[Bugfix]fix output Nan/Inf in marlin if dtype=float16 (#33972)

Signed-off-by: IriKa Qiu <qiujie.jq@gmail.com>

csrc/moe/marlin_moe_wna16/kernel.h

@@ -13,7 +13,7 @@
       const int4 *__restrict__ b_bias_ptr,                            \
       const float *__restrict__ a_scales_ptr,                         \
       const int4 *__restrict__ scales_ptr,                            \
-      const uint16_t *__restrict__ global_scale_ptr,                  \
+      const float *__restrict__ global_scale_ptr,                     \
       const int4 *__restrict__ zp_ptr, const int *__restrict__ g_idx, \
       const int32_t *__restrict__ sorted_token_ids_ptr,               \
       const int32_t *__restrict__ expert_ids_ptr,                     \

csrc/moe/marlin_moe_wna16/marlin_template.h

@@ -260,7 +260,7 @@ __global__ void Marlin(
     // fp16 quantization scales. shape (k/groupsize, n)
     const int4* __restrict__ scales_ptr,
     // fp16 global scale (for nvfp4// only)
-    const uint16_t* __restrict__ global_scale_ptr,
+    const float* __restrict__ global_scale_ptr,
     // 4bit packed zero-points of shape
     // (k/groupsize, n/pack_factor)
     const int4* __restrict__ zp_ptr,
@@ -308,7 +308,14 @@ __global__ void Marlin(
   constexpr int moe_block_size = m_block_size_8 ? 8 : (16 * thread_m_blocks);
 
   #if defined(__CUDA_ARCH__) && __CUDA_ARCH__ == 750
-  constexpr bool use_fp16_accum = a_type_id == vllm::kFloat16.id();
+  static constexpr auto num_bits =
+      vllm::ScalarType::from_id(b_type_id).size_bits();
+  // Disable use_fp16_accum for NVFP4 and cases when group_size == -1 &&
+  // num_bits == 4
+  constexpr bool use_fp16_accum =
+      a_type_id == vllm::kFloat16.id() &&
+      (!(b_type_id == vllm::kFE2M1f.id() && s_type_id == vllm::kFE4M3fn.id()) &&
+       !(group_blocks == -1 && num_bits == 4));
   #else
   constexpr bool use_fp16_accum = false;
   #endif
@@ -357,7 +364,7 @@ __global__ void Marlin(
       has_zp && !is_zp_float && !std::is_same<scalar_t, nv_bfloat16>::value ||
       has_zp && !is_zp_float && !(b_type == vllm::kU8);
 
-  c_scalar_t2 global_scale;
+  float global_scale_f32 = 1.0f;
 
   constexpr bool has_act_order = group_blocks == 0;
 
@@ -507,11 +514,12 @@ __global__ void Marlin(
 
       if (mul_topk_weights) {
         idx = idx < prob_m_top_k ? idx : 0;
-        c_scalar_t2 topk_weight_val =
-            Cdtype::num2num2(Cdtype::float2num(topk_weights_ptr[idx]));
+        float topk_weight_tmp = topk_weights_ptr[idx];
         if constexpr (b_type == vllm::kFE2M1f && s_type == vllm::kFE4M3fn) {
-          topk_weight_val = __hmul2(topk_weight_val, global_scale);
+          topk_weight_tmp *= global_scale_f32;
         }
+        c_scalar_t2 topk_weight_val =
+            Cdtype::num2num2(Cdtype::float2num(topk_weight_tmp));
         sh_block_topk_weights[threadIdx.x] = topk_weight_val;
       }
     }
@@ -532,8 +540,7 @@ __global__ void Marlin(
     expert_id = expert_ids_ptr[block_id];
 
     if constexpr (b_type == vllm::kFE2M1f && s_type == vllm::kFE4M3fn) {
-      uint16_t val = global_scale_ptr[expert_id];
-      global_scale = Cdtype::num2num2(*reinterpret_cast<c_scalar_t*>(&val));
+      global_scale_f32 = global_scale_ptr[expert_id];
     }
 
     B_expert_off = expert_id * prob_n * prob_k / (pack_factor * 4);
@@ -1784,6 +1791,13 @@ __global__ void Marlin(
     // We first reorder in shared memory to guarantee the most efficient final
     // global write patterns
     auto write = [&](int idx, float c0, float c1, FragS& s, FragS& b_bias) {
+      if constexpr (b_type == vllm::kFE2M1f && s_type == vllm::kFE4M3fn) {
+        if (!mul_topk_weights) {
+          c0 *= global_scale_f32;
+          c1 *= global_scale_f32;
+        }
+      }
+
       c_scalar_t2 res =
           Cdtype::nums2num2(Cdtype::float2num(c0), Cdtype::float2num(c1));
 
@@ -1800,11 +1814,6 @@ __global__ void Marlin(
         res = __hmul2(res, tmp_scale);
       }
 
-      if constexpr (b_type == vllm::kFE2M1f && s_type == vllm::kFE4M3fn) {
-        if (!mul_topk_weights) {
-          res = __hmul2(res, global_scale);
-        }
-      }
       if (has_bias && last) {
         c_scalar_t2 tmp_bias = b_bias[0];
         if constexpr (m_block_size_8) {

csrc/moe/marlin_moe_wna16/ops.cu

@@ -382,7 +382,7 @@ void marlin_mm(const void* A, const void* B, void* C, void* C_tmp, void* b_bias,
   const int4* bias_ptr = (const int4*)b_bias;
   const float* a_s_ptr = (const float*)a_s;
   const int4* b_s_ptr = (const int4*)b_s;
-  const uint16_t* g_s_ptr = (const uint16_t*)g_s;
+  const float* g_s_ptr = (const float*)g_s;
   const int4* zp_ptr = (const int4*)zp;
   const int* g_idx_ptr = (const int*)g_idx;
   const int* perm_ptr = (const int*)perm;
@@ -759,7 +759,7 @@ torch::Tensor moe_wna16_marlin_gemm(
     TORCH_CHECK(b_type == vllm::kFE2M1f && s_type == vllm::kFE4M3fn,
                 "global_scale can only be used for nvfp4 format.");
   } else {
-    global_scale = torch::empty({0}, options);
+    global_scale = torch::empty({0}, options_fp32);
     TORCH_CHECK(!(b_type == vllm::kFE2M1f && s_type == vllm::kFE4M3fn),
                 "the global_scale parameter must be passed for nvfp4 format.");
   }
@@ -842,8 +842,8 @@ torch::Tensor moe_wna16_marlin_gemm(
 
   TORCH_CHECK(a_scales.scalar_type() == at::ScalarType::Float,
               "scalar type of a_scales must be float");
-  TORCH_CHECK(global_scale.scalar_type() == c.scalar_type(),
-              "scalar type of global_scale must be the same with c");
+  TORCH_CHECK(global_scale.scalar_type() == at::ScalarType::Float,
+              "scalar type of global_scale must be float");
   if (a_type.size_bits() == 16) {
     TORCH_CHECK(
         a.scalar_type() == c.scalar_type(),

csrc/quantization/activation_kernels.cu

@@ -189,10 +189,7 @@ __device__ __forceinline__ void cp_async_wait<0>() {
 }
 
 __device__ __forceinline__ float clip(float v, float mmin, float mmax) {
-#if __CUDACC_VER_MAJOR__ >= 11 && __CUDA_ARCH__ >= 800
   return fminf(mmax, fmaxf(v, mmin));
-#else
-#endif
 }
 
 __device__ __forceinline__ __nv_bfloat16 clip(__nv_bfloat16 v,

csrc/quantization/marlin/kernel.h

@@ -13,7 +13,7 @@
       const int4 *__restrict__ b_bias_ptr,                                     \
       const float *__restrict__ a_scales_ptr,                                  \
       const int4 *__restrict__ scales_ptr,                                     \
-      const uint16_t *__restrict__ global_scale_ptr,                           \
+      const float *__restrict__ global_scale_ptr,                              \
       const int4 *__restrict__ zp_ptr, const int *__restrict__ g_idx,          \
       int num_groups, int prob_m, int prob_n, int prob_k, int lda, int *locks, \
       bool has_bias, bool use_atomic_add, bool use_fp32_reduce,                \

csrc/quantization/marlin/marlin.cu

@@ -57,7 +57,7 @@ torch::Tensor marlin_gemm(
     int64_t size_k, bool is_k_full, bool use_atomic_add, bool use_fp32_reduce,
     bool is_zp_float) {
   TORCH_CHECK_NOT_IMPLEMENTED(false,
-                              "marlin_gemm(..) requires CUDA_ARCH >= 8.0");
+                              "marlin_gemm(..) requires CUDA_ARCH >= 7.5");
   return torch::empty({1, 1});
 }
 
@@ -356,7 +356,7 @@ void marlin_mm(const void* A, const void* B, void* C, void* C_tmp, void* b_bias,
   const int4* bias_ptr = (const int4*)b_bias;
   const float* a_s_ptr = (const float*)a_s;
   const int4* b_s_ptr = (const int4*)b_s;
-  const uint16_t* g_s_ptr = (const uint16_t*)g_s;
+  const float* g_s_ptr = (const float*)g_s;
 
   const int4* zp_ptr = (const int4*)zp;
   const int* g_idx_ptr = (const int*)g_idx;
@@ -751,7 +751,7 @@ torch::Tensor marlin_gemm(
     TORCH_CHECK(b_type == vllm::kFE2M1f && s_type == vllm::kFE4M3fn,
                 "global_scale can only be used for nvfp4 format.");
   } else {
-    global_scale = torch::empty({0}, options);
+    global_scale = torch::empty({0}, options_fp32);
     TORCH_CHECK(!(b_type == vllm::kFE2M1f && s_type == vllm::kFE4M3fn),
                 "the global_scale parameter must be passed for nvfp4 format.");
   }
@@ -832,8 +832,8 @@ torch::Tensor marlin_gemm(
 
   TORCH_CHECK(a_scales.scalar_type() == at::ScalarType::Float,
               "scalar type of a_scales must be float");
-  TORCH_CHECK(global_scale.scalar_type() == c.scalar_type(),
-              "scalar type of global_scale must be the same with c");
+  TORCH_CHECK(global_scale.scalar_type() == at::ScalarType::Float,
+              "scalar type of global_scale must be float");
   if (a_type.size_bits() == 16) {
     TORCH_CHECK(
         a.scalar_type() == c.scalar_type(),

csrc/quantization/marlin/marlin_template.h

@@ -251,8 +251,8 @@ __global__ void Marlin(
     const float* __restrict__ a_scales_ptr,
     // fp16 quantization scales. shape (k/groupsize, n)
     const int4* __restrict__ scales_ptr,
-    // fp16 global scale (for nvfp4// only)
-    const uint16_t* __restrict__ global_scale_ptr,
+    // float global scale (for nvfp4// only)
+    const float* __restrict__ global_scale_ptr,
     // 4bit packed zero-points of shape
     // (k/groupsize, n/pack_factor)
     const int4* __restrict__ zp_ptr,
@@ -292,7 +292,13 @@ __global__ void Marlin(
   #endif
 
   #if defined(__CUDA_ARCH__) && __CUDA_ARCH__ == 750
-  constexpr bool use_fp16_accum = a_type_id == vllm::kFloat16.id();
+  constexpr auto num_bits = vllm::ScalarType::from_id(b_type_id).size_bits();
+  // Disable use_fp16_accum for NVFP4 and cases when group_size == -1 &&
+  // num_bits == 4
+  constexpr bool use_fp16_accum =
+      a_type_id == vllm::kFloat16.id() &&
+      (!(b_type_id == vllm::kFE2M1f.id() && s_type_id == vllm::kFE4M3fn.id()) &&
+       !(group_blocks == -1 && num_bits == 4));
   #else
   constexpr bool use_fp16_accum = false;
   #endif
@@ -342,11 +348,10 @@ __global__ void Marlin(
       has_zp && !is_zp_float && !std::is_same<scalar_t, nv_bfloat16>::value ||
       has_zp && !is_zp_float && !(b_type == vllm::kU8);
 
-  c_scalar_t2 global_scale;
+  float global_scale_f32 = 1.0f;
 
   if constexpr (b_type == vllm::kFE2M1f && s_type == vllm::kFE4M3fn) {
-    uint16_t val = global_scale_ptr[0];
-    global_scale = Cdtype::num2num2(*reinterpret_cast<c_scalar_t*>(&val));
+    global_scale_f32 = global_scale_ptr[0];
   }
 
   constexpr bool has_act_order = group_blocks == 0;
@@ -1644,6 +1649,10 @@ __global__ void Marlin(
     // We first reorder in shared memory to guarantee the most efficient final
     // global write patterns
     auto write = [&](int idx, float c0, float c1, FragS& s, FragS& b_bias) {
+      if constexpr (b_type == vllm::kFE2M1f && s_type == vllm::kFE4M3fn) {
+        c0 *= global_scale_f32;
+        c1 *= global_scale_f32;
+      }
       c_scalar_t2 res =
           Cdtype::nums2num2(Cdtype::float2num(c0), Cdtype::float2num(c1));
 
@@ -1659,10 +1668,6 @@ __global__ void Marlin(
         }
         res = __hmul2(res, tmp_scale);
       }
-
-      if constexpr (b_type == vllm::kFE2M1f && s_type == vllm::kFE4M3fn) {
-        res = __hmul2(res, global_scale);
-      }
       if (has_bias && last) {
         c_scalar_t2 tmp_bias = b_bias[0];
         if constexpr (m_block_size_8) {

vllm/model_executor/layers/quantization/utils/marlin_utils_fp4.py

@@ -27,10 +27,19 @@ def is_fp4_marlin_supported():
     return current_platform.has_device_capability(75)
 
 
-def _nvfp4_compute_scale_factor(marlin_scales: torch.Tensor) -> float:
+def _nvfp4_compute_scale_factor(
+    marlin_scales: torch.Tensor,
+    a_dtype: torch.dtype | None = None,
+) -> float:
     """Compute the power-of-2 scale_factor needed so that all non-zero
     values in marlin_scales * 2^7 are >= 2 after rescaling.
     Returns a Python float (power of 2, >= 1.0)."""
+
+    # Since half has a smaller dynamic range compared to bfloat16,
+    # no rescaling is applied here if active dtype is half.
+    if a_dtype is not None and a_dtype == torch.half:
+        return 1.0
+
     ws_float = marlin_scales.float() * (2**7)
     nonzero_mask = ws_float > 0
     if nonzero_mask.any():
@@ -44,6 +53,7 @@ def _nvfp4_compute_scale_factor(marlin_scales: torch.Tensor) -> float:
 def nvfp4_marlin_process_scales(
     marlin_scales: torch.Tensor,
     scale_factor: float | None = None,
+    a_dtype: torch.dtype | None = None,
 ) -> tuple[torch.Tensor, float]:
     """Process NVFP4 weight scales into the special S0E5M3 format for Marlin.
 
@@ -91,7 +101,7 @@ def nvfp4_marlin_process_scales(
     # to fully utilize the E4M3 dynamic range (e.g., global_scale=1).
     # The caller must compensate by dividing global_scale by scale_factor.
     if scale_factor is None:
-        scale_factor = _nvfp4_compute_scale_factor(marlin_scales)
+        scale_factor = _nvfp4_compute_scale_factor(marlin_scales, a_dtype)
     if scale_factor > 1.0:
         marlin_scales = (marlin_scales.float() * scale_factor).to(torch.half)
 
@@ -119,12 +129,14 @@ def mxfp4_marlin_process_scales(marlin_scales, input_dtype=None):
     return marlin_scales
 
 
-def nvfp4_marlin_process_global_scale(global_scale):
-    assert global_scale.dtype in [torch.half, torch.bfloat16]
+def nvfp4_marlin_process_global_scale(global_scale, a_dtype: torch.dtype | None = None):
+    if a_dtype is None:
+        a_dtype = global_scale.dtype
+    assert a_dtype in [torch.half, torch.bfloat16]
     fp4_exponent = 2
-    if global_scale.dtype == torch.half:
+    if a_dtype == torch.half:
         target_exponent = 5
-    elif global_scale.dtype == torch.bfloat16:
+    elif a_dtype == torch.bfloat16:
         target_exponent = 8
     # exponent_bias_fp16 = 2 ** 4 - 2 ** 1 = 14
     # exponent_bias_bf16 = 2 ** 7 - 2 ** 1 = 126
@@ -244,11 +256,15 @@ def prepare_fp4_layer_for_marlin(
     )
 
     if is_nvfp4:
-        weight_scale, scale_factor = nvfp4_marlin_process_scales(weight_scale)
+        weight_scale, scale_factor = nvfp4_marlin_process_scales(
+            weight_scale, a_dtype=param_dtype
+        )
         layer.weight_scale = torch.nn.Parameter(weight_scale, requires_grad=False)
 
-        weight_global_scale = layer.weight_global_scale.to(param_dtype)
-        weight_global_scale = nvfp4_marlin_process_global_scale(weight_global_scale)
+        weight_global_scale = layer.weight_global_scale.to(torch.float32)
+        weight_global_scale = nvfp4_marlin_process_global_scale(
+            weight_global_scale, param_dtype
+        )
         weight_global_scale = weight_global_scale / scale_factor
         layer.weight_global_scale = torch.nn.Parameter(
             weight_global_scale, requires_grad=False
@@ -339,7 +355,6 @@ def prepare_nvfp4_moe_layer_for_marlin(
         scales: torch.Tensor, g_scales: torch.Tensor, name: str
     ) -> tuple[torch.Tensor, torch.Tensor]:
         scales = scales.to(param_dtype)
-        g_scales = g_scales.to(param_dtype)
 
         tensor_list = []
         num_shards = 2 if is_act_and_mul else 1
@@ -350,7 +365,7 @@ def prepare_nvfp4_moe_layer_for_marlin(
 
         # All experts share one global_scale, so compute the max
         # scale_factor across all experts first, then apply uniformly.
-        combined_scale_factor = _nvfp4_compute_scale_factor(scales)
+        combined_scale_factor = _nvfp4_compute_scale_factor(scales, param_dtype)
 
         for i in range(E):
             scale = scales[i].T
@@ -362,12 +377,12 @@ def prepare_nvfp4_moe_layer_for_marlin(
                 is_a_8bit=is_a_8bit,
             )
             marlin_scales, _ = nvfp4_marlin_process_scales(
-                marlin_scales, scale_factor=combined_scale_factor
+                marlin_scales, scale_factor=combined_scale_factor, a_dtype=param_dtype
             )
             tensor_list.append(marlin_scales)
 
         scales = torch.cat([x.unsqueeze(0) for x in tensor_list], 0)
-        g_scales = nvfp4_marlin_process_global_scale(g_scales)
+        g_scales = nvfp4_marlin_process_global_scale(g_scales, param_dtype)
         g_scales = g_scales / combined_scale_factor
         return scales, g_scales
 
@@ -438,7 +453,7 @@ def prepare_moe_fp4_layer_for_marlin(
             scales = scales.view(torch.float8_e8m0fnu)
         scales = scales.to(param_dtype)
         if is_nvfp4:
-            global_scale = getattr(layer, name + "_weight_scale_2").to(param_dtype)
+            global_scale = getattr(layer, name + "_weight_scale_2")
 
         tensor_list = []
         if "w13" in name:
@@ -449,7 +464,7 @@ def prepare_moe_fp4_layer_for_marlin(
         # For NVFP4: compute unified scale_factor across all experts
         combined_scale_factor = None
         if is_nvfp4:
-            combined_scale_factor = _nvfp4_compute_scale_factor(scales)
+            combined_scale_factor = _nvfp4_compute_scale_factor(scales, param_dtype)
 
         for i in range(e):
             scale = scales[i].T
@@ -463,7 +478,9 @@ def prepare_moe_fp4_layer_for_marlin(
             )
             if is_nvfp4:
                 marlin_scales, _ = nvfp4_marlin_process_scales(
-                    marlin_scales, scale_factor=combined_scale_factor
+                    marlin_scales,
+                    scale_factor=combined_scale_factor,
+                    a_dtype=param_dtype,
                 )
             else:
                 marlin_scales = mxfp4_marlin_process_scales(
@@ -477,7 +494,7 @@ def prepare_moe_fp4_layer_for_marlin(
 
         if is_nvfp4:
             assert combined_scale_factor is not None
-            global_scale = nvfp4_marlin_process_global_scale(global_scale)
+            global_scale = nvfp4_marlin_process_global_scale(global_scale, param_dtype)
             global_scale = global_scale / combined_scale_factor
             global_scale = torch.nn.Parameter(global_scale, requires_grad=False)
             setattr(layer, name + "_weight_scale_2", global_scale)
@@ -665,7 +682,7 @@ def rand_marlin_weight_nvfp4_like(weight, group_size, input_dtype=None):
     )
     marlin_scales, scale_factor = nvfp4_marlin_process_scales(marlin_scales)
 
-    global_scale = nvfp4_marlin_process_global_scale(global_scale)
+    global_scale = nvfp4_marlin_process_global_scale(global_scale).to(torch.float32)
     global_scale = global_scale / scale_factor
 
     return weight_ref.T, marlin_qweight, marlin_scales, global_scale