint: str
) -> Dict[str, Any]:
"""
Load XLM weights into a Transformer encoder or decoder model.
Args:
state_dict: state dict for either TransformerEncoder or
TransformerDecoder
pretrained_xlm_checkpoint: checkpoint to load XLM weights from
Raises:
AssertionError: If architecture (num layers, attention heads, etc.)
does not match between the current Transformer encoder or
decoder and the pretrained_xlm_checkpoint
"""
if not os.path.exists(pretrained_xlm_checkpoint):
raise IOError("Model file not found: {}".format(pretrained_xlm_checkpoint))
state = checkpoint_utils.load_checkpoint_to_cpu(pretrained_xlm_checkpoint)
xlm_state_dict = state["model"]
for key in xlm_state_dict.keys():
for search_key in ["embed_tokens", "embed_positions", "layers"]:
if search_key in key:
subkey = key[key.find(search_key):]
assert subkey in state_dict, (
"{} Transformer encoder / decoder "
"state_dict does not contain {}. Cannot "
"load {} from pretrained XLM checkpoint "
"{} into Transformer.".format(
str(state_dict.keys()),
subkey, key, pretrained_xlm_checkpoint)
)
state_dict[subkey] = xlm_state_dict[key]
return state_dict | Load XLM weights into a Transformer encoder or decoder model. Args: state_dict: state dict for either TransformerEncoder or TransformerDecoder pretrained_xlm_checkpoint: checkpoint to load XLM weights from Raises: AssertionError: If architecture (num layers, attention heads, etc.) does not match between the current Transformer encoder or decoder and the pretrained_xlm_checkpoint |
19,171 | import os
from typing import Any, Dict
from fairseq import checkpoint_utils
from fairseq.data.legacy.masked_lm_dictionary import MaskedLMDictionary
from fairseq.models import register_model, register_model_architecture
from fairseq.models.transformer import (
TransformerDecoder,
TransformerEncoder,
TransformerModel,
base_architecture as transformer_base_architecture,
)
def base_architecture(args):
transformer_base_architecture(args) | null |
19,172 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import options, utils
from fairseq.models import (
FairseqEncoder,
FairseqIncrementalDecoder,
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.modules import (
AdaptiveSoftmax,
DynamicConv,
LayerNorm,
PositionalEmbedding,
LightweightConv,
MultiheadAttention,
)
def Embedding(num_embeddings, embedding_dim, padding_idx):
m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx)
nn.init.normal_(m.weight, mean=0, std=embedding_dim ** -0.5)
nn.init.constant_(m.weight[padding_idx], 0)
return m | null |
19,173 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import options, utils
from fairseq.models import (
FairseqEncoder,
FairseqIncrementalDecoder,
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.modules import (
AdaptiveSoftmax,
DynamicConv,
LayerNorm,
PositionalEmbedding,
LightweightConv,
MultiheadAttention,
)
def Linear(in_features, out_features, bias=True):
m = nn.Linear(in_features, out_features, bias)
nn.init.xavier_uniform_(m.weight)
if bias:
nn.init.constant_(m.bias, 0.)
return m | null |
19,174 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import options, utils
from fairseq.models import (
FairseqEncoder,
FairseqIncrementalDecoder,
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.modules import (
AdaptiveSoftmax,
DynamicConv,
LayerNorm,
PositionalEmbedding,
LightweightConv,
MultiheadAttention,
)
def base_architecture(args):
args.encoder_embed_path = getattr(args, 'encoder_embed_path', None)
args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 512)
args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 2048)
args.encoder_layers = getattr(args, 'encoder_layers', 7)
args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 8)
args.encoder_normalize_before = getattr(args, 'encoder_normalize_before', False)
args.encoder_learned_pos = getattr(args, 'encoder_learned_pos', False)
args.decoder_embed_path = getattr(args, 'decoder_embed_path', None)
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', args.encoder_embed_dim)
args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', args.encoder_ffn_embed_dim)
args.decoder_layers = getattr(args, 'decoder_layers', 6)
args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 8)
args.decoder_normalize_before = getattr(args, 'decoder_normalize_before', False)
args.decoder_learned_pos = getattr(args, 'decoder_learned_pos', False)
args.attention_dropout = getattr(args, 'attention_dropout', 0.)
args.relu_dropout = getattr(args, 'relu_dropout', 0.)
args.dropout = getattr(args, 'dropout', 0.1)
args.adaptive_softmax_cutoff = getattr(args, 'adaptive_softmax_cutoff', None)
args.adaptive_softmax_dropout = getattr(args, 'adaptive_softmax_dropout', 0)
args.share_decoder_input_output_embed = getattr(args, 'share_decoder_input_output_embed', False)
args.share_all_embeddings = getattr(args, 'share_all_embeddings', False)
args.no_token_positional_embeddings = getattr(args, 'no_token_positional_embeddings', False)
args.decoder_output_dim = getattr(args, 'decoder_output_dim', args.decoder_embed_dim)
args.decoder_input_dim = getattr(args, 'decoder_input_dim', args.decoder_embed_dim)
args.encoder_conv_dim = getattr(args, 'encoder_conv_dim', args.encoder_embed_dim)
args.decoder_conv_dim = getattr(args, 'decoder_conv_dim', args.decoder_embed_dim)
args.encoder_kernel_size_list = getattr(args, 'encoder_kernel_size_list', [3, 7, 15, 31, 31, 31, 31])
args.decoder_kernel_size_list = getattr(args, 'decoder_kernel_size_list', [3, 7, 15, 31, 31, 31])
if len(args.encoder_kernel_size_list) == 1:
args.encoder_kernel_size_list = args.encoder_kernel_size_list * args.encoder_layers
if len(args.decoder_kernel_size_list) == 1:
args.decoder_kernel_size_list = args.decoder_kernel_size_list * args.decoder_layers
assert len(args.encoder_kernel_size_list) == args.encoder_layers, "encoder_kernel_size_list doesn't match encoder_layers"
assert len(args.decoder_kernel_size_list) == args.decoder_layers, "decoder_kernel_size_list doesn't match decoder_layers"
args.encoder_glu = getattr(args, 'encoder_glu', True)
args.decoder_glu = getattr(args, 'decoder_glu', True)
args.input_dropout = getattr(args, 'input_dropout', 0.1)
args.weight_dropout = getattr(args, 'weight_dropout', args.attention_dropout)
def lightconv_iwslt_de_en(args):
args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 512)
args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 1024)
args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 4)
args.encoder_layers = getattr(args, 'encoder_layers', 7)
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 512)
args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 1024)
args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 4)
args.decoder_layers = getattr(args, 'decoder_layers', 6)
args.attention_dropout = getattr(args, 'attention_dropout', 0.1)
args.weight_dropout = getattr(args, 'weight_dropout', 0.1)
args.encoder_glu = getattr(args, 'encoder_glu', False)
args.decoder_glu = getattr(args, 'decoder_glu', False)
args.input_dropout = getattr(args, 'input_dropout', 0.0)
base_architecture(args) | null |
19,175 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import options, utils
from fairseq.models import (
FairseqEncoder,
FairseqIncrementalDecoder,
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.modules import (
AdaptiveSoftmax,
DynamicConv,
LayerNorm,
PositionalEmbedding,
LightweightConv,
MultiheadAttention,
)
def base_architecture(args):
args.encoder_embed_path = getattr(args, 'encoder_embed_path', None)
args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 512)
args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 2048)
args.encoder_layers = getattr(args, 'encoder_layers', 7)
args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 8)
args.encoder_normalize_before = getattr(args, 'encoder_normalize_before', False)
args.encoder_learned_pos = getattr(args, 'encoder_learned_pos', False)
args.decoder_embed_path = getattr(args, 'decoder_embed_path', None)
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', args.encoder_embed_dim)
args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', args.encoder_ffn_embed_dim)
args.decoder_layers = getattr(args, 'decoder_layers', 6)
args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 8)
args.decoder_normalize_before = getattr(args, 'decoder_normalize_before', False)
args.decoder_learned_pos = getattr(args, 'decoder_learned_pos', False)
args.attention_dropout = getattr(args, 'attention_dropout', 0.)
args.relu_dropout = getattr(args, 'relu_dropout', 0.)
args.dropout = getattr(args, 'dropout', 0.1)
args.adaptive_softmax_cutoff = getattr(args, 'adaptive_softmax_cutoff', None)
args.adaptive_softmax_dropout = getattr(args, 'adaptive_softmax_dropout', 0)
args.share_decoder_input_output_embed = getattr(args, 'share_decoder_input_output_embed', False)
args.share_all_embeddings = getattr(args, 'share_all_embeddings', False)
args.no_token_positional_embeddings = getattr(args, 'no_token_positional_embeddings', False)
args.decoder_output_dim = getattr(args, 'decoder_output_dim', args.decoder_embed_dim)
args.decoder_input_dim = getattr(args, 'decoder_input_dim', args.decoder_embed_dim)
args.encoder_conv_dim = getattr(args, 'encoder_conv_dim', args.encoder_embed_dim)
args.decoder_conv_dim = getattr(args, 'decoder_conv_dim', args.decoder_embed_dim)
args.encoder_kernel_size_list = getattr(args, 'encoder_kernel_size_list', [3, 7, 15, 31, 31, 31, 31])
args.decoder_kernel_size_list = getattr(args, 'decoder_kernel_size_list', [3, 7, 15, 31, 31, 31])
if len(args.encoder_kernel_size_list) == 1:
args.encoder_kernel_size_list = args.encoder_kernel_size_list * args.encoder_layers
if len(args.decoder_kernel_size_list) == 1:
args.decoder_kernel_size_list = args.decoder_kernel_size_list * args.decoder_layers
assert len(args.encoder_kernel_size_list) == args.encoder_layers, "encoder_kernel_size_list doesn't match encoder_layers"
assert len(args.decoder_kernel_size_list) == args.decoder_layers, "decoder_kernel_size_list doesn't match decoder_layers"
args.encoder_glu = getattr(args, 'encoder_glu', True)
args.decoder_glu = getattr(args, 'decoder_glu', True)
args.input_dropout = getattr(args, 'input_dropout', 0.1)
args.weight_dropout = getattr(args, 'weight_dropout', args.attention_dropout)
def lightconv_wmt_en_de(args):
base_architecture(args) | null |
19,176 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import options, utils
from fairseq.models import (
FairseqEncoder,
FairseqIncrementalDecoder,
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.modules import (
AdaptiveSoftmax,
DynamicConv,
LayerNorm,
PositionalEmbedding,
LightweightConv,
MultiheadAttention,
)
def lightconv_wmt_en_de_big(args):
args.attention_dropout = getattr(args, 'attention_dropout', 0.1)
args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 1024)
args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 4096)
args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 16)
args.encoder_normalize_before = getattr(args, 'encoder_normalize_before', False)
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 1024)
args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 4096)
args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 16)
args.dropout = getattr(args, 'dropout', 0.3)
base_architecture(args)
def lightconv_wmt_en_fr_big(args):
args.dropout = getattr(args, 'dropout', 0.1)
lightconv_wmt_en_de_big(args) | null |
19,177 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import options, utils
from fairseq.models import (
FairseqEncoder,
FairseqIncrementalDecoder,
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.modules import (
AdaptiveSoftmax,
DynamicConv,
LayerNorm,
PositionalEmbedding,
LightweightConv,
MultiheadAttention,
)
def lightconv_wmt_en_de_big(args):
def lightconv_wmt_zh_en_big(args):
args.dropout = getattr(args, 'dropout', 0.2)
args.attention_dropout = getattr(args, 'attention_dropout', 0.2)
args.weight_dropout = getattr(args, 'weight_dropout', 0.2)
lightconv_wmt_en_de_big(args) | null |
19,178 | import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.models import (
FairseqEncoder,
FairseqEncoderModel,
register_model,
register_model_architecture,
)
from fairseq.modules import (
LayerNorm,
TransformerSentenceEncoder,
)
from fairseq.modules.transformer_sentence_encoder import init_bert_params
from fairseq.modules.quant_noise import quant_noise as apply_quant_noise_
from .hub_interface import RobertaHubInterface
def base_architecture(args):
args.encoder_layers = getattr(args, 'encoder_layers', 12)
args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 768)
args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 3072)
args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 12)
args.activation_fn = getattr(args, 'activation_fn', 'gelu')
args.pooler_activation_fn = getattr(args, 'pooler_activation_fn', 'tanh')
args.dropout = getattr(args, 'dropout', 0.1)
args.attention_dropout = getattr(args, 'attention_dropout', 0.1)
args.activation_dropout = getattr(args, 'activation_dropout', 0.0)
args.pooler_dropout = getattr(args, 'pooler_dropout', 0.0)
args.encoder_layers_to_keep = getattr(args, 'encoder_layers_to_keep', None)
args.encoder_layerdrop = getattr(args, 'encoder_layerdrop', 0.0)
def roberta_base_architecture(args):
base_architecture(args) | null |
19,179 | import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.models import (
FairseqEncoder,
FairseqEncoderModel,
register_model,
register_model_architecture,
)
from fairseq.modules import (
LayerNorm,
TransformerSentenceEncoder,
)
from fairseq.modules.transformer_sentence_encoder import init_bert_params
from fairseq.modules.quant_noise import quant_noise as apply_quant_noise_
from .hub_interface import RobertaHubInterface
def base_architecture(args):
def roberta_large_architecture(args):
args.encoder_layers = getattr(args, 'encoder_layers', 24)
args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 1024)
args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 4096)
args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 16)
base_architecture(args) | null |
19,180 | import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.models import (
FairseqEncoder,
FairseqEncoderModel,
register_model,
register_model_architecture,
)
from fairseq.modules import (
LayerNorm,
TransformerSentenceEncoder,
)
from fairseq.modules.transformer_sentence_encoder import init_bert_params
from fairseq.modules.quant_noise import quant_noise as apply_quant_noise_
from .hub_interface import RobertaHubInterface
def base_architecture(args):
args.encoder_layers = getattr(args, 'encoder_layers', 12)
args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 768)
args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 3072)
args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 12)
args.activation_fn = getattr(args, 'activation_fn', 'gelu')
args.pooler_activation_fn = getattr(args, 'pooler_activation_fn', 'tanh')
args.dropout = getattr(args, 'dropout', 0.1)
args.attention_dropout = getattr(args, 'attention_dropout', 0.1)
args.activation_dropout = getattr(args, 'activation_dropout', 0.0)
args.pooler_dropout = getattr(args, 'pooler_dropout', 0.0)
args.encoder_layers_to_keep = getattr(args, 'encoder_layers_to_keep', None)
args.encoder_layerdrop = getattr(args, 'encoder_layerdrop', 0.0)
def xlm_architecture(args):
args.encoder_layers = getattr(args, 'encoder_layers', 16)
args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 1280)
args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 1280*4)
args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 16)
base_architecture(args) | null |
19,181 | from collections import Counter
from typing import List
import torch
The provided code snippet includes necessary dependencies for implementing the `align_bpe_to_words` function. Write a Python function `def align_bpe_to_words(roberta, bpe_tokens: torch.LongTensor, other_tokens: List[str])` to solve the following problem:
Helper to align GPT-2 BPE to other tokenization formats (e.g., spaCy). Args: roberta (RobertaHubInterface): RoBERTa instance bpe_tokens (torch.LongTensor): GPT-2 BPE tokens of shape `(T_bpe)` other_tokens (List[str]): other tokens of shape `(T_words)` Returns: List[str]: mapping from *other_tokens* to corresponding *bpe_tokens*.
Here is the function:
def align_bpe_to_words(roberta, bpe_tokens: torch.LongTensor, other_tokens: List[str]):
"""
Helper to align GPT-2 BPE to other tokenization formats (e.g., spaCy).
Args:
roberta (RobertaHubInterface): RoBERTa instance
bpe_tokens (torch.LongTensor): GPT-2 BPE tokens of shape `(T_bpe)`
other_tokens (List[str]): other tokens of shape `(T_words)`
Returns:
List[str]: mapping from *other_tokens* to corresponding *bpe_tokens*.
"""
assert bpe_tokens.dim() == 1
assert bpe_tokens[0] == 0
def clean(text):
return text.strip()
# remove whitespaces to simplify alignment
bpe_tokens = [roberta.task.source_dictionary.string([x]) for x in bpe_tokens]
bpe_tokens = [clean(roberta.bpe.decode(x) if x not in {'<s>', ''} else x) for x in bpe_tokens]
other_tokens = [clean(str(o)) for o in other_tokens]
# strip leading <s>
bpe_tokens = bpe_tokens[1:]
assert ''.join(bpe_tokens) == ''.join(other_tokens)
# create alignment from every word to a list of BPE tokens
alignment = []
bpe_toks = filter(lambda item: item[1] != '', enumerate(bpe_tokens, start=1))
j, bpe_tok = next(bpe_toks)
for other_tok in other_tokens:
bpe_indices = []
while True:
if other_tok.startswith(bpe_tok):
bpe_indices.append(j)
other_tok = other_tok[len(bpe_tok):]
try:
j, bpe_tok = next(bpe_toks)
except StopIteration:
j, bpe_tok = None, None
elif bpe_tok.startswith(other_tok):
# other_tok spans multiple BPE tokens
bpe_indices.append(j)
bpe_tok = bpe_tok[len(other_tok):]
other_tok = ''
else:
raise Exception('Cannot align "{}" and "{}"'.format(other_tok, bpe_tok))
if other_tok == '':
break
assert len(bpe_indices) > 0
alignment.append(bpe_indices)
assert len(alignment) == len(other_tokens)
return alignment | Helper to align GPT-2 BPE to other tokenization formats (e.g., spaCy). Args: roberta (RobertaHubInterface): RoBERTa instance bpe_tokens (torch.LongTensor): GPT-2 BPE tokens of shape `(T_bpe)` other_tokens (List[str]): other tokens of shape `(T_words)` Returns: List[str]: mapping from *other_tokens* to corresponding *bpe_tokens*. |
19,182 | from collections import Counter
from typing import List
import torch
The provided code snippet includes necessary dependencies for implementing the `align_features_to_words` function. Write a Python function `def align_features_to_words(roberta, features, alignment)` to solve the following problem:
Align given features to words. Args: roberta (RobertaHubInterface): RoBERTa instance features (torch.Tensor): features to align of shape `(T_bpe x C)` alignment: alignment between BPE tokens and words returned by func:`align_bpe_to_words`.
Here is the function:
def align_features_to_words(roberta, features, alignment):
"""
Align given features to words.
Args:
roberta (RobertaHubInterface): RoBERTa instance
features (torch.Tensor): features to align of shape `(T_bpe x C)`
alignment: alignment between BPE tokens and words returned by
func:`align_bpe_to_words`.
"""
assert features.dim() == 2
bpe_counts = Counter(j for bpe_indices in alignment for j in bpe_indices)
assert bpe_counts[0] == 0 # <s> shouldn't be aligned
denom = features.new([bpe_counts.get(j, 1) for j in range(len(features))])
weighted_features = features / denom.unsqueeze(-1)
output = [weighted_features[0]]
largest_j = -1
for bpe_indices in alignment:
output.append(weighted_features[bpe_indices].sum(dim=0))
largest_j = max(largest_j, *bpe_indices)
for j in range(largest_j + 1, len(features)):
output.append(weighted_features[j])
output = torch.stack(output)
assert torch.all(torch.abs(output.sum(dim=0) - features.sum(dim=0)) < 1e-4)
return output | Align given features to words. Args: roberta (RobertaHubInterface): RoBERTa instance features (torch.Tensor): features to align of shape `(T_bpe x C)` alignment: alignment between BPE tokens and words returned by func:`align_bpe_to_words`. |
19,183 | from collections import Counter
from typing import List
import torch
def spacy_nlp():
if getattr(spacy_nlp, '_nlp', None) is None:
try:
from spacy.lang.en import English
spacy_nlp._nlp = English()
except ImportError:
raise ImportError('Please install spacy with: pip install spacy')
return spacy_nlp._nlp
def spacy_tokenizer():
if getattr(spacy_tokenizer, '_tokenizer', None) is None:
try:
nlp = spacy_nlp()
spacy_tokenizer._tokenizer = nlp.Defaults.create_tokenizer(nlp)
except ImportError:
raise ImportError('Please install spacy with: pip install spacy')
return spacy_tokenizer._tokenizer | null |
19,184 | import math
from typing import Any, Dict, List, Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import options, utils
from fairseq.models import (
FairseqEncoder,
FairseqEncoderDecoderModel,
FairseqIncrementalDecoder,
register_model,
register_model_architecture,
)
from fairseq.models.fairseq_encoder import EncoderOut
from fairseq.modules import (
AdaptiveSoftmax,
LayerDropModuleList,
LayerNorm,
PositionalEmbedding,
SinusoidalPositionalEmbedding,
TransformerDecoderLayer,
TransformerEncoderLayer,
)
from fairseq.modules.quant_noise import quant_noise as apply_quant_noise_
from torch import Tensor
def Embedding(num_embeddings, embedding_dim, padding_idx):
m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx)
nn.init.normal_(m.weight, mean=0, std=embedding_dim ** -0.5)
nn.init.constant_(m.weight[padding_idx], 0)
return m | null |
19,185 | import math
from typing import Any, Dict, List, Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import options, utils
from fairseq.models import (
FairseqEncoder,
FairseqEncoderDecoderModel,
FairseqIncrementalDecoder,
register_model,
register_model_architecture,
)
from fairseq.models.fairseq_encoder import EncoderOut
from fairseq.modules import (
AdaptiveSoftmax,
LayerDropModuleList,
LayerNorm,
PositionalEmbedding,
SinusoidalPositionalEmbedding,
TransformerDecoderLayer,
TransformerEncoderLayer,
)
from fairseq.modules.quant_noise import quant_noise as apply_quant_noise_
from torch import Tensor
def Linear(in_features, out_features, bias=True):
m = nn.Linear(in_features, out_features, bias)
nn.init.xavier_uniform_(m.weight)
if bias:
nn.init.constant_(m.bias, 0.0)
return m | null |
19,186 | import math
from typing import Any, Dict, List, Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import options, utils
from fairseq.models import (
FairseqEncoder,
FairseqEncoderDecoderModel,
FairseqIncrementalDecoder,
register_model,
register_model_architecture,
)
from fairseq.models.fairseq_encoder import EncoderOut
from fairseq.modules import (
AdaptiveSoftmax,
LayerDropModuleList,
LayerNorm,
PositionalEmbedding,
SinusoidalPositionalEmbedding,
TransformerDecoderLayer,
TransformerEncoderLayer,
)
from fairseq.modules.quant_noise import quant_noise as apply_quant_noise_
from torch import Tensor
def base_architecture(args):
args.encoder_embed_path = getattr(args, "encoder_embed_path", None)
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048)
args.encoder_layers = getattr(args, "encoder_layers", 6)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8)
args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False)
args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False)
args.decoder_embed_path = getattr(args, "decoder_embed_path", None)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim)
args.decoder_ffn_embed_dim = getattr(
args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim
)
args.decoder_layers = getattr(args, "decoder_layers", 6)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8)
args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False)
args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False)
args.attention_dropout = getattr(args, "attention_dropout", 0.0)
args.activation_dropout = getattr(args, "activation_dropout", 0.0)
args.activation_fn = getattr(args, "activation_fn", "relu")
args.dropout = getattr(args, "dropout", 0.1)
args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0)
args.share_decoder_input_output_embed = getattr(
args, "share_decoder_input_output_embed", False
)
args.share_all_embeddings = getattr(args, "share_all_embeddings", False)
args.no_token_positional_embeddings = getattr(
args, "no_token_positional_embeddings", False
)
args.adaptive_input = getattr(args, "adaptive_input", False)
args.no_cross_attention = getattr(args, "no_cross_attention", False)
args.cross_self_attention = getattr(args, "cross_self_attention", False)
args.decoder_output_dim = getattr(
args, "decoder_output_dim", args.decoder_embed_dim
)
args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim)
args.no_scale_embedding = getattr(args, "no_scale_embedding", False)
args.layernorm_embedding = getattr(args, "layernorm_embedding", False)
args.tie_adaptive_weights = getattr(args, "tie_adaptive_weights", False)
def transformer_wmt_en_de(args):
base_architecture(args) | null |
19,187 | import math
from typing import Any, Dict, List, Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import options, utils
from fairseq.models import (
FairseqEncoder,
FairseqEncoderDecoderModel,
FairseqIncrementalDecoder,
register_model,
register_model_architecture,
)
from fairseq.models.fairseq_encoder import EncoderOut
from fairseq.modules import (
AdaptiveSoftmax,
LayerDropModuleList,
LayerNorm,
PositionalEmbedding,
SinusoidalPositionalEmbedding,
TransformerDecoderLayer,
TransformerEncoderLayer,
)
from fairseq.modules.quant_noise import quant_noise as apply_quant_noise_
from torch import Tensor
def transformer_vaswani_wmt_en_de_big(args):
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4096)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16)
args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 1024)
args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 4096)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16)
args.dropout = getattr(args, "dropout", 0.3)
base_architecture(args)
def transformer_vaswani_wmt_en_fr_big(args):
args.dropout = getattr(args, "dropout", 0.1)
transformer_vaswani_wmt_en_de_big(args) | null |
19,188 | import math
from typing import Any, Dict, List, Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import options, utils
from fairseq.models import (
FairseqEncoder,
FairseqEncoderDecoderModel,
FairseqIncrementalDecoder,
register_model,
register_model_architecture,
)
from fairseq.models.fairseq_encoder import EncoderOut
from fairseq.modules import (
AdaptiveSoftmax,
LayerDropModuleList,
LayerNorm,
PositionalEmbedding,
SinusoidalPositionalEmbedding,
TransformerDecoderLayer,
TransformerEncoderLayer,
)
from fairseq.modules.quant_noise import quant_noise as apply_quant_noise_
from torch import Tensor
def transformer_vaswani_wmt_en_de_big(args):
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4096)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16)
args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 1024)
args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 4096)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16)
args.dropout = getattr(args, "dropout", 0.3)
base_architecture(args)
def transformer_wmt_en_de_big_t2t(args):
args.encoder_normalize_before = getattr(args, "encoder_normalize_before", True)
args.decoder_normalize_before = getattr(args, "decoder_normalize_before", True)
args.attention_dropout = getattr(args, "attention_dropout", 0.1)
args.activation_dropout = getattr(args, "activation_dropout", 0.1)
transformer_vaswani_wmt_en_de_big(args) | null |
19,189 | import logging
import torch
import torch.nn as nn
from fairseq import utils
from fairseq.models import (
register_model,
register_model_architecture,
)
from fairseq.models.transformer import TransformerModel
from fairseq.modules.transformer_sentence_encoder import init_bert_params
from .hub_interface import BARTHubInterface
def bart_large_architecture(args):
args.encoder_embed_path = getattr(args, 'encoder_embed_path', None)
args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 1024)
args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 4*1024)
args.encoder_layers = getattr(args, 'encoder_layers', 12)
args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 16)
args.encoder_normalize_before = getattr(args, 'encoder_normalize_before', False)
args.encoder_learned_pos = getattr(args, 'encoder_learned_pos', True)
args.decoder_embed_path = getattr(args, 'decoder_embed_path', None)
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', args.encoder_embed_dim)
args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', args.encoder_ffn_embed_dim)
args.decoder_layers = getattr(args, 'decoder_layers', 12)
args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 16)
args.decoder_normalize_before = getattr(args, 'decoder_normalize_before', False)
args.decoder_learned_pos = getattr(args, 'decoder_learned_pos', True)
args.attention_dropout = getattr(args, 'attention_dropout', 0.)
args.relu_dropout = getattr(args, 'relu_dropout', 0.)
args.dropout = getattr(args, 'dropout', 0.1)
args.max_target_positions = getattr(args, 'max_target_positions', 1024)
args.max_source_positions = getattr(args, 'max_source_positions', 1024)
args.adaptive_softmax_cutoff = getattr(args, 'adaptive_softmax_cutoff', None)
args.adaptive_softmax_dropout = getattr(args, 'adaptive_softmax_dropout', 0)
args.share_decoder_input_output_embed = getattr(args, 'share_decoder_input_output_embed', True)
args.share_all_embeddings = getattr(args, 'share_all_embeddings', True)
args.decoder_output_dim = getattr(args, 'decoder_output_dim', args.decoder_embed_dim)
args.decoder_input_dim = getattr(args, 'decoder_input_dim', args.decoder_embed_dim)
args.no_scale_embedding = getattr(args, 'no_scale_embedding', True)
args.layernorm_embedding = getattr(args, 'layernorm_embedding', True)
args.activation_fn = getattr(args, 'activation_fn', 'gelu')
args.pooler_activation_fn = getattr(args, 'pooler_activation_fn', 'tanh')
args.pooler_dropout = getattr(args, 'pooler_dropout', 0.0)
def mbart_large_architecture(args):
args.no_scale_embedding = getattr(args, 'no_scale_embedding', False)
bart_large_architecture(args) | null |
19,190 | import logging
import torch
import torch.nn as nn
from fairseq import utils
from fairseq.models import (
register_model,
register_model_architecture,
)
from fairseq.models.transformer import TransformerModel
from fairseq.modules.transformer_sentence_encoder import init_bert_params
from .hub_interface import BARTHubInterface
def mbart_base_architecture(args):
args.no_scale_embedding = getattr(args, 'no_scale_embedding', False)
bart_base_architecture(args)
def mbart_base_wmt20_architecture(args):
args.layernorm_embedding = getattr(args, 'layernorm_embedding', False)
mbart_base_architecture(args) | null |
19,191 | from fairseq import options, utils
from fairseq.models import (
FairseqLanguageModel,
register_model,
register_model_architecture,
)
from fairseq.models.transformer import (
Embedding,
TransformerDecoder,
)
from fairseq.modules import (
AdaptiveInput,
CharacterTokenEmbedder,
)
def transformer_lm_big(args):
def transformer_lm_baevski_wiki103(args):
args.decoder_layers = getattr(args, 'decoder_layers', 16)
args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 8)
args.dropout = getattr(args, 'dropout', 0.3)
args.adaptive_input = getattr(args, 'adaptive_input', True)
args.tie_adaptive_weights = getattr(args, 'tie_adaptive_weights', True)
args.adaptive_input_cutoff = getattr(args, 'adaptive_input_cutoff', '20000,60000')
args.adaptive_softmax_cutoff = getattr(args, 'adaptive_softmax_cutoff', '20000,60000')
args.adaptive_softmax_dropout = getattr(args, 'adaptive_softmax_dropout', 0.2)
args.attention_dropout = getattr(args, 'attention_dropout', 0.1)
args.activation_dropout = getattr(args, 'activation_dropout', 0.1)
args.no_decoder_final_norm = getattr(args, 'no_decoder_final_norm', True)
args.tie_adaptive_proj = getattr(args, 'tie_adaptive_proj', True)
transformer_lm_big(args) | null |
19,192 | from fairseq import options, utils
from fairseq.models import (
FairseqLanguageModel,
register_model,
register_model_architecture,
)
from fairseq.models.transformer import (
Embedding,
TransformerDecoder,
)
from fairseq.modules import (
AdaptiveInput,
CharacterTokenEmbedder,
)
def transformer_lm_big(args):
def transformer_lm_baevski_gbw(args):
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 512)
args.dropout = getattr(args, 'dropout', 0.1)
args.attention_dropout = getattr(args, 'attention_dropout', 0.1)
args.no_decoder_final_norm = getattr(args, 'no_decoder_final_norm', True)
transformer_lm_big(args) | null |
19,193 | from fairseq import options, utils
from fairseq.models import (
FairseqLanguageModel,
register_model,
register_model_architecture,
)
from fairseq.models.transformer import (
Embedding,
TransformerDecoder,
)
from fairseq.modules import (
AdaptiveInput,
CharacterTokenEmbedder,
)
def base_lm_architecture(args):
# backward compatibility for older model checkpoints
if hasattr(args, 'no_tie_adaptive_proj'):
# previous models defined --no-tie-adaptive-proj, so use the existence of
# that option to determine if this is an "old" model checkpoint
args.no_decoder_final_norm = True # old models always set this to True
if args.no_tie_adaptive_proj is False:
args.tie_adaptive_proj = True
if hasattr(args, 'decoder_final_norm'):
args.no_decoder_final_norm = not args.decoder_final_norm
args.dropout = getattr(args, 'dropout', 0.1)
args.attention_dropout = getattr(args, 'attention_dropout', 0.0)
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 512)
args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 2048)
args.decoder_layers = getattr(args, 'decoder_layers', 6)
args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 8)
args.adaptive_softmax_cutoff = getattr(args, 'adaptive_softmax_cutoff', None)
args.adaptive_softmax_dropout = getattr(args, 'adaptive_softmax_dropout', 0)
args.adaptive_softmax_factor = getattr(args, 'adaptive_softmax_factor', 4)
args.decoder_learned_pos = getattr(args, 'decoder_learned_pos', False)
args.activation_fn = getattr(args, 'activation_fn', 'relu')
args.add_bos_token = getattr(args, 'add_bos_token', False)
args.no_token_positional_embeddings = getattr(args, 'no_token_positional_embeddings', False)
args.share_decoder_input_output_embed = getattr(args, 'share_decoder_input_output_embed', False)
args.character_embeddings = getattr(args, 'character_embeddings', False)
args.decoder_output_dim = getattr(args, 'decoder_output_dim', args.decoder_embed_dim)
args.decoder_input_dim = getattr(args, 'decoder_input_dim', args.decoder_embed_dim)
# Model training is not stable without this
args.decoder_normalize_before = True
args.no_decoder_final_norm = getattr(args, 'no_decoder_final_norm', False)
args.adaptive_input = getattr(args, 'adaptive_input', False)
args.adaptive_input_factor = getattr(args, 'adaptive_input_factor', 4)
args.adaptive_input_cutoff = getattr(args, 'adaptive_input_cutoff', None)
args.tie_adaptive_weights = getattr(args, 'tie_adaptive_weights', False)
args.tie_adaptive_proj = getattr(args, 'tie_adaptive_proj', False)
args.no_scale_embedding = getattr(args, 'no_scale_embedding', False)
args.layernorm_embedding = getattr(args, 'layernorm_embedding', False)
def transformer_lm_gpt(args):
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 768)
args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 3072)
args.decoder_layers = getattr(args, 'decoder_layers', 12)
args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 12)
args.dropout = getattr(args, 'dropout', 0.1)
args.attention_dropout = getattr(args, 'attention_dropout', 0.1)
args.activation_fn = getattr(args, 'activation_fn', 'gelu')
base_lm_architecture(args) | null |
19,194 | from fairseq import options, utils
from fairseq.models import (
FairseqLanguageModel,
register_model,
register_model_architecture,
)
from fairseq.models.transformer import (
Embedding,
TransformerDecoder,
)
from fairseq.modules import (
AdaptiveInput,
CharacterTokenEmbedder,
)
def base_lm_architecture(args):
# backward compatibility for older model checkpoints
if hasattr(args, 'no_tie_adaptive_proj'):
# previous models defined --no-tie-adaptive-proj, so use the existence of
# that option to determine if this is an "old" model checkpoint
args.no_decoder_final_norm = True # old models always set this to True
if args.no_tie_adaptive_proj is False:
args.tie_adaptive_proj = True
if hasattr(args, 'decoder_final_norm'):
args.no_decoder_final_norm = not args.decoder_final_norm
args.dropout = getattr(args, 'dropout', 0.1)
args.attention_dropout = getattr(args, 'attention_dropout', 0.0)
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 512)
args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 2048)
args.decoder_layers = getattr(args, 'decoder_layers', 6)
args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 8)
args.adaptive_softmax_cutoff = getattr(args, 'adaptive_softmax_cutoff', None)
args.adaptive_softmax_dropout = getattr(args, 'adaptive_softmax_dropout', 0)
args.adaptive_softmax_factor = getattr(args, 'adaptive_softmax_factor', 4)
args.decoder_learned_pos = getattr(args, 'decoder_learned_pos', False)
args.activation_fn = getattr(args, 'activation_fn', 'relu')
args.add_bos_token = getattr(args, 'add_bos_token', False)
args.no_token_positional_embeddings = getattr(args, 'no_token_positional_embeddings', False)
args.share_decoder_input_output_embed = getattr(args, 'share_decoder_input_output_embed', False)
args.character_embeddings = getattr(args, 'character_embeddings', False)
args.decoder_output_dim = getattr(args, 'decoder_output_dim', args.decoder_embed_dim)
args.decoder_input_dim = getattr(args, 'decoder_input_dim', args.decoder_embed_dim)
# Model training is not stable without this
args.decoder_normalize_before = True
args.no_decoder_final_norm = getattr(args, 'no_decoder_final_norm', False)
args.adaptive_input = getattr(args, 'adaptive_input', False)
args.adaptive_input_factor = getattr(args, 'adaptive_input_factor', 4)
args.adaptive_input_cutoff = getattr(args, 'adaptive_input_cutoff', None)
args.tie_adaptive_weights = getattr(args, 'tie_adaptive_weights', False)
args.tie_adaptive_proj = getattr(args, 'tie_adaptive_proj', False)
args.no_scale_embedding = getattr(args, 'no_scale_embedding', False)
args.layernorm_embedding = getattr(args, 'layernorm_embedding', False)
def transformer_lm_gpt2_small(args):
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 1024)
args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 4096)
args.decoder_layers = getattr(args, 'decoder_layers', 24)
args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 16)
args.dropout = getattr(args, 'dropout', 0.1)
args.attention_dropout = getattr(args, 'attention_dropout', 0.1)
args.activation_fn = getattr(args, 'activation_fn', 'gelu')
base_lm_architecture(args) | null |
19,195 | from fairseq import options, utils
from fairseq.models import (
FairseqLanguageModel,
register_model,
register_model_architecture,
)
from fairseq.models.transformer import (
Embedding,
TransformerDecoder,
)
from fairseq.modules import (
AdaptiveInput,
CharacterTokenEmbedder,
)
def base_lm_architecture(args):
# backward compatibility for older model checkpoints
if hasattr(args, 'no_tie_adaptive_proj'):
# previous models defined --no-tie-adaptive-proj, so use the existence of
# that option to determine if this is an "old" model checkpoint
args.no_decoder_final_norm = True # old models always set this to True
if args.no_tie_adaptive_proj is False:
args.tie_adaptive_proj = True
if hasattr(args, 'decoder_final_norm'):
args.no_decoder_final_norm = not args.decoder_final_norm
args.dropout = getattr(args, 'dropout', 0.1)
args.attention_dropout = getattr(args, 'attention_dropout', 0.0)
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 512)
args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 2048)
args.decoder_layers = getattr(args, 'decoder_layers', 6)
args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 8)
args.adaptive_softmax_cutoff = getattr(args, 'adaptive_softmax_cutoff', None)
args.adaptive_softmax_dropout = getattr(args, 'adaptive_softmax_dropout', 0)
args.adaptive_softmax_factor = getattr(args, 'adaptive_softmax_factor', 4)
args.decoder_learned_pos = getattr(args, 'decoder_learned_pos', False)
args.activation_fn = getattr(args, 'activation_fn', 'relu')
args.add_bos_token = getattr(args, 'add_bos_token', False)
args.no_token_positional_embeddings = getattr(args, 'no_token_positional_embeddings', False)
args.share_decoder_input_output_embed = getattr(args, 'share_decoder_input_output_embed', False)
args.character_embeddings = getattr(args, 'character_embeddings', False)
args.decoder_output_dim = getattr(args, 'decoder_output_dim', args.decoder_embed_dim)
args.decoder_input_dim = getattr(args, 'decoder_input_dim', args.decoder_embed_dim)
# Model training is not stable without this
args.decoder_normalize_before = True
args.no_decoder_final_norm = getattr(args, 'no_decoder_final_norm', False)
args.adaptive_input = getattr(args, 'adaptive_input', False)
args.adaptive_input_factor = getattr(args, 'adaptive_input_factor', 4)
args.adaptive_input_cutoff = getattr(args, 'adaptive_input_cutoff', None)
args.tie_adaptive_weights = getattr(args, 'tie_adaptive_weights', False)
args.tie_adaptive_proj = getattr(args, 'tie_adaptive_proj', False)
args.no_scale_embedding = getattr(args, 'no_scale_embedding', False)
args.layernorm_embedding = getattr(args, 'layernorm_embedding', False)
def transformer_lm_gpt2_medium(args):
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 1280)
args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 5120)
args.decoder_layers = getattr(args, 'decoder_layers', 36)
args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 20)
args.dropout = getattr(args, 'dropout', 0.1)
args.attention_dropout = getattr(args, 'attention_dropout', 0.1)
args.activation_fn = getattr(args, 'activation_fn', 'gelu')
base_lm_architecture(args) | null |
19,196 | from fairseq import options, utils
from fairseq.models import (
FairseqLanguageModel,
register_model,
register_model_architecture,
)
from fairseq.models.transformer import (
Embedding,
TransformerDecoder,
)
from fairseq.modules import (
AdaptiveInput,
CharacterTokenEmbedder,
)
def base_lm_architecture(args):
# backward compatibility for older model checkpoints
if hasattr(args, 'no_tie_adaptive_proj'):
# previous models defined --no-tie-adaptive-proj, so use the existence of
# that option to determine if this is an "old" model checkpoint
args.no_decoder_final_norm = True # old models always set this to True
if args.no_tie_adaptive_proj is False:
args.tie_adaptive_proj = True
if hasattr(args, 'decoder_final_norm'):
args.no_decoder_final_norm = not args.decoder_final_norm
args.dropout = getattr(args, 'dropout', 0.1)
args.attention_dropout = getattr(args, 'attention_dropout', 0.0)
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 512)
args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 2048)
args.decoder_layers = getattr(args, 'decoder_layers', 6)
args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 8)
args.adaptive_softmax_cutoff = getattr(args, 'adaptive_softmax_cutoff', None)
args.adaptive_softmax_dropout = getattr(args, 'adaptive_softmax_dropout', 0)
args.adaptive_softmax_factor = getattr(args, 'adaptive_softmax_factor', 4)
args.decoder_learned_pos = getattr(args, 'decoder_learned_pos', False)
args.activation_fn = getattr(args, 'activation_fn', 'relu')
args.add_bos_token = getattr(args, 'add_bos_token', False)
args.no_token_positional_embeddings = getattr(args, 'no_token_positional_embeddings', False)
args.share_decoder_input_output_embed = getattr(args, 'share_decoder_input_output_embed', False)
args.character_embeddings = getattr(args, 'character_embeddings', False)
args.decoder_output_dim = getattr(args, 'decoder_output_dim', args.decoder_embed_dim)
args.decoder_input_dim = getattr(args, 'decoder_input_dim', args.decoder_embed_dim)
# Model training is not stable without this
args.decoder_normalize_before = True
args.no_decoder_final_norm = getattr(args, 'no_decoder_final_norm', False)
args.adaptive_input = getattr(args, 'adaptive_input', False)
args.adaptive_input_factor = getattr(args, 'adaptive_input_factor', 4)
args.adaptive_input_cutoff = getattr(args, 'adaptive_input_cutoff', None)
args.tie_adaptive_weights = getattr(args, 'tie_adaptive_weights', False)
args.tie_adaptive_proj = getattr(args, 'tie_adaptive_proj', False)
args.no_scale_embedding = getattr(args, 'no_scale_embedding', False)
args.layernorm_embedding = getattr(args, 'layernorm_embedding', False)
def transformer_lm_gpt2_big(args):
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 1600)
args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 6400)
args.decoder_layers = getattr(args, 'decoder_layers', 48)
args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 25)
args.dropout = getattr(args, 'dropout', 0.1)
args.attention_dropout = getattr(args, 'attention_dropout', 0.1)
args.activation_fn = getattr(args, 'activation_fn', 'gelu')
base_lm_architecture(args) | null |
19,197 | from fairseq.models import register_model, register_model_architecture
from fairseq.models.transformer import (
base_architecture,
transformer_wmt_en_de_big,
TransformerModel,
)
def base_architecture(args):
args.encoder_embed_path = getattr(args, "encoder_embed_path", None)
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048)
args.encoder_layers = getattr(args, "encoder_layers", 6)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8)
args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False)
args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False)
args.decoder_embed_path = getattr(args, "decoder_embed_path", None)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim)
args.decoder_ffn_embed_dim = getattr(
args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim
)
args.decoder_layers = getattr(args, "decoder_layers", 6)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8)
args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False)
args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False)
args.attention_dropout = getattr(args, "attention_dropout", 0.0)
args.activation_dropout = getattr(args, "activation_dropout", 0.0)
args.activation_fn = getattr(args, "activation_fn", "relu")
args.dropout = getattr(args, "dropout", 0.1)
args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0)
args.share_decoder_input_output_embed = getattr(
args, "share_decoder_input_output_embed", False
)
args.share_all_embeddings = getattr(args, "share_all_embeddings", False)
args.no_token_positional_embeddings = getattr(
args, "no_token_positional_embeddings", False
)
args.adaptive_input = getattr(args, "adaptive_input", False)
args.no_cross_attention = getattr(args, "no_cross_attention", False)
args.cross_self_attention = getattr(args, "cross_self_attention", False)
args.decoder_output_dim = getattr(
args, "decoder_output_dim", args.decoder_embed_dim
)
args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim)
args.no_scale_embedding = getattr(args, "no_scale_embedding", False)
args.layernorm_embedding = getattr(args, "layernorm_embedding", False)
args.tie_adaptive_weights = getattr(args, "tie_adaptive_weights", False)
def transformer_align(args):
args.alignment_heads = getattr(args, "alignment_heads", 1)
args.alignment_layer = getattr(args, "alignment_layer", 4)
args.full_context_alignment = getattr(args, "full_context_alignment", False)
base_architecture(args) | null |
19,198 | from fairseq.models import register_model, register_model_architecture
from fairseq.models.transformer import (
base_architecture,
transformer_wmt_en_de_big,
TransformerModel,
)
def transformer_wmt_en_de_big(args):
args.attention_dropout = getattr(args, "attention_dropout", 0.1)
transformer_vaswani_wmt_en_de_big(args)
def transformer_wmt_en_de_big_align(args):
args.alignment_heads = getattr(args, "alignment_heads", 1)
args.alignment_layer = getattr(args, "alignment_layer", 4)
transformer_wmt_en_de_big(args) | null |
19,199 | import logging
import os
import sys
from typing import Dict, List, Optional
import torch
from fairseq.models import (
FairseqIncrementalDecoder,
FairseqLanguageModel,
register_model,
register_model_architecture,
)
def default_architecture(args):
if getattr(args, 'max_target_positions', None) is None:
args.max_target_positions = getattr(
args, 'tokens_per_sample', DEFAULT_MAX_TARGET_POSITIONS
)
args.embed_dim = getattr(args, 'embed_dim', 768)
args.num_attention_heads = getattr(args, 'num_attention_heads', 12)
args.num_layers = getattr(args, 'num_layers', 12)
args.dropout = getattr(args, 'dropout', 0.1)
args.attention_dropout = getattr(args, 'attention_dropout', 0.1)
def hf_gpt2_medium(args):
args.embed_dim = getattr(args, 'embed_dim', 1024)
args.num_attention_heads = getattr(args, 'num_attention_heads', 16)
args.num_layers = getattr(args, 'num_layers', 24)
default_architecture(args) | null |
19,200 | import logging
import os
import sys
from typing import Dict, List, Optional
import torch
from fairseq.models import (
FairseqIncrementalDecoder,
FairseqLanguageModel,
register_model,
register_model_architecture,
)
def default_architecture(args):
if getattr(args, 'max_target_positions', None) is None:
args.max_target_positions = getattr(
args, 'tokens_per_sample', DEFAULT_MAX_TARGET_POSITIONS
)
args.embed_dim = getattr(args, 'embed_dim', 768)
args.num_attention_heads = getattr(args, 'num_attention_heads', 12)
args.num_layers = getattr(args, 'num_layers', 12)
args.dropout = getattr(args, 'dropout', 0.1)
args.attention_dropout = getattr(args, 'attention_dropout', 0.1)
def hf_gpt2_large(args):
args.embed_dim = getattr(args, 'embed_dim', 1280)
args.num_attention_heads = getattr(args, 'num_attention_heads', 20)
args.num_layers = getattr(args, 'num_layers', 36)
default_architecture(args) | null |
19,201 | import logging
import os
import sys
from typing import Dict, List, Optional
import torch
from fairseq.models import (
FairseqIncrementalDecoder,
FairseqLanguageModel,
register_model,
register_model_architecture,
)
def default_architecture(args):
if getattr(args, 'max_target_positions', None) is None:
args.max_target_positions = getattr(
args, 'tokens_per_sample', DEFAULT_MAX_TARGET_POSITIONS
)
args.embed_dim = getattr(args, 'embed_dim', 768)
args.num_attention_heads = getattr(args, 'num_attention_heads', 12)
args.num_layers = getattr(args, 'num_layers', 12)
args.dropout = getattr(args, 'dropout', 0.1)
args.attention_dropout = getattr(args, 'attention_dropout', 0.1)
def hf_gpt2_xl(args):
args.embed_dim = getattr(args, 'embed_dim', 1600)
args.num_attention_heads = getattr(args, 'num_attention_heads', 25)
args.num_layers = getattr(args, 'num_layers', 48)
default_architecture(args) | null |
19,202 | from fairseq import options
from fairseq.models import (
FairseqLanguageModel,
register_model,
register_model_architecture,
)
from fairseq.models.lightconv import (
Embedding,
LightConvDecoder,
)
from fairseq.modules import (
AdaptiveInput,
CharacterTokenEmbedder,
)
def base_lm_architecture(args):
def lightconv_lm_gbw(args):
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 512)
args.dropout = getattr(args, 'dropout', 0.1)
args.attention_dropout = getattr(args, 'attention_dropout', 0.1)
args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 4096)
args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 16)
base_lm_architecture(args) | null |
19,203 | import logging
import math
import sys
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq.models import BaseFairseqModel, register_model, register_model_architecture
from fairseq.modules import (
Fp32GroupNorm,
Fp32LayerNorm,
GumbelVectorQuantizer,
KmeansVectorQuantizer,
)
from fairseq.utils import buffered_arange
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)
def norm_block(is_layer_norm, dim, affine=True):
if is_layer_norm:
mod = nn.Sequential(
TransposeLast(),
Fp32LayerNorm(dim, elementwise_affine=affine),
TransposeLast(),
)
else:
mod = Fp32GroupNorm(1, dim, affine=affine)
return mod | null |
19,204 | import logging
import math
import sys
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq.models import BaseFairseqModel, register_model, register_model_architecture
from fairseq.modules import (
Fp32GroupNorm,
Fp32LayerNorm,
GumbelVectorQuantizer,
KmeansVectorQuantizer,
)
from fairseq.utils import buffered_arange
def base_wav2vec_architecture(args):
conv_feature_layers = "[(512, 10, 5)]"
conv_feature_layers += " + [(512, 8, 4)]"
conv_feature_layers += " + [(512, 4, 2)] * 3"
args.conv_feature_layers = getattr(args, "conv_feature_layers", conv_feature_layers)
args.conv_aggregator_layers = getattr(
args, "conv_aggregator_layers", "[(512, 3, 1)] * 9"
)
args.prediction_steps = getattr(args, "prediction_steps", 12)
args.num_negatives = getattr(args, "num_negatives", 1)
args.sample_distance = getattr(args, "sample_distance", None)
args.cross_sample_negatives = getattr(args, "cross_sample_negatives", 0)
args.dropout = getattr(args, "dropout", 0.0)
args.dropout_features = getattr(args, "dropout_features", 0.0)
args.dropout_agg = getattr(args, "dropout_agg", 0.0)
args.encoder = getattr(args, "encoder", "cnn")
args.aggregator = getattr(args, "aggregator", "cnn")
args.skip_connections_feat = getattr(args, "skip_connections_feat", False)
args.skip_connections_agg = getattr(args, "skip_connections_agg", False)
args.residual_scale = getattr(args, "residual_scale", 0.5)
args.gru_dim = getattr(args, "gru_dim", 512)
args.no_conv_bias = getattr(args, "no_conv_bias", False)
args.agg_zero_pad = getattr(args, "agg_zero_pad", False)
args.log_compression = getattr(args, "log_compression", False)
args.balanced_classes = getattr(args, "balanced_classes", False)
args.infonce = getattr(args, "infonce", False)
args.project_features = getattr(args, "project_features", "none")
args.non_affine_group_norm = getattr(args, "non_affine_group_norm", False)
args.offset = getattr(args, "offset", "auto")
args.activation = getattr(args, "activation", "relu")
args.vq_type = getattr(args, "vq_type", "none")
args.vq_vars = getattr(args, "vq_vars", 320)
args.vq_groups = getattr(args, "vq_groups", 2)
args.vq_dim = getattr(args, "vq_dim", 0)
args.vq_depth = getattr(args, "vq_depth", 1)
args.combine_groups = getattr(args, "combine_groups", False)
args.vq_temp = getattr(args, "vq_temp", "(2.0, 0.5, 0.999995)")
args.vq_gamma = getattr(args, "vq_gamma", 0.25) | null |
19,205 | from fairseq.models import register_model, register_model_architecture
from fairseq.models.nat import NATransformerModel
from fairseq.utils import new_arange
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 _skeptical_unmasking(output_scores, output_masks, p):
sorted_index = output_scores.sort(-1)[1]
boundary_len = (
(output_masks.sum(1, keepdim=True).type_as(output_scores) - 2) * p
).long()
skeptical_mask = new_arange(output_masks) < boundary_len
return skeptical_mask.scatter(1, sorted_index, skeptical_mask) | null |
19,206 | from fairseq.models import register_model, register_model_architecture
from fairseq.models.nat import NATransformerModel
from fairseq.utils import new_arange
def cmlm_base_architecture(args):
args.encoder_embed_path = getattr(args, "encoder_embed_path", None)
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048)
args.encoder_layers = getattr(args, "encoder_layers", 6)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8)
args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False)
args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False)
args.decoder_embed_path = getattr(args, "decoder_embed_path", None)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim)
args.decoder_ffn_embed_dim = getattr(
args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim
)
args.decoder_layers = getattr(args, "decoder_layers", 6)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8)
args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False)
args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False)
args.attention_dropout = getattr(args, "attention_dropout", 0.0)
args.activation_dropout = getattr(args, "activation_dropout", 0.0)
args.activation_fn = getattr(args, "activation_fn", "relu")
args.dropout = getattr(args, "dropout", 0.1)
args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0)
args.share_decoder_input_output_embed = getattr(
args, "share_decoder_input_output_embed", False
)
args.share_all_embeddings = getattr(args, "share_all_embeddings", True)
args.no_token_positional_embeddings = getattr(
args, "no_token_positional_embeddings", False
)
args.adaptive_input = getattr(args, "adaptive_input", False)
args.apply_bert_init = getattr(args, "apply_bert_init", False)
args.decoder_output_dim = getattr(
args, "decoder_output_dim", args.decoder_embed_dim
)
args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim)
# --- special arguments ---
args.sg_length_pred = getattr(args, "sg_length_pred", False)
args.pred_length_offset = getattr(args, "pred_length_offset", False)
args.length_loss_factor = getattr(args, "length_loss_factor", 0.1)
args.ngram_predictor = getattr(args, "ngram_predictor", 1)
args.src_embedding_copy = getattr(args, "src_embedding_copy", False)
def cmlm_wmt_en_de(args):
cmlm_base_architecture(args) | null |
19,207 | import math
import torch
from fairseq.models.transformer import TransformerModel, TransformerEncoder, TransformerDecoder
from fairseq.modules.transformer_sentence_encoder import init_bert_params
def ensemble_encoder(func):
def wrapper(self, *args, **kwargs):
if self.ensemble_models is None or len(self.ensemble_models) == 1:
return func(self, *args, **kwargs)
encoder_outs = [func(model, *args, **kwargs) for model in self.ensemble_models]
_encoder_out = encoder_outs[0]
def stack(key):
outs = [getattr(e, key) for e in encoder_outs]
return torch.stack(outs, -1) if outs[0] is not None else None
return _encoder_out._replace(
encoder_out=stack('encoder_out'),
encoder_embedding=stack('encoder_embedding'),
encoder_states=stack('encoder_states')
)
return wrapper | null |
19,208 | import math
import torch
from fairseq.models.transformer import TransformerModel, TransformerEncoder, TransformerDecoder
from fairseq.modules.transformer_sentence_encoder import init_bert_params
def ensemble_decoder(func):
def wrapper(self, normalize=False, encoder_out=None, *args, **kwargs):
if self.ensemble_models is None or len(self.ensemble_models) == 1:
return func(self, normalize=normalize, encoder_out=encoder_out, *args, **kwargs)
action_outs = [
func(model, normalize=normalize, encoder_out=encoder_out._replace(
encoder_out=encoder_out.encoder_out[:, :, :, i]
), *args, **kwargs)
for i, model in enumerate(self.ensemble_models)
]
if not isinstance(action_outs[0], tuple): # return multiple values
action_outs = [[a] for a in action_outs]
else:
action_outs = [list(a) for a in action_outs]
ensembled_outs = []
for i in range(len(action_outs[0])):
if i == 0 and normalize:
ensembled_outs += [
torch.logsumexp(
torch.stack([a[i] for a in action_outs], -1),
dim=-1) - math.log(len(self.ensemble_models))
]
elif action_outs[0][i] is not None:
ensembled_outs += [
torch.stack([a[i] for a in action_outs], -1)
]
else:
ensembled_outs += [None]
if len(ensembled_outs) == 1:
return ensembled_outs[0]
return tuple(ensembled_outs)
return wrapper | null |
19,209 | from fairseq.models.nat import NATransformerModel, base_architecture
from fairseq.models import register_model, register_model_architecture
from fairseq.modules import DynamicCRF
def nacrf_base_architecture(args):
args.crf_lowrank_approx = getattr(args, "crf_lowrank_approx", 32)
args.crf_beam_approx = getattr(args, "crf_beam_approx", 64)
args.word_ins_loss_factor = getattr(args, "word_ins_loss_factor", 0.5)
args.encoder_normalize_before = getattr(args, "encoder_normalize_before", True)
args.decoder_normalize_before = getattr(args, "decoder_normalize_before", True)
base_architecture(args) | null |
19,210 | import torch
from fairseq.utils import new_arange
def load_libnat():
try:
from fairseq import libnat_cuda
return libnat_cuda, True
except ImportError as e:
print(str(e) + '... fall back to CPU version')
try:
from fairseq import libnat
return libnat, False
except ImportError as e:
import sys
sys.stderr.write("ERROR: missing libnat_cuda. run `python setup.py build_ext --inplace`\n")
raise e
def _get_ins_targets(in_tokens, out_tokens, padding_idx, unk_idx):
libnat, use_cuda = load_libnat()
def _get_ins_targets_cuda(in_tokens, out_tokens, padding_idx, unk_idx):
in_masks = in_tokens.ne(padding_idx)
out_masks = out_tokens.ne(padding_idx)
mask_ins_targets, masked_tgt_masks = libnat.generate_insertion_labels(
out_tokens.int(), libnat.levenshtein_distance(
in_tokens.int(), out_tokens.int(),
in_masks.sum(1).int(), out_masks.sum(1).int()
)
)
masked_tgt_masks = masked_tgt_masks.bool() & out_masks
mask_ins_targets = mask_ins_targets.type_as(
in_tokens)[:, 1:in_masks.size(1)].masked_fill_(~in_masks[:, 1:], 0)
masked_tgt_tokens = out_tokens.masked_fill(masked_tgt_masks, unk_idx)
return masked_tgt_masks, masked_tgt_tokens, mask_ins_targets
def _get_ins_targets_cpu(in_tokens, out_tokens, padding_idx, unk_idx):
in_seq_len, out_seq_len = in_tokens.size(1), out_tokens.size(1)
in_tokens_list = [
[t for t in s if t != padding_idx] for i, s in enumerate(in_tokens.tolist())
]
out_tokens_list = [
[t for t in s if t != padding_idx]
for i, s in enumerate(out_tokens.tolist())
]
full_labels = libnat.suggested_ed2_path(
in_tokens_list, out_tokens_list, padding_idx
)
mask_inputs = [
[len(c) if c[0] != padding_idx else 0 for c in a[:-1]] for a in full_labels
]
# generate labels
masked_tgt_masks = []
for mask_input in mask_inputs:
mask_label = []
for beam_size in mask_input[1:-1]: # HACK 1:-1
mask_label += [0] + [1 for _ in range(beam_size)]
masked_tgt_masks.append(
mask_label + [0 for _ in range(out_seq_len - len(mask_label))]
)
mask_ins_targets = [
mask_input[1:-1] + [0 for _ in range(in_seq_len - 1 - len(mask_input[1:-1]))]
for mask_input in mask_inputs
]
# transform to tensor
masked_tgt_masks = torch.tensor(
masked_tgt_masks, device=out_tokens.device
).bool()
mask_ins_targets = torch.tensor(mask_ins_targets, device=in_tokens.device)
masked_tgt_tokens = out_tokens.masked_fill(masked_tgt_masks, unk_idx)
return masked_tgt_masks, masked_tgt_tokens, mask_ins_targets
if use_cuda:
return _get_ins_targets_cuda(in_tokens, out_tokens, padding_idx, unk_idx)
return _get_ins_targets_cpu(in_tokens, out_tokens, padding_idx, unk_idx) | null |
19,211 | import torch
from fairseq.utils import new_arange
def load_libnat():
def _get_del_targets(in_tokens, out_tokens, padding_idx):
libnat, use_cuda = load_libnat()
def _get_del_targets_cuda(in_tokens, out_tokens, padding_idx):
in_masks = in_tokens.ne(padding_idx)
out_masks = out_tokens.ne(padding_idx)
word_del_targets = libnat.generate_deletion_labels(
in_tokens.int(),
libnat.levenshtein_distance(
in_tokens.int(), out_tokens.int(),
in_masks.sum(1).int(), out_masks.sum(1).int()
)
)
word_del_targets = word_del_targets.type_as(in_tokens).masked_fill_(~in_masks, 0)
return word_del_targets
def _get_del_targets_cpu(in_tokens, out_tokens, padding_idx):
out_seq_len = out_tokens.size(1)
with torch.cuda.device_of(in_tokens):
in_tokens_list = [
[t for t in s if t != padding_idx] for i, s in enumerate(in_tokens.tolist())
]
out_tokens_list = [
[t for t in s if t != padding_idx]
for i, s in enumerate(out_tokens.tolist())
]
full_labels = libnat.suggested_ed2_path(
in_tokens_list, out_tokens_list, padding_idx
)
word_del_targets = [b[-1] for b in full_labels]
word_del_targets = [
labels + [0 for _ in range(out_seq_len - len(labels))]
for labels in word_del_targets
]
# transform to tensor
word_del_targets = torch.tensor(word_del_targets, device=out_tokens.device)
return word_del_targets
if use_cuda:
return _get_del_targets_cuda(in_tokens, out_tokens, padding_idx)
return _get_del_targets_cpu(in_tokens, out_tokens, padding_idx) | null |
19,212 | import torch
from fairseq.utils import new_arange
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 _apply_ins_masks(
in_tokens, in_scores, mask_ins_pred, padding_idx, unk_idx, eos_idx
):
in_masks = in_tokens.ne(padding_idx)
in_lengths = in_masks.sum(1)
# HACK: hacky way to shift all the paddings to eos first.
in_tokens.masked_fill_(~in_masks, eos_idx)
mask_ins_pred.masked_fill_(~in_masks[:, 1:], 0)
out_lengths = in_lengths + mask_ins_pred.sum(1)
out_max_len = out_lengths.max()
out_masks = (
new_arange(out_lengths, out_max_len)[None, :]
< out_lengths[:, None]
)
reordering = (mask_ins_pred + in_masks[:, 1:].long()).cumsum(1)
out_tokens = (
in_tokens.new_zeros(in_tokens.size(0), out_max_len)
.fill_(padding_idx)
.masked_fill_(out_masks, unk_idx)
)
out_tokens[:, 0] = in_tokens[:, 0]
out_tokens.scatter_(1, reordering, in_tokens[:, 1:])
out_scores = None
if in_scores is not None:
in_scores.masked_fill_(~in_masks, 0)
out_scores = in_scores.new_zeros(*out_tokens.size())
out_scores[:, 0] = in_scores[:, 0]
out_scores.scatter_(1, reordering, in_scores[:, 1:])
return out_tokens, out_scores | null |
19,213 | import torch
from fairseq.utils import new_arange
def _apply_ins_words(
in_tokens, in_scores, word_ins_pred, word_ins_scores, unk_idx
):
word_ins_masks = in_tokens.eq(unk_idx)
out_tokens = in_tokens.masked_scatter(word_ins_masks, word_ins_pred[word_ins_masks])
if in_scores is not None:
out_scores = in_scores.masked_scatter(
word_ins_masks, word_ins_scores[word_ins_masks]
)
else:
out_scores = None
return out_tokens, out_scores | null |
19,214 | import torch
from fairseq.utils import new_arange
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 _apply_del_words(
in_tokens, in_scores, in_attn, word_del_pred, padding_idx, bos_idx, eos_idx
):
# apply deletion to a tensor
in_masks = in_tokens.ne(padding_idx)
bos_eos_masks = in_tokens.eq(bos_idx) | in_tokens.eq(eos_idx)
max_len = in_tokens.size(1)
word_del_pred.masked_fill_(~in_masks, 1)
word_del_pred.masked_fill_(bos_eos_masks, 0)
reordering = (
new_arange(in_tokens)
.masked_fill_(word_del_pred, max_len)
.sort(1)[1]
)
out_tokens = in_tokens.masked_fill(word_del_pred, padding_idx).gather(1, reordering)
out_scores = None
if in_scores is not None:
out_scores = in_scores.masked_fill(word_del_pred, 0).gather(1, reordering)
out_attn = None
if in_attn is not None:
_mask = word_del_pred[:, :, None].expand_as(in_attn)
_reordering = reordering[:, :, None].expand_as(in_attn)
out_attn = in_attn.masked_fill(_mask, 0.).gather(1, _reordering)
return out_tokens, out_scores, out_attn | null |
19,215 | import torch
from fairseq.utils import new_arange
The provided code snippet includes necessary dependencies for implementing the `_skip` function. Write a Python function `def _skip(x, mask)` to solve the following problem:
Getting sliced (dim=0) tensor by mask. Supporting tensor and list/dict of tensors.
Here is the function:
def _skip(x, mask):
"""
Getting sliced (dim=0) tensor by mask. Supporting tensor and list/dict of tensors.
"""
if isinstance(x, int):
return x
if x is None:
return None
if isinstance(x, torch.Tensor):
if x.size(0) == mask.size(0):
return x[mask]
elif x.size(1) == mask.size(0):
return x[:, mask]
if isinstance(x, list):
return [_skip(x_i, mask) for x_i in x]
if isinstance(x, dict):
return {k: _skip(v, mask) for k, v in x.items()}
raise NotImplementedError | Getting sliced (dim=0) tensor by mask. Supporting tensor and list/dict of tensors. |
19,216 | import torch
from fairseq.utils import new_arange
def _skip_encoder_out(encoder, encoder_out, mask):
if not mask.any():
return encoder_out
else:
return encoder.reorder_encoder_out(encoder_out, mask.nonzero().squeeze()) | null |
19,217 | import torch
from fairseq.utils import new_arange
The provided code snippet includes necessary dependencies for implementing the `_fill` function. Write a Python function `def _fill(x, mask, y, padding_idx)` to solve the following problem:
Filling tensor x with y at masked positions (dim=0).
Here is the function:
def _fill(x, mask, y, padding_idx):
"""
Filling tensor x with y at masked positions (dim=0).
"""
if x is None:
return y
assert x.dim() == y.dim() and mask.size(0) == x.size(0)
assert x.dim() == 2 or (x.dim() == 3 and x.size(2) == y.size(2))
n_selected = mask.sum()
assert n_selected == y.size(0)
if n_selected == x.size(0):
return y
if x.size(1) < y.size(1):
dims = [x.size(0), y.size(1) - x.size(1)]
if x.dim() == 3:
dims.append(x.size(2))
x = torch.cat([x, x.new_zeros(*dims).fill_(padding_idx)], 1)
x[mask] = y
elif x.size(1) > y.size(1):
x[mask] = padding_idx
if x.dim() == 2:
x[mask, :y.size(1)] = y
else:
x[mask, :y.size(1), :] = y
else:
x[mask] = y
return x | Filling tensor x with y at masked positions (dim=0). |
19,218 | import numpy as np
import torch
import torch.nn.functional as F
from fairseq.models import register_model, register_model_architecture
from fairseq.models.nat import (
LevenshteinTransformerDecoder,
LevenshteinTransformerModel,
FairseqNATModel,
ensemble_decoder
)
from fairseq.models.transformer import Linear
from fairseq.utils import new_arange
from fairseq.modules.transformer_sentence_encoder import init_bert_params
neg_scorer = NegativeDistanceScore()
def _get_ins_targets(in_tokens, out_tokens, padding_idx, unk_idx, vocab_size, tau=None):
try:
from fairseq import libnat
except ImportError as e:
import sys
sys.stderr.write('ERROR: missing libnat. run `pip install --editable .`\n')
raise e
B = in_tokens.size(0)
T = in_tokens.size(1)
V = vocab_size
with torch.cuda.device_of(in_tokens):
in_tokens_list = [
[t for t in s if t != padding_idx] for i, s in enumerate(in_tokens.tolist())
]
out_tokens_list = [
[t for t in s if t != padding_idx]
for i, s in enumerate(out_tokens.tolist())
]
full_labels = libnat.suggested_ed2_path(
in_tokens_list, out_tokens_list, padding_idx
)
insert_labels = [a[:-1] for a in full_labels]
# numericalize1
insert_label_tensors = in_tokens.new_zeros(B * (T - 1) * V).float()
insert_index, insert_labels = zip(
*[
(w + (j + i * (T - 1)) * V, neg_scorer(k, len(label), tau))
for i, labels in enumerate(insert_labels)
for j, label in enumerate(labels[1:-1])
for k, w in enumerate(label)
]
) # HACK 1:-1
insert_index, insert_labels = [
torch.tensor(list(a), device=in_tokens.device)
for a in [insert_index, insert_labels]
]
insert_label_tensors.scatter_(0, insert_index.long(), insert_labels)
insert_label_tensors = insert_label_tensors.view(B, T - 1, V)
return insert_label_tensors | null |
19,219 | import numpy as np
import torch
import torch.nn.functional as F
from fairseq.models import register_model, register_model_architecture
from fairseq.models.nat import (
LevenshteinTransformerDecoder,
LevenshteinTransformerModel,
FairseqNATModel,
ensemble_decoder
)
from fairseq.models.transformer import Linear
from fairseq.utils import new_arange
from fairseq.modules.transformer_sentence_encoder import init_bert_params
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 _apply_ins_words(in_tokens, in_scores, word_ins_pred, word_ins_scores, padding_idx):
padding_masks = in_tokens[:, 1:].eq(padding_idx)
word_ins_scores.masked_fill_(padding_masks, 0.0)
word_ins_pred.masked_fill_(padding_masks, padding_idx)
in_coords = new_arange(in_tokens).type_as(in_scores)
# shift all padding predictions to infinite
out_coords = (in_coords[:, 1:] - 0.5).masked_fill(
word_ins_pred.eq(padding_idx), float("inf")
)
out_coords = torch.cat([in_coords, out_coords], 1).sort(-1)[1]
out_tokens = torch.cat([in_tokens, word_ins_pred], 1).gather(1, out_coords)
out_scores = torch.cat([in_scores, word_ins_scores], 1).gather(1, out_coords)
return out_tokens, out_scores | null |
19,220 | import numpy as np
import torch
import torch.nn.functional as F
from fairseq.models import register_model, register_model_architecture
from fairseq.models.nat import (
LevenshteinTransformerDecoder,
LevenshteinTransformerModel,
FairseqNATModel,
ensemble_decoder
)
from fairseq.models.transformer import Linear
from fairseq.utils import new_arange
from fairseq.modules.transformer_sentence_encoder import init_bert_params
def insertion_base_architecture(args):
args.encoder_embed_path = getattr(args, "encoder_embed_path", None)
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048)
args.encoder_layers = getattr(args, "encoder_layers", 6)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8)
args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False)
args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False)
args.decoder_embed_path = getattr(args, "decoder_embed_path", None)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim)
args.decoder_ffn_embed_dim = getattr(
args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim
)
args.decoder_layers = getattr(args, "decoder_layers", 6)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8)
args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False)
args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False)
args.attention_dropout = getattr(args, "attention_dropout", 0.0)
args.activation_dropout = getattr(args, "activation_dropout", 0.0)
args.activation_fn = getattr(args, "activation_fn", "relu")
args.dropout = getattr(args, "dropout", 0.1)
args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0)
args.share_decoder_input_output_embed = getattr(
args, "share_decoder_input_output_embed", False
)
args.share_all_embeddings = getattr(args, "share_all_embeddings", False)
args.no_token_positional_embeddings = getattr(
args, "no_token_positional_embeddings", False
)
args.adaptive_input = getattr(args, "adaptive_input", False)
args.apply_bert_init = getattr(args, "apply_bert_init", False)
args.decoder_output_dim = getattr(
args, "decoder_output_dim", args.decoder_embed_dim
)
args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim)
# special for insertion transformer
args.label_tau = getattr(args, "label_tau", None) | null |
19,221 | import torch
import torch.nn.functional as F
from fairseq import utils
from fairseq.iterative_refinement_generator import DecoderOut
from fairseq.models import register_model, register_model_architecture
from fairseq.models.transformer import Embedding
from fairseq.models.nat import (
FairseqNATModel,
FairseqNATDecoder,
ensemble_decoder
)
from fairseq.modules.transformer_sentence_encoder import init_bert_params
def _mean_pooling(enc_feats, src_masks):
# enc_feats: T x B x C
# src_masks: B x T or None
if src_masks is None:
enc_feats = enc_feats.mean(0)
else:
src_masks = (~src_masks).transpose(0, 1).type_as(enc_feats)
enc_feats = (
(enc_feats / src_masks.sum(0)[None, :, None]) * src_masks[:, :, None]
).sum(0)
return enc_feats | null |
19,222 | import torch
import torch.nn.functional as F
from fairseq import utils
from fairseq.iterative_refinement_generator import DecoderOut
from fairseq.models import register_model, register_model_architecture
from fairseq.models.transformer import Embedding
from fairseq.models.nat import (
FairseqNATModel,
FairseqNATDecoder,
ensemble_decoder
)
from fairseq.modules.transformer_sentence_encoder import init_bert_params
def _argmax(x, dim):
return (x == x.max(dim, keepdim=True)[0]).type_as(x) | null |
19,223 | import torch
import torch.nn.functional as F
from fairseq import utils
from fairseq.iterative_refinement_generator import DecoderOut
from fairseq.models import register_model, register_model_architecture
from fairseq.models.transformer import Embedding
from fairseq.models.nat import (
FairseqNATModel,
FairseqNATDecoder,
ensemble_decoder
)
from fairseq.modules.transformer_sentence_encoder import init_bert_params
def _uniform_assignment(src_lens, trg_lens):
max_trg_len = trg_lens.max()
steps = (src_lens.float() - 1) / (trg_lens.float() - 1) # step-size
# max_trg_len
index_t = utils.new_arange(trg_lens, max_trg_len).float()
index_t = steps[:, None] * index_t[None, :] # batch_size X max_trg_len
index_t = torch.round(index_t).long().detach()
return index_t | null |
19,224 | import torch
import torch.nn.functional as F
from fairseq import utils
from fairseq.iterative_refinement_generator import DecoderOut
from fairseq.models import register_model, register_model_architecture
from fairseq.models.transformer import Embedding
from fairseq.models.nat import (
FairseqNATModel,
FairseqNATDecoder,
ensemble_decoder
)
from fairseq.modules.transformer_sentence_encoder import init_bert_params
def base_architecture(args):
args.encoder_embed_path = getattr(args, "encoder_embed_path", None)
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048)
args.encoder_layers = getattr(args, "encoder_layers", 6)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8)
args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False)
args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False)
args.decoder_embed_path = getattr(args, "decoder_embed_path", None)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim)
args.decoder_ffn_embed_dim = getattr(
args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim
)
args.decoder_layers = getattr(args, "decoder_layers", 6)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8)
args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False)
args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False)
args.attention_dropout = getattr(args, "attention_dropout", 0.0)
args.activation_dropout = getattr(args, "activation_dropout", 0.0)
args.activation_fn = getattr(args, "activation_fn", "relu")
args.dropout = getattr(args, "dropout", 0.1)
args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0)
args.share_decoder_input_output_embed = getattr(
args, "share_decoder_input_output_embed", False
)
args.share_all_embeddings = getattr(args, "share_all_embeddings", False)
args.no_token_positional_embeddings = getattr(
args, "no_token_positional_embeddings", False
)
args.adaptive_input = getattr(args, "adaptive_input", False)
args.apply_bert_init = getattr(args, "apply_bert_init", False)
args.decoder_output_dim = getattr(
args, "decoder_output_dim", args.decoder_embed_dim
)
args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim)
# --- special arguments ---
args.sg_length_pred = getattr(args, "sg_length_pred", False)
args.pred_length_offset = getattr(args, "pred_length_offset", False)
args.length_loss_factor = getattr(args, "length_loss_factor", 0.1)
args.src_embedding_copy = getattr(args, "src_embedding_copy", False)
"nonautoregressive_transformer", "nonautoregressive_transformer_wmt_en_de"
def nonautoregressive_transformer_wmt_en_de(args):
base_architecture(args) | null |
19,225 | import torch
from fairseq.models import register_model, register_model_architecture
from fairseq.models.nat import NATransformerModel
def _sequential_poisoning(s, V, beta=0.33, bos=2, eos=3, pad=1):
# s: input batch
# V: vocabulary size
rand_words = torch.randint(low=4, high=V, size=s.size(), device=s.device)
choices = torch.rand(size=s.size(), device=s.device)
choices.masked_fill_((s == pad) | (s == bos) | (s == eos), 1)
replace = choices < beta / 3
repeat = (choices >= beta / 3) & (choices < beta * 2 / 3)
swap = (choices >= beta * 2 / 3) & (choices < beta)
safe = choices >= beta
for i in range(s.size(1) - 1):
rand_word = rand_words[:, i]
next_word = s[:, i + 1]
self_word = s[:, i]
replace_i = replace[:, i]
swap_i = swap[:, i] & (next_word != 3)
repeat_i = repeat[:, i] & (next_word != 3)
safe_i = safe[:, i] | ((next_word == 3) & (~replace_i))
s[:, i] = (
self_word * (safe_i | repeat_i).long()
+ next_word * swap_i.long()
+ rand_word * replace_i.long()
)
s[:, i + 1] = (
next_word * (safe_i | replace_i).long()
+ self_word * (swap_i | repeat_i).long()
)
return s | null |
19,226 | import torch
from fairseq.models import register_model, register_model_architecture
from fairseq.models.nat import NATransformerModel
def gumbel_noise(input, TINY=1e-8):
return input.new_zeros(*input.size()).uniform_().add_(
TINY).log_().neg_().add_(TINY).log_().neg_() | null |
19,227 | import torch
from fairseq.models import register_model, register_model_architecture
from fairseq.models.nat import NATransformerModel
def inat_base_architecture(args):
args.encoder_embed_path = getattr(args, "encoder_embed_path", None)
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048)
args.encoder_layers = getattr(args, "encoder_layers", 6)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8)
args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False)
args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False)
args.decoder_embed_path = getattr(args, "decoder_embed_path", None)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim)
args.decoder_ffn_embed_dim = getattr(
args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim
)
args.decoder_layers = getattr(args, "decoder_layers", 6)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8)
args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False)
args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False)
args.attention_dropout = getattr(args, "attention_dropout", 0.0)
args.activation_dropout = getattr(args, "activation_dropout", 0.0)
args.activation_fn = getattr(args, "activation_fn", "relu")
args.dropout = getattr(args, "dropout", 0.1)
args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0)
args.share_decoder_input_output_embed = getattr(
args, "share_decoder_input_output_embed", False
)
args.share_all_embeddings = getattr(args, "share_all_embeddings", False)
args.no_token_positional_embeddings = getattr(
args, "no_token_positional_embeddings", False
)
args.adaptive_input = getattr(args, "adaptive_input", False)
args.apply_bert_init = getattr(args, "apply_bert_init", False)
args.decoder_output_dim = getattr(
args, "decoder_output_dim", args.decoder_embed_dim
)
args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim)
# --- special arguments ---
args.sg_length_pred = getattr(args, "sg_length_pred", False)
args.pred_length_offset = getattr(args, "pred_length_offset", False)
args.length_loss_factor = getattr(args, "length_loss_factor", 0.1)
args.ngram_predictor = getattr(args, "ngram_predictor", 1)
args.src_embedding_copy = getattr(args, "src_embedding_copy", False)
args.train_step = getattr(args, "train_step", 4)
args.dae_ratio = getattr(args, "dae_ratio", 0.5)
args.stochastic_approx = getattr(args, "stochastic_approx", False)
"iterative_nonautoregressive_transformer",
"iterative_nonautoregressive_transformer_wmt_en_de",
def iter_nat_wmt_en_de(args):
inat_base_architecture(args) | null |
19,228 | import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq.iterative_refinement_generator import DecoderOut
from fairseq.models import register_model, register_model_architecture
from fairseq.models.transformer import (
Embedding,
TransformerDecoderLayer
)
from fairseq.models.nat import (
FairseqNATModel,
FairseqNATDecoder,
ensemble_decoder
)
from fairseq.modules.transformer_sentence_encoder import init_bert_params
from .levenshtein_utils import (
_skip, _skip_encoder_out, _fill,
_get_ins_targets, _get_del_targets,
_apply_ins_masks, _apply_ins_words, _apply_del_words
)
def levenshtein_base_architecture(args):
args.encoder_embed_path = getattr(args, "encoder_embed_path", None)
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048)
args.encoder_layers = getattr(args, "encoder_layers", 6)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8)
args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False)
args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False)
args.decoder_embed_path = getattr(args, "decoder_embed_path", None)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim)
args.decoder_ffn_embed_dim = getattr(
args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim
)
args.decoder_layers = getattr(args, "decoder_layers", 6)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8)
args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False)
args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False)
args.attention_dropout = getattr(args, "attention_dropout", 0.0)
args.activation_dropout = getattr(args, "activation_dropout", 0.0)
args.activation_fn = getattr(args, "activation_fn", "relu")
args.dropout = getattr(args, "dropout", 0.1)
args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0)
args.share_decoder_input_output_embed = getattr(
args, "share_decoder_input_output_embed", False
)
args.share_all_embeddings = getattr(args, "share_all_embeddings", False)
args.no_token_positional_embeddings = getattr(
args, "no_token_positional_embeddings", False
)
args.adaptive_input = getattr(args, "adaptive_input", False)
args.apply_bert_init = getattr(args, "apply_bert_init", False)
args.decoder_output_dim = getattr(
args, "decoder_output_dim", args.decoder_embed_dim
)
args.sampling_for_deletion = getattr(args, "sampling_for_deletion", False)
args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim)
args.early_exit = getattr(args, "early_exit", "6,6,6")
args.no_share_discriminator = getattr(args, "no_share_discriminator", False)
args.no_share_maskpredictor = getattr(args, "no_share_maskpredictor", False)
args.share_discriminator_maskpredictor = getattr(args, "share_discriminator_maskpredictor", False)
args.no_share_last_layer = getattr(args, "no_share_last_layer", False)
"levenshtein_transformer", "levenshtein_transformer_wmt_en_de"
def levenshtein_transformer_wmt_en_de(args):
levenshtein_base_architecture(args) | null |
19,229 | import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq.iterative_refinement_generator import DecoderOut
from fairseq.models import register_model, register_model_architecture
from fairseq.models.transformer import (
Embedding,
TransformerDecoderLayer
)
from fairseq.models.nat import (
FairseqNATModel,
FairseqNATDecoder,
ensemble_decoder
)
from fairseq.modules.transformer_sentence_encoder import init_bert_params
from .levenshtein_utils import (
_skip, _skip_encoder_out, _fill,
_get_ins_targets, _get_del_targets,
_apply_ins_masks, _apply_ins_words, _apply_del_words
)
def levenshtein_transformer_vaswani_wmt_en_de_big(args):
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4096)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16)
args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 1024)
args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 4096)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16)
args.dropout = getattr(args, "dropout", 0.3)
levenshtein_base_architecture(args)
"levenshtein_transformer", "levenshtein_transformer_wmt_en_de_big"
def levenshtein_transformer_wmt_en_de_big_t2t(args):
args.encoder_normalize_before = getattr(args, "encoder_normalize_before", True)
args.decoder_normalize_before = getattr(args, "decoder_normalize_before", True)
args.attention_dropout = getattr(args, "attention_dropout", 0.1)
args.activation_dropout = getattr(args, "activation_dropout", 0.1)
levenshtein_transformer_vaswani_wmt_en_de_big(args) | null |
19,230 | import inspect
import torch.nn as nn
from fairseq.legacy_distributed_data_parallel import LegacyDistributedDataParallel
from fairseq.models import BaseFairseqModel
_GOSSIP_DISABLED = False
try:
import gossip
except ImportError:
_GOSSIP_DISABLED = True
class LegacyDistributedDataParallel(nn.Module):
"""Implements distributed data parallelism at the module level.
A simplified version of :class:`torch.nn.parallel.DistributedDataParallel`.
This version uses a c10d process group for communication and does not
broadcast buffers.
Args:
module (~torch.nn.Module): module to be parallelized
world_size (int): number of parallel workers
process_group (optional): the c10d process group to be used for
distributed data all-reduction. If None, the default process group
will be used.
buffer_size (int, optional): number of elements to buffer before
performing all-reduce (default: 256M).
"""
def __init__(self, module, world_size, process_group=None, buffer_size=2**28):
super().__init__()
self.module = module
self.world_size = world_size
self.process_group = process_group
# Never use a bigger buffer than the number of model params
self.buffer_size = min(buffer_size, sum(p.numel() for p in module.parameters()))
self.buffer = None
# Flag used by the NCCL backend to make sure we only reduce gradients
# one time in the execution engine
self.need_reduction = False
# We can also forcibly accumulate grads locally and only do the
# all-reduce at some later time
self.accumulate_grads = False
# For NCCL backend, since every single NCCL call is asynchoronous, we
# therefore directly enqueue all the NCCL reduction calls to the
# default CUDA stream without spawning up other reduction threads.
# This achieves the best performance.
self._register_grad_hook()
def __getstate__(self):
attrs = copy.copy(self.__dict__)
return attrs
def __setstate__(self, state):
super().__setstate__(state)
self._register_grad_hook()
def no_sync(self):
"""A context manager to disable gradient synchronization."""
old_accumulate_grads = self.accumulate_grads
self.accumulate_grads = True
yield
self.accumulate_grads = old_accumulate_grads
def forward(self, *inputs, **kwargs):
return self.module(*inputs, **kwargs)
def _register_grad_hook(self):
"""
This function registers the callback all-reduction function for the
NCCL backend. All gradients will be all reduced in one single step.
The NCCL reduction will directly be enqueued into the default CUDA
stream. Therefore, no synchronization is needed.
"""
def all_reduce(params):
buffer = self.buffer
nonzero_buffer = False
if len(params) > 1:
offset = 0
for p in params:
sz = p.numel()
if p.grad is not None:
buffer[offset:offset+sz].copy_(p.grad.data.view(-1))
nonzero_buffer = True
else:
buffer[offset:offset+sz].zero_()
offset += sz
else:
# we only have a single grad to all-reduce
p = params[0]
if p.grad is not None:
buffer = p.grad.data
nonzero_buffer = True
elif p.numel() <= self.buffer.numel():
buffer = buffer[:p.numel()]
buffer.zero_()
else:
buffer = torch.zeros_like(p)
if nonzero_buffer:
buffer.div_(self.world_size)
distributed_utils.all_reduce(buffer, self.process_group)
# copy all-reduced grads back into their original place
offset = 0
for p in params:
sz = p.numel()
if p.grad is not None:
p.grad.data.copy_(buffer[offset:offset+sz].view_as(p))
else:
p.grad = buffer[offset:offset+sz].view_as(p).clone()
offset += sz
def reduction_fn():
# This function only needs to be called once
if not self.need_reduction or self.accumulate_grads:
return
self.need_reduction = False
if self.buffer is None:
self.buffer = next(self.module.parameters()).new(self.buffer_size)
# All-reduce the gradients in buckets
offset = 0
buffered_params = []
for param in self.module.parameters():
if not param.requires_grad:
continue
if param.grad is None:
param.grad = torch.zeros_like(param)
if param.grad.requires_grad:
raise RuntimeError("DistributedDataParallel only works "
"with gradients that don't require "
"grad")
sz = param.numel()
if sz > self.buffer.numel():
# all-reduce big params directly
all_reduce([param])
else:
if offset + sz > self.buffer.numel():
all_reduce(buffered_params)
offset = 0
buffered_params.clear()
buffered_params.append(param)
offset += sz
if len(buffered_params) > 0:
all_reduce(buffered_params)
# Now register the reduction hook on the parameters
for p in self.module.parameters():
def allreduce_hook(*unused):
self.need_reduction = True
Variable._execution_engine.queue_callback(reduction_fn)
if p.requires_grad:
p.register_hook(allreduce_hook)
The provided code snippet includes necessary dependencies for implementing the `DistributedFairseqModel` function. Write a Python function `def DistributedFairseqModel(args, model, process_group=None)` to solve the following problem:
Wrap a *model* to support distributed data parallel training. This is similar to the built-in DistributedDataParallel, but allows additional configuration of the DistributedDataParallel class to use, and also provides easier access to the wrapped model by forwarding requests for missing attributes to the wrapped model. Args: args (argparse.Namespace): fairseq args model (BaseFairseqModel): model to wrap
Here is the function:
def DistributedFairseqModel(args, model, process_group=None):
"""
Wrap a *model* to support distributed data parallel training.
This is similar to the built-in DistributedDataParallel, but allows
additional configuration of the DistributedDataParallel class to
use, and also provides easier access to the wrapped model by
forwarding requests for missing attributes to the wrapped model.
Args:
args (argparse.Namespace): fairseq args
model (BaseFairseqModel): model to wrap
"""
# determine which DDP class to extend
assert isinstance(model, nn.Module)
if args.distributed_wrapper == 'DDP' and args.ddp_backend == 'c10d':
ddp_class = nn.parallel.DistributedDataParallel
init_kwargs = dict(
module=model,
device_ids=[args.device_id],
output_device=args.device_id,
broadcast_buffers=args.broadcast_buffers,
bucket_cap_mb=args.bucket_cap_mb,
process_group=process_group,
)
# Maintain backward compatibility
if 'check_reduction' in inspect.getargspec(ddp_class)[0]:
init_kwargs['check_reduction'] = True
if 'find_unused_parameters' in inspect.getargspec(ddp_class)[0]:
init_kwargs['find_unused_parameters'] = args.find_unused_parameters
elif args.distributed_wrapper == 'DDP' and args.ddp_backend == 'no_c10d':
ddp_class = LegacyDistributedDataParallel
init_kwargs = dict(
module=model,
world_size=args.distributed_world_size,
buffer_size=2**28,
process_group=process_group,
)
elif args.distributed_wrapper == 'SlowMo':
if _GOSSIP_DISABLED:
raise ImportError(
'Cannot find gossip library. Please install from: '
'github.com/facebookresearch/stochastic_gradient_push'
)
ddp_class = gossip.GossipDataParallel
# The values of slowmo_momentum below were obtained by tuning on the
# En-De 16 dataset by training the transformer_wmt_en_de_large model
if args.slowmo_momentum is None:
if args.distributed_world_size <= 16:
args.slowmo_momentum = 0.0
elif args.distributed_world_size <= 32:
args.slowmo_momentum = 0.2
elif args.distributed_world_size <= 64:
args.slowmo_momentum = 0.5
else:
args.slowmo_momentum = 0.6
init_kwargs = dict(
module=model,
device_ids=[args.device_id],
output_device=args.device_id,
broadcast_buffers=args.broadcast_buffers,
nprocs_per_node=args.nprocs_per_node,
slowmo_momentum=args.slowmo_momentum,
localsgd=(args.slowmo_algorithm == 'LocalSGD'),
localsgd_frequency=args.localsgd_frequency
)
else:
raise ValueError('Unknown --ddp-backend: ' + args.ddp_backend)
class _DistributedFairseqModel(ddp_class):
"""Extend DistributedDataParallel to check for missing
attributes in the wrapped module."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def __getattr__(self, name):
wrapped_module = super().__getattr__('module')
if hasattr(wrapped_module, name):
return getattr(wrapped_module, name)
return super().__getattr__(name)
return _DistributedFairseqModel(**init_kwargs) | Wrap a *model* to support distributed data parallel training. This is similar to the built-in DistributedDataParallel, but allows additional configuration of the DistributedDataParallel class to use, and also provides easier access to the wrapped model by forwarding requests for missing attributes to the wrapped model. Args: args (argparse.Namespace): fairseq args model (BaseFairseqModel): model to wrap |
19,231 | import logging
import math
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import checkpoint_utils
from fairseq.models import (
CompositeEncoder,
FairseqDecoder,
FairseqEncoder,
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.modules import (
DownsampledMultiHeadAttention,
GradMultiply,
LayerNorm,
LearnedPositionalEmbedding,
LinearizedConvolution,
)
from fairseq.incremental_decoding_utils import with_incremental_state
def Embedding(num_embeddings, embedding_dim, padding_idx):
m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx)
m.weight.data.normal_(0, 0.1)
return m | null |
19,232 | import logging
import math
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import checkpoint_utils
from fairseq.models import (
CompositeEncoder,
FairseqDecoder,
FairseqEncoder,
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.modules import (
DownsampledMultiHeadAttention,
GradMultiply,
LayerNorm,
LearnedPositionalEmbedding,
LinearizedConvolution,
)
from fairseq.incremental_decoding_utils import with_incremental_state
def PositionalEmbedding(num_embeddings, embedding_dim, padding_idx):
m = LearnedPositionalEmbedding(num_embeddings, embedding_dim, padding_idx)
m.weight.data.normal_(0, 0.1)
return m | null |
19,233 | import logging
import math
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import checkpoint_utils
from fairseq.models import (
CompositeEncoder,
FairseqDecoder,
FairseqEncoder,
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.modules import (
DownsampledMultiHeadAttention,
GradMultiply,
LayerNorm,
LearnedPositionalEmbedding,
LinearizedConvolution,
)
from fairseq.incremental_decoding_utils import with_incremental_state
The provided code snippet includes necessary dependencies for implementing the `Linear` function. Write a Python function `def Linear(in_features, out_features, dropout=0.)` to solve the following problem:
Weight-normalized Linear layer (input: N x T x C)
Here is the function:
def Linear(in_features, out_features, dropout=0.):
"""Weight-normalized Linear layer (input: N x T x C)"""
m = nn.Linear(in_features, out_features)
m.weight.data.normal_(mean=0, std=math.sqrt((1 - dropout) / in_features))
m.bias.data.zero_()
return m | Weight-normalized Linear layer (input: N x T x C) |
19,234 | import logging
import math
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import checkpoint_utils
from fairseq.models import (
CompositeEncoder,
FairseqDecoder,
FairseqEncoder,
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.modules import (
DownsampledMultiHeadAttention,
GradMultiply,
LayerNorm,
LearnedPositionalEmbedding,
LinearizedConvolution,
)
from fairseq.incremental_decoding_utils import with_incremental_state
The provided code snippet includes necessary dependencies for implementing the `LinearizedConv1d` function. Write a Python function `def LinearizedConv1d(in_channels, out_channels, kernel_size, dropout=0., **kwargs)` to solve the following problem:
Weight-normalized Conv1d layer optimized for decoding
Here is the function:
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))
m.weight.data.normal_(mean=0, std=std)
m.bias.data.zero_()
return m | Weight-normalized Conv1d layer optimized for decoding |
19,235 | import logging
import math
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import checkpoint_utils
from fairseq.models import (
CompositeEncoder,
FairseqDecoder,
FairseqEncoder,
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.modules import (
DownsampledMultiHeadAttention,
GradMultiply,
LayerNorm,
LearnedPositionalEmbedding,
LinearizedConvolution,
)
from fairseq.incremental_decoding_utils import with_incremental_state
The provided code snippet includes necessary dependencies for implementing the `ConvTBC` function. Write a Python function `def ConvTBC(in_channels, out_channels, kernel_size, dropout=0, **kwargs)` to solve the following problem:
Weight-normalized Conv1d layer
Here is the function:
def ConvTBC(in_channels, out_channels, kernel_size, dropout=0, **kwargs):
"""Weight-normalized Conv1d layer"""
from fairseq.modules import ConvTBC
m = ConvTBC(in_channels, out_channels, kernel_size, **kwargs)
std = math.sqrt((4 * (1.0 - dropout)) / (m.kernel_size[0] * in_channels))
m.weight.data.normal_(mean=0, std=std)
m.bias.data.zero_()
return m | Weight-normalized Conv1d layer |
19,236 | import logging
import math
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import checkpoint_utils
from fairseq.models import (
CompositeEncoder,
FairseqDecoder,
FairseqEncoder,
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.modules import (
DownsampledMultiHeadAttention,
GradMultiply,
LayerNorm,
LearnedPositionalEmbedding,
LinearizedConvolution,
)
from fairseq.incremental_decoding_utils import with_incremental_state
def base_architecture(args):
args.dropout = getattr(args, 'dropout', 0.1)
args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 512)
args.encoder_layers = getattr(args, 'encoder_layers', '[(512, 3)] * 3')
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 512)
args.decoder_layers = getattr(args, 'decoder_layers', '[(512, 3)] * 8')
args.decoder_out_embed_dim = getattr(args, 'decoder_out_embed_dim', 256)
args.decoder_attention = getattr(args, 'decoder_attention', 'True')
args.self_attention = getattr(args, 'self_attention', 'False')
args.encoder_attention = getattr(args, 'encoder_attention', 'False')
args.multihead_attention_nheads = getattr(args, 'multihead_attention_nheads', 1)
args.multihead_self_attention_nheads = getattr(args, 'multihead_self_attention_nheads', 1)
args.encoder_attention_nheads = getattr(args, 'encoder_attention_nheads', 1)
args.project_input = getattr(args, 'project_input', 'False')
args.gated_attention = getattr(args, 'gated_attention', 'False')
args.downsample = getattr(args, 'downsample', 'False')
args.pretrained_checkpoint = getattr(args, 'pretrained_checkpoint', '')
args.pretrained = getattr(args, 'pretrained', 'False')
def fconv_self_att_wp(args):
args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 256)
args.encoder_layers = getattr(args, 'encoder_layers', '[(128, 3)] * 2 + [(512,3)] * 1')
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 256)
args.decoder_layers = getattr(args, 'decoder_layers', '[(512, 4)] * 4 + [(768, 4)] * 2 + [(1024, 4)] * 1')
args.decoder_out_embed_dim = getattr(args, 'decoder_out_embed_dim', 256)
args.self_attention = getattr(args, 'self_attention', 'True')
args.multihead_self_attention_nheads = getattr(args, 'multihead_self_attention_nheads', 4)
args.project_input = getattr(args, 'project_input', 'True')
args.gated_attention = getattr(args, 'gated_attention', 'True')
args.downsample = getattr(args, 'downsample', 'True')
base_architecture(args) | null |
19,237 | from typing import List, Optional
import torch
from torch import Tensor
def script_skip_tensor_list(x: List[Tensor], mask):
res = [xi[mask] if xi.size(0) == mask.size(0) else xi[:, mask] for xi in x]
outputs = []
for i, t in enumerate(res):
if t.numel() != 0:
outputs.append(t)
else:
outputs.append(x[i])
return outputs | null |
19,238 | from typing import List, Optional
import torch
from torch import Tensor
def script_skip_tensor(x: Tensor, mask):
# None case
if x.size(0) == 0:
return x
res = x[mask] if x.size(0) == mask.size(0) else x[:, mask]
if res.numel() == 0:
return x
else:
return res | null |
19,239 | from typing import List, Optional
import torch
from torch import Tensor
def coalesce(x: Optional[Tensor], y: Tensor) -> Tensor:
return x if x is not None else y | null |
19,240 | from typing import List, Optional
import torch
from torch import Tensor
def expand_2d_or_3d_tensor(x, trg_dim: int, padding_idx: int):
"""
Expand 2D/3D tensor on dim=1
"""
if x is None:
return None
assert x.dim() == 2 or x.dim() == 3
assert trg_dim >= x.size(1), (trg_dim, x.size())
if trg_dim == x.size(1):
return x
dims = [x.size(0), trg_dim - x.size(1)]
if x.dim() == 3:
dims.append(x.size(2))
x = torch.cat([x, torch.zeros(dims).to(x).fill_(padding_idx)], 1)
return x
The provided code snippet includes necessary dependencies for implementing the `fill_tensors` function. Write a Python function `def fill_tensors(x: Optional[Tensor], mask, y: Optional[Tensor], padding_idx: int) -> Optional[Tensor]` to solve the following problem:
Filling tensor x with y at masked positions (dim=0).
Here is the function:
def fill_tensors(x: Optional[Tensor], mask, y: Optional[Tensor], padding_idx: int) -> Optional[Tensor]:
"""
Filling tensor x with y at masked positions (dim=0).
"""
if x is None or x.size()[0] == 0 or y is None:
return x
assert x.dim() == y.dim() and mask.size(0) == x.size(0)
assert x.dim() == 2 or (x.dim() == 3 and x.size(2) == y.size(2))
n_selected = mask.sum()
if n_selected == 0:
return x
assert n_selected == y.size(0)
if n_selected == x.size(0):
return y
if x.size(1) < y.size(1):
x = expand_2d_or_3d_tensor(x, y.size(1), padding_idx)
x[mask] = y
elif x.size(1) > y.size(1):
x[mask] = torch.tensor(padding_idx).type_as(x)
if x.dim() == 2:
x[mask, :y.size(1)] = y
else:
x[mask, :y.size(1), :] = y
else:
x[mask] = y
return x | Filling tensor x with y at masked positions (dim=0). |
19,241 | from fairseq import options, utils
from fairseq.models import (
FairseqLanguageModel, register_model, register_model_architecture
)
from fairseq.models.lstm import (
LSTMDecoder, Embedding
)
def base_architecture(args):
args.dropout = getattr(args, 'dropout', 0.1)
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 512)
args.decoder_embed_path = getattr(args, 'decoder_embed_path', None)
args.decoder_hidden_size = getattr(args, 'decoder_hidden_size', args.decoder_embed_dim)
args.decoder_layers = getattr(args, 'decoder_layers', 1)
args.decoder_out_embed_dim = getattr(args, 'decoder_out_embed_dim', 512)
args.decoder_attention = getattr(args, 'decoder_attention', '0')
args.decoder_dropout_in = getattr(args, 'decoder_dropout_in', args.dropout)
args.decoder_dropout_out = getattr(args, 'decoder_dropout_out', args.dropout)
args.share_decoder_input_output_embed = getattr(args, 'share_decoder_input_output_embed', False)
args.adaptive_softmax_cutoff = getattr(args, 'adaptive_softmax_cutoff', '10000,50000,200000')
args.residuals = getattr(args, 'residuals', False) | null |
19,242 | from fairseq import options
from fairseq.models import (
FairseqLanguageModel,
register_model,
register_model_architecture,
)
from fairseq.models.fconv import FConvDecoder
def base_lm_architecture(args):
args.dropout = getattr(args, 'dropout', 0.1)
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 128)
args.decoder_layers = getattr(args, 'decoder_layers', '[(1268, 4)] * 13')
args.decoder_attention = getattr(args, 'decoder_attention', 'False')
args.adaptive_softmax_cutoff = getattr(args, 'adaptive_softmax_cutoff', None)
args.adaptive_softmax_dropout = getattr(args, 'adaptive_softmax_dropout', 0)
def fconv_lm_dauphin_wikitext103(args):
layers = '[(850, 6)] * 3'
layers += ' + [(850, 1)] * 1'
layers += ' + [(850, 5)] * 4'
layers += ' + [(850, 1)] * 1'
layers += ' + [(850, 4)] * 3'
layers += ' + [(1024, 4)] * 1'
layers += ' + [(2048, 4)] * 1'
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 280)
args.decoder_layers = getattr(args, 'decoder_layers', layers)
args.decoder_attention = getattr(args, 'decoder_attention', 'False')
args.adaptive_softmax_cutoff = getattr(args, 'adaptive_softmax_cutoff', '10000,20000,200000')
base_lm_architecture(args) | null |
19,243 | from fairseq import options
from fairseq.models import (
FairseqLanguageModel,
register_model,
register_model_architecture,
)
from fairseq.models.fconv import FConvDecoder
def base_lm_architecture(args):
args.dropout = getattr(args, 'dropout', 0.1)
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 128)
args.decoder_layers = getattr(args, 'decoder_layers', '[(1268, 4)] * 13')
args.decoder_attention = getattr(args, 'decoder_attention', 'False')
args.adaptive_softmax_cutoff = getattr(args, 'adaptive_softmax_cutoff', None)
args.adaptive_softmax_dropout = getattr(args, 'adaptive_softmax_dropout', 0)
def fconv_lm_dauphin_gbw(args):
layers = '[(512, 5)]'
layers += ' + [(128, 1, 0), (128, 5, 0), (512, 1, 3)] * 3'
layers += ' + [(512, 1, 0), (512, 5, 0), (1024, 1, 3)] * 3'
layers += ' + [(1024, 1, 0), (1024, 5, 0), (2048, 1, 3)] * 6'
layers += ' + [(1024, 1, 0), (1024, 5, 0), (4096, 1, 3)]'
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 128)
args.decoder_layers = getattr(args, 'decoder_layers', layers)
args.decoder_attention = getattr(args, 'decoder_attention', 'False')
args.adaptive_softmax_cutoff = getattr(args, 'adaptive_softmax_cutoff', '10000,50000,200000')
base_lm_architecture(args) | null |
19,244 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.models import (
FairseqEncoder,
FairseqIncrementalDecoder,
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.modules import (
AdaptiveSoftmax, BeamableMM, GradMultiply, LearnedPositionalEmbedding,
LinearizedConvolution,
)
The provided code snippet includes necessary dependencies for implementing the `extend_conv_spec` function. Write a Python function `def extend_conv_spec(convolutions)` to solve the following problem:
Extends convolutional spec that is a list of tuples of 2 or 3 parameters (kernel size, dim size and optionally how many layers behind to look for residual) to default the residual propagation param if it is not specified
Here is the function:
def extend_conv_spec(convolutions):
"""
Extends convolutional spec that is a list of tuples of 2 or 3 parameters
(kernel size, dim size and optionally how many layers behind to look for residual)
to default the residual propagation param if it is not specified
"""
extended = []
for spec in convolutions:
if len(spec) == 3:
extended.append(spec)
elif len(spec) == 2:
extended.append(spec + (1,))
else:
raise Exception('invalid number of parameters in convolution spec ' + str(spec) + '. expected 2 or 3')
return tuple(extended) | Extends convolutional spec that is a list of tuples of 2 or 3 parameters (kernel size, dim size and optionally how many layers behind to look for residual) to default the residual propagation param if it is not specified |
19,245 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.models import (
FairseqEncoder,
FairseqIncrementalDecoder,
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.modules import (
AdaptiveSoftmax, BeamableMM, GradMultiply, LearnedPositionalEmbedding,
LinearizedConvolution,
)
def Embedding(num_embeddings, embedding_dim, padding_idx):
m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx)
nn.init.normal_(m.weight, 0, 0.1)
nn.init.constant_(m.weight[padding_idx], 0)
return m | null |
19,246 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.models import (
FairseqEncoder,
FairseqIncrementalDecoder,
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.modules import (
AdaptiveSoftmax, BeamableMM, GradMultiply, LearnedPositionalEmbedding,
LinearizedConvolution,
)
def PositionalEmbedding(num_embeddings, embedding_dim, padding_idx):
m = LearnedPositionalEmbedding(num_embeddings, embedding_dim, padding_idx)
nn.init.normal_(m.weight, 0, 0.1)
nn.init.constant_(m.weight[padding_idx], 0)
return m | null |
19,247 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.models import (
FairseqEncoder,
FairseqIncrementalDecoder,
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.modules import (
AdaptiveSoftmax, BeamableMM, GradMultiply, LearnedPositionalEmbedding,
LinearizedConvolution,
)
The provided code snippet includes necessary dependencies for implementing the `Linear` function. Write a Python function `def Linear(in_features, out_features, dropout=0)` to solve the following problem:
Weight-normalized Linear layer (input: N x T x C)
Here is the function:
def Linear(in_features, out_features, dropout=0):
"""Weight-normalized Linear layer (input: N x T x C)"""
m = nn.Linear(in_features, out_features)
nn.init.normal_(m.weight, mean=0, std=math.sqrt((1 - dropout) / in_features))
nn.init.constant_(m.bias, 0)
return nn.utils.weight_norm(m) | Weight-normalized Linear layer (input: N x T x C) |
19,248 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.models import (
FairseqEncoder,
FairseqIncrementalDecoder,
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.modules import (
AdaptiveSoftmax, BeamableMM, GradMultiply, LearnedPositionalEmbedding,
LinearizedConvolution,
)
The provided code snippet includes necessary dependencies for implementing the `LinearizedConv1d` function. Write a Python function `def LinearizedConv1d(in_channels, out_channels, kernel_size, dropout=0, **kwargs)` to solve the following problem:
Weight-normalized Conv1d layer optimized for decoding
Here is the function:
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) | Weight-normalized Conv1d layer optimized for decoding |
19,249 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.models import (
FairseqEncoder,
FairseqIncrementalDecoder,
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.modules import (
AdaptiveSoftmax, BeamableMM, GradMultiply, LearnedPositionalEmbedding,
LinearizedConvolution,
)
The provided code snippet includes necessary dependencies for implementing the `ConvTBC` function. Write a Python function `def ConvTBC(in_channels, out_channels, kernel_size, dropout=0, **kwargs)` to solve the following problem:
Weight-normalized Conv1d layer
Here is the function:
def ConvTBC(in_channels, out_channels, kernel_size, dropout=0, **kwargs):
"""Weight-normalized Conv1d layer"""
from fairseq.modules import ConvTBC
m = ConvTBC(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) | Weight-normalized Conv1d layer |
19,250 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.models import (
FairseqEncoder,
FairseqIncrementalDecoder,
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.modules import (
AdaptiveSoftmax, BeamableMM, GradMultiply, LearnedPositionalEmbedding,
LinearizedConvolution,
)
def base_architecture(args):
args.dropout = getattr(args, 'dropout', 0.1)
args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 512)
args.encoder_embed_path = getattr(args, 'encoder_embed_path', None)
args.encoder_layers = getattr(args, 'encoder_layers', '[(512, 3)] * 20')
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 512)
args.decoder_embed_path = getattr(args, 'decoder_embed_path', None)
args.decoder_layers = getattr(args, 'decoder_layers', '[(512, 3)] * 20')
args.decoder_out_embed_dim = getattr(args, 'decoder_out_embed_dim', 256)
args.decoder_attention = getattr(args, 'decoder_attention', 'True')
args.share_input_output_embed = getattr(args, 'share_input_output_embed', False)
def fconv_iwslt_de_en(args):
args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 256)
args.encoder_layers = getattr(args, 'encoder_layers', '[(256, 3)] * 4')
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 256)
args.decoder_layers = getattr(args, 'decoder_layers', '[(256, 3)] * 3')
args.decoder_out_embed_dim = getattr(args, 'decoder_out_embed_dim', 256)
base_architecture(args) | null |
19,251 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.models import (
FairseqEncoder,
FairseqIncrementalDecoder,
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.modules import (
AdaptiveSoftmax, BeamableMM, GradMultiply, LearnedPositionalEmbedding,
LinearizedConvolution,
)
def base_architecture(args):
def fconv_wmt_en_ro(args):
args.decoder_out_embed_dim = getattr(args, 'decoder_out_embed_dim', 512)
base_architecture(args) | null |
19,252 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.models import (
FairseqEncoder,
FairseqIncrementalDecoder,
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.modules import (
AdaptiveSoftmax, BeamableMM, GradMultiply, LearnedPositionalEmbedding,
LinearizedConvolution,
)
def base_architecture(args):
args.dropout = getattr(args, 'dropout', 0.1)
args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 512)
args.encoder_embed_path = getattr(args, 'encoder_embed_path', None)
args.encoder_layers = getattr(args, 'encoder_layers', '[(512, 3)] * 20')
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 512)
args.decoder_embed_path = getattr(args, 'decoder_embed_path', None)
args.decoder_layers = getattr(args, 'decoder_layers', '[(512, 3)] * 20')
args.decoder_out_embed_dim = getattr(args, 'decoder_out_embed_dim', 256)
args.decoder_attention = getattr(args, 'decoder_attention', 'True')
args.share_input_output_embed = getattr(args, 'share_input_output_embed', False)
def fconv_wmt_en_de(args):
convs = '[(512, 3)] * 9' # first 9 layers have 512 units
convs += ' + [(1024, 3)] * 4' # next 4 layers have 1024 units
convs += ' + [(2048, 1)] * 2' # final 2 layers use 1x1 convolutions
args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 768)
args.encoder_layers = getattr(args, 'encoder_layers', convs)
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 768)
args.decoder_layers = getattr(args, 'decoder_layers', convs)
args.decoder_out_embed_dim = getattr(args, 'decoder_out_embed_dim', 512)
base_architecture(args) | null |
19,253 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.models import (
FairseqEncoder,
FairseqIncrementalDecoder,
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.modules import (
AdaptiveSoftmax, BeamableMM, GradMultiply, LearnedPositionalEmbedding,
LinearizedConvolution,
)
def base_architecture(args):
args.dropout = getattr(args, 'dropout', 0.1)
args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 512)
args.encoder_embed_path = getattr(args, 'encoder_embed_path', None)
args.encoder_layers = getattr(args, 'encoder_layers', '[(512, 3)] * 20')
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 512)
args.decoder_embed_path = getattr(args, 'decoder_embed_path', None)
args.decoder_layers = getattr(args, 'decoder_layers', '[(512, 3)] * 20')
args.decoder_out_embed_dim = getattr(args, 'decoder_out_embed_dim', 256)
args.decoder_attention = getattr(args, 'decoder_attention', 'True')
args.share_input_output_embed = getattr(args, 'share_input_output_embed', False)
def fconv_wmt_en_fr(args):
convs = '[(512, 3)] * 6' # first 6 layers have 512 units
convs += ' + [(768, 3)] * 4' # next 4 layers have 768 units
convs += ' + [(1024, 3)] * 3' # next 3 layers have 1024 units
convs += ' + [(2048, 1)] * 1' # next 1 layer uses 1x1 convolutions
convs += ' + [(4096, 1)] * 1' # final 1 layer uses 1x1 convolutions
args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 768)
args.encoder_layers = getattr(args, 'encoder_layers', convs)
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 768)
args.decoder_layers = getattr(args, 'decoder_layers', convs)
args.decoder_out_embed_dim = getattr(args, 'decoder_out_embed_dim', 512)
base_architecture(args) | null |
19,254 | import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import options, utils
from fairseq.models import (
FairseqEncoder,
FairseqIncrementalDecoder,
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.modules import AdaptiveSoftmax
from torch import Tensor
from typing import Dict, List, Optional, Tuple
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 | null |
19,255 | import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import options, utils
from fairseq.models import (
FairseqEncoder,
FairseqIncrementalDecoder,
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.modules import AdaptiveSoftmax
from torch import Tensor
from typing import Dict, List, Optional, Tuple
def LSTM(input_size, hidden_size, **kwargs):
m = nn.LSTM(input_size, hidden_size, **kwargs)
for name, param in m.named_parameters():
if 'weight' in name or 'bias' in name:
param.data.uniform_(-0.1, 0.1)
return m | null |
19,256 | import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import options, utils
from fairseq.models import (
FairseqEncoder,
FairseqIncrementalDecoder,
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.modules import AdaptiveSoftmax
from torch import Tensor
from typing import Dict, List, Optional, Tuple
def LSTMCell(input_size, hidden_size, **kwargs):
m = nn.LSTMCell(input_size, hidden_size, **kwargs)
for name, param in m.named_parameters():
if 'weight' in name or 'bias' in name:
param.data.uniform_(-0.1, 0.1)
return m | null |
19,257 | import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import options, utils
from fairseq.models import (
FairseqEncoder,
FairseqIncrementalDecoder,
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.modules import AdaptiveSoftmax
from torch import Tensor
from typing import Dict, List, Optional, Tuple
The provided code snippet includes necessary dependencies for implementing the `Linear` function. Write a Python function `def Linear(in_features, out_features, bias=True, dropout=0)` to solve the following problem:
Linear layer (input: N x T x C)
Here is the function:
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 | Linear layer (input: N x T x C) |
19,258 | import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import options, utils
from fairseq.models import (
FairseqEncoder,
FairseqIncrementalDecoder,
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.modules import AdaptiveSoftmax
from torch import Tensor
from typing import Dict, List, Optional, Tuple
def base_architecture(args):
args.dropout = getattr(args, 'dropout', 0.1)
args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 512)
args.encoder_embed_path = getattr(args, 'encoder_embed_path', None)
args.encoder_freeze_embed = getattr(args, 'encoder_freeze_embed', False)
args.encoder_hidden_size = getattr(args, 'encoder_hidden_size', args.encoder_embed_dim)
args.encoder_layers = getattr(args, 'encoder_layers', 1)
args.encoder_bidirectional = getattr(args, 'encoder_bidirectional', False)
args.encoder_dropout_in = getattr(args, 'encoder_dropout_in', args.dropout)
args.encoder_dropout_out = getattr(args, 'encoder_dropout_out', args.dropout)
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 512)
args.decoder_embed_path = getattr(args, 'decoder_embed_path', None)
args.decoder_freeze_embed = getattr(args, 'decoder_freeze_embed', False)
args.decoder_hidden_size = getattr(args, 'decoder_hidden_size', args.decoder_embed_dim)
args.decoder_layers = getattr(args, 'decoder_layers', 1)
args.decoder_out_embed_dim = getattr(args, 'decoder_out_embed_dim', 512)
args.decoder_attention = getattr(args, 'decoder_attention', '1')
args.decoder_dropout_in = getattr(args, 'decoder_dropout_in', args.dropout)
args.decoder_dropout_out = getattr(args, 'decoder_dropout_out', args.dropout)
args.share_decoder_input_output_embed = getattr(args, 'share_decoder_input_output_embed', False)
args.share_all_embeddings = getattr(args, 'share_all_embeddings', False)
args.adaptive_softmax_cutoff = getattr(args, 'adaptive_softmax_cutoff', '10000,50000,200000')
def lstm_wiseman_iwslt_de_en(args):
args.dropout = getattr(args, 'dropout', 0.1)
args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 256)
args.encoder_dropout_in = getattr(args, 'encoder_dropout_in', 0)
args.encoder_dropout_out = getattr(args, 'encoder_dropout_out', 0)
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 256)
args.decoder_out_embed_dim = getattr(args, 'decoder_out_embed_dim', 256)
args.decoder_dropout_in = getattr(args, 'decoder_dropout_in', 0)
args.decoder_dropout_out = getattr(args, 'decoder_dropout_out', args.dropout)
base_architecture(args) | null |
19,259 | import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import options, utils
from fairseq.models import (
FairseqEncoder,
FairseqIncrementalDecoder,
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.modules import AdaptiveSoftmax
from torch import Tensor
from typing import Dict, List, Optional, Tuple
def base_architecture(args):
args.dropout = getattr(args, 'dropout', 0.1)
args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 512)
args.encoder_embed_path = getattr(args, 'encoder_embed_path', None)
args.encoder_freeze_embed = getattr(args, 'encoder_freeze_embed', False)
args.encoder_hidden_size = getattr(args, 'encoder_hidden_size', args.encoder_embed_dim)
args.encoder_layers = getattr(args, 'encoder_layers', 1)
args.encoder_bidirectional = getattr(args, 'encoder_bidirectional', False)
args.encoder_dropout_in = getattr(args, 'encoder_dropout_in', args.dropout)
args.encoder_dropout_out = getattr(args, 'encoder_dropout_out', args.dropout)
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 512)
args.decoder_embed_path = getattr(args, 'decoder_embed_path', None)
args.decoder_freeze_embed = getattr(args, 'decoder_freeze_embed', False)
args.decoder_hidden_size = getattr(args, 'decoder_hidden_size', args.decoder_embed_dim)
args.decoder_layers = getattr(args, 'decoder_layers', 1)
args.decoder_out_embed_dim = getattr(args, 'decoder_out_embed_dim', 512)
args.decoder_attention = getattr(args, 'decoder_attention', '1')
args.decoder_dropout_in = getattr(args, 'decoder_dropout_in', args.dropout)
args.decoder_dropout_out = getattr(args, 'decoder_dropout_out', args.dropout)
args.share_decoder_input_output_embed = getattr(args, 'share_decoder_input_output_embed', False)
args.share_all_embeddings = getattr(args, 'share_all_embeddings', False)
args.adaptive_softmax_cutoff = getattr(args, 'adaptive_softmax_cutoff', '10000,50000,200000')
def lstm_luong_wmt_en_de(args):
args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 1000)
args.encoder_layers = getattr(args, 'encoder_layers', 4)
args.encoder_dropout_out = getattr(args, 'encoder_dropout_out', 0)
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 1000)
args.decoder_layers = getattr(args, 'decoder_layers', 4)
args.decoder_out_embed_dim = getattr(args, 'decoder_out_embed_dim', 1000)
args.decoder_dropout_out = getattr(args, 'decoder_dropout_out', 0)
base_architecture(args) | null |
19,260 | import logging
import re
import torch
import torch.nn as nn
from fairseq import utils
from fairseq.models import (
register_model,
register_model_architecture,
)
from fairseq.models.transformer import TransformerModel
from fairseq.modules.transformer_sentence_encoder import init_bert_params
from .hub_interface import VECOHubInterface
def veco_large_architecture(args):
args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 1024)
args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 4*1024)
args.encoder_layers = getattr(args, 'encoder_layers', 24)
args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 16)
args.encoder_normalize_before = getattr(args, 'encoder_normalize_before', False)
args.encoder_learned_pos = getattr(args, 'encoder_learned_pos', True)
args.decoder_embed_path = getattr(args, 'decoder_embed_path', None)
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', args.encoder_embed_dim)
args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', args.encoder_ffn_embed_dim)
args.keep_decoder_layers = getattr(args, 'keep_decoder_layers', None)
if args.keep_decoder_layers is not None:
args.decoder_layers = len(args.keep_decoder_layers.split(','))
else:
args.decoder_layers = getattr(args, 'decoder_layers', 6)
args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 16)
args.decoder_normalize_before = getattr(args, 'decoder_normalize_before', False)
args.decoder_learned_pos = getattr(args, 'decoder_learned_pos', True)
args.attention_dropout = getattr(args, 'attention_dropout', 0.)
args.relu_dropout = getattr(args, 'relu_dropout', 0.)
args.dropout = getattr(args, 'dropout', 0.1)
args.max_target_positions = getattr(args, 'max_target_positions', 512)
args.max_source_positions = getattr(args, 'max_source_positions', 512)
args.adaptive_softmax_cutoff = getattr(args, 'adaptive_softmax_cutoff', None)
args.adaptive_softmax_dropout = getattr(args, 'adaptive_softmax_dropout', 0)
args.share_decoder_input_output_embed = getattr(args, 'share_decoder_input_output_embed', True)
args.share_all_embeddings = getattr(args, 'share_all_embeddings', True)
args.decoder_output_dim = getattr(args, 'decoder_output_dim', args.decoder_embed_dim)
args.decoder_input_dim = getattr(args, 'decoder_input_dim', args.decoder_embed_dim)
args.no_scale_embedding = getattr(args, 'no_scale_embedding', False)
args.layernorm_embedding = getattr(args, 'layernorm_embedding', True)
args.activation_fn = getattr(args, 'activation_fn', 'gelu')
args.pooler_activation_fn = getattr(args, 'pooler_activation_fn', 'tanh')
args.pooler_dropout = getattr(args, 'pooler_dropout', 0.0)
def veco_xlarge_architecture(args):
args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 1024)
args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 4 * 1024)
args.encoder_layers = getattr(args, 'encoder_layers', 24)
args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 16)
args.decoder_layers = getattr(args, 'decoder_layers', 24)
args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 16)
args.no_scale_embedding = getattr(args, 'no_scale_embedding', False)
args.max_target_positions = getattr(args, 'max_target_positions', 512)
args.max_source_positions = getattr(args, 'max_source_positions', 512)
veco_large_architecture(args) | null |
19,261 | import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.models import (
FairseqEncoderModel,
FairseqEncoder,
register_model,
register_model_architecture,
)
from fairseq.modules import (
LayerNorm,
SinusoidalPositionalEmbedding,
TransformerSentenceEncoder,
)
from fairseq.modules.transformer_sentence_encoder import init_bert_params
def bert_base_architecture(args):
args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 768)
args.share_encoder_input_output_embed = getattr(
args, 'share_encoder_input_output_embed', True)
args.no_token_positional_embeddings = getattr(
args, 'no_token_positional_embeddings', False)
args.encoder_learned_pos = getattr(args, 'encoder_learned_pos', True)
args.num_segment = getattr(args, 'num_segment', 2)
args.encoder_layers = getattr(args, 'encoder_layers', 12)
args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 12)
args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 3072)
args.sentence_class_num = getattr(args, 'sentence_class_num', 2)
args.sent_loss = getattr(args, 'sent_loss', True)
args.apply_bert_init = getattr(args, 'apply_bert_init', True)
args.activation_fn = getattr(args, 'activation_fn', 'gelu')
args.pooler_activation_fn = getattr(args, 'pooler_activation_fn', 'tanh')
args.encoder_normalize_before = getattr(args, 'encoder_normalize_before', True)
base_architecture(args)
def bert_large_architecture(args):
args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 1024)
args.encoder_layers = getattr(args, 'encoder_layers', 24)
args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 16)
args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 4096)
bert_base_architecture(args) | null |
19,262 | import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.models import (
FairseqEncoderModel,
FairseqEncoder,
register_model,
register_model_architecture,
)
from fairseq.modules import (
LayerNorm,
SinusoidalPositionalEmbedding,
TransformerSentenceEncoder,
)
from fairseq.modules.transformer_sentence_encoder import init_bert_params
def base_architecture(args):
args.dropout = getattr(args, 'dropout', 0.1)
args.attention_dropout = getattr(args, 'attention_dropout', 0.1)
args.act_dropout = getattr(args, 'act_dropout', 0.0)
args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 4096)
args.encoder_layers = getattr(args, 'encoder_layers', 6)
args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 8)
args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 1024)
args.share_encoder_input_output_embed = getattr(args, 'share_encoder_input_output_embed', False)
args.encoder_learned_pos = getattr(args, 'encoder_learned_pos', False)
args.no_token_positional_embeddings = getattr(args, 'no_token_positional_embeddings', False)
args.num_segment = getattr(args, 'num_segment', 2)
args.sentence_class_num = getattr(args, 'sentence_class_num', 2)
args.sent_loss = getattr(args, 'sent_loss', False)
args.apply_bert_init = getattr(args, 'apply_bert_init', False)
args.activation_fn = getattr(args, 'activation_fn', 'relu')
args.pooler_activation_fn = getattr(args, 'pooler_activation_fn', 'tanh')
args.encoder_normalize_before = getattr(args, 'encoder_normalize_before', False)
def xlm_architecture(args):
args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 1024)
args.share_encoder_input_output_embed = getattr(
args, 'share_encoder_input_output_embed', True)
args.no_token_positional_embeddings = getattr(
args, 'no_token_positional_embeddings', False)
args.encoder_learned_pos = getattr(args, 'encoder_learned_pos', True)
args.num_segment = getattr(args, 'num_segment', 1)
args.encoder_layers = getattr(args, 'encoder_layers', 6)
args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 8)
args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 4096)
args.sent_loss = getattr(args, 'sent_loss', False)
args.activation_fn = getattr(args, 'activation_fn', 'gelu')
args.encoder_normalize_before = getattr(args, 'encoder_normalize_before', False)
args.pooler_activation_fn = getattr(args, 'pooler_activation_fn', 'tanh')
args.apply_bert_init = getattr(args, 'apply_bert_init', True)
base_architecture(args) | null |
19,263 | import argparse
import os
import re
import shutil
import sys
def parse_checkpoints(files):
entries = []
for f in files:
m = pt_regexp_epoch_based.fullmatch(f)
if m is not None:
entries.append((int(m.group(1)), m.group(0)))
else:
m = pt_regexp_update_based.fullmatch(f)
if m is not None:
entries.append((int(m.group(1)), m.group(0)))
return entries
def last_n_checkpoints(files, n):
entries = parse_checkpoints(files)
return [x[1] for x in sorted(entries, reverse=True)[:n]] | null |
19,264 | import argparse
import os
import re
import shutil
import sys
def parse_checkpoints(files):
def every_n_checkpoints(files, n):
entries = parse_checkpoints(files)
return [x[1] for x in sorted(sorted(entries)[::-n])] | null |
19,265 | import argparse
from fairseq.data import data_utils, Dictionary, indexed_dataset
def get_parser():
parser = argparse.ArgumentParser(
description='writes text from binarized file to stdout')
# fmt: off
parser.add_argument('--dataset-impl', help='dataset implementation',
choices=indexed_dataset.get_available_dataset_impl())
parser.add_argument('--dict', metavar='FP', help='dictionary containing known words', default=None)
parser.add_argument('--input', metavar='FP', required=True, help='binarized file to read')
# fmt: on
return parser | null |
19,266 | import argparse
import collections
import torch
import os
import re
from fairseq.file_io import PathManager
class PathManager:
"""
Wrapper for insulating OSS I/O (using Python builtin operations) from
fvcore's PathManager abstraction (for transparently handling various
internal backends).
"""
def open(
path: str,
mode: str = "r",
buffering: int = -1,
encoding: Optional[str] = None,
errors: Optional[str] = None,
newline: Optional[str] = None,
):
if FVCorePathManager:
return FVCorePathManager.open(
path=path,
mode=mode,
buffering=buffering,
encoding=encoding,
errors=errors,
newline=newline,
)
return open(
path,
mode=mode,
buffering=buffering,
encoding=encoding,
errors=errors,
newline=newline,
)
def copy(src_path: str, dst_path: str, overwrite: bool = False) -> bool:
if FVCorePathManager:
return FVCorePathManager.copy(
src_path=src_path, dst_path=dst_path, overwrite=overwrite
)
return shutil.copyfile(src_path, dst_path)
def get_local_path(path: str, **kwargs) -> str:
if FVCorePathManager:
return FVCorePathManager.get_local_path(path, **kwargs)
return path
def exists(path: str) -> bool:
if FVCorePathManager:
return FVCorePathManager.exists(path)
return os.path.exists(path)
def isfile(path: str) -> bool:
if FVCorePathManager:
return FVCorePathManager.isfile(path)
return os.path.isfile(path)
def ls(path: str) -> List[str]:
if FVCorePathManager:
return FVCorePathManager.ls(path)
return os.listdir(path)
def mkdirs(path: str) -> None:
if FVCorePathManager:
return FVCorePathManager.mkdirs(path)
os.makedirs(path, exist_ok=True)
def rm(path: str) -> None:
if FVCorePathManager:
return FVCorePathManager.rm(path)
os.remove(path)
def register_handler(handler) -> None:
if FVCorePathManager:
return FVCorePathManager.register_handler(handler=handler)
The provided code snippet includes necessary dependencies for implementing the `average_checkpoints` function. Write a Python function `def average_checkpoints(inputs)` to solve the following problem:
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.
Here is the function:
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 | 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. |
19,267 | import argparse
import collections
import torch
import os
import re
from fairseq.file_io import PathManager
class PathManager:
"""
Wrapper for insulating OSS I/O (using Python builtin operations) from
fvcore's PathManager abstraction (for transparently handling various
internal backends).
"""
def open(
path: str,
mode: str = "r",
buffering: int = -1,
encoding: Optional[str] = None,
errors: Optional[str] = None,
newline: Optional[str] = None,
):
if FVCorePathManager:
return FVCorePathManager.open(
path=path,
mode=mode,
buffering=buffering,
encoding=encoding,
errors=errors,
newline=newline,
)
return open(
path,
mode=mode,
buffering=buffering,
encoding=encoding,
errors=errors,
newline=newline,
)
def copy(src_path: str, dst_path: str, overwrite: bool = False) -> bool:
if FVCorePathManager:
return FVCorePathManager.copy(
src_path=src_path, dst_path=dst_path, overwrite=overwrite
)
return shutil.copyfile(src_path, dst_path)
def get_local_path(path: str, **kwargs) -> str:
if FVCorePathManager:
return FVCorePathManager.get_local_path(path, **kwargs)
return path
def exists(path: str) -> bool:
if FVCorePathManager:
return FVCorePathManager.exists(path)
return os.path.exists(path)
def isfile(path: str) -> bool:
if FVCorePathManager:
return FVCorePathManager.isfile(path)
return os.path.isfile(path)
def ls(path: str) -> List[str]:
if FVCorePathManager:
return FVCorePathManager.ls(path)
return os.listdir(path)
def mkdirs(path: str) -> None:
if FVCorePathManager:
return FVCorePathManager.mkdirs(path)
os.makedirs(path, exist_ok=True)
def rm(path: str) -> None:
if FVCorePathManager:
return FVCorePathManager.rm(path)
os.remove(path)
def register_handler(handler) -> None:
if FVCorePathManager:
return FVCorePathManager.register_handler(handler=handler)
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]] | null |
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