gemini-code-assist[bot] commented on code in PR #18403:
URL: https://github.com/apache/tvm/pull/18403#discussion_r2474140433
##########
tests/python/relax/test_frontend_from_exported_program.py:
##########
@@ -1198,16 +1190,27 @@ def forward(self, input):
class expected_tril:
@R.function
def main(
- input_1: R.Tensor((10, 10), dtype="float32")
+ input: R.Tensor((10, 10), dtype="float32")
) -> R.Tuple(R.Tensor((10, 10), dtype="float32")):
# block 0
with R.dataflow():
- lv: R.Tensor((10, 10), dtype="float32") = R.tril(input_1, 1)
- gv: R.Tuple(R.Tensor((10, 10), dtype="float32")) = (lv,)
+ lv: R.Tensor((10,), dtype="int64") = R.arange(
+ R.prim_value(0), R.prim_value(10), R.prim_value(1),
dtype="int64"
+ )
+ lv1: R.Tensor((1, 10), dtype="int64") = R.expand_dims(lv,
axis=[-2])
+ lv2: R.Tensor((10,), dtype="int64") = R.arange(
+ R.prim_value(0), R.prim_value(10), R.prim_value(1),
dtype="int64"
+ )
+ lv3: R.Tensor((10, 1), dtype="int64") = R.expand_dims(lv2,
axis=[-1])
Review Comment:
The `arange` call to create `lv2` is redundant, as `lv` created on line 1197
is identical. You can reuse `lv` to create `lv3` and remove the definition of
`lv2` to make the IR more concise and avoid a redundant operation. This same
issue is present in `expected_triu` as well.
```python
lv3: R.Tensor((10, 1), dtype="int64") = R.expand_dims(lv,
axis=[-1])
```
##########
tests/python/relax/test_frontend_from_exported_program.py:
##########
@@ -1217,16 +1220,27 @@ def forward(self, input):
class expected_triu:
@R.function
def main(
- input_1: R.Tensor((10, 10), dtype="float32")
+ input: R.Tensor((10, 10), dtype="float32")
) -> R.Tuple(R.Tensor((10, 10), dtype="float32")):
# block 0
with R.dataflow():
- lv: R.Tensor((10, 10), dtype="float32") = R.triu(input_1, 1)
- gv: R.Tuple(R.Tensor((10, 10), dtype="float32")) = (lv,)
+ lv: R.Tensor((10,), dtype="int64") = R.arange(
+ R.prim_value(0), R.prim_value(10), R.prim_value(1),
dtype="int64"
+ )
+ lv1: R.Tensor((1, 10), dtype="int64") = R.expand_dims(lv,
axis=[-2])
+ lv2: R.Tensor((10,), dtype="int64") = R.arange(
+ R.prim_value(0), R.prim_value(10), R.prim_value(1),
dtype="int64"
+ )
+ lv3: R.Tensor((10, 1), dtype="int64") = R.expand_dims(lv2,
axis=[-1])
Review Comment:
Similar to `expected_tril`, the `arange` call to create `lv2` is redundant.
You can reuse `lv` (defined on line 1227) to create `lv3` and remove the
definition of `lv2`.
```python
lv3: R.Tensor((10, 1), dtype="int64") = R.expand_dims(lv,
axis=[-1])
```
##########
tests/python/relax/test_frontend_from_exported_program.py:
##########
@@ -1159,32 +1159,24 @@ def main(
input: R.Tensor((1, 3, 10, 10), dtype="float32"),
) -> R.Tuple(R.Tensor((1, 3, 10, 10), dtype="float32")):
with R.dataflow():
- lv: R.Tensor((1, 3, 10, 10), dtype="float32") = R.subtract(
- input, R.const(0.5, "float32")
- )
- lv1: R.Tensor((1, 3, 10, 10), dtype="bool") = R.greater(
- input, R.const(0.5, "float32")
+ lv: R.Tensor((1, 3, 10, 10), dtype="float32") = R.abs(input)
+ lv1: R.Tensor((1, 3, 10, 10), dtype="bool") = R.greater(lv,
R.const(0.5, "float32"))
+ lv2: R.Tensor((1, 3, 10, 10), dtype="float32") = R.sign(input)
+ lv3: R.Tensor((1, 3, 10, 10), dtype="float32") = R.multiply(
+ lv2, R.const(0.5, "float32")
)
- lv2: R.Tensor((1, 3, 10, 10), dtype="float32") = R.astype(lv1,
"float32")
- lv3: R.Tensor((1, 3, 10, 10), dtype="float32") =
R.multiply(lv, lv2)
-
- lv4: R.Tensor((1, 3, 10, 10), dtype="float32") = R.add(
- input, R.const(0.5, "float32")
+ lv4: R.Tensor((1, 3, 10, 10), dtype="float32") =
R.subtract(input, lv3)
+ lv5: R.Tensor((1, 3, 10, 10), dtype="float32") = R.multiply(
+ input, R.const(0.0, "float32")
)
Review Comment:
Using `R.zeros_like(input)` would be more idiomatic and clearer to express
the intent of creating a zero tensor with the same shape and dtype as `input`.
```python
lv5: R.Tensor((1, 3, 10, 10), dtype="float32") =
R.zeros_like(input)
```
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