kernel: support slightly faster merge_state_v2 cuda kernel

sgl-kernel/CMakeLists.txt

@@ -170,6 +170,7 @@ string(REPLACE "-D__CUDA_NO_HALF2_OPERATORS__"      "" CMAKE_CUDA_FLAGS "${CMAKE
 set(SOURCES
     "csrc/allreduce/custom_all_reduce.cu"
     "csrc/attention/cascade.cu"
+    "csrc/attention/merge_attn_states.cu"
     "csrc/attention/cutlass_mla_kernel.cu"
     "csrc/attention/lightning_attention_decode_kernel.cu"
     "csrc/elementwise/activation.cu"

sgl-kernel/csrc/attention/merge_attn_states.cu

@@ -0,0 +1,201 @@
+#include <ATen/cuda/CUDAContext.h>
+#include <c10/cuda/CUDAGuard.h>
+
+#include <algorithm>
+#include <optional>
+
+#include "pytorch_extension_utils.h"
+
+// Helper functions to convert between different data types
+// (float, half, bfloat16) for the merge attention states kernel.
+inline __device__ float to_float(float u) {
+  return u;
+}
+inline __device__ float to_float(half u) {
+  return __half2float(u);
+}
+inline __device__ float to_float(__nv_bfloat16 u) {
+  return __bfloat162float(u);
+}
+inline __device__ void from_float(float& d, float s) {
+  d = s;
+}
+inline __device__ void from_float(half& d, float s) {
+  d = __float2half(s);
+}
+inline __device__ void from_float(__nv_bfloat16& d, float s) {
+  d = __float2bfloat16(s);
+}
+
+// Implements section 2.2 of https://www.arxiv.org/pdf/2501.01005
+template <typename scalar_t, const uint NUM_THREADS>
+__global__ void merge_attn_states_kernel(
+    scalar_t* output,
+    float* output_lse,
+    const scalar_t* prefix_output,
+    const float* prefix_lse,
+    const scalar_t* suffix_output,
+    const float* suffix_lse,
+    const uint num_tokens,
+    const uint num_heads,
+    const uint head_size) {
+  using pack_128b_t = uint4;
+  const uint pack_size = 16 / sizeof(scalar_t);
+  const uint threads_per_head = head_size / pack_size;
+
+  const uint global_idx = blockIdx.x * NUM_THREADS + threadIdx.x;
+  const uint token_head_threads = num_tokens * num_heads * threads_per_head;
+
+  if (global_idx >= token_head_threads) return;
+
+  // global_idx -> token_idx + head_idx + pack_idx
+  const uint token_head_idx = global_idx / threads_per_head;
+  const uint pack_idx = global_idx % threads_per_head;
+
+  const uint token_idx = token_head_idx / num_heads;
+  const uint head_idx = token_head_idx % num_heads;
+
+  const uint pack_offset = pack_idx * pack_size;  // (0~15)*8, etc.
+  const uint head_offset = token_idx * num_heads * head_size + head_idx * head_size;
+  const scalar_t* prefix_head_ptr = prefix_output + head_offset;
+  const scalar_t* suffix_head_ptr = suffix_output + head_offset;
+  scalar_t* output_head_ptr = output + head_offset;
+
+  // float p_lse = prefix_lse[head_idx * num_tokens + token_idx];
+  // float s_lse = suffix_lse[head_idx * num_tokens + token_idx];
+  float p_lse = prefix_lse[token_idx * num_heads + head_idx];
+  float s_lse = suffix_lse[token_idx * num_heads + head_idx];
+  p_lse = std::isinf(p_lse) ? -std::numeric_limits<float>::infinity() : p_lse;
+  s_lse = std::isinf(s_lse) ? -std::numeric_limits<float>::infinity() : s_lse;
+
+  const float max_lse = fmaxf(p_lse, s_lse);
+  p_lse = p_lse - max_lse;
