[Bugfix] Correctly call cudaProfilerStop in benchmarks script (vllm-project#14183)

Signed-off-by: Brayden Zhong <[email protected]>

Author: b8zhong

benchmarks/kernels/benchmark_layernorm.py

@@ -40,7 +40,7 @@ def run_cuda_benchmark(num_iters: int, profile: bool = False) -> float:
 
         end_time = time.perf_counter()
         if profile:
-            torch.cuda.cudart().cudaProfilerStart()
+            torch.cuda.cudart().cudaProfilerStop()
         return (end_time - start_time) / num_iters
 
     # Warmup.

benchmarks/kernels/benchmark_lora.py

@@ -153,7 +153,6 @@ def ref_group_gemm(ref_out: torch.Tensor, input: torch.Tensor,
         result = torch.nn.functional.linear(x, w)
         result *= scaling
         out_list.append(result)
-    torch.cat(out_list, dim=0)
 
     cat_result = torch.cat(out_list, dim=0)
 

benchmarks/kernels/benchmark_machete.py

@@ -45,7 +45,6 @@ def terse_type_name(dt):
         torch.float16: "fp16",
         torch.int8: "int8",
         torch.float8_e4m3fn: "fp8",
-        torch.bfloat16: "bf16",
         torch.float: "float",
         torch.int: "int",
     }[dt]
@@ -259,7 +258,7 @@ def machete_create_bench_fn(bt: BenchmarkTensors,
 
     return lambda: ops.machete_mm(
         a=bt.a,
-        b_q=bt.w_q,
+        b_q=w_q,
         b_type=bt.wtype,
         b_group_scales=bt.w_g_s,
         b_group_zeros=w_g_zp,

benchmarks/kernels/benchmark_moe.py

@@ -1,6 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 
 import argparse
+import json
 import time
 from contextlib import nullcontext
 from datetime import datetime

benchmarks/kernels/benchmark_paged_attention.py

@@ -176,7 +176,7 @@ def run_cuda_benchmark(num_iters: int, profile: bool = False) -> float:
 
         end_time = time.perf_counter()
         if profile:
-            torch.cuda.cudart().cudaProfilerStart()
+            torch.cuda.cudart().cudaProfilerStop()
         return (end_time - start_time) / num_iters
 
     # Warmup.

benchmarks/kernels/benchmark_quant.py

@@ -40,7 +40,7 @@ def run_cuda_benchmark(num_iters: int, profile: bool = False) -> float:
 
         end_time = time.perf_counter()
         if profile:
-            torch.cuda.cudart().cudaProfilerStart()
+            torch.cuda.cudart().cudaProfilerStop()
         return (end_time - start_time) / num_iters
 
     # Warmup.