Faster weight processing (trtllm-gen moe nvfp4) (#9162)

python/sglang/srt/layers/quantization/modelopt_quant.py

@@ -737,6 +737,7 @@ class ModelOptNvFp4FusedMoEMethod(FusedMoEMethodBase):
                 " above."
             )
         self.enable_flashinfer_trtllm_moe = should_use_flashinfer_trtllm_moe()
+        self._cache_permute_indices = {}
 
     @property
     def enable_flashinfer_cutlass_moe(self) -> bool:
@@ -900,10 +901,15 @@ class ModelOptNvFp4FusedMoEMethod(FusedMoEMethodBase):
             e2m1_and_ufp8sf_scale_to_float,
             fp4_quantize,
             next_positive_power_of_2,
+            nvfp4_block_scale_interleave,
             reorder_rows_for_gated_act_gemm,
             shuffle_matrix_a,
             shuffle_matrix_sf_a,
         )
+        from flashinfer.fused_moe.core import (
+            _maybe_get_cached_w2_permute_indices,
+            _maybe_get_cached_w3_w1_permute_indices,
+        )
 
         """Prepare quantized weights for kernel (done offline with weights)."""
         epilogue_tile_m = 128  # FIXME: this depends on the kernel internals
@@ -927,50 +933,66 @@ class ModelOptNvFp4FusedMoEMethod(FusedMoEMethodBase):
             num_experts, hidden_size, intermediate_size // 16
         )  # fp8 scaling factors
 
-        # Reorder rows of W1 and scales for fused gated activation
-        gemm1_weights_fp4_interleaved = []
-        gemm1_scales_fp4_interleaved = []
-        for i in range(num_experts):
-            gemm1_weights_fp4_interleaved.append(
-                reorder_rows_for_gated_act_gemm(gemm1_weights_fp4[i].clone())
-            )
-            gemm1_scales_fp4_interleaved.append(
-                reorder_rows_for_gated_act_gemm(gemm1_scales_linear_fp4[i].clone())
-            )
-
-        # Stack weights and scales for all experts
-        gemm1_weights_fp4_interleaved = torch.stack(
-            gemm1_weights_fp4_interleaved
-        ).reshape(num_experts, 2 * intermediate_size, hidden_size // 2)
-        gemm1_scales_fp4_interleaved = torch.stack(
-            gemm1_scales_fp4_interleaved
-        ).reshape(num_experts, 2 * intermediate_size, hidden_size // 16)
-
-        # Shuffle weights and scaling factors for transposed mma output
         gemm1_weights_fp4_shuffled = []
         gemm1_scales_fp4_shuffled = []
         gemm2_weights_fp4_shuffled = []
         gemm2_scales_fp4_shuffled = []
         for i in range(num_experts):
+            # Calculate the permute indices for the following:
+            # 1. Reorder rows of W1 and scales for fused gated activation
+            # 2. Shuffle weights and scaling factors for transposed mma output
+            # for both w3_w1 and w2 weights and scale factors
+            permute_indices = _maybe_get_cached_w3_w1_permute_indices(
+                self._cache_permute_indices,
+                gemm1_weights_fp4[i].view(torch.uint8),
+                epilogue_tile_m,
+            )
             gemm1_weights_fp4_shuffled.append(
-                shuffle_matrix_a(
-                    gemm1_weights_fp4_interleaved[i].view(torch.uint8), epilogue_tile_m
-                )
+                gemm1_weights_fp4[i]
+                .view(torch.uint8)[permute_indices.to(gemm1_weights_fp4.device)]
+                .contiguous()
+            )
+
+            permute_sf_indices = _maybe_get_cached_w3_w1_permute_indices(
+                self._cache_permute_indices,
+                gemm1_scales_linear_fp4[i].view(torch.uint8),
+                epilogue_tile_m,
+                num_elts_per_sf=16,
             )
             gemm1_scales_fp4_shuffled.append(
-                shuffle_matrix_sf_a(
-                    gemm1_scales_fp4_interleaved[i].view(torch.uint8), epilogue_tile_m
+                nvfp4_block_scale_interleave(
+                    gemm1_scales_linear_fp4[i]
+                    .view(torch.uint8)[
+                        permute_sf_indices.to(gemm1_scales_linear_fp4.device)
+                    ]
+                    .contiguous()
                 )
             )
 
+            permute_indices = _maybe_get_cached_w2_permute_indices(
+                self._cache_permute_indices,
+                gemm2_weights_fp4[i].view(torch.uint8),
+                epilogue_tile_m,
+            )
             gemm2_weights_fp4_shuffled.append(
-                shuffle_matrix_a(
-                    gemm2_weights_fp4[i].view(torch.uint8), epilogue_tile_m
-                )
+                gemm2_weights_fp4[i]
+                .view(torch.uint8)[permute_indices.to(gemm2_weights_fp4.device)]
+                .contiguous()
+            )
+
+            permute_sf_indices = _maybe_get_cached_w2_permute_indices(
+                self._cache_permute_indices,
+                gemm2_scales_linear_fp4[i].view(torch.uint8),
+                epilogue_tile_m,
+                num_elts_per_sf=16,
             )
             gemm2_scales_fp4_shuffled.append(
-                shuffle_matrix_sf_a(
-                    gemm2_scales_linear_fp4[i].view(torch.uint8), epilogue_tile_m
+                nvfp4_block_scale_interleave(
+                    gemm2_scales_linear_fp4[i]
+                    .view(torch.uint8)[
+                        permute_sf_indices.to(gemm2_scales_linear_fp4.device)
+                    ]
+                    .contiguous()
                 )
             )