mirror of
https://github.com/k2-fsa/icefall.git
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87843e9382
* Add k2SSL * fix flake8 * fix for black * fix for black * fix for black * Update ssl_datamodule.py * Fix bugs in HubertDataset * update comments * add librilight * add checkpoint convert script * format --------- Co-authored-by: yifanyeung <yifanyeung@yifanyeung.local> Co-authored-by: zzasdf <15218404468@163.com>
841 lines
32 KiB
Python
841 lines
32 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates.
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#
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# Permission is hereby granted, free of charge, to any person obtaining a copy
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# of this software and associated documentation files (the "Software"), to deal
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# in the Software without restriction, including without limitation the rights
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# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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# copies of the Software, and to permit persons to whom the Software is
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# furnished to do so, subject to the following conditions:
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#
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# The above copyright notice and this permission notice shall be included in all
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# copies or substantial portions of the Software.
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#
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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# SOFTWARE.
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import math
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from typing import Dict, List, Optional, Tuple
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import torch
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import torch.nn.functional as F
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import utils
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from torch import Tensor, nn
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from torch.nn import Parameter
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from utils import FairseqDropout, quant_noise
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_xformers_available = False
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# TODO: move this into xformers?
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# TODO: uint8 input type should just output a bool
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def _mask_for_xformers(mask: Tensor, to_dtype: Optional[torch.dtype] = None):
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"""
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call to pytorch multihead accepts three mask types:
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- ByteTensor where non-zero means to mask
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- FloatTensor which is an additive mask
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- BoolTensor where True means to mask
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xFormers currently accepts boolean and additive maks. For boolean masks
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the values have opposite meaning. For a BoolTensor True mean to keep the value.
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"""
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float_types = [torch.float, torch.float16]
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# If an input mask is a float it is an additive mask. Otherwise it is either uint8 or bool.
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additive = mask.dtype in float_types
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# If to_dype is not specified, keep same dtype as mask.
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to_dtype = mask.dtype if to_dtype is None else to_dtype
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to_additive = to_dtype in float_types
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if additive:
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if to_additive:
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return mask.to(to_dtype)
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mask = mask < 0
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if to_additive:
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# return additive mask
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new_mask = torch.zeros_like(mask, dtype=to_dtype)
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new_mask = new_mask.masked_fill_(mask, -float("inf"))
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return new_mask
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# In xFormers True is value to keep rather than value to mask
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mask = ~mask.to(torch.bool)
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mask = mask.to(to_dtype)
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return mask
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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_(data.cpu().normal_(mean=0.0, std=0.02).to(data.device))
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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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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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dictionary=None,
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q_noise=0.0,
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qn_block_size=8,
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# TODO: pass in config rather than string.
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# config defined in xformers.components.attention.AttentionConfig
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xformers_att_config: Optional[str] = None,
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xformers_blocksparse_layout: Optional[
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torch.Tensor
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] = None, # This should be part of the config
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xformers_blocksparse_blocksize: Optional[
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int
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] = 16, # This should be part of the config
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):
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super().__init__()
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self.use_xformers = False
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if self.use_xformers and not _xformers_available:
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raise ImportError("\n\n Please install xFormers.")
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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 = FairseqDropout(
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dropout, module_name=self.__class__.__name__
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)
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self.head_dim = embed_dim // num_heads
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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 (
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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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self.k_proj = quant_noise(
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nn.Linear(self.kdim, embed_dim, bias=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, 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.beam_size = 1
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self.reset_parameters()
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self.onnx_trace = False
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self.skip_embed_dim_check = False
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def prepare_for_onnx_export_(self):
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self.onnx_trace = True
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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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def _get_reserve_head_index(self, num_heads_to_keep: int):
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k_proj_heads_norm = []
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q_proj_heads_norm = []
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v_proj_heads_norm = []
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for i in range(self.num_heads):
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start_idx = i * self.head_dim
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end_idx = (i + 1) * self.head_dim
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k_proj_heads_norm.append(
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torch.sum(
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torch.abs(
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self.k_proj.weight[
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start_idx:end_idx,
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]
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)
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).tolist()
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+ torch.sum(torch.abs(self.k_proj.bias[start_idx:end_idx])).tolist()
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)
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q_proj_heads_norm.append(
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torch.sum(
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torch.abs(
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self.q_proj.weight[
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start_idx:end_idx,
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]
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)
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).tolist()
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+ torch.sum(torch.abs(self.q_proj.bias[start_idx:end_idx])).tolist()
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)
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v_proj_heads_norm.append(
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torch.sum(
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torch.abs(
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self.v_proj.weight[
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start_idx:end_idx,
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]
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)
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).tolist()
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+ torch.sum(torch.abs(self.v_proj.bias[start_idx:end_idx])).tolist()
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)
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heads_norm = []
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for i in range(self.num_heads):
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heads_norm.append(
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k_proj_heads_norm[i] + q_proj_heads_norm[i] + v_proj_heads_norm[i]
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)
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sorted_head_index = sorted(
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range(self.num_heads), key=lambda k: heads_norm[k], reverse=True
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)
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reserve_head_index = []
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for i in range(num_heads_to_keep):
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start = sorted_head_index[i] * self.head_dim
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end = (sorted_head_index[i] + 1) * self.head_dim
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reserve_head_index.append((start, end))
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return reserve_head_index
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def _adaptive_prune_heads(self, reserve_head_index: List[Tuple[int, int]]):
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new_q_weight = []
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new_q_bias = []
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new_k_weight = []
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new_k_bias = []
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new_v_weight = []
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new_v_bias = []
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new_out_proj_weight = []
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for ele in reserve_head_index:
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start_idx, end_idx = ele
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new_q_weight.append(
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self.q_proj.weight[
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start_idx:end_idx,
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]
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)
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new_q_bias.append(self.q_proj.bias[start_idx:end_idx])
