[Optimization] 1.fix tp+ep moe_forward; 2.set max_prefill_batch=env.MAX_PREFILL_NUM (#5315)

Author: carryyu

fastdeploy/config.py

@@ -1586,7 +1586,11 @@ def __init__(
             self.max_prefill_batch = int(os.getenv("MAX_PREFILL_NUM", "3"))
             if current_platform.is_xpu():
                 self.max_prefill_batch = 1
-            if self.model_config is not None and self.model_config.enable_mm:
+            if (
+                int(envs.ENABLE_V1_KVCACHE_SCHEDULER) == 0
+                and self.model_config is not None
+                and self.model_config.enable_mm
+            ):
                 self.max_prefill_batch = 1  # TODO:当前多模prefill阶段只支持并行度为1,待优化
         else:
             self.max_prefill_batch = self.scheduler_config.max_num_seqs

fastdeploy/model_executor/layers/moe/moe.py

@@ -163,6 +163,9 @@ def __init__(
             self.tp_size = 1
             self.tp_rank = 0
 
+        self.attn_tp_size = fd_config.parallel_config.tensor_parallel_size
+        self.attn_tp_rank = fd_config.parallel_config.tensor_parallel_rank
+
         assert (self.tp_size >= 1 and self.ep_size == 1) or (
             self.tp_size == 1 and self.ep_size > 1
         ), "MoE only support parallelism on TP or EP dimension."
@@ -598,18 +601,18 @@ def forward_split_allgather(self, x: paddle.Tensor, gate: nn.Layer):
         Forward split allgather function.
         """
         token_num = x.shape[0]
-        token_num_per_rank = (token_num + self.tp_size - 1) // self.tp_size
+        token_num_per_rank = (token_num + self.attn_tp_size - 1) // self.attn_tp_size
         # AllGather will hang when the data shapes on multi-ranks are different!
         part_x = paddle.zeros(shape=[token_num_per_rank, x.shape[1]], dtype=x.dtype)
-        start_offset = self.tp_rank * token_num_per_rank
-        end_offset = (self.tp_rank + 1) * token_num_per_rank
+        start_offset = self.attn_tp_rank * token_num_per_rank
+        end_offset = (self.attn_tp_rank + 1) * token_num_per_rank
         if start_offset >= token_num:
             start_offset = token_num
         if end_offset > token_num:
             end_offset = token_num
         part_x[: (end_offset - start_offset), :] = x[start_offset:end_offset, :]
         out = self.quant_method.apply(self, part_x, gate)
-        multi_outs = paddle.zeros([token_num_per_rank * self.tp_size, x.shape[1]], dtype=x.dtype)
+        multi_outs = paddle.zeros([token_num_per_rank * self.attn_tp_size, x.shape[1]], dtype=x.dtype)
         paddle.distributed.all_gather(multi_outs, out, self.tp_group)
         out = multi_outs[:token_num, :]
 
@@ -629,9 +632,9 @@ def forward(self, x: paddle.Tensor, gate: nn.Layer):
         token_num = x.shape[0]
         if (
             self.ep_size > 1
-            and self.tp_size > 1
+            and self.attn_tp_size > 1
             and (not self.fd_config.parallel_config.use_sequence_parallel_moe)
-            and token_num >= self.tp_size
+            and token_num >= self.attn_tp_size
         ):
             out = self.forward_split_allgather(x, gate)
         elif self.fd_config.parallel_config.use_ep and self.fd_config.parallel_config.enable_chunked_moe: