id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
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19,068 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq.modules.scalar_bias import scalar_bias
def Linear(in_features, out_features, dropout=0., bias=True):
"""Weight-normalized Linear layer (input: B x T x C)"""
m = nn.Linear(in_features, out_features, bias=bias)
m.weight.data.normal_(mean=0, std=math.sqrt((1 - dropout) / in_features))
m.bias.data.zero_()
return nn.utils.weight_norm(m)
The provided code snippet includes necessary dependencies for implementing the `GatedLinear` function. Write a Python function `def GatedLinear(in_features, out_features, dropout=0., bias=True)` to solve the following problem:
Weight-normalized Linear layer (input: B x T x C) with interspersed GLU units
Here is the function:
def GatedLinear(in_features, out_features, dropout=0., bias=True):
"""Weight-normalized Linear layer (input: B x T x C) with interspersed GLU units"""
return nn.Sequential(
Linear(in_features, out_features*4, dropout, bias),
nn.GLU(),
Linear(out_features*2, out_features*2, dropout, bias),
nn.GLU(),
Linear(out_features, out_features, dropout, bias)
) | Weight-normalized Linear layer (input: B x T x C) with interspersed GLU units |
19,069 | import torch
import torch.nn as nn
The provided code snippet includes necessary dependencies for implementing the `quant_noise` function. Write a Python function `def quant_noise(module, p, block_size)` to solve the following problem:
Wraps modules and applies quantization noise to the weights for subsequent quantization with Iterative Product Quantization as described in "Training with Quantization Noise for Extreme Model Compression" Args: - module: nn.Module - p: amount of Quantization Noise - block_size: size of the blocks for subsequent quantization with iPQ Remarks: - Module weights must have the right sizes wrt the block size - Only Linear, Embedding and Conv2d modules are supported for the moment - For more detail on how to quantize by blocks with convolutional weights, see "And the Bit Goes Down: Revisiting the Quantization of Neural Networks" - We implement the simplest form of noise here as stated in the paper which consists in randomly dropping blocks
Here is the function:
def quant_noise(module, p, block_size):
"""
Wraps modules and applies quantization noise to the weights for
subsequent quantization with Iterative Product Quantization as
described in "Training with Quantization Noise for Extreme Model Compression"
Args:
- module: nn.Module
- p: amount of Quantization Noise
- block_size: size of the blocks for subsequent quantization with iPQ
Remarks:
- Module weights must have the right sizes wrt the block size
- Only Linear, Embedding and Conv2d modules are supported for the moment
- For more detail on how to quantize by blocks with convolutional weights,
see "And the Bit Goes Down: Revisiting the Quantization of Neural Networks"
- We implement the simplest form of noise here as stated in the paper
which consists in randomly dropping blocks
"""
# if no quantization noise, don't register hook
if p <= 0:
return module
# supported modules
assert isinstance(module, (nn.Linear, nn.Embedding, nn.Conv2d))
# test whether module.weight has the right sizes wrt block_size
is_conv = module.weight.ndim == 4
# 2D matrix
if not is_conv:
assert module.weight.size(1) % block_size == 0, "Input features must be a multiple of block sizes"
# 4D matrix
else:
# 1x1 convolutions
if module.kernel_size == (1, 1):
assert module.in_channels % block_size == 0, "Input channels must be a multiple of block sizes"
# regular convolutions
else:
k = module.kernel_size[0] * module.kernel_size[1]
assert k % block_size == 0, "Kernel size must be a multiple of block size"
def _forward_pre_hook(mod, input):
# no noise for evaluation
if mod.training:
if not is_conv:
# gather weight and sizes
weight = mod.weight
in_features = weight.size(1)
out_features = weight.size(0)
# split weight matrix into blocks and randomly drop selected blocks
mask = torch.zeros(in_features // block_size * out_features, device=weight.device)
mask.bernoulli_(p)
mask = mask.repeat_interleave(block_size, -1).view(-1, in_features)
else:
# gather weight and sizes
weight = mod.weight
in_channels = mod.in_channels
out_channels = mod.out_channels
# split weight matrix into blocks and randomly drop selected blocks
if mod.kernel_size == (1, 1):
mask = torch.zeros(int(in_channels // block_size * out_channels), device=weight.device)
mask.bernoulli_(p)
mask = mask.repeat_interleave(block_size, -1).view(-1, in_channels)
else:
mask = torch.zeros(weight.size(0), weight.size(1), device=weight.device)
mask.bernoulli_(p)
mask = mask.unsqueeze(2).unsqueeze(3).repeat(1, 1, mod.kernel_size[0], mod.kernel_size[1])
# scale weights and apply mask
mask = mask.to(torch.bool) # x.bool() is not currently supported in TorchScript
s = 1 / (1 - p)
mod.weight.data = s * weight.masked_fill(mask, 0)
module.register_forward_pre_hook(_forward_pre_hook)
return module | Wraps modules and applies quantization noise to the weights for subsequent quantization with Iterative Product Quantization as described in "Training with Quantization Noise for Extreme Model Compression" Args: - module: nn.Module - p: amount of Quantization Noise - block_size: size of the blocks for subsequent quantization with iPQ Remarks: - Module weights must have the right sizes wrt the block size - Only Linear, Embedding and Conv2d modules are supported for the moment - For more detail on how to quantize by blocks with convolutional weights, see "And the Bit Goes Down: Revisiting the Quantization of Neural Networks" - We implement the simplest form of noise here as stated in the paper which consists in randomly dropping blocks |
19,070 | import logging
import re
from operator import attrgetter, itemgetter
import numpy as np
import torch.nn as nn
import torch.distributed as dist
from .modules import PQConv2d, PQLinear, PQEmbedding
from .pq import PQ
def get_layers(model, filter_regexp):
"""
Filters out the layers according to a regexp. Note that
we omit biases.
Args:
- model: a nn.Module
- filter_regexp: a regexp to filter the layers to keep
according to their name in model.named_parameters().
For instance, the regexp:
down_layers\\.[123456]\\.(conv[12]|identity\\.conv))
is keeping blocks down_layers from 1 to 6, and inside
each block is keeping conv1, conv2 and identity.conv.
Remarks:
- We add (module\\.)? at the beginning of the regexp to
account for the possible use of nn.parallel.DataParallel
"""
# get all parameter names
all_layers = map(itemgetter(0), model.named_parameters())
# remove biases
all_layers = filter(lambda x: "bias" not in x, all_layers)
# remove .weight in all other names (or .weight_orig is spectral norm)
all_layers = map(lambda x: x.replace(".weight_orig", ""), all_layers)
all_layers = map(lambda x: x.replace(".weight", ""), all_layers)
# return filtered layers
filter_regexp = "(module\\.)?" + "(" + filter_regexp + ")"
r = re.compile(filter_regexp)
return list(filter(r.match, all_layers))
def get_param(module, layer_name, param_config):
"""
Given a quantization configuration, get the right parameter
for the module to be quantized.
Args:
- module: a nn.Module
- layer_name: the name of the layer
- param_config: a dict like
{
'Conv2d': ('kernel_size', {'(3, 3)': 9, '(1, 1)': 4}),
'Linear': ('in_features', {'*': 8})
}
For instance, all conv2d layers with kernel size 3x3 have
a block size of 9 and all Linear layers are quantized with
a block size of 8, irrespective of their size.
Remarks:
- if 'fuzzy_name' is passed as a parameter, layers whose layer_name
include 'fuzzy_name' will be assigned the given parameter.
In the following example, conv.expand layers will have a block
size of 9 while conv.reduce will have a block size of 4 and all
other layers will have a block size of 2.
{
'Conv2d': ('fuzzy_name', {'expand': 9, 'reduce': 4, '*': 2}),
'Linear': ('fuzzy_name', {'classifier': 8, 'projection': 4})
}
"""
layer_type = module.__class__.__name__
if layer_type not in param_config:
raise KeyError(f"Layer type {layer_type} not in config for layer {module}")
feature, params = param_config[module.__class__.__name__]
if feature != "fuzzy_name":
feature_value = str(getattr(module, feature))
if feature_value not in params:
if "*" in params:
feature_value = "*"
else:
raise KeyError(
f"{feature}={feature_value} not in config for layer {module}"
)
else:
feature_values = [name for name in params if name in layer_name]
if len(feature_values) == 0:
if "*" in params:
feature_value = "*"
else:
raise KeyError(
f"name={layer_name} not in config for {module}"
)
else:
feature_value = feature_values[0]
return params[feature_value]
def attrsetter(*items):
def resolve_attr(obj, attr):
attrs = attr.split(".")
head = attrs[:-1]
tail = attrs[-1]
for name in head:
obj = getattr(obj, name)
return obj, tail
def g(obj, val):
for attr in items:
resolved_obj, resolved_attr = resolve_attr(obj, attr)
setattr(resolved_obj, resolved_attr, val)
return g
class PQ(EM):
"""
Quantizes the layer weights W with the standard Product Quantization
technique. This learns a codebook of codewords or centroids of size
block_size from W. For further reference on using PQ to quantize
neural networks, see "And the Bit Goes Down: Revisiting the Quantization
of Neural Networks", Stock et al., ICLR 2020.
PQ is performed in two steps:
(1) The matrix W (weights or fully-connected or convolutional layer)
is reshaped to (block_size, -1).
- If W is fully-connected (2D), its columns are split into
blocks of size block_size.
- If W is convolutional (4D), its filters are split along the
spatial dimension.
(2) We apply the standard EM/k-means algorithm to the resulting reshaped matrix.
Args:
- W: weight matrix to quantize of size (in_features x out_features)
- block_size: size of the blocks (subvectors)
- n_centroids: number of centroids
- n_iter: number of k-means iterations
- eps: for cluster reassignment when an empty cluster is found
- max_tentatives for cluster reassignment when an empty cluster is found
- verbose: print information after each iteration
Remarks:
- block_size be compatible with the shape of W
"""
def __init__(
self,
W,
block_size,
n_centroids=256,
n_iter=20,
eps=1e-6,
max_tentatives=30,
verbose=True,
):
self.block_size = block_size
W_reshaped = self._reshape(W)
super(PQ, self).__init__(
W_reshaped,
n_centroids=n_centroids,
n_iter=n_iter,
eps=eps,
max_tentatives=max_tentatives,
verbose=verbose,
)
def _reshape(self, W):
"""
Reshapes the matrix W as expained in step (1).
"""
# fully connected: by convention the weight has size out_features x in_features
if len(W.size()) == 2:
self.out_features, self.in_features = W.size()
assert (
self.in_features % self.block_size == 0
), "Linear: n_blocks must be a multiple of in_features"
return (
W.reshape(self.out_features, -1, self.block_size)
.permute(2, 1, 0)
.flatten(1, 2)
)
# convolutional: we reshape along the spatial dimension
elif len(W.size()) == 4:
self.out_channels, self.in_channels, self.k_h, self.k_w = W.size()
assert (
self.in_channels * self.k_h * self.k_w
) % self.block_size == 0, (
"Conv2d: n_blocks must be a multiple of in_channels * k_h * k_w"
)
return (
W.reshape(self.out_channels, -1, self.block_size)
.permute(2, 1, 0)
.flatten(1, 2)
)
# not implemented
else:
raise NotImplementedError(W.size())
def encode(self):
"""
Performs self.n_iter EM steps.
"""
self.initialize_centroids()
for i in range(self.n_iter):
try:
self.step(i)
except EmptyClusterResolveError:
break
def decode(self):
"""
Returns the encoded full weight matrix. Must be called after
the encode function.
"""
# fully connected case
if "k_h" not in self.__dict__:
return (
self.centroids[self.assignments]
.reshape(-1, self.out_features, self.block_size)
.permute(1, 0, 2)
.flatten(1, 2)
)
# convolutional case
else:
return (
self.centroids[self.assignments]
.reshape(-1, self.out_channels, self.block_size)
.permute(1, 0, 2)
.reshape(self.out_channels, self.in_channels, self.k_h, self.k_w)
)
The provided code snippet includes necessary dependencies for implementing the `quantize_model_` function. Write a Python function `def quantize_model_( model, size_tracker, layers_to_quantize, block_sizes_config, n_centroids_config, step=0, n_iter=15, eps=1e-6, max_tentatives=100, verbose=True, )` to solve the following problem:
Quantize a model in-place by stages. All the targeted layers are replaced by their quantized counterpart, and the model is ready for the finetuning of the centroids in a standard training loop (no modifications required). Note that we do not quantize biases. Args: - model: a nn.Module - size_tracker: useful for tracking quatization statistics - layers_to_quantize: a list containing regexps for filtering the layers to quantize at each stage according to their name (as in model.named_parameters()) - block_sizes_config: dict like { 'Conv2d': ('kernel_size', {'(3, 3)': 9, '(1, 1)': 4}), 'Linear': ('in_features', {'*': 8}) } For instance, all conv2d layers with kernel size 3x3 have a block size of 9 and all Linear layers are quantized with a block size of 8, irrespective of their size. - n_centroids_config: dict like { 'Conv2d': ('kernel_size', {'*': 256}), 'Linear': ('in_features', {'*': 256}) } For instance, all conv2d layers are quantized with 256 centroids - step: the layers to quantize inplace corresponding to layers_to_quantize[step]
Here is the function:
def quantize_model_(
model,
size_tracker,
layers_to_quantize,
block_sizes_config,
n_centroids_config,
step=0,
n_iter=15,
eps=1e-6,
max_tentatives=100,
verbose=True,
):
"""
Quantize a model in-place by stages. All the targeted
layers are replaced by their quantized counterpart,
and the model is ready for the finetuning of the
centroids in a standard training loop (no modifications
required). Note that we do not quantize biases.
Args:
- model: a nn.Module
- size_tracker: useful for tracking quatization statistics
- layers_to_quantize: a list containing regexps for
filtering the layers to quantize at each stage according
to their name (as in model.named_parameters())
- block_sizes_config: dict like
{
'Conv2d': ('kernel_size', {'(3, 3)': 9, '(1, 1)': 4}),
'Linear': ('in_features', {'*': 8})
}
For instance, all conv2d layers with kernel size 3x3 have
a block size of 9 and all Linear layers are quantized with
a block size of 8, irrespective of their size.
- n_centroids_config: dict like
{
'Conv2d': ('kernel_size', {'*': 256}),
'Linear': ('in_features', {'*': 256})
}
For instance, all conv2d layers are quantized with 256 centroids
- step: the layers to quantize inplace corresponding
to layers_to_quantize[step]
"""
quantized_layers = get_layers(model, layers_to_quantize[step])
for layer in quantized_layers:
# book-keeping
is_master_process = (not dist.is_initialized()) or (dist.is_initialized() and dist.get_rank() == 0)
verbose = verbose and is_master_process
# get block size and centroids
module = attrgetter(layer)(model)
block_size = get_param(module, layer, block_sizes_config)
n_centroids = get_param(module, layer, n_centroids_config)
if verbose:
logging.info(f"Quantizing layer {layer} with block size {block_size} and {n_centroids} centroids")
# quantize layer
weight = module.weight.data.clone()
is_bias = 'bias' in [x[0] for x in module.named_parameters()]
bias = module.bias.data.clone() if is_bias else None
quantizer = PQ(
weight,
block_size,
n_centroids=n_centroids,
n_iter=n_iter,
eps=eps,
max_tentatives=max_tentatives,
verbose=verbose,
)
# quantization performed on all GPUs with same seed
quantizer.encode()
centroids = quantizer.centroids.contiguous()
assignments = quantizer.assignments.contiguous()
# broadcast results to make sure weights are up-to-date
if dist.is_initialized():
dist.broadcast(centroids, 0)
dist.broadcast(assignments, 0)
# instantiate the quantized counterpart
if isinstance(module, nn.Linear):
out_features, in_features = map(
lambda k: module.__dict__[k], ["out_features", "in_features"]
)
quantized_module = PQLinear(
centroids, assignments, bias, in_features, out_features
)
elif isinstance(module, nn.Embedding):
num_embeddings, embedding_dim = map(
lambda k: module.__dict__[k], ["num_embeddings", "embedding_dim"]
)
quantized_module = PQEmbedding(
centroids, assignments, num_embeddings, embedding_dim
)
elif isinstance(module, nn.Conv2d):
out_channels, in_channels, kernel_size = map(
lambda k: module.__dict__[k],
["out_channels", "in_channels", "kernel_size"],
)
stride, padding, dilation, groups, padding_mode = map(
lambda k: module.__dict__[k],
["stride", "padding", "dilation", "groups", "padding_mode"],
)
quantized_module = PQConv2d(
centroids,
assignments,
bias,
in_channels,
out_channels,
kernel_size,
stride=stride,
padding=padding,
dilation=dilation,
groups=groups,
padding_mode=padding_mode,
)
else:
raise ValueError(f"Module {module} not yet supported for quantization")
# replace layer by its quantized counterpart
attrsetter(layer)(model, quantized_module)
# update statistics
size_tracker.update(weight, block_size, n_centroids)
# return name of quantized layers
return quantized_layers | Quantize a model in-place by stages. All the targeted layers are replaced by their quantized counterpart, and the model is ready for the finetuning of the centroids in a standard training loop (no modifications required). Note that we do not quantize biases. Args: - model: a nn.Module - size_tracker: useful for tracking quatization statistics - layers_to_quantize: a list containing regexps for filtering the layers to quantize at each stage according to their name (as in model.named_parameters()) - block_sizes_config: dict like { 'Conv2d': ('kernel_size', {'(3, 3)': 9, '(1, 1)': 4}), 'Linear': ('in_features', {'*': 8}) } For instance, all conv2d layers with kernel size 3x3 have a block size of 9 and all Linear layers are quantized with a block size of 8, irrespective of their size. - n_centroids_config: dict like { 'Conv2d': ('kernel_size', {'*': 256}), 'Linear': ('in_features', {'*': 256}) } For instance, all conv2d layers are quantized with 256 centroids - step: the layers to quantize inplace corresponding to layers_to_quantize[step] |
19,071 | def convert_yaml_to_tuple(yaml_dictionary):
"""Converts a yaml dictionary with two keys: `key` and `value` into a two
argument tuple of those values."""
return (yaml_dictionary["key"], yaml_dictionary["value"])
def parse_config_yaml(yaml_data):
# Initialize to default options.
quantization_options = {
"n_centroids": {
"Linear": ["in_features", {"*": 256}],
"Embedding": ["embedding_dim", {"*": 256}],
},
"block_sizes": {
"Linear": ["fuzzy_name", {"fc": 8, "attn": 4, "emb": 4}],
"Embedding": ["fuzzy_name", {"emb": 8}],
},
"layers_to_quantize": [
"decoder\\.layers\\.\\d+\\.fc[12]",
"decoder\\.embed_tokens\\.embeddings\\.[012]\\.[01]",
"decoder\\.layers\\.\\d+\\.self_attn\\.(k_proj|v_proj|q_proj|out_proj)",
],
}
if "n_centroids" in yaml_data:
quantization_options["n_centroids"] = {
layer: convert_yaml_to_tuple(layer_data)
for layer, layer_data in yaml_data["n_centroids"].items()
}
if "block_sizes" in yaml_data:
quantization_options["block_sizes"] = {
layer: convert_yaml_to_tuple(layer_data)
for layer, layer_data in yaml_data["block_sizes"].items()
}
if "layers_to_quantize" in yaml_data:
quantization_options["layers_to_quantize"] = yaml_data["layers_to_quantize"]
return quantization_options | null |
19,072 | import logging
from operator import attrgetter
import torch.nn as nn
import torch.distributed as dist
from ..pq.utils import get_layers, attrsetter
from .modules import IntConv2d, IntLinear, IntEmbedding, ActivationQuantizer
MAPPING = {nn.Linear: IntLinear, nn.Embedding: IntEmbedding, nn.Conv2d: IntConv2d}
def get_layers(model, filter_regexp):
"""
Filters out the layers according to a regexp. Note that
we omit biases.
Args:
- model: a nn.Module
- filter_regexp: a regexp to filter the layers to keep
according to their name in model.named_parameters().
For instance, the regexp:
down_layers\\.[123456]\\.(conv[12]|identity\\.conv))
is keeping blocks down_layers from 1 to 6, and inside
each block is keeping conv1, conv2 and identity.conv.
Remarks:
- We add (module\\.)? at the beginning of the regexp to
account for the possible use of nn.parallel.DataParallel
"""
# get all parameter names
all_layers = map(itemgetter(0), model.named_parameters())
# remove biases
all_layers = filter(lambda x: "bias" not in x, all_layers)
# remove .weight in all other names (or .weight_orig is spectral norm)
all_layers = map(lambda x: x.replace(".weight_orig", ""), all_layers)
all_layers = map(lambda x: x.replace(".weight", ""), all_layers)
# return filtered layers
filter_regexp = "(module\\.)?" + "(" + filter_regexp + ")"
r = re.compile(filter_regexp)
return list(filter(r.match, all_layers))
def attrsetter(*items):
def resolve_attr(obj, attr):
attrs = attr.split(".")
head = attrs[:-1]
tail = attrs[-1]
for name in head:
obj = getattr(obj, name)
return obj, tail
def g(obj, val):
for attr in items:
resolved_obj, resolved_attr = resolve_attr(obj, attr)
setattr(resolved_obj, resolved_attr, val)
return g
The provided code snippet includes necessary dependencies for implementing the `quantize_model_` function. Write a Python function `def quantize_model_(model, p=0.2, bits=8, update_step=3000)` to solve the following problem:
Replaces all modules with their scalar quantized counterpart and registers hooks to quantize the post-ativations of those modules. Args: - model: a nn.Module - p: amount of noise (0 for no noise, 1 to quantize all the weights/activations) - bits: number of bits - update_step: update quantization parameters every update_step steps
Here is the function:
def quantize_model_(model, p=0.2, bits=8, update_step=3000):
"""
Replaces all modules with their scalar quantized counterpart and
registers hooks to quantize the post-ativations of those modules.
Args:
- model: a nn.Module
- p: amount of noise (0 for no noise, 1 to quantize all the weights/activations)
- bits: number of bits
- update_step: update quantization parameters every update_step steps
"""
# quantize all layers
quantized_layers = get_layers(model, "(.*?)")
for layer in quantized_layers:
# book-keeping
is_master_process = (not dist.is_initialized()) or (dist.is_initialized() and dist.get_rank() == 0)
# recover module
module = attrgetter(layer)(model)
if is_master_process:
logging.info(f"Quantizing layer {layer} with bits={bits} and QuantNoise={p}")
# quantization params
q_params = {"p": p, "update_step": update_step, "bits": bits, "method": "histogram", "counter": 0}
# instantiate the quantized counterpart
if isinstance(module, tuple(MAPPING.keys())):
QuantizedModule = MAPPING[module.__class__]
quantized_module = QuantizedModule.__new__(QuantizedModule)
params = module.__dict__
params.update(q_params)
quantized_module.__dict__.update(params)
else:
if is_master_process:
logging.info(f"Module {module} not yet supported for quantization")
continue
# activation quantization
a_q = ActivationQuantizer(quantized_module, p=0, bits=bits, method="histogram")
# replace layer by its quantized counterpart
attrsetter(layer)(model, quantized_module)
# return name of quantized layers
return quantized_layers | Replaces all modules with their scalar quantized counterpart and registers hooks to quantize the post-ativations of those modules. Args: - model: a nn.Module - p: amount of noise (0 for no noise, 1 to quantize all the weights/activations) - bits: number of bits - update_step: update quantization parameters every update_step steps |
19,073 | import torch
def emulate_int(w, bits, method, scale=None, zero_point=None):
q = globals()[f"emulate_int{bits}_{method}"]
return q(w, scale=scale, zero_point=zero_point) | null |
19,074 | import torch
def quantize(w, scale, zero_point):
return (torch.clamp(torch.round(w / scale + zero_point), 0, 255) - zero_point) * scale
def emulate_int8_histogram(w, scale=None, zero_point=None):
if scale is None:
obs = torch.quantization.observer.HistogramObserver()
_ = obs(w.float())
scale, zero_point = obs.calculate_qparams()
scale = scale.cuda().type_as(w)
zero_point = zero_point.cuda().type_as(w)
return quantize(w, scale, zero_point), scale, zero_point | null |
19,075 | import torch
def quantize(w, scale, zero_point):
return (torch.clamp(torch.round(w / scale + zero_point), 0, 255) - zero_point) * scale
def emulate_int8_channel(w, scale=None, zero_point=None):
if scale is None:
obs = torch.quantization.observer.PerChannelMinMaxObserver(
ch_axis=-1, qscheme=torch.per_channel_symmetric
)
_ = obs(w)
scale, zero_point, ch_axis = obs.get_qparams()
scale = scale.cuda().type_as(w)
zero_point = zero_point.cuda().type_as(w)
return quantize(w, scale, zero_point), scale, zero_point | null |
19,076 | import torch
def quantize(w, scale, zero_point):
def emulate_int8_tensor(w, scale=None, zero_point=None):
if scale is None:
obs = torch.quantization.observer.MinMaxObserver()
_ = obs(w)
scale, zero_point = obs.calculate_qparams()
scale = scale.cuda().type_as(w)
zero_point = zero_point.cuda().type_as(w)
return quantize(w, scale, zero_point), scale, zero_point | null |
19,077 | import torch.nn as nn
from .learned_positional_embedding import LearnedPositionalEmbedding
from .sinusoidal_positional_embedding import SinusoidalPositionalEmbedding
class LearnedPositionalEmbedding(nn.Embedding):
def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int):
def forward(
self,
input: Tensor,
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
positions: Optional[Tensor] = None,
):
class SinusoidalPositionalEmbedding(nn.Module):
def __init__(self, embedding_dim, padding_idx, init_size=1024):
def prepare_for_onnx_export_(self):
def get_embedding(
num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None
):
def forward(
self,
input,
incremental_state: Optional[Any] = None,
timestep: Optional[Tensor] = None,
positions: Optional[Any] = None,
):
def PositionalEmbedding(
num_embeddings: int,
embedding_dim: int,
padding_idx: int,
learned: bool = False,
):
if learned:
# if padding_idx is specified then offset the embedding ids by
# this index and adjust num_embeddings appropriately
# TODO: The right place for this offset would be inside
# LearnedPositionalEmbedding. Move this there for a cleaner implementation.
if padding_idx is not None:
num_embeddings = num_embeddings + padding_idx + 1
m = LearnedPositionalEmbedding(num_embeddings, embedding_dim, padding_idx)
nn.init.normal_(m.weight, mean=0, std=embedding_dim ** -0.5)
if padding_idx is not None:
nn.init.constant_(m.weight[padding_idx], 0)
else:
m = SinusoidalPositionalEmbedding(
embedding_dim, padding_idx, init_size=num_embeddings + padding_idx + 1,
)
return m | null |
19,078 | import collections
import logging
import os
import re
import traceback
from collections import OrderedDict
from typing import Union
import torch
from fairseq.file_io import PathManager
from fairseq.models import FairseqDecoder, FairseqEncoder
from torch.serialization import default_restore_location
def save_checkpoint(args, trainer, epoch_itr, val_loss):
from fairseq import distributed_utils, meters
# only one worker should attempt to create the required dir
if args.distributed_rank == 0:
os.makedirs(args.save_dir, exist_ok=True)
prev_best = getattr(save_checkpoint, "best", val_loss)
if val_loss is not None:
best_function = max if args.maximize_best_checkpoint_metric else min
save_checkpoint.best = best_function(val_loss, prev_best)
if args.no_save or not trainer.is_data_parallel_master:
return
def is_better(a, b):
return a >= b if args.maximize_best_checkpoint_metric else a <= b
write_timer = meters.StopwatchMeter()
write_timer.start()
epoch = epoch_itr.epoch
end_of_epoch = epoch_itr.end_of_epoch()
updates = trainer.get_num_updates()
suffix = getattr(args, "checkpoint_suffix", "")
checkpoint_conds = collections.OrderedDict()
checkpoint_conds["checkpoint{}{}.pt".format(epoch, suffix)] = (
end_of_epoch
and not args.no_epoch_checkpoints
and epoch % args.save_interval == 0
)
checkpoint_conds["checkpoint_{}_{}{}.pt".format(epoch, updates, suffix)] = (
not end_of_epoch
and args.save_interval_updates > 0
and updates % args.save_interval_updates == 0
)
checkpoint_conds["checkpoint_best{}.pt".format(suffix)] = val_loss is not None and (
not hasattr(save_checkpoint, "best")
or is_better(val_loss, save_checkpoint.best)
)
if val_loss is not None and args.keep_best_checkpoints > 0:
checkpoint_conds["checkpoint.best_{}_{:.2f}.pt".format(
args.best_checkpoint_metric, val_loss)] = (
not hasattr(save_checkpoint, "best")
or is_better(val_loss, save_checkpoint.best)
)
checkpoint_conds["checkpoint_last{}.pt".format(suffix)] = not args.no_last_checkpoints
extra_state = {"train_iterator": epoch_itr.state_dict(), "val_loss": val_loss}
if hasattr(save_checkpoint, "best"):
extra_state.update({"best": save_checkpoint.best})
checkpoints = [
os.path.join(args.save_dir, fn) for fn, cond in checkpoint_conds.items() if cond
]
if len(checkpoints) > 0:
trainer.save_checkpoint(checkpoints[0], extra_state)
for cp in checkpoints[1:]:
PathManager.copy(checkpoints[0], cp, overwrite=True)
write_timer.stop()
logger.info(
"saved checkpoint {} (epoch {} @ {} updates, score {}) (writing took {} seconds)".format(
checkpoints[0], epoch, updates, val_loss, write_timer.sum
)
)
if not end_of_epoch and args.keep_interval_updates > 0:
# remove old checkpoints; checkpoints are sorted in descending order
checkpoints = checkpoint_paths(
args.save_dir, pattern=r"checkpoint_\d+_(\d+)\.pt"
)
for old_chk in checkpoints[args.keep_interval_updates :]:
if os.path.lexists(old_chk):
os.remove(old_chk)
if args.keep_last_epochs > 0:
# remove old epoch checkpoints; checkpoints are sorted in descending order
checkpoints = checkpoint_paths(args.save_dir, pattern=r"checkpoint(\d+)\.pt")
for old_chk in checkpoints[args.keep_last_epochs :]:
if os.path.lexists(old_chk):
os.remove(old_chk)
if args.keep_best_checkpoints > 0:
# only keep the best N checkpoints according to validation metric
checkpoints = checkpoint_paths(
args.save_dir, pattern=r"checkpoint\.best_{}_(\d+\.?\d*)\.pt".format(args.best_checkpoint_metric))
if not args.maximize_best_checkpoint_metric:
checkpoints = checkpoints[::-1]
for old_chk in checkpoints[args.keep_best_checkpoints:]:
if os.path.lexists(old_chk):
os.remove(old_chk)
The provided code snippet includes necessary dependencies for implementing the `load_checkpoint` function. Write a Python function `def load_checkpoint(args, trainer, **passthrough_args)` to solve the following problem:
Load a checkpoint and restore the training iterator. *passthrough_args* will be passed through to ``trainer.get_train_iterator``.
Here is the function:
def load_checkpoint(args, trainer, **passthrough_args):
"""
Load a checkpoint and restore the training iterator.
*passthrough_args* will be passed through to
``trainer.get_train_iterator``.
"""
suffix = getattr(args, "checkpoint_suffix", "")
if args.restore_file == "checkpoint_last.pt":
checkpoint_path = os.path.join(args.save_dir, "checkpoint_last{}.pt".format(suffix))
else:
checkpoint_path = args.restore_file
extra_state = trainer.load_checkpoint(
checkpoint_path,
args.reset_optimizer,
args.reset_lr_scheduler,
eval(args.optimizer_overrides),
reset_meters=args.reset_meters,
)
if (
extra_state is not None
and "best" in extra_state
and not args.reset_optimizer
and not args.reset_meters
):
save_checkpoint.best = extra_state["best"]
if extra_state is not None and not args.reset_dataloader:
# restore iterator from checkpoint
itr_state = extra_state["train_iterator"]
epoch_itr = trainer.get_train_iterator(
epoch=itr_state["epoch"], load_dataset=True, **passthrough_args
)
epoch_itr.load_state_dict(itr_state)
else:
epoch_itr = trainer.get_train_iterator(
epoch=1, load_dataset=True, **passthrough_args
)
trainer.lr_step(epoch_itr.epoch)
return extra_state, epoch_itr | Load a checkpoint and restore the training iterator. *passthrough_args* will be passed through to ``trainer.get_train_iterator``. |
19,079 | import collections
import logging
import os
import re
import traceback
from collections import OrderedDict
from typing import Union
import torch
from fairseq.file_io import PathManager
from fairseq.models import FairseqDecoder, FairseqEncoder
from torch.serialization import default_restore_location
def torch_persistent_save(*args, **kwargs):
for i in range(3):
try:
return torch.save(*args, **kwargs)
except Exception:
if i == 2:
logger.error(traceback.format_exc())
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 save_state(
filename,
args,
model_state_dict,
criterion,
optimizer,
lr_scheduler,
num_updates,
optim_history=None,
extra_state=None,
):
from fairseq import utils
if optim_history is None:
optim_history = []
if extra_state is None:
extra_state = {}
state_dict = {
"args": args,
"model": model_state_dict or {},
"optimizer_history": optim_history
+ [
{
"criterion_name": criterion.__class__.__name__,
"optimizer_name": optimizer.__class__.__name__,
"lr_scheduler_state": lr_scheduler.state_dict(),
"num_updates": num_updates,
}
],
"extra_state": extra_state,
}
if utils.has_parameters(criterion):
state_dict["criterion"] = criterion.state_dict()
if not args.no_save_optimizer_state:
state_dict["last_optimizer_state"] = optimizer.state_dict()
# convert all state to CPU
state_dict = utils.move_to_cpu(state_dict)
with PathManager.open(filename, "wb") as f:
torch_persistent_save(state_dict, f) | null |
19,080 | import collections
import logging
import os
import re
import traceback
from collections import OrderedDict
from typing import Union
import torch
from fairseq.file_io import PathManager
from fairseq.models import FairseqDecoder, FairseqEncoder
from torch.serialization import default_restore_location
logger = logging.getLogger(__name__)
The provided code snippet includes necessary dependencies for implementing the `prune_state_dict` function. Write a Python function `def prune_state_dict(state_dict, args)` to solve the following problem:
Prune the given state_dict if desired for LayerDrop (https://arxiv.org/abs/1909.11556). Training with LayerDrop allows models to be robust to pruning at inference time. This function prunes state_dict to allow smaller models to be loaded from a larger model and re-maps the existing state_dict for this to occur. It's called by functions that load models from checkpoints and does not need to be called directly.
Here is the function:
def prune_state_dict(state_dict, args):
"""Prune the given state_dict if desired for LayerDrop
(https://arxiv.org/abs/1909.11556).
Training with LayerDrop allows models to be robust to pruning at inference
time. This function prunes state_dict to allow smaller models to be loaded
from a larger model and re-maps the existing state_dict for this to occur.
It's called by functions that load models from checkpoints and does not
need to be called directly.
"""
if not args or args.arch == "ptt_transformer":
# args should not be none, but don't crash if it is.
return state_dict
encoder_layers_to_keep = (
args.encoder_layers_to_keep if "encoder_layers_to_keep" in vars(args) else None
)
decoder_layers_to_keep = (
args.decoder_layers_to_keep if "decoder_layers_to_keep" in vars(args) else None
)
if not encoder_layers_to_keep and not decoder_layers_to_keep:
return state_dict
# apply pruning
logger.info(
"Pruning model to specified layer configuration - this works best if the model was trained with LayerDrop"
)
def create_pruning_pass(layers_to_keep, layer_name):
keep_layers = sorted(
[int(layer_string) for layer_string in layers_to_keep.split(",")]
)
mapping_dict = {}
for i in range(len(keep_layers)):
mapping_dict[str(keep_layers[i])] = str(i)
regex = re.compile(r"^{layer}.*\.layers\.(\d+)".format(layer=layer_name))
return {"substitution_regex": regex, "mapping_dict": mapping_dict}
pruning_passes = []
if encoder_layers_to_keep:
pruning_passes.append(create_pruning_pass(encoder_layers_to_keep, "encoder"))
if decoder_layers_to_keep:
pruning_passes.append(create_pruning_pass(decoder_layers_to_keep, "decoder"))
new_state_dict = {}
for layer_name in state_dict.keys():
match = re.search(r"\.layers\.(\d+)\.", layer_name)
# if layer has no number in it, it is a supporting layer, such as an
# embedding
if not match:
new_state_dict[layer_name] = state_dict[layer_name]
continue
# otherwise, layer should be pruned.
original_layer_number = match.group(1)
# figure out which mapping dict to replace from
for pruning_pass in pruning_passes:
if original_layer_number in pruning_pass["mapping_dict"] and pruning_pass[
"substitution_regex"
].search(layer_name):
new_layer_number = pruning_pass["mapping_dict"][original_layer_number]
substitution_match = pruning_pass["substitution_regex"].search(
layer_name
)
new_state_key = (
layer_name[: substitution_match.start(1)]
+ new_layer_number
+ layer_name[substitution_match.end(1) :]
)
new_state_dict[new_state_key] = state_dict[layer_name]
# Since layers are now pruned, *_layers_to_keep are no longer needed.
# This is more of "It would make it work fix" rather than a proper fix.
if "encoder_layers_to_keep" in vars(args):
args.encoder_layers_to_keep = None
if "decoder_layers_to_keep" in vars(args):
args.decoder_layers_to_keep = None
return new_state_dict | Prune the given state_dict if desired for LayerDrop (https://arxiv.org/abs/1909.11556). Training with LayerDrop allows models to be robust to pruning at inference time. This function prunes state_dict to allow smaller models to be loaded from a larger model and re-maps the existing state_dict for this to occur. It's called by functions that load models from checkpoints and does not need to be called directly. |
19,081 | import collections
import logging
import os
import re
import traceback
from collections import OrderedDict
from typing import Union
import torch
from fairseq.file_io import PathManager
from fairseq.models import FairseqDecoder, FairseqEncoder
from torch.serialization import default_restore_location
def load_checkpoint_to_cpu(path, arg_overrides=None):
"""Loads a checkpoint to CPU (with upgrading for backward compatibility)."""
with PathManager.open(path, "rb") as f:
state = torch.load(
f, map_location=lambda s, l: default_restore_location(s, "cpu")
)
args = state["args"]
if arg_overrides is not None:
for arg_name, arg_val in arg_overrides.items():
setattr(args, arg_name, arg_val)
state = _upgrade_state_dict(state)
return state
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 `load_pretrained_component_from_model` function. Write a Python function `def load_pretrained_component_from_model( component: Union[FairseqEncoder, FairseqDecoder], checkpoint: str )` to solve the following problem:
Load a pretrained FairseqEncoder or FairseqDecoder from checkpoint into the provided `component` object. If state_dict fails to load, there may be a mismatch in the architecture of the corresponding `component` found in the `checkpoint` file.
Here is the function:
def load_pretrained_component_from_model(
component: Union[FairseqEncoder, FairseqDecoder], checkpoint: str
):
"""
Load a pretrained FairseqEncoder or FairseqDecoder from checkpoint into the
provided `component` object. If state_dict fails to load, there may be a
mismatch in the architecture of the corresponding `component` found in the
`checkpoint` file.
"""
if not PathManager.exists(checkpoint):
raise IOError("Model file not found: {}".format(checkpoint))
state = load_checkpoint_to_cpu(checkpoint)
if isinstance(component, FairseqEncoder):
component_type = "encoder"
elif isinstance(component, FairseqDecoder):
component_type = "decoder"
else:
raise ValueError(
"component to load must be either a FairseqEncoder or "
"FairseqDecoder. Loading other component types are not supported."
)
component_state_dict = OrderedDict()
for key in state["model"].keys():
if key.startswith(component_type):
# encoder.input_layers.0.0.weight --> input_layers.0.0.weight
component_subkey = key[len(component_type) + 1 :]
component_state_dict[component_subkey] = state["model"][key]
component.load_state_dict(component_state_dict, strict=True)
return component | Load a pretrained FairseqEncoder or FairseqDecoder from checkpoint into the provided `component` object. If state_dict fails to load, there may be a mismatch in the architecture of the corresponding `component` found in the `checkpoint` file. |
19,082 | import collections
import logging
import os
import re
import traceback
from collections import OrderedDict
from typing import Union
import torch
from fairseq.file_io import PathManager
from fairseq.models import FairseqDecoder, FairseqEncoder
from torch.serialization import default_restore_location
logger = logging.getLogger(__name__)
def verify_checkpoint_directory(save_dir: str) -> None:
if not os.path.exists(save_dir):
os.makedirs(save_dir, exist_ok=True)
temp_file_path = os.path.join(save_dir, "dummy")
try:
with open(temp_file_path, "w"):
pass
except OSError as e:
logger.warning("Unable to access checkpoint save directory: {}".format(save_dir))
raise e
else:
os.remove(temp_file_path) | null |
19,083 | import contextlib
import copy
import importlib.util
import logging
import math
import os
import sys
import warnings
from collections import defaultdict
from itertools import accumulate
from typing import Callable, Dict, List, Optional
import numpy as np
import torch
import torch.nn.functional as F
from fairseq.logging.meters import safe_round
from fairseq.modules import gelu, gelu_accurate
from fairseq.modules.multihead_attention import MultiheadAttention
from torch import Tensor
def deprecation_warning(message, stacklevel=3):
# don't use DeprecationWarning, since it's ignored by default
warnings.warn(message, stacklevel=stacklevel)
def load_ensemble_for_inference(filenames, task, model_arg_overrides=None):
from fairseq import checkpoint_utils
deprecation_warning(
"utils.load_ensemble_for_inference is deprecated. "
"Please use checkpoint_utils.load_model_ensemble instead."
)
return checkpoint_utils.load_model_ensemble(
filenames, arg_overrides=model_arg_overrides, task=task
) | null |
19,084 | import contextlib
import copy
import importlib.util
import logging
import math
import os
import sys
import warnings
from collections import defaultdict
from itertools import accumulate
from typing import Callable, Dict, List, Optional
import numpy as np
import torch
import torch.nn.functional as F
from fairseq.logging.meters import safe_round
from fairseq.modules import gelu, gelu_accurate
from fairseq.modules.multihead_attention import MultiheadAttention
from torch import Tensor
class MultiheadAttention(nn.Module):
"""Multi-headed attention.
See "Attention Is All You Need" for more details.
"""
def __init__(
self,
embed_dim,
num_heads,
kdim=None,
vdim=None,
dropout=0.0,
bias=True,
add_bias_kv=False,
add_zero_attn=False,
self_attention=False,
encoder_decoder_attention=False,
q_noise=0.0,
qn_block_size=8,
):
super().__init__()
self.embed_dim = embed_dim
self.kdim = kdim if kdim is not None else embed_dim
self.vdim = vdim if vdim is not None else embed_dim
self.qkv_same_dim = self.kdim == embed_dim and self.vdim == embed_dim
self.num_heads = num_heads
self.dropout = dropout
self.head_dim = embed_dim // num_heads
assert (
self.head_dim * num_heads == self.embed_dim
), "embed_dim must be divisible by num_heads"
self.scaling = self.head_dim ** -0.5
self.self_attention = self_attention
self.encoder_decoder_attention = encoder_decoder_attention
assert not self.self_attention or self.qkv_same_dim, (
"Self-attention requires query, key and " "value to be of the same size"
)
self.k_proj = quant_noise(nn.Linear(self.kdim, embed_dim, bias=bias), q_noise, qn_block_size)
self.v_proj = quant_noise(nn.Linear(self.vdim, embed_dim, bias=bias), q_noise, qn_block_size)
self.q_proj = quant_noise(nn.Linear(embed_dim, embed_dim, bias=bias), q_noise, qn_block_size)
self.out_proj = quant_noise(nn.Linear(embed_dim, embed_dim, bias=bias), q_noise, qn_block_size)
if add_bias_kv:
self.bias_k = Parameter(torch.Tensor(1, 1, embed_dim))
self.bias_v = Parameter(torch.Tensor(1, 1, embed_dim))
else:
self.bias_k = self.bias_v = None
self.add_zero_attn = add_zero_attn
self.reset_parameters()
self.onnx_trace = False
self.tpu = False
def prepare_for_onnx_export_(self):
self.onnx_trace = True
def prepare_for_tpu_(self, **kwargs):
self.tpu = True
def reset_parameters(self):
if self.qkv_same_dim:
# Empirically observed the convergence to be much better with
# the scaled initialization
nn.init.xavier_uniform_(self.k_proj.weight, gain=1 / math.sqrt(2))
nn.init.xavier_uniform_(self.v_proj.weight, gain=1 / math.sqrt(2))
nn.init.xavier_uniform_(self.q_proj.weight, gain=1 / math.sqrt(2))
else:
nn.init.xavier_uniform_(self.k_proj.weight)
nn.init.xavier_uniform_(self.v_proj.weight)
nn.init.xavier_uniform_(self.q_proj.weight)
nn.init.xavier_uniform_(self.out_proj.weight)
if self.out_proj.bias is not None:
nn.init.constant_(self.out_proj.bias, 0.)
if self.bias_k is not None:
nn.init.xavier_normal_(self.bias_k)
if self.bias_v is not None:
nn.init.xavier_normal_(self.bias_v)
def forward(
self,
query,
key: Optional[Tensor],
value: Optional[Tensor],
key_padding_mask: Optional[Tensor] = None,
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
need_weights: bool = True,
static_kv: bool = False,
attn_mask: Optional[Tensor] = None,
before_softmax: bool = False,
need_head_weights: bool = False,
) -> Tuple[Tensor, Optional[Tensor]]:
"""Input shape: Time x Batch x Channel
Args:
key_padding_mask (ByteTensor, optional): mask to exclude
keys that are pads, of shape `(batch, src_len)`, where
padding elements are indicated by 1s.
need_weights (bool, optional): return the attention weights,
averaged over heads (default: False).
attn_mask (ByteTensor, optional): typically used to
implement causal attention, where the mask prevents the
attention from looking forward in time (default: None).
before_softmax (bool, optional): return the raw attention
weights and values before the attention softmax.
need_head_weights (bool, optional): return the attention
weights for each head. Implies *need_weights*. Default:
return the average attention weights over all heads.
"""
if need_head_weights:
need_weights = True
tgt_len, bsz, embed_dim = query.size()
assert embed_dim == self.embed_dim
assert list(query.size()) == [tgt_len, bsz, embed_dim]
if (
not self.onnx_trace
and not self.tpu # don't use PyTorch version on TPUs
and incremental_state is None
and not static_kv
# A workaround for quantization to work. Otherwise JIT compilation
# treats bias in linear module as method.
and not torch.jit.is_scripting()
):
assert key is not None and value is not None
return F.multi_head_attention_forward(
query,
key,
value,
self.embed_dim,
self.num_heads,
torch.empty([0]),
torch.cat((self.q_proj.bias, self.k_proj.bias, self.v_proj.bias)),
self.bias_k,
self.bias_v,
self.add_zero_attn,
self.dropout,
self.out_proj.weight,
self.out_proj.bias,
self.training,
key_padding_mask,
need_weights,
attn_mask,
use_separate_proj_weight=True,
q_proj_weight=self.q_proj.weight,
k_proj_weight=self.k_proj.weight,
v_proj_weight=self.v_proj.weight,
)
if incremental_state is not None:
saved_state = self._get_input_buffer(incremental_state)
if saved_state is not None and "prev_key" in saved_state:
# previous time steps are cached - no need to recompute
# key and value if they are static
if static_kv:
assert self.encoder_decoder_attention and not self.self_attention
key = value = None
else:
saved_state = None
if self.self_attention:
q = self.q_proj(query)
k = self.k_proj(query)
v = self.v_proj(query)
elif self.encoder_decoder_attention:
# encoder-decoder attention
q = self.q_proj(query)
if key is None:
assert value is None
k = v = None
else:
k = self.k_proj(key)
v = self.v_proj(key)
else:
assert key is not None and value is not None
q = self.q_proj(query)
k = self.k_proj(key)
v = self.v_proj(value)
q *= self.scaling
if self.bias_k is not None:
assert self.bias_v is not None
k = torch.cat([k, self.bias_k.repeat(1, bsz, 1)])
v = torch.cat([v, self.bias_v.repeat(1, bsz, 1)])
if attn_mask is not None:
attn_mask = torch.cat(
[attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1
)
if key_padding_mask is not None:
key_padding_mask = torch.cat(
[
key_padding_mask,
key_padding_mask.new_zeros(key_padding_mask.size(0), 1),
],
dim=1,
)
q = (
q.contiguous()
.view(tgt_len, bsz * self.num_heads, self.head_dim)
.transpose(0, 1)
)
if k is not None:
k = (
k.contiguous()
.view(-1, bsz * self.num_heads, self.head_dim)
.transpose(0, 1)
)
if v is not None:
v = (
v.contiguous()
.view(-1, bsz * self.num_heads, self.head_dim)
.transpose(0, 1)
)
if saved_state is not None:
# saved states are stored with shape (bsz, num_heads, seq_len, head_dim)
if "prev_key" in saved_state:
_prev_key = saved_state["prev_key"]
assert _prev_key is not None
prev_key = _prev_key.view(bsz * self.num_heads, -1, self.head_dim)
if static_kv:
k = prev_key
else:
assert k is not None
k = torch.cat([prev_key, k], dim=1)
if "prev_value" in saved_state:
_prev_value = saved_state["prev_value"]
assert _prev_value is not None
prev_value = _prev_value.view(bsz * self.num_heads, -1, self.head_dim)
if static_kv:
v = prev_value
else:
assert v is not None
v = torch.cat([prev_value, v], dim=1)
prev_key_padding_mask: Optional[Tensor] = None
if "prev_key_padding_mask" in saved_state:
prev_key_padding_mask = saved_state["prev_key_padding_mask"]
assert k is not None and v is not None
key_padding_mask = MultiheadAttention._append_prev_key_padding_mask(
key_padding_mask=key_padding_mask,
prev_key_padding_mask=prev_key_padding_mask,
batch_size=bsz,
src_len=k.size(1),
static_kv=static_kv,
)
saved_state["prev_key"] = k.view(bsz, self.num_heads, -1, self.head_dim)
saved_state["prev_value"] = v.view(bsz, self.num_heads, -1, self.head_dim)
saved_state["prev_key_padding_mask"] = key_padding_mask
# In this branch incremental_state is never None
assert incremental_state is not None
incremental_state = self._set_input_buffer(incremental_state, saved_state)
assert k is not None
src_len = k.size(1)
# This is part of a workaround to get around fork/join parallelism
# not supporting Optional types.
if key_padding_mask is not None and key_padding_mask.dim() == 0:
key_padding_mask = None
if key_padding_mask is not None:
assert key_padding_mask.size(0) == bsz
assert key_padding_mask.size(1) == src_len
if self.add_zero_attn:
assert v is not None
src_len += 1
k = torch.cat([k, k.new_zeros((k.size(0), 1) + k.size()[2:])], dim=1)
v = torch.cat([v, v.new_zeros((v.size(0), 1) + v.size()[2:])], dim=1)
if attn_mask is not None:
attn_mask = torch.cat(
[attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1
)
if key_padding_mask is not None:
key_padding_mask = torch.cat(
[
key_padding_mask,
torch.zeros(key_padding_mask.size(0), 1).type_as(
key_padding_mask
),
],
dim=1,
)
attn_weights = torch.bmm(q, k.transpose(1, 2))
attn_weights = MultiheadAttention.apply_sparse_mask(attn_weights, tgt_len, src_len, bsz)
assert list(attn_weights.size()) == [bsz * self.num_heads, tgt_len, src_len]
if attn_mask is not None:
attn_mask = attn_mask.unsqueeze(0)
if self.onnx_trace:
attn_mask = attn_mask.repeat(attn_weights.size(0), 1, 1)
attn_weights += attn_mask
if key_padding_mask is not None:
# don't attend to padding symbols
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
if not self.tpu:
attn_weights = attn_weights.masked_fill(
key_padding_mask.unsqueeze(1).unsqueeze(2).to(torch.bool),
float("-inf")
)
else:
attn_weights = attn_weights.transpose(0, 2)
attn_weights = attn_weights.masked_fill(key_padding_mask, float('-inf'))
attn_weights = attn_weights.transpose(0, 2)
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
if before_softmax:
return attn_weights, v
attn_weights_float = utils.softmax(
attn_weights, dim=-1, onnx_trace=self.onnx_trace
)
attn_weights = attn_weights_float.type_as(attn_weights)
attn_probs = F.dropout(
attn_weights,
p=self.dropout,
training=self.training,
)
assert v is not None
attn = torch.bmm(attn_probs, v)
assert list(attn.size()) == [bsz * self.num_heads, tgt_len, self.head_dim]
if self.onnx_trace and attn.size(1) == 1:
# when ONNX tracing a single decoder step (sequence length == 1)
# the transpose is a no-op copy before view, thus unnecessary
attn = attn.contiguous().view(tgt_len, bsz, embed_dim)
else:
attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim)
attn = self.out_proj(attn)
attn_weights: Optional[Tensor] = None
if need_weights:
attn_weights = attn_weights_float.view(
bsz, self.num_heads, tgt_len, src_len
).transpose(1, 0)
if not need_head_weights:
# average attention weights over heads
attn_weights = attn_weights.mean(dim=0)
return attn, attn_weights
def _append_prev_key_padding_mask(
key_padding_mask: Optional[Tensor],
prev_key_padding_mask: Optional[Tensor],
batch_size: int,
src_len: int,
static_kv: bool,
) -> Optional[Tensor]:
# saved key padding masks have shape (bsz, seq_len)
if prev_key_padding_mask is not None and static_kv:
new_key_padding_mask = prev_key_padding_mask
elif prev_key_padding_mask is not None and key_padding_mask is not None:
new_key_padding_mask = torch.cat(
[prev_key_padding_mask.float(), key_padding_mask.float()], dim=1
)
# During incremental decoding, as the padding token enters and
# leaves the frame, there will be a time when prev or current
# is None
elif prev_key_padding_mask is not None:
filler = torch.zeros(
(batch_size, src_len - prev_key_padding_mask.size(1)),
device=prev_key_padding_mask.device,
)
new_key_padding_mask = torch.cat(
[prev_key_padding_mask.float(), filler.float()], dim=1
)
elif key_padding_mask is not None:
filler = torch.zeros(
(batch_size, src_len - key_padding_mask.size(1)),
device=key_padding_mask.device,
)
new_key_padding_mask = torch.cat(
[filler.float(), key_padding_mask.float()], dim=1
)
else:
new_key_padding_mask = prev_key_padding_mask
return new_key_padding_mask
def reorder_incremental_state(
self, incremental_state: Dict[str, Dict[str, Optional[Tensor]]], new_order: Tensor
):
"""Reorder buffered internal state (for incremental generation)."""
input_buffer = self._get_input_buffer(incremental_state)
if input_buffer is not None:
for k in input_buffer.keys():
input_buffer_k = input_buffer[k]
if input_buffer_k is not None:
if self.encoder_decoder_attention and input_buffer_k.size(0) == new_order.size(0):
break
input_buffer[k] = input_buffer_k.index_select(0, new_order)
incremental_state = self._set_input_buffer(incremental_state, input_buffer)
return incremental_state
def _get_input_buffer(
self, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]]
) -> Dict[str, Optional[Tensor]]:
result = self.get_incremental_state(incremental_state, "attn_state")
if result is not None:
return result
else:
empty_result: Dict[str, Optional[Tensor]] = {}
return empty_result
def _set_input_buffer(
self,
incremental_state: Dict[str, Dict[str, Optional[Tensor]]],
buffer: Dict[str, Optional[Tensor]],
):
return self.set_incremental_state(incremental_state, "attn_state", buffer)
def apply_sparse_mask(attn_weights, tgt_len: int, src_len: int, bsz: int):
return attn_weights
def upgrade_state_dict_named(self, state_dict, name):
prefix = name + "." if name != "" else ""
items_to_add = {}
keys_to_remove = []
for k in state_dict.keys():
if k.endswith(prefix + "in_proj_weight"):
# in_proj_weight used to be q + k + v with same dimensions
dim = int(state_dict[k].shape[0] / 3)
items_to_add[prefix + "q_proj.weight"] = state_dict[k][:dim]
items_to_add[prefix + "k_proj.weight"] = state_dict[k][dim : 2 * dim]
items_to_add[prefix + "v_proj.weight"] = state_dict[k][2 * dim :]
keys_to_remove.append(k)
k_bias = prefix + "in_proj_bias"
if k_bias in state_dict.keys():
dim = int(state_dict[k].shape[0] / 3)
items_to_add[prefix + "q_proj.bias"] = state_dict[k_bias][:dim]
items_to_add[prefix + "k_proj.bias"] = state_dict[k_bias][
dim : 2 * dim
]
items_to_add[prefix + "v_proj.bias"] = state_dict[k_bias][2 * dim :]
keys_to_remove.append(prefix + "in_proj_bias")
for k in keys_to_remove:
del state_dict[k]
for key, value in items_to_add.items():
state_dict[key] = value
The provided code snippet includes necessary dependencies for implementing the `get_incremental_state` function. Write a Python function `def get_incremental_state( module: MultiheadAttention, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]], key: str, ) -> Optional[Dict[str, Optional[Tensor]]]` to solve the following problem:
Helper for getting incremental state for an nn.Module.
Here is the function:
def get_incremental_state(
module: MultiheadAttention,
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]],
key: str,
) -> Optional[Dict[str, Optional[Tensor]]]:
"""Helper for getting incremental state for an nn.Module."""
return module.get_incremental_state(incremental_state, key) | Helper for getting incremental state for an nn.Module. |
19,085 | import contextlib
import copy
import importlib.util
import logging
import math
import os
import sys
import warnings
from collections import defaultdict
from itertools import accumulate
from typing import Callable, Dict, List, Optional
import numpy as np
import torch
import torch.nn.functional as F
from fairseq.logging.meters import safe_round
from fairseq.modules import gelu, gelu_accurate
from fairseq.modules.multihead_attention import MultiheadAttention
from torch import Tensor
class MultiheadAttention(nn.Module):
"""Multi-headed attention.
See "Attention Is All You Need" for more details.
"""
def __init__(
self,
embed_dim,
num_heads,
kdim=None,
vdim=None,
dropout=0.0,
bias=True,
add_bias_kv=False,
add_zero_attn=False,
self_attention=False,
encoder_decoder_attention=False,
q_noise=0.0,
qn_block_size=8,
):
super().__init__()
self.embed_dim = embed_dim
self.kdim = kdim if kdim is not None else embed_dim
self.vdim = vdim if vdim is not None else embed_dim
self.qkv_same_dim = self.kdim == embed_dim and self.vdim == embed_dim
self.num_heads = num_heads
self.dropout = dropout
self.head_dim = embed_dim // num_heads
assert (
self.head_dim * num_heads == self.embed_dim
), "embed_dim must be divisible by num_heads"
self.scaling = self.head_dim ** -0.5
self.self_attention = self_attention
self.encoder_decoder_attention = encoder_decoder_attention
assert not self.self_attention or self.qkv_same_dim, (
"Self-attention requires query, key and " "value to be of the same size"
)
self.k_proj = quant_noise(nn.Linear(self.kdim, embed_dim, bias=bias), q_noise, qn_block_size)
self.v_proj = quant_noise(nn.Linear(self.vdim, embed_dim, bias=bias), q_noise, qn_block_size)
self.q_proj = quant_noise(nn.Linear(embed_dim, embed_dim, bias=bias), q_noise, qn_block_size)
self.out_proj = quant_noise(nn.Linear(embed_dim, embed_dim, bias=bias), q_noise, qn_block_size)
if add_bias_kv:
self.bias_k = Parameter(torch.Tensor(1, 1, embed_dim))
self.bias_v = Parameter(torch.Tensor(1, 1, embed_dim))
else:
self.bias_k = self.bias_v = None
self.add_zero_attn = add_zero_attn
self.reset_parameters()
self.onnx_trace = False
self.tpu = False
def prepare_for_onnx_export_(self):
self.onnx_trace = True
def prepare_for_tpu_(self, **kwargs):
self.tpu = True
def reset_parameters(self):
if self.qkv_same_dim:
# Empirically observed the convergence to be much better with
# the scaled initialization
nn.init.xavier_uniform_(self.k_proj.weight, gain=1 / math.sqrt(2))
nn.init.xavier_uniform_(self.v_proj.weight, gain=1 / math.sqrt(2))
nn.init.xavier_uniform_(self.q_proj.weight, gain=1 / math.sqrt(2))
else:
nn.init.xavier_uniform_(self.k_proj.weight)
nn.init.xavier_uniform_(self.v_proj.weight)
nn.init.xavier_uniform_(self.q_proj.weight)
nn.init.xavier_uniform_(self.out_proj.weight)
if self.out_proj.bias is not None:
nn.init.constant_(self.out_proj.bias, 0.)
if self.bias_k is not None:
nn.init.xavier_normal_(self.bias_k)
if self.bias_v is not None:
nn.init.xavier_normal_(self.bias_v)
def forward(
self,
query,
key: Optional[Tensor],
value: Optional[Tensor],
key_padding_mask: Optional[Tensor] = None,
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
need_weights: bool = True,
static_kv: bool = False,
attn_mask: Optional[Tensor] = None,
before_softmax: bool = False,
need_head_weights: bool = False,
) -> Tuple[Tensor, Optional[Tensor]]:
"""Input shape: Time x Batch x Channel
Args:
key_padding_mask (ByteTensor, optional): mask to exclude
keys that are pads, of shape `(batch, src_len)`, where
padding elements are indicated by 1s.
need_weights (bool, optional): return the attention weights,
averaged over heads (default: False).
attn_mask (ByteTensor, optional): typically used to
implement causal attention, where the mask prevents the
attention from looking forward in time (default: None).
before_softmax (bool, optional): return the raw attention
weights and values before the attention softmax.
need_head_weights (bool, optional): return the attention
weights for each head. Implies *need_weights*. Default:
return the average attention weights over all heads.
"""
if need_head_weights:
need_weights = True
tgt_len, bsz, embed_dim = query.size()
assert embed_dim == self.embed_dim
assert list(query.size()) == [tgt_len, bsz, embed_dim]
if (
not self.onnx_trace
and not self.tpu # don't use PyTorch version on TPUs
and incremental_state is None
and not static_kv
# A workaround for quantization to work. Otherwise JIT compilation
# treats bias in linear module as method.
and not torch.jit.is_scripting()
):
assert key is not None and value is not None
return F.multi_head_attention_forward(
query,
key,
value,
self.embed_dim,
self.num_heads,
torch.empty([0]),
torch.cat((self.q_proj.bias, self.k_proj.bias, self.v_proj.bias)),
self.bias_k,
self.bias_v,
self.add_zero_attn,
self.dropout,
self.out_proj.weight,
self.out_proj.bias,
self.training,
key_padding_mask,
need_weights,
attn_mask,
use_separate_proj_weight=True,
q_proj_weight=self.q_proj.weight,
k_proj_weight=self.k_proj.weight,
v_proj_weight=self.v_proj.weight,
)
if incremental_state is not None:
saved_state = self._get_input_buffer(incremental_state)
if saved_state is not None and "prev_key" in saved_state:
# previous time steps are cached - no need to recompute
# key and value if they are static
if static_kv:
assert self.encoder_decoder_attention and not self.self_attention
key = value = None
else:
saved_state = None
if self.self_attention:
q = self.q_proj(query)
k = self.k_proj(query)
v = self.v_proj(query)
elif self.encoder_decoder_attention:
# encoder-decoder attention
q = self.q_proj(query)
if key is None:
assert value is None
k = v = None
else:
k = self.k_proj(key)
v = self.v_proj(key)
else:
assert key is not None and value is not None
q = self.q_proj(query)
k = self.k_proj(key)
v = self.v_proj(value)
q *= self.scaling
if self.bias_k is not None:
assert self.bias_v is not None
k = torch.cat([k, self.bias_k.repeat(1, bsz, 1)])
v = torch.cat([v, self.bias_v.repeat(1, bsz, 1)])
if attn_mask is not None:
attn_mask = torch.cat(
[attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1
)
if key_padding_mask is not None:
key_padding_mask = torch.cat(
[
key_padding_mask,
key_padding_mask.new_zeros(key_padding_mask.size(0), 1),
],
dim=1,
)
q = (
q.contiguous()
.view(tgt_len, bsz * self.num_heads, self.head_dim)
.transpose(0, 1)
)
if k is not None:
k = (
k.contiguous()
.view(-1, bsz * self.num_heads, self.head_dim)
.transpose(0, 1)
)
if v is not None:
v = (
v.contiguous()
.view(-1, bsz * self.num_heads, self.head_dim)
.transpose(0, 1)
)
if saved_state is not None:
# saved states are stored with shape (bsz, num_heads, seq_len, head_dim)
if "prev_key" in saved_state:
_prev_key = saved_state["prev_key"]
assert _prev_key is not None
prev_key = _prev_key.view(bsz * self.num_heads, -1, self.head_dim)
if static_kv:
k = prev_key
else:
assert k is not None
k = torch.cat([prev_key, k], dim=1)
if "prev_value" in saved_state:
_prev_value = saved_state["prev_value"]
assert _prev_value is not None
prev_value = _prev_value.view(bsz * self.num_heads, -1, self.head_dim)
if static_kv:
v = prev_value
else:
assert v is not None
v = torch.cat([prev_value, v], dim=1)
prev_key_padding_mask: Optional[Tensor] = None
if "prev_key_padding_mask" in saved_state:
prev_key_padding_mask = saved_state["prev_key_padding_mask"]
assert k is not None and v is not None
key_padding_mask = MultiheadAttention._append_prev_key_padding_mask(
key_padding_mask=key_padding_mask,
prev_key_padding_mask=prev_key_padding_mask,
batch_size=bsz,
src_len=k.size(1),
static_kv=static_kv,
)
saved_state["prev_key"] = k.view(bsz, self.num_heads, -1, self.head_dim)
saved_state["prev_value"] = v.view(bsz, self.num_heads, -1, self.head_dim)
saved_state["prev_key_padding_mask"] = key_padding_mask
# In this branch incremental_state is never None
assert incremental_state is not None
incremental_state = self._set_input_buffer(incremental_state, saved_state)
assert k is not None
src_len = k.size(1)
# This is part of a workaround to get around fork/join parallelism
# not supporting Optional types.
if key_padding_mask is not None and key_padding_mask.dim() == 0:
key_padding_mask = None
if key_padding_mask is not None:
assert key_padding_mask.size(0) == bsz
assert key_padding_mask.size(1) == src_len
if self.add_zero_attn:
assert v is not None
src_len += 1
k = torch.cat([k, k.new_zeros((k.size(0), 1) + k.size()[2:])], dim=1)
v = torch.cat([v, v.new_zeros((v.size(0), 1) + v.size()[2:])], dim=1)
if attn_mask is not None:
attn_mask = torch.cat(
[attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1
)
if key_padding_mask is not None:
key_padding_mask = torch.cat(
[
key_padding_mask,
torch.zeros(key_padding_mask.size(0), 1).type_as(
key_padding_mask
),
],
dim=1,
)
attn_weights = torch.bmm(q, k.transpose(1, 2))
attn_weights = MultiheadAttention.apply_sparse_mask(attn_weights, tgt_len, src_len, bsz)
assert list(attn_weights.size()) == [bsz * self.num_heads, tgt_len, src_len]
if attn_mask is not None:
attn_mask = attn_mask.unsqueeze(0)
if self.onnx_trace:
attn_mask = attn_mask.repeat(attn_weights.size(0), 1, 1)
attn_weights += attn_mask
if key_padding_mask is not None:
# don't attend to padding symbols
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
if not self.tpu:
attn_weights = attn_weights.masked_fill(
key_padding_mask.unsqueeze(1).unsqueeze(2).to(torch.bool),
float("-inf")
)
else:
attn_weights = attn_weights.transpose(0, 2)
attn_weights = attn_weights.masked_fill(key_padding_mask, float('-inf'))
attn_weights = attn_weights.transpose(0, 2)
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
if before_softmax:
return attn_weights, v
attn_weights_float = utils.softmax(
attn_weights, dim=-1, onnx_trace=self.onnx_trace
)
attn_weights = attn_weights_float.type_as(attn_weights)
attn_probs = F.dropout(
attn_weights,
p=self.dropout,
training=self.training,
)
assert v is not None
attn = torch.bmm(attn_probs, v)
assert list(attn.size()) == [bsz * self.num_heads, tgt_len, self.head_dim]
if self.onnx_trace and attn.size(1) == 1:
# when ONNX tracing a single decoder step (sequence length == 1)
# the transpose is a no-op copy before view, thus unnecessary
attn = attn.contiguous().view(tgt_len, bsz, embed_dim)
else:
attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim)
attn = self.out_proj(attn)
attn_weights: Optional[Tensor] = None
if need_weights:
attn_weights = attn_weights_float.view(
bsz, self.num_heads, tgt_len, src_len
).transpose(1, 0)
if not need_head_weights:
# average attention weights over heads
attn_weights = attn_weights.mean(dim=0)
return attn, attn_weights
def _append_prev_key_padding_mask(
key_padding_mask: Optional[Tensor],
prev_key_padding_mask: Optional[Tensor],
batch_size: int,
src_len: int,
static_kv: bool,
) -> Optional[Tensor]:
# saved key padding masks have shape (bsz, seq_len)
if prev_key_padding_mask is not None and static_kv:
new_key_padding_mask = prev_key_padding_mask
elif prev_key_padding_mask is not None and key_padding_mask is not None:
new_key_padding_mask = torch.cat(
[prev_key_padding_mask.float(), key_padding_mask.float()], dim=1
)
# During incremental decoding, as the padding token enters and
# leaves the frame, there will be a time when prev or current
# is None
elif prev_key_padding_mask is not None:
filler = torch.zeros(
(batch_size, src_len - prev_key_padding_mask.size(1)),
device=prev_key_padding_mask.device,
)
new_key_padding_mask = torch.cat(
[prev_key_padding_mask.float(), filler.float()], dim=1
)
elif key_padding_mask is not None:
filler = torch.zeros(
(batch_size, src_len - key_padding_mask.size(1)),
device=key_padding_mask.device,
)
new_key_padding_mask = torch.cat(
[filler.float(), key_padding_mask.float()], dim=1
)
else:
new_key_padding_mask = prev_key_padding_mask
return new_key_padding_mask
def reorder_incremental_state(
self, incremental_state: Dict[str, Dict[str, Optional[Tensor]]], new_order: Tensor
):
"""Reorder buffered internal state (for incremental generation)."""
input_buffer = self._get_input_buffer(incremental_state)
if input_buffer is not None:
for k in input_buffer.keys():
input_buffer_k = input_buffer[k]
if input_buffer_k is not None:
if self.encoder_decoder_attention and input_buffer_k.size(0) == new_order.size(0):
break
input_buffer[k] = input_buffer_k.index_select(0, new_order)
incremental_state = self._set_input_buffer(incremental_state, input_buffer)
return incremental_state
def _get_input_buffer(
self, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]]
) -> Dict[str, Optional[Tensor]]:
result = self.get_incremental_state(incremental_state, "attn_state")
if result is not None:
return result
else:
empty_result: Dict[str, Optional[Tensor]] = {}
return empty_result
def _set_input_buffer(
self,
incremental_state: Dict[str, Dict[str, Optional[Tensor]]],
buffer: Dict[str, Optional[Tensor]],
):
return self.set_incremental_state(incremental_state, "attn_state", buffer)
def apply_sparse_mask(attn_weights, tgt_len: int, src_len: int, bsz: int):
return attn_weights
def upgrade_state_dict_named(self, state_dict, name):
prefix = name + "." if name != "" else ""
items_to_add = {}
keys_to_remove = []
for k in state_dict.keys():
if k.endswith(prefix + "in_proj_weight"):
# in_proj_weight used to be q + k + v with same dimensions
dim = int(state_dict[k].shape[0] / 3)
items_to_add[prefix + "q_proj.weight"] = state_dict[k][:dim]
items_to_add[prefix + "k_proj.weight"] = state_dict[k][dim : 2 * dim]
items_to_add[prefix + "v_proj.weight"] = state_dict[k][2 * dim :]
keys_to_remove.append(k)
k_bias = prefix + "in_proj_bias"
if k_bias in state_dict.keys():
dim = int(state_dict[k].shape[0] / 3)
items_to_add[prefix + "q_proj.bias"] = state_dict[k_bias][:dim]
items_to_add[prefix + "k_proj.bias"] = state_dict[k_bias][
dim : 2 * dim
]
items_to_add[prefix + "v_proj.bias"] = state_dict[k_bias][2 * dim :]
keys_to_remove.append(prefix + "in_proj_bias")
for k in keys_to_remove:
del state_dict[k]
for key, value in items_to_add.items():
state_dict[key] = value
The provided code snippet includes necessary dependencies for implementing the `set_incremental_state` function. Write a Python function `def set_incremental_state( module: MultiheadAttention, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]], key: str, value: Dict[str, Optional[Tensor]], ) -> Optional[Dict[str, Dict[str, Optional[Tensor]]]]` to solve the following problem:
Helper for setting incremental state for an nn.Module.
Here is the function:
def set_incremental_state(
module: MultiheadAttention,
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]],
key: str,
value: Dict[str, Optional[Tensor]],
) -> Optional[Dict[str, Dict[str, Optional[Tensor]]]]:
"""Helper for setting incremental state for an nn.Module."""
if incremental_state is not None:
result = module.set_incremental_state(incremental_state, key, value)
if result is not None:
incremental_state = result
return incremental_state | Helper for setting incremental state for an nn.Module. |
19,086 | import contextlib
import copy
import importlib.util
import logging
import math
import os
import sys
import warnings
from collections import defaultdict
from itertools import accumulate
from typing import Callable, Dict, List, Optional
import numpy as np
import torch
import torch.nn.functional as F
from fairseq.logging.meters import safe_round
from fairseq.modules import gelu, gelu_accurate
from fairseq.modules.multihead_attention import MultiheadAttention
from torch import Tensor
logger = logging.getLogger(__name__)
def print_embed_overlap(embed_dict, vocab_dict):
embed_keys = set(embed_dict.keys())
vocab_keys = set(vocab_dict.symbols)
overlap = len(embed_keys & vocab_keys)
logger.info("found {}/{} types in embedding file".format(overlap, len(vocab_dict))) | null |
19,087 | import contextlib
import copy
import importlib.util
import logging
import math
import os
import sys
import warnings
from collections import defaultdict
from itertools import accumulate
from typing import Callable, Dict, List, Optional
import numpy as np
import torch
import torch.nn.functional as F
from fairseq.logging.meters import safe_round
from fairseq.modules import gelu, gelu_accurate
from fairseq.modules.multihead_attention import MultiheadAttention
from torch import Tensor
The provided code snippet includes necessary dependencies for implementing the `parse_embedding` function. Write a Python function `def parse_embedding(embed_path)` to solve the following problem:
Parse embedding text file into a dictionary of word and embedding tensors. The first line can have vocabulary size and dimension. The following lines should contain word and embedding separated by spaces. Example: 2 5 the -0.0230 -0.0264 0.0287 0.0171 0.1403 at -0.0395 -0.1286 0.0275 0.0254 -0.0932
Here is the function:
def parse_embedding(embed_path):
"""Parse embedding text file into a dictionary of word and embedding tensors.
The first line can have vocabulary size and dimension. The following lines
should contain word and embedding separated by spaces.
Example:
2 5
the -0.0230 -0.0264 0.0287 0.0171 0.1403
at -0.0395 -0.1286 0.0275 0.0254 -0.0932
"""
embed_dict = {}
with open(embed_path) as f_embed:
next(f_embed) # skip header
for line in f_embed:
pieces = line.rstrip().split(" ")
embed_dict[pieces[0]] = torch.Tensor(
[float(weight) for weight in pieces[1:]]
)
return embed_dict | Parse embedding text file into a dictionary of word and embedding tensors. The first line can have vocabulary size and dimension. The following lines should contain word and embedding separated by spaces. Example: 2 5 the -0.0230 -0.0264 0.0287 0.0171 0.1403 at -0.0395 -0.1286 0.0275 0.0254 -0.0932 |
19,088 | import contextlib
import copy
import importlib.util
import logging
import math
import os
import sys
import warnings
from collections import defaultdict
from itertools import accumulate
from typing import Callable, Dict, List, Optional
import numpy as np
import torch
import torch.nn.functional as F
from fairseq.logging.meters import safe_round
from fairseq.modules import gelu, gelu_accurate
from fairseq.modules.multihead_attention import MultiheadAttention
from torch import Tensor
def load_embedding(embed_dict, vocab, embedding):
for idx in range(len(vocab)):
token = vocab[idx]
if token in embed_dict:
embedding.weight.data[idx] = embed_dict[token]
return embedding | null |
19,089 | import contextlib
import copy
import importlib.util
import logging
import math
import os
import sys
import warnings
from collections import defaultdict
from itertools import accumulate
from typing import Callable, Dict, List, Optional
import numpy as np
import torch
import torch.nn.functional as F
from fairseq.logging.meters import safe_round
from fairseq.modules import gelu, gelu_accurate
from fairseq.modules.multihead_attention import MultiheadAttention
from torch import Tensor
The provided code snippet includes necessary dependencies for implementing the `make_positions` function. Write a Python function `def make_positions(tensor, padding_idx: int, onnx_trace: bool = False)` to solve the following problem:
Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols are ignored.
Here is the function:
def make_positions(tensor, padding_idx: int, onnx_trace: bool = False):
"""Replace non-padding symbols with their position numbers.
Position numbers begin at padding_idx+1. Padding symbols are ignored.
"""
# The series of casts and type-conversions here are carefully
# balanced to both work with ONNX export and XLA. In particular XLA
# prefers ints, cumsum defaults to output longs, and ONNX doesn't know
# how to handle the dtype kwarg in cumsum.
mask = tensor.ne(padding_idx).int()
return (torch.cumsum(mask, dim=1).type_as(mask) * mask).long() + padding_idx | Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols are ignored. |
19,090 | import contextlib
import copy
import importlib.util
import logging
import math
import os
import sys
import warnings
from collections import defaultdict
from itertools import accumulate
from typing import Callable, Dict, List, Optional
import numpy as np
import torch
import torch.nn.functional as F
from fairseq.logging.meters import safe_round
from fairseq.modules import gelu, gelu_accurate
from fairseq.modules.multihead_attention import MultiheadAttention
from torch import Tensor
def buffered_arange(max):
if not hasattr(buffered_arange, "buf"):
buffered_arange.buf = torch.LongTensor()
if max > buffered_arange.buf.numel():
buffered_arange.buf.resize_(max)
torch.arange(max, out=buffered_arange.buf)
return buffered_arange.buf[:max] | null |
19,091 | import contextlib
import copy
import importlib.util
import logging
import math
import os
import sys
import warnings
from collections import defaultdict
from itertools import accumulate
from typing import Callable, Dict, List, Optional
import numpy as np
import torch
import torch.nn.functional as F
from fairseq.logging.meters import safe_round
from fairseq.modules import gelu, gelu_accurate
from fairseq.modules.multihead_attention import MultiheadAttention
from torch import Tensor
def convert_padding_direction(
src_tokens, padding_idx, right_to_left: bool = False, left_to_right: bool = False
):
assert right_to_left ^ left_to_right
pad_mask = src_tokens.eq(padding_idx)
if not pad_mask.any():
# no padding, return early
return src_tokens
if left_to_right and not pad_mask[:, 0].any():
# already right padded
return src_tokens
if right_to_left and not pad_mask[:, -1].any():
# already left padded
return src_tokens
max_len = src_tokens.size(1)
buffered = torch.empty(0).long()
if max_len > 0:
torch.arange(max_len, out=buffered)
range = buffered.type_as(src_tokens).expand_as(src_tokens)
num_pads = pad_mask.long().sum(dim=1, keepdim=True)
if right_to_left:
index = torch.remainder(range - num_pads, max_len)
else:
index = torch.remainder(range + num_pads, max_len)
return src_tokens.gather(1, index) | null |
19,092 | import contextlib
import copy
import importlib.util
import logging
import math
import os
import sys
import warnings
from collections import defaultdict
from itertools import accumulate
from typing import Callable, Dict, List, Optional
import numpy as np
import torch
import torch.nn.functional as F
from fairseq.logging.meters import safe_round
from fairseq.modules import gelu, gelu_accurate
from fairseq.modules.multihead_attention import MultiheadAttention
from torch import Tensor
def clip_grad_norm_(params, max_norm, aggregate_norm_fn=None) -> torch.Tensor:
if isinstance(params, torch.Tensor):
params = [params]
params = list(params)
grads = [p.grad.detach() for p in filter(lambda p: p.grad is not None, params)]
if len(grads) == 0:
if len(params) > 0:
return params[0].new_tensor(0.)
else:
return torch.tensor(0.)
if len(grads) == 1:
total_norm = torch.norm(grads[0], p=2, dtype=torch.float32)
else:
total_norm = torch.norm(
torch.stack([torch.norm(g, p=2, dtype=torch.float32) for g in grads])
)
if aggregate_norm_fn is not None:
total_norm = aggregate_norm_fn(total_norm)
if max_norm > 0:
max_norm = float(max_norm)
clip_coef = (max_norm / (total_norm + 1e-6)).clamp_(max=1)
for g in grads:
g.mul_(clip_coef)
return total_norm | null |
19,093 | import contextlib
import copy
import importlib.util
import logging
import math
import os
import sys
import warnings
from collections import defaultdict
from itertools import accumulate
from typing import Callable, Dict, List, Optional
import numpy as np
import torch
import torch.nn.functional as F
from fairseq.logging.meters import safe_round
from fairseq.modules import gelu, gelu_accurate
from fairseq.modules.multihead_attention import MultiheadAttention
from torch import Tensor
The provided code snippet includes necessary dependencies for implementing the `fill_with_neg_inf` function. Write a Python function `def fill_with_neg_inf(t)` to solve the following problem:
FP16-compatible function that fills a tensor with -inf.
Here is the function:
def fill_with_neg_inf(t):
"""FP16-compatible function that fills a tensor with -inf."""
return t.float().fill_(float("-inf")).type_as(t) | FP16-compatible function that fills a tensor with -inf. |
19,094 | import contextlib
import copy
import importlib.util
import logging
import math
import os
import sys
import warnings
from collections import defaultdict
from itertools import accumulate
from typing import Callable, Dict, List, Optional
import numpy as np
import torch
import torch.nn.functional as F
from fairseq.logging.meters import safe_round
from fairseq.modules import gelu, gelu_accurate
from fairseq.modules.multihead_attention import MultiheadAttention
from torch import Tensor
def softmax(x, dim: int, onnx_trace: bool = False):
if onnx_trace:
return F.softmax(x.float(), dim=dim)
else:
return F.softmax(x, dim=dim, dtype=torch.float32) | null |
19,095 | import contextlib
import copy
import importlib.util
import logging
import math
import os
import sys
import warnings
from collections import defaultdict
from itertools import accumulate
from typing import Callable, Dict, List, Optional
import numpy as np
import torch
import torch.nn.functional as F
from fairseq.logging.meters import safe_round
from fairseq.modules import gelu, gelu_accurate
from fairseq.modules.multihead_attention import MultiheadAttention
from torch import Tensor
def safe_round(number, ndigits):
if hasattr(number, '__round__'):
return round(number, ndigits)
elif torch is not None and torch.is_tensor(number) and number.numel() == 1:
return safe_round(number.item(), ndigits)
elif np is not None and np.ndim(number) == 0 and hasattr(number, 'item'):
return safe_round(number.item(), ndigits)
else:
return number
def get_perplexity(loss, round=2, base=2):
if loss is None:
return 0.
try:
return safe_round(base ** loss, round)
except OverflowError:
return float('inf') | null |
19,096 | import contextlib
import copy
import importlib.util
import logging
import math
import os
import sys
import warnings
from collections import defaultdict
from itertools import accumulate
from typing import Callable, Dict, List, Optional
import numpy as np
import torch
import torch.nn.functional as F
from fairseq.logging.meters import safe_round
from fairseq.modules import gelu, gelu_accurate
from fairseq.modules.multihead_attention import MultiheadAttention
from torch import Tensor
def deprecation_warning(message, stacklevel=3):
# don't use DeprecationWarning, since it's ignored by default
warnings.warn(message, stacklevel=stacklevel)
def gelu(x: torch.Tensor) -> torch.Tensor:
return torch.nn.functional.gelu(x.float()).type_as(x)
The provided code snippet includes necessary dependencies for implementing the `get_activation_fn` function. Write a Python function `def get_activation_fn(activation: str) -> Callable` to solve the following problem:
Returns the activation function corresponding to `activation`
Here is the function:
def get_activation_fn(activation: str) -> Callable:
""" Returns the activation function corresponding to `activation` """
if activation == "relu":
return F.relu
elif activation == "gelu":
return gelu
elif activation == "gelu_fast":
deprecation_warning(
"--activation-fn=gelu_fast has been renamed to gelu_accurate"
)
return gelu_accurate
elif activation == "gelu_accurate":
return gelu_accurate
elif activation == "tanh":
return torch.tanh
elif activation == "linear":
return lambda x: x
else:
raise RuntimeError("--activation-fn {} not supported".format(activation)) | Returns the activation function corresponding to `activation` |
19,097 | import contextlib
import copy
import importlib.util
import logging
import math
import os
import sys
import warnings
from collections import defaultdict
from itertools import accumulate
from typing import Callable, Dict, List, Optional
import numpy as np
import torch
import torch.nn.functional as F
from fairseq.logging.meters import safe_round
from fairseq.modules import gelu, gelu_accurate
from fairseq.modules.multihead_attention import MultiheadAttention
from torch import Tensor
def get_available_activation_fns() -> List:
return [
"relu",
"gelu",
"gelu_fast", # deprecated
"gelu_accurate",
"tanh",
"linear",
] | null |
19,098 | import contextlib
import copy
import importlib.util
import logging
import math
import os
import sys
import warnings
from collections import defaultdict
from itertools import accumulate
from typing import Callable, Dict, List, Optional
import numpy as np
import torch
import torch.nn.functional as F
from fairseq.logging.meters import safe_round
from fairseq.modules import gelu, gelu_accurate
from fairseq.modules.multihead_attention import MultiheadAttention
from torch import Tensor
def set_torch_seed(seed):
# Set seed based on args.seed and the update number so that we get
# reproducible results when resuming from checkpoints
assert isinstance(seed, int)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
def with_torch_seed(seed):
assert isinstance(seed, int)
rng_state = torch.get_rng_state()
cuda_rng_state = torch.cuda.get_rng_state()
set_torch_seed(seed)
yield
torch.set_rng_state(rng_state)
torch.cuda.set_rng_state(cuda_rng_state) | null |
19,099 | import contextlib
import copy
import importlib.util
import logging
import math
import os
import sys
import warnings
from collections import defaultdict
from itertools import accumulate
from typing import Callable, Dict, List, Optional
import numpy as np
import torch
import torch.nn.functional as F
from fairseq.logging.meters import safe_round
from fairseq.modules import gelu, gelu_accurate
from fairseq.modules.multihead_attention import MultiheadAttention
from torch import Tensor
The provided code snippet includes necessary dependencies for implementing the `parse_alignment` function. Write a Python function `def parse_alignment(line)` to solve the following problem:
Parses a single line from the alingment file. Args: line (str): String containing the alignment of the format: <src_idx_1>-<tgt_idx_1> <src_idx_2>-<tgt_idx_2> .. <src_idx_m>-<tgt_idx_m>. All indices are 0 indexed. Returns: torch.IntTensor: packed alignments of shape (2 * m).
Here is the function:
def parse_alignment(line):
"""
Parses a single line from the alingment file.
Args:
line (str): String containing the alignment of the format:
<src_idx_1>-<tgt_idx_1> <src_idx_2>-<tgt_idx_2> ..
<src_idx_m>-<tgt_idx_m>. All indices are 0 indexed.
Returns:
torch.IntTensor: packed alignments of shape (2 * m).
"""
alignments = line.strip().split()
parsed_alignment = torch.IntTensor(2 * len(alignments))
for idx, alignment in enumerate(alignments):
src_idx, tgt_idx = alignment.split("-")
parsed_alignment[2 * idx] = int(src_idx)
parsed_alignment[2 * idx + 1] = int(tgt_idx)
return parsed_alignment | Parses a single line from the alingment file. Args: line (str): String containing the alignment of the format: <src_idx_1>-<tgt_idx_1> <src_idx_2>-<tgt_idx_2> .. <src_idx_m>-<tgt_idx_m>. All indices are 0 indexed. Returns: torch.IntTensor: packed alignments of shape (2 * m). |
19,100 | import contextlib
import copy
import importlib.util
import logging
import math
import os
import sys
import warnings
from collections import defaultdict
from itertools import accumulate
from typing import Callable, Dict, List, Optional
import numpy as np
import torch
import torch.nn.functional as F
from fairseq.logging.meters import safe_round
from fairseq.modules import gelu, gelu_accurate
from fairseq.modules.multihead_attention import MultiheadAttention
from torch import Tensor
def item(tensor):
if hasattr(tensor, "item"):
return tensor.item()
if hasattr(tensor, "__getitem__"):
return tensor[0]
return tensor
def get_token_to_word_mapping(tokens, exclude_list):
n = len(tokens)
word_start = [int(token not in exclude_list) for token in tokens]
word_idx = list(accumulate(word_start))
token_to_word = {i: word_idx[i] for i in range(n)}
return token_to_word
def extract_hard_alignment(attn, src_sent, tgt_sent, pad, eos):
tgt_valid = ((tgt_sent != pad) & (tgt_sent != eos)).nonzero().squeeze(dim=-1)
src_invalid = ((src_sent == pad) | (src_sent == eos)).nonzero().squeeze(dim=-1)
src_token_to_word = get_token_to_word_mapping(src_sent, [eos, pad])
tgt_token_to_word = get_token_to_word_mapping(tgt_sent, [eos, pad])
alignment = []
if len(tgt_valid) != 0 and len(src_invalid) < len(src_sent):
attn_valid = attn[tgt_valid]
attn_valid[:, src_invalid] = float("-inf")
_, src_indices = attn_valid.max(dim=1)
for tgt_idx, src_idx in zip(tgt_valid, src_indices):
alignment.append(
(
src_token_to_word[src_idx.item()] - 1,
tgt_token_to_word[tgt_idx.item()] - 1,
)
)
return alignment | null |
19,101 | import os
from collections import Counter
from fairseq.tokenizer import tokenize_line
import torch
def safe_readline(f):
pos = f.tell()
while True:
try:
return f.readline()
except UnicodeDecodeError:
pos -= 1
f.seek(pos) # search where this character begins | null |
19,102 | import atexit
import json
import logging
import os
import sys
from collections import OrderedDict
from contextlib import contextmanager
from numbers import Number
from typing import Optional
import torch
from .meters import AverageMeter, StopwatchMeter, TimeMeter
def progress_bar(
iterator,
log_format: Optional[str] = None,
log_interval: int = 100,
epoch: Optional[int] = None,
prefix: Optional[str] = None,
tensorboard_logdir: Optional[str] = None,
default_log_format: str = 'tqdm',
):
if log_format is None:
log_format = default_log_format
if log_format == 'tqdm' and not sys.stderr.isatty():
log_format = 'simple'
if log_format == 'json':
bar = JsonProgressBar(iterator, epoch, prefix, log_interval)
elif log_format == 'none':
bar = NoopProgressBar(iterator, epoch, prefix)
elif log_format == 'simple':
bar = SimpleProgressBar(iterator, epoch, prefix, log_interval)
elif log_format == 'tqdm':
bar = TqdmProgressBar(iterator, epoch, prefix)
else:
raise ValueError('Unknown log format: {}'.format(log_format))
if tensorboard_logdir:
try:
# [FB only] custom wrapper for TensorBoard
import palaas # noqa
from .fb_tbmf_wrapper import FbTbmfWrapper
bar = FbTbmfWrapper(bar, log_interval)
except ImportError:
bar = TensorboardProgressBarWrapper(bar, tensorboard_logdir)
return bar
The provided code snippet includes necessary dependencies for implementing the `build_progress_bar` function. Write a Python function `def build_progress_bar( args, iterator, epoch: Optional[int] = None, prefix: Optional[str] = None, default: str = 'tqdm', no_progress_bar: str = 'none', )` to solve the following problem:
Legacy wrapper that takes an argparse.Namespace.
Here is the function:
def build_progress_bar(
args,
iterator,
epoch: Optional[int] = None,
prefix: Optional[str] = None,
default: str = 'tqdm',
no_progress_bar: str = 'none',
):
"""Legacy wrapper that takes an argparse.Namespace."""
if getattr(args, 'no_progress_bar', False):
default = no_progress_bar
if getattr(args, 'distributed_rank', 0) == 0:
tensorboard_logdir = getattr(args, 'tensorboard_logdir', None)
else:
tensorboard_logdir = None
return progress_bar(
iterator,
log_format=args.log_format,
log_interval=args.log_interval,
epoch=epoch,
prefix=prefix,
tensorboard_logdir=tensorboard_logdir,
default_log_format=default,
) | Legacy wrapper that takes an argparse.Namespace. |
19,103 | import atexit
import json
import logging
import os
import sys
from collections import OrderedDict
from contextlib import contextmanager
from numbers import Number
from typing import Optional
import torch
from .meters import AverageMeter, StopwatchMeter, TimeMeter
class AverageMeter(Meter):
"""Computes and stores the average and current value"""
def __init__(self, round: Optional[int] = None):
self.round = round
self.reset()
def reset(self):
self.val = None # most recent update
self.sum = 0 # sum from all updates
self.count = 0 # total n from all updates
def update(self, val, n=1):
if val is not None:
self.val = val
if n > 0:
self.sum = type_as(self.sum, val) + (val * n)
self.count = type_as(self.count, n) + n
def state_dict(self):
return {
'val': self.val,
'sum': self.sum,
'count': self.count,
'round': self.round,
}
def load_state_dict(self, state_dict):
self.val = state_dict['val']
self.sum = state_dict['sum']
self.count = state_dict['count']
self.round = state_dict.get('round', None)
def avg(self):
return self.sum / self.count if self.count > 0 else self.val
def smoothed_value(self) -> float:
val = self.avg
if self.round is not None and val is not None:
val = safe_round(val, self.round)
return val
class TimeMeter(Meter):
"""Computes the average occurrence of some event per second"""
def __init__(
self,
init: int = 0,
n: int = 0,
round: Optional[int] = None,
):
self.round = round
self.reset(init, n)
def reset(self, init=0, n=0):
self.init = init
self.start = time.perf_counter()
self.n = n
self.i = 0
def update(self, val=1):
self.n = type_as(self.n, val) + val
self.i += 1
def state_dict(self):
return {
'init': self.elapsed_time,
'n': self.n,
'round': self.round,
}
def load_state_dict(self, state_dict):
if 'start' in state_dict:
# backwards compatibility for old state_dicts
self.reset(init=state_dict['init'])
else:
self.reset(init=state_dict['init'], n=state_dict['n'])
self.round = state_dict.get('round', None)
def avg(self):
return self.n / self.elapsed_time
def elapsed_time(self):
return self.init + (time.perf_counter() - self.start)
def smoothed_value(self) -> float:
val = self.avg
if self.round is not None and val is not None:
val = safe_round(val, self.round)
return val
class StopwatchMeter(Meter):
"""Computes the sum/avg duration of some event in seconds"""
def __init__(self, round: Optional[int] = None):
self.round = round
self.sum = 0
self.n = 0
self.start_time = None
def start(self):
self.start_time = time.perf_counter()
def stop(self, n=1, prehook=None):
if self.start_time is not None:
if prehook is not None:
prehook()
delta = time.perf_counter() - self.start_time
self.sum = self.sum + delta
self.n = type_as(self.n, n) + n
def reset(self):
self.sum = 0 # cumulative time during which stopwatch was active
self.n = 0 # total n across all start/stop
self.start()
def state_dict(self):
return {
'sum': self.sum,
'n': self.n,
'round': self.round,
}
def load_state_dict(self, state_dict):
self.sum = state_dict['sum']
self.n = state_dict['n']
self.start_time = None
self.round = state_dict.get('round', None)
def avg(self):
return self.sum / self.n if self.n > 0 else self.sum
def elapsed_time(self):
if self.start_time is None:
return 0.
return time.perf_counter() - self.start_time
def smoothed_value(self) -> float:
val = self.avg if self.sum > 0 else self.elapsed_time
if self.round is not None and val is not None:
val = safe_round(val, self.round)
return val
def format_stat(stat):
if isinstance(stat, Number):
stat = '{:g}'.format(stat)
elif isinstance(stat, AverageMeter):
stat = '{:.3f}'.format(stat.avg)
elif isinstance(stat, TimeMeter):
stat = '{:g}'.format(round(stat.avg))
elif isinstance(stat, StopwatchMeter):
stat = '{:g}'.format(round(stat.sum))
elif torch.is_tensor(stat):
stat = stat.tolist()
return stat | null |
19,104 | import atexit
import json
import logging
import os
import sys
from collections import OrderedDict
from contextlib import contextmanager
from numbers import Number
from typing import Optional
import torch
from .meters import AverageMeter, StopwatchMeter, TimeMeter
def rename_logger(logger, new_name):
old_name = logger.name
if new_name is not None:
logger.name = new_name
yield logger
logger.name = old_name | null |
19,105 | import atexit
import json
import logging
import os
import sys
from collections import OrderedDict
from contextlib import contextmanager
from numbers import Number
from typing import Optional
import torch
from .meters import AverageMeter, StopwatchMeter, TimeMeter
try:
_tensorboard_writers = {}
from tensorboardX import SummaryWriter
except ImportError:
SummaryWriter = None
def _close_writers():
for w in _tensorboard_writers.values():
w.close() | null |
19,106 | from collections import defaultdict, OrderedDict
import contextlib
import time
from typing import Callable, Dict, List, Optional
import uuid
from .meters import *
def get_active_aggregators() -> List[MetersDict]:
return list(_active_aggregators.values())
class AverageMeter(Meter):
"""Computes and stores the average and current value"""
def __init__(self, round: Optional[int] = None):
self.round = round
self.reset()
def reset(self):
self.val = None # most recent update
self.sum = 0 # sum from all updates
self.count = 0 # total n from all updates
def update(self, val, n=1):
if val is not None:
self.val = val
if n > 0:
self.sum = type_as(self.sum, val) + (val * n)
self.count = type_as(self.count, n) + n
def state_dict(self):
return {
'val': self.val,
'sum': self.sum,
'count': self.count,
'round': self.round,
}
def load_state_dict(self, state_dict):
self.val = state_dict['val']
self.sum = state_dict['sum']
self.count = state_dict['count']
self.round = state_dict.get('round', None)
def avg(self):
return self.sum / self.count if self.count > 0 else self.val
def smoothed_value(self) -> float:
val = self.avg
if self.round is not None and val is not None:
val = safe_round(val, self.round)
return val
The provided code snippet includes necessary dependencies for implementing the `log_scalar` function. Write a Python function `def log_scalar( key: str, value: float, weight: float = 1, priority: int = 10, round: Optional[int] = None, )` to solve the following problem:
Log a scalar value. Args: key (str): name of the field to log value (float): value to log weight (float): weight that this value contributes to the average. A weight of 0 will always log the latest value. priority (int): smaller values are logged earlier in the output round (Optional[int]): number of digits to round to when displaying
Here is the function:
def log_scalar(
key: str,
value: float,
weight: float = 1,
priority: int = 10,
round: Optional[int] = None,
):
"""Log a scalar value.
Args:
key (str): name of the field to log
value (float): value to log
weight (float): weight that this value contributes to the average.
A weight of 0 will always log the latest value.
priority (int): smaller values are logged earlier in the output
round (Optional[int]): number of digits to round to when displaying
"""
for agg in get_active_aggregators():
if key not in agg:
agg.add_meter(key, AverageMeter(round=round), priority)
agg[key].update(value, weight) | Log a scalar value. Args: key (str): name of the field to log value (float): value to log weight (float): weight that this value contributes to the average. A weight of 0 will always log the latest value. priority (int): smaller values are logged earlier in the output round (Optional[int]): number of digits to round to when displaying |
19,107 | from collections import defaultdict, OrderedDict
import contextlib
import time
from typing import Callable, Dict, List, Optional
import uuid
from .meters import *
def get_active_aggregators() -> List[MetersDict]:
return list(_active_aggregators.values())
class MetersDict(OrderedDict):
"""A sorted dictionary of :class:`Meters`.
Meters are sorted according to a priority that is given when the
meter is first added to the dictionary.
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.priorities = []
def __setitem__(self, key, value):
assert key not in self, "MetersDict doesn't support reassignment"
priority, value = value
bisect.insort(self.priorities, (priority, len(self.priorities), key))
super().__setitem__(key, value)
for _, _, key in self.priorities: # reorder dict to match priorities
self.move_to_end(key)
def add_meter(self, key, meter, priority):
self.__setitem__(key, (priority, meter))
def state_dict(self):
return [
(pri, key, self[key].__class__.__name__, self[key].state_dict())
for pri, _, key in self.priorities
# can't serialize DerivedMeter instances
if not isinstance(self[key], MetersDict._DerivedMeter)
]
def load_state_dict(self, state_dict):
self.clear()
self.priorities.clear()
for pri, key, meter_cls, meter_state in state_dict:
meter = globals()[meter_cls]()
meter.load_state_dict(meter_state)
self.add_meter(key, meter, pri)
def get_smoothed_value(self, key: str) -> float:
"""Get a single smoothed value."""
meter = self[key]
if isinstance(meter, MetersDict._DerivedMeter):
return meter.fn(self)
else:
return meter.smoothed_value
def get_smoothed_values(self) -> Dict[str, float]:
"""Get all smoothed values."""
return OrderedDict([
(key, self.get_smoothed_value(key))
for key in self.keys()
if not key.startswith("_")
])
def reset(self):
"""Reset Meter instances."""
for meter in self.values():
if isinstance(meter, MetersDict._DerivedMeter):
continue
meter.reset()
class _DerivedMeter(Meter):
"""A Meter whose values are derived from other Meters."""
def __init__(self, fn):
self.fn = fn
def reset(self):
pass
The provided code snippet includes necessary dependencies for implementing the `log_derived` function. Write a Python function `def log_derived(key: str, fn: Callable[[MetersDict], float], priority: int = 20)` to solve the following problem:
Log a scalar value derived from other meters. Args: key (str): name of the field to log fn (Callable[[MetersDict], float]): function that takes a single argument *meters* and returns the derived value priority (int): smaller values are logged earlier in the output
Here is the function:
def log_derived(key: str, fn: Callable[[MetersDict], float], priority: int = 20):
"""Log a scalar value derived from other meters.
Args:
key (str): name of the field to log
fn (Callable[[MetersDict], float]): function that takes a single
argument *meters* and returns the derived value
priority (int): smaller values are logged earlier in the output
"""
for agg in get_active_aggregators():
if key not in agg:
agg.add_meter(key, MetersDict._DerivedMeter(fn), priority) | Log a scalar value derived from other meters. Args: key (str): name of the field to log fn (Callable[[MetersDict], float]): function that takes a single argument *meters* and returns the derived value priority (int): smaller values are logged earlier in the output |
19,108 | from collections import defaultdict, OrderedDict
import contextlib
import time
from typing import Callable, Dict, List, Optional
import uuid
from .meters import *
def reset() -> None:
"""Reset all metrics aggregators."""
_aggregators.clear()
_active_aggregators.clear()
_active_aggregators_cnt.clear()
# The "default" aggregator observes all logged values.
_aggregators["default"] = MetersDict()
_active_aggregators["default"] = _aggregators["default"]
_active_aggregators_cnt["default"] = 1
reset()
def get_active_aggregators() -> List[MetersDict]:
return list(_active_aggregators.values())
class TimeMeter(Meter):
"""Computes the average occurrence of some event per second"""
def __init__(
self,
init: int = 0,
n: int = 0,
round: Optional[int] = None,
):
self.round = round
self.reset(init, n)
def reset(self, init=0, n=0):
self.init = init
self.start = time.perf_counter()
self.n = n
self.i = 0
def update(self, val=1):
self.n = type_as(self.n, val) + val
self.i += 1
def state_dict(self):
return {
'init': self.elapsed_time,
'n': self.n,
'round': self.round,
}
def load_state_dict(self, state_dict):
if 'start' in state_dict:
# backwards compatibility for old state_dicts
self.reset(init=state_dict['init'])
else:
self.reset(init=state_dict['init'], n=state_dict['n'])
self.round = state_dict.get('round', None)
def avg(self):
return self.n / self.elapsed_time
def elapsed_time(self):
return self.init + (time.perf_counter() - self.start)
def smoothed_value(self) -> float:
val = self.avg
if self.round is not None and val is not None:
val = safe_round(val, self.round)
return val
The provided code snippet includes necessary dependencies for implementing the `log_speed` function. Write a Python function `def log_speed( key: str, value: float, priority: int = 30, round: Optional[int] = None, )` to solve the following problem:
Log the rate of some quantity per second. Args: key (str): name of the field to log value (float): value to log priority (int): smaller values are logged earlier in the output round (Optional[int]): number of digits to round to when displaying
Here is the function:
def log_speed(
key: str,
value: float,
priority: int = 30,
round: Optional[int] = None,
):
"""Log the rate of some quantity per second.
Args:
key (str): name of the field to log
value (float): value to log
priority (int): smaller values are logged earlier in the output
round (Optional[int]): number of digits to round to when displaying
"""
for agg in get_active_aggregators():
if key not in agg:
agg.add_meter(key, TimeMeter(round=round), priority)
agg[key].reset() # reset meter on the first call
else:
agg[key].update(value) | Log the rate of some quantity per second. Args: key (str): name of the field to log value (float): value to log priority (int): smaller values are logged earlier in the output round (Optional[int]): number of digits to round to when displaying |
19,109 | from collections import defaultdict, OrderedDict
import contextlib
import time
from typing import Callable, Dict, List, Optional
import uuid
from .meters import *
def get_active_aggregators() -> List[MetersDict]:
return list(_active_aggregators.values())
class StopwatchMeter(Meter):
"""Computes the sum/avg duration of some event in seconds"""
def __init__(self, round: Optional[int] = None):
self.round = round
self.sum = 0
self.n = 0
self.start_time = None
def start(self):
self.start_time = time.perf_counter()
def stop(self, n=1, prehook=None):
if self.start_time is not None:
if prehook is not None:
prehook()
delta = time.perf_counter() - self.start_time
self.sum = self.sum + delta
self.n = type_as(self.n, n) + n
def reset(self):
self.sum = 0 # cumulative time during which stopwatch was active
self.n = 0 # total n across all start/stop
self.start()
def state_dict(self):
return {
'sum': self.sum,
'n': self.n,
'round': self.round,
}
def load_state_dict(self, state_dict):
self.sum = state_dict['sum']
self.n = state_dict['n']
self.start_time = None
self.round = state_dict.get('round', None)
def avg(self):
return self.sum / self.n if self.n > 0 else self.sum
def elapsed_time(self):
if self.start_time is None:
return 0.
return time.perf_counter() - self.start_time
def smoothed_value(self) -> float:
val = self.avg if self.sum > 0 else self.elapsed_time
if self.round is not None and val is not None:
val = safe_round(val, self.round)
return val
The provided code snippet includes necessary dependencies for implementing the `log_start_time` function. Write a Python function `def log_start_time(key: str, priority: int = 40, round: Optional[int] = None)` to solve the following problem:
Log the duration of some event in seconds. The duration will be computed once :func:`log_stop_time` is called. Args: key (str): name of the field to log priority (int): smaller values are logged earlier in the output round (Optional[int]): number of digits to round to when displaying
Here is the function:
def log_start_time(key: str, priority: int = 40, round: Optional[int] = None):
"""Log the duration of some event in seconds.
The duration will be computed once :func:`log_stop_time` is called.
Args:
key (str): name of the field to log
priority (int): smaller values are logged earlier in the output
round (Optional[int]): number of digits to round to when displaying
"""
for agg in get_active_aggregators():
if key not in agg:
agg.add_meter(key, StopwatchMeter(round=round), priority)
agg[key].start() | Log the duration of some event in seconds. The duration will be computed once :func:`log_stop_time` is called. Args: key (str): name of the field to log priority (int): smaller values are logged earlier in the output round (Optional[int]): number of digits to round to when displaying |
19,110 | from collections import defaultdict, OrderedDict
import contextlib
import time
from typing import Callable, Dict, List, Optional
import uuid
from .meters import *
def get_active_aggregators() -> List[MetersDict]:
return list(_active_aggregators.values())
The provided code snippet includes necessary dependencies for implementing the `log_stop_time` function. Write a Python function `def log_stop_time(key: str, weight: float = 0., prehook=None)` to solve the following problem:
Log the duration of some event in seconds. The duration will be computed since :func:`log_start_time` was called. Set weight > 0 to report the average time instead of the sum. Args: key (str): name of the field to log weight (float): weight that this time contributes to the average prehook (function, no arguments): will be called before the timer is stopped. For example, use prehook=torch.cuda.synchronize to make sure all gpu operations are done before timer is stopped.