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new_k_weight.append(
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self.k_proj.weight[
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start_idx:end_idx,
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]
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)
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new_k_bias.append(self.k_proj.bias[start_idx:end_idx])
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new_v_weight.append(
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self.v_proj.weight[
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start_idx:end_idx,
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]
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)
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new_v_bias.append(self.v_proj.bias[start_idx:end_idx])
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new_out_proj_weight.append(self.out_proj.weight[:, start_idx:end_idx])
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new_q_weight = torch.cat(new_q_weight).detach()
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new_k_weight = torch.cat(new_k_weight).detach()
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new_v_weight = torch.cat(new_v_weight).detach()
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new_out_proj_weight = torch.cat(new_out_proj_weight, dim=-1).detach()
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new_q_weight.requires_grad = True
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new_k_weight.requires_grad = True
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new_v_weight.requires_grad = True
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new_out_proj_weight.requires_grad = True
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new_q_bias = torch.cat(new_q_bias).detach()
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new_q_bias.requires_grad = True
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new_k_bias = torch.cat(new_k_bias).detach()
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new_k_bias.requires_grad = True
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new_v_bias = torch.cat(new_v_bias).detach()
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new_v_bias.requires_grad = True
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self.q_proj.weight = torch.nn.Parameter(new_q_weight)
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self.q_proj.bias = torch.nn.Parameter(new_q_bias)
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self.k_proj.weight = torch.nn.Parameter(new_k_weight)
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self.k_proj.bias = torch.nn.Parameter(new_k_bias)
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self.v_proj.weight = torch.nn.Parameter(new_v_weight)
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self.v_proj.bias = torch.nn.Parameter(new_v_bias)
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self.out_proj.weight = torch.nn.Parameter(new_out_proj_weight)
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self.num_heads = len(reserve_head_index)
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self.embed_dim = self.head_dim * self.num_heads
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self.q_proj.out_features = self.embed_dim
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self.k_proj.out_features = self.embed_dim
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self.v_proj.out_features = self.embed_dim
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def _set_skip_embed_dim_check(self):
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self.skip_embed_dim_check = True
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def _pad_masks(
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self,
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key_padding_mask: Optional[Tensor],
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attn_mask: Optional[Tensor],
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) -> Tuple[Optional[Tensor], Optional[Tensor]]:
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if attn_mask is not None:
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shape = attn_mask.size()[:-1] + torch.Size([1])
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attn_mask = torch.cat([attn_mask, attn_mask.new_zeros(shape)], dim=-1)
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if key_padding_mask is not None:
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shape = key_padding_mask.size()[:-1] + torch.Size([1])
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key_padding_mask = torch.cat(
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[
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key_padding_mask,
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key_padding_mask.new_zeros(shape),
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],
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dim=-1,
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)
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return key_padding_mask, attn_mask
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def _add_bias(
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self,
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k: Tensor,
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v: Tensor,
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key_padding_mask: Optional[Tensor],
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attn_mask: Optional[Tensor],
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bsz: int,
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) -> Tuple[Tensor, Tensor, Optional[Tensor], Optional[Tensor]]:
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assert self.bias_k is not None
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assert self.bias_v is not None
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k = torch.cat([k, self.bias_k.repeat(1, bsz, 1)])
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v = torch.cat([v, self.bias_v.repeat(1, bsz, 1)])
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key_padding_mask, attn_mask = self._pad_masks(
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key_padding_mask=key_padding_mask, attn_mask=attn_mask
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)
