[Relax][ONNX] Complete ShapeExpr reshape handling in ONNX frontend (#18956)

## Summary

Complete `Reshape` handling for shape values in the Relax ONNX frontend.

## Changes

- keep `ShapeExpr -> Reshape([-1])` on the shape-specialized path
- materialize `ShapeExpr` to an `int64` tensor for other reshape targets
and apply regular tensor reshape semantics
- add frontend coverage for `Shape -> Reshape([-1])`
- add frontend coverage for reshaping shape outputs to non-`[-1]`
targets such as `[1, 3]` and `[3, 1]`
- extend symbolic shape deduction coverage to include the common `Shape
-> Reshape([-1]) -> Gather -> Unsqueeze` shape-construction pattern

## Validation

- `pytest -k 'test_symbolic_shape_deduction or test_reshape_shape_output
or test_reshape'`

This PR completes the `Reshape` limitation in the Relax ONNX frontend
operator work tracked in #18945.

Author: LudovicoYIN

python/tvm/relax/frontend/onnx/onnx_frontend.py

@@ -1016,11 +1016,16 @@ def _impl_v13(cls, bb, inputs, attr, params):
         data = inputs[0]
         new_shape = get_constant(inputs[1], params)
 
-        if isinstance(data, relax.ShapeExpr) and isinstance(new_shape, relax.Constant):
-            new_shape = new_shape.data.numpy().tolist()
-            if new_shape != [-1]:
-                raise NotImplementedError("Need to fix this case")
-            return data
+        if isinstance(data, relax.ShapeExpr):
+            # Preserve identity flatten for shape values to keep shape-specialized
+            # handling in downstream shape-construction patterns.
+            if isinstance(new_shape, relax.Constant):
+                new_shape_values = new_shape.data.numpy().tolist()
+                if new_shape_values == [-1]:
+                    return data
+
+            # Other reshape targets follow regular int64 tensor reshape semantics.
+            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())

tests/python/relax/test_frontend_onnx.py

@@ -971,6 +971,38 @@ def test_reshape(in_shape, shape, out_shape):
     check_correctness(model, inputs=input_values)
 
 
+@pytest.mark.parametrize(
+    "target_shape, output_shape",
+    [
+        ([-1], [3]),
+        ([1, 3], [1, 3]),
+        ([3, 1], [3, 1]),
+    ],
+)
+def test_reshape_shape_output(target_shape, output_shape):
+    shape_node = helper.make_node("Shape", ["data"], ["shape_out"])
+    reshape_node = helper.make_node("Reshape", ["shape_out", "target_shape"], ["reshaped"])
+
+    data_shape = [2, 3, 4]
+
+    graph = helper.make_graph(
+        [shape_node, reshape_node],
+        "reshape_shape_output",
+        inputs=[
+            helper.make_tensor_value_info("data", TensorProto.FLOAT, data_shape),
+        ],
+        initializer=[
+            helper.make_tensor("target_shape", TensorProto.INT64, [len(target_shape)], target_shape)
+        ],
+        outputs=[helper.make_tensor_value_info("reshaped", TensorProto.INT64, output_shape)],
+    )
+    input_values = {
+        "data": np.random.randn(*data_shape).astype("float32"),
+    }
+    model = helper.make_model(graph, producer_name="reshape_shape_output")
+    check_correctness(model, inputs=input_values)
+
+
 def test_transpose():
     verify_unary("Transpose", [32, 32, 32], attrs={"perm": [1, 2, 0]})
 
@@ -3630,29 +3662,46 @@ def test_optional_get_element_empty_raises():
         from_onnx(model, opset=18, keep_params_in_input=True)
 
 
-def test_symbolic_shape_deduction():
+@pytest.mark.parametrize("with_reshape_flatten", [False, True])
+def test_symbolic_shape_deduction(with_reshape_flatten):
     index_node = helper.make_node(
         "Constant",
         inputs=[],
         outputs=["indices"],
         value=helper.make_tensor("indices", TensorProto.INT64, [], [0]),
     )
     shape_node = helper.make_node("Shape", ["data"], ["shape_output"])
-    gather_node = helper.make_node("Gather", ["shape_output", "indices"], ["gather_output"])
+    nodes = [index_node, shape_node]
+    gather_input = "shape_output"
+
+    if with_reshape_flatten:
+        reshape_node = helper.make_node(
+            "Reshape", ["shape_output", "target_shape"], ["reshaped_shape"]
+        )
+        nodes.append(reshape_node)
+        gather_input = "reshaped_shape"
+
+    gather_node = helper.make_node("Gather", [gather_input, "indices"], ["gather_output"])
     unsqueeze_node = helper.make_node("Unsqueeze", ["gather_output", "axes"], ["unsqueeze_output"])
     constant_of_shape_node = helper.make_node(
         "ConstantOfShape",
         ["unsqueeze_output"],
         ["output"],
         value=helper.make_tensor("value", TensorProto.FLOAT, [], [1]),
     )
+    nodes.extend([gather_node, unsqueeze_node, constant_of_shape_node])
+
+    initializers = [helper.make_tensor("axes", TensorProto.INT64, [1], vals=[0])]
+    if with_reshape_flatten:
+        initializers.append(helper.make_tensor("target_shape", TensorProto.INT64, [1], vals=[-1]))
+
     graph = helper.make_graph(
-        [index_node, shape_node, gather_node, unsqueeze_node, constant_of_shape_node],
+        nodes,
         "test_shape_deduction",
         inputs=[
             helper.make_tensor_value_info("data", TensorProto.FLOAT, ["batch", "seq"]),
         ],
-        initializer=[helper.make_tensor("axes", TensorProto.INT64, [1], vals=[0])],
+        initializer=initializers,
         outputs=[helper.make_tensor_value_info("output", TensorProto.INT64, [1])],
     )
     model = helper.make_model(graph, producer_name="test_shape_deduction")