enable select for NVFP4Tensor

Summary:

This is useful for vLLM 2d -> 3d MoE weight surgery

Test Plan:

unit tests:
```
pytest test/prototype/mx_formats/ -s
```

Also, after this PR we can run a Qwen 1.5 MoE model quantized with nvfp4
in vLLM, with vllm-project/vllm#25480

Reviewers:

Subscribers:

Tasks:

Tags:
ghstack-source-id: db79143
ghstack-comment-id: 3361104836
Pull-Request: #3117

Author: vkuzo

torchao/prototype/mx_formats/nvfp4_tensor.py

@@ -634,6 +634,25 @@ def nvfp4_view_op(func, types, args, kwargs):
     )
 
 
+@implements([aten.select.int])
+def nvfp4_select(func, types, args, kwargs):
+    old, dim, index = args
+    assert dim == 0, f"NVFP4Tensor aten.select.int with {dim=} is not yet supported"
+    assert len(old.qdata.shape) == len(old._scale_e4m3.shape), "unsupported"
+    new = old.__class__(
+        old.qdata[index],
+        old._scale_e4m3[index],
+        old._block_size,
+        old._orig_dtype,
+        old._per_tensor_scale,
+        old._act_per_tensor_scale,
+        old._is_swizzled_scales,
+        old.use_triton_kernel,
+        old.act_quant_kwargs,
+    )
+    return return_and_correct_aliasing(func, args, kwargs, new)
+
+
 def _addmm_nvfp4_dispatch(
     a: NVFP4Tensor, b: NVFP4Tensor, aten_op, bias: Optional[torch.Tensor] = None
 ) -> torch.Tensor:

torchao/testing/utils.py

@@ -626,8 +626,9 @@ def _test_narrow_similar_to_vllm(self, config: AOBaseConfig):
             )
 
     def _test_quantize_3d_param_similar_to_vllm(self, config: AOBaseConfig):
-        # this happens when vLLM loads empty MoE weights and quantizes
-        # them
+        # this happens when vLLM loads empty MoE weights, quantizes
+        # them, and stitches 2d params from the checkpoint into a 3d param
+        # in memory
 
         dtype = torch.bfloat16
         with torch.device("meta"):
@@ -636,6 +637,7 @@ def _test_quantize_3d_param_similar_to_vllm(self, config: AOBaseConfig):
             torch.randn(60, 2816, 2048, device="cuda", dtype=dtype)
         )
         quantize_(l, config)
+        _w_slice = l.weight[0]
 
 
 common_utils.instantiate_parametrized_tests(TorchAOBasicTestCase)