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return k, v, key_padding_mask, attn_mask
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def _append_zero_attn(
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self,
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k: Tensor,
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v: Tensor,
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key_padding_mask: Optional[Tensor],
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attn_mask: Optional[Tensor],
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) -> Tuple[Tensor, Tensor, Optional[Tensor], Optional[Tensor]]:
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zero_attn_shape = k.size()[:-2] + torch.Size([1]) + k.size()[-1:]
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k = torch.cat(
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[k, torch.zeros(zero_attn_shape, dtype=k.dtype, device=k.device)],
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dim=-2,
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)
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v = torch.cat(
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[v, torch.zeros(zero_attn_shape, dtype=v.dtype, device=v.device)],
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dim=-2,
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)
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key_padding_mask, attn_mask = self._pad_masks(
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key_padding_mask=key_padding_mask, attn_mask=attn_mask
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)
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return k, v, key_padding_mask, attn_mask
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def forward(
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self,
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query: Tensor,
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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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) -> Tuple[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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if not self.skip_embed_dim_check:
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assert (
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embed_dim == self.embed_dim
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), f"query dim {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 value is not None
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assert src_len, key_bsz == value.shape[:2]
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if (
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not self.onnx_trace
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and not is_tpu # don't use PyTorch version on TPUs
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and incremental_state is None
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and not static_kv
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# A workaround for quantization to work. Otherwise JIT compilation
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# treats bias in linear module as method.
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and not torch.jit.is_scripting()
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# The Multihead attention implemented in pytorch forces strong dimension check
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# for input embedding dimention and K,Q,V projection dimension.
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# Since pruning will break the dimension check and it is not easy to modify the pytorch API,
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# it is preferred to bypass the pytorch MHA when we need to skip embed_dim_check
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and not self.skip_embed_dim_check
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):
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assert key is not None and value is not None
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return F.multi_head_attention_forward(
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query,
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key,
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value,
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self.embed_dim,
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self.num_heads,
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|
torch.empty([0]),
|
|
torch.cat((self.q_proj.bias, self.k_proj.bias, self.v_proj.bias)),
|
|
self.bias_k,
|
|
self.bias_v,
|
|
self.add_zero_attn,
|
|
self.dropout_module.p,
|
|
self.out_proj.weight,
|
|
self.out_proj.bias,
|
|
self.training or self.dropout_module.apply_during_inference,
|
|
key_padding_mask.bool() if key_padding_mask is not None else None,
|
|
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:
|
|
if self.beam_size > 1 and bsz == key.size(1):
|
|
# key is [T, bsz*beam_size, C], reduce to [T, bsz, C]
|
|
key = key.view(key.size(0), -1, self.beam_size, key.size(2))[
|
|
:, :, 0, :
|
|
]
|
|
if key_padding_mask is not None:
|
|
key_padding_mask = key_padding_mask.view(
|
|
-1, self.beam_size, key_padding_mask.size(1)
|
|
)[:, 0, :]
|
|
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, v, attn_mask, key_padding_mask = self._add_bias(
|
|
k, v, attn_mask, key_padding_mask, bsz
|
|
)
|
|
|
|
q = (
|
|
q.contiguous()
|
|
.view(tgt_len, bsz * self.num_heads, self.head_dim)
|
|
.transpose(0, 1)
|
|
)
|
|
kv_bsz = bsz # need default value for scripting
|
|
if k is not None:
|
|
kv_bsz = k.size(1)
|
|
k = (
|
|
k.contiguous()
|
|
.view(-1, kv_bsz * self.num_heads, self.head_dim)
|
|
.transpose(0, 1)
|
|
)
|
|
if v is not None:
|
|
v = (
|
|
v.contiguous()
|
|
.view(-1, kv_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
|
|
kv_bsz = _prev_key.size(0)
|
|
prev_key = _prev_key.view(kv_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
|
|
assert kv_bsz == _prev_value.size(0)
|
|
prev_value = _prev_value.view(
|
|
kv_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=kv_bsz,
|
|
src_len=k.size(1),
|
|
static_kv=static_kv,
|
|
)
|
|
|
|
saved_state["prev_key"] = k.view(kv_bsz, self.num_heads, -1, self.head_dim)
|
|
saved_state["prev_value"] = v.view(
|
|
kv_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) == kv_bsz
|
|
assert key_padding_mask.size(1) == src_len
|
|
|
|
if self.add_zero_attn:
|
|
assert v is not None
|
