829 lines
31 KiB
Python
829 lines
31 KiB
Python
# --------------------------------------------------------
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# WavLM: Large-Scale Self-Supervised Pre-training for Full Stack Speech Processing (https://arxiv.org/abs/2110.13900.pdf)
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# Github source: https://github.com/microsoft/unilm/tree/master/wavlm
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# Copyright (c) 2021 Microsoft
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# Licensed under The MIT License [see LICENSE for details]
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# Based on fairseq code bases
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# https://github.com/pytorch/fairseq
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# --------------------------------------------------------
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import math
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import warnings
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from typing import Dict, Optional, Tuple
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import torch
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import torch.nn.functional as F
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from torch import Tensor, nn
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from torch.nn import Parameter
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class TransposeLast(nn.Module):
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def __init__(self, deconstruct_idx=None):
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super().__init__()
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self.deconstruct_idx = deconstruct_idx
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def forward(self, x):
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if self.deconstruct_idx is not None:
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x = x[self.deconstruct_idx]
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return x.transpose(-2, -1)
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class Fp32LayerNorm(nn.LayerNorm):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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def forward(self, input):
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output = F.layer_norm(
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input.float(),
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self.normalized_shape,
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self.weight.float() if self.weight is not None else None,
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self.bias.float() if self.bias is not None else None,
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self.eps,
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)
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return output.type_as(input)
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class Fp32GroupNorm(nn.GroupNorm):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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def forward(self, input):
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output = F.group_norm(
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input.float(),
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self.num_groups,
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self.weight.float() if self.weight is not None else None,
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self.bias.float() if self.bias is not None else None,
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self.eps,
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)
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return output.type_as(input)
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class GradMultiply(torch.autograd.Function):
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@staticmethod
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def forward(ctx, x, scale):
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ctx.scale = scale
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res = x.new(x)
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return res
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@staticmethod
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def backward(ctx, grad):
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return grad * ctx.scale, None
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class SamePad(nn.Module):
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def __init__(self, kernel_size, causal=False):
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super().__init__()
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if causal:
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self.remove = kernel_size - 1
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else:
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self.remove = 1 if kernel_size % 2 == 0 else 0
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def forward(self, x):
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if self.remove > 0:
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x = x[:, :, : -self.remove]
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return x
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class Swish(nn.Module):
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"""Swish function
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"""
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def __init__(self):
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"""Construct an MultiHeadedAttention object."""
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super(Swish, self).__init__()
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self.act = torch.nn.Sigmoid()
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def forward(self, x):
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return x * self.act(x)
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class GLU_Linear(nn.Module):
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def __init__(self, input_dim, output_dim, glu_type="sigmoid", bias_in_glu=True):
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super(GLU_Linear, self).__init__()
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self.glu_type = glu_type
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self.output_dim = output_dim
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if glu_type == "sigmoid":
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self.glu_act = torch.nn.Sigmoid()
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elif glu_type == "swish":
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self.glu_act = Swish()
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elif glu_type == "relu":
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self.glu_act = torch.nn.ReLU()
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elif glu_type == "gelu":
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self.glu_act = torch.nn.GELU()
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if bias_in_glu:
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self.linear = nn.Linear(input_dim, output_dim * 2, True)
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else:
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self.linear = nn.Linear(input_dim, output_dim * 2, False)
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def forward(self, x):
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# to be consistent with GLU_Linear, we assume the input always has the #channel (#dim) in the last dimension of the tensor, so need to switch the dimension first for 1D-Conv case
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x = self.linear(x)
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if self.glu_type == "bilinear":
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x = (x[:, :, 0:self.output_dim] * x[:, :, self.output_dim:self.output_dim * 2])
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else:
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x = (x[:, :, 0:self.output_dim] * self.glu_act(x[:, :, self.output_dim:self.output_dim * 2]))
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return x
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def gelu_accurate(x):
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if not hasattr(gelu_accurate, "_a"):
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gelu_accurate._a = math.sqrt(2 / math.pi)
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return (
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0.5 * x * (1 + torch.tanh(gelu_accurate._a * (x + 0.044715 * torch.pow(x, 3))))
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)
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def gelu(x: torch.Tensor) -> torch.Tensor:
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return torch.nn.functional.gelu(x.float()).type_as(x)
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def get_activation_fn(activation: str):
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"""Returns the activation function corresponding to `activation`"""
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if activation == "relu":
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return F.relu
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elif activation == "gelu":
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return gelu
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elif activation == "gelu_fast":
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warnings.warn(
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"--activation-fn=gelu_fast has been renamed to gelu_accurate"
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)
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return gelu_accurate
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elif activation == "gelu_accurate":
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return gelu_accurate
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elif activation == "tanh":
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return torch.tanh
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elif activation == "linear":
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return lambda x: x
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elif activation == "glu":
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return lambda x: x
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else:
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raise RuntimeError("--activation-fn {} not supported".format(activation))
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def init_bert_params(module):
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"""
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Initialize the weights specific to the BERT Model.
