fix: remove custom scaled bmm op on cpu and fix fp8 test

fms_mo/aiu_addons/fp8/fp8_spyre_op.py

@@ -17,6 +17,7 @@
 from typing import Optional
 
 # Third Party
+from packaging.version import Version
 from torch import Tensor
 import torch
 import torch.nn.functional as F
@@ -29,60 +30,63 @@
 # open issue in PyLint: https://github.com/pytorch/pytorch/issues/119482
 
 
-def _scaled_mm_cpu_out(
-    mat1: Tensor,
-    mat2: Tensor,
-    scale1: Tensor,
-    scale2: Tensor,
-    bias: Optional[Tensor] = None,
-    scale_result: Optional[Tensor] = None,
-    out_dtype: Optional[torch.dtype] = None,
-    use_fast_accum: bool = False,
-    *,
-    out: Optional[Tensor] = None,
-) -> Tensor:
-    if out_dtype is None:
-        out_dtype = torch.float32
-    mat1 = (mat1.to(dtype=out_dtype) * scale1).to(dtype=out_dtype)
-    mat2 = (mat2.to(dtype=out_dtype) * scale2).to(dtype=out_dtype)
-
-    if bias is not None:
-        ret = torch.addmm(bias, mat1, mat2).to(dtype=out_dtype)
-    else:
-        ret = torch.mm(mat1, mat2).to(dtype=out_dtype)
-
-    if out is not None:
-        out.copy_(ret)
-        return out
-    return ret
-
-
-torch.library.register_kernel(torch.ops.aten._scaled_mm.out, "cpu", _scaled_mm_cpu_out)
-
-
-@torch.library.register_kernel("aten::_scaled_mm", "cpu")
-def _scaled_mm_cpu(
-    mat1: Tensor,
-    mat2: Tensor,
-    scale1: Tensor,
-    scale2: Tensor,
-    bias: Optional[Tensor] = None,
-    scale_result: Optional[Tensor] = None,
-    out_dtype: Optional[torch.dtype] = None,
-    use_fast_accum: bool = False,
-) -> Tensor:
-    return _scaled_mm_cpu_out(
-        mat1,
-        mat2,
-        scale1,
-        scale2,
-        bias,
-        scale_result,
-        out_dtype,
-        use_fast_accum,
-        out=None,
+if Version(torch.__version__) <= Version("2.7"):
+    # PyTorch 2.8 adds scaled_mm_out op for CPU in the ATen set,
+    # while for earlier versions we need a custom definition
+    def _scaled_mm_cpu_out(
+        mat1: Tensor,
+        mat2: Tensor,
+        scale1: Tensor,
+        scale2: Tensor,
+        bias: Optional[Tensor] = None,
+        scale_result: Optional[Tensor] = None,
+        out_dtype: Optional[torch.dtype] = None,
+        use_fast_accum: bool = False,
+        *,
+        out: Optional[Tensor] = None,
+    ) -> Tensor:
+        if out_dtype is None:
+            out_dtype = torch.float32
+        mat1 = (mat1.to(dtype=out_dtype) * scale1).to(dtype=out_dtype)
+        mat2 = (mat2.to(dtype=out_dtype) * scale2).to(dtype=out_dtype)
+
+        if bias is not None:
+            ret = torch.addmm(bias, mat1, mat2).to(dtype=out_dtype)
+        else:
+            ret = torch.mm(mat1, mat2).to(dtype=out_dtype)
+
+        if out is not None:
+            out.copy_(ret)
+            return out
+        return ret
+
+    torch.library.register_kernel(
+        torch.ops.aten._scaled_mm.out, "cpu", _scaled_mm_cpu_out
     )
 
+    @torch.library.register_kernel("aten::_scaled_mm", "cpu")
+    def _scaled_mm_cpu(
+        mat1: Tensor,
+        mat2: Tensor,
+        scale1: Tensor,
+        scale2: Tensor,
+        bias: Optional[Tensor] = None,
+        scale_result: Optional[Tensor] = None,
+        out_dtype: Optional[torch.dtype] = None,
+        use_fast_accum: bool = False,
+    ) -> Tensor:
+        return _scaled_mm_cpu_out(
+            mat1,
+            mat2,
+            scale1,
+            scale2,
+            bias,
+            scale_result,
+            out_dtype,
+            use_fast_accum,
+            out=None,
+        )
+
 
 @torch.library.custom_op("spyre::scaled_bmm", mutates_args=())
 def spyre_scaled_bmm(

fms_mo/aiu_addons/i8i8/i8i8_aiu_op.py

@@ -36,10 +36,8 @@ def implement_op_decorator(op_namespace_id):
     Always compare against pytorch version in current environment.
     """
 
-    torch_version = Version(torch.__version__.split("+", maxsplit=1)[0])
-
     def decorator(func):
-        if torch_version < Version("2.4"):
+        if Version(torch.__version__) < Version("2.4"):
             return torch.library.impl(op_namespace_id, "default")(func)
         return torch.library.custom_op(op_namespace_id, mutates_args=())(func)
 
@@ -51,10 +49,8 @@ def register_op_decorator(op_namespace_id):
     Always compare against pytorch version in current environment.
     """
 
-    torch_version = Version(torch.__version__.split("+", maxsplit=1)[0])
-
     def decorator(func):
-        if torch_version < Version("2.4"):
+        if Version(torch.__version__) < Version("2.4"):
             return torch.library.impl_abstract(op_namespace_id)(func)
         return torch.library.register_fake(op_namespace_id)(func)
 
@@ -73,7 +69,7 @@ def register_aiu_i8i8_op():
         logger.warning("AIU op has already been registered")
         return
     op_namespace_id = "fms_mo::i8i8_aiu"
-    if Version(torch.__version__.split("+", maxsplit=1)[0]) < Version("2.4"):
+    if Version(torch.__version__) < Version("2.4"):
         torch.library.define(
             op_namespace_id,
             "(Tensor x, Tensor weight, Tensor bias, Tensor qdata, "

tests/aiu_addons/test_fp8_addon.py

@@ -51,9 +51,9 @@ def test_fp8_op() -> None:
     # Local
     from fms_mo.aiu_addons.fp8.fp8_attn import _math_fp8_compute_op
 
-    query = torch.randn((1, 32, 64, 128), dtype=torch.bfloat16, device="cuda")
-    key = torch.randn((1, 32, 64, 128), dtype=torch.bfloat16, device="cuda")
-    value = torch.randn((1, 32, 64, 128), dtype=torch.bfloat16, device="cuda")
+    query = torch.randn((1, 64, 32, 128), dtype=torch.bfloat16, device="cuda")
+    key = torch.randn((1, 64, 32, 128), dtype=torch.bfloat16, device="cuda")
+    value = torch.randn((1, 64, 32, 128), dtype=torch.bfloat16, device="cuda")
 
     out = _math_fp8_compute_op(query, key, value, 32, 32, 0.0, None)
     assert out.size() == query.size()