gemini-code-assist[bot] commented on code in PR #20006:
URL: https://github.com/apache/tvm/pull/20006#discussion_r3584422302
##########
tests/python/relax/test_frontend_onnx.py:
##########
@@ -5148,64 +5193,97 @@ def create_composite_reduce_test_parameters_axes_attr():
return output
[email protected]("func, dynamic, opset",
create_reduce_test_parameters_axes_attr())
-def test_all_reduce_funcs_axes_attr(func, dynamic, opset):
- def verify_reduce_func(func, data, axis, keepdims):
- inshape = data.shape
- outshape = np.sum(data, axis=axis, keepdims=keepdims == 1).shape
+def _verify_reduce_numerical(
+ func: str,
+ opset: int,
+ *,
+ axes_as_input: bool,
+ axes,
+ noop_with_empty_axes: bool = False,
+ dynamic: bool = False,
+ keepdims: bool = False,
+):
+ input_shape = [3, 3, 3]
+ node_inputs = ["x"]
+ initializers = []
+ node_attrs = {"keepdims": keepdims}
- if axis:
- node = onnx.helper.make_node(
- func, inputs=["x"], outputs=["y"], axes=axis, keepdims=keepdims
+ if axes_as_input:
+ node_attrs["noop_with_empty_axes"] = noop_with_empty_axes
+ if axes is not None:
+ axes_np = np.asarray(axes, dtype=np.int64)
+ initializers.append(
+ helper.make_tensor(
+ name="reduce_axes",
+ data_type=TensorProto.INT64,
+ dims=axes_np.shape,
+ vals=axes_np,
+ )
)
- else:
- node = onnx.helper.make_node(func, inputs=["x"], outputs=["y"],
keepdims=keepdims)
-
- if dynamic:
- in_list = ["?" for _ in range(len(inshape))]
- out_list = ["?" for _ in range(len(outshape))]
- else:
- in_list = list(inshape)
- out_list = list(outshape)
- graph = helper.make_graph(
- [node],
- "reduce_test",
- inputs=[helper.make_tensor_value_info("x", TensorProto.FLOAT,
in_list)],
- outputs=[helper.make_tensor_value_info("y", TensorProto.FLOAT,
out_list)],
- )
+ node_inputs.append("reduce_axes")
+ elif axes:
+ node_attrs["axes"] = axes
- model = helper.make_model(graph, producer_name="reduce_test")
- inputs_dict = {"x": data}
- # Reduction ops accumulate arithmetic errors, so we use a higher
tolerance.
- check_correctness(model, inputs_dict, opset=opset, rtol=1e-4,
atol=1e-4)
+ if axes_as_input and noop_with_empty_axes and not axes:
Review Comment:
When `axes` is `None` (representing an omitted axes input), `not axes`
evaluates to `True`. This incorrectly triggers the `noop_with_empty_axes`
behavior and sets `output_shape = input_shape`, whereas an omitted `axes` input
should trigger reduction over all dimensions. We should explicitly check that
`axes` is not `None` and is empty.
```suggestion
if axes_as_input and noop_with_empty_axes and axes is not None and
len(axes) == 0:
```
##########
tests/python/relax/test_frontend_onnx.py:
##########
@@ -5233,8 +5311,19 @@ def expected_input_shape(shape):
parser_vars["axes_shape"] = axes_shape
params.append(' reduce_axes: R.Tensor(axes_shape,
dtype="int64")')
+ basic_reduce_op = {
+ "ReduceMax": R.max,
+ "ReduceMean": R.mean,
+ "ReduceMin": R.min,
+ "ReduceProd": R.prod,
+ "ReduceSum": R.sum,
+ }.get(func)
+
if noop_with_empty_axes and not axes:
Review Comment:
When `axes` is `None` (representing an omitted axes input), `not axes`
evaluates to `True`. This incorrectly triggers the `noop_with_empty_axes`
condition and generates a no-op body (`gv = x`), whereas an omitted `axes`
input should trigger reduction over all dimensions. We should explicitly check
that `axes` is not `None` and is empty.
```suggestion
if noop_with_empty_axes and axes is not None and len(axes) == 0:
```
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