modify cast from hp to mx to help inductor fuse

Summary:

Thanks to investigation from @eellison, moving the reshape
to the end of the cast helps inductor fuse the cast into a single
kernel.  This doesn't yet work with fp4, but let's unblock fp8 and deal
with fp4 later.

Fixes #1690

Note: in the repro with swizzling from
#1773, we go from 3 to 2 kernels.
Further investigation is needed whether we can fuse the swizzling.

Test Plan:

```
pytest test/prototype/mx_formats/test_mx_tensor.py -x -s -k test_to_mx_inductor_single_kernel
```

Reviewers:

Subscribers:

Tasks:

Tags:

test/prototype/mx_formats/test_mx_tensor.py

@@ -6,6 +6,8 @@
 
 import pytest
 import torch
+from torch._inductor.utils import run_and_get_code
+from torch.testing import FileCheck
 
 from torchao.prototype.mx_formats.config import MXGemmKernelChoice
 from torchao.prototype.mx_formats.constants import (
@@ -284,3 +286,25 @@ def test_to_mx_from_mx_compile_numerics(elem_dtype, hp_dtype, all_zeros):
         use_fp4_custom_triton_dequant_kernel,
     )
     torch.testing.assert_close(x_mx_dq, x_mx_c_dq, atol=0, rtol=0)
+
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
+@pytest.mark.skipif(
+    is_sm_at_least_100(), reason="triton does not work yet on CUDA capability 10.0"
+)
+@pytest.mark.skipif(
+    not is_sm_at_least_89(), reason="float8 in triton requires CUDA capability 8.9 or greater"
+)
+def test_to_mx_inductor_single_kernel():
+    """
+    Verify that inductor can fuse the cast of a high precision tensor to mx
+    into a single kernel
+    """
+    # TODO(future PR): add fp4 and fp6 here
+    # TODO(#1773): add swizzled scale format here
+    x = torch.randn(2048, 2048, dtype=torch.bfloat16, device="cuda")
+    block_size = 32
+    to_mx_c = torch.compile(MXTensor.to_mx, fullgraph=True)
+    out, code = run_and_get_code(to_mx_c, x, torch.float8_e4m3fn, block_size)
+    FileCheck().check("def call(").check_count(
+        ".run(", 1, exactly=True
+    ).run(code[0])

torchao/prototype/mx_formats/mx_tensor.py

@@ -205,16 +205,25 @@ def to_mx(
     data_lp = torch.clamp(
         data_hp / scale_fp.unsqueeze(1), min=-1 * max_pos, max=max_pos
     )
-    data_lp = data_lp.reshape(orig_shape)
 
     # cast to target dtype
     if elem_dtype in (torch.float8_e4m3fn, torch.float8_e5m2):
         data_lp = data_lp.to(elem_dtype)
+        # need to reshape at the end to help inductor fuse things
+        data_lp = data_lp.reshape(orig_shape)
     elif elem_dtype == DTYPE_FP6_E2M3:
         data_lp = f32_to_f6_e2m3_unpacked(data_lp)
+        # need to reshape at the end to help inductor fuse things
+        data_lp = data_lp.reshape(orig_shape)
     elif elem_dtype == DTYPE_FP6_E3M2:
         data_lp = f32_to_f6_e3m2_unpacked(data_lp)
+        # need to reshape at the end to help inductor fuse things
+        data_lp = data_lp.reshape(orig_shape)
     elif elem_dtype == DTYPE_FP4:
+        # can't reshape at the end without handling it in the packing code,
+        # punt until later since we'll need to rethink the torch.compile
+        # approach for fp4x2 in any case
+        data_lp = data_lp.reshape(orig_shape)
         data_lp = f32_to_f4_unpacked(data_lp)
         data_lp = pack_uint4(data_lp)
     else: