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Support serving DeepSeek-R1-Channel-INT8 with 32 L40S. (#4418)

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This commit is contained in:
Wenbo Yang
2025-03-17 15:03:43 +08:00
committed by GitHub
parent 0f52fb55ec
commit 75b656488a
7 changed files with 489 additions and 11 deletions
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27
benchmark/deepseek_v3/README.md
View File

@@ -218,6 +218,33 @@ python3 -m sglang.bench_serving --dataset-path /path/to/ShareGPT_V3_unfiltered_c
> **Note: using `--parallel 200` can accelerate accuracy benchmarking**.
### Example: Serving with 32 L40S with int8 Quantization
Running with per-channel quantization model:
- [meituan/DeepSeek-R1-Channel-INT8](https://huggingface.co/meituan/DeepSeek-R1-Channel-INT8)
Assuming that master node IP is `MASTER_IP`, checkpoint path is `/path/to/DeepSeek-R1-Channel-INT8` and port=5000, we can have following commands to launch the server:
```bash
#master
python3 -m sglang.launch_server --model meituan/DeepSeek-R1-Channel-INT8 --tp 32 --quantization w8a8_int8 \
--dist-init-addr MASTER_IP:5000 --nnodes 4 --node-rank 0 --trust-remote \
--enable-torch-compile --torch-compile-max-bs 32
#cluster
python3 -m sglang.launch_server --model meituan/DeepSeek-R1-Channel-INT8 --tp 32 --quantization w8a8_int8 \
--dist-init-addr MASTER_IP:5000 --nnodes 4 --node-rank 1 --trust-remote \
--enable-torch-compile --torch-compile-max-bs 32
python3 -m sglang.launch_server --model meituan/DeepSeek-R1-Channel-INT8 --tp 32 --quantization w8a8_int8 \
--dist-init-addr MASTER_IP:5000 --nnodes 4 --node-rank 2 --trust-remote \
--enable-torch-compile --torch-compile-max-bs 32
python3 -m sglang.launch_server --model meituan/DeepSeek-R1-Channel-INT8 --tp 32 --quantization w8a8_int8 \
--dist-init-addr MASTER_IP:5000 --nnodes 4 --node-rank 3 --trust-remote \
--enable-torch-compile --torch-compile-max-bs 32
```
The benchmarking method is the same as describted in the previous [16 x A100](https://github.com/sgl-project/sglang/tree/main/benchmark/deepseek_v3#example-serving-with-16-a100a800-with-int8-quantization) example.
### Example: Serving on any cloud or Kubernetes with SkyPilot
SkyPilot helps find cheapest available GPUs across any cloud or existing Kubernetes clusters and launch distributed serving with a single command. See details [here](https://github.com/skypilot-org/skypilot/tree/master/llm/deepseek-r1).

2
docs/references/deepseek.md
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@@ -18,6 +18,7 @@ SGLang is recognized as one of the top engines for [DeepSeek model inference](ht
| **Quantized weights (AWQ)** | 8 x H100/800/20 |
| | 8 x A100/A800 |
| **Quantized weights (int8)** | 16 x A100/800 |
| | 32 x L40S |
<style>
.md-typeset__table {
@@ -56,6 +57,7 @@ Detailed commands for reference:
- [4 x 8 x A100](https://github.com/sgl-project/sglang/tree/main/benchmark/deepseek_v3#example-serving-with-four-a1008-nodes)
- [8 x A100 (AWQ)](https://github.com/sgl-project/sglang/tree/main/benchmark/deepseek_v3#example-serving-with-8-a100a800-with-awq-quantization)
- [16 x A100 (int8)](https://github.com/sgl-project/sglang/tree/main/benchmark/deepseek_v3#example-serving-with-16-a100a800-with-int8-quantization)
- [32 x L40S (int8)](https://github.com/sgl-project/sglang/tree/main/benchmark/deepseek_v3#example-serving-with-32-l40s-with-int8-quantization)
### Download Weights