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This overrides the default initializations depending on the specified arguments.
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1. If normal_init_linear_weights is set then weights of linear
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layer will be initialized using the normal distribution and
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bais will be set to the specified value.
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2. If normal_init_embed_weights is set then weights of embedding
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layer will be initialized using the normal distribution.
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3. If normal_init_proj_weights is set then weights of
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in_project_weight for MultiHeadAttention initialized using
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the normal distribution (to be validated).
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"""
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def normal_(data):
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# with FSDP, module params will be on CUDA, so we cast them back to CPU
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# so that the RNG is consistent with and without FSDP
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data.copy_(
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data.cpu().normal_(mean=0.0, std=0.02).to(data.device)
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)
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if isinstance(module, nn.Linear):
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normal_(module.weight.data)
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if module.bias is not None:
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module.bias.data.zero_()
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if isinstance(module, nn.Embedding):
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normal_(module.weight.data)
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if module.padding_idx is not None:
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module.weight.data[module.padding_idx].zero_()
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if isinstance(module, MultiheadAttention):
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normal_(module.q_proj.weight.data)
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normal_(module.k_proj.weight.data)
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normal_(module.v_proj.weight.data)
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def quant_noise(module, p, block_size):
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"""
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Wraps modules and applies quantization noise to the weights for
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subsequent quantization with Iterative Product Quantization as
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described in "Training with Quantization Noise for Extreme Model Compression"
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Args:
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- module: nn.Module
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- p: amount of Quantization Noise
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- block_size: size of the blocks for subsequent quantization with iPQ
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Remarks:
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- Module weights must have the right sizes wrt the block size
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- Only Linear, Embedding and Conv2d modules are supported for the moment
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- For more detail on how to quantize by blocks with convolutional weights,
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see "And the Bit Goes Down: Revisiting the Quantization of Neural Networks"
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- We implement the simplest form of noise here as stated in the paper
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which consists in randomly dropping blocks
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"""
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# if no quantization noise, don't register hook
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if p <= 0:
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return module
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# supported modules
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assert isinstance(module, (nn.Linear, nn.Embedding, nn.Conv2d))
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# test whether module.weight has the right sizes wrt block_size
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is_conv = module.weight.ndim == 4
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# 2D matrix
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if not is_conv:
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assert (
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module.weight.size(1) % block_size == 0
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), "Input features must be a multiple of block sizes"
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# 4D matrix
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else:
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# 1x1 convolutions
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if module.kernel_size == (1, 1):
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assert (
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module.in_channels % block_size == 0
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), "Input channels must be a multiple of block sizes"
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# regular convolutions
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else:
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k = module.kernel_size[0] * module.kernel_size[1]
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assert k % block_size == 0, "Kernel size must be a multiple of block size"
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def _forward_pre_hook(mod, input):
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# no noise for evaluation
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if mod.training:
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if not is_conv:
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# gather weight and sizes
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weight = mod.weight
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in_features = weight.size(1)
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out_features = weight.size(0)
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# split weight matrix into blocks and randomly drop selected blocks
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mask = torch.zeros(
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in_features // block_size * out_features, device=weight.device
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)
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mask.bernoulli_(p)
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mask = mask.repeat_interleave(block_size, -1).view(-1, in_features)
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else:
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# gather weight and sizes
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weight = mod.weight
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in_channels = mod.in_channels
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out_channels = mod.out_channels
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# split weight matrix into blocks and randomly drop selected blocks
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if mod.kernel_size == (1, 1):
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mask = torch.zeros(
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int(in_channels // block_size * out_channels),
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device=weight.device,
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)
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mask.bernoulli_(p)
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mask = mask.repeat_interleave(block_size, -1).view(-1, in_channels)
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else:
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mask = torch.zeros(
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weight.size(0), weight.size(1), device=weight.device
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)
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mask.bernoulli_(p)
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mask = (
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mask.unsqueeze(2)
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.unsqueeze(3)
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.repeat(1, 1, mod.kernel_size[0], mod.kernel_size[1])
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)
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# scale weights and apply mask
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mask = mask.to(
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torch.bool
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) # x.bool() is not currently supported in TorchScript
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s = 1 / (1 - p)
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mod.weight.data = s * weight.masked_fill(mask, 0)
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module.register_forward_pre_hook(_forward_pre_hook)
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return module
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class MultiheadAttention(nn.Module):
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"""Multi-headed attention.
