use ceil_div in cutlass block scaling shape check

benchmarks/cutlass_benchmarks/w8a8_benchmarks.py

@@ -115,8 +115,16 @@ def bench_fp8(
     a_cont = a.contiguous()
     scale_a = torch.tensor(1.0, device="cuda", dtype=torch.float32)
     scale_b = torch.tensor(1.0, device="cuda", dtype=torch.float32)
-    block_scale_a = torch.rand((m, k // 128), device="cuda", dtype=torch.float32)
-    block_scale_b = torch.rand((k // 128, n // 128), device="cuda", dtype=torch.float32)
+
+    def ceil_div(x: int, y: int) -> int:
+        return (x + y - 1) // y
+
+    block_scale_a = torch.rand(
+        (m, ceil_div(k, 128)), device="cuda", dtype=torch.float32
+    )
+    block_scale_b = torch.rand(
+        ceil_div(k, 128), ceil_div(n, 128), device="cuda", dtype=torch.float32
+    )
     block_scale_a_M_major = block_scale_a.t().contiguous().t()
     block_scale_b_K_major = block_scale_b.t().contiguous().t()
     bias = torch.zeros((n,), device="cuda", dtype=torch.bfloat16)

csrc/quantization/cutlass_w8a8/c3x/scaled_mm_helper.hpp

@@ -1,5 +1,6 @@
 #include <torch/all.h>
 #include "cuda_utils.h"
+#include "cutlass_extensions/common.hpp"
 
 template <typename Fp8Func, typename Int8Func, typename BlockwiseFunc>
 void dispatch_scaled_mm(torch::Tensor& c, torch::Tensor const& a,
@@ -28,29 +29,46 @@ void dispatch_scaled_mm(torch::Tensor& c, torch::Tensor const& a,
       }
     }
   } else {
-    using GroupShape = std::array<int64_t, 2>;
-    auto make_group_shape = [](torch::Tensor const& x,
-                               torch::Tensor const& s) -> GroupShape {
-      TORCH_CHECK(s.dim() == 2, "cutlass_scaled_mm group scales must be 2D");
-      return {cuda_utils::ceil_div(x.size(0), s.size(0)),
-              cuda_utils::ceil_div(x.size(1), s.size(1))};
-    };
+    TORCH_CHECK(a_scales.dim() == 2, "a scale must be 2d tensor.");
+    TORCH_CHECK(b_scales.dim() == 2, "b scale must be 2d tensor.");
+    int32_t version_num = get_sm_version_num();
+    if (version_num >= 100) {
+      TORCH_CHECK(
+          a.size(0) == a_scales.size(0) &&
+              cuda_utils::ceil_div(a.size(1), int64_t(128)) == a_scales.size(1),
+          "a_scale_group_shape must be [1, 128].");
+      TORCH_CHECK(
+          cuda_utils::ceil_div(b.size(0), int64_t(128)) == b_scales.size(0) &&
+              cuda_utils::ceil_div(b.size(1), int64_t(128)) == b_scales.size(1),
+          "b_scale_group_shape must be [128, 128].");
+    } else {
+      // TODO: Remove this after using cutlass sm90 blockwise scaling gemm
+      // kernel, or introducing ceil_div to the load_init() of mainloop.
+      using GroupShape = std::array<int64_t, 2>;
+      auto make_group_shape = [](torch::Tensor const& x,
+                                 torch::Tensor const& s) -> GroupShape {
+        TORCH_CHECK(s.dim() == 2, "cutlass_scaled_mm group scales must be 2D");
+        return {cuda_utils::ceil_div(x.size(0), s.size(0)),
+                cuda_utils::ceil_div(x.size(1), s.size(1))};
+      };
+
+      GroupShape a_scale_group_shape = make_group_shape(a, a_scales);
+      GroupShape b_scale_group_shape = make_group_shape(b, b_scales);
 
-    GroupShape a_scale_group_shape = make_group_shape(a, a_scales);
-    GroupShape b_scale_group_shape = make_group_shape(b, b_scales);
+      // 1x128 per-token group scales for activations
+      // 128x128 blockwise scales for weights
+      TORCH_CHECK((a_scale_group_shape == GroupShape{1, 128} &&
+                   b_scale_group_shape == GroupShape{128, 128} &&
+                   a.dtype() == torch::kFloat8_e4m3fn &&
+                   b.dtype() == torch::kFloat8_e4m3fn),
+                  "cutlass_scaled_mm only supports datatype float8_e4m3fn.\n"
+                  "a_scale_group_shape must be [1, 128]. Got: [",
+                  a_scale_group_shape[0], ", ", a_scale_group_shape[1],
+                  "]\n"
+                  "b_scale_group_shape must be [128, 128]. Got: [",
+                  b_scale_group_shape[0], ", ", b_scale_group_shape[1], "]");
+    }
 
-    // 1x128 per-token group scales for activations
-    // 128x128 blockwise scales for weights
-    TORCH_CHECK((a_scale_group_shape == GroupShape{1, 128} &&
-                 b_scale_group_shape == GroupShape{128, 128} &&
-                 a.dtype() == torch::kFloat8_e4m3fn &&
-                 b.dtype() == torch::kFloat8_e4m3fn),
-                "cutlass_scaled_mm only supports datatype float8_e4m3fn.\n"
-                "a_scale_group_shape must be [1, 128]. Got: [",
-                a_scale_group_shape[0], ", ", a_scale_group_shape[1],
-                "]\n"
-                "b_scale_group_shape must be [128, 128]. Got: [",
-                b_scale_group_shape[0], ", ", b_scale_group_shape[1], "]");
     TORCH_CHECK(!bias, "Bias not yet supported blockwise scaled_mm");
     blockwise_func(c, a, b, a_scales, b_scales);
   }

vllm/model_executor/layers/quantization/utils/fp8_utils.py

@@ -115,8 +115,19 @@ def apply_w8a8_block_fp8_linear(
     output_shape = [*input.shape[:-1], weight.shape[0]]
 
     if current_platform.is_cuda():
-        use_cutlass = cutlass_block_fp8_supported and (
-            weight.shape[0] % 128 == 0 and weight.shape[1] % 128 == 0)
+        if current_platform.has_device_capability(100):
+
+            def ceil_div(x: int, y: int) -> int:
+                return (x + y - 1) // y
+
+            use_cutlass = cutlass_block_fp8_supported and (
+                ceil_div(weight.shape[0], 128) == weight_scale.shape[0]
+                and ceil_div(weight.shape[1], 128) == weight_scale.shape[1])
+        else:
+            # TODO: update this after switching to public sm90 block scale gemm
+            # as it also supports weight.shape % 128 != 0
+            use_cutlass = cutlass_block_fp8_supported and (
+                weight.shape[0] % 128 == 0 and weight.shape[1] % 128 == 0)
     else:
         use_cutlass = False