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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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21
fastdeploy/model_executor/layers/embeddings.py
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@@ -68,13 +68,13 @@ class VocabParallelEmbedding(nn.Layer):
self.params_dtype: str = params_dtype
if self.use_ep:
self.word_embeddings = nn.Embedding(
self.embeddings = nn.Embedding(
num_embeddings,
embedding_dim,
)
else:
if not self.column_cut:
self.word_embeddings = fleet.meta_parallel.VocabParallelEmbedding(
self.embeddings = fleet.meta_parallel.VocabParallelEmbedding(
num_embeddings,
embedding_dim,
mp_group=fleet.get_hybrid_communicate_group().
@@ -85,13 +85,13 @@ class VocabParallelEmbedding(nn.Layer):
)
else:
# column cut embedding
self.word_embeddings = nn.Embedding(
self.embeddings = nn.Embedding(
num_embeddings,
embedding_dim // self.world_size,
)
self.word_embeddings.weight.is_distributed = True
self.word_embeddings.weight.split_axis = 1
self.embeddings.weight.is_distributed = True
self.embeddings.weight.split_axis = 1
if not self.use_rope:
self.position_embeddings = nn.Embedding(
@@ -112,13 +112,12 @@ class VocabParallelEmbedding(nn.Layer):
Args:
state_dict (dict): A dictionary containing the checkpoint weights and biases.
"""
a = state_dict[self.prefix + ".weight"]
if self.tie_word_embeddings:
self.word_embeddings.weight.set_value(
self.embeddings.weight.set_value(
get_tensor(state_dict[self.prefix + ".weight"]).astype(
paddle.get_default_dtype()))
else:
self.word_embeddings.weight.set_value(
self.embeddings.weight.set_value(
get_tensor(state_dict.pop(self.prefix + ".weight")).astype(
paddle.get_default_dtype()))
@@ -134,10 +133,10 @@ class VocabParallelEmbedding(nn.Layer):
Tensor: Embedded tensor representation of the input IDs.
"""
if self.use_ep:
input_embedings = self.word_embeddings(ids_remove_padding)
input_embedings = self.embeddings(ids_remove_padding)
else:
if self.column_cut:
input_embedings = self.word_embeddings(ids_remove_padding)
input_embedings = self.embeddings(ids_remove_padding)
inputs_embeds_temp = []
paddle.distributed.all_gather(
inputs_embeds_temp,
@@ -148,6 +147,6 @@ class VocabParallelEmbedding(nn.Layer):
)
input_embedings = paddle.concat(inputs_embeds_temp, -1)
else:
input_embedings = self.word_embeddings(ids_remove_padding)
input_embedings = self.embeddings(ids_remove_padding)
return input_embedings