+  s_lse = s_lse - max_lse;
+  const float p_se = expf(p_lse);
+  const float s_se = expf(s_lse);
+  const float out_se = p_se + s_se;
+  const float p_scale = p_se / out_se;
+  const float s_scale = s_se / out_se;
+
+  if (pack_offset < head_size) {
+    // Pack 128b load
+    pack_128b_t p_out_pack = reinterpret_cast<const pack_128b_t*>(prefix_head_ptr)[pack_offset / pack_size];
+    pack_128b_t s_out_pack = reinterpret_cast<const pack_128b_t*>(suffix_head_ptr)[pack_offset / pack_size];
+    pack_128b_t o_out_pack;
+
+#pragma unroll
+    for (uint i = 0; i < pack_size; ++i) {
+      // Always use float for FMA to keep high precision.
+      // half(uint16_t), bfloat16, float -> float.
+      const float p_out_f = to_float(reinterpret_cast<const scalar_t*>(&p_out_pack)[i]);
+      const float s_out_f = to_float(reinterpret_cast<const scalar_t*>(&s_out_pack)[i]);
+      // fma: a * b + c = p_out_f * p_scale + (s_out_f * s_scale)
+      const float o_out_f = p_out_f * p_scale + (s_out_f * s_scale);
+      // float -> half(uint16_t), bfloat16, float.
+      from_float(reinterpret_cast<scalar_t*>(&o_out_pack)[i], o_out_f);
+    }
+
+    // Pack 128b storage
+    reinterpret_cast<pack_128b_t*>(output_head_ptr)[pack_offset / pack_size] = o_out_pack;
+  }
+  // We only need to write to output_lse once per head.
+  if (output_lse != nullptr && pack_idx == 0) {
+    float out_lse = logf(out_se) + max_lse;
+    output_lse[token_idx * num_heads + head_idx] = out_lse;
+  }
+}
+
+// The following macro is used to dispatch the conversion function based on
+// the output data type. The FN is a macro that calls a function with
+// template<typename scalar_t>.
+#define DISPATCH_BY_SCALAR_DTYPE(scalar_dtype, fn)                      \
+  {                                                                     \
+    if (scalar_dtype == at::ScalarType::Float) {                        \
+      fn(float);                                                        \
+    } else if (scalar_dtype == at::ScalarType::Half) {                  \
+      fn(half);                                                         \
+    } else if (scalar_dtype == at::ScalarType::BFloat16) {              \
+      fn(__nv_bfloat16);                                                \
+    } else {                                                            \
+      TORCH_CHECK(false, "Unsupported data type of O: ", scalar_dtype); \
+    }                                                                   \
+  }
+
+#define LAUNCH_MERGE_ATTN_STATES(scalar_t, NUM_THREADS)               \
+  {                                                                   \
+    merge_attn_states_kernel<scalar_t, NUM_THREADS><<<grid, block>>>( \
+        reinterpret_cast<scalar_t*>(output.data_ptr()),               \
+        reinterpret_cast<float*>(output_lse.data_ptr()),              \
+        reinterpret_cast<scalar_t*>(prefix_output.data_ptr()),        \
+        reinterpret_cast<float*>(prefix_lse.data_ptr()),              \
+        reinterpret_cast<scalar_t*>(suffix_output.data_ptr()),        \
+        reinterpret_cast<float*>(suffix_lse.data_ptr()),              \
+        num_tokens,                                                   \
+        num_heads,                                                    \
+        head_size);                                                   \
+  }
+
+/*@brief Merges the attention states from prefix and suffix
+ * into the output tensor. NUM_TOKENS: n, NUM_HEADS: h, HEAD_SIZE: d
+ *
+ * @param output [n,h,d] The output tensor to store the merged attention states.
+ * @param output_lse [h,d] Optional tensor to store the log-sum-exp values.
+ * @param prefix_output [n,h,d] The prefix attention states.
+ * @param prefix_lse [n,h] The log-sum-exp values for the prefix attention
+ * states.
+ * @param suffix_output [n,h,d] The suffix attention states.
+ * @param suffix_lse [n,h] The log-sum-exp values for the suffix attention
+ * states.
+ */
+template <typename scalar_t>
+void merge_attn_states_launcher(
+    const at::Tensor& prefix_output,  // [NUM_TOKENS, NUM_HEADS, HEAD_SIZE]
+    const at::Tensor& prefix_lse,     // [NUM_TOKENS, NUM_HEADS]
+    const at::Tensor& suffix_output,  // [NUM_TOKENS, NUM_HEADS, HEAD_SIZE]
+    const at::Tensor& suffix_lse,     // [NUM_TOKENS, NUM_HEADS]
+    at::Tensor& output,               // [NUM_TOKENS, NUM_HEADS, HEAD_SIZE]
+    at::Tensor& output_lse            // [NUM_TOKENS, NUM_HEADS]
+) {
+  constexpr uint NUM_THREADS = 128;
+  const uint num_tokens = output.size(0);
+  const uint num_heads = output.size(1);
+  const uint head_size = output.size(2);
+  const uint pack_size = 16 / sizeof(scalar_t);
+  TORCH_CHECK(head_size % pack_size == 0, "headsize must be multiple of pack_size:", pack_size);
+  // Process one pack elements per thread. for float, the
+  // pack_size is 4 for half/bf16, the pack_size is 8.
+  const uint threads_per_head = head_size / pack_size;
+  const uint total_threads = num_tokens * num_heads * threads_per_head;
+
+  dim3 block(NUM_THREADS);
+  dim3 grid((total_threads + NUM_THREADS - 1) / NUM_THREADS);
+
+  LAUNCH_MERGE_ATTN_STATES(scalar_t, NUM_THREADS);
+}
+
+#define CALL_MERGE_ATTN_STATES_LAUNCHER(scalar_t) \
+  { merge_attn_states_launcher<scalar_t>(v_a, s_a, v_b, s_b, v_merged, s_merged); }
+
+void merge_state_v2(
+    at::Tensor v_a, at::Tensor s_a, at::Tensor v_b, at::Tensor s_b, at::Tensor v_merged, at::Tensor s_merged) {
+  // Input tensors must be contiguous
+  CHECK_INPUT(v_a);  // v_a prefix_output (seq_len, num_heads, head_dim)
+  CHECK_INPUT(s_a);  // s_a prefix_lse (seq_len, num_heads)
+  CHECK_INPUT(v_b);  // v_b suffix_output (seq_len, num_heads, head_dim)
+  CHECK_INPUT(s_b);  // s_b suffix_lse (seq_len, num_heads)
+  // v_merged output (seq_len, num_heads, head_dim)
+  // s_merged output_lse (seq_len, num_heads)
+  auto device = v_a.device();
+  CHECK_EQ(s_a.device(), device);
+  CHECK_EQ(v_b.device(), device);
+  CHECK_EQ(s_b.device(), device);
+  CHECK_DIM(3, v_a);
+  CHECK_DIM(2, s_a);
+  CHECK_DIM(3, v_b);
+  CHECK_DIM(2, s_b);
+  CHECK_SHAPE(v_a, v_b);
+  CHECK_SHAPE(s_a, s_b);
+  CHECK_EQ(v_a.size(0), s_a.size(0));
+  CHECK_EQ(v_a.size(1), s_b.size(1));
+  DISPATCH_BY_SCALAR_DTYPE(v_merged.dtype(), CALL_MERGE_ATTN_STATES_LAUNCHER);
+}

