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[BugFix] fix real_bsz in ep (#3366)

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* fix ep

* delete cuda_graph
This commit is contained in:
lizexu123
2025-08-14 17:31:19 +08:00
committed by GitHub
parent 0ea8712018
commit 7b596d0877
3 changed files with 6 additions and 2 deletions
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2
fastdeploy/model_executor/models/ernie4_5_moe.py
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@@ -450,7 +450,7 @@ class Ernie4_5_MoeForCausalLM(ModelForCasualLM):
self.fd_config.model_config.moe_layer_start_index,
self.fd_config.model_config.num_hidden_layers,
):
self.ernie.layers[i].mlp.expert(fake_hidden_states)
self.ernie.layers[i].mlp.experts(fake_hidden_states, self.ernie.layers[i].mlp.gate)
def forward(
self,

4
fastdeploy/worker/gpu_model_runner.py
View File

@@ -636,7 +636,9 @@ class GPUModelRunner(ModelRunnerBase):
self.share_inputs["max_length"] = paddle.full(
[max_num_seqs, 1], self.model_config.max_model_len, dtype="int64"
)
self.seq_lens_this_time_buffer = paddle.full(max_num_seqs, 0, dtype="int32")
self.seq_lens_this_time_buffer = paddle.full([max_num_seqs, 1], 0, dtype="int32")
if self.fd_config.parallel_config.enable_expert_parallel:
self.share_inputs["seq_lens_this_time"] = paddle.full([max_num_seqs, 1], 0, dtype="int32")
self.share_inputs["seq_lens_encoder"] = paddle.full([max_num_seqs, 1], 0, dtype="int32")
self.share_inputs["seq_lens_decoder"] = paddle.full([max_num_seqs, 1], 0, dtype="int32")
self.share_inputs["step_seq_lens_encoder"] = paddle.full([max_num_seqs, 1], 0, dtype="int32")

2
fastdeploy/worker/worker_process.py
View File

@@ -250,6 +250,7 @@ class PaddleDisWorkerProc:
while True:
self.worker_healthy_live_signal.value[self.local_rank % self.max_chips_per_node] = int(time.time())
num_running_requests = 0
if self.fd_config.parallel_config.tensor_parallel_rank == 0 and self.task_queue.num_tasks() > 0:
tasks, read_finish = self.task_queue.get_tasks()
@@ -276,6 +277,7 @@ class PaddleDisWorkerProc:
self.nnode = int((self.parallel_config.tensor_parallel_size + 7) // 8)
mp_num_per_node = self.parallel_config.tensor_parallel_size // self.nnode
req_ids = []
num_running_requests = 0
while True:
if self.local_rank == 0:
if self.model_weights_status.value[0] != 0: