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refactor rl get_name_mappings_to_training (#2847)
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* 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
This commit is contained in:
Yuanle Liu
2025-07-15 22:31:42 +08:00
committed by GitHub
parent e7bcbbab52
commit 61b3997b85
47 changed files with 1591 additions and 1629 deletions
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16
fastdeploy/model_executor/models/deepseek_v3.py
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@@ -61,7 +61,7 @@ class DeepSeekV3MLP(nn.Layer):
) -> None:
super().__init__()
self.gate_up_proj = MergedColumnParallelLinear(
self.up_gate_proj = MergedColumnParallelLinear(
fd_config=fd_config,
prefix=f"{prefix}.up_gate_proj",
input_size=fd_config.model_config.hidden_size,
@@ -88,13 +88,13 @@ class DeepSeekV3MLP(nn.Layer):
def load_state_dict(self, state_dict):
"""
"""
self.gate_up_proj.load_state_dict(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.gate_up_proj(x)
gate_up_out = self.up_gate_proj(x)
act_out = self.act_fn(gate_up_out)
down_out = self.down_proj(act_out)
return down_out
@@ -115,9 +115,9 @@ class DeepSeekV3MoE(nn.Layer):
"gate_weight_key": f"{prefix}.gate.weight",
"gate_correction_bias_key":
f"{prefix}.gate.e_score_correction_bias",
"ffn1_expert_weight_key":
"up_gate_proj_expert_weight_key":
f"{prefix}.experts.{{}}.up_gate_proj.weight",
"ffn2_expert_weight_key":
"down_proj_expert_weight_key":
f"{prefix}.experts.{{}}.down_proj.weight",
}
@@ -528,7 +528,7 @@ class DeepSeekV3Model(nn.Layer):
self.num_layers = fd_config.model_config.num_hidden_layers
fd_config.model_config.pretrained_config.prefix_name = "deepseek_v3"
self.embeddings = VocabParallelEmbedding(
self.embed_tokens = VocabParallelEmbedding(
fd_config,
num_embeddings=fd_config.model_config.vocab_size,
embedding_dim=fd_config.model_config.hidden_size,
@@ -554,7 +554,7 @@ class DeepSeekV3Model(nn.Layer):
"""
Load model parameters from a given state dictionary.
"""
self.embeddings.load_state_dict(state_dict)
self.embed_tokens.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}")
@@ -569,7 +569,7 @@ class DeepSeekV3Model(nn.Layer):
):
"""
"""
hidden_states = self.embeddings(ids_remove_padding=ids_remove_padding)
hidden_states = self.embed_tokens(ids_remove_padding=ids_remove_padding)
residual = None
for i in range(self.num_layers):