fix: enable multi-GPU Triton fused MoE tuning (#6295)

benchmark/kernels/fused_moe_triton/tuning_fused_moe_triton.py

@@ -3,6 +3,7 @@ import argparse
 import json
 import time
 from datetime import datetime
+from contextlib import nullcontext
 from typing import Any, Dict, List, Tuple, TypedDict
 
 import ray
@@ -21,7 +22,7 @@ from sglang.srt.layers.moe.fused_moe_triton.fused_moe import (
 )
 from sglang.srt.layers.moe.moe_runner import MoeRunnerConfig
 from sglang.srt.layers.moe.topk import TopKConfig, select_experts
-from sglang.srt.utils import is_hip
+from sglang.srt.utils import is_hip, is_rocm
 
 _is_hip = is_hip()
 
@@ -245,6 +246,9 @@ class BenchmarkWorker:
         torch.set_default_device("cuda")
         torch.cuda.manual_seed_all(0)
         self.seed = seed
+        # Get the device ID to allocate tensors and kernels
+        # on the respective GPU.
+        self.device_id = int(ray.get_gpu_ids()[0])
 
     def benchmark(
         self,
@@ -283,19 +287,20 @@ class BenchmarkWorker:
             )
         else:
             config = op_config[min(op_config.keys(), key=lambda x: abs(x - num_tokens))]
-        kernel_time = benchmark_config(
-            config,
-            num_tokens,
-            num_experts,
-            shard_intermediate_size,
-            hidden_size,
-            topk,
-            dtype,
-            use_fp8_w8a8,
-            use_int8_w8a8,
-            use_int8_w8a16,
-            block_shape,
-        )
+        with torch.cuda.device(self.device_id) if is_rocm() else nullcontext():
+            kernel_time = benchmark_config(
+                config,
+                num_tokens,
+                num_experts,
+                shard_intermediate_size,
+                hidden_size,
+                topk,
+                dtype,
+                use_fp8_w8a8,
+                use_int8_w8a8,
+                use_int8_w8a16,
+                block_shape,
+            )
         return config, kernel_time
 
     def tune(
@@ -314,29 +319,30 @@ class BenchmarkWorker:
     ) -> Dict[str, int]:
         best_config = None
         best_time = float("inf")
-        for config in tqdm(search_space):
-            try:
-                kernel_time = benchmark_config(
-                    config,
-                    num_tokens,
-                    num_experts,
-                    shard_intermediate_size,
-                    hidden_size,
-                    topk,
-                    dtype,
-                    use_fp8_w8a8,
-                    use_int8_w8a8,
-                    use_int8_w8a16,
-                    block_shape,
-                    num_iters=10,
-                )
-            except triton.runtime.autotuner.OutOfResources:
-                # Some configurations may be invalid and fail to compile.
-                continue
+        with torch.cuda.device(self.device_id) if is_rocm() else nullcontext():
+            for config in tqdm(search_space):
+                try:
+                    kernel_time = benchmark_config(
+                        config,
+                        num_tokens,
+                        num_experts,
+                        shard_intermediate_size,
+                        hidden_size,
+                        topk,
+                        dtype,
+                        use_fp8_w8a8,
+                        use_int8_w8a8,
+                        use_int8_w8a16,
+                        block_shape,
+                        num_iters=10,
+                    )
+                except triton.runtime.autotuner.OutOfResources:
+                    # Some configurations may be invalid and fail to compile.
+                    continue
 
-            if kernel_time < best_time:
-                best_time = kernel_time
-                best_config = config
+                if kernel_time < best_time:
+                    best_time = kernel_time
+                    best_config = config
         now = datetime.now()
         print(f"{now.ctime()}] Completed tuning for batch_size={num_tokens}")
         assert best_config is not None