mx: support inference_mode and rank 3+

test/prototype/mx_formats/test_inference_workflow.py

@@ -71,11 +71,20 @@ def cuda_kernel_profiler(kernel_pattern):
 @pytest.mark.parametrize("bias", [True, False])
 @pytest.mark.parametrize("compile", [True, False])
 @pytest.mark.parametrize("emulate", [True, False])
+@pytest.mark.parametrize("use_inference_mode", [True, False])
+@pytest.mark.parametrize("x_rank", [2, 3])
 @torch.no_grad()
 @skip_if_rocm(
     "ROCm float4 gemm require gfx950"
 )  # TODO(future): deploy gfx950 in ROCM CI
-def test_inference_workflow_mx(elem_dtype, bias: bool, compile: bool, emulate: bool):
+def test_inference_workflow_mx(
+    elem_dtype,
+    bias: bool,
+    compile: bool,
+    emulate: bool,
+    use_inference_mode: bool,
+    x_rank: int,
+):
     """
     Smoke test for inference compile
     """
@@ -112,8 +121,15 @@ def test_inference_workflow_mx(elem_dtype, bias: bool, compile: bool, emulate: b
         m_mx = torch.compile(m_mx, fullgraph=True)
 
     x = torch.randn(128, 32, device="cuda", dtype=torch.bfloat16)
+    if x_rank == 3:
+        x = x.unsqueeze(0)
+
     y_ref = m(x)
-    y_mx = m_mx(x)
+    if use_inference_mode:
+        with torch.inference_mode():
+            y_mx = m_mx(x)
+    else:
+        y_mx = m_mx(x)
     sqnr = compute_error(y_ref, y_mx)
     SQNR_THRESHOLD = 25.0 if elem_dtype == torch.float8_e4m3fn else 15.0
     assert sqnr >= SQNR_THRESHOLD, (

torchao/prototype/mx_formats/mx_tensor.py

@@ -775,6 +775,27 @@ def mx_addmm(func, types, args, kwargs):
     return _addmm_mx_dispatch(a, b, func, bias=bias)
 
 
+@implements([aten.linear.default])
+def mx_linear(func, types, args, kwargs):
+    assert isinstance(args[0], torch.Tensor) and isinstance(args[1], MXTensor)
+    a = args[0]
+
+    # make a 2d
+    orig_a_shape = a.shape
+    a_2d = a.view(-1, orig_a_shape[-1])
+
+    b = args[1].t()
+    if len(args) > 2:
+        bias = args[2]
+        res = _addmm_mx_dispatch(a_2d, b, aten.addmm.default, bias)
+    else:
+        res = _addmm_mx_dispatch(a_2d, b, aten.mm.default)
+
+    # reshape back to original shape
+    res = res.view(*orig_a_shape[:-1], res.shape[-1])
+    return res
+
+
 @implements([aten.t.default])
 def mx_t(func, types, args, kwargs):
     # For now, only transpose(input, 0, 1) is supported.