mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-04 16:22:57 +08:00
61b3997b85
Some checks failed
Deploy GitHub Pages / deploy (push) Has been cancelled
* refactor rl get_name_mappings_to_training * fix tp>1 * change variable name(ffn1->up_gate_proj/ffn2->down_proj) * change variable name(linear_weight->weight/linear_bias->bias) * add rl names mapping for vl * fix ernie 0.3B error * fix develop code * fix
325 lines
13 KiB
Python
325 lines
13 KiB
Python
"""
|
|
# Copyright (c) 2025 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.
|
|
"""
|
|
|
|
import paddle
|
|
from paddle import nn
|
|
from paddleformers.utils.log import logger
|
|
|
|
from fastdeploy import envs
|
|
from fastdeploy.model_executor.layers.utils import get_tensor
|
|
|
|
|
|
def get_moe_method():
|
|
"""
|
|
return moe method based on device platform
|
|
"""
|
|
from fastdeploy.platforms import current_platform
|
|
if current_platform.is_cuda():
|
|
from .fused_moe_cutlass_backend import CutlassMoEMethod
|
|
return CutlassMoEMethod(None)
|
|
elif current_platform.is_xpu():
|
|
from .fused_moe_xpu_backend import XPUMoEMethod
|
|
return XPUMoEMethod(None)
|
|
elif current_platform.is_gcu():
|
|
from fastdeploy.model_executor.layers.backends import GCUFusedMoeMethod
|
|
return GCUFusedMoeMethod(None)
|
|
raise NotImplementedError()
|
|
|
|
class FusedMoE(nn.Layer):
|
|
"""
|
|
FusedMoE is a layer that performs MoE (Mixture of Experts) computation.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
fd_config,
|
|
reduce_results: bool = True,
|
|
moe_intermediate_size: int = -1,
|
|
num_experts: int = -1,
|
|
expert_id_offset: int = 0,
|
|
top_k: int = -1,
|
|
topk_method: str = "",
|
|
topk_group: int = -1,
|
|
n_group: int = -1,
|
|
routed_scaling_factor: float = 1.0,
|
|
layer_idx: int = -1,
|
|
moe_tag: str = "",
|
|
weight_key_map: dict = {},
|
|
):
|
|
"""
|
|
Initialize the Moe layer with given parameters.
|
|
Args:
|
|
fd_config (FDConfig): Arguments related to inference, containing
|
|
attributes such as weight_dtype, act_dtype, mp_size, hidden_size, head_dim,
|
|
num_attention_heads, and ffn_hidden_size.
|
|
"""
|
|
super().__init__()
|
|
|
|
self.fd_config = fd_config
|
|
self.layer_idx = layer_idx
|
|
self.reduce_results = reduce_results
|
|
|
|
self.tp_size = fd_config.parallel_config.tensor_parallel_size
|
|
self.ep_size = fd_config.parallel_config.expert_parallel_size
|
|
self.ep_rank = fd_config.parallel_config.expert_parallel_rank
|
|
|
|
assert (self.tp_size >= 1 and self.ep_size == 1) or \
|
|
(self.tp_size == 1 and self.ep_size > 1), \
|
|
'MoE only support parallelism on TP or EP dimension.'
|
|
|
|
self.hidden_size = fd_config.model_config.hidden_size
|
|
self.num_experts = num_experts
|
|
self.num_local_experts = self.num_experts // self.ep_size
|
|
|
|
self.moe_intermediate_size = moe_intermediate_size // self.tp_size
|
|
|
|
self.top_k = top_k
|
|
self.weight_key_map = weight_key_map
|
|
|
|
self.use_method = envs.FD_MOE_BACKEND.lower()
|
|
self.gate_correction_bias = None
|
|
self.moe_tag = moe_tag
|
|
if self.ep_size > 1:
|
|
expert_id_offset = expert_id_offset + self.ep_rank * self.num_local_experts
|
|
|
|
self.expert_id_offset = expert_id_offset
|
|
|
|
# used for deepseek_v3
|
|
self.topk_method = topk_method
|
|
self.topk_group = topk_group
|
|
self.n_group = n_group
|
|
self.routed_scaling_factor = routed_scaling_factor
|
|
|
|
moe_quant_config = fd_config.quant_config
|
|
self.moe_quant_type = None
|
|
if moe_quant_config:
|
|
self.quant_method = moe_quant_config.get_quant_method(self)
|
|
self.moe_quant_type = moe_quant_config.name()
|
|
else:
|
|
# now, no quant method(w_fp16 a_fp16) can't get from quant_config, we will optimize it in future
|
|
self.quant_method = get_moe_method()
|
|
|
|
if self.ep_size > 1:
|
|
self.quant_method.init_ep(self)
|
|
|
|
if fd_config.load_config.dynamic_load_weight:
|
|
# It's for RL to build model
|
|
self.init_moe_weights()
|
|
|
|
logger.info(
|
|
f"{moe_tag}MoE config is {num_experts=}[{expert_id_offset}, {expert_id_offset+self.num_local_experts}), \
|
|
{top_k=}, hidden_size={self.hidden_size}, {moe_intermediate_size=}, \
|
|
, ep_size={self.ep_size}, \
|
|
tp_size={self.tp_size}.")
