147 lines
5.9 KiB
Python
147 lines
5.9 KiB
Python
# Copyright 2024 Bytedance Ltd. and/or its affiliates
|
|
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
|
|
#
|
|
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
|
|
# and OPT implementations in this library. It has been modified from its
|
|
# original forms to accommodate minor architectural differences compared
|
|
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
|
|
#
|
|
# 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.
|
|
|
|
from typing import Optional, Tuple
|
|
|
|
import torch
|
|
from torch import nn
|
|
from transformers import LlamaConfig
|
|
from megatron.core import ModelParallelConfig
|
|
|
|
from .parallel_attention import ParallelLlamaAttention, ParallelLlamaAttentionRmPad
|
|
from .parallel_mlp import ParallelLlamaMLP
|
|
from .parallel_rmsnorm import ParallelLlamaRMSNorm
|
|
|
|
|
|
class ParallelLlamaDecoderLayer(nn.Module):
|
|
|
|
def __init__(self, config: LlamaConfig, megatron_config: ModelParallelConfig):
|
|
super().__init__()
|
|
self.hidden_size = config.hidden_size
|
|
self.self_attn = ParallelLlamaAttention(config=config, megatron_config=megatron_config)
|
|
|
|
self.mlp = ParallelLlamaMLP(config, megatron_config=megatron_config)
|
|
self.input_layernorm = ParallelLlamaRMSNorm(config, megatron_config)
|
|
self.post_attention_layernorm = ParallelLlamaRMSNorm(config, megatron_config)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
|
|
"""
|
|
Args:
|
|
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
|
|
attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
|
|
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
|
|
output_attentions (`bool`, *optional*):
|
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
|
|
returned tensors for more detail.
|
|
use_cache (`bool`, *optional*):
|
|
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
|
|
(see `past_key_values`).
|
|
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
|
|
"""
|
|
|
|
residual = hidden_states
|
|
|
|
hidden_states = self.input_layernorm(hidden_states)
|
|
|
|
# Note: sequence parallel is hidden inside ColumnParallelLinear
|
|
# reduce scatter is hidden inside RowParallelLinear
|
|
|
|
# Self Attention
|
|
hidden_states = self.self_attn(
|
|
hidden_states=hidden_states,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
)
|
|
|
|
# TODO: add sequence parallel operator reduce_scatter here
|
|
|
|
hidden_states = residual + hidden_states
|
|
|
|
# Fully Connected
|
|
residual = hidden_states
|
|
hidden_states = self.post_attention_layernorm(hidden_states)
|
|
|
|
# TODO: add sequence parallel operator all_gather here
|
|
|
|
hidden_states = self.mlp(hidden_states)
|
|
|
|
# TODO: add sequence parallel operator reduce_scatter here
|
|
|
|
hidden_states = residual + hidden_states
|
|
|
|
outputs = hidden_states
|
|
|
|
return outputs
|
|
|
|
|
|
class ParallelLlamaDecoderLayerRmPad(nn.Module):
|
|
|
|
def __init__(self, config: LlamaConfig, megatron_config: ModelParallelConfig):
|
|
super().__init__()
|
|
self.config = config
|
|
self.megatron_config = megatron_config
|
|
self.hidden_size = config.hidden_size
|
|
self.self_attn = ParallelLlamaAttentionRmPad(config=config, megatron_config=megatron_config)
|
|
|
|
self.mlp = ParallelLlamaMLP(config, megatron_config=megatron_config)
|
|
self.input_layernorm = ParallelLlamaRMSNorm(config, megatron_config)
|
|
self.post_attention_layernorm = ParallelLlamaRMSNorm(config, megatron_config)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
sequence_length: int = None,
|
|
indices: torch.Tensor = None,
|
|
cu_seqlens: int = None,
|
|
max_seqlen_in_batch: int = None
|
|
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
|
|
residual = hidden_states # (total_nnz // sp, 1, hidden_size)
|
|
|
|
hidden_states = self.input_layernorm(hidden_states)
|
|
|
|
# Self Attention
|
|
# (total_nnz // sp, 1, hidden_size) -> all-gather (total_nnz, 1, hidden_size)
|
|
# -> col + row -> reduce-scatter -> (total_nnz // sp, 1, hidden_size)
|
|
hidden_states = self.self_attn(hidden_states=hidden_states,
|
|
position_ids=position_ids,
|
|
sequence_length=sequence_length,
|
|
indices=indices,
|
|
cu_seqlens=cu_seqlens,
|
|
max_seqlen_in_batch=max_seqlen_in_batch)
|
|
|
|
hidden_states = residual + hidden_states
|
|
|
|
# Fully Connected
|
|
# shape changes same as attn
|
|
residual = hidden_states
|
|
hidden_states = self.post_attention_layernorm(hidden_states)
|
|
hidden_states = self.mlp(hidden_states)
|
|
hidden_states = residual + hidden_states
|
|
|
|
outputs = hidden_states
|
|
|
|
return outputs
|