[Kernel] Support decode context parallelism on Blackwell with CUTLASS MLA (#24385)

Signed-off-by: Ming Yang <minos.future@gmail.com>
Signed-off-by: youkaichao <youkaichao@gmail.com>
Co-authored-by: youkaichao <youkaichao@gmail.com>

csrc/attention/mla/sm100_cutlass_mla_kernel.cu

@@ -36,6 +36,7 @@ limitations under the License.
 #if !defined(CUDA_VERSION) || CUDA_VERSION < 12040
 void sm100_cutlass_mla_decode(
     torch::Tensor const& out,
+    torch::Tensor const& lse,
     torch::Tensor const& q_nope,
     torch::Tensor const& q_pe,
     torch::Tensor const& kv_c_and_k_pe_cache,
@@ -99,6 +100,7 @@ struct MlaSm100 {
 template <typename T>
 typename T::Fmha::Arguments args_from_options(
     at::Tensor const& out,
+    at::Tensor const& lse,
     at::Tensor const& q_nope,
     at::Tensor const& q_pe,
     at::Tensor const& kv_c_and_k_pe_cache,
@@ -162,7 +164,10 @@ typename T::Fmha::Arguments args_from_options(
        stride_PT,
        page_count_total,
        page_size},
-      {static_cast<ElementOut*>(out.data_ptr()), stride_O, static_cast<ElementAcc*>(nullptr), stride_LSE},
+      {static_cast<ElementOut*>(out.data_ptr()),
+       stride_O,
+       static_cast<ElementAcc*>(lse.defined() ? lse.data_ptr() : nullptr),
+       stride_LSE},
       hw_info,
       // TODO(trevor-m): Change split_kv back to -1 when
       // https://github.com/NVIDIA/cutlass/issues/2274 is fixed. Split_kv=1 will
@@ -181,6 +186,7 @@ typename T::Fmha::Arguments args_from_options(
 template <typename Element, typename ElementOut, bool IsPaged128, typename PersistenceOption>
 void runMla(
     at::Tensor const& out,
+    at::Tensor const& lse,
     at::Tensor const& q_nope,
     at::Tensor const& q_pe,
     at::Tensor const& kv_c_and_k_pe_cache,
@@ -192,7 +198,7 @@ void runMla(
     cudaStream_t stream) {
   using MlaSm100Type = MlaSm100<Element, ElementOut, IsPaged128, PersistenceOption>;
   typename MlaSm100Type::Fmha fmha;
-  auto arguments = args_from_options<MlaSm100Type>(out, q_nope, q_pe, kv_c_and_k_pe_cache, seq_lens, page_table, sm_scale, num_kv_splits);
+  auto arguments = args_from_options<MlaSm100Type>(out, lse, q_nope, q_pe, kv_c_and_k_pe_cache, seq_lens, page_table, sm_scale, num_kv_splits);
 
   CUTLASS_CHECK(fmha.can_implement(arguments));
 
@@ -214,6 +220,7 @@ void runMla(
 
 void sm100_cutlass_mla_decode(
     torch::Tensor const& out,
+    torch::Tensor const& lse,
     torch::Tensor const& q_nope,
     torch::Tensor const& q_pe,
     torch::Tensor const& kv_c_and_k_pe_cache,
@@ -234,13 +241,13 @@ void sm100_cutlass_mla_decode(
     DISPATCH_BOOL(num_kv_splits <= 1, NotManualSplitKV, [&] {
       if (in_dtype == at::ScalarType::Half) {
         runMla<cutlass::half_t, cutlass::half_t, IsPaged128, IsPersistent<NotManualSplitKV>>(
-          out, q_nope, q_pe, kv_c_and_k_pe_cache, seq_lens, page_table, workspace, sm_scale, num_kv_splits, stream);
+          out, lse, q_nope, q_pe, kv_c_and_k_pe_cache, seq_lens, page_table, workspace, sm_scale, num_kv_splits, stream);
       } else if (in_dtype == at::ScalarType::BFloat16) {
         runMla<cutlass::bfloat16_t, cutlass::bfloat16_t, IsPaged128, IsPersistent<NotManualSplitKV>>(
-          out, q_nope, q_pe, kv_c_and_k_pe_cache, seq_lens, page_table, workspace, sm_scale, num_kv_splits, stream);
+          out, lse, q_nope, q_pe, kv_c_and_k_pe_cache, seq_lens, page_table, workspace, sm_scale, num_kv_splits, stream);
       } else if (in_dtype == at::ScalarType::Float8_e4m3fn) {
         runMla<cutlass::float_e4m3_t, cutlass::bfloat16_t, IsPaged128, IsPersistent<NotManualSplitKV>>(
-          out, q_nope, q_pe, kv_c_and_k_pe_cache, seq_lens, page_table, workspace, sm_scale, num_kv_splits, stream);
+          out, lse, q_nope, q_pe, kv_c_and_k_pe_cache, seq_lens, page_table, workspace, sm_scale, num_kv_splits, stream);
       } else {
         TORCH_CHECK(false, "Unsupported input data type of MLA");
       }

