apps/FastDeploy
1
0
Fork 0
mirror of https://github.com/PaddlePaddle/FastDeploy.git synced 2025-10-06 09:07:10 +08:00
Code Issues Actions 2 Packages Projects Releases Wiki Activity

Supports DP+TP+EP hybrid parallel deployment strategy (#3489)

Browse Source 
* Support DP+TP+EP hybrid parallel deployment strategy

* Support DP+TP+EP hybrid parallel deployment strategy

* fix conflict

* add moe_tp_ep function split_allgather_out

* del tp_group in moe_cutlass_backend

* for ci

* fix parallel_config for ci

* del log
This commit is contained in:
lzy
2025-08-26 15:04:01 +08:00
committed by GitHub
parent 52eda7fdb3
commit d339df2e90
15 changed files with 304 additions and 224 deletions
Download Patch File Download Diff File Expand all files Collapse all files

69
fastdeploy/model_executor/layers/embeddings.py
View File

@@ -57,43 +57,37 @@ class VocabParallelEmbedding(nn.Layer):
hcg = fleet.get_hybrid_communicate_group()
self.mp_rank: int = hcg.get_model_parallel_rank()
self.column_cut = False
self.world_size: int = hcg.get_model_parallel_world_size()
self.ring_id: int = hcg.get_model_parallel_group().id
self.use_ep: bool = fd_config.parallel_config.use_ep
self.world_size: int = fd_config.parallel_config.tensor_parallel_size
self.tensor_parallel_rank = fd_config.parallel_config.tensor_parallel_rank
self.tp_group = fd_config.parallel_config.tp_group
self.hidden_dropout_prob: float = fd_config.model_config.hidden_dropout_prob
self.initializer_range: float = fd_config.model_config.initializer_range
self.max_position_embeddings: int = fd_config.model_config.max_position_embeddings
self.tie_word_embeddings: bool = fd_config.model_config.tie_word_embeddings
self.params_dtype: str = params_dtype
if self.use_ep:
self.embeddings = nn.Embedding(
if not self.column_cut:
self.embeddings = fleet.meta_parallel.VocabParallelEmbedding(
num_embeddings,
embedding_dim,
mp_group=self.tp_group,
weight_attr=paddle.ParamAttr(
initializer=nn.initializer.Normal(mean=0.0, std=self.initializer_range),
),
)
if self.world_size > 1:
set_weight_attrs(self.embeddings.weight, {"output_dim": False})
else:
if not self.column_cut:
self.embeddings = fleet.meta_parallel.VocabParallelEmbedding(
num_embeddings,
embedding_dim,
mp_group=fleet.get_hybrid_communicate_group().get_model_parallel_group(),
weight_attr=paddle.ParamAttr(
initializer=nn.initializer.Normal(mean=0.0, std=self.initializer_range),
),
)
if self.world_size > 1:
set_weight_attrs(self.embeddings.weight, {"output_dim": False})
else:
# column cut embedding
self.embeddings = nn.Embedding(
num_embeddings,
embedding_dim // self.world_size,
)
# column cut embedding
self.embeddings = nn.Embedding(
num_embeddings,
embedding_dim // self.world_size,
)
self.embeddings.weight.is_distributed = True
self.embeddings.weight.split_axis = 1
if self.world_size > 1:
set_weight_attrs(self.embeddings.weight, {"output_dim": True})
self.embeddings.weight.is_distributed = True
self.embeddings.weight.split_axis = 1
if self.world_size > 1:
set_weight_attrs(self.embeddings.weight, {"output_dim": True})
self.prefix = prefix
self.dropout = nn.Dropout(self.hidden_dropout_prob)
@@ -125,20 +119,17 @@ class VocabParallelEmbedding(nn.Layer):
Returns:
Tensor: Embedded tensor representation of the input IDs.
"""
if self.use_ep:
if self.column_cut:
input_embedings = self.embeddings(ids_remove_padding)
inputs_embeds_temp = []
paddle.distributed.all_gather(
inputs_embeds_temp,
input_embedings,
group=self.tp_group,
sync_op=True,
)
input_embedings = paddle.concat(inputs_embeds_temp, -1)
else:
if self.column_cut:
input_embedings = self.embeddings(ids_remove_padding)
inputs_embeds_temp = []
paddle.distributed.all_gather(
inputs_embeds_temp,
input_embedings,
group=fleet.get_hybrid_communicate_group().get_model_parallel_group(),
sync_op=True,
)
input_embedings = paddle.concat(inputs_embeds_temp, -1)
else:
input_embedings = self.embeddings(ids_remove_padding)
input_embedings = self.embeddings(ids_remove_padding)
return input_embedings