Here is the function:
def log_stop_time(key: str, weight: float = 0., prehook=None):
"""Log the duration of some event in seconds.
The duration will be computed since :func:`log_start_time` was called.
Set weight > 0 to report the average time instead of the sum.
Args:
key (str): name of the field to log
weight (float): weight that this time contributes to the average
prehook (function, no arguments): will be called before the timer
is stopped. For example, use prehook=torch.cuda.synchronize to
make sure all gpu operations are done before timer is stopped.
"""
for agg in get_active_aggregators():
if key in agg:
agg[key].stop(weight, prehook) | Log the duration of some event in seconds. The duration will be computed since :func:`log_start_time` was called. Set weight > 0 to report the average time instead of the sum. Args: key (str): name of the field to log weight (float): weight that this time contributes to the average prehook (function, no arguments): will be called before the timer is stopped. For example, use prehook=torch.cuda.synchronize to make sure all gpu operations are done before timer is stopped. |
19,111 | from collections import defaultdict, OrderedDict
import contextlib
import time
from typing import Callable, Dict, List, Optional
import uuid
from .meters import *
def get_active_aggregators() -> List[MetersDict]:
return list(_active_aggregators.values())
class Meter(object):
"""Base class for Meters."""
def __init__(self):
pass
def state_dict(self):
return {}
def load_state_dict(self, state_dict):
pass
def reset(self):
raise NotImplementedError
def smoothed_value(self) -> float:
"""Smoothed value used for logging."""
raise NotImplementedError
The provided code snippet includes necessary dependencies for implementing the `log_custom` function. Write a Python function `def log_custom( new_meter_fn: Callable[[], Meter], key: str, *args, priority: int = 50, **kwargs, )` to solve the following problem:
Log using a custom Meter. Any extra *args* or *kwargs* will be passed through to the Meter's *update* method. Args: new_meter_fn (Callable[[], Meter]): function that returns a new Meter instance key (str): name of the field to log priority (int): smaller values are logged earlier in the output
Here is the function:
def log_custom(
new_meter_fn: Callable[[], Meter],
key: str,
*args,
priority: int = 50,
**kwargs,
):
"""Log using a custom Meter.
Any extra *args* or *kwargs* will be passed through to the Meter's
*update* method.
Args:
new_meter_fn (Callable[[], Meter]): function that returns a new
Meter instance
key (str): name of the field to log
priority (int): smaller values are logged earlier in the output
"""
for agg in get_active_aggregators():
if key not in agg:
agg.add_meter(key, new_meter_fn(), priority)
agg[key].update(*args, **kwargs) | Log using a custom Meter. Any extra *args* or *kwargs* will be passed through to the Meter's *update* method. Args: new_meter_fn (Callable[[], Meter]): function that returns a new Meter instance key (str): name of the field to log priority (int): smaller values are logged earlier in the output |
19,112 | from collections import defaultdict, OrderedDict
import contextlib
import time
from typing import Callable, Dict, List, Optional
import uuid
from .meters import *
def reset() -> None:
"""Reset all metrics aggregators."""
_aggregators.clear()
_active_aggregators.clear()
_active_aggregators_cnt.clear()
# The "default" aggregator observes all logged values.
_aggregators["default"] = MetersDict()
_active_aggregators["default"] = _aggregators["default"]
_active_aggregators_cnt["default"] = 1
reset()
def get_meter(name: str, key: str) -> Meter:
"""Get a single Meter instance aggregated under *name* and *key*.
Returns:
Meter or None if no metrics have been logged under *name* and *key*.
"""
if name not in _aggregators:
return None
return _aggregators[name].get(key, None)
The provided code snippet includes necessary dependencies for implementing the `reset_meter` function. Write a Python function `def reset_meter(name: str, key: str) -> None` to solve the following problem:
Reset Meter instance aggregated under a given *name* and *key*.
Here is the function:
def reset_meter(name: str, key: str) -> None:
"""Reset Meter instance aggregated under a given *name* and *key*."""
meter = get_meter(name, key)
if meter is not None:
meter.reset() | Reset Meter instance aggregated under a given *name* and *key*. |
19,113 | from collections import defaultdict, OrderedDict
import contextlib
import time
from typing import Callable, Dict, List, Optional
import uuid
from .meters import *
_aggregators = OrderedDict()
The provided code snippet includes necessary dependencies for implementing the `get_smoothed_value` function. Write a Python function `def get_smoothed_value(name: str, key: str) -> float` to solve the following problem:
Get a single smoothed value. Raises: KeyError: if no metrics have been logged under *name* and *key*.
Here is the function:
def get_smoothed_value(name: str, key: str) -> float:
"""Get a single smoothed value.
Raises:
KeyError: if no metrics have been logged under *name* and *key*.
"""
return _aggregators[name].get_smoothed_value(key) | Get a single smoothed value. Raises: KeyError: if no metrics have been logged under *name* and *key*. |
19,114 | from collections import defaultdict, OrderedDict
import contextlib
import time
from typing import Callable, Dict, List, Optional
import uuid
from .meters import *
_aggregators = OrderedDict()
def state_dict():
return OrderedDict([
(name, agg.state_dict())
for name, agg in _aggregators.items()
]) | null |
19,115 | from collections import defaultdict, OrderedDict
import contextlib
import time
from typing import Callable, Dict, List, Optional
import uuid
from .meters import *
_aggregators = OrderedDict()
class MetersDict(OrderedDict):
"""A sorted dictionary of :class:`Meters`.
Meters are sorted according to a priority that is given when the
meter is first added to the dictionary.
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.priorities = []
def __setitem__(self, key, value):
assert key not in self, "MetersDict doesn't support reassignment"
priority, value = value
bisect.insort(self.priorities, (priority, len(self.priorities), key))
super().__setitem__(key, value)
for _, _, key in self.priorities: # reorder dict to match priorities
self.move_to_end(key)
def add_meter(self, key, meter, priority):
self.__setitem__(key, (priority, meter))
def state_dict(self):
return [
(pri, key, self[key].__class__.__name__, self[key].state_dict())
for pri, _, key in self.priorities
# can't serialize DerivedMeter instances
if not isinstance(self[key], MetersDict._DerivedMeter)
]
def load_state_dict(self, state_dict):
self.clear()
self.priorities.clear()
for pri, key, meter_cls, meter_state in state_dict:
meter = globals()[meter_cls]()
meter.load_state_dict(meter_state)
self.add_meter(key, meter, pri)
def get_smoothed_value(self, key: str) -> float:
"""Get a single smoothed value."""
meter = self[key]
if isinstance(meter, MetersDict._DerivedMeter):
return meter.fn(self)
else:
return meter.smoothed_value
def get_smoothed_values(self) -> Dict[str, float]:
"""Get all smoothed values."""
return OrderedDict([
(key, self.get_smoothed_value(key))
for key in self.keys()
if not key.startswith("_")
])
def reset(self):
"""Reset Meter instances."""
for meter in self.values():
if isinstance(meter, MetersDict._DerivedMeter):
continue
meter.reset()
class _DerivedMeter(Meter):
"""A Meter whose values are derived from other Meters."""
def __init__(self, fn):
self.fn = fn
def reset(self):
pass
def load_state_dict(state_dict):
for name, agg_state in state_dict.items():
_aggregators[name] = MetersDict()
_aggregators[name].load_state_dict(agg_state) | null |
19,116 | import contextlib
from itertools import chain
import logging
import sys
from typing import Any, Dict, List
import torch
from fairseq import checkpoint_utils, distributed_utils, models, optim, utils
from fairseq.file_io import PathManager
from fairseq.logging import meters, metrics
from fairseq.nan_detector import NanDetector
from fairseq.optim import lr_scheduler
def _catalog_shared_params(module, memo=None, prefix=''):
if memo is None:
first_call = True
memo = {}
else:
first_call = False
for name, param in module._parameters.items():
param_prefix = prefix + ('.' if prefix else '') + name
if param not in memo:
memo[param] = []
memo[param].append(param_prefix)
for name, m in module._modules.items():
if m is None:
continue
submodule_prefix = prefix + ('.' if prefix else '') + name
_catalog_shared_params(m, memo, submodule_prefix)
if first_call:
return [x for x in memo.values() if len(x) > 1] | null |
19,117 | import contextlib
from itertools import chain
import logging
import sys
from typing import Any, Dict, List
import torch
from fairseq import checkpoint_utils, distributed_utils, models, optim, utils
from fairseq.file_io import PathManager
from fairseq.logging import meters, metrics
from fairseq.nan_detector import NanDetector
from fairseq.optim import lr_scheduler
def _get_module_by_path(module, path):
path = path.split('.')
for name in path:
module = getattr(module, name)
return module | null |
19,118 | import contextlib
from itertools import chain
import logging
import sys
from typing import Any, Dict, List
import torch
from fairseq import checkpoint_utils, distributed_utils, models, optim, utils
from fairseq.file_io import PathManager
from fairseq.logging import meters, metrics
from fairseq.nan_detector import NanDetector
from fairseq.optim import lr_scheduler
def _set_module_by_path(module, path, value):
path = path.split('.')
for name in path[:-1]:
module = getattr(module, name)
setattr(module, path[-1], value) | null |
19,119 | import fnmatch
from functools import wraps, partial
from hashlib import sha256
from io import open
import json
import logging
import os
import shutil
import tarfile
import tempfile
The provided code snippet includes necessary dependencies for implementing the `filename_to_url` function. Write a Python function `def filename_to_url(filename, cache_dir=None)` to solve the following problem:
Return the url and etag (which may be ``None``) stored for `filename`. Raise ``EnvironmentError`` if `filename` or its stored metadata do not exist.
Here is the function:
def filename_to_url(filename, cache_dir=None):
"""
Return the url and etag (which may be ``None``) stored for `filename`.
Raise ``EnvironmentError`` if `filename` or its stored metadata do not exist.
"""
if cache_dir is None:
cache_dir = PYTORCH_FAIRSEQ_CACHE
if isinstance(cache_dir, Path):
cache_dir = str(cache_dir)
cache_path = os.path.join(cache_dir, filename)
if not os.path.exists(cache_path):
raise EnvironmentError("file {} not found".format(cache_path))
meta_path = cache_path + '.json'
if not os.path.exists(meta_path):
raise EnvironmentError("file {} not found".format(meta_path))
with open(meta_path, encoding="utf-8") as meta_file:
metadata = json.load(meta_file)
url = metadata['url']
etag = metadata['etag']
return url, etag | Return the url and etag (which may be ``None``) stored for `filename`. Raise ``EnvironmentError`` if `filename` or its stored metadata do not exist. |
19,120 | import fnmatch
from functools import wraps, partial
from hashlib import sha256
from io import open
import json
import logging
import os
import shutil
import tarfile
import tempfile
The provided code snippet includes necessary dependencies for implementing the `s3_request` function. Write a Python function `def s3_request(func)` to solve the following problem:
Wrapper function for s3 requests in order to create more helpful error messages.
Here is the function:
def s3_request(func):
"""
Wrapper function for s3 requests in order to create more helpful error
messages.
"""
@wraps(func)
def wrapper(url, *args, **kwargs):
from botocore.exceptions import ClientError
try:
return func(url, *args, **kwargs)
except ClientError as exc:
if int(exc.response["Error"]["Code"]) == 404:
raise EnvironmentError("file {} not found".format(url))
else:
raise
return wrapper | Wrapper function for s3 requests in order to create more helpful error messages. |
19,121 | import fnmatch
from functools import wraps, partial
from hashlib import sha256
from io import open
import json
import logging
import os
import shutil
import tarfile
import tempfile
The provided code snippet includes necessary dependencies for implementing the `read_set_from_file` function. Write a Python function `def read_set_from_file(filename)` to solve the following problem:
Extract a de-duped collection (set) of text from a file. Expected file format is one item per line.
Here is the function:
def read_set_from_file(filename):
'''
Extract a de-duped collection (set) of text from a file.
Expected file format is one item per line.
'''
collection = set()
with open(filename, 'r', encoding='utf-8') as file_:
for line in file_:
collection.add(line.rstrip())
return collection | Extract a de-duped collection (set) of text from a file. Expected file format is one item per line. |
19,122 | import fnmatch
from functools import wraps, partial
from hashlib import sha256
from io import open
import json
import logging
import os
import shutil
import tarfile
import tempfile
def get_file_extension(path, dot=True, lower=True):
ext = os.path.splitext(path)[1]
ext = ext if dot else ext[1:]
return ext.lower() if lower else ext | null |
19,123 | import logging
import os
import pickle
import random
import socket
import struct
import subprocess
import warnings
from collections import OrderedDict
from typing import Any, Dict, Mapping
import torch
import torch.distributed as dist
from fairseq import utils
def get_rank():
return dist.get_rank()
def get_world_size():
return dist.get_world_size()
def all_reduce(tensor, group=None):
if isinstance(group, tuple) and group[0] == 'tpu':
import torch_xla.core.xla_model as xm
return xm.all_reduce('sum', [tensor], groups=group[1])
else:
if group is None:
group = get_default_group()
return dist.all_reduce(tensor, group=group)
The provided code snippet includes necessary dependencies for implementing the `all_gather_list` function. Write a Python function `def all_gather_list(data, group=None, max_size=16384)` to solve the following problem:
Gathers arbitrary data from all nodes into a list. Similar to :func:`~torch.distributed.all_gather` but for arbitrary Python data. Note that *data* must be picklable. Args: data (Any): data from the local worker to be gathered on other workers group (optional): group of the collective max_size (int, optional): maximum size of the data to be gathered across workers
Here is the function:
def all_gather_list(data, group=None, max_size=16384):
"""Gathers arbitrary data from all nodes into a list.
Similar to :func:`~torch.distributed.all_gather` but for arbitrary Python
data. Note that *data* must be picklable.
Args:
data (Any): data from the local worker to be gathered on other workers
group (optional): group of the collective
max_size (int, optional): maximum size of the data to be gathered
across workers
"""
rank = get_rank()
world_size = get_world_size()
buffer_size = max_size * world_size
if not hasattr(all_gather_list, '_buffer') or \
all_gather_list._buffer.numel() < buffer_size:
all_gather_list._buffer = torch.cuda.ByteTensor(buffer_size)
all_gather_list._cpu_buffer = torch.ByteTensor(max_size).pin_memory()
buffer = all_gather_list._buffer
buffer.zero_()
cpu_buffer = all_gather_list._cpu_buffer
data = utils.move_to_cpu(data)
enc = pickle.dumps(data)
enc_size = len(enc)
header_size = 4 # size of header that contains the length of the encoded data
size = header_size + enc_size
if size > max_size:
raise ValueError('encoded data size ({}) exceeds max_size ({})'.format(size, max_size))
header = struct.pack(">I", enc_size)
cpu_buffer[:size] = torch.ByteTensor(list(header + enc))
start = rank * max_size
buffer[start:start + size].copy_(cpu_buffer[:size])
all_reduce(buffer, group=group)
buffer = buffer.cpu()
try:
result = []
for i in range(world_size):
out_buffer = buffer[i * max_size:(i + 1) * max_size]
enc_size, = struct.unpack(">I", bytes(out_buffer[:header_size].tolist()))
if enc_size > 0:
result.append(pickle.loads(bytes(out_buffer[header_size:header_size + enc_size].tolist())))
return result
except pickle.UnpicklingError:
raise Exception(
'Unable to unpickle data from other workers. all_gather_list requires all '
'workers to enter the function together, so this error usually indicates '
'that the workers have fallen out of sync somehow. Workers can fall out of '
'sync if one of them runs out of memory, or if there are other conditions '
'in your training script that can cause one worker to finish an epoch '
'while other workers are still iterating over their portions of the data. '
'Try rerunning with --ddp-backend=no_c10d and see if that helps.'
) | Gathers arbitrary data from all nodes into a list. Similar to :func:`~torch.distributed.all_gather` but for arbitrary Python data. Note that *data* must be picklable. Args: data (Any): data from the local worker to be gathered on other workers group (optional): group of the collective max_size (int, optional): maximum size of the data to be gathered across workers |
19,124 | import logging
import os
import pickle
import random
import socket
import struct
import subprocess
import warnings
from collections import OrderedDict
from typing import Any, Dict, Mapping
import torch
import torch.distributed as dist
from fairseq import utils
def all_reduce(tensor, group=None):
if isinstance(group, tuple) and group[0] == 'tpu':
import torch_xla.core.xla_model as xm
return xm.all_reduce('sum', [tensor], groups=group[1])
else:
if group is None:
group = get_default_group()
return dist.all_reduce(tensor, group=group)
The provided code snippet includes necessary dependencies for implementing the `all_reduce_dict` function. Write a Python function `def all_reduce_dict( data: Mapping[str, Any], device, group=None, ) -> Dict[str, Any]` to solve the following problem:
AllReduce a dictionary of values across workers. We separately reduce items that are already on the device and items on CPU for better performance. Args: data (Mapping[str, Any]): dictionary of data to all-reduce, but cannot be a nested dictionary device (torch.device): device for the reduction group (optional): group of the collective
Here is the function:
def all_reduce_dict(
data: Mapping[str, Any],
device,
group=None,
) -> Dict[str, Any]:
"""
AllReduce a dictionary of values across workers. We separately
reduce items that are already on the device and items on CPU for
better performance.
Args:
data (Mapping[str, Any]): dictionary of data to all-reduce, but
cannot be a nested dictionary
device (torch.device): device for the reduction
group (optional): group of the collective
"""
data_keys = list(data.keys())
# We want to separately reduce items that are already on the
# device and items on CPU for performance reasons.
cpu_data = OrderedDict()
device_data = OrderedDict()
for k in data_keys:
t = data[k]
if not torch.is_tensor(t):
cpu_data[k] = torch.tensor(t, dtype=torch.double)
elif t.device.type != device.type:
cpu_data[k] = t.to(dtype=torch.double)
else:
device_data[k] = t.to(dtype=torch.double)
def _all_reduce_dict(data: OrderedDict):
if len(data) == 0:
return data
buf = torch.stack(list(data.values())).to(device=device)
all_reduce(buf, group=group)
return {k: buf[i] for i, k in enumerate(data)}
cpu_data = _all_reduce_dict(cpu_data)
device_data = _all_reduce_dict(device_data)
def get_from_stack(key):
if key in cpu_data:
return cpu_data[key]
elif key in device_data:
return device_data[key]
raise KeyError
return OrderedDict([(key, get_from_stack(key)) for key in data_keys]) | AllReduce a dictionary of values across workers. We separately reduce items that are already on the device and items on CPU for better performance. Args: data (Mapping[str, Any]): dictionary of data to all-reduce, but cannot be a nested dictionary device (torch.device): device for the reduction group (optional): group of the collective |
19,125 | import numpy as np
from fairseq.data import data_utils
from . import BaseWrapperDataset
class TruncateDataset(BaseWrapperDataset):
"""Truncate a sequence by returning the first truncation_length tokens
"""
def __init__(self, dataset, truncation_length):
super().__init__(dataset)
assert truncation_length is not None
self.truncation_length = truncation_length
self.dataset = dataset
def __getitem__(self, index):
item = self.dataset[index]
item_len = item.size(0)
if item_len > self.truncation_length:
item = item[:self.truncation_length]
return item
def sizes(self):
return np.minimum(self.dataset.sizes, self.truncation_length)
def __len__(self):
return len(self.dataset)
class RandomCropDataset(TruncateDataset):
"""Truncate a sequence by returning a random crop of truncation_length tokens
"""
def __init__(self, dataset, truncation_length, seed=1):
super().__init__(dataset, truncation_length)
self.seed = seed
self.epoch = 0
def set_epoch(self, epoch, **unused):
super().set_epoch(epoch)
self.epoch = epoch
def __getitem__(self, index):
with data_utils.numpy_seed(self.seed, self.epoch, index):
item = self.dataset[index]
item_len = item.size(0)
excess = item_len - self.truncation_length
if excess > 0:
start_idx = np.random.randint(0, excess)
item = item[start_idx:start_idx+self.truncation_length]
return item
def maybe_shorten_dataset(dataset, split, shorten_data_split_whitelist, shorten_method, tokens_per_sample, seed):
truncate_split = split in shorten_data_split_whitelist.split(',') \
or len(shorten_data_split_whitelist) == 0
if shorten_method == 'truncate' and truncate_split:
dataset = TruncateDataset(dataset, tokens_per_sample)
elif shorten_method == 'random_crop' and truncate_split:
dataset = RandomCropDataset(dataset, tokens_per_sample, seed)
return dataset | null |
19,126 | import numpy as np
import torch
import math
from . import data_utils, FairseqDataset
def collate(
samples,
pad_idx,
eos_idx,
vocab,
left_pad_source=False,
left_pad_target=False,
input_feeding=True,
):
assert input_feeding
if len(samples) == 0:
return {}
def merge(key, left_pad, move_eos_to_beginning=False):
return data_utils.collate_tokens(
[s[key] for s in samples],
pad_idx, eos_idx, left_pad, move_eos_to_beginning,
)
id = torch.LongTensor([s['id'] for s in samples])
src_tokens = merge('source', left_pad=left_pad_source)
# sort by descending source length
src_lengths = torch.LongTensor([s['source'].numel() for s in samples])
src_lengths, sort_order = src_lengths.sort(descending=True)
id = id.index_select(0, sort_order)
src_tokens = src_tokens.index_select(0, sort_order)
prev_output_tokens = None
target = None
if samples[0].get('target', None) is not None:
target = merge('target', left_pad=left_pad_target)
target = target.index_select(0, sort_order)
ntokens = sum(len(s['target']) for s in samples)
if input_feeding:
# we create a shifted version of targets for feeding the
# previous output token(s) into the next decoder step
prev_output_tokens = merge(
'target',
left_pad=left_pad_target,
move_eos_to_beginning=True,
)
prev_output_tokens = prev_output_tokens.index_select(0, sort_order)
else:
ntokens = sum(len(s['source']) for s in samples)
batch = {
'id': id,
'ntokens': ntokens,
'net_input': {
'src_tokens': src_tokens,
'src_lengths': src_lengths,
},
'target': target,
'nsentences': samples[0]['source'].size(0),
}
if prev_output_tokens is not None:
batch['net_input']['prev_output_tokens'] = prev_output_tokens
return batch | null |
19,127 | from collections import OrderedDict
from typing import Callable, Dict, List
import numpy as np
from . import FairseqDataset
def uniform_sampler(x):
# Sample from uniform distribution
return np.random.choice(x, 1).item() | null |
19,128 | import contextlib
import itertools
import logging
import os
import sys
import types
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `infer_language_pair` function. Write a Python function `def infer_language_pair(path)` to solve the following problem:
Infer language pair from filename: <split>.<lang1>-<lang2>.(...).idx
Here is the function:
def infer_language_pair(path):
"""Infer language pair from filename: <split>.<lang1>-<lang2>.(...).idx"""
src, dst = None, None
for filename in os.listdir(path):
parts = filename.split('.')