|
|
|
|
def init_moe_weights(self):
|
|
"""
|
|
Initialize the weight shapes and parameters for the MoE layer.
|
|
Combines weight shape initialization and parameter creation into a single function.
|
|
"""
|
|
# Initialize weight shapes
|
|
self._dtype = self._helper.get_default_dtype()
|
|
self.weight_dtype = self._dtype
|
|
gate_weight_shape = [self.hidden_size, self.num_experts]
|
|
gate_correction_bias_shape = [1, self.num_experts]
|
|
|
|
self.gate_weight = self.create_parameter(
|
|
shape=gate_weight_shape,
|
|
dtype="float32",
|
|
)
|
|
if self.model_config.moe_use_aux_free:
|
|
self.gate_correction_bias = self.create_parameter(
|
|
shape=gate_correction_bias_shape,
|
|
dtype="float32",
|
|
)
|
|
up_gate_proj_output_dim = self.moe_intermediate_size * 2
|
|
if self.moe_quant_type in ["fp8", "wint8"]:
|
|
up_gate_proj_weight_shape = [self.num_local_experts, up_gate_proj_output_dim, self.hidden_size]
|
|
down_proj_weight_shape = [self.num_local_experts, self.hidden_size, self.moe_intermediate_size]
|
|
else:
|
|
up_gate_proj_weight_shape = [self.num_local_experts, self.hidden_size, up_gate_proj_output_dim]
|
|
down_proj_weight_shape = [self.num_local_experts, self.moe_intermediate_size, self.hidden_size]
|
|
|
|
# Create parameters
|
|
if self.moe_quant_type == "fp8":
|
|
#(TODO:gaoziyuan)
|
|
pass
|
|
elif self.moe_quant_type == "wint8":
|
|
self.weight_dtype = "int8"
|
|
self.init_weight_only_scale()
|
|
|
|
# up_gate_proj parameters
|
|
self.up_gate_proj_weight = self.create_parameter(
|
|
shape=up_gate_proj_weight_shape,
|
|
dtype=self.weight_dtype,
|
|
default_initializer=paddle.nn.initializer.Constant(0),
|
|
)
|
|
# down_proj parameters
|
|
self.down_proj_weight = self.create_parameter(
|
|
shape=down_proj_weight_shape,
|
|
dtype=self.weight_dtype,
|
|
default_initializer=paddle.nn.initializer.Constant(0),
|
|
)
|
|
|
|
def init_weight_only_scale(self):
|
|
"""
|
|
Initialize the weight scale.
|
|
"""
|
|
self.up_gate_proj_weight_scale = self.create_parameter(
|
|
shape=[self.num_local_experts, self.moe_intermediate_size * 2],
|
|
dtype=self._dtype,
|
|
)
|
|
self.down_proj_weight_scale = self.create_parameter(
|
|
shape=[self.num_local_experts, self.hidden_size],
|
|
dtype=self._dtype,
|
|
)
|
|
|
|
def load_experts_weight(self, state_dict: dict,
|
|
up_gate_proj_expert_weight_key: str,
|
|
down_proj_expert_weight_key: str):
|
|
"""
|
|
Load experts weight from state_dict.
|
|
Args:
|
|
state_dict (dict): The state_dict of model.
|
|
up_gate_proj_expert_weight_key (str): The key of up_gate_proj expert weight.
|
|
down_proj_expert_weight_key (str): The key of down_proj expert weight.
|
|
"""
|
|
up_gate_proj_weights = []
|
|
down_proj_weights = []
|
|
is_ffn_merged = up_gate_proj_expert_weight_key.format(
|
|
self.expert_id_offset) in state_dict
|
|
if is_ffn_merged:
|
|
for i in range(self.num_local_experts):
|
|
expert_idx = self.expert_id_offset + i
|
|
up_gate_proj_weights.append(
|
|
get_tensor(
|
|
state_dict.pop(
|
|
up_gate_proj_expert_weight_key.format(expert_idx))))
|
|
down_proj_weights.append(
|
|
get_tensor(
|
|
state_dict.pop(
|
|
down_proj_expert_weight_key.format(expert_idx))))
|
|
else:
|
|
gate_expert_weight_key = up_gate_proj_expert_weight_key.replace(
|
|
"up_gate_proj", "gate_proj")
|
|
up_expert_weight_key = up_gate_proj_expert_weight_key.replace(
|
|
"up_gate_proj", "up_proj")
|
|
for j in range(self.num_local_experts):
|
|
expert_idx = self.expert_id_offset + j
|
|
gate = get_tensor(
|
|
state_dict.pop(gate_expert_weight_key.format(expert_idx)))
|
|
up = get_tensor(
|
|
state_dict.pop(up_expert_weight_key.format(expert_idx)))
|
|
up_gate_proj_weights.append(paddle.concat([gate, up], axis=-1))
|
|
down_proj_weights.append(
|
|
get_tensor(
|
|
state_dict.pop(
|
|
down_proj_expert_weight_key.format(expert_idx))))
|
|
return up_gate_proj_weights, down_proj_weights
|
|
|
|
def extract_moe_ffn_weights(self, state_dict: dict):
|
|
"""
|
|
Extract MoE FFN weights from state dict based on weight key mapping.
|
|
|
|
Args:
|
|
state_dict (dict): Model state dictionary containing the weights.
|
|
|
|
Returns:
|
|
tuple: A tuple containing two lists:
|
|
- up_gate_proj_weights: List of tensors for first FFN layer weights
|
|
- down_proj_weights: List of tensors for second FFN layer weights
|
|
|
|
Raises:
|
|
AssertionError: If required weight keys are missing or number of weights
|
|
doesn't match number of local experts.
|
|
"""
|
|
up_gate_proj_expert_weight_key = self.weight_key_map.get(
|
|
"up_gate_proj_expert_weight_key", None)
|
|
down_proj_expert_weight_key = self.weight_key_map.get(
|
|
"down_proj_expert_weight_key", None)
|
|
assert up_gate_proj_expert_weight_key is not None, "up_gate_proj_expert_weight_key should not be none."
|
|
assert down_proj_expert_weight_key is not None, "down_proj_expert_weight_key should not be none."
|
|
|
|
up_gate_proj_weights, down_proj_weights = self.load_experts_weight(
|
|
state_dict, up_gate_proj_expert_weight_key, down_proj_expert_weight_key)
|
|
assert len(
|
|
up_gate_proj_weights
|
|
) == self.num_local_experts, "up_gate_proj_weights length should be equal to num_local_experts."
|
|
assert len(
|
|
down_proj_weights
|
|
) == self.num_local_experts, "down_proj_weights length should be equal to num_local_experts."
|
|
|
|
return up_gate_proj_weights, down_proj_weights
|
|
|
|
def extract_gate_correction_bias(self, gate_correction_bias_key,
|
|
state_dict):
|
|
"""
|
|
extract_gate_correction_bias function.
|
|
"""
|
|
gate_correction_bias_tensor = get_tensor(
|
|
state_dict.pop(gate_correction_bias_key)).astype("float32")
|
|
return gate_correction_bias_tensor
|
|
|
|
def load_state_dict(self, state_dict):
|
|
"""
|
|
load_state_dict function.
|
|
"""
|
|
self.gate_correction_bias_key = self.weight_key_map.get(
|
|
"gate_correction_bias_key", None)
|
|
if self.gate_correction_bias_key is not None and self.gate_correction_bias_key in state_dict:
|
|
self.moe_use_gate_correction_bias = True
|
|
else:
|
|
self.moe_use_gate_correction_bias = False
|
|
if self.moe_use_gate_correction_bias:
|
|
gate_correction_bias_tensor = self.extract_gate_correction_bias(
|
|
self.gate_correction_bias_key, state_dict)
|
|
self.gate_correction_bias = self.create_parameter(
|
|
shape=gate_correction_bias_tensor.shape,
|
|
dtype="float32",
|
|
)
|
|
self.gate_correction_bias.set_value(gate_correction_bias_tensor)
|
|
|
|
gate_weight_key = self.weight_key_map.get("gate_weight_key", None)
|
|
assert gate_weight_key is not None, "gate_weight_key should not be None, please check model checkpoints"
|
|
|
|
gate_weight_tensor = get_tensor(state_dict.pop(gate_weight_key))
|
|
|
|
self.gate_weight = self.create_parameter(
|
|
shape=gate_weight_tensor.shape,
|
|
dtype="float32",
|
|
)
|
|
self.gate_weight.set_value(gate_weight_tensor.astype("float32"))
|
|
|
|
if self.fd_config.model_config.is_quantized:
|
|
self.quant_method.process_prequanted_weights(self, state_dict)
|
|
else:
|
|
self.quant_method.create_weights(self, state_dict)
|
|
|
|
def forward(self, x: paddle.Tensor):
|
|
"""
|
|
Defines the forward computation of the moe layer.
|
|
|
|
Args:
|
|
x (Tensor): Input tensor to the moe layer.
|
|
|
|
Returns:
|
|
Tensor: Output tensor.s
|
|
|
|
"""
|
|
gate_out = paddle.matmul(x.cast("float32"), self.gate_weight)
|
|
out = self.quant_method.apply(self, x, gate_out)
|
|
return out
|