add test for supporting torch.float16 and torch.bfloat16

Summary:
# context
* We found the new operator `permute_multi_embedding` can't support `torch.float16` in an inference test
* added test to cover the dtype support
* before the operator change, we see the following error
```
Failures:

  1) torchrec.sparse.tests.test_jagged_tensor.TestKeyedTensorRegroupOp: test_multi_permute_dtype
    1) RuntimeError: expected scalar type Float but found Half
      File "torchrec/sparse/tests/test_jagged_tensor.py", line 2798, in test_multi_permute_dtype
        outputs = torch.ops.fbgemm.permute_multi_embedding(
      File "torch/_ops.py", line 1113, in __call__
        return self._op(*args, **(kwargs or {}))
```
* suspicion is that in the cpu operator, there are tensor data access with `data_ptr<float>` in the code, which limited the dtype could only be `float32`
```
          auto outp = outputs[out_tensor][b].data_ptr<float>() + out_offset;
          auto inp = inputs[in_tensor][b].data_ptr<float>() + in_offset;
```

# changes
* use `FBGEMM_DISPATCH_FLOATING_TYPES` to dispatch the dtype to template `scalar_t`.
* after the change the operator can support `float16`, `bfloat16`

WARNING: somehow this operator still can't support `int` types.

Differential Revision: D57143637

torchrec/sparse/tests/test_jagged_tensor.py

@@ -2775,6 +2775,52 @@ def test_multi_permute_forward(self, device_str: str, batch_size: int) -> None:
             for out, ref in zip(outputs, refs):
                 torch.testing.assert_close(out, ref)
 
+    @repeat_test(
+        device_str=["meta", "cpu", "cuda"],
+        dtype=[
+            # torch.int,
+            # torch.uint8,
+            # torch.int8,
+            # torch.int16,
+            # torch.float64,
+            torch.float,
+            torch.float32,
+            torch.float16,
+            torch.bfloat16,
+        ],
+    )
+    def test_multi_permute_dtype(self, device_str: str, dtype: torch.dtype) -> None:
+        if device_str == "cuda" and not torch.cuda.is_available():
+            return
+        else:
+            device = torch.device(device_str)
+        batch_size = 4
+        keys = [["f1", "f2"], ["f3", "f4", "f5"], ["f6"]]
+        lengths = [[3, 4], [5, 6, 7], [8]]
+        groups = [["f1", "f3"], ["f2"], ["f4", "f1", "f6"], ["f1", "f5"]]
+        values = [
+            torch.randn(batch_size, sum(L), device=device, dtype=dtype) for L in lengths
+        ]
+        permutes, in_shapes, out_shapes, out_lengths = _kt_regroup_arguments(
+            values[0], keys, lengths, groups
+        )
+        outputs = torch.ops.fbgemm.permute_multi_embedding(
+            values, permutes, in_shapes, out_shapes, out_lengths
+        )
+
+        if device_str == "meta":
+            for out, ref in zip(outputs, out_lengths):
+                self.assertEqual(out.shape, (batch_size, ref))
+        else:
+            refs = [[] for _ in groups]
+            for i in range(permutes.size(0)):
+                in_idx, out, in_start, _, length, _ = permutes[i].tolist()
+                refs[out].append(values[in_idx][:, in_start : (in_start + length)])
+            refs = [torch.cat(ref, dim=1) for ref in refs]
+            for out, ref in zip(outputs, refs):
+                torch.testing.assert_close(out, ref)
+                self.assertEqual(out.dtype, ref.dtype)
+
     @repeat_test(
         ["cpu", 32, [[3, 4], [5, 6, 7], [8]]],
         ["cuda", 128, [[96, 256], [512, 128, 768], [1024]]],