Source code for cerebras.modelzoo.layers.TransformerDecoder
# Copyright 2022 Cerebras Systems.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
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 cerebras.modelzoo.layers.RotaryPositionEmbeddingHelper import (
RotaryPositionEmbeddingHelper,
)
from cerebras.modelzoo.layers.utils import _get_clones
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,
extract_layer_idx: Optional[int] = None,
**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).
extract_layer_idx: (inclusive)layer index in range [0, self.num_layers) (zero-indexed)
Applies decoder layers up to (and including) `extract_layer_idx`
instead of all decoder layers.
For ex: extract_layer_idx=3 would run fwd pass from decoder_block_0 to decoder_block_3
and return outputs from decoder_block_3.
If `extract_layer_idx` = None and `norm` != None, then
the output returned would be decoder_block_{self.num_layers-1} -> norm -> output (return)
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 = []
_is_extract_idx_was_none = extract_layer_idx is None
if extract_layer_idx == None:
extract_layer_idx = self.num_layers - 1
for layer_idx in range(extract_layer_idx + 1):
mod = self.layers[layer_idx]
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 and _is_extract_idx_was_none:
output = self.norm(output)
if cache_present_kv:
return (output, present_kv)
else:
return output