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See "Attention Is All You Need" for more details.
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"""
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def __init__(
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self,
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embed_dim,
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num_heads,
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kdim=None,
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vdim=None,
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dropout=0.0,
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bias=True,
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add_bias_kv=False,
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add_zero_attn=False,
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self_attention=False,
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encoder_decoder_attention=False,
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q_noise=0.0,
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qn_block_size=8,
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has_relative_attention_bias=False,
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num_buckets=32,
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max_distance=128,
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gru_rel_pos=False,
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rescale_init=False,
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):
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super().__init__()
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self.embed_dim = embed_dim
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self.kdim = kdim if kdim is not None else embed_dim
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self.vdim = vdim if vdim is not None else embed_dim
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self.qkv_same_dim = self.kdim == embed_dim and self.vdim == embed_dim
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self.num_heads = num_heads
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self.dropout_module = nn.Dropout(dropout)
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self.has_relative_attention_bias = has_relative_attention_bias
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self.num_buckets = num_buckets
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self.max_distance = max_distance
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if self.has_relative_attention_bias:
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self.relative_attention_bias = nn.Embedding(num_buckets, num_heads)
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self.head_dim = embed_dim // num_heads
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self.q_head_dim = self.head_dim
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self.k_head_dim = self.head_dim
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assert (
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self.head_dim * num_heads == self.embed_dim
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), "embed_dim must be divisible by num_heads"
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self.scaling = self.head_dim ** -0.5
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self.self_attention = self_attention
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self.encoder_decoder_attention = encoder_decoder_attention
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assert not self.self_attention or self.qkv_same_dim, (
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"Self-attention requires query, key and " "value to be of the same size"
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)
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k_bias = True
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if rescale_init:
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k_bias = False
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k_embed_dim = embed_dim
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q_embed_dim = embed_dim
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self.k_proj = quant_noise(
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nn.Linear(self.kdim, k_embed_dim, bias=k_bias), q_noise, qn_block_size
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)
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self.v_proj = quant_noise(
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nn.Linear(self.vdim, embed_dim, bias=bias), q_noise, qn_block_size
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)
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self.q_proj = quant_noise(
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nn.Linear(embed_dim, q_embed_dim, bias=bias), q_noise, qn_block_size
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)
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self.out_proj = quant_noise(
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nn.Linear(embed_dim, embed_dim, bias=bias), q_noise, qn_block_size
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)
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if add_bias_kv:
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self.bias_k = Parameter(torch.Tensor(1, 1, embed_dim))
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self.bias_v = Parameter(torch.Tensor(1, 1, embed_dim))
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else:
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self.bias_k = self.bias_v = None
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self.add_zero_attn = add_zero_attn
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self.gru_rel_pos = gru_rel_pos
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if self.gru_rel_pos:
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self.grep_linear = nn.Linear(self.q_head_dim, 8)
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self.grep_a = nn.Parameter(torch.ones(1, num_heads, 1, 1))
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self.reset_parameters()
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def reset_parameters(self):
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if self.qkv_same_dim:
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# Empirically observed the convergence to be much better with
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# the scaled initialization
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nn.init.xavier_uniform_(self.k_proj.weight, gain=1 / math.sqrt(2))
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nn.init.xavier_uniform_(self.v_proj.weight, gain=1 / math.sqrt(2))
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nn.init.xavier_uniform_(self.q_proj.weight, gain=1 / math.sqrt(2))
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else:
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nn.init.xavier_uniform_(self.k_proj.weight)