19
python/sglang/srt/layers/attention/triton_ops/extend_attention.py
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@@ -341,12 +341,21 @@ def extend_attention_fwd(
else:
BLOCK_M, BLOCK_N = (32, 64)
elif is_cuda_available and CUDA_CAPABILITY[0] >= 8:
if Lq <= 128:
BLOCK_M, BLOCK_N = (128, 128)
elif Lq <= 256:
BLOCK_M, BLOCK_N = (64, 64)
# 8.9 has a much smaller shared memory size (100K) than 8.0 (160K)
if CUDA_CAPABILITY[1] == 9:
if Lq <= 128:
BLOCK_M, BLOCK_N = (64, 128)
elif Lq <= 256:
BLOCK_M, BLOCK_N = (64, 64)
else:
BLOCK_M, BLOCK_N = (32, 32)
else:
BLOCK_M, BLOCK_N = (32, 64)
if Lq <= 128:
BLOCK_M, BLOCK_N = (128, 128)
elif Lq <= 256:
BLOCK_M, BLOCK_N = (64, 64)
else:
BLOCK_M, BLOCK_N = (32, 64)
else:
BLOCK_M, BLOCK_N = (64, 64) if Lq <= 128 else (32, 32)

146
python/sglang/srt/layers/moe/fused_moe_triton/configs/E=256,N=64,device_name=NVIDIA_L20,dtype=int8_w8a8.json Normal file
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@@ -0,0 +1,146 @@
{
"1": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 2
},
"2": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 2
},
"4": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 2
},
"8": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 2
},
"16": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 2
},
"24": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 2
},
"32": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 64,
"num_warps": 8,
"num_stages": 2
},
"48": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 3
},
"64": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 3
},
"96": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 3
},
"128": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 3
},
"256": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 256,
"GROUP_SIZE_M": 32,
"num_warps": 8,
"num_stages": 2
},
"512": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 2
},
"1024": {
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 2
},
"1536": {
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 2
},
"2048": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 2
},
"3072": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 2
},
"4096": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 2
}
}

146
python/sglang/srt/layers/moe/fused_moe_triton/configs/E=256,N=64,device_name=NVIDIA_L40S,dtype=int8_w8a8.json Normal file
View File

@@ -0,0 +1,146 @@
{
"1": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 3
},
"2": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 3
},
"4": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 3
},
"8": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 3
},
"16": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_K": 256,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 2
},
"24": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 256,
"GROUP_SIZE_M": 64,
"num_warps": 8,
"num_stages": 2
},
"32": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 256,
"GROUP_SIZE_M": 32,
"num_warps": 8,
"num_stages": 2
},
"48": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 256,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"64": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 256,
"GROUP_SIZE_M": 64,
"num_warps": 8,
"num_stages": 2
},
"96": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 256,
"GROUP_SIZE_M": 1,
"num_warps": 8,
"num_stages": 2
},
"128": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 256,
"GROUP_SIZE_M": 1,
"num_warps": 8,
"num_stages": 2
},
"256": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 256,
"GROUP_SIZE_M": 1,
"num_warps": 8,
"num_stages": 2
},
"512": {
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 256,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 2
},
"1024": {
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 256,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 2
},
"1536": {
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 8,
"num_stages": 3
},
"2048": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 2
},
"3072": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 256,
"GROUP_SIZE_M": 16,
"num_warps": 8,
"num_stages": 2
},
"4096": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 8,
"num_stages": 4
}
}

