feat: Add mask to merge_state_in_place

include/flashinfer/attention/cascade.cuh

@@ -81,16 +81,22 @@ __global__ void MergeStateKernel(DTypeIn* __restrict__ v_a, float* __restrict__
  * \param s The logsumexp value to be updated in-place. (n, h)
  * \param v_other The other v to be merged. (n, h, d)
  * \param s_other The other logsumexp value to be merged. (n, h)
+ * \param mask Optional mask of whether to merge given sequences or not. (n)
  * \param num_heads The number of heads of v and v_other.
  * \param head_dim The dimension of each head.
  * \note Both s and s_other are logsumexp values with base 2.
  */
 template <uint32_t vec_size, typename DType>
 __global__ void MergeStateInPlaceKernel(DType* __restrict__ v, float* __restrict__ s,
                                         DType* __restrict__ v_other, float* __restrict__ s_other,
+                                        uint8_t* __restrict__ mask,
                                         uint32_t num_heads, uint32_t head_dim) {
-  uint32_t tx = threadIdx.x, ty = threadIdx.y;
   uint32_t pos = blockIdx.x;
+
+  if (mask != nullptr && mask[pos] == 0)
+    return;
+
+  uint32_t tx = threadIdx.x, ty = threadIdx.y;
   uint32_t head_idx = ty;
 
   float s_val = s[pos * num_heads + head_idx];
@@ -383,13 +389,15 @@ cudaError_t MergeState(DTypeIn* v_a, float* s_a, DTypeIn* v_b, float* s_b, DType
  * \param seq_len The sequence length.
  * \param num_heads The number of heads of v and v_other.
  * \param head_dim The dimension of each head.
+ * \param mask Optional mask of whether to merge given sequences or not. (n)
  * \param stream The CUDA stream to execute the kernel.
  * \return status Indicates whether CUDA calls are successful
  * \note Both s and s_other are logsumexp values with base 2.
  */
 template <typename DType>
 cudaError_t MergeStateInPlace(DType* v, float* s, DType* v_other, float* s_other, uint32_t seq_len,
                               uint32_t num_heads, uint32_t head_dim,
+                              uint8_t* mask = nullptr,
                               cudaStream_t stream = nullptr) {
   DISPATCH_HEAD_DIM(head_dim, HEAD_DIM, {
     constexpr uint32_t vec_size = std::max(16U / sizeof(DType), HEAD_DIM / 32U);
@@ -398,7 +406,7 @@ cudaError_t MergeStateInPlace(DType* v, float* s, DType* v_other, float* s_other
     dim3 nblks(seq_len);
     dim3 nthrs(bdx, bdy);
     auto kernel = MergeStateInPlaceKernel<vec_size, DType>;
-    void* args[] = {&v, &s, &v_other, &s_other, &num_heads, &head_dim};
+    void* args[] = {&v, &s, &v_other, &s_other, &mask, &num_heads, &head_dim};
     FLASHINFER_CUDA_CALL(cudaLaunchKernel((void*)kernel, nblks, nthrs, args, 0, stream));
   });
   return cudaSuccess;

python/csrc/cascade.cu

@@ -63,7 +63,7 @@ std::vector<torch::Tensor> merge_state(torch::Tensor v_a, torch::Tensor s_a, tor
 }
 
 void merge_state_in_place(torch::Tensor v, torch::Tensor s, torch::Tensor v_other,
-                          torch::Tensor s_other) {
+                          torch::Tensor s_other, std::optional<torch::Tensor> mask) {
   CHECK_INPUT(v);
   CHECK_INPUT(s);
   CHECK_INPUT(v_other);
@@ -82,6 +82,13 @@ void merge_state_in_place(torch::Tensor v, torch::Tensor s, torch::Tensor v_othe
   CHECK_EQ(v.size(1), s.size(1));
   CHECK_EQ(s.scalar_type(), torch::kFloat32);
   CHECK_EQ(s_other.scalar_type(), torch::kFloat32);
+  uint8_t* mask_ptr = nullptr;
+  if (mask.has_value()) {
+    CHECK_DIM(1, mask.value());
+    CHECK_EQ(v.size(0), mask.value().size(0));
+    CHECK_EQ(mask.value().device(), device);
+    mask_ptr = static_cast<uint8_t*>(mask.value().data_ptr());
+  }
   unsigned int seq_len = v.size(0);
   unsigned int num_heads = v.size(1);
   unsigned int head_dim = v.size(2);
@@ -91,7 +98,7 @@ void merge_state_in_place(torch::Tensor v, torch::Tensor s, torch::Tensor v_othe
     cudaError_t status = MergeStateInPlace(
         static_cast<c_type*>(v.data_ptr()), static_cast<float*>(s.data_ptr()),
         static_cast<c_type*>(v_other.data_ptr()), static_cast<float*>(s_other.data_ptr()), seq_len,
-        num_heads, head_dim, torch_current_stream);
+        num_heads, head_dim, mask_ptr, torch_current_stream);
     TORCH_CHECK(status == cudaSuccess,
                 "MergeStateInPlace kernel launch failed: ", cudaGetErrorString(status));
     return true;

python/csrc/flashinfer_ops.h

@@ -46,7 +46,7 @@ std::vector<torch::Tensor> merge_state(torch::Tensor v_a, torch::Tensor s_a, tor
                                        torch::Tensor s_b);
 
 void merge_state_in_place(torch::Tensor v, torch::Tensor s, torch::Tensor v_other,
-                          torch::Tensor s_other);
+                          torch::Tensor s_other, std::optional<torch::Tensor> mask = std::nullopt);
 
 std::vector<torch::Tensor> merge_states(torch::Tensor v, torch::Tensor s);
 

python/flashinfer/cascade.py

@@ -98,7 +98,11 @@ def merge_state(
 
 
 def merge_state_in_place(
-    v: torch.Tensor, s: torch.Tensor, v_other: torch.Tensor, s_other: torch.Tensor
+    v: torch.Tensor,
+    s: torch.Tensor,
+    v_other: torch.Tensor,
+    s_other: torch.Tensor,
+    mask: Optional[torch.Tensor] = None,
 ):
     r"""Merge the self-attention state ``(v, s)`` with another state
     ``(v_other, s_other)`` in-place.
@@ -117,6 +121,11 @@ def merge_state_in_place(
     s_other : torch.Tensor
         The other logsumexp value to be merged, expected to be a float32 tensor,
         shape: ``(seq_len, num_heads)``.
+    mask : Optional[torch.Tensor]
+        The boolean mask tensor for whether to merge the state for a corresponding sequence
+        or not. Useful for CUDA graphs. If not specified (default), will merge states for
+        all sequences.
+        shape: ``[seq_len]``
 
     Example
     -------
@@ -131,7 +140,7 @@ def merge_state_in_place(
     >>> s_other = torch.randn(seq_len, num_heads, dtype=torch.float32).to("cuda:0")
     >>> flashinfer.merge_state_in_place(v, s, v_other, s_other)
     """
-    _kernels.merge_state_in_place(v, s, v_other, s_other)
+    _kernels.merge_state_in_place(v, s, v_other, s_other, mask)
 
 
 def merge_states(v: torch.Tensor, s: torch.Tensor):

python/tests/test_shared_prefix_kernels.py

@@ -199,6 +199,63 @@ def test_batch_attention_with_shared_prefix_paged_kv_cache(
         o_baseline.cpu().numpy(), o_cascade.cpu().numpy(), rtol=1e-3, atol=1e-3
     )
 
+@pytest.mark.parametrize("seed", [0])
+@pytest.mark.parametrize("num_tries", [50])
+def test_merge_state_in_place_with_mask(seed, num_tries):
+    seq_len = 512
+    num_heads = 32
+    head_dim = 128
+    va = torch.randn(seq_len, num_heads, head_dim).half().to("cuda:0")
+    sa = torch.randn(seq_len, num_heads, dtype=torch.float32).to("cuda:0")
+    vb = torch.randn(seq_len, num_heads, head_dim).half().to("cuda:0")
+    sb = torch.randn(seq_len, num_heads, dtype=torch.float32).to("cuda:0")
+    va_orginal = va.clone()
+    sa_original = sa.clone()
+
+    # No mask.
+    flashinfer.merge_state_in_place(va, sa, vb, sb)
+    va_merged_ref = va.clone()
+    sa_merged_ref = sa.clone()
+    assert not torch.allclose(va_merged_ref, va_orginal)
+    assert not torch.allclose(sa_merged_ref, sa_original)
+
+    # Mask with all 1s. Should be identical to no mask.
+    mask = torch.ones(seq_len, dtype=torch.bool).to("cuda:0")
+    va = va_orginal.clone()
+    sa = sa_original.clone()
+    flashinfer.merge_state_in_place(va, sa, vb, sb, mask=mask)
+    va_merged = va
+    sa_merged = sa
+    numpy.testing.assert_allclose(va_merged.cpu().numpy(), va_merged_ref.cpu().numpy(), rtol=1e-3, atol=1e-3)
+    numpy.testing.assert_allclose(sa_merged.cpu().numpy(), sa_merged_ref.cpu().numpy(), rtol=1e-3, atol=1e-3)
+
+    # Mask with all zeros. Input and output should be identical.
+    mask = torch.zeros(seq_len, dtype=torch.bool).to("cuda:0")
+    va = va_orginal.clone()
+    sa = sa_original.clone()
+    flashinfer.merge_state_in_place(va, sa, vb, sb, mask=mask)
+    va_merged = va
+    sa_merged = sa
+    numpy.testing.assert_allclose(va_merged.cpu().numpy(), va_orginal.cpu().numpy(), rtol=1e-3, atol=1e-3)
+    numpy.testing.assert_allclose(sa_merged.cpu().numpy(), sa_original.cpu().numpy(), rtol=1e-3, atol=1e-3)
+
+    # Test some random masks.
+    randgen = torch.Generator(device="cuda:0")
+    randgen.manual_seed(seed)
+    for _ in range(num_tries):
+        rand_mask = (torch.rand(seq_len, generator=randgen, dtype=torch.float32, device="cuda:0") > 0.5).to(dtype=torch.bool)
+        true_indices = rand_mask.nonzero()
+        false_indices = (rand_mask==0).nonzero()
+        va = va_orginal.clone()
+        sa = sa_original.clone()
+        flashinfer.merge_state_in_place(va, sa, vb, sb, mask=rand_mask)
+        va_merged = va
+        sa_merged = sa
+
+        numpy.testing.assert_allclose(va_merged[false_indices].cpu().numpy(), va_orginal[false_indices].cpu().numpy(), rtol=1e-3, atol=1e-3)
+        numpy.testing.assert_allclose(sa_merged[false_indices].cpu().numpy(), sa_original[false_indices].cpu().numpy(), rtol=1e-3, atol=1e-3)
+        numpy.testing.assert_allclose(va_merged[true_indices].cpu().numpy(), va_merged_ref[true_indices].cpu().numpy(), rtol=1e-3, atol=1e-3)
+        numpy.testing.assert_allclose(sa_merged[true_indices].cpu().numpy(), sa_merged_ref[true_indices].cpu().numpy(), rtol=1e-3, atol=1e-3)
 
 if __name__ == "__main__":
     test_batch_attention_with_shared_prefix_paged_kv_cache(