mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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59071268b6
* Use PEP 563 in attention.py and fix conflict * merge commit * Change what was left out last time
433 lines
13 KiB
Python
433 lines
13 KiB
Python
"""
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# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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from __future__ import annotations
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from functools import partial
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import paddle
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from paddle import nn
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from paddleformers.transformers import PretrainedModel
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from paddleformers.utils.log import logger
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from fastdeploy.config import FDConfig, ModelConfig
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from fastdeploy.model_executor.graph_optimization.decorator import \
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support_graph_optimization
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from fastdeploy.model_executor.layers.activation import SiluAndMul
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from fastdeploy.model_executor.layers.attention.attention import Attention
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from fastdeploy.model_executor.layers.embeddings import VocabParallelEmbedding
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from fastdeploy.model_executor.layers.linear import (
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MergedColumnParallelLinear, QKVParallelLinear, RowParallelLinear)
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from fastdeploy.model_executor.layers.lm_head import ParallelLMHead
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from fastdeploy.model_executor.layers.normalization import RMSNorm
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from fastdeploy.model_executor.models.model_base import ModelForCasualLM
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from fastdeploy.model_executor.forward_meta import ForwardMeta
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class Qwen2MLP(nn.Layer):
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"""
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"""
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def __init__(
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self,
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fd_config: FDConfig,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.nranks = fd_config.parallel_config.tensor_parallel_degree
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self.gate_up_proj = MergedColumnParallelLinear(
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fd_config=fd_config,
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prefix=f"{prefix}.up_gate_proj",
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input_size=fd_config.model_config.hidden_size,
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output_size=fd_config.model_config.ffn_hidden_size * 2,
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with_bias=False,
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activation=fd_config.model_config.hidden_act,
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)
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self.down_proj = RowParallelLinear(
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fd_config=fd_config,
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prefix=f"{prefix}.down_proj",
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input_size=fd_config.model_config.ffn_hidden_size,
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output_size=fd_config.model_config.hidden_size,
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with_bias=False,
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)
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self.act_fn = SiluAndMul(
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fd_config=fd_config,
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bias=getattr(self.gate_up_proj, "linear_bias", None),
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act_method=fd_config.model_config.hidden_act,
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)
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def load_state_dict(self, state_dict):
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"""
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"""
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self.gate_up_proj.load_state_dict(state_dict)
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self.down_proj.load_state_dict(state_dict)
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def forward(self, x):
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"""
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"""
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gate_up_out = self.gate_up_proj(x)
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act_out = self.act_fn(gate_up_out)
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down_out = self.down_proj(act_out)
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return down_out
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class Qwen2Attention(nn.Layer):
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"""
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"""
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def __init__(self,
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fd_config: FDConfig,
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layer_id: int,
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prefix: str = "") -> None:
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super().__init__()
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self.qkv_proj = QKVParallelLinear(fd_config=fd_config,
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prefix=f"{prefix}.qkv_proj",
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with_bias=True)
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self.o_proj = RowParallelLinear(
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fd_config=fd_config,
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prefix=f"{prefix}.o_proj",
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input_size=fd_config.model_config.hidden_size,
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output_size=fd_config.model_config.hidden_size,
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)
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self.attn = Attention(fd_config=fd_config,
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layer_id=layer_id,
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prefix=prefix,
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use_neox_rotary_style=True)
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def load_state_dict(self, state_dict):
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"""
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"""
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self.qkv_proj.load_state_dict(state_dict)
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self.o_proj.load_state_dict(state_dict)
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def forward(
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self,
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forward_meta: ForwardMeta,
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hidden_states: paddle.Tensor,
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):
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"""
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"""
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qkv_out = self.qkv_proj(hidden_states)
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atten_out = self.attn(
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qkv=qkv_out,
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forward_meta=forward_meta,
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)
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output = self.o_proj(atten_out)
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return output
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class Qwen2DecoderLayer(nn.Layer):
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"""
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"""
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def __init__(
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self,
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fd_config: FDConfig,
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prefix: str = "",
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) -> None:
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super().__init__()
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layer_id = int(prefix.split(sep='.')[-1])
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self.self_attn = Qwen2Attention(
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fd_config=fd_config,
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layer_id=layer_id,
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prefix=f"{prefix}.self_attn",
