mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-05 16:48:03 +08:00
Simplify the Config code (#2770)
* simplify the code * fix vl * delete config * fix * perfect code * fix ci * fix xpu * fix xpu * fix server * resolve conflict * fix mtp * resolve conflict * fix xpu * fix xpu * fix vl * fix log * fix qwen moe * fix qwen moe * fix qwen moe
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YuanRisheng
@@ -27,6 +27,7 @@ from paddleformers.utils.log import logger
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from fastdeploy.config import FDConfig
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from fastdeploy.distributed.communication_op import \
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tensor_model_parallel_all_reduce
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from fastdeploy.model_executor.forward_meta import ForwardMeta
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from fastdeploy.model_executor.layers.activation import SiluAndMul
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from fastdeploy.model_executor.layers.attention.attention import Attention
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from fastdeploy.model_executor.layers.embeddings import VocabParallelEmbedding
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@@ -40,7 +41,6 @@ from fastdeploy.model_executor.layers.rotary_embedding import \
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DeepseekScalingRotaryEmbedding
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from fastdeploy.model_executor.models.model_base import ModelForCasualLM
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from fastdeploy.platforms import current_platform
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from fastdeploy.model_executor.forward_meta import ForwardMeta
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if current_platform.is_cuda():
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from fastdeploy.model_executor.ops.gpu import \
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@@ -109,7 +109,7 @@ class DeepSeekV3MoE(nn.Layer):
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prefix: str) -> None:
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super().__init__()
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self.tp_size = fd_config.parallel_config.tensor_parallel_degree
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self.tp_size = fd_config.parallel_config.tensor_parallel_size
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weight_key_map = {
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"gate_weight_key": f"{prefix}.gate.weight",
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@@ -124,23 +124,23 @@ class DeepSeekV3MoE(nn.Layer):
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self.fused_moe = FusedMoE(
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fd_config=fd_config,
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reduce_results=False,
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moe_intermediate_size=fd_config.model_config.deepseekv3.
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moe_intermediate_size=fd_config.model_config.
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moe_intermediate_size,
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num_experts=fd_config.model_config.deepseekv3.n_routed_experts,
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top_k=fd_config.model_config.deepseekv3.num_experts_per_tok,
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topk_method=fd_config.model_config.deepseekv3.topk_method,
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topk_group=fd_config.model_config.deepseekv3.topk_group,
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n_group=fd_config.model_config.deepseekv3.n_group,
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routed_scaling_factor=fd_config.model_config.deepseekv3.
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num_experts=fd_config.model_config.n_routed_experts,
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top_k=fd_config.model_config.num_experts_per_tok,
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topk_method=fd_config.model_config.topk_method,
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topk_group=fd_config.model_config.topk_group,
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n_group=fd_config.model_config.n_group,
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routed_scaling_factor=fd_config.model_config.
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routed_scaling_factor,
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layer_idx=layer_id,
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weight_key_map=weight_key_map,
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)
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self.num_shared_experts = fd_config.model_config.deepseekv3.n_shared_experts
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self.num_shared_experts = fd_config.model_config.n_shared_experts
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shared_experts_intermediate_size = (
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self.num_shared_experts *
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fd_config.model_config.deepseekv3.moe_intermediate_size)
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fd_config.model_config.moe_intermediate_size)
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self.shared_experts = DeepSeekV3MLP(
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fd_config=fd_config,
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@@ -178,18 +178,18 @@ class DeepseekV3MLAAttention(nn.Layer):
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prefix: str = "") -> None:
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super().__init__()
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self.tp_size = fd_config.parallel_config.tensor_parallel_degree
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self.tp_size = fd_config.parallel_config.tensor_parallel_size
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self.hidden_size = fd_config.model_config.hidden_size
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self.num_attention_heads = fd_config.model_config.num_attention_heads
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self.num_attention_heads_tp = self.num_attention_heads // self.tp_size
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# MLA
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self.qk_nope_head_dim = fd_config.model_config.deepseekv3.qk_nope_head_dim
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self.qk_rope_head_dim = fd_config.model_config.deepseekv3.qk_rope_head_dim
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self.qk_nope_head_dim = fd_config.model_config.qk_nope_head_dim
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self.qk_rope_head_dim = fd_config.model_config.qk_rope_head_dim
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self.qk_head_dim = self.qk_nope_head_dim + self.qk_rope_head_dim
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self.v_head_dim = fd_config.model_config.deepseekv3.v_head_dim
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self.q_lora_rank = fd_config.model_config.deepseekv3.q_lora_rank
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self.kv_lora_rank = fd_config.model_config.deepseekv3.kv_lora_rank
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self.v_head_dim = fd_config.model_config.v_head_dim
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self.q_lora_rank = fd_config.model_config.q_lora_rank
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self.kv_lora_rank = fd_config.model_config.kv_lora_rank
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self.attn_softmax_scale = self.qk_head_dim**-0.5
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self.rope_theta = fd_config.model_config.rope_theta
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@@ -255,7 +255,7 @@ class DeepseekV3MLAAttention(nn.Layer):
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qk_nope_head_dim=self.qk_nope_head_dim,
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v_head_dim=self.v_head_dim)
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self.rope_scaling = fd_config.model_config.deepseekv3.rope_scaling
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self.rope_scaling = fd_config.model_config.rope_scaling
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if self.rope_scaling:
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mscale_all_dim = self.rope_scaling.get("mscale_all_dim", False)
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scaling_factor = self.rope_scaling["factor"]
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@@ -449,9 +449,9 @@ class DeepSeekV3DecoderLayer(nn.Layer):
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prefix=f"{prefix}.self_attn",
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)
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if (fd_config.model_config.deepseekv3.n_routed_experts is not None
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if (fd_config.model_config.n_routed_experts is not None
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and layer_id
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>= fd_config.model_config.deepseekv3.first_k_dense_replace):
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>= fd_config.model_config.first_k_dense_replace):
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self.mlp = DeepSeekV3MoE(
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fd_config=fd_config,
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layer_id=layer_id,
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@@ -525,8 +525,8 @@ class DeepSeekV3Model(nn.Layer):
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Initializer for the DeepSeekV3Model class.
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"""
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super().__init__()
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self.num_layers = fd_config.model_config.num_layers
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fd_config.model_config.prefix_name = "deepseek_v3"
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self.num_layers = fd_config.model_config.num_hidden_layers
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fd_config.model_config.pretrained_config.prefix_name = "deepseek_v3"
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self.embeddings = VocabParallelEmbedding(
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fd_config,
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@@ -539,7 +539,7 @@ class DeepSeekV3Model(nn.Layer):
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self.decoder_layers = nn.LayerList([
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DeepSeekV3DecoderLayer(
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fd_config,
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prefix=f"{fd_config.model_config.prefix_name}.layers.{i}")
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prefix=f"{fd_config.model_config.pretrained_config.prefix_name}.layers.{i}")
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for i in range(self.num_layers)
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])
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@@ -755,5 +755,5 @@ class DeepSeekV3PretrainedModel(PretrainedModel):
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return final_actions
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mappings = get_tensor_parallel_split_mappings(config.num_layers)
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mappings = get_tensor_parallel_split_mappings(config.num_hidden_layers)
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return mappings
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