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nn.init.xavier_uniform_(self.v_proj.weight)
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nn.init.xavier_uniform_(self.q_proj.weight)
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nn.init.xavier_uniform_(self.out_proj.weight)
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if self.out_proj.bias is not None:
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nn.init.constant_(self.out_proj.bias, 0.0)
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if self.bias_k is not None:
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nn.init.xavier_normal_(self.bias_k)
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if self.bias_v is not None:
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nn.init.xavier_normal_(self.bias_v)
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if self.has_relative_attention_bias:
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nn.init.xavier_normal_(self.relative_attention_bias.weight)
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def _relative_positions_bucket(self, relative_positions, bidirectional=True):
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num_buckets = self.num_buckets
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max_distance = self.max_distance
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relative_buckets = 0
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if bidirectional:
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num_buckets = num_buckets // 2
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relative_buckets += (relative_positions > 0).to(torch.long) * num_buckets
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relative_positions = torch.abs(relative_positions)
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else:
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relative_positions = -torch.min(relative_positions, torch.zeros_like(relative_positions))
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max_exact = num_buckets // 2
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is_small = relative_positions < max_exact
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relative_postion_if_large = max_exact + (
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torch.log(relative_positions.float() / max_exact)
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/ math.log(max_distance / max_exact)
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* (num_buckets - max_exact)
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).to(torch.long)
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relative_postion_if_large = torch.min(
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relative_postion_if_large, torch.full_like(relative_postion_if_large, num_buckets - 1)
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)
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relative_buckets += torch.where(is_small, relative_positions, relative_postion_if_large)
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return relative_buckets
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def compute_bias(self, query_length, key_length):
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context_position = torch.arange(query_length, dtype=torch.long)[:, None]
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memory_position = torch.arange(key_length, dtype=torch.long)[None, :]
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relative_position = memory_position - context_position
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relative_position_bucket = self._relative_positions_bucket(
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relative_position,
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bidirectional=True
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)
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relative_position_bucket = relative_position_bucket.to(self.relative_attention_bias.weight.device)
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values = self.relative_attention_bias(relative_position_bucket)
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values = values.permute([2, 0, 1])
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return values
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def forward(
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self,
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query,
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key: Optional[Tensor],
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value: Optional[Tensor],
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key_padding_mask: Optional[Tensor] = None,
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incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
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need_weights: bool = True,
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static_kv: bool = False,
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attn_mask: Optional[Tensor] = None,
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before_softmax: bool = False,
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need_head_weights: bool = False,
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position_bias: Optional[Tensor] = None
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) -> Tuple[Tensor, Optional[Tensor], Optional[Tensor]]:
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"""Input shape: Time x Batch x Channel
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Args:
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key_padding_mask (ByteTensor, optional): mask to exclude
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keys that are pads, of shape `(batch, src_len)`, where
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padding elements are indicated by 1s.
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need_weights (bool, optional): return the attention weights,
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averaged over heads (default: False).
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attn_mask (ByteTensor, optional): typically used to
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implement causal attention, where the mask prevents the
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attention from looking forward in time (default: None).
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before_softmax (bool, optional): return the raw attention
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weights and values before the attention softmax.
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need_head_weights (bool, optional): return the attention
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weights for each head. Implies *need_weights*. Default:
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return the average attention weights over all heads.
