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polish code with new pre-commit rule (#2923)

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Zero Rains
2025-07-19 23:19:27 +08:00
committed by GitHub
parent b8676d71a8
commit 25698d56d1
424 changed files with 14307 additions and 13518 deletions
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140
fastdeploy/model_executor/models/qwen2.py
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@@ -25,21 +25,24 @@ from paddleformers.utils.log import logger
from fastdeploy.config import FDConfig, ModelConfig
from fastdeploy.model_executor.forward_meta import ForwardMeta
from fastdeploy.model_executor.graph_optimization.decorator import \
support_graph_optimization
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.attention import Attention
from fastdeploy.model_executor.layers.embeddings import VocabParallelEmbedding
from fastdeploy.model_executor.layers.linear import (
MergedColumnParallelLinear, QKVParallelLinear, RowParallelLinear)
MergedColumnParallelLinear,
QKVParallelLinear,
RowParallelLinear,
)
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
class Qwen2MLP(nn.Layer):
"""
"""
""" """
def __init__(
self,
@@ -72,14 +75,12 @@ class Qwen2MLP(nn.Layer):
)
def load_state_dict(self, state_dict):
"""
"""
""" """
self.up_gate_proj.load_state_dict(state_dict)
self.down_proj.load_state_dict(state_dict)
def forward(self, x):
"""
"""
""" """
gate_up_out = self.up_gate_proj(x)
act_out = self.act_fn(gate_up_out)
down_out = self.down_proj(act_out)
@@ -87,18 +88,12 @@ class Qwen2MLP(nn.Layer):
class Qwen2Attention(nn.Layer):
"""
"""
""" """
def __init__(self,
fd_config: FDConfig,
layer_id: int,
prefix: str = "") -> None:
def __init__(self, fd_config: FDConfig, layer_id: int, prefix: str = "") -> None:
super().__init__()
self.qkv_proj = QKVParallelLinear(fd_config=fd_config,
prefix=f"{prefix}.qkv_proj",
with_bias=True)
self.qkv_proj = QKVParallelLinear(fd_config=fd_config, prefix=f"{prefix}.qkv_proj", with_bias=True)
self.o_proj = RowParallelLinear(
fd_config=fd_config,
@@ -107,14 +102,15 @@ class Qwen2Attention(nn.Layer):
output_size=fd_config.model_config.hidden_size,
)
self.attn = Attention(fd_config=fd_config,
layer_id=layer_id,
prefix=prefix,
use_neox_rotary_style=True)
self.attn = Attention(
fd_config=fd_config,
layer_id=layer_id,
prefix=prefix,
use_neox_rotary_style=True,
)
def load_state_dict(self, state_dict):
"""
"""
""" """
self.qkv_proj.load_state_dict(state_dict)
self.o_proj.load_state_dict(state_dict)
@@ -123,8 +119,7 @@ class Qwen2Attention(nn.Layer):
forward_meta: ForwardMeta,
hidden_states: paddle.Tensor,
):
"""
"""
""" """
qkv_out = self.qkv_proj(hidden_states)
atten_out = self.attn(
@@ -136,8 +131,7 @@ class Qwen2Attention(nn.Layer):
class Qwen2DecoderLayer(nn.Layer):
"""
"""
""" """
def __init__(
self,
@@ -145,7 +139,7 @@ class Qwen2DecoderLayer(nn.Layer):
prefix: str = "",
) -> None:
super().__init__()
layer_id = int(prefix.split(sep='.')[-1])
layer_id = int(prefix.split(sep=".")[-1])
self.self_attn = Qwen2Attention(
fd_config=fd_config,
@@ -173,8 +167,7 @@ class Qwen2DecoderLayer(nn.Layer):
)
def load_state_dict(self, state_dict):
"""
"""
""" """
self.self_attn.load_state_dict(state_dict)
self.mlp.load_state_dict(state_dict)
self.input_layernorm.load_state_dict(state_dict)
@@ -186,15 +179,13 @@ class Qwen2DecoderLayer(nn.Layer):
hidden_states: paddle.Tensor,
residual: paddle.Tensor = None,
):
"""
"""
""" """
# Self Attention
if residual is None:
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
else:
hidden_states, residual = self.input_layernorm(
hidden_states, residual)
hidden_states, residual = self.input_layernorm(hidden_states, residual)
hidden_states = self.self_attn(
hidden_states=hidden_states,
@@ -202,8 +193,7 @@ class Qwen2DecoderLayer(nn.Layer):
)
# Fully Connected
hidden_states, residual = self.post_attention_layernorm(
hidden_states, residual)
hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
hidden_states = self.mlp(hidden_states)
@@ -212,8 +202,7 @@ class Qwen2DecoderLayer(nn.Layer):
@support_graph_optimization
class Qwen2Model(nn.Layer):
"""
"""
""" """
def __init__(
self,
@@ -238,12 +227,15 @@ class Qwen2Model(nn.Layer):
prefix=(f"{fd_config.model_config.pretrained_config.prefix_name}.embed_tokens"),
)
self.layers = nn.LayerList([
Qwen2DecoderLayer(
fd_config=fd_config,
prefix=f"{fd_config.model_config.pretrained_config.prefix_name}.layers.{i}")
for i in range(self.num_layers)
])
self.layers = nn.LayerList(
[
Qwen2DecoderLayer(
fd_config=fd_config,
prefix=f"{fd_config.model_config.pretrained_config.prefix_name}.layers.{i}",
)
for i in range(self.num_layers)
]
)
self.norm = RMSNorm(
fd_config,
@@ -272,16 +264,14 @@ class Qwen2Model(nn.Layer):
ids_remove_padding: paddle.Tensor,
forward_meta: ForwardMeta,
):
"""
"""
""" """
hidden_states = self.embed_tokens(ids_remove_padding=ids_remove_padding)
residual = None
for i in range(self.num_layers):
hidden_states, residual = self.layers[i](forward_meta,
hidden_states, residual)
hidden_states, residual = self.layers[i](forward_meta, hidden_states, residual)
hidden_states = hidden_states + residual
@@ -302,7 +292,7 @@ class Qwen2ForCausalLM(ModelForCasualLM):
"""
super(Qwen2ForCausalLM, self).__init__(fd_config)
self.fd_config =fd_config
self.fd_config = fd_config
self.qwen2 = Qwen2Model(fd_config=fd_config)
self.ori_vocab_size = fd_config.model_config.ori_vocab_size
@@ -316,8 +306,7 @@ class Qwen2ForCausalLM(ModelForCasualLM):
@classmethod
def name(self):
"""
"""
""" """
return "Qwen2ForCausalLM"
@paddle.no_grad()
@@ -334,11 +323,10 @@ class Qwen2ForCausalLM(ModelForCasualLM):
self.lm_head.load_state_dict(state_dict)
def compute_logits(self, hidden_states: paddle.Tensor):
"""
"""
""" """
logits = self.lm_head(hidden_states)
logits = paddle.cast(logits, paddle.float32)
logits[:, self.ori_vocab_size:] = -float("inf")
logits[:, self.ori_vocab_size :] = -float("inf")
return logits
@@ -347,10 +335,8 @@ class Qwen2ForCausalLM(ModelForCasualLM):
ids_remove_padding: paddle.Tensor,
forward_meta: ForwardMeta,
):
"""
"""
hidden_states = self.qwen2(ids_remove_padding=ids_remove_padding,
forward_meta=forward_meta)
""" """
hidden_states = self.qwen2(ids_remove_padding=ids_remove_padding, forward_meta=forward_meta)
return hidden_states
@@ -371,8 +357,7 @@ class Qwen2PretrainedModel(PretrainedModel):
@classmethod
def _get_tensor_parallel_mappings(cls, config: ModelConfig, is_split=True):
from paddleformers.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,
@@ -388,41 +373,30 @@ class Qwen2PretrainedModel(PretrainedModel):
"lm_head.weight": partial(fn, is_column=True),
# Row Linear
"embed_tokens.weight": partial(fn, is_column=False),
"layers.0.self_attn.o_proj.weight": partial(fn,
is_column=False),
"layers.0.self_attn.o_proj.weight": partial(fn, is_column=False),
"layers.0.mlp.down_proj.weight": partial(fn, is_column=False),
}
# Column Linear
if config.fuse_attention_qkv:
base_actions["layers.0.self_attn.qkv_proj.weight"] = partial(
fn, is_column=True)
base_actions["layers.0.self_attn.qkv_proj.weight"] = partial(fn, is_column=True)
else:
base_actions["layers.0.self_attn.q_proj.weight"] = partial(
fn, is_column=True)
base_actions["layers.0.self_attn.q_proj.bias"] = partial(
fn, is_column=True)
base_actions["layers.0.self_attn.q_proj.weight"] = partial(fn, is_column=True)
base_actions["layers.0.self_attn.q_proj.bias"] = partial(fn, is_column=True)
# if we have enough num_key_value_heads to split, then split it.
if config.num_key_value_heads % config.tensor_parallel_degree == 0:
base_actions["layers.0.self_attn.k_proj.weight"] = partial(
fn, is_column=True)
base_actions["layers.0.self_attn.v_proj.weight"] = partial(
fn, is_column=True)
base_actions["layers.0.self_attn.k_proj.bias"] = partial(
fn, is_column=True)
base_actions["layers.0.self_attn.v_proj.bias"] = partial(
fn, is_column=True)
base_actions["layers.0.self_attn.k_proj.weight"] = partial(fn, is_column=True)
base_actions["layers.0.self_attn.v_proj.weight"] = partial(fn, is_column=True)
base_actions["layers.0.self_attn.k_proj.bias"] = partial(fn, is_column=True)
base_actions["layers.0.self_attn.v_proj.bias"] = partial(fn, is_column=True)
base_actions["layers.0.mlp.gate_proj.weight"] = partial(
fn, is_column=True)
base_actions["layers.0.mlp.up_proj.weight"] = partial(
fn, is_column=True)
base_actions["layers.0.mlp.gate_proj.weight"] = partial(fn, is_column=True)
base_actions["layers.0.mlp.up_proj.weight"] = partial(fn, is_column=True)
for key, action in base_actions.items():
if "layers.0." in key:
for i in range(num_layers):
final_actions[key.replace("layers.0.",
f"layers.{i}.")] = action
final_actions[key.replace("layers.0.", f"layers.{i}.")] = action
final_actions[key] = action
return final_actions