Rename files in sgl kernel to avoid nested folder structure (#4213)

Co-authored-by: zhyncs <me@zhyncs.com>

sgl-kernel/csrc/gemm/per_token_quant_fp8.cu

@@ -0,0 +1,111 @@
+#include <ATen/cuda/CUDAContext.h>
+
+#include <cmath>
+#include <cub/block/block_reduce.cuh>
+#include <flashinfer/vec_dtypes.cuh>
+
+#include "utils.h"
+
+template <typename T>
+__global__ void per_token_quant_fp8_kernel(
+    const T* __restrict__ input,
+    FP8_TYPE* __restrict__ output_q,
+    float* __restrict__ output_s,
+    const int64_t hidden_dim,
+    const int64_t num_tokens) {
+  const int token_idx = blockIdx.x;
+
+  if (token_idx >= num_tokens) return;
+
+  const int tid = threadIdx.x;
+  const int block_dim = blockDim.x;
+
+  const T* token_input = input + token_idx * hidden_dim;
+  FP8_TYPE* token_output = output_q + token_idx * hidden_dim;
+
+  float max_value = 0.0f;
+
+  for (int i = tid; i < hidden_dim; i += block_dim) {
+    float val = static_cast<float>(token_input[i]);
+    max_value = fmaxf(max_value, fabsf(val));
+  }
+
+  max_value = blockReduceMax(max_value);
+
+  __shared__ float block_max;
+  if (tid == 0) {
+    block_max = max_value / FP8_E4M3_MAX;
+    output_s[token_idx] = block_max;
+  }
+  __syncthreads();
+
+  const float scale_val = 1.0f / block_max;
+
+  constexpr uint32_t vec_size = 16 / sizeof(T);
+  using vec_t = flashinfer::vec_t<T, vec_size>;
+
+  const int32_t num_vec_elems = hidden_dim / vec_size;
+
+  for (int32_t i = tid; i < num_vec_elems; i += block_dim) {
+    vec_t input_vec;
+    input_vec.cast_load(token_input + i * vec_size);
+
+    FP8_TYPE output_arr[vec_size];
+#pragma unroll
+    for (uint32_t j = 0; j < vec_size; ++j) {
+      float val = fmax(fmin(static_cast<float>(input_vec[j]) * scale_val, FP8_E4M3_MAX), -FP8_E4M3_MAX);
+#ifndef USE_ROCM
+      output_arr[j] = static_cast<FP8_TYPE>(val);
+#else
+      output_arr[j] = c10::Float8_e4m3fnuz(
+          __hip_cvt_float_to_fp8(val, fp8::fp8_type::__default_saturation, fp8::fp8_type::__default_interpret),
+          c10::Float8_e4m3fnuz::from_bits());
+#endif
+    }
+
+#pragma unroll
+    for (uint32_t j = 0; j < vec_size; ++j) {
+      token_output[i * vec_size + j] = output_arr[j];
+    }
+  }
+
+  const int32_t remaining_start = num_vec_elems * vec_size;
+  for (int32_t idx = remaining_start + tid; idx < hidden_dim; idx += block_dim) {
+    float val = fmax(-FP8_E4M3_MAX, fmin(static_cast<float>(token_input[idx]) * scale_val, FP8_E4M3_MAX));
+#ifndef USE_ROCM
+    token_output[idx] = static_cast<FP8_TYPE>(val);
+#else
+    token_output[idx] = c10::Float8_e4m3fnuz(
+        __hip_cvt_float_to_fp8(val, fp8::fp8_type::__default_saturation, fp8::fp8_type::__default_interpret),
+        c10::Float8_e4m3fnuz::from_bits());
+#endif
+  }
+}
+
+void sgl_per_token_quant_fp8(torch::Tensor input, torch::Tensor output_q, torch::Tensor output_s) {
+  CHECK_INPUT(input);
+  CHECK_INPUT(output_q);
+  CHECK_INPUT(output_s);
+
+  const auto input_sizes = input.sizes();
+  const int64_t num_tokens = input_sizes[0];
+  const int64_t hidden_dim = input_sizes[1];
+
+  const int block_size = 128;
+  const int num_blocks = num_tokens;
+
+  dim3 grid(num_blocks);
+  dim3 block(block_size);
+
+  cudaStream_t stream = at::cuda::getCurrentCUDAStream();
+
+  DISPATCH_PYTORCH_DTYPE_TO_CTYPE_FLOAT_FP16(input.scalar_type(), scalar_t, [&] {
+    per_token_quant_fp8_kernel<scalar_t><<<grid, block, 0, stream>>>(
+        static_cast<scalar_t*>(input.data_ptr()),
+        static_cast<FP8_TYPE*>(output_q.data_ptr()),
+        static_cast<float*>(output_s.data_ptr()),
+        hidden_dim,
+        num_tokens);
+    return true;
+  });
+}