Upstream triton fp4 weight preshuffle (#28888)

Signed-off-by: Aleksandr Malyshev <maleksan@amd.com>
Co-authored-by: Aleksandr Malyshev <maleksan@amd.com>

vllm/_aiter_ops.py

@@ -948,6 +948,31 @@ class rocm_aiter_ops:
             (8192, 32768),
         ]
 
+    @staticmethod
+    def is_triton_gemm_afp4wfp4_presh_ws_tuned(n: int, k: int) -> bool:
+        return (n, k) in [
+            (8192, 4096),
+            (1280, 8192),
+            (16384, 53248),
+            (106496, 16384),
+            (57344, 8192),
+            (8192, 2048),
+            (2560, 8192),
+            (10240, 8192),
+            (16384, 16384),
+            (8192, 28672),
+            (28672, 8192),
+            (18432, 16384),
+            (8192, 1024),
+            (7168, 8192),
+            (5120, 8192),
+            (8192, 8192),
+            (8192, 7168),
+            (14336, 8192),
+            (8192, 14336),
+            (8192, 3584),
+        ]
+
     @staticmethod
     def shuffle_weight(
         self, tensor: torch.Tensor, layout: tuple[int, int] = (16, 16)

vllm/model_executor/layers/quantization/quark/schemes/quark_ocp_mx.py

@@ -10,6 +10,7 @@ import torch
 import torch.nn.functional as F
 
 from vllm import envs
+from vllm._aiter_ops import rocm_aiter_ops
 from vllm.logger import init_logger
 from vllm.model_executor.layers.quantization.utils.mxfp4_utils import (
     dequant_mxfp4,
@@ -49,7 +50,10 @@ def is_rocm_aiter_fp4_asm_gemm_enabled() -> bool:
 
 try:
     from aiter.ops.shuffle import shuffle_weight
-    from aiter.ops.triton.gemm_afp4wfp4 import gemm_afp4wfp4
+    from aiter.ops.triton.gemm_afp4wfp4 import (
+        gemm_afp4wfp4,
+        gemm_afp4wfp4_preshuffled_weight_scales,
+    )
     from aiter.ops.triton.quant import dynamic_mxfp4_quant
 
     from vllm.utils.torch_utils import direct_register_custom_op
@@ -66,23 +70,56 @@ try:
         x_scales: torch.Tensor | None = None,
     ) -> torch.Tensor:
         M = x.shape[0]
+        N = weight.shape[0]
+        K = weight.shape[1]
         if rocm_use_aiter_fp4_asm_gemm:
-            if x_scales is None:
-                # use hip quant kernel for performance
-                x_q, x_s = per_1x32_f4_quant_hip(x, shuffle=True)
+            if M <= 64 and rocm_aiter_ops.is_triton_gemm_afp4wfp4_presh_ws_tuned(N, K):
+                if x_scales is None:
+                    # use hip quant kernel for performance
+                    if M >= 32:
+                        x_q, x_s = per_1x32_f4_quant_hip(x, shuffle=True)
+                    else:
+                        x_q, x_s = per_1x32_f4_quant_hip(x, shuffle=False)
+                else:
+                    x_q = x
+                    x_s = x_scales
+
+                if M >= 32:
+                    x_s = x_s.view(torch.uint8).view(x_s.shape[0] // 32, -1)
+                else:
+                    x_s = x_s[:M, ...].view(torch.uint8)
+
+                y = torch.empty(M, N, device=x_q.device, dtype=out_dtype)
+                gemm_afp4wfp4_preshuffled_weight_scales(
+                    x_q.view(torch.uint8),
+                    weight.view(torch.uint8).view(weight.shape[0] // 16, -1),
+                    x_s,
+                    weight_scale.view(torch.uint8).view(
+                        weight_scale.shape[0] // 32, -1
+                    ),
+                    out_dtype,
+                    y,
+                )
             else:
-                x_q = x
-                x_s = x_scales
-
-            # 32 alignment is enough for dim0 padding of output for
-            # gemm_a4w4 kernel
-            y = torch.empty(
-                (M + 31) // 32 * 32, weight.shape[0], device=x_q.device, dtype=out_dtype
-            )
+                if x_scales is None:
+                    # use hip quant kernel for performance
+                    x_q, x_s = per_1x32_f4_quant_hip(x, shuffle=True)
+                else:
+                    x_q = x
+                    x_s = x_scales
+
+                # 32 alignment is enough for dim0 padding of output for
+                # gemm_a4w4 kernel
+                y = torch.empty(
+                    (M + 31) // 32 * 32,
+                    weight.shape[0],
+                    device=x_q.device,
+                    dtype=out_dtype,
+                )
 
-            gemm_a4w4(
-                x_q, weight, x_s, weight_scale.view(x_s.dtype), y, bpreshuffle=True
-            )
+                gemm_a4w4(
+                    x_q, weight, x_s, weight_scale.view(x_s.dtype), y, bpreshuffle=True
+                )
             return y[:M]
         else:
             if x_scales is None: