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new 6734aa2256 [Fix][Relax] Honor ONNX Reshape zero semantics (#20161)
6734aa2256 is described below
commit 6734aa22568bdce3da2d95008e8486d97e1250a7
Author: Zhewen Tan <[email protected]>
AuthorDate: Mon Aug 24 12:45:13 2026 +0800
[Fix][Relax] Honor ONNX Reshape zero semantics (#20161)
ONNX Reshape uses zero entries to copy the corresponding input dimension
by default. The Relax importer currently constant-folds those shapes
through NumPy without applying that rule, so a valid shape such as `[0,
3]` fails for a `(2, 3)` tensor. Conversely, when `allowzero=1`, passing
the zero through `relax.op.reshape` invokes Relax's own zero-copy
convention instead of preserving the literal zero dimension.
This change normalizes copied dimensions before constant folding when
`allowzero` is disabled. When it is enabled, the shape tensor is
materialized as a symbolic `ShapeExpr`, preserving literal zero
dimensions without applying Relax's zero-copy shortcut.
The regression tests cover both the default all-constant path and an
executable `allowzero=1` model with a `(0, 2)` output.
Fixes #20151
Testing:
```
pre-commit run --files python/tvm/relax/frontend/onnx/onnx_frontend.py
tests/python/relax/test_frontend_onnx.py
pytest -q tests/python/relax/test_frontend_onnx.py -k reshape
pytest -q -n 4 tests/python/relax/test_frontend_onnx.py -k 'not
test_clip_v13'
```
The first two commands pass, and the broad run reports 493 passed, 9
skipped, and 4 xfailed. Running the entire file also reports three
`test_clip_v13` failures that reproduce unchanged on a clean `main`
worktree in the same environment.
---
python/tvm/relax/frontend/onnx/onnx_frontend.py | 18 ++++++-
tests/python/relax/test_frontend_onnx.py | 65 +++++++++++++++++++++++++
2 files changed, 81 insertions(+), 2 deletions(-)
diff --git a/python/tvm/relax/frontend/onnx/onnx_frontend.py
b/python/tvm/relax/frontend/onnx/onnx_frontend.py
index 65bd5bfe1a..7e8616f65f 100644
--- a/python/tvm/relax/frontend/onnx/onnx_frontend.py
+++ b/python/tvm/relax/frontend/onnx/onnx_frontend.py
@@ -1561,6 +1561,7 @@ class Reshape(OnnxOpConverter):
def _impl_v13(cls, bb, inputs, attr, params):
data = inputs[0]
new_shape = get_constant(inputs[1], params)
+ allowzero = attr.get("allowzero", 0)
if isinstance(data, relax.ShapeExpr):
# Preserve identity flatten for shape values to keep
shape-specialized
@@ -1574,10 +1575,23 @@ class Reshape(OnnxOpConverter):
data = bb.normalize(relax.op.shape_to_tensor(data))
if isinstance(data, relax.Constant) and isinstance(new_shape,
relax.Constant):
- out = _np.reshape(data.data.numpy(),
new_shape.data.numpy().tolist())
+ data_array = data.data.numpy()
+ new_shape_values = new_shape.data.numpy().tolist()
+ if not allowzero:
+ new_shape_values = [
+ data_array.shape[i] if dim == 0 else dim
+ for i, dim in enumerate(new_shape_values)
+ ]
+ out = _np.reshape(data_array, new_shape_values)
return relax.const(out, out.dtype)
if isinstance(new_shape, relax.Constant):
- new_shape = new_shape.data.numpy().tolist()
+ new_shape_values = new_shape.data.numpy().tolist()
+ if allowzero and 0 in new_shape_values:
+ new_shape = _tensor_to_shape_expr(
+ bb, new_shape, len(new_shape_values), "reshape_dim"
+ )
+ else:
+ new_shape = new_shape_values
out = relax.op.reshape(data, new_shape)
return out
diff --git a/tests/python/relax/test_frontend_onnx.py
b/tests/python/relax/test_frontend_onnx.py
index 673c25bcdd..09f3a7b01d 100644
--- a/tests/python/relax/test_frontend_onnx.py
+++ b/tests/python/relax/test_frontend_onnx.py
@@ -2274,6 +2274,71 @@ def test_reshape():
verify_reshape([7, 32, 32, 8], [0, 32, 32, 8], [7, 32, 32, 8],
ExpectedCopyInputDim)
+def test_reshape_constant_zero_copy_dimension():
+ data = np.arange(6, dtype="float32").reshape(2, 3)
+ reshape_node = helper.make_node("Reshape", ["data", "shape"], ["reshaped"])
+ graph = helper.make_graph(
+ [reshape_node],
+ "reshape_constant_zero_copy_test",
+ inputs=[],
+ initializer=[
+ numpy_helper.from_array(data, "data"),
+ helper.make_tensor("shape", TensorProto.INT64, [2], [0, 3]),
+ ],
+ outputs=[helper.make_tensor_value_info("reshaped", TensorProto.FLOAT,
[2, 3])],
+ )
+ model = helper.make_model(
+ graph,
+ producer_name="reshape_constant_zero_copy_test",
+ opset_imports=[helper.make_opsetid("", 14)],
+ )
+
+ output = run_in_tvm(model, opset=14)
+
+ tvm.testing.assert_allclose(output.numpy(), data)
+
+
+def test_reshape_allowzero_literal_zero_dimension():
+ reshape_node = helper.make_node("Reshape", ["data", "shape"],
["reshaped"], allowzero=1)
+ graph = helper.make_graph(
+ [reshape_node],
+ "reshape_allowzero_test",
+ inputs=[helper.make_tensor_value_info("data", TensorProto.FLOAT, [2,
0])],
+ initializer=[helper.make_tensor("shape", TensorProto.INT64, [2], [0,
2])],
+ outputs=[helper.make_tensor_value_info("reshaped", TensorProto.FLOAT,
[0, 2])],
+ )
+ model = helper.make_model(
+ graph,
+ producer_name="reshape_allowzero_test",
+ opset_imports=[helper.make_opsetid("", 14)],
+ )
+
+ output = run_in_tvm(model, {"data": np.zeros((2, 0), dtype="float32")},
opset=14)
+
+ assert tuple(output.shape) == (0, 2)
+
+
+def test_reshape_allowzero_infers_dimension_without_literal_zero():
+ reshape_node = helper.make_node("Reshape", ["data", "shape"],
["reshaped"], allowzero=1)
+ graph = helper.make_graph(
+ [reshape_node],
+ "reshape_allowzero_infer_dimension_test",
+ inputs=[helper.make_tensor_value_info("data", TensorProto.FLOAT, [3,
4])],
+ initializer=[helper.make_tensor("shape", TensorProto.INT64, [2], [-1,
2])],
+ outputs=[helper.make_tensor_value_info("reshaped", TensorProto.FLOAT,
[6, 2])],
+ )
+ model = helper.make_model(
+ graph,
+ producer_name="reshape_allowzero_infer_dimension_test",
+ opset_imports=[helper.make_opsetid("", 14)],
+ )
+ data = np.arange(12, dtype="float32").reshape(3, 4)
+
+ output = run_in_tvm(model, {"data": data}, opset=14)
+
+ tvm.testing.assert_allclose(output.numpy(), data.reshape(6, 2))
+
+
def test_reshape_shape_output():
def verify_reshape_shape_output(target_shape, output_shape, expected):
shape_node = helper.make_node("Shape", ["data"], ["shape_out"])
