Source code for modelzoo.common.pytorch.layers.TransformerDecoderLayer
# Copyright 2022 Cerebras Systems.
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"""
Adapted from https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/transformer.py
"""
from typing import Callable, Optional, Tuple, Type, Union
import torch.nn as nn
from torch import Tensor
from torch.nn import Dropout, LayerNorm
from modelzoo.common.pytorch.layers.AttentionHelper import get_attention_module
from modelzoo.common.pytorch.layers.FeedForwardNetwork import FeedForwardNetwork
from modelzoo.common.pytorch.model_utils.RotaryPositionEmbeddingHelper import (
RotaryPositionEmbeddingHelper,
)
SelfAttnKV = Tuple[Tensor, Tensor]
CrossAttnKV = Tuple[Tensor, Tensor]
SelfAndCrossAttnKV = Tuple[Tensor, Tensor, Tensor, Tensor]
[docs]class TransformerDecoderLayer(nn.Module):
r"""
TransformerDecoderLayer is made up of self-attn, multihead-attn and feedforward network.
This standard decoder layer is based on the paper "Attention Is All You Need".
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez,
Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in
Neural Information Processing Systems, pages 6000-6010. Users may modify or implement
in a different way during application.
Args:
d_model: the number of expected features in the input (required).
nhead: the number of heads in the multihead-attention models (required).
dim_feedforward: the dimension of the feedforward network model (default=2048).
dropout: the dropout value (default=0.1).
activation: the activation function of the intermediate layer, can be a string
("relu" or "gelu") or a unary callable. Default: gelu
layer_norm_eps: the eps value in layer normalization components (default=1e-5).
batch_first: If ``True``, then the input and output tensors are provided
as (batch, seq, feature). Default: ``False`` (seq, batch, feature).
norm_layer: the normalization class that will be used before/after FF layers (default=nn.LayerNorm)
norm_first: if ``True``, layer norm is done prior to self attention, multihead
attention and feedforward operations, respectively. Otherwise it's done after.
Default: ``False`` (after).
attention_dropout_rate: Attention dropout rate. If None, defaults to dropout.
attention_softmax_fp32: Use FP32 softmax in attention block.
use_projection_bias_in_attention: Add bias to Q,K,V projections
in the Attention layer. Defaults to False.
attention_type: Should be in ["scaled_dot_product", "dot_product"]
scale_qk_dot_by_d (bool): If ``True`` scales QK^T dot product by d(=hidden/d_head) instead of sqrt(d).
attention_inner_dim (int): Number of output units in attention query/key/value projection. Defaults to d_model
add_cross_attention: If ``True``, adds cross-attention layer between encoder/decoder,
otherwise, only self-attention is used in the decoder (GPT-style models should set to ``False``)
use_ffn_bias_in_attention: Add bias in the concluding FFN
in the Attention layer. Defaults to False.
use_ffn_bias: Add bias in all dense layers of the decoder's ffn sublayer
attention_initializer: Attention layer initializer. Defaults to "xavier_uniform".
attention_q_initializer: Query projection kernel initializer. If not
specified, the query will be initialized via ``attention_initializer``
attention_output_layer_initializer: attention output layer projection initializer. If not
specified, the output will be initialized via ``attention_initializer``
ffn_initializer: FFN layer initializer. Defaults to "xavier_uniform".
ffn_output_layer_initializer: If not None, initialize the last FFN layer
with this initializer. Defaults to None.
use_ff_layer1_dropout: If ``True``, dropout will be enabled after the first feed forward layer. Default: True
use_ff_layer2_dropout = If ``True``, dropout will be enabled after the second feed forward layer. Default: True
ffn_dropout_rate: Controls dropout rate of FF's first layer. If None, defaults to dropout.
Examples:
>>> decoder_layer = nn.TransformerDecoderLayer(d_model=512, nhead=8, batch_first=True)
>>> memory = torch.rand(32, 10, 512)
>>> tgt = torch.rand(32, 20, 512)
>>> out = decoder_layer(tgt, memory)
"""
[docs] def __init__(
self,
d_model: int,
nhead: int,
dim_feedforward: int = 2048,
dropout: float = 0.1,
activation: Union[str, Callable[[Tensor], Tensor]] = "gelu",
layer_norm_eps: float = 1e-5,
batch_first: bool = True,
norm_layer: Type[nn.Module] = LayerNorm,
norm_first: bool = False,
attention_module: Union[str, nn.Module] = "aiayn_attention",
extra_attention_params={},
device=None,
add_cross_attention: bool = True,
attention_dropout_rate: Optional[float] = None,
attention_softmax_fp32: Optional[bool] = True,
attention_type="scaled_dot_product",
scale_qk_dot_by_d=False,
scale_qk_dot_by_layer_idx=False,
attention_inner_dim=None,
use_projection_bias_in_attention=False,
use_ffn_bias_in_attention=False,
use_ffn_bias=False,
attention_initializer="xavier_uniform",
attention_q_initializer=None,
attention_output_layer_initializer=None,
ffn_initializer="xavier_uniform",
ffn_output_layer_initializer=None,
use_ff_layer1_dropout: bool = True,
use_ff_layer2_dropout: bool = True,
ffn_dropout_rate: Optional[float] = None,
) -> None:
super(TransformerDecoderLayer, self).__init__()
assert batch_first, "Currently, only batch_first=True is supported"
self.add_cross_attention = add_cross_attention
if attention_dropout_rate is None:
attention_dropout_rate = dropout
AttentionModule = get_attention_module(
attention_module, extra_attention_params
)
self.self_attn = AttentionModule(
d_model,
nhead,
inner_dim=attention_inner_dim,
dropout=attention_dropout_rate,
batch_first=batch_first,
attention_type=attention_type,
scale_qk_dot_by_d=scale_qk_dot_by_d,
softmax_dtype_fp32=attention_softmax_fp32,
use_projection_bias=use_projection_bias_in_attention,
use_ffn_bias=use_ffn_bias_in_attention,
attention_initializer=attention_initializer,
attention_q_initializer=attention_q_initializer,
output_layer_initializer=attention_output_layer_initializer,
scale_qk_dot_by_layer_idx=scale_qk_dot_by_layer_idx,
device=device,
**extra_attention_params,
)
self.norm_first = norm_first
self.norm1 = norm_layer(d_model, eps=layer_norm_eps, device=device,)
self.dropout1 = Dropout(dropout)
self.norm3 = norm_layer(d_model, eps=layer_norm_eps, device=device,)
if self.add_cross_attention:
self.multihead_attn = AttentionModule(
d_model,
nhead,
inner_dim=attention_inner_dim,
dropout=attention_dropout_rate,
batch_first=batch_first,
attention_type=attention_type,
softmax_dtype_fp32=attention_softmax_fp32,
use_projection_bias=use_projection_bias_in_attention,
use_ffn_bias=use_ffn_bias_in_attention,
attention_initializer=attention_initializer,
attention_q_initializer=attention_q_initializer,
output_layer_initializer=attention_output_layer_initializer,
scale_qk_dot_by_layer_idx=scale_qk_dot_by_layer_idx,
device=device,
**extra_attention_params,
)
self.norm2 = norm_layer(d_model, eps=layer_norm_eps, device=device,)
self.dropout2 = Dropout(dropout)
if ffn_dropout_rate is None:
ffn_dropout_rate = dropout
self.ffn = FeedForwardNetwork(
input_unit=d_model,
layers_units=[dim_feedforward, d_model],
layers_activation=[activation, None],
layers_dropout_rates=[
ffn_dropout_rate if use_ff_layer1_dropout else None,
dropout if use_ff_layer2_dropout else None,
],
use_bias=use_ffn_bias,
kernel_initializer=ffn_initializer,
output_layer_initializer=ffn_output_layer_initializer,
bias_initializer="zeros",
device=device,
)
self.__reset_parameters()
def reset_parameters(self):
self.__reset_parameters()
def __reset_parameters(self):
self.self_attn.reset_parameters()
self.ffn.reset_parameters()
if hasattr(self.norm1, 'bias'):
self.norm1.bias.data.zero_()
if hasattr(self.norm1, 'weight') and hasattr(self.norm1.weight, 'data'):
self.norm1.weight.data.fill_(1.0)
if self.norm3 is not None:
if hasattr(self.norm3, 'bias'):
self.norm3.bias.data.zero_()
if hasattr(self.norm3, 'weight') and hasattr(
self.norm3.weight, 'data'
):
self.norm3.weight.data.fill_(1.0)
if self.add_cross_attention:
self.multihead_attn.reset_parameters()
if hasattr(self.norm2, 'bias'):
self.norm2.bias.data.zero_()
if hasattr(self.norm2, 'weight') and hasattr(
self.norm2.weight, 'data'
):
self.norm2.weight.data.fill_(1.0)
[docs] def forward(
self,
tgt: Tensor,
memory: Optional[Tensor] = None,
tgt_mask: Optional[Tensor] = None,
memory_mask: Optional[Tensor] = None,
tgt_key_padding_mask: Optional[Tensor] = None,
memory_key_padding_mask: Optional[Tensor] = None,
rotary_position_embedding_helper: Optional[
RotaryPositionEmbeddingHelper
] = None,
past_kv: Optional[Union[SelfAttnKV, SelfAndCrossAttnKV]] = None,
cache_present_kv: bool = False,
self_attn_position_bias: Optional[Tensor] = None,
cross_attn_position_bias: Optional[Tensor] = None,
layer_idx: Optional[int] = None,
**extra_args,
) -> Union[Tensor, Tuple[Tensor, Union[SelfAttnKV, SelfAndCrossAttnKV]]]:
r"""Pass the inputs (and mask) through the decoder layer.
