Source code for modelzoo.common.pytorch.layers.TransformerDecoder

# Copyright 2022 Cerebras Systems.
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# Licensed under the Apache License, Version 2.0 (the "License");
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#     http://www.apache.org/licenses/LICENSE-2.0
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"""
Adapted from https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/transformer.py
"""

from typing import List, Optional, Tuple, Union

import torch.nn as nn
from torch import Tensor

from modelzoo.common.pytorch.layers.utils import _get_clones
from modelzoo.common.pytorch.model_utils.RotaryPositionEmbeddingHelper import (
    RotaryPositionEmbeddingHelper,
)

SelfAttnKV = Tuple[Tensor, Tensor]
SelfAndCrossAttnKV = Tuple[Tensor, Tensor, Tensor, Tensor]


[docs]class TransformerDecoder(nn.Module): r"""TransformerDecoder is a stack of N decoder layers Args: decoder_layer: an instance of the TransformerDecoderLayer() class (required). num_layers: the number of sub-decoder-layers in the decoder (required). norm: the layer normalization component (optional). Examples:: >>> decoder_layer = nn.TransformerDecoderLayer(d_model=512, nhead=8) >>> transformer_decoder = nn.TransformerDecoder(decoder_layer, num_layers=6) >>> memory = torch.rand(10, 32, 512) >>> tgt = torch.rand(20, 32, 512) >>> out = transformer_decoder(tgt, memory) """
[docs] def __init__(self, decoder_layer, num_layers, norm=None): super(TransformerDecoder, self).__init__() self.layers = _get_clones(decoder_layer, num_layers) self.num_layers = num_layers self.norm = norm # Re-initialize all layers to get new set of weights for each layer self.__reset_parameters()
def reset_parameters(self): self.__reset_parameters() def __reset_parameters(self): for layer in self.layers: layer.reset_parameters() if self.norm: if hasattr(self.norm, 'bias'): self.norm.bias.data.zero_() self.norm.weight.data.fill_(1.0)
[docs] def forward( self, tgt: Tensor, memory: Optional[Tensor] = None, tgt_mask: Optional[Tensor] = None, sparse_mask: Optional[Tensor] = None, memory_mask: Optional[Tensor] = None, tgt_key_padding_mask: Optional[Tensor] = None, memory_key_padding_mask: Optional[Tensor] = None, self_attn_position_bias: Optional[Tensor] = None, cross_attn_position_bias: Optional[Tensor] = None, rotary_position_embedding_helper: Optional[ RotaryPositionEmbeddingHelper ] = None, past_kv: Optional[List[Union[SelfAttnKV, SelfAndCrossAttnKV]]] = None, cache_present_kv: bool = False, **extra_args, ) -> Union[ Tensor, Tuple[Tensor, List[Union[SelfAttnKV, SelfAndCrossAttnKV]]] ]: r"""Pass the inputs (and mask) through the decoder layer in turn. Args: tgt: the sequence to the decoder (required). memory: the sequence from the last layer of the encoder (optional). 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). self_attn_position_bias: the tensor containing position bias to apply in self-attention, can be obtained from relative or alibi position embeddings. cross_attn_position_bias: similar to self_attn_position_bias, this is the tensor containing position bias to apply in cross-attention. rotary_position_embedding_helper (Optional[RotaryPositionEmbeddingHelper]): A helper class to apply rotary embedding on the input tensor. past_kv: Past keys and values for each of the decoder layers (optional). cache_present_kv: Specifies if the present keys and values must be cached and returned. (optional). Shape: see the docs in Transformer class. """ assert ( past_kv is None and not cache_present_kv ), "Cannot provide past_kv because inference is not supported yet." output = tgt present_kv = [] for layer_idx, mod in enumerate(self.layers): output = mod( output, memory=memory, # Alternate between dense and fixed sparse attention, # This is used in GPT-3 model. tgt_mask=sparse_mask if layer_idx % 2 != 0 and sparse_mask is not None else tgt_mask, memory_mask=memory_mask, tgt_key_padding_mask=tgt_key_padding_mask, memory_key_padding_mask=memory_key_padding_mask, rotary_position_embedding_helper=rotary_position_embedding_helper, past_kv=None if past_kv is None else past_kv[layer_idx], cache_present_kv=cache_present_kv, self_attn_position_bias=self_attn_position_bias, cross_attn_position_bias=cross_attn_position_bias, layer_idx=layer_idx, **extra_args, ) if cache_present_kv: present_kv.append(output[1]) output = output[0] if self.norm is not None: output = self.norm(output) if cache_present_kv: return (output, present_kv) else: return output