Add more compile compatibility for Float8Tensor ops (#285)

Summary: Pull Request resolved: #285

Reviewed By: vkuzo

Differential Revision: D59068281

Pulled By: drisspg

fbshipit-source-id: 18fa34db74cf60e85ff372ff1091c107119403a0

float8_experimental/float8_ops.py

@@ -42,6 +42,9 @@ def decorator(func):
         aten.as_strided.default,
         aten.clone.default,
         aten.detach.default,
+        aten.slice.Tensor,
+        aten.transpose.int,
+        aten.fill_.Scalar,
     ]
 )
 def float8_desugar_op(aten_op, args, kwargs=None):
@@ -263,3 +266,55 @@ def wait_tensor_fp8(aten_op, args, kwargs=None):
     return Float8Tensor(
         fp8_out, fp8_input._scale, fp8_input._orig_dtype, fp8_input._mm_config
     )
+
+
+@implements([aten.index_put_.default])
+def index_put_fp8(aten_op, args, kwargs=None):
+    fp8_self = args[0]
+    fp8_values = args[2]
+    assert isinstance(fp8_self, Float8Tensor)
+    assert isinstance(fp8_values, Float8Tensor)
+    assert fp8_self._scale == fp8_values._scale
+    assert fp8_self.dtype == fp8_values.dtype
+    assert fp8_self._orig_dtype == fp8_values._orig_dtype
+
+    fp8_data = fp8_self._data
+    fp8_values_data = fp8_values._data
+    fp8_out = aten_op(fp8_data, args[1], fp8_values_data, *args[3:], **kwargs)
+    return Float8Tensor(
+        fp8_out, fp8_self._scale, fp8_self._orig_dtype, fp8_self._mm_config
+    )
+
+
+@implements([aten.copy_.default])
+def copy_fp8(aten_op, args, kwargs=None):
+    # For a copy op with Float8Tensors involved, only the following combinations are allowed:
+    # 1. self is a high precision (hp) tensor, src is a Float8Tensor:
+    #    in this case src is upcasted and unscaled to go into the hp tensor
+    # 2. self and src are Float8Tensors:
+    #    the copy is only allowed if all the Float8Tensor properties are equal (a la torch.cat)
+    # Every other combination is banned as the semantics are not well defined
+
+    self = args[0]
+    src = args[1]
+
+    if not isinstance(self, Float8Tensor) and isinstance(src, Float8Tensor):
+        src_hp = src.to_original_precision()
+        return aten_op(self, src_hp, *args[2:], **kwargs)
+    elif isinstance(self, Float8Tensor) and isinstance(src, Float8Tensor):
+        assert (
+            self._orig_dtype == src._orig_dtype
+        ), "Expecting both Float8Tensors to be of the same dtype"
+        assert (
+            self._scale == src._scale
+        ), "Expecting both Float8Tensors to have thee same scale"
+        assert (
+            self._mm_config == src._mm_config
+        ), "Expecting both Float8Tensors to have thee same mm config"
+        assert (
+            self._data.dtype == src._data.dtype
+        ), "Expecting both Float8Tensors to be of the same dtypet"
+        fp8_out = aten_op(self._data, src._data, *args[2:], **kwargs)
+        return Float8Tensor(fp8_out, self._scale, self._orig_dtype, self._mm_config)
+    else:
+        raise RuntimeError("Unsupported semantics for copy_ in Float8Tensor")

test/test_base.py

@@ -83,6 +83,44 @@ def test_split_cat(self):
         catted = torch.cat(splits, dim=0)
         assert bitwise_identical(fp8_a, catted)
 
+    def test_index_put(self):
+        a = torch.rand(16, dtype=torch.bfloat16)
+        scale_a = tensor_to_scale(a, torch.float8_e4m3fn)
+        fp8_a = Float8Tensor.to_float8(a, scale_a, torch.float8_e4m3fn)
+
+        index = torch.randint(0, 15, (16,), dtype=torch.long)
+
+        b = torch.rand(16, 16, dtype=torch.bfloat16)
+        scale_b = tensor_to_scale(b, torch.float8_e4m3fn)
+        fp8_b = Float8Tensor.to_float8(b, scale_a, torch.float8_e4m3fn)
+        fp8_b_bad = Float8Tensor.to_float8(b, scale_b, torch.float8_e4m3fn)
+
+        with self.assertRaises(AssertionError):
+            b[index] = fp8_a
+            fp8_b[index] = a
+            fp8_b_bad[index] = fp8_a
+        fp8_b[index] = fp8_a
+
+    def test_copy_(self):
+        a = torch.rand(16, dtype=torch.bfloat16)
+        scale_a = tensor_to_scale(a, torch.float8_e4m3fn)
+        fp8_a = Float8Tensor.to_float8(a, scale_a, torch.float8_e4m3fn)
+
+        b = torch.empty(16, dtype=torch.bfloat16)
+        b.copy_(fp8_a)  # Should work
+        torch.testing.assert_close(b, fp8_a.to_original_precision())
+        with self.assertRaises(RuntimeError):
+            fp8_a.copy_(b)  # Should fail
+
+        fp8_b = Float8Tensor(
+            torch.empty(16, dtype=torch.float8_e4m3fn),
+            scale_a,
+            torch.bfloat16,
+            fp8_a._mm_config,
+        )
+        fp8_b.copy_(fp8_a)
+        torch.testing.assert_close(fp8_a._data, fp8_b._data)
+
 
 class TestFloat8Linear:
     def _test_linear_impl(