158
sgl-kernel/csrc/gemm/int8_gemm_kernel.cu
View File

@@ -279,6 +279,143 @@ void sm80_dispatch_shape(
}
}
// Dispatch shape for sm89 (L40S, L20, RTX 4090), according to:
// https://github.com/vllm-project/vllm/blob/main/csrc/quantization/cutlass_w8a8/scaled_mm_c2x_sm89_int8_dispatch.cuh
template <typename ElementOutput, typename ArchTag, typename InstructionShape>
void sm89_dispatch_shape(
torch::Tensor& out,
const torch::Tensor& mat_a,
const torch::Tensor& mat_b,
const torch::Tensor& scales_a,
const torch::Tensor& scales_b,
const c10::optional<torch::Tensor>& bias) {
int m = mat_a.size(0);
int n = mat_b.size(1);
if (m <= 16) {
if (n <= 8192) {
cutlass_int8_scaled_mm<
ElementOutput,
ArchTag,
cutlass::gemm::GemmShape<16, 64, 128>,
cutlass::gemm::GemmShape<16, 64, 64>,
InstructionShape,
5>(out, mat_a, mat_b, scales_a, scales_b, bias);
} else {
cutlass_int8_scaled_mm<
ElementOutput,
ArchTag,
cutlass::gemm::GemmShape<16, 128, 128>,
cutlass::gemm::GemmShape<16, 64, 64>,
InstructionShape,
4>(out, mat_a, mat_b, scales_a, scales_b, bias);
}
} else if (m <= 32) {
if (n <= 8192) {
cutlass_int8_scaled_mm<
ElementOutput,
ArchTag,
cutlass::gemm::GemmShape<32, 64, 128>,
cutlass::gemm::GemmShape<16, 64, 64>,
InstructionShape,
5>(out, mat_a, mat_b, scales_a, scales_b, bias);
} else {
cutlass_int8_scaled_mm<
ElementOutput,
ArchTag,
cutlass::gemm::GemmShape<32, 128, 128>,
cutlass::gemm::GemmShape<32, 64, 64>,
InstructionShape,
4>(out, mat_a, mat_b, scales_a, scales_b, bias);
}
} else if (m <= 64) {
if (n <= 8192) {
cutlass_int8_scaled_mm<
ElementOutput,
ArchTag,
cutlass::gemm::GemmShape<64, 64, 128>,
cutlass::gemm::GemmShape<32, 64, 64>,
InstructionShape,
5>(out, mat_a, mat_b, scales_a, scales_b, bias);
} else {
cutlass_int8_scaled_mm<
ElementOutput,
ArchTag,
cutlass::gemm::GemmShape<64, 128, 128>,
cutlass::gemm::GemmShape<64, 64, 64>,
InstructionShape,
3>(out, mat_a, mat_b, scales_a, scales_b, bias);
}
} else if (m <= 128) {
if (n <= 8192) {
cutlass_int8_scaled_mm<
ElementOutput,
ArchTag,
cutlass::gemm::GemmShape<64, 128, 128>,
cutlass::gemm::GemmShape<32, 64, 64>,
InstructionShape,
3>(out, mat_a, mat_b, scales_a, scales_b, bias);
} else if (n <= 16384) {
cutlass_int8_scaled_mm<
ElementOutput,
ArchTag,
cutlass::gemm::GemmShape<128, 128, 64>,
cutlass::gemm::GemmShape<64, 64, 64>,
InstructionShape,
5>(out, mat_a, mat_b, scales_a, scales_b, bias);
} else {
cutlass_int8_scaled_mm<
ElementOutput,
ArchTag,
cutlass::gemm::GemmShape<64, 64, 128>,
cutlass::gemm::GemmShape<32, 64, 64>,
InstructionShape,
5>(out, mat_a, mat_b, scales_a, scales_b, bias);
}
} else if (m <= 256) {
if (n <= 4096) {
cutlass_int8_scaled_mm<
ElementOutput,
ArchTag,
cutlass::gemm::GemmShape<64, 128, 128>,
cutlass::gemm::GemmShape<64, 64, 64>,
InstructionShape,
