enable all_reduce_fusion kernel + cudagraph function

tests/comm/test_trtllm_allreduce_fusion_paddle.py

@@ -1,10 +1,10 @@
 import socket
 import pytest
-
 import paddle
 import paddle.distributed as dist_pp
 
 paddle.enable_compat()
+from paddle.device.cuda.graphs import CUDAGraph
 import flashinfer.comm as comm
 
 import os
@@ -36,15 +36,12 @@ def kernel(workspace_tensor, rank, world_size):
     residual_out = paddle.zeros(message_size, dtype=dtype, device=device)
     norm_out = paddle.zeros(message_size, dtype=dtype, device=device)
     quant_out = paddle.zeros(message_size, dtype=dtype, device=device)
-    scale_out = paddle.zeros(
-        message_size // 16, dtype=dtype, device=device
-    )  # SF_VEC_SIZE = 16
+    scale_out = paddle.zeros(message_size // 16, dtype=dtype, device=device)
     rms_gamma = paddle.randn(hidden_dim, dtype=dtype, device=device)
     rms_eps = 1e-3
     scale_factor = paddle.tensor(0.5, dtype=paddle.float32, device=device)
 
     # Run fusion operation
-    print("Running fusion operation...")
     comm.trtllm_allreduce_fusion(
         allreduce_in=allreduce_in,
         world_size=world_size,
@@ -69,7 +66,7 @@ def kernel(workspace_tensor, rank, world_size):
         layout_code=layout_code,
     )
 
-    paddle.cuda.synchronize()
+    # paddle.cuda.synchronize()
 
     return allreduce_in, all_reduce_out
 
@@ -91,6 +88,7 @@ def _run_simple_worker(world_size, rank, distributed_init_port):
     os.environ["PADDLE_CURRENT_ENDPOINT"] = (
         f"127.0.0.1:{distributed_init_port + rank + 10}"
     )
+
     # Set NCCL related environment variables (optional but recommended)
     os.environ["FLAGS_SYNC_NCCL_ALLREDUCE"] = "1"
 
@@ -117,7 +115,25 @@ def _run_simple_worker(world_size, rank, distributed_init_port):
         dist_pp.barrier(group=group_pp)
 
         # Run fusion operation
-        allreduce_in_clone, all_reduce_out = kernel(workspace_tensor, rank, world_size)
+        loop = 5
+        s = paddle.cuda.Stream()
+        s.wait_stream(paddle.cuda.current_stream())
+        with paddle.cuda.stream(s):
+            for _ in range(loop):
+                allreduce_in_clone, all_reduce_out = kernel(
+                    workspace_tensor, rank, world_size
+                )
+
+        g = CUDAGraph()
+        g.capture_begin()
+        for _ in range(loop):
+            allreduce_in_clone, all_reduce_out = kernel(
+                workspace_tensor, rank, world_size
+            )
+        g.capture_end()
+
+        g.replay()
+        paddle.cuda.synchronize()
 
         # # Calculate reference result
         dist_pp.all_reduce(allreduce_in_clone, group=group_pp)