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Sync v2.0 version of code to github repo

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Jiang-Jia-Jun
2025-06-29 23:29:37 +00:00
parent d151496038
commit 92c2cfa2e7
597 changed files with 78776 additions and 22905 deletions
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108
fastdeploy/model_executor/models/qwen2.py
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@@ -20,9 +20,12 @@ from functools import partial
import paddle
from paddle import nn
from paddlenlp.transformers import PretrainedModel
from paddleformers.transformers import PretrainedModel
from paddleformers.utils.log import logger
from fastdeploy.config import LLMConfig, ModelConfig
from fastdeploy.config import FDConfig, ModelConfig
from fastdeploy.model_executor.graph_optimization.decorator import \
support_graph_optimization
from fastdeploy.model_executor.layers.activation import SiluAndMul
from fastdeploy.model_executor.layers.attention import Attention
from fastdeploy.model_executor.layers.embeddings import VocabParallelEmbedding
@@ -31,7 +34,7 @@ from fastdeploy.model_executor.layers.linear import (
from fastdeploy.model_executor.layers.lm_head import ParallelLMHead
from fastdeploy.model_executor.layers.normalization import RMSNorm
from fastdeploy.model_executor.models.model_base import ModelForCasualLM
from fastdeploy.worker.model_runner import ForwardMeta
from fastdeploy.worker.forward_meta import ForwardMeta
class Qwen2MLP(nn.Layer):
@@ -40,32 +43,33 @@ class Qwen2MLP(nn.Layer):
def __init__(
self,
llm_config: LLMConfig,
fd_config: FDConfig,
prefix: str = "",
) -> None:
super().__init__()
self.nranks = llm_config.parallel_config.mp_size
self.nranks = fd_config.parallel_config.tensor_parallel_degree
self.gate_up_proj = MergedColumnParallelLinear(
llm_config=llm_config,
fd_config=fd_config,
prefix=f"{prefix}.up_gate_proj",
input_size=fd_config.model_config.hidden_size,
output_size=fd_config.model_config.ffn_hidden_size * 2,
with_bias=False,
activation=llm_config.model_config.hidden_act,
activation=fd_config.model_config.hidden_act,
use_fast_ffn=True,
)
self.down_proj = RowParallelLinear(
llm_config=llm_config,
fd_config=fd_config,
prefix=f"{prefix}.down_proj",
input_size=(llm_config.model_config.ffn_hidden_size //
self.nranks),
output_size=llm_config.model_config.hidden_size,
input_size=(fd_config.model_config.ffn_hidden_size // self.nranks),
output_size=fd_config.model_config.hidden_size,
with_bias=False,
)
self.act_fn = SiluAndMul(
llm_config=llm_config,
fd_config=fd_config,
bias=getattr(self.gate_up_proj, "linear_bias", None),
act_method=llm_config.model_config.hidden_act,
act_method=fd_config.model_config.hidden_act,
)
def load_state_dict(self, state_dict):
@@ -88,25 +92,25 @@ class Qwen2Attention(nn.Layer):
"""
def __init__(self,
llm_config: LLMConfig,
fd_config: FDConfig,
layer_id: int,
prefix: str = "") -> None:
super().__init__()
nranks = llm_config.parallel_config.mp_size
nranks = fd_config.parallel_config.tensor_parallel_degree
self.qkv_proj = QKVParallelLinear(llm_config=llm_config,
self.qkv_proj = QKVParallelLinear(fd_config=fd_config,
prefix=f"{prefix}.qkv_proj",
with_bias=True)
self.o_proj = RowParallelLinear(
llm_config=llm_config,
fd_config=fd_config,
prefix=f"{prefix}.o_proj",
input_size=(llm_config.model_config.hidden_size // nranks),
output_size=llm_config.model_config.hidden_size,
input_size=(fd_config.model_config.hidden_size // nranks),
output_size=fd_config.model_config.hidden_size,
)
self.attn = Attention(llm_config=llm_config,
self.attn = Attention(fd_config=fd_config,
layer_id=layer_id,
prefix=prefix,
use_neox_rotary_style=True)
@@ -140,33 +144,33 @@ class Qwen2DecoderLayer(nn.Layer):
def __init__(
self,
llm_config: LLMConfig,
fd_config: FDConfig,
prefix: str = "",
) -> None:
super().__init__()
layer_id = int(prefix.split(sep='.')[-1])
self.self_attn = Qwen2Attention(
llm_config=llm_config,
fd_config=fd_config,
layer_id=layer_id,
prefix=f"{prefix}.self_attn",
)
self.mlp = Qwen2MLP(
llm_config=llm_config,
fd_config=fd_config,
prefix=f"{prefix}.mlp",
)
self.input_layernorm = RMSNorm(
llm_config,
hidden_size=llm_config.model_config.hidden_size,
fd_config,
hidden_size=fd_config.model_config.hidden_size,
eps=1e-6,
prefix=f"{prefix}.input_layernorm",
)
self.post_attention_layernorm = RMSNorm(
llm_config,
hidden_size=llm_config.model_config.hidden_size,
fd_config,
hidden_size=fd_config.model_config.hidden_size,
eps=1e-6,
prefix=f"{prefix}.post_attention_layernorm",
)
@@ -209,13 +213,14 @@ class Qwen2DecoderLayer(nn.Layer):
return hidden_states, residual
@support_graph_optimization
class Qwen2Model(nn.Layer):
"""
"""
def __init__(
self,
llm_config: LLMConfig = None,
fd_config: FDConfig = None,
):
"""
Initializer for the Qwen2Model class.
@@ -225,29 +230,29 @@ class Qwen2Model(nn.Layer):
"""
super().__init__()
self.num_layers = llm_config.model_config.num_layers
llm_config.model_config.prefix_name = "qwen2"
self.num_layers = fd_config.model_config.num_layers
fd_config.model_config.prefix_name = "qwen2"
self.embeddings = VocabParallelEmbedding(
llm_config=llm_config,
num_embeddings=llm_config.model_config.vocab_size,
embedding_dim=llm_config.model_config.hidden_size,
fd_config=fd_config,
num_embeddings=fd_config.model_config.vocab_size,
embedding_dim=fd_config.model_config.hidden_size,
params_dtype=paddle.get_default_dtype,
prefix=(f"{llm_config.model_config.prefix_name}.embed_tokens"),
prefix=(f"{fd_config.model_config.prefix_name}.embed_tokens"),
)
self.layers = nn.LayerList([
Qwen2DecoderLayer(
llm_config=llm_config,
prefix=f"{llm_config.model_config.prefix_name}.layers.{i}")
fd_config=fd_config,
prefix=f"{fd_config.model_config.prefix_name}.layers.{i}")
for i in range(self.num_layers)
])
self.norm = RMSNorm(
llm_config,
hidden_size=llm_config.model_config.hidden_size,
fd_config,
hidden_size=fd_config.model_config.hidden_size,
eps=1e-5,
prefix=f"{llm_config.model_config.prefix_name}.norm",
prefix=f"{fd_config.model_config.prefix_name}.norm",
)
def load_state_dict(self, state_dict):
@@ -262,6 +267,7 @@ class Qwen2Model(nn.Layer):
self.embeddings.load_state_dict(state_dict)
self.norm.load_state_dict(state_dict)
for i in range(self.num_layers):
logger.info(f"Start load layer {i}")
self.layers[i].load_state_dict(state_dict)
def forward(
@@ -292,21 +298,21 @@ class Qwen2ForCausalLM(ModelForCasualLM):
Qwen2ForCausalLM
"""
def __init__(self, llm_config: LLMConfig):
def __init__(self, fd_config: FDConfig):
"""
Args:
llm_config (LLMConfig): Configurations for the LLM model.
fd_config (FDConfig): Configurations for the LLM model.
"""
super(Qwen2ForCausalLM, self).__init__(llm_config)
super(Qwen2ForCausalLM, self).__init__(fd_config)
self.model = Qwen2Model(llm_config=llm_config)
self.model = Qwen2Model(fd_config=fd_config)
self.ori_vocab_size = llm_config.model_config.ori_vocab_size
self.ori_vocab_size = fd_config.model_config.ori_vocab_size
self.lm_head = ParallelLMHead(
llm_config=llm_config,
embedding_dim=llm_config.model_config.hidden_size,
num_embeddings=llm_config.model_config.vocab_size,
fd_config=fd_config,
embedding_dim=fd_config.model_config.hidden_size,
num_embeddings=fd_config.model_config.vocab_size,
prefix="lm_head",
)
@@ -345,7 +351,8 @@ class Qwen2ForCausalLM(ModelForCasualLM):
):
"""
"""
hidden_states = self.model(ids_remove_padding, forward_meta)
hidden_states = self.model(ids_remove_padding=ids_remove_padding,
forward_meta=forward_meta)
return hidden_states
@@ -355,7 +362,7 @@ class Qwen2PretrainedModel(PretrainedModel):
Qwen2PretrainedModel
"""
config_class = LLMConfig
config_class = FDConfig
def _init_weight(self, layer):
"""
@@ -366,7 +373,8 @@ class Qwen2PretrainedModel(PretrainedModel):
@classmethod
def _get_tensor_parallel_mappings(cls, config: ModelConfig, is_split=True):
from paddlenlp.transformers.conversion_utils import split_or_merge_func
from paddleformers.transformers.conversion_utils import \
split_or_merge_func
fn = split_or_merge_func(
is_split=is_split,