Args:
tgt: the sequence to the decoder layer (required).
memory: the sequence from the last layer of the encoder (required).
tgt_mask: the mask for the tgt sequence (optional).
memory_mask: the mask for the memory sequence (optional).
tgt_key_padding_mask: the mask for the tgt keys per batch (optional).
memory_key_padding_mask: the mask for the memory keys per batch (optional).
rotary_position_embedding_helper (Optional[RotaryPositionEmbeddingHelper]):
A helper class to apply rotary embedding on the input tensor.
past_kv: Past keys and values for self attention and (if applicable) cross
attention modules. Key/value tensors have shape
``[batch_size, num_heads, seq_length, embed_dim / num_heads]``. (optional).
cache_present_kv: Specifies if the present keys and values
must be cached and returned. Needed to speed up the
computations when the decoder is called within an
autoregressive loop. (optional).
self_attn_position_bias: the tensor containing position bias to apply in self-attention,
can be obtained from relative or alibi position embeddings.
Shape:
see the docs in Transformer class.
"""
# see Fig. 1 of https://arxiv.org/pdf/2002.04745v1.pdf
assert (
past_kv is None and not cache_present_kv
), "Cannot provide past_kv because inference is not supported yet."
x = tgt
if self.norm_first:
attn1_out = self._sa_block(
self.norm1(x),
tgt_mask,
tgt_key_padding_mask,
rotary_position_embedding_helper=rotary_position_embedding_helper,
past_kv=past_kv[:2] if past_kv is not None else None,
cache_present_kv=cache_present_kv,
self_attn_position_bias=self_attn_position_bias,
layer_idx=layer_idx,
**extra_args,
)
x = x + attn1_out[0]
if self.add_cross_attention:
attn2_out = self._mha_block(
self.norm2(x),
memory,
memory_mask,
memory_key_padding_mask,
past_kv=past_kv[2:] if past_kv is not None else None,
cache_present_kv=cache_present_kv,
cross_attn_position_bias=cross_attn_position_bias,
layer_idx=layer_idx,
**extra_args,
)
x = x + attn2_out[0]
x = (
x + self.ffn(self.norm3(x))
if self.norm3 is not None
else x + self.ffn(x)
)
else:
attn1_out = self._sa_block(
x,
tgt_mask,
tgt_key_padding_mask,
rotary_position_embedding_helper=rotary_position_embedding_helper,
past_kv=past_kv[:2] if past_kv is not None else None,
cache_present_kv=cache_present_kv,
self_attn_position_bias=self_attn_position_bias,
layer_idx=layer_idx,
**extra_args,
)
x = self.norm1(x + attn1_out[0])
if self.add_cross_attention:
attn2_out = self._mha_block(
x,
memory,
memory_mask,
memory_key_padding_mask,
past_kv=past_kv[2:] if past_kv is not None else None,
cache_present_kv=cache_present_kv,
cross_attn_position_bias=cross_attn_position_bias,
layer_idx=layer_idx,
**extra_args,
)
x = self.norm2(x + attn2_out[0])
x = (
self.norm3(x + self.ffn(x))
if self.norm3 is not None
else x + self.ffn(x)
)
if not cache_present_kv:
return x
else:
present_kv = (
attn1_out[1]
if not self.add_cross_attention
else attn1_out[1] + attn2_out[1]
)
return x, present_kv
# self-attention block
def _sa_block(
self,
x: Tensor,
attn_mask: Optional[Tensor],
key_padding_mask: Optional[Tensor],
rotary_position_embedding_helper: Optional[
RotaryPositionEmbeddingHelper
] = None,
past_kv: Optional[SelfAttnKV] = None,
cache_present_kv: bool = False,
self_attn_position_bias: Optional[Tensor] = None,
layer_idx: Optional[int] = None,
**extra_args,
) -> Tensor:
attn_out = self.self_attn(
x,
x,
x,
attn_mask=attn_mask,
key_padding_mask=key_padding_mask,
need_weights=False,
rotary_position_embedding_helper=rotary_position_embedding_helper,
past_kv=past_kv,
cache_present_kv=cache_present_kv,
position_bias=self_attn_position_bias,
layer_idx=layer_idx,
**extra_args,
)
if cache_present_kv:
out, present_kv = attn_out
else:
out = attn_out
out = (self.dropout1(out),)
if cache_present_kv:
out += (present_kv,)
return out
# multihead attention block
def _mha_block(
self,
x: Tensor,
mem: Tensor,
attn_mask: Optional[Tensor],
key_padding_mask: Optional[Tensor],
past_kv: Optional[CrossAttnKV] = None,
cache_present_kv: bool = False,
cross_attn_position_bias: Optional[Tensor] = None,
layer_idx: Optional[int] = None,
**extra_args,
) -> Tensor:
attn_out = self.multihead_attn(
x,
mem,
mem,
attn_mask=attn_mask,
key_padding_mask=key_padding_mask,
need_weights=False,
past_kv=past_kv,
cache_present_kv=cache_present_kv,
past_kv_self_attn=False,
position_bias=cross_attn_position_bias,
layer_idx=layer_idx,
**extra_args,
)
if cache_present_kv:
x, present_kv = attn_out
else:
x = attn_out
out = (self.dropout2(x),)
if cache_present_kv:
out += (present_kv,)
return out