mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-01-24 12:42:54 +08:00
deaae40bd7
* Update TensorRT-LLM --------- Co-authored-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
210 lines
8.2 KiB
Python
210 lines
8.2 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
|
|
# SPDX-License-Identifier: Apache-2.0
|
|
#
|
|
# 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 ..._utils import pad_vocab_size
|
|
from ...functional import Tensor
|
|
from ...layers import (MLP, Attention, AttentionMaskType, ColumnLinear,
|
|
LayerNorm, PositionEmbeddingType)
|
|
from ...module import Module
|
|
from ..gpt.model import GPTEmbedding
|
|
from ..modeling_utils import DecoderLayerList, DecoderModelForCausalLM
|
|
|
|
|
|
class OPTDecoderLayer(Module):
|
|
|
|
def __init__(self, config, layer_idx):
|
|
super().__init__()
|
|
self.layer_idx = layer_idx
|
|
self.config = config
|
|
self.do_layer_norm_before = self.config.do_layer_norm_before
|
|
|
|
hidden_size = config.hidden_size
|
|
dtype = config.dtype
|
|
tp_group = config.mapping.tp_group
|
|
tp_size = config.mapping.tp_size
|
|
|
|
self.input_layernorm = LayerNorm(normalized_shape=hidden_size,
|
|
dtype=dtype)
|
|
|
|
self.attention = Attention(
|
|
hidden_size,
|
|
config.num_attention_heads,
|
|
max_position_embeddings=config.max_position_embeddings,
|
|
attention_mask_type=AttentionMaskType.causal,
|
|
dtype=dtype,
|
|
tp_group=tp_group,
|
|
tp_size=tp_size,
|
|
instance_id=2 * layer_idx)
|
|
|
|
mlp_hidden_size = hidden_size * 4 if config.intermediate_size is None else config.intermediate_size
|
|
|
|
self.mlp = MLP(hidden_size=hidden_size,
|
|
ffn_hidden_size=mlp_hidden_size,
|
|
hidden_act=config.hidden_act,
|
|
dtype=dtype,
|
|
tp_group=tp_group,
|
|
tp_size=tp_size,
|
|
instance_id=2 * layer_idx + 1)
|
|
self.post_layernorm = LayerNorm(normalized_shape=hidden_size,
|
|
dtype=dtype)
|
|
|
|
def forward(self,
|
|
hidden_states: Tensor,
|
|
attention_mask=None,
|
|
use_cache=False,
|
|
kv_cache_params=None,
|
|
attention_params=None,
|
|
all_reduce_workspace=None):
|
|
residual = hidden_states
|
|
|
|
attention_input = hidden_states
|
|
if self.do_layer_norm_before:
|
|
attention_input = self.input_layernorm(hidden_states)
|
|
|
|
# At this point the hidden_states object must be a Tensor.
|
|
assert isinstance(attention_input, Tensor)
|
|
|
|
attention_output = self.attention(attention_input,
|
|
attention_mask=attention_mask,
|
|
use_cache=use_cache,
|
|
kv_cache_params=kv_cache_params,
|
|
attention_params=attention_params,
|
|
workspace=all_reduce_workspace)
|
|
if use_cache:
|
|
attention_output, presents = attention_output
|
|
|
|
hidden_states = residual + attention_output
|
|
if not self.do_layer_norm_before:
|
|
hidden_states = self.input_layernorm(hidden_states)
|
|
|
|
residual = hidden_states
|
|
if self.do_layer_norm_before:
|
|
hidden_states = self.post_layernorm(hidden_states)
|
|
|
|
hidden_states = self.mlp(hidden_states, all_reduce_workspace)
|
|
|
|
hidden_states = residual + hidden_states
|
|
|
|
if not self.do_layer_norm_before:
|
|
hidden_states = self.post_layernorm(hidden_states)
|
|
|
|
if use_cache:
|
|
return (hidden_states, presents)
|
|
return hidden_states
|
|
|
|
|
|
class OPTModel(Module):
|
|
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.do_layer_norm_before = config.do_layer_norm_before
|
|
use_parallel_embedding = config.use_parallel_embedding
|
|
embedding_sharding_dim = config.embedding_sharding_dim
|
|
use_prompt_tuning = config.use_prompt_tuning
|
|
mapping = config.mapping
|
|
|
|
self.embedding = GPTEmbedding(
|
|
config.vocab_size,
|
|
config.hidden_size,
|
|
config.max_position_embeddings,
|
|
position_embedding_type=PositionEmbeddingType.learned_absolute,
|
|
dtype=config.dtype,
|
|
use_prompt_tuning=use_prompt_tuning,
|
|
tensor_parallel=mapping.tp_size if use_parallel_embedding else 1,
|
|
tensor_parallel_group=mapping.tp_group
|
|
if use_parallel_embedding else None,
|
|
sharding_dim=embedding_sharding_dim,
|
|
tp_rank=mapping.tp_rank)
|
|
|
|
self.layers = DecoderLayerList(OPTDecoderLayer, config)
|
|
|
|
if self.do_layer_norm_before:
|
|
self.ln_f = LayerNorm(normalized_shape=config.hidden_size,
|
|
dtype=config.dtype)
|
|
|
|
def forward(self,
|
|
input_ids: Tensor,
|
|
position_ids=None,
|
|
use_cache=False,
|
|
attention_mask=None,
|
|
kv_cache_params=None,
|
|
attention_params=None,
|
|
prompt_embedding_table=None,
|
|
prompt_tasks=None,
|
|
prompt_vocab_size=None,
|
|
all_reduce_workspace=None):
|
|
|
|
hidden_states = self.embedding(input_ids, position_ids,
|
|
prompt_embedding_table, prompt_tasks,
|
|
prompt_vocab_size, all_reduce_workspace)
|
|
|
|
hidden_states = self.layers(hidden_states,
|
|
use_cache=use_cache,
|
|
attention_mask=attention_mask,
|
|
kv_cache_params=kv_cache_params,
|
|
attention_params=attention_params,
|
|
all_reduce_workspace=all_reduce_workspace)
|
|
|
|
if use_cache:
|
|
hidden_states, presents = hidden_states
|
|
|
|
if self.do_layer_norm_before:
|
|
hidden_states = self.ln_f(hidden_states)
|
|
|
|
if use_cache:
|
|
return (hidden_states, tuple(presents))
|
|
return hidden_states
|
|
|
|
|
|
class OPTForCausalLM(DecoderModelForCausalLM):
|
|
|
|
def __init__(self, config):
|
|
use_parallel_embedding = config.use_parallel_embedding
|
|
embedding_sharding_dim = config.embedding_sharding_dim
|
|
share_embedding_table = config.share_embedding_table
|
|
mapping = config.mapping
|
|
|
|
if share_embedding_table and mapping.tp_size > 1:
|
|
if (not use_parallel_embedding) or (use_parallel_embedding and
|
|
embedding_sharding_dim == 1):
|
|
raise NotImplementedError(
|
|
'For multiple-processes cases, sharing the embedding table must set use_parallel_embedding=True and embedding_sharding_dim=0'
|
|
)
|
|
|
|
transformer = OPTModel(config)
|
|
vocab_size_padded = pad_vocab_size(config.vocab_size,
|
|
config.mapping.tp_size)
|
|
|
|
share_weight = None
|
|
if share_embedding_table:
|
|
share_weight = transformer.embedding.vocab_embedding.weight
|
|
|
|
lm_head = ColumnLinear(config.hidden_size,
|
|
vocab_size_padded,
|
|
bias=False,
|
|
dtype=config.dtype,
|
|
tp_group=mapping.tp_group,
|
|
tp_size=mapping.tp_size,
|
|
gather_output=True,
|
|
share_weight=share_weight)
|
|
|
|
super().__init__(config, transformer, lm_head)
|
|
|
|
def check_config(self):
|
|
self.config.set_if_not_exist('use_parallel_embedding', False)
|
|
self.config.set_if_not_exist('embedding_sharding_dim', 0)
|
|
self.config.set_if_not_exist('share_embedding_table', False)
|
|
self.config.set_if_not_exist('do_layer_norm_before', False)
|