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 14, 05:30 UTC
[Perf] Add Triton config for DeepSeek V3 FP8 EP32 H200 (#23504)
Signed-off-by: Ming Yang <minos.future@gmail.com>
Ming Yang committed
Aug 24, 2025 at 18:06 UTC
504d91431499e302bbd5a3d8a1432cd427ec8d5d
4 files changed
+184
-1
benchmarks/kernels/benchmark_w8a8_block_fp8.py
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-1
@@ -11,8 +11,8 @@ from datetime import datetime
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from typing import Any
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import torch
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-import tqdm
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import triton
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+from tqdm import tqdm
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from vllm.model_executor.layers.quantization.utils.fp8_utils import (
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_w8a8_block_fp8_matmul,
vllm/model_executor/layers/fused_moe/configs/E=8,N=2048,device_name=NVIDIA_H200,dtype=fp8_w8a8,block_shape=[128,128].json
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@@ -0,0 +1,154 @@
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+{
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+ "1": {
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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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+ "2": {
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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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+ "4": {
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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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+ "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": 1,
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+ "num_warps": 4,
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+ "num_stages": 4
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+ },
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+ "16": {
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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": 3
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+ },
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+ "24": {
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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": 32,
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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": 64,
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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": 4,
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+ "num_stages": 4
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+ },
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+ "48": {
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+ "BLOCK_SIZE_M": 64,
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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": 4,
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+ "num_stages": 4
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+ },
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+ "64": {
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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": 32,
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+ "num_warps": 4,
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+ "num_stages": 4
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+ },
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+ "96": {
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+ "BLOCK_SIZE_M": 64,
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+ "BLOCK_SIZE_N": 128,
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+ "BLOCK_SIZE_K": 256,
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+ "GROUP_SIZE_M": 16,
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+ "num_warps": 4,
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+ "num_stages": 4
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+ },
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+ "128": {
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+ "BLOCK_SIZE_M": 64,
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+ "BLOCK_SIZE_N": 128,
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+ "BLOCK_SIZE_K": 256,
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+ "GROUP_SIZE_M": 16,
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+ "num_warps": 4,
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+ "num_stages": 4
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+ },
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+ "256": {
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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": 16,
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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": 256,
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+ "GROUP_SIZE_M": 64,
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+ "num_warps": 4,
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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": 256,
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+ "GROUP_SIZE_M": 16,
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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": 64,
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+ "BLOCK_SIZE_N": 128,
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+ "BLOCK_SIZE_K": 128,
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+ "GROUP_SIZE_M": 64,
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+ "num_warps": 4,
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+ "num_stages": 3
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+ },
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+ "2048": {
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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": 32,
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+ "num_warps": 4,
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+ "num_stages": 3
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+ },
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+ "3072": {
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+ "BLOCK_SIZE_M": 64,
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+ "BLOCK_SIZE_N": 128,
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+ "BLOCK_SIZE_K": 256,
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+ "GROUP_SIZE_M": 32,
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+ "num_warps": 4,
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+ "num_stages": 3
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+ },
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+ "4096": {
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+ "BLOCK_SIZE_M": 64,
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+ "BLOCK_SIZE_N": 128,
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+ "BLOCK_SIZE_K": 256,
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+ "GROUP_SIZE_M": 32,
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+ "num_warps": 4,
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+ "num_stages": 3
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+ },
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+ "8192": {
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+ "BLOCK_SIZE_M": 64,
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+ "BLOCK_SIZE_N": 128,
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+ "BLOCK_SIZE_K": 256,
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+ "GROUP_SIZE_M": 32,
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+ "num_warps": 4,
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+ "num_stages": 3
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+ }
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+}
vllm/model_executor/layers/quantization/utils/configs/N=2112,K=7168,device_name=NVIDIA_H200,dtype=fp8_w8a8,block_shape=[128,128].json
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@@ -0,0 +1,26 @@
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+{
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+ "2048": {
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+ "BLOCK_SIZE_M": 256,
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+ "BLOCK_SIZE_N": 64,
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+ "BLOCK_SIZE_K": 128,
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+ "GROUP_SIZE_M": 32,
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+ "num_warps": 8,
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+ "num_stages": 3
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+ },
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+ "3072": {
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+ "BLOCK_SIZE_M": 128,
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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": 3
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+ },
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+ "4096": {
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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": 32,
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+ "num_warps": 4,
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+ "num_stages": 4
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+ }
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+}
vllm/model_executor/layers/quantization/utils/configs/README.md
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+# Quantization Kernel Config
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+
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+Use scripts under `benchmarks/kernels/` to generate these config files.