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"""
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if need_head_weights:
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need_weights = True
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is_tpu = query.device.type == "xla"
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tgt_len, bsz, embed_dim = query.size()
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src_len = tgt_len
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assert embed_dim == self.embed_dim
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assert list(query.size()) == [tgt_len, bsz, embed_dim]
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if key is not None:
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src_len, key_bsz, _ = key.size()
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if not torch.jit.is_scripting():
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|
assert key_bsz == bsz
|
|
assert value is not None
|
|
assert src_len, bsz == value.shape[:2]
|
|
|
|
if self.has_relative_attention_bias and position_bias is None:
|
|
position_bias = self.compute_bias(tgt_len, src_len)
|
|
position_bias = position_bias.unsqueeze(0).repeat(bsz, 1, 1, 1).view(bsz * self.num_heads, tgt_len, src_len)
|
|
|
|
if (
|
|
not is_tpu # don't use PyTorch version on TPUs
|
|
and incremental_state is None
|
|
and not static_kv
|
|
# A workaround for quantization to work. Otherwise JIT compilation
|
|
# treats bias in linear module as method.
|
|
and not torch.jit.is_scripting()
|
|
and self.q_head_dim == self.head_dim
|
|
):
|
|
assert key is not None and value is not None
|
|
assert attn_mask is None
|
|
|
|
attn_mask_rel_pos = None
|
|
if position_bias is not None:
|
|
attn_mask_rel_pos = position_bias
|
|
if self.gru_rel_pos:
|
|
query_layer = query.transpose(0, 1)
|
|
new_x_shape = query_layer.size()[:-1] + (self.num_heads, -1)
|
|
query_layer = query_layer.view(*new_x_shape)
|
|
query_layer = query_layer.permute(0, 2, 1, 3)
|
|
_B, _H, _L, __ = query_layer.size()
|
|
|
|
gate_a, gate_b = torch.sigmoid(self.grep_linear(query_layer).view(
|
|
_B, _H, _L, 2, 4).sum(-1, keepdim=False)).chunk(2, dim=-1)
|
|
gate_a_1 = gate_a * (gate_b * self.grep_a - 1.0) + 2.0
|
|
attn_mask_rel_pos = gate_a_1.view(bsz * self.num_heads, -1, 1) * position_bias
|
|
|
|
attn_mask_rel_pos = attn_mask_rel_pos.view((-1, tgt_len, tgt_len))
|
|
k_proj_bias = self.k_proj.bias
|
|
if k_proj_bias is None:
|
|
k_proj_bias = torch.zeros_like(self.q_proj.bias)
|
|
|
|
x, attn = F.multi_head_attention_forward(
|
|
query,
|
|
key,
|
|
value,
|
|
self.embed_dim,
|
|
self.num_heads,
|
|
torch.empty([0]),
|
|
torch.cat((self.q_proj.bias, self.k_proj.bias, self.v_proj.bias)),
|
|
self.bias_k,
|
|
self.bias_v,
|
|
self.add_zero_attn,
|
|
self.dropout_module.p,
|
|
self.out_proj.weight,
|
|
self.out_proj.bias,
|
|
self.training,
|
|
# self.training or self.dropout_module.apply_during_inference,
|
|
key_padding_mask,
|
|
need_weights,
|
|
attn_mask_rel_pos,
|
|
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,
|
|
)
|
|
return x, attn, position_bias
|
|
|
|
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.q_head_dim)
|
|
.transpose(0, 1)
|
|
)
|
|
if k is not None:
|
|
k = (
|
|
k.contiguous()
|
|
.view(-1, bsz * self.num_heads, self.k_head_dim)
|
|
.transpose(0, 1)
|
|
)
|
|
if v is not None:
|
|
v = (
|
|
v.contiguous()
|
|
.view(-1, bsz * self.num_heads, self.head_dim)
|
|
.transpose(0, 1)
|
|
)
|
|
|
|
if saved_state is not None:
|
|
# saved states are stored with shape (bsz, num_heads, seq_len, head_dim)
|
|
if "prev_key" in saved_state:
|
|
_prev_key = saved_state["prev_key"]
|
|
assert _prev_key is not None
|
|
prev_key = _prev_key.view(bsz * self.num_heads, -1, self.head_dim)
|
|
if static_kv:
|
|
k = prev_key
|
|
else:
|
|
assert k is not None
|
|
k = torch.cat([prev_key, k], dim=1)
|
|
src_len = k.size(1)
|
|
if "prev_value" in saved_state:
|
|
_prev_value = saved_state["prev_value"]
|
|
assert _prev_value is not None
|
|
prev_value = _prev_value.view(bsz * self.num_heads, -1, self.head_dim)
|
|
if static_kv:
|
|
v = prev_value
|
|
else:
|
|
assert v is not None
|
|
v = torch.cat([prev_value, v], dim=1)
|
|
prev_key_padding_mask: Optional[Tensor] = None
|
|
if "prev_key_padding_mask" in saved_state:
|
|
prev_key_padding_mask = saved_state["prev_key_padding_mask"]
|
|
assert k is not None and v is not None
|
|
key_padding_mask = MultiheadAttention._append_prev_key_padding_mask(
|
|
key_padding_mask=key_padding_mask,
|
|
prev_key_padding_mask=prev_key_padding_mask,
|
|
batch_size=bsz,
|
|
src_len=k.size(1),
|
|
static_kv=static_kv,
|
|
)
|
|
|
|
saved_state["prev_key"] = k.view(bsz, self.num_heads, -1, self.head_dim)
|
|
saved_state["prev_value"] = v.view(bsz, self.num_heads, -1, self.head_dim)
|
|
saved_state["prev_key_padding_mask"] = key_padding_mask
|
|
# In this branch incremental_state is never None
|
|
assert incremental_state is not None
|
|
incremental_state = self._set_input_buffer(incremental_state, saved_state)
|
|
assert k is not None
|
|
assert k.size(1) == src_len
|
|
|
|
# This is part of a workaround to get around fork/join parallelism
|
|
# not supporting Optional types.
|
|
if key_padding_mask is not None and key_padding_mask.dim() == 0:
|
|
key_padding_mask = None
|
|
|
|
if key_padding_mask is not None:
|
|
assert key_padding_mask.size(0) == bsz
|
|
assert key_padding_mask.size(1) == src_len
|
|
|
|
if self.add_zero_attn:
|
|
assert v is not None
|
|
src_len += 1
|
|
k = torch.cat([k, k.new_zeros((k.size(0), 1) + k.size()[2:])], dim=1)
|
|
v = torch.cat([v, v.new_zeros((v.size(0), 1) + v.size()[2:])], dim=1)
|
|
if attn_mask is not None:
|
|
attn_mask = torch.cat(
|
|
[attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1
|
|
)
|
|
if key_padding_mask is not None:
|
|
key_padding_mask = torch.cat(
|
|
[
|
|
key_padding_mask,
|
|
torch.zeros(key_padding_mask.size(0), 1).type_as(
|
|
key_padding_mask
|
|
),
|
|
],
|
|
dim=1,
|
|
)
|
|
|
|
attn_weights = torch.bmm(q, k.transpose(1, 2))
|
|
attn_weights = self.apply_sparse_mask(attn_weights, tgt_len, src_len, bsz)
|
|
|
|
assert list(attn_weights.size()) == [bsz * self.num_heads, tgt_len, src_len]
|
|
|
|
if attn_mask is not None:
|
|
attn_mask = attn_mask.unsqueeze(0)
|
|
attn_weights += attn_mask
|
|
|
|
if key_padding_mask is not None:
|
|
# don't attend to padding symbols
|
|
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
|
|
if not is_tpu:
|
|
attn_weights = attn_weights.masked_fill(
|
|
key_padding_mask.unsqueeze(1).unsqueeze(2).to(torch.bool),
|
|
float("-inf"),
|
|
)
|
|
else:
|
|
attn_weights = attn_weights.transpose(0, 2)
|
|
attn_weights = attn_weights.masked_fill(key_padding_mask, float("-inf"))
|
|
