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"""
# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
#
# 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 __future__ import annotations
from functools import partial
import paddle
from paddle import nn
from paddleformers.transformers import PretrainedModel
from paddleformers.utils.log import logger
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
from fastdeploy.model_executor.layers.linear import (
MergedColumnParallelLinear, QKVParallelLinear, RowParallelLinear)
from fastdeploy.model_executor.layers.lm_head import ParallelLMHead
from fastdeploy.model_executor.layers.moe.moe import FusedMoE
from fastdeploy.model_executor.layers.normalization import RMSNorm
from fastdeploy.model_executor.models.model_base import ModelForCasualLM
from fastdeploy.worker.forward_meta import ForwardMeta
class Qwen3MLP(nn.Layer):
"""
"""
def __init__(
self,
fd_config: FDConfig,
prefix: str = "",
) -> None:
super().__init__()
self.nranks = fd_config.parallel_config.tensor_parallel_degree
self.gate_up_proj = MergedColumnParallelLinear(
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=fd_config.model_config.hidden_act,
use_fast_ffn=True,
)
self.down_proj = RowParallelLinear(
fd_config,
prefix=f"{prefix}.down_proj",
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(
fd_config,
bias=getattr(self.gate_up_proj, "linear_bias", None),
act_method=fd_config.model_config.hidden_act,
)
def load_state_dict(self, state_dict):
"""
"""
self.gate_up_proj.load_state_dict(state_dict)
self.down_proj.load_state_dict(state_dict)
def forward(self, x):
"""
"""
gate_up_out = self.gate_up_proj(x)
act_out = self.act_fn(gate_up_out)
down_out = self.down_proj(act_out)
return down_out
class Qwen3Attention(nn.Layer):
"""
"""
def __init__(self,
fd_config: FDConfig,
layer_id: int,
prefix: str = "") -> None:
super().__init__()
self.fd_config = fd_config
self.head_dim = fd_config.model_config.head_dim
self.qkv_proj = QKVParallelLinear(fd_config,
prefix=f"{prefix}.qkv_proj",
with_bias=False)
nranks = fd_config.parallel_config.tensor_parallel_degree
self.o_proj = RowParallelLinear(
fd_config,
prefix=f"{prefix}.o_proj",
input_size=fd_config.model_config.head_dim *
fd_config.model_config.num_attention_heads // nranks,
output_size=fd_config.model_config.hidden_size,
)
self.attn = Attention(fd_config,
layer_id=layer_id,
prefix=prefix,
use_neox_rotary_style=True)
self.q_norm = RMSNorm(fd_config,
hidden_size=self.head_dim,
eps=1e-6,
prefix=f"{prefix}.q_norm",
begin_norm_axis=2)
self.k_norm = RMSNorm(fd_config,
hidden_size=self.head_dim,
eps=1e-6,
prefix=f"{prefix}.k_norm",
begin_norm_axis=2)
self.q_size = fd_config.model_config.num_attention_heads * self.head_dim // nranks
self.kv_size = fd_config.model_config.num_key_value_heads * self.head_dim // nranks
def load_state_dict(self, state_dict):
"""
"""
self.qkv_proj.load_state_dict(state_dict)
self.o_proj.load_state_dict(state_dict)
self.q_norm.load_state_dict(state_dict)
self.k_norm.load_state_dict(state_dict)
def forward(
self,
forward_meta: ForwardMeta,
hidden_states: paddle.Tensor,
):
"""
"""
qkv_out = self.qkv_proj(hidden_states)
# origin_qkv_out = qkv_out
q, k, v = qkv_out.split([self.q_size, self.kv_size, self.kv_size],
axis=-1)
q_by_head = q.reshape(
[*q.shape[:-1], q.shape[-1] // self.head_dim, self.head_dim])
q_by_head = self.q_norm(q_by_head)
q = q_by_head.reshape(q.shape)
k_by_head = k.reshape(
[*k.shape[:-1], k.shape[-1] // self.head_dim, self.head_dim])
k_by_head = self.k_norm(k_by_head)
k = k_by_head.reshape(k.shape)
qkv_out = paddle.concat([q, k, v], axis=-1)
atten_out = self.attn(
qkv=qkv_out,
forward_meta=forward_meta,
)
output = self.o_proj(atten_out)
return output
class Qwen3DecoderLayer(nn.Layer):
"""
"""
def __init__(
self,
fd_config: FDConfig,
prefix: str = "",
) -> None:
super().__init__()
layer_id = int(prefix.split(sep='.')[-1])
self.self_attn = Qwen3Attention(
fd_config=fd_config,
layer_id=layer_id,
prefix=f"{prefix}.self_attn",
)
weight_key_map = {
"gate_weight_key":
f"{prefix}.mlp.gate.weight",
"ffn1_expert_weight_key":
f"{prefix}.mlp.experts.{{}}.up_gate_proj.weight",
"ffn2_expert_weight_key":
f"{prefix}.mlp.experts.{{}}.down_proj.weight",
}
if (fd_config.moe_config.num_experts is not None
and layer_id >= fd_config.moe_config.moe_layer_start_index):
self.mlp = FusedMoE(fd_config,
moe_intermediate_size=fd_config.moe_config.
moe_intermediate_size,
num_experts=fd_config.moe_config.num_experts,
top_k=fd_config.moe_config.top_k,
layer_idx=layer_id,
weight_key_map=weight_key_map)
else:
self.mlp = Qwen3MLP(
fd_config,
prefix=f"{prefix}.mlp",
)
self.input_layernorm = RMSNorm(
fd_config,
hidden_size=fd_config.model_config.hidden_size,
eps=1e-6,
prefix=f"{prefix}.input_layernorm",
)
self.post_attention_layernorm = RMSNorm(
fd_config,
hidden_size=fd_config.model_config.hidden_size,
eps=1e-6,
prefix=f"{prefix}.post_attention_layernorm",
)
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)
self.post_attention_layernorm.load_state_dict(state_dict)
def forward(
self,
forward_meta: ForwardMeta,
hidden_states: paddle.Tensor,
residual: paddle.Tensor = None,
):
"""
"""
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 = self.self_attn(
hidden_states=hidden_states,
forward_meta=forward_meta,
)
# Fully Connected
hidden_states, residual = self.post_attention_layernorm(
hidden_states, residual)
hidden_states = self.mlp(hidden_states)
return hidden_states, residual
@support_graph_optimization
class Qwen3MoeModel(nn.Layer):
"""
"""
def __init__(
self,
fd_config: FDConfig = None,
):
"""
Initializer for the Qwen2Model class.
Args:
"""
super().__init__()
self.num_layers = fd_config.model_config.num_layers
fd_config.model_config.prefix_name = "model"
self.embeddings = VocabParallelEmbedding(
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"{fd_config.model_config.prefix_name}.embed_tokens"),
)
self.layers = nn.LayerList([
Qwen3DecoderLayer(
fd_config,
prefix=f"{fd_config.model_config.prefix_name}.layers.{i}")
for i in range(self.num_layers)
])
self.norm = RMSNorm(
fd_config,
hidden_size=fd_config.model_config.hidden_size,
eps=1e-6,
prefix=f"{fd_config.model_config.prefix_name}.norm",
)
def load_state_dict(self, state_dict):
"""
Load model parameters from a given state dictionary.
Args:
state_dict (dict[str, np.ndarray | paddle.Tensor]):
A dictionary containing model parameters, where keys are parameter names
and values are NumPy arrays or PaddlePaddle tensors.
"""
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(
self,
ids_remove_padding: paddle.Tensor,
forward_meta: ForwardMeta,
):
"""
"""
hidden_states = self.embeddings(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 = hidden_states + residual
out = self.norm(hidden_states)
return out
class Qwen3MoeForCausalLM(ModelForCasualLM):
"""
Qwen3MoeForCausalLM
"""
def __init__(self, fd_config: FDConfig):
"""
Args:
fd_config (FDConfig): Configurations for the LLM model.
"""
super(Qwen3MoeForCausalLM, self).__init__(fd_config)
self.model = Qwen3MoeModel(fd_config)
self.ori_vocab_size = fd_config.model_config.ori_vocab_size
self.lm_head = ParallelLMHead(
fd_config,
embedding_dim=fd_config.model_config.hidden_size,
num_embeddings=fd_config.model_config.vocab_size,
prefix="lm_head",
)
@classmethod
def name(self):
"""
"""
return "Qwen3MoeForCausalLM"
@paddle.no_grad()
def set_state_dict(self, state_dict):
"""
Load model parameters from a given state dictionary.
Args:
state_dict (dict[str, np.ndarray | paddle.Tensor]):
A dictionary containing model parameters, where keys are parameter names
and values are NumPy arrays or PaddlePaddle tensors.
"""
self.model.load_state_dict(state_dict)
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")
return logits
def forward(
self,
ids_remove_padding: paddle.Tensor,
forward_meta: ForwardMeta,
):
"""
"""
hidden_states = self.model(ids_remove_padding=ids_remove_padding,
forward_meta=forward_meta)
return hidden_states
class Qwen3MoePretrainedModel(PretrainedModel):
"""
Qwen3MoePretrainedModel
"""
config_class = FDConfig
def _init_weight(self, layer):
"""
_init_weight
"""
return None
@classmethod
def _get_tensor_parallel_mappings(cls, config: ModelConfig, is_split=True):
# TODO not support TP split now, next PR will support TP.
from paddleformers.transformers.conversion_utils import \
split_or_merge_func
fn = split_or_merge_func(
is_split=is_split,
tensor_parallel_degree=config.tensor_parallel_degree,
tensor_parallel_rank=config.tensor_parallel_rank,
num_attention_heads=config.num_attention_heads,
)
def get_tensor_parallel_split_mappings(num_layers, moe_num_experts):
final_actions = {}
base_actions = {
"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),
}
# Column Linear
config.fuse_attention_qkv = False
if config.fuse_attention_qkv:
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)
# 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)
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] = action
base_actions = {
"layers.0.mlp.experts.0.gate_proj.weight":
partial(fn, is_column=True),
"layers.0.mlp.experts.0.down_proj.weight":
partial(fn, is_column=False),
"layers.0.mlp.experts.0.up_proj.weight":
partial(fn, is_column=True),
}
for key, action in base_actions.items():
for i in range(num_layers):
newkey = key.replace("layers.0.", f"layers.{i}.")
for j in range(moe_num_experts):
newkey2 = newkey.replace("experts.0.", f"experts.{j}.")
final_actions[newkey2] = action
return final_actions
moe_num_experts = 0
if isinstance(config.moe_num_experts, list):
moe_num_experts = sum(config.moe_num_experts)
elif isinstance(config.moe_num_experts, int):
moe_num_experts = config.moe_num_experts
else:
raise ValueError(
f"Not support type of moe_num_experts [{type(config.moe_num_experts)}]"
)
mappings = get_tensor_parallel_split_mappings(config.num_layers,
moe_num_experts)
return mappings