[Kernel] FP8 support for MoE kernel / Mixtral

CMakeLists.txt

@@ -167,6 +167,7 @@ set(VLLM_EXT_SRC
   "csrc/layernorm_kernels.cu"
   "csrc/quantization/squeezellm/quant_cuda_kernel.cu"
   "csrc/quantization/gptq/q_gemm.cu"
+  "csrc/quantization/fp8/fp8_cuda_kernels.cu"
   "csrc/cuda_utils_kernels.cu"
   "csrc/moe_align_block_size_kernels.cu"
   "csrc/pybind.cpp")

csrc/ops.h

@@ -131,6 +131,11 @@ void gptq_shuffle(
   torch::Tensor q_perm,
   int bit);
 
+void scaled_fp8_quant(
+  torch::Tensor& out,
+  torch::Tensor& input,
+  torch::Tensor& scale);
+
 void moe_align_block_size(
   torch::Tensor topk_ids,
   int num_experts,

csrc/pybind.cpp

@@ -71,6 +71,7 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
   ops.def("gptq_gemm", &gptq_gemm, "Quantized GEMM for GPTQ");
   ops.def("gptq_shuffle", &gptq_shuffle, "Post processing for GPTQ");
   ops.def("squeezellm_gemm", &squeezellm_gemm, "Quantized GEMM for SqueezeLLM");
+  ops.def("scaled_fp8_quant", &scaled_fp8_quant, "Compute FP8 quantized tensor and scaling factor");
   ops.def(
     "moe_align_block_size",
     &moe_align_block_size,

csrc/quantization/fp8/fp8_cuda_kernels.cu

@@ -0,0 +1,103 @@
+#include <ATen/cuda/CUDAContext.h>
+#include <torch/extension.h>
+#include <c10/cuda/CUDAGuard.h>
+
+#include <cmath>
+
+#include "cuda_compat.h"
+#include "dispatch_utils.h"
+
+namespace vllm {
+
+__device__ __forceinline__ float atomicMaxFloat(float* addr, float value) {
+    float old;
+    old = (value >= 0) ? __int_as_float(atomicMax((int*)addr, __float_as_int(value))) :
+         __uint_as_float(atomicMin((unsigned int*)addr, __float_as_uint(value)));
+
+    return old;
+}
+
+// Compute the absolute maximum m of the input tensor and store
+// m / float8_e4m3::max() in *scale. Each thread block performs a
+// reduction tree and the memory in scale is atomically updated.
+// So to get the right answer, *scale needs to be initialized to
+// a value <= 0.0 and we need to wait for all thread blocks to
+// finish before consuming *scale.
+template<typename scalar_t>
+__global__ void segmented_max_reduction(
+  float* __restrict__ scale,
+  const scalar_t* __restrict__ input,
+  int64_t num_elems) {
+  __shared__ float cache[1024];
+  int i = blockDim.x * blockIdx.x + threadIdx.x;
+
+  // First store maximum for all values processes by
+  // the current thread in cache[threadIdx.x]
+  scalar_t tmp = 0.0;
+  while (i < num_elems) {
+    float x = static_cast<float>(input[i]);
+    tmp = max(tmp, fabs(x));
+    i += blockDim.x * gridDim.x;
+  }
+  cache[threadIdx.x] = tmp;
+
+  __syncthreads();
+
+  // Now perform parallel reduction within the thread block
+  int ib = blockDim.x / 2;
+  while (ib != 0) {
+    if (threadIdx.x < ib && cache[threadIdx.x + ib] > cache[threadIdx.x]) {
+        cache[threadIdx.x] = cache[threadIdx.x + ib];
+    }
+    __syncthreads();
+    ib /= 2;
+  }
+  // Finally, since cache[0] contains the maximum for this thread block,
+  // atomically write the max to the target location
+  if (threadIdx.x == 0) {
+    atomicMaxFloat(scale, cache[0] / std::numeric_limits<c10::Float8_e4m3fn>::max());
+  }
+}
+
+template<typename scalar_t>
+__global__ void scaled_fp8_quant_kernel(
+  c10::Float8_e4m3fn* __restrict__ out,
+  const scalar_t* __restrict__ input,
+  const float* __restrict__ scale,
+  int64_t num_elems) {
+  int i = blockDim.x * blockIdx.x + threadIdx.x;
+  while (i < num_elems) {
+    out[i] = static_cast<c10::Float8_e4m3fn>(input[i] / *scale);
+    i += blockDim.x * gridDim.x;
+  }
+}
+
+} // namespace vllm
+
+void scaled_fp8_quant(
+  torch::Tensor& out,      // [..., d]
+  torch::Tensor& input,    // [..., d]
+  torch::Tensor& scale)    // [1]
+{
+  int64_t num_tokens = input.numel() / input.size(-1);
+  int64_t num_elems = input.numel();
+  dim3 grid(num_tokens);
+  dim3 block(1024);
+  const at::cuda::OptionalCUDAGuard device_guard(device_of(input));
+  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
+  VLLM_DISPATCH_FLOATING_TYPES(
+    input.scalar_type(),
+    "scaled_fp8_quant_kernel",
+    [&] {
+      vllm::segmented_max_reduction<scalar_t><<<grid, block, 0, stream>>>(
+        scale.data_ptr<float>(),
+        input.data_ptr<scalar_t>(),
+        num_elems);
+      vllm::scaled_fp8_quant_kernel<scalar_t><<<grid, block, 0, stream>>>(
+        out.data_ptr<c10::Float8_e4m3fn>(),
+        input.data_ptr<scalar_t>(),
+        scale.data_ptr<float>(),
+        num_elems);
+      });
+}
+

vllm/_custom_ops.py

@@ -1,4 +1,4 @@
-from typing import Dict, Optional
+from typing import Dict, Optional, Tuple
 
 import torch
 
@@ -153,6 +153,14 @@ def marlin_gemm(a: torch.Tensor, b_q_weight: torch.Tensor,
                                 size_n, size_k)
 
 
+# fp8
+def scaled_fp8_quant(input: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+    scale = torch.zeros(1, device=input.device, dtype=torch.float32)
+    output = torch.empty_like(input, dtype=torch.float8_e4m3fn)
+    vllm_ops.scaled_fp8_quant(output, input, scale)
+    return output, scale
+
+
 # moe
 def moe_align_block_size(topk_ids: torch.Tensor, num_experts: int,
                          block_size: int, sorted_token_ids: torch.Tensor,

vllm/model_executor/layers/fused_moe/configs/E=8,N=7168,device_name=NVIDIA_H100_80GB_HBM3,dtype=float8.json

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