attn_weights = attn_weights.transpose(0, 2)
|
|
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
|
|
|
|
if before_softmax:
|
|
return attn_weights, v, position_bias
|
|
|
|
if position_bias is not None:
|
|
if self.gru_rel_pos == 1:
|
|
query_layer = q.view(bsz, self.num_heads, tgt_len, self.q_head_dim)
|
|
_B, _H, _L, __ = query_layer.size()
|
|
gate_a, gate_b = torch.sigmoid(self.grep_linear(query_layer).view(
|
|
_B, _H, _L, 2, 4).sum(-1, keepdim=False)).chunk(2, dim=-1)
|
|
gate_a_1 = gate_a * (gate_b * self.grep_a - 1.0) + 2.0
|
|
position_bias = gate_a_1.view(bsz * self.num_heads, -1, 1) * position_bias
|
|
|
|
position_bias = position_bias.view(attn_weights.size())
|
|
|
|
attn_weights = attn_weights + position_bias
|
|
|
|
attn_weights_float = F.softmax(
|
|
attn_weights, dim=-1
|
|
)
|
|
attn_weights = attn_weights_float.type_as(attn_weights)
|
|
attn_probs = self.dropout_module(attn_weights)
|
|
|
|
assert v is not None
|
|
attn = torch.bmm(attn_probs, v)
|
|
assert list(attn.size()) == [bsz * self.num_heads, tgt_len, self.head_dim]
|
|
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, position_bias
|
|
|
|
@staticmethod
|
|
def _append_prev_key_padding_mask(
|
|
key_padding_mask: Optional[Tensor],
|
|
prev_key_padding_mask: Optional[Tensor],
|
|
batch_size: int,
|
|
src_len: int,
|
|
static_kv: bool,
|
|
) -> Optional[Tensor]:
|
|
# saved key padding masks have shape (bsz, seq_len)
|
|
if prev_key_padding_mask is not None and static_kv:
|
|
new_key_padding_mask = prev_key_padding_mask
|
|
elif prev_key_padding_mask is not None and key_padding_mask is not None:
|
|
new_key_padding_mask = torch.cat(
|
|
[prev_key_padding_mask.float(), key_padding_mask.float()], dim=1
|
|
)
|
|
# During incremental decoding, as the padding token enters and
|
|
# leaves the frame, there will be a time when prev or current
|
|
# is None
|
|
elif prev_key_padding_mask is not None:
|
|
if src_len > prev_key_padding_mask.size(1):
|
|
filler = torch.zeros(
|
|
(batch_size, src_len - prev_key_padding_mask.size(1)),
|
|
device=prev_key_padding_mask.device,
|
|
)
|
|
new_key_padding_mask = torch.cat(
|
|
[prev_key_padding_mask.float(), filler.float()], dim=1
|
|
)
|
|
else:
|
|
new_key_padding_mask = prev_key_padding_mask.float()
|
|
elif key_padding_mask is not None:
|
|
if src_len > key_padding_mask.size(1):
|
|
filler = torch.zeros(
|
|
(batch_size, src_len - key_padding_mask.size(1)),
|
|
device=key_padding_mask.device,
|
|
)
|
|
new_key_padding_mask = torch.cat(
|
|
[filler.float(), key_padding_mask.float()], dim=1
|
|
)
|
|
else:
|
|
new_key_padding_mask = key_padding_mask.float()
|
|
else:
|
|
new_key_padding_mask = prev_key_padding_mask
|
|
return new_key_padding_mask
|
|
|
|
def _get_input_buffer(
|
|
self, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]]
|
|
) -> Dict[str, Optional[Tensor]]:
|
|
result = self.get_incremental_state(incremental_state, "attn_state")
|
|
if result is not None:
|
|
return result
|
|
else:
|
|
empty_result: Dict[str, Optional[Tensor]] = {}
|
|
return empty_result
|
|
|
|
def _set_input_buffer(
|
|
self,
|
|
incremental_state: Dict[str, Dict[str, Optional[Tensor]]],
|
|
buffer: Dict[str, Optional[Tensor]],
|
|
):
|
|
return self.set_incremental_state(incremental_state, "attn_state", buffer)
|
|
|
|
def apply_sparse_mask(self, attn_weights, tgt_len: int, src_len: int, bsz: int):
|
|
return attn_weights
|