sgl-kernel/csrc/common_extension.cc

@@ -47,6 +47,8 @@ TORCH_LIBRARY_FRAGMENT(sgl_kernel, m) {
   m.impl("lightning_attention_decode", torch::kCUDA, &lightning_attention_decode);
   m.def("merge_state(Tensor v_a, Tensor s_a, Tensor v_b, Tensor s_b, Tensor! v_merged, Tensor! s_merged) -> ()");
   m.impl("merge_state", torch::kCUDA, &merge_state);
+  m.def("merge_state_v2(Tensor v_a, Tensor s_a, Tensor v_b, Tensor s_b, Tensor! v_merged, Tensor! s_merged) -> ()");
+  m.impl("merge_state_v2", torch::kCUDA, &merge_state_v2);
   m.def(
       "cutlass_mla_decode(Tensor! out, Tensor q_nope_and_q_pe, Tensor kv_c_and_k_pe_cache, Tensor seq_lens, Tensor "
       "page_table, Tensor workspace) -> ()");

sgl-kernel/include/sgl_kernel_ops.h

@@ -89,6 +89,8 @@ void lightning_attention_decode(
     torch::Tensor new_kv);
 void merge_state(
     at::Tensor v_a, at::Tensor s_a, at::Tensor v_b, at::Tensor s_b, at::Tensor v_merged, at::Tensor s_merged);
+void merge_state_v2(
+    at::Tensor v_a, at::Tensor s_a, at::Tensor v_b, at::Tensor s_b, at::Tensor v_merged, at::Tensor s_merged);
 void cutlass_mla_decode(
     torch::Tensor const& out,
     torch::Tensor const& q_nope_and_q_pe,

sgl-kernel/python/sgl_kernel/__init__.py

@@ -16,6 +16,7 @@
     cutlass_mla_get_workspace_size,
     lightning_attention_decode,
     merge_state,
+    merge_state_v2,
 )
 from sgl_kernel.elementwise import (
     apply_rope_with_cos_sin_cache_inplace,

sgl-kernel/python/sgl_kernel/attention.py

@@ -1,4 +1,4 @@
-from typing import Tuple
+from typing import Optional, Tuple
 
 import torch
 
@@ -10,16 +10,47 @@ def lightning_attention_decode(q, k, v, past_kv, slope, output, new_kv):
 
 
 def merge_state(
-    v_a: torch.Tensor, s_a: torch.Tensor, v_b: torch.Tensor, s_b: torch.Tensor
+    v_a: torch.Tensor,
+    s_a: torch.Tensor,
+    v_b: torch.Tensor,
+    s_b: torch.Tensor,
+    v_merged: Optional[torch.Tensor] = None,
+    s_merged: Optional[torch.Tensor] = None,
 ) -> Tuple[torch.Tensor, torch.Tensor]:
     s_a = s_a.to(torch.float32)
     s_b = s_b.to(torch.float32)
-    v_merged = torch.empty_like(v_a)
-    s_merged = torch.empty_like(s_a)
+    # Avoid creating new tensors if they are already provided
+    if v_merged is None:
+        v_merged = torch.empty_like(v_a)
+    if s_merged is None:
+        s_merged = torch.empty_like(s_a)
     torch.ops.sgl_kernel.merge_state.default(v_a, s_a, v_b, s_b, v_merged, s_merged)
     return v_merged, s_merged
 
 
+def merge_state_v2(
+    v_a: torch.Tensor,
+    s_a: torch.Tensor,
+    v_b: torch.Tensor,
+    s_b: torch.Tensor,
+    v_merged: Optional[torch.Tensor] = None,
+    s_merged: Optional[torch.Tensor] = None,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    s_a = s_a.to(torch.float32)
+    s_b = s_b.to(torch.float32)
+    # TODO(DefTruth): Currently, the custom merge_attn_states kernel
+    # does not support the FP8 data type and non - CUDA devices.
+    # It may be necessary to fall back to using the Triton kernel.
+
+    # Avoid creating new tensors if they are already provided
+    if v_merged is None:
+        v_merged = torch.empty_like(v_a)
+    if s_merged is None:
+        s_merged = torch.empty_like(s_a)
+    torch.ops.sgl_kernel.merge_state_v2.default(v_a, s_a, v_b, s_b, v_merged, s_merged)
+    return v_merged, s_merged
+
+
 def cutlass_mla_decode(
     q_nope_and_q_pe: torch.Tensor,
     kv_c_and_k_pe_cache: torch.Tensor,