[Kernel] Apply torch.Tag.needs_fixed_stride_order only for torch==2.6.0 (#19346)

Signed-off-by: rzou <zou3519@gmail.com>

csrc/torch_bindings.cpp

@@ -20,13 +20,17 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
   // vLLM custom ops
   //
 
-  // The default behavior in PyTorch 2.6 is "requires_contiguous", so we need
+  // The default behavior in PyTorch 2.6 was changed to "requires_contiguous",
+  // so we need
   // to override this for many GEMMs with the following tag. Otherwise,
   // torch.compile will force all input tensors to be contiguous(), which
   // will break many custom ops that require column-major weight matrices.
-  // TODO: remove this for PyTorch 2.8, when the default is planned to switch
-  // to match exact eager-mode strides.
-  at::Tag stride_tag = at::Tag::needs_fixed_stride_order;
+  // This was a bug and PyTorch 2.7 has since fixed this.
+#if TORCH_VERSION_MAJOR == 2 && TORCH_VERSION_MINOR == 6
+  #define stride_tag at::Tag::needs_fixed_stride_order
+#else
+  #define stride_tag
+#endif
 
   ops.def("weak_ref_tensor(Tensor input) -> Tensor");
   ops.impl("weak_ref_tensor", torch::kCUDA, &weak_ref_tensor);

vllm/attention/ops/rocm_aiter_mla.py

@@ -6,7 +6,7 @@ from typing import Optional
 import torch
 
 from vllm.platforms import current_platform
-from vllm.utils import direct_register_custom_op
+from vllm.utils import direct_register_custom_op, is_torch_equal_or_newer
 
 
 def get_aiter_mla_metadata(max_batch_size: int, block_size: int,
@@ -93,8 +93,12 @@ def mla_decode_fwd_fake(
 
 
 if current_platform.is_rocm():
+    if is_torch_equal_or_newer("2.7.0"):
+        tags = ()
+    else:
+        tags = (torch.Tag.needs_fixed_stride_order, ),
     direct_register_custom_op(op_name="rocm_aiter_mla_decode_fwd",
                               op_func=mla_decode_fwd_impl,
                               mutates_args=["o"],
                               fake_impl=mla_decode_fwd_fake,
-                              tags=[torch.Tag.needs_fixed_stride_order])
+                              tags=tags)

vllm/model_executor/layers/fused_moe/fused_moe.py

@@ -33,7 +33,7 @@ from vllm.model_executor.layers.quantization.utils.mxfp4_utils import (
     dequant_mxfp4)
 from vllm.platforms import current_platform
 from vllm.triton_utils import tl, triton
-from vllm.utils import direct_register_custom_op
+from vllm.utils import direct_register_custom_op, is_torch_equal_or_newer
 from vllm.utils.deep_gemm import is_blackwell_deep_gemm_used
 
 from .rocm_aiter_fused_moe import is_rocm_aiter_moe_enabled
@@ -1056,7 +1056,8 @@ direct_register_custom_op(
     op_func=inplace_fused_experts,
     mutates_args=["hidden_states"],
     fake_impl=inplace_fused_experts_fake,
-    tags=(torch.Tag.needs_fixed_stride_order, ),
+    tags=(() if is_torch_equal_or_newer("2.7.0") else
+          (torch.Tag.needs_fixed_stride_order, )),
 )
 
 
@@ -1122,7 +1123,8 @@ direct_register_custom_op(
     op_func=outplace_fused_experts,
     mutates_args=[],
     fake_impl=outplace_fused_experts_fake,
-    tags=(torch.Tag.needs_fixed_stride_order, ),
+    tags=(() if is_torch_equal_or_newer("2.7.0") else
+          (torch.Tag.needs_fixed_stride_order, )),
 )