nvfp4: support inference_mode and rank 3

test/prototype/mx_formats/test_inference_workflow.py

@@ -172,6 +172,8 @@ def test_inference_workflow_mx(
     ],
     ids=lambda s: f"{s[0]}x{s[1]}x{s[2]}",
 )
+@pytest.mark.parametrize("use_inference_mode", [False, True])
+@pytest.mark.parametrize("x_rank", [2, 3])
 @torch.no_grad()
 @skip_if_rocm("ROCm float4 gemm require gfx950")
 def test_inference_workflow_nvfp4(
@@ -182,6 +184,8 @@ def test_inference_workflow_nvfp4(
     use_triton_kernel: bool,
     use_dynamic_per_tensor_scale: bool,
     shapes: tuple,
+    use_inference_mode: bool,
+    x_rank: int,
 ):
     """
     Test NVFP4 recipe with scale_dtype=float8_e4m3fn and block_size=16
@@ -196,6 +200,16 @@ def test_inference_workflow_nvfp4(
 
     if mm_config == NVFP4MMConfig.WEIGHT_ONLY and compile:
         pytest.skip("TODO: NVFP4MMConfig.WEIGHT_ONLY currently errors w/ compile")
+
+    if use_inference_mode and (
+        shapes != (128, 64, 256) or inpt_dtype != torch.bfloat16 or use_triton_kernel
+    ):
+        pytest.skip("skipping unnecessary tests for inference mode")
+    if x_rank == 3 and (
+        shapes != (128, 64, 256) or inpt_dtype != torch.bfloat16 or use_triton_kernel
+    ):
+        pytest.skip("skipping unnecessary tests for x_rank 3")
+
     batch_size, in_features, out_features = shapes
 
     m = nn.Linear(in_features, out_features, bias=bias, dtype=inpt_dtype, device="cuda")
@@ -212,14 +226,21 @@ def test_inference_workflow_nvfp4(
         m_mx = torch.compile(m_mx, fullgraph=True, backend="aot_eager")
 
     x = torch.randn(batch_size, in_features, device="cuda", dtype=inpt_dtype)
+    if x_rank == 3:
+        x = x.unsqueeze(0)
+
     y_ref = m(x)
 
     if use_triton_kernel and mm_config != NVFP4MMConfig.WEIGHT_ONLY:
         with cuda_kernel_profiler("quantize_nvfp4_triton_kernel") as result:
             y_mx = m_mx(x)
         assert result["found"], "Expected quantize_nvfp4 kernel to be found"
     else:
-        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)
 

torchao/prototype/mx_formats/nvfp4_tensor.py

@@ -502,6 +502,7 @@ def _addmm_nvfp4_dispatch(
     assert b.scale.t().is_contiguous()
     assert a._block_size == 16, f"NVFP4 requires block_size=16, got {a._block_size}"
     assert b._block_size == 16, f"NVFP4 requires block_size=16, got {b._block_size}"
+    assert len(a.shape) == 2 and len(b.shape) == 2
 
     M, K = a.shape[0], a.shape[1]
     N = b.shape[1]
@@ -576,15 +577,19 @@ def nvfp4_linear(func, types, args, kwargs):
             tensor_amax = torch.max(torch.abs(input_tensor))
             per_tensor_scale = per_tensor_amax_to_scale(tensor_amax)
         else:
-            per_tensor_scale = weight_tensor._act_per_tensor_scale
+            per_tensor_scale = weight_tensor.act_per_tensor_scale
+        orig_shape = input_tensor.shape
+        input_tensor = input_tensor.view(-1, orig_shape[-1])
         input_tensor = NVFP4Tensor.to_nvfp4(
             input_tensor,
             block_size=k.block_size,
             per_tensor_scale=per_tensor_scale,
             is_swizzled_scales=k.is_swizzled_scales,
             use_triton_kernel=k.use_triton_kernel,
         )
-        return _addmm_nvfp4_dispatch(input_tensor, weight_tensor.t(), func, bias=bias)
+        res = _addmm_nvfp4_dispatch(input_tensor, weight_tensor.t(), func, bias=bias)
+        res = res.reshape(*orig_shape[:-1], res.shape[-1])
+        return res
 
 
 @implements([aten.mm.default, aten.matmul.default])