|
src_len += 1
|
|
k, v, key_padding_mask, attn_mask = self._append_zero_attn(
|
|
k=k, v=v, key_padding_mask=key_padding_mask, attn_mask=attn_mask
|
|
)
|
|
|
|
if self.encoder_decoder_attention and bsz != kv_bsz:
|
|
attn_weights = torch.einsum(
|
|
"bxhtd,bhsd->bxhts",
|
|
q.view((kv_bsz, -1, self.num_heads) + q.size()[1:]),
|
|
k.view((kv_bsz, self.num_heads) + k.size()[1:]),
|
|
)
|
|
attn_weights = attn_weights.reshape((-1,) + attn_weights.size()[-2:])
|
|
else:
|
|
attn_weights = torch.bmm(q, k.transpose(1, 2))
|
|
attn_weights = self.apply_sparse_mask(attn_weights, tgt_len, src_len, bsz)
|
|
|
|
assert list(attn_weights.size()) == [
|
|
bsz * self.num_heads,
|
|
tgt_len,
|
|
src_len,
|
|
]
|
|
|
|
if attn_mask is not None:
|
|
attn_mask = attn_mask.unsqueeze(0)
|
|
if self.onnx_trace:
|
|
attn_mask = attn_mask.repeat(attn_weights.size(0), 1, 1)
|
|
attn_weights += attn_mask
|
|
|
|
if key_padding_mask is not None:
|
|
# don't attend to padding symbols
|
|
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
|
|
if not is_tpu:
|
|
attn_weights = attn_weights.view(
|
|
kv_bsz, -1, self.num_heads, tgt_len, src_len
|
|
)
|
|
attn_weights = attn_weights.masked_fill(
|
|
key_padding_mask.unsqueeze(1)
|
|
.unsqueeze(2)
|
|
.unsqueeze(3)
|
|
.to(torch.bool),
|
|
float("-inf"),
|
|
)
|
|
else:
|
|
attn_weights = attn_weights.transpose(0, 2)
|
|
attn_weights = attn_weights.masked_fill(key_padding_mask, float("-inf"))
|
|
attn_weights = attn_weights.transpose(0, 2)
|
|
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
|
|
|
|
if before_softmax:
|
|
return attn_weights, v
|
|
|
|
attn_weights_float = utils.softmax(
|
|
attn_weights, dim=-1, onnx_trace=self.onnx_trace
|
|
)
|
|
attn_weights = attn_weights_float.type_as(attn_weights)
|
|
attn_probs = self.dropout_module(attn_weights)
|
|
|
|
assert v is not None
|
|
attn: Optional[Tensor] = None
|
|
if self.encoder_decoder_attention and bsz != kv_bsz:
|
|
attn = torch.einsum(
|
|
"bxhts,bhsd->bxhtd",
|
|
attn_probs.view(
|
|
(
|
|
kv_bsz,
|
|
-1,
|
|
self.num_heads,
|
|
)
|
|
+ attn_probs.size()[1:]
|
|
),
|
|
v.view(
|
|
(
|
|
kv_bsz,
|
|
self.num_heads,
|
|
)
|
|
+ v.size()[1:]
|
|
),
|
|
)
|
|
attn = attn.reshape((-1,) + attn.size()[-2:])
|
|
else:
|
|
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, self.embed_dim)
|
|
else:
|
|
attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, self.embed_dim)
|
|
attn = self.out_proj(attn)
|
|
attn_weights: Optional[Tensor] = None
|
|
if need_weights:
|
|
attn_weights = attn_weights_float.view(
|
|
bsz, self.num_heads, tgt_len, src_len
|
|
).transpose(1, 0)
|
|
if not need_head_weights:
|
|
# average attention weights over heads
|
|
attn_weights = attn_weights.mean(dim=0)
|
|
|
|
return attn, attn_weights
|
|
|
|
@staticmethod
|
|
def _append_prev_key_padding_mask(
|
|
key_padding_mask: Optional[Tensor],
|
|
prev_key_padding_mask: Optional[Tensor],
|
|
batch_size: int,
|
|
src_len: int,
|
|
static_kv: bool,
|
|
) -> Optional[Tensor]:
|
|
# saved key padding masks have shape (bsz, seq_len)
|
|
if prev_key_padding_mask is not None and static_kv:
|
|
new_key_padding_mask = prev_key_padding_mask
|
|
elif prev_key_padding_mask is not None and key_padding_mask is not None:
|
|
new_key_padding_mask = torch.cat(
|
|
[prev_key_padding_mask.float(), key_padding_mask.float()], dim=1
|
|
)
|
|
# During incremental decoding, as the padding token enters and
|
|
# leaves the frame, there will be a time when prev or current
|
|
# is None
|
|
elif prev_key_padding_mask is not None:
|
|
if src_len > prev_key_padding_mask.size(1):
|
|
filler = torch.zeros(
|
|
(batch_size, src_len - prev_key_padding_mask.size(1)),
|
|
device=prev_key_padding_mask.device,
|
|
)
|
|
new_key_padding_mask = torch.cat(
|
|
[prev_key_padding_mask.float(), filler.float()], dim=1
|
|
)
|
|
else:
|
|
new_key_padding_mask = prev_key_padding_mask.float()
|
|
elif key_padding_mask is not None:
|
|
if src_len > key_padding_mask.size(1):
|
|
filler = torch.zeros(
|
|
(batch_size, src_len - key_padding_mask.size(1)),
|
|
device=key_padding_mask.device,
|
|
)
|
|
new_key_padding_mask = torch.cat(
|
|
[filler.float(), key_padding_mask.float()], dim=1
|
|
)
|
|
else:
|
|
new_key_padding_mask = key_padding_mask.float()
|
|
else:
|
|
new_key_padding_mask = prev_key_padding_mask
|
|
return new_key_padding_mask
|
|
|
|
@torch.jit.export
|
|
def reorder_incremental_state(
|
|
self,
|
|
incremental_state: Optional[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:
|
|
if input_buffer_k.size(0) * self.beam_size == new_order.size(0):
|
|
return incremental_state
|
|
elif self.beam_size > 1:
|
|
input_buffer[k] = input_buffer_k.index_select(
|
|
0,
|
|
new_order.reshape(-1, self.beam_size)[:, 0]
|
|
// self.beam_size,
|
|
)
|
|
else:
|
|
input_buffer[k] = input_buffer_k.index_select(0, new_order)
|
|
else:
|
|
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 set_beam_size(self, beam_size):
|
|
"""Used for effiecient beamable enc-dec attention"""
|
|
self.beam_size = beam_size
|
|
|
|
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: Optional[Dict[str, Dict[str, Optional[Tensor]]]],
|
|
buffer: Dict[str, Optional[Tensor]],
|
|
):
|
|
return self.set_incremental_state(incremental_state, "attn_state", buffer)
|
|
|
|
def apply_sparse_mask(self, attn_weights, tgt_len: int, src_len: int, bsz: int):
|
|
return attn_weights
|
|
|
|
def upgrade_state_dict_named(self, state_dict, name):
|
|
prefix = name + "." if name != "" else ""
|
|
items_to_add = {}
|
|
keys_to_remove = []
|
|
for k in state_dict.keys():
|
|
if k.endswith(prefix + "in_proj_weight"):
|
|
# in_proj_weight used to be q + k + v with same dimensions
|
|
dim = int(state_dict[k].shape[0] / 3)
|
|
items_to_add[prefix + "q_proj.weight"] = state_dict[k][:dim]
|
|
items_to_add[prefix + "k_proj.weight"] = state_dict[k][dim : 2 * dim]
|
|
items_to_add[prefix + "v_proj.weight"] = state_dict[k][2 * dim :]
|
|
|
|
keys_to_remove.append(k)
|
|
|
|
k_bias = prefix + "in_proj_bias"
|
|
if k_bias in state_dict.keys():
|
|
dim = int(state_dict[k].shape[0] / 3)
|
|
items_to_add[prefix + "q_proj.bias"] = state_dict[k_bias][:dim]
|
|
items_to_add[prefix + "k_proj.bias"] = state_dict[k_bias][
|
|
dim : 2 * dim
|
|
]
|
|
items_to_add[prefix + "v_proj.bias"] = state_dict[k_bias][2 * dim :]
|
|
|
|
keys_to_remove.append(prefix + "in_proj_bias")
|
|
|
|
for k in keys_to_remove:
|
|
del state_dict[k]
|
|
|
|
for key, value in items_to_add.items():
|
|
state_dict[key] = value
|