A high-throughput and memory-efficient inference and serving engine for LLMs
Mirrored from https://github.com/tduncan-lum/Nvidia-Optimization-Stack---VLLM.git
Synced Nov 15, 03:30 UTC
[Benchmark] Add support for multiple batch size benchmark through CLI in `benchmark_moe.py` (#20516)
Signed-off-by: Brayden Zhong <b8zhong@uwaterloo.ca>
Brayden Zhong committed
Jul 6, 2025 at 05:20 UTC
cede942b87b5d8baa0b95447f3e87e3c600ff5f5
2 files changed
+149
-2
benchmarks/kernels/benchmark_moe.py
+2
-2
@@ -620,7 +620,7 @@ def main(args: argparse.Namespace):
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4096,
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]
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else:
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- batch_sizes = [args.batch_size]
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+ batch_sizes = args.batch_size
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use_deep_gemm = bool(args.use_deep_gemm)
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@@ -728,7 +728,7 @@ if __name__ == "__main__":
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)
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parser.add_argument("--use-deep-gemm", action="store_true")
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parser.add_argument("--seed", type=int, default=0)
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- parser.add_argument("--batch-size", type=int, required=False)
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+ parser.add_argument("--batch-size", type=int, nargs="+", required=False)
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parser.add_argument("--tune", action="store_true")
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parser.add_argument("--trust-remote-code", action="store_true")
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parser.add_argument("--model-prefix", type=str, required=False)
vllm/model_executor/layers/fused_moe/configs/E=16,N=1024,device_name=NVIDIA_B200,dtype=fp8_w8a8.json
new
+147
@@ -0,0 +1,147 @@
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+{
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+ "1": {
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+ "BLOCK_SIZE_M": 16,
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+ "BLOCK_SIZE_N": 32,
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+ "BLOCK_SIZE_K": 256,
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+ "GROUP_SIZE_M": 64,
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+ "num_warps": 4,
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+ "num_stages": 4
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+ },
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+ "2": {
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+ "BLOCK_SIZE_M": 16,
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+ "BLOCK_SIZE_N": 32,
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+ "BLOCK_SIZE_K": 256,
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+ "GROUP_SIZE_M": 1,
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+ "num_warps": 4,
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+ "num_stages": 3
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+ },
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+ "4": {
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+ "BLOCK_SIZE_M": 16,
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+ "BLOCK_SIZE_N": 32,
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+ "BLOCK_SIZE_K": 256,
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+ "GROUP_SIZE_M": 1,
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+ "num_warps": 4,
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+ "num_stages": 3
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+ },
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+ "8": {
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+ "BLOCK_SIZE_M": 16,
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+ "BLOCK_SIZE_N": 128,
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+ "BLOCK_SIZE_K": 128,
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+ "GROUP_SIZE_M": 16,
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+ "num_warps": 4,
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+ "num_stages": 5
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+ },
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+ "16": {
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+ "BLOCK_SIZE_M": 16,
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+ "BLOCK_SIZE_N": 64,
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+ "BLOCK_SIZE_K": 128,
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+ "GROUP_SIZE_M": 1,
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+ "num_warps": 4,
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+ "num_stages": 4
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+ },
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+ "24": {
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+ "BLOCK_SIZE_M": 16,
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+ "BLOCK_SIZE_N": 128,
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+ "BLOCK_SIZE_K": 128,
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+ "GROUP_SIZE_M": 1,
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+ "num_warps": 4,
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+ "num_stages": 4
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+ },
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+ "32": {
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+ "BLOCK_SIZE_M": 16,
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+ "BLOCK_SIZE_N": 64,
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+ "BLOCK_SIZE_K": 128,
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+ "GROUP_SIZE_M": 1,
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+ "num_warps": 4,
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+ "num_stages": 5
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+ },
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+ "48": {
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+ "BLOCK_SIZE_M": 16,
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+ "BLOCK_SIZE_N": 64,
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+ "BLOCK_SIZE_K": 128,
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+ "GROUP_SIZE_M": 1,
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+ "num_warps": 4,
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+ "num_stages": 5
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+ },
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+ "64": {
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+ "BLOCK_SIZE_M": 16,
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+ "BLOCK_SIZE_N": 128,
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+ "BLOCK_SIZE_K": 256,
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+ "GROUP_SIZE_M": 1,
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+ "num_warps": 8,
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+ "num_stages": 3
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+ },
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+ "96": {
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+ "BLOCK_SIZE_M": 16,
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+ "BLOCK_SIZE_N": 128,
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+ "BLOCK_SIZE_K": 256,
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+ "GROUP_SIZE_M": 1,
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+ "num_warps": 8,
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+ "num_stages": 3
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+ },
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+ "128": {
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+ "BLOCK_SIZE_M": 16,
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+ "BLOCK_SIZE_N": 128,
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+ "BLOCK_SIZE_K": 256,
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+ "GROUP_SIZE_M": 1,
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+ "num_warps": 8,
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+ "num_stages": 3
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+ },
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+ "256": {
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+ "BLOCK_SIZE_M": 16,
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+ "BLOCK_SIZE_N": 128,
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+ "BLOCK_SIZE_K": 128,
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+ "GROUP_SIZE_M": 1,
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+ "num_warps": 4,
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+ "num_stages": 4
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+ },
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+ "512": {
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+ "BLOCK_SIZE_M": 64,
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+ "BLOCK_SIZE_N": 128,
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+ "BLOCK_SIZE_K": 128,
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+ "GROUP_SIZE_M": 1,
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+ "num_warps": 8,
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+ "num_stages": 3
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+ },
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+ "1024": {
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+ "BLOCK_SIZE_M": 64,
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+ "BLOCK_SIZE_N": 128,
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+ "BLOCK_SIZE_K": 128,
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+ "GROUP_SIZE_M": 1,
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+ "num_warps": 4,
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+ "num_stages": 3
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+ },
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+ "1536": {
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+ "BLOCK_SIZE_M": 128,
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+ "BLOCK_SIZE_N": 128,
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+ "BLOCK_SIZE_K": 64,
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+ "GROUP_SIZE_M": 1,
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+ "num_warps": 4,
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+ "num_stages": 4
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+ },
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+ "2048": {
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+ "BLOCK_SIZE_M": 128,
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+ "BLOCK_SIZE_N": 128,
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+ "BLOCK_SIZE_K": 64,
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+ "GROUP_SIZE_M": 1,
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+ "num_warps": 4,
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+ "num_stages": 4
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+ },
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+ "3072": {
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+ "BLOCK_SIZE_M": 256,
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+ "BLOCK_SIZE_N": 256,
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+ "BLOCK_SIZE_K": 128,
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+ "GROUP_SIZE_M": 1,
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+ "num_warps": 8,
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+ "num_stages": 3
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+ },
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+ "4096": {
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+ "BLOCK_SIZE_M": 128,
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+ "BLOCK_SIZE_N": 256,
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+ "BLOCK_SIZE_K": 128,
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+ "GROUP_SIZE_M": 1,
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+ "num_warps": 8,
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+ "num_stages": 4
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+ }
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+}
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+