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)
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self.mlp = Qwen2MLP(
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fd_config=fd_config,
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prefix=f"{prefix}.mlp",
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)
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self.input_layernorm = RMSNorm(
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fd_config,
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hidden_size=fd_config.model_config.hidden_size,
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eps=fd_config.model_config.rms_norm_eps,
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prefix=f"{prefix}.input_layernorm",
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)
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self.post_attention_layernorm = RMSNorm(
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fd_config,
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hidden_size=fd_config.model_config.hidden_size,
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eps=fd_config.model_config.rms_norm_eps,
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prefix=f"{prefix}.post_attention_layernorm",
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)
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def load_state_dict(self, state_dict):
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"""
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"""
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self.self_attn.load_state_dict(state_dict)
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self.mlp.load_state_dict(state_dict)
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self.input_layernorm.load_state_dict(state_dict)
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self.post_attention_layernorm.load_state_dict(state_dict)
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def forward(
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self,
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forward_meta: ForwardMeta,
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hidden_states: paddle.Tensor,
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residual: paddle.Tensor = None,
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):
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"""
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"""
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# Self Attention
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if residual is None:
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residual = hidden_states
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hidden_states = self.input_layernorm(hidden_states)
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else:
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hidden_states, residual = self.input_layernorm(
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hidden_states, residual)
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hidden_states = self.self_attn(
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hidden_states=hidden_states,
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forward_meta=forward_meta,
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)
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# Fully Connected
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hidden_states, residual = self.post_attention_layernorm(
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hidden_states, residual)
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hidden_states = self.mlp(hidden_states)
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return hidden_states, residual
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@support_graph_optimization
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class Qwen2Model(nn.Layer):
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"""
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"""
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def __init__(
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self,
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fd_config: FDConfig = None,
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):
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"""
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Initializer for the Qwen2Model class.
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Args:
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"""
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super().__init__()
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self.num_layers = fd_config.model_config.num_layers
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fd_config.model_config.prefix_name = "qwen2"
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self.embeddings = VocabParallelEmbedding(
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fd_config=fd_config,
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num_embeddings=fd_config.model_config.vocab_size,
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embedding_dim=fd_config.model_config.hidden_size,
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params_dtype=paddle.get_default_dtype,
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prefix=(f"{fd_config.model_config.prefix_name}.embed_tokens"),
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)
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self.layers = nn.LayerList([
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Qwen2DecoderLayer(
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fd_config=fd_config,
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prefix=f"{fd_config.model_config.prefix_name}.layers.{i}")
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for i in range(self.num_layers)
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])
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self.norm = RMSNorm(
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fd_config,
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hidden_size=fd_config.model_config.hidden_size,
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eps=fd_config.model_config.rms_norm_eps,
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prefix=f"{fd_config.model_config.prefix_name}.norm",
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)
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def load_state_dict(self, state_dict):
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"""
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Load model parameters from a given state dictionary.
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Args:
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state_dict (dict[str, np.ndarray | paddle.Tensor]):
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A dictionary containing model parameters, where keys are parameter names
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and values are NumPy arrays or PaddlePaddle tensors.
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"""
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self.embeddings.load_state_dict(state_dict)
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self.norm.load_state_dict(state_dict)
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for i in range(self.num_layers):
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logger.info(f"Start load layer {i}")
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self.layers[i].load_state_dict(state_dict)
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def forward(
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self,
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ids_remove_padding: paddle.Tensor,
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forward_meta: ForwardMeta,
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):
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"""
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"""
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hidden_states = self.embeddings(ids_remove_padding=ids_remove_padding)
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residual = None
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for i in range(self.num_layers):
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hidden_states, residual = self.layers[i](forward_meta,
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hidden_states, residual)
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hidden_states = hidden_states + residual
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out = self.norm(hidden_states)
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return out
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class Qwen2ForCausalLM(ModelForCasualLM):
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"""
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Qwen2ForCausalLM
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"""
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def __init__(self, fd_config: FDConfig):
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"""
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Args:
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fd_config (FDConfig): Configurations for the LLM model.
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"""
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super(Qwen2ForCausalLM, self).__init__(fd_config)
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self.fd_config =fd_config
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self.model = Qwen2Model(fd_config=fd_config)
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self.ori_vocab_size = fd_config.model_config.ori_vocab_size
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self.lm_head = ParallelLMHead(
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fd_config=fd_config,
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embedding_dim=fd_config.model_config.hidden_size,
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num_embeddings=fd_config.model_config.vocab_size,
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prefix="lm_head",
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)
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@classmethod
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def name(self):
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"""
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"""
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return "Qwen2ForCausalLM"
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@paddle.no_grad()
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def set_state_dict(self, state_dict):
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"""
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Load model parameters from a given state dictionary.
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Args:
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state_dict (dict[str, np.ndarray | paddle.Tensor]):
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A dictionary containing model parameters, where keys are parameter names
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and values are NumPy arrays or PaddlePaddle tensors.
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"""
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self.model.load_state_dict(state_dict)
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self.lm_head.load_state_dict(state_dict)
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def compute_logits(self, hidden_states: paddle.Tensor):
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"""
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"""
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logits = self.lm_head(hidden_states)
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logits = paddle.cast(logits, paddle.float32)
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logits[:, self.ori_vocab_size:] = -float("inf")
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return logits
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def forward(
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self,
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ids_remove_padding: paddle.Tensor,
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forward_meta: ForwardMeta,
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):
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"""
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"""
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hidden_states = self.model(ids_remove_padding=ids_remove_padding,
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forward_meta=forward_meta)
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return hidden_states
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class Qwen2PretrainedModel(PretrainedModel):
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"""
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Qwen2PretrainedModel
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"""
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config_class = FDConfig
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def _init_weight(self, layer):
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"""
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_init_weight
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"""
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return None
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@classmethod
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def _get_tensor_parallel_mappings(cls, config: ModelConfig, is_split=True):
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from paddleformers.transformers.conversion_utils import \
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split_or_merge_func
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fn = split_or_merge_func(
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is_split=is_split,
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tensor_parallel_degree=config.tensor_parallel_degree,
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tensor_parallel_rank=config.tensor_parallel_rank,
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num_attention_heads=config.num_attention_heads,
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)
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def get_tensor_parallel_split_mappings(num_layers):
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final_actions = {}
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base_actions = {
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"lm_head.weight": partial(fn, is_column=True),
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# Row Linear
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"embed_tokens.weight": partial(fn, is_column=False),
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"layers.0.self_attn.o_proj.weight": partial(fn,
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is_column=False),
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"layers.0.mlp.down_proj.weight": partial(fn, is_column=False),
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}
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# Column Linear
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if config.fuse_attention_qkv:
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base_actions["layers.0.self_attn.qkv_proj.weight"] = partial(
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fn, is_column=True)
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else:
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base_actions["layers.0.self_attn.q_proj.weight"] = partial(
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fn, is_column=True)
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base_actions["layers.0.self_attn.q_proj.bias"] = partial(
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fn, is_column=True)
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# if we have enough num_key_value_heads to split, then split it.
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if config.num_key_value_heads % config.tensor_parallel_degree == 0:
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base_actions["layers.0.self_attn.k_proj.weight"] = partial(
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fn, is_column=True)
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base_actions["layers.0.self_attn.v_proj.weight"] = partial(
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fn, is_column=True)
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base_actions["layers.0.self_attn.k_proj.bias"] = partial(
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fn, is_column=True)
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base_actions["layers.0.self_attn.v_proj.bias"] = partial(
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fn, is_column=True)
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base_actions["layers.0.mlp.gate_proj.weight"] = partial(
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fn, is_column=True)
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base_actions["layers.0.mlp.up_proj.weight"] = partial(
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fn, is_column=True)
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for key, action in base_actions.items():
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if "layers.0." in key:
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for i in range(num_layers):
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final_actions[key.replace("layers.0.",
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f"layers.{i}.")] = action
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final_actions[key] = action
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return final_actions
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mappings = get_tensor_parallel_split_mappings(config.num_layers)
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return mappings
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