3>(out, mat_a, mat_b, scales_a, scales_b, bias);
} else if (n <= 8192) {
cutlass_int8_scaled_mm<
ElementOutput,
ArchTag,
cutlass::gemm::GemmShape<128, 128, 64>,
cutlass::gemm::GemmShape<64, 64, 64>,
InstructionShape,
5>(out, mat_a, mat_b, scales_a, scales_b, bias);
} else if (n <= 16384) {
cutlass_int8_scaled_mm<
ElementOutput,
ArchTag,
cutlass::gemm::GemmShape<256, 128, 64>,
cutlass::gemm::GemmShape<64, 64, 64>,
InstructionShape,
3>(out, mat_a, mat_b, scales_a, scales_b, bias);
} else {
cutlass_int8_scaled_mm<
ElementOutput,
ArchTag,
cutlass::gemm::GemmShape<128, 128, 64>,
cutlass::gemm::GemmShape<64, 64, 64>,
InstructionShape,
5>(out, mat_a, mat_b, scales_a, scales_b, bias);
}
} else {
cutlass_int8_scaled_mm<
ElementOutput,
ArchTag,
cutlass::gemm::GemmShape<32, 64, 128>,
cutlass::gemm::GemmShape<16, 64, 64>,
InstructionShape,
5>(out, mat_a, mat_b, scales_a, scales_b, bias);
}
}
template <
typename ElementOutput,
typename TileShape,
@@ -566,12 +703,23 @@ torch::Tensor int8_scaled_mm(
sm75_dispatch_shape<cutlass::half_t, cutlass::arch::Sm75, cutlass::gemm::GemmShape<8, 8, 16>>(
out, mat_a, mat_b, scales_a, scales_b, bias);
} else if (sm_version >= 80 && sm_version < 90) {
if (out_dtype == torch::kBFloat16) {
sm80_dispatch_shape<cutlass::bfloat16_t, cutlass::arch::Sm80, cutlass::gemm::GemmShape<16, 8, 32>>(
out, mat_a, mat_b, scales_a, scales_b, bias);
// sm89 has a much smaller shared memory size (100K) than sm80 (160K)
if (sm_version == 89) {
if (out_dtype == torch::kBFloat16) {
sm89_dispatch_shape<cutlass::bfloat16_t, cutlass::arch::Sm80, cutlass::gemm::GemmShape<16, 8, 32>>(
out, mat_a, mat_b, scales_a, scales_b, bias);
} else {
sm89_dispatch_shape<cutlass::half_t, cutlass::arch::Sm80, cutlass::gemm::GemmShape<16, 8, 32>>(
out, mat_a, mat_b, scales_a, scales_b, bias);
}
} else {
sm80_dispatch_shape<cutlass::half_t, cutlass::arch::Sm80, cutlass::gemm::GemmShape<16, 8, 32>>(
out, mat_a, mat_b, scales_a, scales_b, bias);
if (out_dtype == torch::kBFloat16) {
sm80_dispatch_shape<cutlass::bfloat16_t, cutlass::arch::Sm80, cutlass::gemm::GemmShape<16, 8, 32>>(
out, mat_a, mat_b, scales_a, scales_b, bias);
} else {
sm80_dispatch_shape<cutlass::half_t, cutlass::arch::Sm80, cutlass::gemm::GemmShape<16, 8, 32>>(
out, mat_a, mat_b, scales_a, scales_b, bias);
}
}
} else if (sm_version == 90) {
#if defined CUDA_VERSION && CUDA_VERSION >= 12000

2
sgl-kernel/tests/test_int8_gemm.py
View File

@@ -37,7 +37,7 @@ class TestInt8Gemm(unittest.TestCase):
print(f"M={M}, N={N}, K={K}, with_bias={with_bias}, out_dtype={out_dtype}: OK")
def test_accuracy(self):
Ms = [1, 128, 512, 1024, 4096, 8192]
Ms = [1, 16, 32, 64, 128, 512, 1024, 4096, 8192]
Ns = [16, 128, 512, 1024, 4096, 8192, 16384]
Ks = [512, 1024, 4096, 8192, 16384]
bias_opts = [True, False]