[https://nvbugs/5753788][chore] fix empty tensor cutlass moe

Signed-off-by: leslie-fang25 <[email protected]>

Author: leslie-fang25

cpp/tensorrt_llm/kernels/quantization.cu

@@ -146,7 +146,10 @@ void invokeFP4Quantization(int b, int m, int n, T const* input, float const* SFS
         int const numBlocksPerSM = std::max(1u, 2048u / block.x);
         // The number of blocks for m. The m dimension will be padded to 128 for swizzled layout.
         int numBlocksForM = layout == QuantizationSFLayout::SWIZZLED ? PadUpFn(m, 128) : m;
-        dim3 grid(std::min(numBlocksForM, multiProcessorCount * numBlocksPerSM));
+        int gridSize = std::min(numBlocksForM, multiProcessorCount * numBlocksPerSM);
+        // Ensure gridSize is not zero.
+        gridSize = std::max(1, gridSize);
+        dim3 grid(gridSize);
 
         // Launch the cvt kernel.
         auto* kernel_instance = useUE8M0
@@ -165,7 +168,10 @@ void invokeFP4Quantization(int b, int m, int n, T const* input, float const* SFS
         int const numBlocksPerSM = std::max(1u, 2048u / block.x);
         // The number of blocks for m. The m dimension will be padded to 128 for swizzled layout.
         int numBlocksForM = layout == QuantizationSFLayout::SWIZZLED ? PadUpFn(m, 128) : m;
-        dim3 grid(std::min(numBlocksForM, multiProcessorCount * numBlocksPerSM));
+        int gridSize = std::min(numBlocksForM, multiProcessorCount * numBlocksPerSM);
+        // Ensure gridSize is not zero.
+        gridSize = std::max(1, gridSize);
+        dim3 grid(gridSize);
 
         // Launch the cvt kernel.
         auto* kernel_instance = useUE8M0

tensorrt_llm/_torch/modules/fused_moe/fused_moe_cutlass.py

@@ -319,11 +319,17 @@ def quantize_input(
                         x_row = x.shape[0]
                     else:
                         x_row = x.shape[0]
+                        hidden_size = x.shape[-1]
                         x, x_sf = torch.ops.trtllm.fp4_quantize(
                             x, self.fc31_input_scale, self.scaling_vector_size,
                             False, False)
+                        if x_sf.numel() == 0 and x_sf.dim() == 1:
+                            # View torch.Size[0] in to (0, -1) is not supported
+                            x_sf = x_sf.view(
+                                (0,
+                                 hidden_size // int(self.scaling_vector_size)))
                     # Reshape x_sf to 2D for post-quant communication
-                    if x_sf is not None:
+                    if x_sf is not None and x_sf.numel() != 0:
                         x_sf = x_sf.view((x_row, -1))
                 else:
                     if not isinstance(x, Fp4QuantizedTensor):
@@ -494,8 +500,20 @@ def forward_chunk(
         self._load_balancer_start_wait_gpu_stage(is_first_call)
 
         # apply routing
-        token_selected_experts, token_final_scales = self.routing_method.apply(
-            router_logits)
+        if router_logits.numel() == 0:
+            # For dtype, refer to https://github.com/NVIDIA/TensorRT-LLM/blob/55f3cda66d05a2e5686c9c7512721beb522bc8b7/tensorrt_llm/_torch/modules/fused_moe/routing.py#L327
+            token_selected_experts = torch.empty(
+                (0, self.routing_method.experts_per_token),
+                dtype=torch.int32,
+                device=router_logits.device)
+            token_final_scales = torch.empty(
+                (0, self.routing_method.experts_per_token),
+                dtype=torch.float32,
+                device=router_logits.device)
+        else:
+            token_selected_experts, token_final_scales = self.routing_method.apply(
+                router_logits)
+
         assert token_selected_experts.shape[
             1] == self.routing_method.experts_per_token
         assert token_selected_experts.shape == token_final_scales.shape