if len(parts) >= 3 and len(parts[1].split('-')) == 2:
return parts[1].split('-')
return src, dst | Infer language pair from filename: <split>.<lang1>-<lang2>.(...).idx |
19,129 | import contextlib
import itertools
import logging
import os
import sys
import types
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `numpy_seed` function. Write a Python function `def numpy_seed(seed, *addl_seeds)` to solve the following problem:
Context manager which seeds the NumPy PRNG with the specified seed and restores the state afterward
Here is the function:
def numpy_seed(seed, *addl_seeds):
"""Context manager which seeds the NumPy PRNG with the specified seed and
restores the state afterward"""
if seed is None:
yield
return
if len(addl_seeds) > 0:
seed = int(hash((seed, *addl_seeds)) % 1e6)
state = np.random.get_state()
np.random.seed(seed)
try:
yield
finally:
np.random.set_state(state) | Context manager which seeds the NumPy PRNG with the specified seed and restores the state afterward |
19,130 | import contextlib
import itertools
import logging
import os
import sys
import types
import numpy as np
logger = logging.getLogger(__name__)
def _filter_by_size_dynamic(indices, size_fn, max_positions, raise_exception=False):
def check_size(idx):
if isinstance(max_positions, float) or isinstance(max_positions, int):
return size_fn(idx) <= max_positions
elif isinstance(max_positions, dict):
idx_size = size_fn(idx)
assert isinstance(idx_size, dict)
intersect_keys = set(max_positions.keys()) & set(idx_size.keys())
return all(
all(a is None or b is None or a <= b
for a, b in zip(idx_size[key], max_positions[key]))
for key in intersect_keys
)
else:
# Hacky as heck, for the specific case of multilingual training with RoundRobin.
if isinstance(size_fn(idx), dict) and isinstance(max_positions, tuple):
return all(
a is None or b is None or a <= b
for a, b in zip(size_fn(idx).values(), max_positions)
)
# For MultiCorpusSampledDataset, will generalize it later
if not isinstance(size_fn(idx), Iterable):
return all(size_fn(idx) <= b for b in max_positions)
return all(
a is None or b is None or a <= b
for a, b in zip(size_fn(idx), max_positions)
)
ignored = []
itr = collect_filtered(check_size, indices, ignored)
indices = np.fromiter(itr, dtype=np.int64, count=-1)
return indices, ignored
The provided code snippet includes necessary dependencies for implementing the `filter_by_size` function. Write a Python function `def filter_by_size(indices, dataset, max_positions, raise_exception=False)` to solve the following problem:
Filter indices based on their size. Args: indices (List[int]): ordered list of dataset indices dataset (FairseqDataset): fairseq dataset instance max_positions (tuple): filter elements larger than this size. Comparisons are done component-wise. raise_exception (bool, optional): if ``True``, raise an exception if any elements are filtered (default: False).
Here is the function:
def filter_by_size(indices, dataset, max_positions, raise_exception=False):
"""
Filter indices based on their size.
Args:
indices (List[int]): ordered list of dataset indices
dataset (FairseqDataset): fairseq dataset instance
max_positions (tuple): filter elements larger than this size.
Comparisons are done component-wise.
raise_exception (bool, optional): if ``True``, raise an exception if
any elements are filtered (default: False).
"""
if isinstance(max_positions, float) or isinstance(max_positions, int):
if hasattr(dataset, 'sizes') and isinstance(dataset.sizes, np.ndarray):
ignored = indices[dataset.sizes[indices] > max_positions].tolist()
indices = indices[dataset.sizes[indices] <= max_positions]
elif hasattr(dataset, 'sizes') and isinstance(dataset.sizes, list) and len(dataset.sizes) == 1:
ignored = indices[dataset.sizes[0][indices] > max_positions].tolist()
indices = indices[dataset.sizes[0][indices] <= max_positions]
else:
indices, ignored = _filter_by_size_dynamic(indices, dataset.size, max_positions)
else:
indices, ignored = _filter_by_size_dynamic(indices, dataset.size, max_positions)
if len(ignored) > 0 and raise_exception:
raise Exception((
'Size of sample #{} is invalid (={}) since max_positions={}, '
'skip this example with --skip-invalid-size-inputs-valid-test'
).format(ignored[0], dataset.size(ignored[0]), max_positions))
if len(ignored) > 0:
logger.warning((
'{} samples have invalid sizes and will be skipped, '
'max_positions={}, first few sample ids={}'
).format(len(ignored), max_positions, ignored[:10]))
return indices | Filter indices based on their size. Args: indices (List[int]): ordered list of dataset indices dataset (FairseqDataset): fairseq dataset instance max_positions (tuple): filter elements larger than this size. Comparisons are done component-wise. raise_exception (bool, optional): if ``True``, raise an exception if any elements are filtered (default: False). |
19,131 | import contextlib
import itertools
import logging
import os
import sys
import types
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `batch_by_size` function. Write a Python function `def batch_by_size( indices, num_tokens_fn, max_tokens=None, max_sentences=None, required_batch_size_multiple=1, )` to solve the following problem:
Yield mini-batches of indices bucketed by size. Batches may contain sequences of different lengths. Args: indices (List[int]): ordered list of dataset indices num_tokens_fn (callable): function that returns the number of tokens at a given index max_tokens (int, optional): max number of tokens in each batch (default: None). max_sentences (int, optional): max number of sentences in each batch (default: None). required_batch_size_multiple (int, optional): require batch size to be a multiple of N (default: 1).
Here is the function:
def batch_by_size(
indices, num_tokens_fn, max_tokens=None, max_sentences=None,
required_batch_size_multiple=1,
):
"""
Yield mini-batches of indices bucketed by size. Batches may contain
sequences of different lengths.
Args:
indices (List[int]): ordered list of dataset indices
num_tokens_fn (callable): function that returns the number of tokens at
a given index
max_tokens (int, optional): max number of tokens in each batch
(default: None).
max_sentences (int, optional): max number of sentences in each
batch (default: None).
required_batch_size_multiple (int, optional): require batch size to
be a multiple of N (default: 1).
"""
try:
from fairseq.data.data_utils_fast import batch_by_size_fast
except ImportError:
raise ImportError(
'Please build Cython components with: `pip install --editable .` '
'or `python setup.py build_ext --inplace`'
)
max_tokens = max_tokens if max_tokens is not None else -1
max_sentences = max_sentences if max_sentences is not None else -1
bsz_mult = required_batch_size_multiple
if isinstance(indices, types.GeneratorType):
indices = np.fromiter(indices, dtype=np.int64, count=-1)
return batch_by_size_fast(indices, num_tokens_fn, max_tokens, max_sentences, bsz_mult) | Yield mini-batches of indices bucketed by size. Batches may contain sequences of different lengths. Args: indices (List[int]): ordered list of dataset indices num_tokens_fn (callable): function that returns the number of tokens at a given index max_tokens (int, optional): max number of tokens in each batch (default: None). max_sentences (int, optional): max number of sentences in each batch (default: None). required_batch_size_multiple (int, optional): require batch size to be a multiple of N (default: 1). |
19,132 | import contextlib
import itertools
import logging
import os
import sys
import types
import numpy as np
def process_bpe_symbol(sentence: str, bpe_symbol: str):
if bpe_symbol == 'sentencepiece':
sentence = sentence.replace(' ', '').replace('\u2581', ' ').strip()
elif bpe_symbol == '_EOW':
sentence = sentence.replace(' ', '').replace('_EOW', ' ').strip()
elif bpe_symbol is not None:
sentence = (sentence + ' ').replace(bpe_symbol, '').rstrip()
return sentence | null |
19,133 | import itertools
import math
import operator
import os
import time
import numpy as np
import torch
import queue
import logging
from threading import Thread
from . import data_utils
def _chunk_iterator(itr, chunk_size):
chunk = []
for x in itr:
chunk.append(x)
if len(chunk) == chunk_size:
yield chunk
chunk = []
if len(chunk) > 0:
yield chunk | null |
19,134 | import logging
import numpy as np
import torch
from . import data_utils, FairseqDataset
logger = logging.getLogger(__name__)
def collate(
samples, pad_idx, eos_idx, left_pad_source=True, left_pad_target=False,
input_feeding=True,
):
if len(samples) == 0:
return {}
def merge(key, left_pad, move_eos_to_beginning=False):
return data_utils.collate_tokens(
[s[key] for s in samples],
pad_idx, eos_idx, left_pad, move_eos_to_beginning,
)
def check_alignment(alignment, src_len, tgt_len):
if alignment is None or len(alignment) == 0:
return False
if alignment[:, 0].max().item() >= src_len - 1 or alignment[:, 1].max().item() >= tgt_len - 1:
logger.warning("alignment size mismatch found, skipping alignment!")
return False
return True
def compute_alignment_weights(alignments):
"""
Given a tensor of shape [:, 2] containing the source-target indices
corresponding to the alignments, a weight vector containing the
inverse frequency of each target index is computed.
For e.g. if alignments = [[5, 7], [2, 3], [1, 3], [4, 2]], then
a tensor containing [1., 0.5, 0.5, 1] should be returned (since target
index 3 is repeated twice)
"""
align_tgt = alignments[:, 1]
_, align_tgt_i, align_tgt_c = torch.unique(align_tgt, return_inverse=True, return_counts=True)
align_weights = align_tgt_c[align_tgt_i[np.arange(len(align_tgt))]]
return 1. / align_weights.float()
id = torch.LongTensor([s['id'] for s in samples])
src_tokens = merge('source', left_pad=left_pad_source)
# sort by descending source length
src_lengths = torch.LongTensor([s['source'].numel() for s in samples])
src_lengths, sort_order = src_lengths.sort(descending=True)
id = id.index_select(0, sort_order)
src_tokens = src_tokens.index_select(0, sort_order)
prev_output_tokens = None
target = None
if samples[0].get('target', None) is not None:
target = merge('target', left_pad=left_pad_target)
target = target.index_select(0, sort_order)
tgt_lengths = torch.LongTensor([s['target'].numel() for s in samples]).index_select(0, sort_order)
ntokens = sum(len(s['target']) for s in samples)
if input_feeding:
# we create a shifted version of targets for feeding the
# previous output token(s) into the next decoder step
prev_output_tokens = merge(
'target',
left_pad=left_pad_target,
move_eos_to_beginning=True,
)
prev_output_tokens = prev_output_tokens.index_select(0, sort_order)
else:
ntokens = sum(len(s['source']) for s in samples)
batch = {
'id': id,
'nsentences': len(samples),
'ntokens': ntokens,
'net_input': {
'src_tokens': src_tokens,
'src_lengths': src_lengths,
},
'target': target,
}
if prev_output_tokens is not None:
batch['net_input']['prev_output_tokens'] = prev_output_tokens
if samples[0].get('alignment', None) is not None:
bsz, tgt_sz = batch['target'].shape
src_sz = batch['net_input']['src_tokens'].shape[1]
offsets = torch.zeros((len(sort_order), 2), dtype=torch.long)
offsets[:, 1] += (torch.arange(len(sort_order), dtype=torch.long) * tgt_sz)
if left_pad_source:
offsets[:, 0] += (src_sz - src_lengths)
if left_pad_target:
offsets[:, 1] += (tgt_sz - tgt_lengths)
alignments = [
alignment + offset
for align_idx, offset, src_len, tgt_len in zip(sort_order, offsets, src_lengths, tgt_lengths)
for alignment in [samples[align_idx]['alignment'].view(-1, 2)]
if check_alignment(alignment, src_len, tgt_len)
]
if len(alignments) > 0:
alignments = torch.cat(alignments, dim=0)
align_weights = compute_alignment_weights(alignments)
batch['alignments'] = alignments
batch['align_weights'] = align_weights
return batch | null |
19,135 | from collections import OrderedDict
import torch
from torch.utils.data.dataloader import default_collate
from . import FairseqDataset
The provided code snippet includes necessary dependencies for implementing the `_flatten` function. Write a Python function `def _flatten(dico, prefix=None)` to solve the following problem:
Flatten a nested dictionary.
Here is the function:
def _flatten(dico, prefix=None):
"""Flatten a nested dictionary."""
new_dico = OrderedDict()
if isinstance(dico, dict):
prefix = prefix + '.' if prefix is not None else ''
for k, v in dico.items():
if v is None:
continue
new_dico.update(_flatten(v, prefix + k))
elif isinstance(dico, list):
for i, v in enumerate(dico):
new_dico.update(_flatten(v, prefix + '.[' + str(i) + ']'))
else:
new_dico = OrderedDict({prefix: dico})
return new_dico | Flatten a nested dictionary. |
19,136 | from collections import OrderedDict
import torch
from torch.utils.data.dataloader import default_collate
from . import FairseqDataset
The provided code snippet includes necessary dependencies for implementing the `_unflatten` function. Write a Python function `def _unflatten(dico)` to solve the following problem:
Unflatten a flattened dictionary into a nested dictionary.
Here is the function:
def _unflatten(dico):
"""Unflatten a flattened dictionary into a nested dictionary."""
new_dico = OrderedDict()
for full_k, v in dico.items():
full_k = full_k.split('.')
node = new_dico
for k in full_k[:-1]:
if k.startswith('[') and k.endswith(']'):
k = int(k[1:-1])
if k not in node:
node[k] = OrderedDict()
node = node[k]
node[full_k[-1]] = v
return new_dico | Unflatten a flattened dictionary into a nested dictionary. |
19,137 | import numpy as np
import torch
from . import data_utils, FairseqDataset
def collate(samples, pad_idx, eos_idx):
if len(samples) == 0:
return {}
def merge(key, is_list=False):
if is_list:
res = []
for i in range(len(samples[0][key])):
res.append(data_utils.collate_tokens(
[s[key][i] for s in samples], pad_idx, eos_idx, left_pad=False,
))
return res
else:
return data_utils.collate_tokens(
[s[key] for s in samples], pad_idx, eos_idx, left_pad=False,
)
src_tokens = merge('source')
if samples[0]['target'] is not None:
is_target_list = isinstance(samples[0]['target'], list)
target = merge('target', is_target_list)
else:
target = src_tokens
return {
'id': torch.LongTensor([s['id'] for s in samples]),
'nsentences': len(samples),
'ntokens': sum(len(s['source']) for s in samples),
'net_input': {
'src_tokens': src_tokens,
'src_lengths': torch.LongTensor([
s['source'].numel() for s in samples
]),
},
'target': target,
} | null |
19,138 | import torch
from fairseq.data import encoders
def get_whole_word_mask(args, dictionary):
bpe = encoders.build_bpe(args)
if bpe is not None:
def is_beginning_of_word(i):
if i < dictionary.nspecial:
# special elements are always considered beginnings
return True
tok = dictionary[i]
if tok.startswith('madeupword'):
return True
try:
return bpe.is_beginning_of_word(tok)
except ValueError:
return True
mask_whole_words = torch.ByteTensor(list(
map(is_beginning_of_word, range(len(dictionary)))
))
return mask_whole_words
return None | null |
19,139 | from functools import lru_cache
import json
The provided code snippet includes necessary dependencies for implementing the `bytes_to_unicode` function. Write a Python function `def bytes_to_unicode()` to solve the following problem:
Returns list of utf-8 byte and a corresponding list of unicode strings. The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. This is a signficant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup tables between utf-8 bytes and unicode strings. And avoids mapping to whitespace/control characters the bpe code barfs on.
Here is the function:
def bytes_to_unicode():
"""
Returns list of utf-8 byte and a corresponding list of unicode strings.
The reversible bpe codes work on unicode strings.
This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
This is a signficant percentage of your normal, say, 32K bpe vocab.
To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
And avoids mapping to whitespace/control characters the bpe code barfs on.
"""
bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
cs = bs[:]
n = 0
for b in range(2**8):
if b not in bs:
bs.append(b)
cs.append(2**8+n)
n += 1
cs = [chr(n) for n in cs]
return dict(zip(bs, cs)) | Returns list of utf-8 byte and a corresponding list of unicode strings. The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. This is a signficant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup tables between utf-8 bytes and unicode strings. And avoids mapping to whitespace/control characters the bpe code barfs on. |
19,140 | from functools import lru_cache
import json
The provided code snippet includes necessary dependencies for implementing the `get_pairs` function. Write a Python function `def get_pairs(word)` to solve the following problem:
Return set of symbol pairs in a word. Word is represented as tuple of symbols (symbols being variable-length strings).
Here is the function:
def get_pairs(word):
"""Return set of symbol pairs in a word.
Word is represented as tuple of symbols (symbols being variable-length strings).
"""
pairs = set()
prev_char = word[0]
for char in word[1:]:
pairs.add((prev_char, char))
prev_char = char
return pairs | Return set of symbol pairs in a word. Word is represented as tuple of symbols (symbols being variable-length strings). |
19,141 | from functools import lru_cache
import json
class Encoder:
def __init__(self, encoder, bpe_merges, errors='replace'):
self.encoder = encoder
self.decoder = {v:k for k,v in self.encoder.items()}
self.errors = errors # how to handle errors in decoding
self.byte_encoder = bytes_to_unicode()
self.byte_decoder = {v:k for k, v in self.byte_encoder.items()}
self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
self.cache = {}
try:
import regex as re
self.re = re
except ImportError:
raise ImportError('Please install regex with: pip install regex')
# Should haved added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
self.pat = self.re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")
def bpe(self, token):
if token in self.cache:
return self.cache[token]
word = tuple(token)
pairs = get_pairs(word)
if not pairs:
return token
while True:
bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf')))
if bigram not in self.bpe_ranks:
break
first, second = bigram
new_word = []
i = 0
while i < len(word):
try:
j = word.index(first, i)
new_word.extend(word[i:j])
i = j
except:
new_word.extend(word[i:])
break
if word[i] == first and i < len(word)-1 and word[i+1] == second:
new_word.append(first+second)
i += 2
else:
new_word.append(word[i])
i += 1
new_word = tuple(new_word)
word = new_word
if len(word) == 1:
break
else:
pairs = get_pairs(word)
word = ' '.join(word)
self.cache[token] = word
return word
def encode(self, text):
bpe_tokens = []
for token in self.re.findall(self.pat, text):
token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8'))
bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' '))
return bpe_tokens
def decode(self, tokens):
text = ''.join([self.decoder.get(token, token) for token in tokens])
text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors=self.errors)
return text
def get_encoder(encoder_json_path, vocab_bpe_path):
with open(encoder_json_path, 'r') as f:
encoder = json.load(f)
with open(vocab_bpe_path, 'r', encoding="utf-8") as f:
bpe_data = f.read()
bpe_merges = [tuple(merge_str.split()) for merge_str in bpe_data.split('\n')[1:-1]]
return Encoder(
encoder=encoder,
bpe_merges=bpe_merges,
) | null |
19,142 | import re
def byte_decode(x: str) -> str:
try:
return bytes([BCHAR_TO_BYTE[bc] for bc in x]).decode('utf-8')
except ValueError:
return ''
def smart_byte_decode(x: str) -> str:
output = byte_decode(x)
if output == '':
# DP the best recovery (max valid chars) if it's broken
n_bytes = len(x)
f = [0 for _ in range(n_bytes + 1)]
pt = [0 for _ in range(n_bytes + 1)]
for i in range(1, n_bytes + 1):
f[i], pt[i] = f[i - 1], i - 1
for j in range(1, min(4, i) + 1):
if f[i - j] + 1 > f[i] and len(byte_decode(x[i - j: i])) > 0:
f[i], pt[i] = f[i - j] + 1, i - j
cur_pt = n_bytes
while cur_pt > 0:
if f[cur_pt] == f[pt[cur_pt]] + 1:
output = byte_decode(x[pt[cur_pt]: cur_pt]) + output
cur_pt = pt[cur_pt]
return output | null |
19,143 | import torch
from fairseq import utils
from . import FairseqDataset
The provided code snippet includes necessary dependencies for implementing the `backtranslate_samples` function. Write a Python function `def backtranslate_samples(samples, collate_fn, generate_fn, cuda=True)` to solve the following problem:
Backtranslate a list of samples. Given an input (*samples*) of the form: [{'id': 1, 'source': 'hallo welt'}] this will return: [{'id': 1, 'source': 'hello world', 'target': 'hallo welt'}] Args: samples (List[dict]): samples to backtranslate. Individual samples are expected to have a 'source' key, which will become the 'target' after backtranslation. collate_fn (callable): function to collate samples into a mini-batch generate_fn (callable): function to generate backtranslations cuda (bool): use GPU for generation (default: ``True``) Returns: List[dict]: an updated list of samples with a backtranslated source
Here is the function:
def backtranslate_samples(samples, collate_fn, generate_fn, cuda=True):
"""Backtranslate a list of samples.
