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https://github.com/PaddlePaddle/FastDeploy.git
synced 2026-04-23 00:17:25 +08:00
polish code with new pre-commit rule (#2923)
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Zero Rains
@@ -27,16 +27,21 @@ def get_moe_method():
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return moe method based on device platform
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"""
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from fastdeploy.platforms import current_platform
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if current_platform.is_cuda():
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from .fused_moe_cutlass_backend import CutlassMoEMethod
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return CutlassMoEMethod(None)
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elif current_platform.is_xpu():
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from .fused_moe_xpu_backend import XPUMoEMethod
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return XPUMoEMethod(None)
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elif current_platform.is_gcu():
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from fastdeploy.model_executor.layers.backends import GCUFusedMoeMethod
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return GCUFusedMoeMethod(None)
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raise NotImplementedError()
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raise NotImplementedError
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class FusedMoE(nn.Layer):
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"""
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@@ -76,9 +81,9 @@ class FusedMoE(nn.Layer):
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self.ep_size = fd_config.parallel_config.expert_parallel_size
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self.ep_rank = fd_config.parallel_config.expert_parallel_rank
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assert (self.tp_size >= 1 and self.ep_size == 1) or \
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(self.tp_size == 1 and self.ep_size > 1), \
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'MoE only support parallelism on TP or EP dimension.'
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assert (self.tp_size >= 1 and self.ep_size == 1) or (
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self.tp_size == 1 and self.ep_size > 1
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), "MoE only support parallelism on TP or EP dimension."
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self.hidden_size = fd_config.model_config.hidden_size
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self.num_experts = num_experts
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@@ -123,7 +128,8 @@ class FusedMoE(nn.Layer):
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f"{moe_tag}MoE config is {num_experts=}[{expert_id_offset}, {expert_id_offset+self.num_local_experts}), \
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{top_k=}, hidden_size={self.hidden_size}, {moe_intermediate_size=}, \
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, ep_size={self.ep_size}, \
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tp_size={self.tp_size}.")
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tp_size={self.tp_size}."
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)
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def init_moe_weights(self):
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"""
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@@ -147,15 +153,31 @@ class FusedMoE(nn.Layer):
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)
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up_gate_proj_output_dim = self.moe_intermediate_size * 2
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if self.moe_quant_type in ["fp8", "wint8"]:
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up_gate_proj_weight_shape = [self.num_local_experts, up_gate_proj_output_dim, self.hidden_size]
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down_proj_weight_shape = [self.num_local_experts, self.hidden_size, self.moe_intermediate_size]
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up_gate_proj_weight_shape = [
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self.num_local_experts,
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up_gate_proj_output_dim,
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self.hidden_size,
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]
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down_proj_weight_shape = [
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self.num_local_experts,
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self.hidden_size,
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self.moe_intermediate_size,
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]
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else:
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up_gate_proj_weight_shape = [self.num_local_experts, self.hidden_size, up_gate_proj_output_dim]
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down_proj_weight_shape = [self.num_local_experts, self.moe_intermediate_size, self.hidden_size]
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up_gate_proj_weight_shape = [
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self.num_local_experts,
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self.hidden_size,
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up_gate_proj_output_dim,
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]
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down_proj_weight_shape = [
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self.num_local_experts,
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self.moe_intermediate_size,
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self.hidden_size,
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]
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# Create parameters
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if self.moe_quant_type == "fp8":
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#(TODO:gaoziyuan)
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# (TODO:gaoziyuan)
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pass
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elif self.moe_quant_type == "wint8":
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self.weight_dtype = "int8"
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@@ -187,9 +209,12 @@ class FusedMoE(nn.Layer):
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dtype=self._dtype,
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)
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def load_experts_weight(self, state_dict: dict,
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up_gate_proj_expert_weight_key: str,
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down_proj_expert_weight_key: str):
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def load_experts_weight(
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self,
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state_dict: dict,
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up_gate_proj_expert_weight_key: str,
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down_proj_expert_weight_key: str,
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):
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"""
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Load experts weight from state_dict.
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Args:
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@@ -199,35 +224,23 @@ class FusedMoE(nn.Layer):
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"""
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up_gate_proj_weights = []
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down_proj_weights = []
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is_ffn_merged = up_gate_proj_expert_weight_key.format(
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self.expert_id_offset) in state_dict
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is_ffn_merged = up_gate_proj_expert_weight_key.format(self.expert_id_offset) in state_dict
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if is_ffn_merged:
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for i in range(self.num_local_experts):
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expert_idx = self.expert_id_offset + i
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up_gate_proj_weights.append(
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get_tensor(
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state_dict.pop(
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up_gate_proj_expert_weight_key.format(expert_idx))))
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down_proj_weights.append(
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get_tensor(
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state_dict.pop(
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down_proj_expert_weight_key.format(expert_idx))))
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get_tensor(state_dict.pop(up_gate_proj_expert_weight_key.format(expert_idx)))
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)
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down_proj_weights.append(get_tensor(state_dict.pop(down_proj_expert_weight_key.format(expert_idx))))
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else:
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gate_expert_weight_key = up_gate_proj_expert_weight_key.replace(
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"up_gate_proj", "gate_proj")
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up_expert_weight_key = up_gate_proj_expert_weight_key.replace(
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"up_gate_proj", "up_proj")
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gate_expert_weight_key = up_gate_proj_expert_weight_key.replace("up_gate_proj", "gate_proj")
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up_expert_weight_key = up_gate_proj_expert_weight_key.replace("up_gate_proj", "up_proj")
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for j in range(self.num_local_experts):
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expert_idx = self.expert_id_offset + j
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gate = get_tensor(
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state_dict.pop(gate_expert_weight_key.format(expert_idx)))
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up = get_tensor(
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state_dict.pop(up_expert_weight_key.format(expert_idx)))
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gate = get_tensor(state_dict.pop(gate_expert_weight_key.format(expert_idx)))
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up = get_tensor(state_dict.pop(up_expert_weight_key.format(expert_idx)))
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up_gate_proj_weights.append(paddle.concat([gate, up], axis=-1))
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down_proj_weights.append(
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get_tensor(
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state_dict.pop(
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down_proj_expert_weight_key.format(expert_idx))))
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down_proj_weights.append(get_tensor(state_dict.pop(down_proj_expert_weight_key.format(expert_idx))))
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return up_gate_proj_weights, down_proj_weights
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def extract_moe_ffn_weights(self, state_dict: dict):
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@@ -246,46 +259,43 @@ class FusedMoE(nn.Layer):
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AssertionError: If required weight keys are missing or number of weights
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doesn't match number of local experts.
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"""
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up_gate_proj_expert_weight_key = self.weight_key_map.get(
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"up_gate_proj_expert_weight_key", None)
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down_proj_expert_weight_key = self.weight_key_map.get(
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"down_proj_expert_weight_key", None)
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up_gate_proj_expert_weight_key = self.weight_key_map.get("up_gate_proj_expert_weight_key", None)
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down_proj_expert_weight_key = self.weight_key_map.get("down_proj_expert_weight_key", None)
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assert up_gate_proj_expert_weight_key is not None, "up_gate_proj_expert_weight_key should not be none."
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assert down_proj_expert_weight_key is not None, "down_proj_expert_weight_key should not be none."
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up_gate_proj_weights, down_proj_weights = self.load_experts_weight(
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state_dict, up_gate_proj_expert_weight_key, down_proj_expert_weight_key)
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assert len(
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up_gate_proj_weights
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) == self.num_local_experts, "up_gate_proj_weights length should be equal to num_local_experts."
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assert len(
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down_proj_weights
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) == self.num_local_experts, "down_proj_weights length should be equal to num_local_experts."
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state_dict,
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up_gate_proj_expert_weight_key,
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down_proj_expert_weight_key,
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)
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assert (
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len(up_gate_proj_weights) == self.num_local_experts
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), "up_gate_proj_weights length should be equal to num_local_experts."
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assert (
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len(down_proj_weights) == self.num_local_experts
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), "down_proj_weights length should be equal to num_local_experts."
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return up_gate_proj_weights, down_proj_weights
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def extract_gate_correction_bias(self, gate_correction_bias_key,
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state_dict):
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def extract_gate_correction_bias(self, gate_correction_bias_key, state_dict):
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"""
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extract_gate_correction_bias function.
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"""
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gate_correction_bias_tensor = get_tensor(
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state_dict.pop(gate_correction_bias_key)).astype("float32")
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gate_correction_bias_tensor = get_tensor(state_dict.pop(gate_correction_bias_key)).astype("float32")
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return gate_correction_bias_tensor
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def load_state_dict(self, state_dict):
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"""
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load_state_dict function.
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"""
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self.gate_correction_bias_key = self.weight_key_map.get(
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"gate_correction_bias_key", None)
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self.gate_correction_bias_key = self.weight_key_map.get("gate_correction_bias_key", None)
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if self.gate_correction_bias_key is not None and self.gate_correction_bias_key in state_dict:
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self.moe_use_gate_correction_bias = True
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else:
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self.moe_use_gate_correction_bias = False
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if self.moe_use_gate_correction_bias:
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gate_correction_bias_tensor = self.extract_gate_correction_bias(
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self.gate_correction_bias_key, state_dict)
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gate_correction_bias_tensor = self.extract_gate_correction_bias(self.gate_correction_bias_key, state_dict)
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self.gate_correction_bias = self.create_parameter(
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shape=gate_correction_bias_tensor.shape,
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dtype="float32",
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