csrc/torch_bindings.cpp

@@ -516,10 +516,10 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
 
   // SM100 CUTLASS MLA decode
   ops.def(
-      "sm100_cutlass_mla_decode(Tensor! out, Tensor q_nope, Tensor q_pe,"
-      "                         Tensor kv_c_and_k_pe_cache, Tensor seq_lens,"
-      "                         Tensor page_table, Tensor workspace, float "
-      "scale,"
+      "sm100_cutlass_mla_decode(Tensor! out, Tensor! lse, Tensor q_nope,"
+      "                         Tensor q_pe, Tensor kv_c_and_k_pe_cache,"
+      "                         Tensor seq_lens, Tensor page_table,"
+      "                         Tensor workspace, float scale,"
       "                         int num_kv_splits) -> ()");
   // conditionally compiled so impl in source file
 

tests/kernels/test_cutlass_mla_decode.py

@@ -2,6 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import math
 import random
+from typing import Optional
 
 import pytest
 import torch
@@ -14,10 +15,16 @@ from vllm.triton_utils import triton
 def cal_diff(x: torch.Tensor,
              y: torch.Tensor,
              name: str,
-             use_fp8: bool = False) -> None:
+             use_fp8: bool = False,
+             diff_threshold: Optional[float] = None) -> None:
     x, y = x.double(), y.double()
     cos_diff = 1 - 2 * (x * y).sum().item() / max(
         (x * x + y * y).sum().item(), 1e-12)
+    if diff_threshold is not None:
+        # directly compare the cos_diff with the threshold
+        assert cos_diff < diff_threshold
+    else:
+        # use the default threshold
         if (use_fp8):
             assert cos_diff < 1e-4
         else:
@@ -118,11 +125,13 @@ def test_cutlass_mla_decode(b, s_q, mean_sk, h_q, h_kv, d, dv, block_size,
                                 dtype=torch.uint8)
 
         out_ans = torch.empty(b, MAX_HEADS, dv, dtype=init_dtype)
-
-        ops.sm100_cutlass_mla_decode(out_ans, q_nope, q_pe, kv_cache_flat,
-                                     cache_seqlens, block_table, workspace,
-                                     scale, 1)
-        return out_ans[:, :h_q].contiguous()
+        output_lse = torch.empty((b, MAX_HEADS),
+                                 dtype=torch.float32,
+                                 device=q_nope.device)
+        ops.sm100_cutlass_mla_decode(out_ans, output_lse, q_nope, q_pe,
+                                     kv_cache_flat, cache_seqlens, block_table,
+                                     workspace, scale, 1)
+        return out_ans[:, :h_q].contiguous(), output_lse[:, :h_q].contiguous()
 
     def scaled_dot_product_attention(query, key, value, is_causal=False):
         query = query.float()
@@ -165,11 +174,14 @@ def test_cutlass_mla_decode(b, s_q, mean_sk, h_q, h_kv, d, dv, block_size,
             lse[i] = lse_i
         return out, lse
 
-    out_cutlass = cutlass_mla()
+    out_cutlass, lse_cutlass = cutlass_mla()
     out_torch, lse_torch = ref_mla()
     # Extract the single token (s_q=1) slice to match cutlass output shape
     out_torch_slice = out_torch[:, 0, :, :]  # [b, h_q, dv]
+    lse_torch_slice = lse_torch[:, 0, :]  # [b, h_q]
     cal_diff(out_cutlass, out_torch_slice, "out", use_fp8)
+    # lse has larger numerical error, so use a larger threshold
+    cal_diff(lse_cutlass, lse_torch_slice, "lse", use_fp8, diff_threshold=1e-3)
 
     t = triton.testing.do_bench(cutlass_mla)
     FLOPS = s_q * total_seqlens * h_q * (d + dv) * 2

vllm/_custom_ops.py

@@ -1833,13 +1833,13 @@ def cutlass_mla_decode(out: torch.Tensor, q_nope: torch.Tensor,
     return out
 
 
-def sm100_cutlass_mla_decode(out: torch.Tensor, q_nope: torch.Tensor,
-                             q_pe: torch.Tensor,
+def sm100_cutlass_mla_decode(out: torch.Tensor, lse: torch.Tensor,
+                             q_nope: torch.Tensor, q_pe: torch.Tensor,
                              kv_c_and_k_pe_cache: torch.Tensor,
                              seq_lens: torch.Tensor, page_table: torch.Tensor,
                              workspace: torch.Tensor, scale: float,
                              num_kv_splits: int) -> torch.Tensor:
-    torch.ops._C.sm100_cutlass_mla_decode(out, q_nope, q_pe,
+    torch.ops._C.sm100_cutlass_mla_decode(out, lse, q_nope, q_pe,
                                           kv_c_and_k_pe_cache, seq_lens,
                                           page_table, workspace, scale,
                                           num_kv_splits)

vllm/v1/attention/backends/mla/cutlass_mla.py

@@ -76,6 +76,7 @@ g_sm100_workspace = SM100Workspace(128 * 1024 * 1024)  # 128MB
 
 
 class CutlassMLAImpl(MLACommonImpl[MLACommonMetadata]):
+    can_return_lse_for_decode: bool = True
 
     def __init__(
             self,
@@ -138,7 +139,7 @@ class CutlassMLAImpl(MLACommonImpl[MLACommonMetadata]):
         workspace: torch.Tensor,
         sm_scale: float,
         num_kv_splits: int,
-    ) -> torch.Tensor:
+    ) -> tuple[torch.Tensor, torch.Tensor]:
         assert (q_nope.ndim == 3
                 ), f"q_nope must be a 3D tensor, but got {q_nope.ndim}"
         assert (
@@ -193,9 +194,13 @@ class CutlassMLAImpl(MLACommonImpl[MLACommonMetadata]):
         dtype = (torch.bfloat16 if is_quantized_kv_cache(self.kv_cache_dtype)
                  else q_nope.dtype)
         out = q_nope.new_empty((B_q, MAX_HEADS, D_latent), dtype=dtype)
+        lse = (torch.empty(
+            (B_q, MAX_HEADS), dtype=torch.float32, device=q_nope.device)
+               if self.need_to_return_lse_for_decode else torch.Tensor())
 
         ops.sm100_cutlass_mla_decode(
             out,
+            lse,
             q_nope,
             q_pe,
             kv_c_and_k_pe_cache,
@@ -205,7 +210,9 @@ class CutlassMLAImpl(MLACommonImpl[MLACommonMetadata]):
             sm_scale,
             num_kv_splits,
         )
-        return out[:, :H].contiguous()
+        returned_lse = lse[:, :H].contiguous(
+        ) if self.need_to_return_lse_for_decode else lse
+        return out[:, :H].contiguous(), returned_lse
 
     def _sm100_forward_decode(
         self,
@@ -213,7 +220,7 @@ class CutlassMLAImpl(MLACommonImpl[MLACommonMetadata]):
         q_pe: torch.Tensor,
         kv_c_and_k_pe_cache: torch.Tensor,
         attn_metadata: MLACommonMetadata,
-    ) -> torch.Tensor:
+    ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
         assert kv_c_and_k_pe_cache.numel() > 0
         assert attn_metadata.decode is not None
 
@@ -226,13 +233,18 @@ class CutlassMLAImpl(MLACommonImpl[MLACommonMetadata]):
         q_nope = q_nope.clone()
         q_pe = q_pe.clone()
 
-        o = self._sm100_cutlass_mla_decode(q_nope, q_pe, kv_c_and_k_pe_cache,
+        o, lse = self._sm100_cutlass_mla_decode(
+            q_nope,
+            q_pe,
+            kv_c_and_k_pe_cache,
             attn_metadata.decode.seq_lens,
             attn_metadata.decode.block_table,
             self._workspace.get_buf(),
-                                           self.scale, self._num_kv_splits)
+            self.scale,
+            self._num_kv_splits,
+        )
 
-        return o
+        return o, (lse if self.need_to_return_lse_for_decode else None)
 
     # TODO: Currently we leave it here only for backup in case something is
     #       wrong with the new SM100 CUTLASS MLA kernel
@@ -286,4 +298,4 @@ class CutlassMLAImpl(MLACommonImpl[MLACommonMetadata]):
                                             attn_metadata), None
 
         return self._sm100_forward_decode(q_nope, q_pe, kv_c_and_k_pe_cache,
-                                          attn_metadata), None
+                                          attn_metadata)