Given an input (*samples*) of the form:
[{'id': 1, 'source': 'hallo welt'}]
this will return:
[{'id': 1, 'source': 'hello world', 'target': 'hallo welt'}]
Args:
samples (List[dict]): samples to backtranslate. Individual samples are
expected to have a 'source' key, which will become the 'target'
after backtranslation.
collate_fn (callable): function to collate samples into a mini-batch
generate_fn (callable): function to generate backtranslations
cuda (bool): use GPU for generation (default: ``True``)
Returns:
List[dict]: an updated list of samples with a backtranslated source
"""
collated_samples = collate_fn(samples)
s = utils.move_to_cuda(collated_samples) if cuda else collated_samples
generated_sources = generate_fn(s)
id_to_src = {
sample['id']: sample['source'] for sample in samples
}
# Go through each tgt sentence in batch and its corresponding best
# generated hypothesis and create a backtranslation data pair
# {id: id, source: generated backtranslation, target: original tgt}
return [
{'id': id.item(), 'target': id_to_src[id.item()], 'source': hypos[0]['tokens'].cpu()}
for id, hypos in zip(collated_samples['id'], generated_sources)
] | Backtranslate a list of samples. Given an input (*samples*) of the form: [{'id': 1, 'source': 'hallo welt'}] this will return: [{'id': 1, 'source': 'hello world', 'target': 'hallo welt'}] Args: samples (List[dict]): samples to backtranslate. Individual samples are expected to have a 'source' key, which will become the 'target' after backtranslation. collate_fn (callable): function to collate samples into a mini-batch generate_fn (callable): function to generate backtranslations cuda (bool): use GPU for generation (default: ``True``) Returns: List[dict]: an updated list of samples with a backtranslated source |
19,144 | from functools import lru_cache
import os
import shutil
import struct
import numpy as np
import torch
from . import FairseqDataset
def read_longs(f, n):
a = np.empty(n, dtype=np.int64)
f.readinto(a)
return a | null |
19,145 | from functools import lru_cache
import os
import shutil
import struct
import numpy as np
import torch
from . import FairseqDataset
def write_longs(f, a):
f.write(np.array(a, dtype=np.int64)) | null |
19,146 | from functools import lru_cache
import os
import shutil
import struct
import numpy as np
import torch
from . import FairseqDataset
dtypes = {
1: np.uint8,
2: np.int8,
3: np.int16,
4: np.int32,
5: np.int64,
6: np.float,
7: np.double,
8: np.uint16
}
def code(dtype):
for k in dtypes.keys():
if dtypes[k] == dtype:
return k
raise ValueError(dtype) | null |
19,147 | from functools import lru_cache
import os
import shutil
import struct
import numpy as np
import torch
from . import FairseqDataset
def data_file_path(prefix_path):
return prefix_path + '.bin' | null |
19,148 | from functools import lru_cache
import os
import shutil
import struct
import numpy as np
import torch
from . import FairseqDataset
def _warmup_mmap_file(path):
with open(path, 'rb') as stream:
while stream.read(100 * 1024 * 1024):
pass | null |
19,149 | import argparse
REGISTRIES = {}
def set_defaults(args, cls):
"""Helper to set default arguments based on *add_args*."""
if not hasattr(cls, 'add_args'):
return
parser = argparse.ArgumentParser(argument_default=argparse.SUPPRESS, allow_abbrev=False)
cls.add_args(parser)
# copied from argparse.py:
defaults = argparse.Namespace()
for action in parser._actions:
if action.dest is not argparse.SUPPRESS:
if not hasattr(defaults, action.dest):
if action.default is not argparse.SUPPRESS:
setattr(defaults, action.dest, action.default)
for key, default_value in vars(defaults).items():
if not hasattr(args, key):
setattr(args, key, default_value)
def setup_registry(
registry_name: str,
base_class=None,
default=None,
):
assert registry_name.startswith('--')
registry_name = registry_name[2:].replace('-', '_')
REGISTRY = {}
REGISTRY_CLASS_NAMES = set()
# maintain a registry of all registries
if registry_name in REGISTRIES:
return # registry already exists
REGISTRIES[registry_name] = {
'registry': REGISTRY,
'default': default,
}
def build_x(args, *extra_args, **extra_kwargs):
choice = getattr(args, registry_name, None)
if choice is None:
return None
cls = REGISTRY[choice]
if hasattr(cls, 'build_' + registry_name):
builder = getattr(cls, 'build_' + registry_name)
else:
builder = cls
set_defaults(args, cls)
return builder(args, *extra_args, **extra_kwargs)
def register_x(name):
def register_x_cls(cls):
if name in REGISTRY:
raise ValueError('Cannot register duplicate {} ({})'.format(registry_name, name))
if cls.__name__ in REGISTRY_CLASS_NAMES:
raise ValueError(
'Cannot register {} with duplicate class name ({})'.format(
registry_name, cls.__name__,
)
)
if base_class is not None and not issubclass(cls, base_class):
raise ValueError('{} must extend {}'.format(cls.__name__, base_class.__name__))
REGISTRY[name] = cls
REGISTRY_CLASS_NAMES.add(cls.__name__)
return cls
return register_x_cls
return build_x, register_x, REGISTRY | null |
19,150 | import argparse
import copy
import logging
import os
from typing import List, Dict, Iterator, Tuple, Any
import torch
from torch import nn
from fairseq import utils
from fairseq.data import encoders
def from_pretrained(
model_name_or_path,
checkpoint_file='model.pt',
data_name_or_path='.',
archive_map=None,
**kwargs
):
from fairseq import checkpoint_utils, file_utils
if archive_map is not None:
if model_name_or_path in archive_map:
model_name_or_path = archive_map[model_name_or_path]
if data_name_or_path is not None and data_name_or_path in archive_map:
data_name_or_path = archive_map[data_name_or_path]
# allow archive_map to set default arg_overrides (e.g., tokenizer, bpe)
# for each model
if isinstance(model_name_or_path, dict):
for k, v in model_name_or_path.items():
if k == 'checkpoint_file':
checkpoint_file = v
elif (
k != 'path'
# only set kwargs that don't already have overrides
and k not in kwargs
):
kwargs[k] = v
model_name_or_path = model_name_or_path['path']
model_path = file_utils.load_archive_file(model_name_or_path)
# convenience hack for loading data and BPE codes from model archive
if data_name_or_path.startswith('.'):
kwargs['data'] = os.path.abspath(os.path.join(model_path, data_name_or_path))
else:
kwargs['data'] = file_utils.load_archive_file(data_name_or_path)
for file, arg in {
'code': 'bpe_codes',
'bpecodes': 'bpe_codes',
'sentencepiece.bpe.model': 'sentencepiece_vocab',
}.items():
path = os.path.join(model_path, file)
if os.path.exists(path):
kwargs[arg] = path
if 'user_dir' in kwargs:
utils.import_user_module(argparse.Namespace(user_dir=kwargs['user_dir']))
models, args, task = checkpoint_utils.load_model_ensemble_and_task(
[os.path.join(model_path, cpt) for cpt in checkpoint_file.split(os.pathsep)],
arg_overrides=kwargs,
)
return {
'args': args,
'task': task,
'models': models,
} | null |
19,151 | from argparse import Namespace
import json
import itertools
import logging
import os
import numpy as np
from fairseq import metrics, options, utils
from fairseq.data import (
AppendTokenDataset,
ConcatDataset,
data_utils,
encoders,
indexed_dataset,
LanguagePairDataset,
PrependTokenDataset,
StripTokenDataset,
TruncateDataset,
)
from fairseq.tasks import FairseqTask, register_task
logger = logging.getLogger(__name__)
def load_langpair_dataset(
data_path, split,
src, src_dict,
tgt, tgt_dict,
combine, dataset_impl, upsample_primary,
left_pad_source, left_pad_target, max_source_positions,
max_target_positions, prepend_bos=False, load_alignments=False,
truncate_source=False, append_source_id=False
):
def split_exists(split, src, tgt, lang, data_path):
filename = os.path.join(data_path, '{}.{}-{}.{}'.format(split, src, tgt, lang))
return indexed_dataset.dataset_exists(filename, impl=dataset_impl)
src_datasets = []
tgt_datasets = []
for k in itertools.count():
split_k = split + (str(k) if k > 0 else '')
# infer langcode
if split_exists(split_k, src, tgt, src, data_path):
prefix = os.path.join(data_path, '{}.{}-{}.'.format(split_k, src, tgt))
elif split_exists(split_k, tgt, src, src, data_path):
prefix = os.path.join(data_path, '{}.{}-{}.'.format(split_k, tgt, src))
else:
if k > 0:
break
else:
raise FileNotFoundError('Dataset not found: {} ({})'.format(split, data_path))
src_dataset = data_utils.load_indexed_dataset(prefix + src, src_dict, dataset_impl)
if truncate_source:
src_dataset = AppendTokenDataset(
TruncateDataset(
StripTokenDataset(src_dataset, src_dict.eos()),
max_source_positions - 1,
),
src_dict.eos(),
)
src_datasets.append(src_dataset)
tgt_dataset = data_utils.load_indexed_dataset(prefix + tgt, tgt_dict, dataset_impl)
if tgt_dataset is not None:
tgt_datasets.append(tgt_dataset)
logger.info('{} {} {}-{} {} examples'.format(
data_path, split_k, src, tgt, len(src_datasets[-1])
))
if not combine:
break
assert len(src_datasets) == len(tgt_datasets) or len(tgt_datasets) == 0
if len(src_datasets) == 1:
src_dataset = src_datasets[0]
tgt_dataset = tgt_datasets[0] if len(tgt_datasets) > 0 else None
else:
sample_ratios = [1] * len(src_datasets)
sample_ratios[0] = upsample_primary
src_dataset = ConcatDataset(src_datasets, sample_ratios)
if len(tgt_datasets) > 0:
tgt_dataset = ConcatDataset(tgt_datasets, sample_ratios)
else:
tgt_dataset = None
if prepend_bos:
assert hasattr(src_dict, "bos_index") and hasattr(tgt_dict, "bos_index")
src_dataset = PrependTokenDataset(src_dataset, src_dict.bos())
if tgt_dataset is not None:
tgt_dataset = PrependTokenDataset(tgt_dataset, tgt_dict.bos())
eos = None
if append_source_id:
src_dataset = AppendTokenDataset(src_dataset, src_dict.index('[{}]'.format(src)))
if tgt_dataset is not None:
tgt_dataset = AppendTokenDataset(tgt_dataset, tgt_dict.index('[{}]'.format(tgt)))
eos = tgt_dict.index('[{}]'.format(tgt))
align_dataset = None
if load_alignments:
align_path = os.path.join(data_path, '{}.align.{}-{}'.format(split, src, tgt))
if indexed_dataset.dataset_exists(align_path, impl=dataset_impl):
align_dataset = data_utils.load_indexed_dataset(align_path, None, dataset_impl)
tgt_dataset_sizes = tgt_dataset.sizes if tgt_dataset is not None else None
return LanguagePairDataset(
src_dataset, src_dataset.sizes, src_dict,
tgt_dataset, tgt_dataset_sizes, tgt_dict,
left_pad_source=left_pad_source,
left_pad_target=left_pad_target,
max_source_positions=max_source_positions,
max_target_positions=max_target_positions,
align_dataset=align_dataset, eos=eos
) | null |
19,152 | from collections import OrderedDict
import logging
import os
import torch
from fairseq import metrics, options
from fairseq.data import (
Dictionary,
LanguagePairDataset,
RoundRobinZipDatasets,
TransformEosLangPairDataset,
)
from fairseq.models import FairseqMultiModel
from fairseq.tasks.translation import load_langpair_dataset
from . import FairseqTask, register_task
from fairseq import utils
def _lang_token(lang: str):
return '__{}__'.format(lang)
The provided code snippet includes necessary dependencies for implementing the `_lang_token_index` function. Write a Python function `def _lang_token_index(dic: Dictionary, lang: str)` to solve the following problem:
Return language token index.
Here is the function:
def _lang_token_index(dic: Dictionary, lang: str):
"""Return language token index."""
idx = dic.index(_lang_token(lang))
assert idx != dic.unk_index, \
'cannot find language token for lang {}'.format(lang)
return idx | Return language token index. |
19,153 | from collections import OrderedDict
import logging
import os
from fairseq.data import (
BacktranslationDataset,
data_utils,
indexed_dataset,
IndexedCachedDataset,
IndexedDataset,
IndexedRawTextDataset,
LanguagePairDataset,
NoisingDataset,
RoundRobinZipDatasets,
)
from fairseq.models import FairseqMultiModel
from fairseq.sequence_generator import SequenceGenerator
from .multilingual_translation import MultilingualTranslationTask
from . import register_task
from fairseq import utils
def _get_bt_dataset_key(lang_pair):
return "bt:" + lang_pair | null |
19,154 | from collections import OrderedDict
import logging
import os
from fairseq.data import (
BacktranslationDataset,
data_utils,
indexed_dataset,
IndexedCachedDataset,
IndexedDataset,
IndexedRawTextDataset,
LanguagePairDataset,
NoisingDataset,
RoundRobinZipDatasets,
)
from fairseq.models import FairseqMultiModel
from fairseq.sequence_generator import SequenceGenerator
from .multilingual_translation import MultilingualTranslationTask
from . import register_task
from fairseq import utils
def _get_denoising_dataset_key(lang_pair):
return "denoising:" + lang_pair | null |
19,155 | from collections import OrderedDict
import logging
import os
from fairseq.data import (
BacktranslationDataset,
data_utils,
indexed_dataset,
IndexedCachedDataset,
IndexedDataset,
IndexedRawTextDataset,
LanguagePairDataset,
NoisingDataset,
RoundRobinZipDatasets,
)
from fairseq.models import FairseqMultiModel
from fairseq.sequence_generator import SequenceGenerator
from .multilingual_translation import MultilingualTranslationTask
from . import register_task
from fairseq import utils
The provided code snippet includes necessary dependencies for implementing the `parse_lambda_config` function. Write a Python function `def parse_lambda_config(x)` to solve the following problem:
Parse the configuration of lambda coefficient (for scheduling). x = "3" # lambda will be a constant equal to x x = "0:1,1000:0" # lambda will start from 1 and linearly decrease # to 0 during the first 1000 iterations x = "0:0,1000:0,2000:1" # lambda will be equal to 0 for the first 1000 # iterations, then will linearly increase to 1 until iteration 2000
Here is the function:
def parse_lambda_config(x):
"""
Parse the configuration of lambda coefficient (for scheduling).
x = "3" # lambda will be a constant equal to x
x = "0:1,1000:0" # lambda will start from 1 and linearly decrease
# to 0 during the first 1000 iterations
x = "0:0,1000:0,2000:1" # lambda will be equal to 0 for the first 1000
# iterations, then will linearly increase to 1 until iteration 2000
"""
split = x.split(',')
if len(split) == 1:
return float(x), None
else:
split = [s.split(os.pathsep) for s in split]
assert all(len(s) == 2 for s in split)
assert all(k.isdigit() for k, _ in split)
assert all(int(split[i][0]) < int(split[i + 1][0]) for i in range(len(split) - 1))
return float(split[0][1]), [(int(k), float(v)) for k, v in split] | Parse the configuration of lambda coefficient (for scheduling). x = "3" # lambda will be a constant equal to x x = "0:1,1000:0" # lambda will start from 1 and linearly decrease # to 0 during the first 1000 iterations x = "0:0,1000:0,2000:1" # lambda will be equal to 0 for the first 1000 # iterations, then will linearly increase to 1 until iteration 2000 |
19,156 | import argparse
import sys
from typing import Callable, List, Optional
import torch
from fairseq import utils
from fairseq.data.indexed_dataset import get_available_dataset_impl
def get_generation_parser(interactive=False, default_task="translation"):
parser = get_parser("Generation", default_task)
add_dataset_args(parser, gen=True)
add_generation_args(parser)
if interactive:
add_interactive_args(parser)
return parser
def get_interactive_generation_parser(default_task="translation"):
return get_generation_parser(interactive=True, default_task=default_task) | null |
19,157 | import argparse
import sys
from typing import Callable, List, Optional
import torch
from fairseq import utils
from fairseq.data.indexed_dataset import get_available_dataset_impl
def eval_bool(x, default=False):
if x is None:
return default
try:
return bool(eval(x))
except TypeError:
return default | null |
19,158 | import math
from fairseq import metrics, utils
from fairseq.criterions import FairseqCriterion, register_criterion
def label_smoothed_nll_loss(lprobs, target, epsilon, ignore_index=None, reduce=True):
if target.dim() == lprobs.dim() - 1:
target = target.unsqueeze(-1)
nll_loss = -lprobs.gather(dim=-1, index=target)
smooth_loss = -lprobs.sum(dim=-1, keepdim=True)
if ignore_index is not None:
pad_mask = target.eq(ignore_index)
nll_loss.masked_fill_(pad_mask, 0.)
smooth_loss.masked_fill_(pad_mask, 0.)
else:
nll_loss = nll_loss.squeeze(-1)
smooth_loss = smooth_loss.squeeze(-1)
if reduce:
nll_loss = nll_loss.sum()
smooth_loss = smooth_loss.sum()
eps_i = epsilon / lprobs.size(-1)
loss = (1. - epsilon) * nll_loss + eps_i * smooth_loss
return loss, nll_loss | null |
19,159 | import math
import torch
import torch.nn.functional as F
from fairseq import utils
from fairseq.criterions import FairseqCriterion, register_criterion
The provided code snippet includes necessary dependencies for implementing the `compute_cross_entropy_loss` function. Write a Python function `def compute_cross_entropy_loss(logits, targets, ignore_index=-100)` to solve the following problem:
Function to compute the cross entropy loss. The default value of ignore_index is the same as the default value for F.cross_entropy in pytorch.
Here is the function:
def compute_cross_entropy_loss(logits, targets, ignore_index=-100):
"""
Function to compute the cross entropy loss. The default value of
ignore_index is the same as the default value for F.cross_entropy in
pytorch.
"""
assert logits.size(0) == targets.size(-1), \
"Logits and Targets tensor shapes don't match up"
loss = F.nll_loss(
F.log_softmax(logits, -1, dtype=torch.float32),
targets,
reduction="sum",
ignore_index=ignore_index,
)
return loss | Function to compute the cross entropy loss. The default value of ignore_index is the same as the default value for F.cross_entropy in pytorch. |
19,160 | import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.models import (
FairseqEncoder,
register_model,
register_model_architecture,
)
from fairseq.models.roberta import (
RobertaModel,
RobertaEncoder,
RobertaLMHead,
RobertaClassificationHead,
)
from fairseq.modules import (
LayerNorm,
TransformerSentenceEncoder,
)
from fairseq.model_parallel.modules import (
ModelParallelTransformerSentenceEncoder,
)
from fairseq.modules.transformer_sentence_encoder import init_bert_params
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 |
19,161 | import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.models import (
FairseqEncoder,
register_model,
register_model_architecture,
)
from fairseq.models.roberta import (
RobertaModel,
RobertaEncoder,
RobertaLMHead,
RobertaClassificationHead,
)
from fairseq.modules import (
LayerNorm,
TransformerSentenceEncoder,
)
from fairseq.model_parallel.modules import (
ModelParallelTransformerSentenceEncoder,
)
from fairseq.modules.transformer_sentence_encoder import init_bert_params
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_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 |
19,162 | import torch.nn as nn
from fairseq.models import (
register_model,
register_model_architecture,
)
from fairseq.models.transformer_lm import (
base_lm_architecture,
TransformerLanguageModel,
)
from fairseq.model_parallel.models.transformer import (
ModelParallelTransformerDecoder,
)
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_megatron(args):
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 3072)
args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 3072 * 4)
args.decoder_layers = getattr(args, 'decoder_layers', 72)
args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 32)
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 |
19,163 | import torch.nn as nn
from fairseq.models import (
register_model,
register_model_architecture,
)
from fairseq.models.transformer_lm import (
base_lm_architecture,
TransformerLanguageModel,
)
from fairseq.model_parallel.models.transformer import (
ModelParallelTransformerDecoder,
)
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_megatron_11b(args):
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 3072)
args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 3072 * 6)
args.decoder_layers = getattr(args, 'decoder_layers', 72)
args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 32)
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 |
19,164 | import multiprocessing
import os
import pdb
import sys
class MultiprocessingPdb(pdb.Pdb):
"""A Pdb wrapper that works in a multiprocessing environment.
Usage: `from fairseq import pdb; pdb.set_trace()`
"""
def __init__(self):
pdb.Pdb.__init__(self, nosigint=True)
def _cmdloop(self):
stdin_bak = sys.stdin
with _stdin_lock:
try:
if _stdin_fd is not None:
if not _stdin[0]:
_stdin[0] = os.fdopen(_stdin_fd)
sys.stdin = _stdin[0]
self.cmdloop()
finally:
sys.stdin = stdin_bak
import pdb
import sys
sys.modules['fairseq.meters'] = meters
sys.modules['fairseq.metrics'] = metrics
sys.modules['fairseq.progress_bar'] = progress_bar
def set_trace():
pdb = MultiprocessingPdb()
pdb.set_trace(sys._getframe().f_back) | null |
19,165 | from typing import Dict, Optional
import uuid
from torch import Tensor
class FairseqIncrementalState(object):
def __init__(self, *args, **kwargs):
def init_incremental_state(self):
def _get_full_incremental_state_key(self, key: str) -> str:
def get_incremental_state(
self,
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]],
key: str,
) -> Optional[Dict[str, Optional[Tensor]]]:
def set_incremental_state(
self,
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]],
key: str,
value: Dict[str, Optional[Tensor]],
) -> Optional[Dict[str, Dict[str, Optional[Tensor]]]]:
def with_incremental_state(cls):
cls.__bases__ = (FairseqIncrementalState,) + tuple(b for b in cls.__bases__ if b != FairseqIncrementalState)
return cls | null |
19,166 | import logging
from fairseq.modules.quantization import pq, quantization_options, scalar
def quantize_model_scalar(model, args):
quant_noise_scalar = getattr(args, 'quant_noise_scalar', 0)
if quant_noise_scalar > 0:
# quantize_model edits the model in place
scalar.quantize_model_(model, p=quant_noise_scalar, bits=8, update_step=1000)
return model | null |
19,167 | import torch.nn as nn
import torch.nn.functional as F
from fairseq.data import Dictionary
from fairseq.models import (
FairseqDecoder,
FairseqLanguageModel,
register_model,
register_model_architecture,
)
def base_architecture(args):
pass | null |
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