[Perf][Kernel] Optimize FP4 quantization kernels (SM100F) (#32520)

Signed-off-by: LopezCastroRoberto <rocastro@redhat.com>

benchmarks/kernels/bench_nvfp4_quant.py

@@ -20,8 +20,12 @@ FLOAT4_E2M1_MAX = scalar_types.float4_e2m1f.max()
 FLOAT8_E4M3_MAX = torch.finfo(torch.float8_e4m3fn).max
 
 PROVIDER_CFGS = {
-    "vllm": dict(backend="vllm", enabled=True),
-    "flashinfer": dict(backend="flashinfer", enabled=True),
+    "vllm": dict(backend="vllm", is_sf_swizzled_layout=False, enabled=True),
+    "vllm-swizzle": dict(backend="vllm", is_sf_swizzled_layout=True, enabled=True),
+    "flashinfer": dict(backend="flashinfer", is_sf_swizzled_layout=False, enabled=True),
+    "flashinfer-swizzle": dict(
+        backend="flashinfer", is_sf_swizzled_layout=True, enabled=True
+    ),
 }
 
 _enabled = [k for k, v in PROVIDER_CFGS.items() if v["enabled"]]
@@ -36,7 +40,7 @@ def compute_global_scale(tensor: torch.Tensor) -> torch.Tensor:
 @triton.testing.perf_report(
     triton.testing.Benchmark(
         x_names=["batch_size"],
-        x_vals=[1, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096],
+        x_vals=[1, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192],
         x_log=False,
         line_arg="provider",
         line_vals=_enabled,
@@ -63,19 +67,36 @@ def benchmark(batch_size, provider, N, K):
 
     if cfg["backend"] == "vllm":
         # vLLM's FP4 quantization
+        if cfg["is_sf_swizzled_layout"]:
             ms, min_ms, max_ms = triton.testing.do_bench_cudagraph(
-            lambda: ops.scaled_fp4_quant(a, a_global_scale),
+                lambda: ops.scaled_fp4_quant(
+                    a, a_global_scale, is_sf_swizzled_layout=True
+                ),
+                quantiles=quantiles,
+            )
+        else:
+            ms, min_ms, max_ms = triton.testing.do_bench_cudagraph(
+                lambda: ops.scaled_fp4_quant(
+                    a, a_global_scale, is_sf_swizzled_layout=False
+                ),
                 quantiles=quantiles,
             )
     elif cfg["backend"] == "flashinfer":
         # FlashInfer's FP4 quantization
-        # Use is_sf_swizzled_layout=True to match vLLM's output format
+        if cfg["is_sf_swizzled_layout"]:
             ms, min_ms, max_ms = triton.testing.do_bench_cudagraph(
                 lambda: flashinfer_fp4_quantize(
                     a, a_global_scale, is_sf_swizzled_layout=True
                 ),
                 quantiles=quantiles,
             )
+        else:
+            ms, min_ms, max_ms = triton.testing.do_bench_cudagraph(
+                lambda: flashinfer_fp4_quantize(
+                    a, a_global_scale, is_sf_swizzled_layout=False
+                ),
+                quantiles=quantiles,
+            )
 
     # Convert ms to us for better readability at small batch sizes
     to_us = lambda t_ms: t_ms * 1000
@@ -92,7 +113,9 @@ def prepare_shapes(args):
     return out
 
 
-def _test_accuracy_once(M: int, K: int, dtype: torch.dtype, device: str):
+def _test_accuracy_once(
+    M: int, K: int, dtype: torch.dtype, device: str, is_sf_swizzled_layout: bool
+):
     """Test accuracy between vLLM and FlashInfer FP4 quantization."""
     # Create input tensor
     a = torch.randn((M, K), device=device, dtype=dtype)
@@ -101,11 +124,13 @@ def _test_accuracy_once(M: int, K: int, dtype: torch.dtype, device: str):
     a_global_scale = compute_global_scale(a)
 
     # vLLM quantization
-    vllm_fp4, vllm_scale = ops.scaled_fp4_quant(a, a_global_scale)
+    vllm_fp4, vllm_scale = ops.scaled_fp4_quant(
+        a, a_global_scale, is_sf_swizzled_layout=is_sf_swizzled_layout
+    )
 
     # FlashInfer quantization (with swizzled layout to match vLLM's output)
     flashinfer_fp4, flashinfer_scale = flashinfer_fp4_quantize(
-        a, a_global_scale, is_sf_swizzled_layout=True
+        a, a_global_scale, is_sf_swizzled_layout=is_sf_swizzled_layout
     )
     flashinfer_scale = flashinfer_scale.view(torch.float8_e4m3fn)
 
@@ -114,7 +139,14 @@ def _test_accuracy_once(M: int, K: int, dtype: torch.dtype, device: str):
         vllm_fp4,
         flashinfer_fp4,
     )
-    print(f"M={M}, K={K}, dtype={dtype}: PASSED")
+    # Compare scales
+    torch.testing.assert_close(
+        vllm_scale,
+        flashinfer_scale,
+    )
+    print(
+        f"M={M}, K={K}, dtype={dtype}, is_sf_swizzled_layout={is_sf_swizzled_layout}: PASSED"  # noqa: E501
+    )
 
 
 def test_accuracy():
@@ -130,9 +162,10 @@ def test_accuracy():
     Ms = [1, 1024]
     Ks = [4096]
 
+    for is_sf_swizzled_layout in [True, False]:
         for M in Ms:
             for K in Ks:
-            _test_accuracy_once(M, K, dtype, device)
+                _test_accuracy_once(M, K, dtype, device, is_sf_swizzled_layout)
 
     print("\nAll accuracy tests passed!")
 
@@ -145,7 +178,7 @@ if __name__ == "__main__":
         "--models",
         nargs="+",
         type=str,
-        default=["meta-llama/Llama-3.1-8B-Instruct"],
+        default=["meta-llama/Llama-3.3-70B-Instruct"],
         choices=list(WEIGHT_SHAPES.keys()),
     )
     parser.add_argument("--tp-sizes", nargs="+", type=int, default=[1])

csrc/ops.h

@@ -293,7 +293,8 @@ std::vector<torch::Tensor> cutlass_sparse_compress(torch::Tensor const& a);
 
 void scaled_fp4_quant(torch::Tensor& output, torch::Tensor const& input,
                       torch::Tensor& output_scale,
-                      torch::Tensor const& input_scale);
+                      torch::Tensor const& input_scale,
+                      bool is_sf_swizzled_layout);
 
 void scaled_fp4_experts_quant(
     torch::Tensor& output, torch::Tensor& output_scale,

csrc/quantization/fp4/activation_nvfp4_quant_fusion_kernels.cu

@@ -27,17 +27,24 @@
 
 #include "cuda_utils.h"
 #include "launch_bounds_utils.h"
+
+// Define before including nvfp4_utils.cuh so the header
+// can use this macro during compilation.
+#define NVFP4_ENABLE_ELTS16 1
 #include "nvfp4_utils.cuh"
 
 namespace vllm {
 
 // Use UE4M3 by default.
 template <class Type, bool UE8M0_SF = false>
-__global__ void __launch_bounds__(1024, VLLM_BLOCKS_PER_SM(1024))
-    silu_mul_cvt_fp16_to_fp4(int32_t numRows, int32_t numCols, Type const* in,
-                             float const* SFScale, uint32_t* out,
-                             uint32_t* SFout) {
-  using PackedVec = PackedVec<Type>;
+__global__ void __launch_bounds__(512, VLLM_BLOCKS_PER_SM(512))
+    silu_mul_cvt_fp16_to_fp4(int32_t numRows, int32_t numCols,
+                             int32_t num_padded_cols,
+                             Type const* __restrict__ in,
+                             float const* __restrict__ SFScale,
+                             uint32_t* __restrict__ out,
+                             uint32_t* __restrict__ SFout) {
+  using PackedVec = vllm::PackedVec<Type>;
   static constexpr int CVT_FP4_NUM_THREADS_PER_SF =
       (CVT_FP4_SF_VEC_SIZE / CVT_FP4_ELTS_PER_THREAD);
   static_assert(sizeof(PackedVec) == sizeof(Type) * CVT_FP4_ELTS_PER_THREAD,
@@ -49,34 +56,60 @@ __global__ void __launch_bounds__(1024, VLLM_BLOCKS_PER_SM(1024))
   // Get the global scaling factor, which will be applied to the SF.
   // Note SFScale is the same as next GEMM's alpha, which is
   // (448.f / (Alpha_A / 6.f)).
-  float const SFScaleVal = SFScale == nullptr ? 1.0f : SFScale[0];
+  float const SFScaleVal = (SFScale == nullptr) ? 1.0f : SFScale[0];
+
+  int32_t const colIdx = blockDim.x * blockIdx.y + threadIdx.x;
+  int elem_idx = colIdx * CVT_FP4_ELTS_PER_THREAD;
 
   // Input tensor row/col loops.
   for (int rowIdx = blockIdx.x; rowIdx < numRows; rowIdx += gridDim.x) {
-    for (int colIdx = threadIdx.x; colIdx < numCols / CVT_FP4_ELTS_PER_THREAD;
-         colIdx += blockDim.x) {
+    if (colIdx < num_padded_cols) {
+      PackedVec in_vec;
+      PackedVec in_vec2;
       int64_t inOffset =
           rowIdx * (numCols * 2 / CVT_FP4_ELTS_PER_THREAD) + colIdx;
       int64_t inOffset2 = rowIdx * (numCols * 2 / CVT_FP4_ELTS_PER_THREAD) +
                           numCols / CVT_FP4_ELTS_PER_THREAD + colIdx;
-      PackedVec in_vec = reinterpret_cast<PackedVec const*>(in)[inOffset];
-      PackedVec in_vec2 = reinterpret_cast<PackedVec const*>(in)[inOffset2];
 
-      // Get the output tensor offset.
-      // Same as inOffset because 8 elements are packed into one uint32_t.
-      int64_t outOffset = rowIdx * (numCols / CVT_FP4_ELTS_PER_THREAD) + colIdx;
-      auto& out_pos = out[outOffset];
+      bool valid = (rowIdx < numRows) && (elem_idx < numCols);
+      if constexpr (CVT_FP4_PACK16) {
+        ld256_or_zero_cg_u32<Type>(
+            in_vec, &reinterpret_cast<const uint32_t*>(in)[inOffset * 8],
+            valid);
+        ld256_or_zero_cg_u32<Type>(
+            in_vec2, &reinterpret_cast<const uint32_t*>(in)[inOffset2 * 8],
+            valid);
+      } else {
+        ld128_or_zero_cg_u32<Type>(
+            in_vec, &reinterpret_cast<const uint32_t*>(in)[inOffset * 4],
+            valid);
+        ld128_or_zero_cg_u32<Type>(
+            in_vec2, &reinterpret_cast<const uint32_t*>(in)[inOffset2 * 4],
+            valid);
+      }
 
       // Compute silu and mul
-      PackedVec out_silu_mul = compute_silu_mul(in_vec, in_vec2);
+      PackedVec out_silu_mul = compute_silu_mul<Type>(in_vec, in_vec2);
 
       auto sf_out =
           cvt_quant_to_fp4_get_sf_out_offset<uint32_t,
                                              CVT_FP4_NUM_THREADS_PER_SF>(
               rowIdx, colIdx, numKTiles, SFout);
 
-      out_pos = cvt_warp_fp16_to_fp4<Type, UE8M0_SF>(out_silu_mul, SFScaleVal,
-                                                     sf_out);
+      auto out_val =
+          cvt_warp_fp16_to_fp4<Type, CVT_FP4_NUM_THREADS_PER_SF, UE8M0_SF>(
+              out_silu_mul, SFScaleVal, sf_out);
+
+      if (valid) {
+        if constexpr (CVT_FP4_PACK16) {
+          int64_t outOffset = rowIdx * (numCols / 8) + colIdx * 2;
+          uint64_t packed64 =
+              (uint64_t(out_val.hi) << 32) | uint64_t(out_val.lo);
+          reinterpret_cast<uint64_t*>(out)[outOffset >> 1] = packed64;
+        } else {
+          out[inOffset] = out_val;
+        }
+      }
     }
   }
 }
@@ -103,17 +136,23 @@ void silu_and_mul_nvfp4_quant_sm1xxa(torch::Tensor& output,  // [..., d]
   auto output_ptr = static_cast<int64_t*>(output.data_ptr());
   const at::cuda::OptionalCUDAGuard device_guard(device_of(input));
   auto stream = at::cuda::getCurrentCUDAStream(input.get_device());
-  dim3 block(std::min(int(n / ELTS_PER_THREAD), 1024));
+  dim3 block(std::min(int(n / ELTS_PER_THREAD), 512));
   int const numBlocksPerSM =
       vllm_runtime_blocks_per_sm(static_cast<int>(block.x));
-  dim3 grid(std::min(int(m), multiProcessorCount * numBlocksPerSM));
+
+  int sf_n_unpadded = int(n / CVT_FP4_SF_VEC_SIZE);
+
+  int grid_y = vllm::div_round_up(sf_n_unpadded, static_cast<int>(block.x));
+  int grid_x = std::min(
+      int(m), std::max(1, (multiProcessorCount * numBlocksPerSM) / grid_y));
+  dim3 grid(grid_x, grid_y);
 
   VLLM_DISPATCH_HALF_TYPES(
       input.scalar_type(), "silu_and_mul_nvfp4_quant_kernel", [&] {
         using cuda_type = vllm::CUDATypeConverter<scalar_t>::Type;
         auto input_ptr = static_cast<cuda_type const*>(input.data_ptr());
         vllm::silu_mul_cvt_fp16_to_fp4<cuda_type><<<grid, block, 0, stream>>>(
-            m, n, input_ptr, input_sf_ptr,
+            m, n, sf_n_unpadded, input_ptr, input_sf_ptr,
             reinterpret_cast<uint32_t*>(output_ptr),
             reinterpret_cast<uint32_t*>(sf_out));
       });

csrc/quantization/fp4/nvfp4_experts_quant.cu

@@ -140,8 +140,8 @@ __global__ void __launch_bounds__(512, VLLM_BLOCKS_PER_SM(512))
                                            CVT_FP4_NUM_THREADS_PER_SF>(
             rowIdx_in_expert, colIdx, numKTiles, SFout_in_expert);
 
-    out_pos =
-        cvt_warp_fp16_to_fp4<Type, UE8M0_SF>(quant_input, SFScaleVal, sf_out);
+    out_pos = cvt_warp_fp16_to_fp4<Type, CVT_FP4_NUM_THREADS_PER_SF, UE8M0_SF>(
+        quant_input, SFScaleVal, sf_out);
   }
 }
 
@@ -246,8 +246,8 @@ __global__ void __launch_bounds__(1024, VLLM_BLOCKS_PER_SM(1024))
                                            CVT_FP4_NUM_THREADS_PER_SF>(
             rowIdx_in_expert, colIdx, numKTiles, SFout_in_expert);
 
-    out_pos =
-        cvt_warp_fp16_to_fp4<Type, UE8M0_SF>(quant_input, SFScaleVal, sf_out);
+    out_pos = cvt_warp_fp16_to_fp4<Type, CVT_FP4_NUM_THREADS_PER_SF, UE8M0_SF>(
+        quant_input, SFScaleVal, sf_out);
   }
 }
 

csrc/quantization/fp4/nvfp4_quant_entry.cu

@@ -21,7 +21,8 @@
 void scaled_fp4_quant_sm1xxa(torch::Tensor const& output,
                              torch::Tensor const& input,
                              torch::Tensor const& output_sf,
-                             torch::Tensor const& input_sf);
+                             torch::Tensor const& input_sf,
+                             bool is_sf_swizzled_layout);
 #endif
 
 #if (defined(ENABLE_NVFP4_SM100) && ENABLE_NVFP4_SM100) || \
@@ -51,10 +52,12 @@ void silu_and_mul_scaled_fp4_experts_quant_sm1xxa(
 #endif
 
 void scaled_fp4_quant(torch::Tensor& output, torch::Tensor const& input,
-                      torch::Tensor& output_sf, torch::Tensor const& input_sf) {
+                      torch::Tensor& output_sf, torch::Tensor const& input_sf,
+                      bool is_sf_swizzled_layout) {
 #if (defined(ENABLE_NVFP4_SM100) && ENABLE_NVFP4_SM100) || \
     (defined(ENABLE_NVFP4_SM120) && ENABLE_NVFP4_SM120)
-  return scaled_fp4_quant_sm1xxa(output, input, output_sf, input_sf);
+  return scaled_fp4_quant_sm1xxa(output, input, output_sf, input_sf,
+                                 is_sf_swizzled_layout);
 #endif
   TORCH_CHECK_NOT_IMPLEMENTED(false, "No compiled nvfp4 quantization kernel");
 }

csrc/quantization/fp4/nvfp4_quant_kernels.cu

@@ -27,29 +27,23 @@
 
 #include "cuda_utils.h"
 #include "launch_bounds_utils.h"
+
+// Define before including nvfp4_utils.cuh so the header
+// can use this macro during compilation.
+#define NVFP4_ENABLE_ELTS16 1
 #include "nvfp4_utils.cuh"
 
 namespace vllm {
 
-template <typename Int>
-__host__ __device__ inline Int round_up(Int x, Int y) {
-  static_assert(std::is_integral_v<Int>,
-                "round_up argument must be integral type");
-  return ((x + y - 1) / y) * y;
-}
-
-// Compute effective rows for grid configuration with swizzled SF layouts.
-inline int computeEffectiveRows(int m) {
-  constexpr int ROW_TILE = 128;
-  return round_up(m, ROW_TILE);
-}
-
 // Use UE4M3 by default.
 template <class Type, bool UE8M0_SF = false>
 __global__ void __launch_bounds__(512, VLLM_BLOCKS_PER_SM(512))
-    cvt_fp16_to_fp4(int32_t numRows, int32_t numCols, Type const* in,
-                    float const* SFScale, uint32_t* out, uint32_t* SFout) {
-  using PackedVec = PackedVec<Type>;
+    cvt_fp16_to_fp4(int32_t numRows, int32_t numCols, int32_t num_padded_cols,
+                    Type const* __restrict__ in,
+                    float const* __restrict__ SFScale,
+                    uint32_t* __restrict__ out, uint32_t* __restrict__ SFout) {
+  using PackedVec = vllm::PackedVec<Type>;
+
   static constexpr int CVT_FP4_NUM_THREADS_PER_SF =
       (CVT_FP4_SF_VEC_SIZE / CVT_FP4_ELTS_PER_THREAD);
   static_assert(sizeof(PackedVec) == sizeof(Type) * CVT_FP4_ELTS_PER_THREAD,
@@ -59,33 +53,31 @@ __global__ void __launch_bounds__(512, VLLM_BLOCKS_PER_SM(512))
   int32_t const numKTiles = (numCols + 63) / 64;
 
   int sf_m = round_up<int>(numRows, 128);
-  int sf_n_unpadded = numCols / CVT_FP4_SF_VEC_SIZE;
-  int sf_n_int = round_up<int>(sf_n_unpadded, 4) / 4;
-  int num_padded_cols = sf_n_int * 4 * CVT_FP4_SF_VEC_SIZE;
+  int32_t const colIdx = blockDim.x * blockIdx.y + threadIdx.x;
+  int elem_idx = colIdx * CVT_FP4_ELTS_PER_THREAD;
 
   // Get the global scaling factor, which will be applied to the SF.
   // Note SFScale is the same as next GEMM's alpha, which is
   // (448.f / (Alpha_A / 6.f)).
-  float const global_scale = SFScale == nullptr ? 1.0f : SFScale[0];
+  float const global_scale = (SFScale == nullptr) ? 1.0f : SFScale[0];
 
   // Iterate over all rows and cols including padded ones -
   //  ensures we visit every single scale factor address to initialize it.
   for (int rowIdx = blockIdx.x; rowIdx < sf_m; rowIdx += gridDim.x) {
-    for (int colIdx = threadIdx.x;
-         colIdx < num_padded_cols / CVT_FP4_ELTS_PER_THREAD;
-         colIdx += blockDim.x) {
-      int elem_idx = colIdx * CVT_FP4_ELTS_PER_THREAD;
-
+    if (colIdx < num_padded_cols) {
       PackedVec in_vec;
       int64_t inOffset = rowIdx * (numCols / CVT_FP4_ELTS_PER_THREAD) + colIdx;
 
       // If we are outside valid rows OR outside valid columns -> Use Zeros
-      if (rowIdx >= numRows || elem_idx >= numCols) {
-        memset(&in_vec, 0, sizeof(PackedVec));
-
+      bool valid = (rowIdx < numRows) && (elem_idx < numCols);
+      if constexpr (CVT_FP4_PACK16) {
+        ld256_or_zero_cg_u32<Type>(
+            in_vec, &reinterpret_cast<const uint32_t*>(in)[inOffset * 8],
+            valid);
       } else {
-        // Valid Region: Load actual data
-        in_vec = reinterpret_cast<PackedVec const*>(in)[inOffset];
+        ld128_or_zero_cg_u32<Type>(
+            in_vec, &reinterpret_cast<const uint32_t*>(in)[inOffset * 4],
+            valid);
       }
 
       auto sf_out =
@@ -94,16 +86,88 @@ __global__ void __launch_bounds__(512, VLLM_BLOCKS_PER_SM(512))
               rowIdx, colIdx, numKTiles, SFout);
 
       auto out_val =
-          cvt_warp_fp16_to_fp4<Type, UE8M0_SF>(in_vec, global_scale, sf_out);
+          cvt_warp_fp16_to_fp4<Type, CVT_FP4_NUM_THREADS_PER_SF, UE8M0_SF>(
+              in_vec, global_scale, sf_out);
+
+      // We do NOT write output for padding because the 'out' tensor is not
+      // padded.
+      if (valid) {
+        if constexpr (CVT_FP4_PACK16) {
+          int64_t outOffset = rowIdx * (numCols / 8) + colIdx * 2;
+          uint64_t packed64 =
+              (uint64_t(out_val.hi) << 32) | uint64_t(out_val.lo);
+          reinterpret_cast<uint64_t*>(out)[outOffset >> 1] = packed64;
+        } else {
+          out[inOffset] = out_val;
+        }
+      }
+    }
+  }
+}
+
+// Use UE4M3 by default.
+template <class Type, bool UE8M0_SF = false>
+__global__ void __launch_bounds__(512, VLLM_BLOCKS_PER_SM(512))
+    cvt_fp16_to_fp4_sf_major(int32_t numRows, int32_t numCols,
+                             int32_t sf_n_unpadded, Type const* __restrict__ in,
+                             float const* __restrict__ SFScale,
+                             uint32_t* __restrict__ out,
+                             uint32_t* __restrict__ SFout) {
+  using PackedVec = PackedVec<Type>;
+
+  static constexpr int CVT_FP4_NUM_THREADS_PER_SF =
+      (CVT_FP4_SF_VEC_SIZE / CVT_FP4_ELTS_PER_THREAD);
+  static_assert(sizeof(PackedVec) == sizeof(Type) * CVT_FP4_ELTS_PER_THREAD,
+                "Vec size is not matched.");
+
+  int32_t const colIdx = blockDim.x * blockIdx.y + threadIdx.x;
+  int elem_idx = colIdx * CVT_FP4_ELTS_PER_THREAD;
+
+  // Get the global scaling factor, which will be applied to the SF.
+  // Note SFScale is the same as next GEMM's alpha, which is
+  // (448.f / (Alpha_A / 6.f)).
+  float const global_scale = (SFScale == nullptr) ? 1.0f : SFScale[0];
+
+  // Iterate over all rows and cols including padded ones -
+  //  ensures we visit every single scale factor address to initialize it.
+  for (int rowIdx = blockIdx.x; rowIdx < numRows; rowIdx += gridDim.x) {
+    if (colIdx < sf_n_unpadded) {
+      PackedVec in_vec;
+      int64_t inOffset = rowIdx * (numCols / CVT_FP4_ELTS_PER_THREAD) + colIdx;
+
+      // If we are outside valid rows OR outside valid columns -> Use Zeros
+      bool valid = (rowIdx < numRows) && (elem_idx < numCols);
+      if constexpr (CVT_FP4_PACK16) {
+        ld256_or_zero_cg_u32<Type>(
+            in_vec, &reinterpret_cast<const uint32_t*>(in)[inOffset * 8],
+            valid);
+      } else {
+        ld128_or_zero_cg_u32<Type>(
+            in_vec, &reinterpret_cast<const uint32_t*>(in)[inOffset * 4],
+            valid);
+      }
+
+      auto sf_out =
+          sf_out_rowmajor_u8<uint32_t>(rowIdx, colIdx, sf_n_unpadded, SFout);
+
+      auto out_val =
+          cvt_warp_fp16_to_fp4<Type, CVT_FP4_NUM_THREADS_PER_SF, UE8M0_SF>(
+              in_vec, global_scale, sf_out);
 
       // We do NOT write output for padding because the 'out' tensor is not
       // padded.
-      if (rowIdx < numRows && elem_idx < numCols) {
-        // Same as inOffset because 8 elements are packed into one uint32_t.
+      if (valid) {
+        if constexpr (CVT_FP4_PACK16) {
+          int64_t outOffset = rowIdx * (numCols / 8) + colIdx * 2;
+          uint64_t packed64 =
+              (uint64_t(out_val.hi) << 32) | uint64_t(out_val.lo);
+          reinterpret_cast<uint64_t*>(out)[outOffset >> 1] = packed64;
+        } else {
           out[inOffset] = out_val;
         }
       }
     }
+  }
 }
 
 }  // namespace vllm
@@ -111,7 +175,8 @@ __global__ void __launch_bounds__(512, VLLM_BLOCKS_PER_SM(512))
 void scaled_fp4_quant_sm1xxa(torch::Tensor const& output,
                              torch::Tensor const& input,
                              torch::Tensor const& output_sf,
-                             torch::Tensor const& input_sf) {
+                             torch::Tensor const& input_sf,
+                             bool is_sf_swizzled_layout) {
   int32_t m = input.size(0);
   int32_t n = input.size(1);
 
@@ -129,19 +194,48 @@ void scaled_fp4_quant_sm1xxa(torch::Tensor const& output,
   const at::cuda::OptionalCUDAGuard device_guard(device_of(input));
   auto stream = at::cuda::getCurrentCUDAStream(input.get_device());
 
+  int sf_n_unpadded = int(n / CVT_FP4_SF_VEC_SIZE);
+
   // Grid, Block size. Each thread converts 8 values.
   dim3 block(std::min(int(n / ELTS_PER_THREAD), 512));
   int const numBlocksPerSM =
       vllm_runtime_blocks_per_sm(static_cast<int>(block.x));
-  int effectiveRows = vllm::computeEffectiveRows(m);
-  dim3 grid(std::min(effectiveRows, multiProcessorCount * numBlocksPerSM));
+
+  if (is_sf_swizzled_layout) {
+    int sf_n_int = int(vllm::round_up(sf_n_unpadded, 4) / 4);
+    int32_t num_padded_cols =
+        sf_n_int * 4 * CVT_FP4_SF_VEC_SIZE / CVT_FP4_ELTS_PER_THREAD;
+
+    int grid_y = vllm::div_round_up(num_padded_cols, static_cast<int>(block.x));
+    int grid_x =
+        std::min(vllm::computeEffectiveRows(m),
+                 std::max(1, (multiProcessorCount * numBlocksPerSM) / grid_y));
+    dim3 grid(grid_x, grid_y);
 
     VLLM_DISPATCH_HALF_TYPES(input.scalar_type(), "nvfp4_quant_kernel", [&] {
       using cuda_type = vllm::CUDATypeConverter<scalar_t>::Type;
       auto input_ptr = static_cast<cuda_type const*>(input.data_ptr());
       // NOTE: We don't support e8m0 scales at this moment.
       vllm::cvt_fp16_to_fp4<cuda_type, false><<<grid, block, 0, stream>>>(
-        m, n, input_ptr, input_sf_ptr, reinterpret_cast<uint32_t*>(output_ptr),
+          m, n, num_padded_cols, input_ptr, input_sf_ptr,
+          reinterpret_cast<uint32_t*>(output_ptr),
+          reinterpret_cast<uint32_t*>(sf_out));
+    });
+  } else {
+    int grid_y = vllm::div_round_up(sf_n_unpadded, static_cast<int>(block.x));
+    int grid_x = std::min(
+        m, std::max(1, (multiProcessorCount * numBlocksPerSM) / grid_y));
+    dim3 grid(grid_x, grid_y);
+
+    VLLM_DISPATCH_HALF_TYPES(input.scalar_type(), "nvfp4_quant_kernel", [&] {
+      using cuda_type = vllm::CUDATypeConverter<scalar_t>::Type;
+      auto input_ptr = static_cast<cuda_type const*>(input.data_ptr());
+      // NOTE: We don't support e8m0 scales at this moment.
+      vllm::cvt_fp16_to_fp4_sf_major<cuda_type, false>
+          <<<grid, block, 0, stream>>>(m, n, sf_n_unpadded, input_ptr,
+                                       input_sf_ptr,
+                                       reinterpret_cast<uint32_t*>(output_ptr),
                                        reinterpret_cast<uint32_t*>(sf_out));
     });
+  }
 }

csrc/quantization/fp4/nvfp4_utils.cuh

@@ -19,9 +19,17 @@
 #include <cuda_runtime.h>
 #include <cuda_fp8.h>
 
-#define ELTS_PER_THREAD 8
-
+#if (defined(NVFP4_ENABLE_ELTS16) && (CUDART_VERSION >= 12090) && \
+     defined(ENABLE_NVFP4_SM100) && ENABLE_NVFP4_SM100)
+  #define ELTS_PER_THREAD 16
+constexpr int CVT_FP4_ELTS_PER_THREAD = 16;
+constexpr bool CVT_FP4_PACK16 = true;
+#else
+  #define ELTS_PER_THREAD 8
 constexpr int CVT_FP4_ELTS_PER_THREAD = 8;
+constexpr bool CVT_FP4_PACK16 = false;
+#endif
+
 constexpr int CVT_FP4_SF_VEC_SIZE = 16;
 
 namespace vllm {
@@ -68,19 +76,46 @@ struct TypeConverter<__nv_bfloat16> {
   using Type = __nv_bfloat162;
 };
 
+#if (defined(NVFP4_ENABLE_ELTS16) && (CUDART_VERSION >= 12090) && \
+     defined(ENABLE_NVFP4_SM100) && ENABLE_NVFP4_SM100)
+// Define a 32 bytes packed data type.
+template <class Type>
+struct alignas(32) PackedVec {
+  typename TypeConverter<Type>::Type elts[8];
+};
+#else
 // Define a 16 bytes packed data type.
 template <class Type>
-struct PackedVec {
+struct alignas(16) PackedVec {
   typename TypeConverter<Type>::Type elts[4];
 };
+#endif
 
 template <>
 struct PackedVec<__nv_fp8_e4m3> {
   __nv_fp8x2_e4m3 elts[8];
 };
 
+template <typename Int>
+__host__ __device__ inline Int round_up(Int x, Int y) {
+  static_assert(std::is_integral_v<Int>,
+                "round_up argument must be integral type");
+  return ((x + y - 1) / y) * y;
+}
+
+template <typename Int>
+__host__ __device__ __forceinline__ Int div_round_up(Int x, Int y) {
+  return (x + y - 1) / y;
+}
+
+// Compute effective rows for grid configuration with swizzled SF layouts.
+inline int computeEffectiveRows(int m) {
+  constexpr int ROW_TILE = 128;
+  return round_up(m, ROW_TILE);
+}
+
 // Convert 8 float32 values into 8 e2m1 values (represented as one uint32_t).
-inline __device__ uint32_t fp32_vec_to_e2m1(float (&array)[8]) {
+inline __device__ uint32_t fp32_vec8_to_e2m1(float (&array)[8]) {
   uint32_t val;
   asm volatile(
       "{\n"
@@ -101,7 +136,7 @@ inline __device__ uint32_t fp32_vec_to_e2m1(float (&array)[8]) {
 }
 
 // Convert 4 float2 values into 8 e2m1 values (represented as one uint32_t).
-inline __device__ uint32_t fp32_vec_to_e2m1(float2 (&array)[4]) {
+__device__ __forceinline__ uint32_t fp32_vec8_to_e2m1(float2 (&array)[4]) {
   uint32_t val;
   asm volatile(
       "{\n"
@@ -114,20 +149,115 @@ inline __device__ uint32_t fp32_vec_to_e2m1(float2 (&array)[4]) {
       "cvt.rn.satfinite.e2m1x2.f32   byte2, %6, %5;\n"
       "cvt.rn.satfinite.e2m1x2.f32   byte3, %8, %7;\n"
       "mov.b32 %0, {byte0, byte1, byte2, byte3};\n"
-      "}"
+      "}\n"
       : "=r"(val)
       : "f"(array[0].x), "f"(array[0].y), "f"(array[1].x), "f"(array[1].y),
         "f"(array[2].x), "f"(array[2].y), "f"(array[3].x), "f"(array[3].y));
   return val;
 }
 
+struct u32x2 {
+  uint32_t lo, hi;
+};
+
+using fp4_packed_t = std::conditional_t<CVT_FP4_PACK16, u32x2, uint32_t>;
+
+__device__ __forceinline__ u32x2 fp32_vec16_to_e2m1(float2 (&array)[8]) {
+  u32x2 out;
+  asm volatile(
+      "{\n"
+      ".reg .b8 b0;\n"
+      ".reg .b8 b1;\n"
+      ".reg .b8 b2;\n"
+      ".reg .b8 b3;\n"
+      ".reg .b8 b4;\n"
+      ".reg .b8 b5;\n"
+      ".reg .b8 b6;\n"
+      ".reg .b8 b7;\n"
+      "cvt.rn.satfinite.e2m1x2.f32   b0,  %3,  %2;\n"
+      "cvt.rn.satfinite.e2m1x2.f32   b1,  %5,  %4;\n"
+      "cvt.rn.satfinite.e2m1x2.f32   b2,  %7,  %6;\n"
+      "cvt.rn.satfinite.e2m1x2.f32   b3,  %9,  %8;\n"
+      "cvt.rn.satfinite.e2m1x2.f32   b4, %11, %10;\n"
+      "cvt.rn.satfinite.e2m1x2.f32   b5, %13, %12;\n"
+      "cvt.rn.satfinite.e2m1x2.f32   b6, %15, %14;\n"
+      "cvt.rn.satfinite.e2m1x2.f32   b7, %17, %16;\n"
+      "mov.b32 %0, {b0, b1, b2, b3};\n"
+      "mov.b32 %1, {b4, b5, b6, b7};\n"
+      "}\n"
+      : "=r"(out.lo), "=r"(out.hi)
+      : "f"(array[0].x), "f"(array[0].y), "f"(array[1].x), "f"(array[1].y),
+        "f"(array[2].x), "f"(array[2].y), "f"(array[3].x), "f"(array[3].y),
+        "f"(array[4].x), "f"(array[4].y), "f"(array[5].x), "f"(array[5].y),
+        "f"(array[6].x), "f"(array[6].y), "f"(array[7].x), "f"(array[7].y));
+  return out;
+}
+
+__device__ __forceinline__ uint32_t pack_fp4(float2 (&v)[4]) {
+  return fp32_vec8_to_e2m1(v);
+}
+
+__device__ __forceinline__ u32x2 pack_fp4(float2 (&v)[8]) {
+  return fp32_vec16_to_e2m1(v);
+}
+
 // Fast reciprocal.
-inline __device__ float reciprocal_approximate_ftz(float a) {
+__device__ __forceinline__ float reciprocal_approximate_ftz(float a) {
   float b;
-  asm volatile("rcp.approx.ftz.f32 %0, %1;\n" : "=f"(b) : "f"(a));
+  asm volatile("rcp.approx.ftz.f32 %0, %1;" : "=f"(b) : "f"(a));
   return b;
 }
 
+template <class Type>
+__device__ __forceinline__ void ld128_or_zero_cg_u32(PackedVec<Type>& out,
+                                                     const void* ptr,
+                                                     bool pred) {
+  uint32_t r0, r1, r2, r3;
+
+  asm volatile(
+      "{\n"
+      "  .reg .pred pr;\n"
+      "  setp.ne.u32 pr, %4, 0;\n"
+      "  mov.u32 %0, 0;\n"
+      "  mov.u32 %1, 0;\n"
+      "  mov.u32 %2, 0;\n"
+      "  mov.u32 %3, 0;\n"
+      "  @pr ld.global.cg.v4.u32 {%0,%1,%2,%3}, [%5];\n"
+      "}\n"
+      : "=r"(r0), "=r"(r1), "=r"(r2), "=r"(r3)
+      : "r"((int)pred), "l"(ptr));
+
+  *reinterpret_cast<uint4*>(&out) = uint4{r0, r1, r2, r3};
+}
+
+template <class Type>
+__device__ __forceinline__ void ld256_or_zero_cg_u32(PackedVec<Type>& out,
+                                                     const void* ptr,
+                                                     bool pred) {
+  uint32_t r0, r1, r2, r3, r4, r5, r6, r7;
+
+  asm volatile(
+      "{\n"
+      "  .reg .pred pr;\n"
+      "  setp.ne.u32 pr, %8, 0;\n"
+      "  mov.u32 %0, 0;\n"
+      "  mov.u32 %1, 0;\n"
+      "  mov.u32 %2, 0;\n"
+      "  mov.u32 %3, 0;\n"
+      "  mov.u32 %4, 0;\n"
+      "  mov.u32 %5, 0;\n"
+      "  mov.u32 %6, 0;\n"
+      "  mov.u32 %7, 0;\n"
+      "  @pr ld.global.cg.v8.u32 {%0,%1,%2,%3,%4,%5,%6,%7}, [%9];\n"
+      "}\n"
+      : "=r"(r0), "=r"(r1), "=r"(r2), "=r"(r3), "=r"(r4), "=r"(r5), "=r"(r6),
+        "=r"(r7)
+      : "r"((int)pred), "l"(ptr));
+
+  reinterpret_cast<uint4*>(&out)[0] = uint4{r0, r1, r2, r3};
+  reinterpret_cast<uint4*>(&out)[1] = uint4{r4, r5, r6, r7};
+}
+
 // Compute SF output offset for swizzled tensor core layout.
 // SF layout: [numMTiles, numKTiles, 32, 4, 4]
 // Caller must precompute: numKTiles = (numCols + 63) / 64
@@ -166,21 +296,41 @@ __device__ __forceinline__ uint8_t* cvt_quant_to_fp4_get_sf_out_offset(
   return reinterpret_cast<uint8_t*>(SFout) + SFOffset;
 }
 
+template <class SFType>
+__device__ __forceinline__ uint8_t* sf_out_rowmajor_u8(int row, int pack,
+                                                       int packs_per_row_sf,
+                                                       SFType* SFout) {
+  constexpr int PACK = CVT_FP4_ELTS_PER_THREAD;
+  constexpr int THREADS_PER_SF =
+      CVT_FP4_SF_VEC_SIZE / PACK;  // 1 if PACK=16, 2 else PACK=8
+
+  if (threadIdx.x % THREADS_PER_SF != 0) return nullptr;
+
+  int sf_col =
+      pack / THREADS_PER_SF;  // PACK=16 => sf_col=pack; PACK=8 => sf_col=pack/2
+  int64_t off = (int64_t)row * packs_per_row_sf + sf_col;
+
+  return (uint8_t*)SFout + off;
+}
+
 // Quantizes the provided PackedVec into the uint32_t output
-template <class Type, bool UE8M0_SF = false>
-__device__ uint32_t cvt_warp_fp16_to_fp4(PackedVec<Type>& vec, float SFScaleVal,
-                                         uint8_t* SFout) {
+template <class Type, int CVT_FP4_NUM_THREADS_PER_SF, bool UE8M0_SF = false>
+__device__ __forceinline__ fp4_packed_t
+cvt_warp_fp16_to_fp4(PackedVec<Type>& vec, float SFScaleVal, uint8_t* SFout) {
   // Get absolute maximum values among the local 8 values.
   auto localMax = __habs2(vec.elts[0]);
 
-// Local maximum value.
+  // Local maximum value.
 #pragma unroll
   for (int i = 1; i < CVT_FP4_ELTS_PER_THREAD / 2; i++) {
     localMax = __hmax2(localMax, __habs2(vec.elts[i]));
   }
 
   // Get the absolute maximum among all 16 values (two threads).
-  localMax = __hmax2(__shfl_xor_sync(uint32_t(-1), localMax, 1), localMax);
+
+  if constexpr (CVT_FP4_NUM_THREADS_PER_SF == 2) {
+    localMax = __hmax2(__shfl_xor_sync(0xffffffffu, localMax, 1), localMax);
+  }
   // Get the final absolute maximum values.
   float vecMax = float(__hmax(localMax.x, localMax.y));
 
@@ -205,19 +355,18 @@ __device__ uint32_t cvt_warp_fp16_to_fp4(PackedVec<Type>& vec, float SFScaleVal,
     // Convert back to fp32.
     SFValue = float(tmp);
   }
+
+  // Write the SF to global memory (STG.8).
+  if (SFout) *SFout = fp8SFVal;
+
   // Get the output scale.
   // Recipe: final_scale = reciprocal(fp32(fp8(SFValue * SFScaleVal))) *
   //                       reciprocal(SFScaleVal))
   float outputScale =
-      SFValue != 0 ? reciprocal_approximate_ftz(
+      SFValue != 0.0f ? reciprocal_approximate_ftz(
                             SFValue * reciprocal_approximate_ftz(SFScaleVal))
                       : 0.0f;
 
-  if (SFout) {
-    // Write the SF to global memory (STG.8).
-    *SFout = fp8SFVal;
-  }
-
   // Convert the input to float.
   float2 fp2Vals[CVT_FP4_ELTS_PER_THREAD / 2];
 
@@ -233,10 +382,7 @@ __device__ uint32_t cvt_warp_fp16_to_fp4(PackedVec<Type>& vec, float SFScaleVal,
   }
 
   // Convert to e2m1 values.
-  uint32_t e2m1Vec = fp32_vec_to_e2m1(fp2Vals);
-
-  // Write the e2m1 values to global memory.
-  return e2m1Vec;
+  return pack_fp4(fp2Vals);
 }
 
 // silu in float32

csrc/torch_bindings.cpp

@@ -546,7 +546,8 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
   // Compute NVFP4 block quantized tensor.
   ops.def(
       "scaled_fp4_quant(Tensor! output, Tensor input,"
-      "                 Tensor! output_scale, Tensor input_scale) -> ()");
+      "                 Tensor! output_scale, Tensor input_scale, bool "
+      "is_sf_swizzled_layout) -> ()");
   ops.impl("scaled_fp4_quant", torch::kCUDA, &scaled_fp4_quant);
 
   // Compute NVFP4 experts quantization.

tests/kernels/quantization/test_flashinfer_nvfp4_scaled_mm.py

@@ -107,10 +107,14 @@ def test_flashinfer_nvfp4_gemm(
     # from checkpoints are in linear scales.
     # So instead of needing to swizzle for cutlass as in modelopt.py,
     # we need to unswizzle for trtllm here.
-    a_fp4, a_scale_interleaved = ops.scaled_fp4_quant(a_dtype, a_global_scale, backend)
+    a_fp4, a_scale_interleaved = ops.scaled_fp4_quant(
+        a_dtype, a_global_scale, is_sf_swizzled_layout=True, backend=backend
+    )
     is_sf_128x4_layout = not (backend == "trtllm" and m <= 32)
 
-    b_fp4, b_scale_interleaved = ops.scaled_fp4_quant(b_dtype, b_global_scale)
+    b_fp4, b_scale_interleaved = ops.scaled_fp4_quant(
+        b_dtype, b_global_scale, is_sf_swizzled_layout=True
+    )
 
     # get_ref_results unswizzles the scales internally.
     expected_out = get_ref_results(

tests/kernels/quantization/test_nvfp4_quant.py

@@ -27,6 +27,12 @@ PAD_SHAPES = [
     (150, 128),
     (150, 48),
     (90, 80),
+    (128, 512),
+    (128, 1024),
+    (128, 2048),
+    (64, 7168),
+    (64, 7152),
+    (32, 14336),
 ]
 SEEDS = [42]
 CUDA_DEVICES = ["cuda:0"]
@@ -173,3 +179,25 @@ def test_quantize_to_fp4_padded(pad_shape: tuple[int, int]) -> None:
     out_ans = cast_from_fp4(out, m, n)
     torch.testing.assert_close(out_ans, out_ref)
     torch.testing.assert_close(scale_ans, scale_ref)
+
+
+@pytest.mark.parametrize("pad_shape", PAD_SHAPES)
+@torch.inference_mode()
+def test_quantize_to_fp4_padded_no_sf_swizzled(pad_shape: tuple[int, int]) -> None:
+    dtype = torch.float16
+    set_random_seed(42)
+    torch.set_default_device("cuda:0")
+
+    m, n = pad_shape
+
+    x = torch.randn((m, n), dtype=dtype)
+
+    tensor_amax = torch.abs(x).max().to(torch.float32)
+    global_scale = FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX / tensor_amax
+    out_ref, scale_ref = ref_nvfp4_quant(x, global_scale)
+
+    out, out_scale = ops.scaled_fp4_quant(x, global_scale, is_sf_swizzled_layout=False)
+    scale_ans = out_scale.to(torch.float32)
+    out_ans = cast_from_fp4(out, m, n)
+    torch.testing.assert_close(out_ans, out_ref)
+    torch.testing.assert_close(scale_ans, scale_ref)

vllm/_custom_ops.py

@@ -1534,6 +1534,7 @@ def permute_cols(a: torch.Tensor, perm: torch.Tensor) -> torch.Tensor:
 def scaled_fp4_quant(
     input: torch.Tensor,
     input_global_scale: torch.Tensor,
+    is_sf_swizzled_layout: bool = True,
     backend: str = "none",
 ) -> tuple[torch.Tensor, torch.Tensor]:
     """
@@ -1577,7 +1578,7 @@ def scaled_fp4_quant(
     else:
         # Two fp4 values will be packed into an uint8.
         output = torch.empty((m, n // 2), device=device, dtype=torch.uint8)
-
+        if is_sf_swizzled_layout:
             # We use the rounded values to store the swizzled values. Due to the
             # requirement of the Tensor Core, the minimum tile is 128x4 for the scales.
             # So, we first pad the scales to multiples of 128 and 4. Then, the scales
@@ -1590,8 +1591,12 @@ def scaled_fp4_quant(
             output_scale = torch.empty(
                 (rounded_m, rounded_n // 4), device=device, dtype=torch.int32
             )
+        else:
+            output_scale = torch.empty((m, n // 16), device=device, dtype=torch.uint8)
 
-        torch.ops._C.scaled_fp4_quant(output, input, output_scale, input_global_scale)
+        torch.ops._C.scaled_fp4_quant(
+            output, input, output_scale, input_global_scale, is_sf_swizzled_layout
+        )
 
     output_scale = output_scale.view(torch.float8_e4m3fn)
     return output, output_scale

vllm/compilation/activation_quant_fusion.py

@@ -152,6 +152,7 @@ class SiluMulNvfp4QuantPattern(ActivationQuantPattern):
                 input=result_silu_mul,
                 output_scale=output_scale,
                 input_scale=scale,
+                is_sf_swizzled_layout=True,
             )
             return at[1], at[2]
 

vllm/compilation/collective_fusion.py

@@ -946,6 +946,7 @@ class AllReduceFusedRMSNormStaticQuantNVFP4Pattern(BasePattern):
                 input=rms,
                 output_scale=output_scale,
                 input_scale=input_global_scale,
+                is_sf_swizzled_layout=True,
             )
 
             # quant_out, allreduce_output, output_scale
@@ -1043,6 +1044,7 @@ class AllReduceFusedAddRMSNormStaticQuantNVFP4Pattern(BasePattern):
                 input=rms,
                 output_scale=output_scale,
                 input_scale=input_global_scale,
+                is_sf_swizzled_layout=True,
             )
 
             # quant_out, allreduce_output, output_scale

vllm/compilation/fusion_attn.py

@@ -248,6 +248,7 @@ class AttentionNvfp4QuantPattern(AttentionQuantPattern):
                 input=attn_out_view,
                 output_scale=output_scale,
                 input_scale=input_scale,
+                is_sf_swizzled_layout=True,
             )
             output_scale_view = torch.ops.aten.view.dtype(at2[2], FP8_DTYPE)
             return at2[1], output_scale_view

vllm/model_executor/layers/fused_moe/utils.py

@@ -24,7 +24,6 @@ from vllm.model_executor.layers.quantization.utils.mxfp8_utils import (
     mxfp8_e4m3_quantize,
 )
 from vllm.triton_utils import tl, triton
-from vllm.utils.flashinfer import flashinfer_fp4_quantize
 from vllm.utils.math_utils import cdiv
 from vllm.utils.torch_utils import is_torch_equal_or_newer
 
@@ -117,9 +116,7 @@ def _nvfp4_quantize(
     A_scale: torch.Tensor | None,
     is_sf_swizzled_layout: bool,
 ) -> tuple[torch.Tensor, torch.Tensor]:
-    return flashinfer_fp4_quantize(
-        A, A_scale, is_sf_swizzled_layout=is_sf_swizzled_layout
-    )
+    return ops.scaled_fp4_quant(A, A_scale, is_sf_swizzled_layout=is_sf_swizzled_layout)
 
 
 def _fp8_quantize(

vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w4a4_nvfp4.py

@@ -191,7 +191,10 @@ class CompressedTensorsW4A4Fp4(CompressedTensorsScheme):
 
         # quantize BF16 or FP16 to (FP4 and interleaved block scale)
         x_fp4, x_blockscale = scaled_fp4_quant(
-            x, layer.input_global_scale, self.backend
+            x,
+            layer.input_global_scale,
+            is_sf_swizzled_layout=True,
+            backend=self.backend,
         )
 
         mm_args = (

vllm/model_executor/layers/quantization/modelopt.py

@@ -1307,7 +1307,9 @@ class ModelOptNvFp4LinearMethod(LinearMethodBase):
         output_shape = [x.shape[0], layer.weight.shape[0]]
 
         # quantize BF16 or FP16 to (FP4 and interleaved block scale)
-        x_fp4, x_blockscale = scaled_fp4_quant(x, layer.input_scale_inv, self.backend)
+        x_fp4, x_blockscale = scaled_fp4_quant(
+            x, layer.input_scale_inv, is_sf_swizzled_layout=True, backend=self.backend
+        )
 
         # validate dtypes of quantized input, input block scale,
         # weight and weight_blockscale

vllm/model_executor/layers/quantization/utils/flashinfer_fp4_moe.py

@@ -8,6 +8,7 @@ import torch
 
 import vllm.envs as envs
 import vllm.model_executor.layers.fused_moe.modular_kernel as mk
+from vllm import _custom_ops as ops
 from vllm.logger import init_logger
 from vllm.model_executor.layers.fused_moe.config import (
     FusedMoEConfig,
@@ -341,10 +342,8 @@ def flashinfer_trtllm_fp4_moe(
         hidden_states_fp4, hidden_states_scale_linear_fp4 = x
     else:
         # hidden_states is the already quantized
-        (hidden_states_fp4, hidden_states_scale_linear_fp4) = flashinfer.fp4_quantize(
-            x,
-            layer.a1_gscale,
-            is_sf_swizzled_layout=False,
+        (hidden_states_fp4, hidden_states_scale_linear_fp4) = ops.scaled_fp4_quant(
+            x, layer.a1_gscale, is_sf_swizzled_layout=False
         )
 
     # Determine routing method type
@@ -443,10 +442,8 @@ def flashinfer_trtllm_fp4_routed_moe(
         hidden_states_fp4, hidden_states_scale_linear_fp4 = x
     else:
         # Quantize input to FP4
-        (hidden_states_fp4, hidden_states_scale_linear_fp4) = flashinfer.fp4_quantize(
-            x,
-            layer.a1_gscale,
-            is_sf_swizzled_layout=False,
+        (hidden_states_fp4, hidden_states_scale_linear_fp4) = ops.scaled_fp4_quant(
+            x, layer.a1_gscale, is_sf_swizzled_layout=False
         )
 
     # Call TRT-LLM FP4 block-scale MoE kernel