Migrate MiniCPMVImageInputs to TensorSchema

[bbeckca]

Purpose

This PR migrates MiniCPMVImageInputs from a TypedDict-based definition to a structured TensorSchema model with runtime shape validation. This brings it in line with recent changes to Phi3VImagePixelInputs, and is part of a broader effort to improve input contract enforcement and debug-ability across multi-modal models.

Test Plan

Confirm validation works via standalone tests in tests/standalone_test/test_tensor_schema.py and rely on CI to check integration.

Test Result

tests/standalone_tests/test_tensor_schema.py::test_tensor_schema_valid_tensor PASSED                                                                                                                                      [  6%]
tests/standalone_tests/test_tensor_schema.py::test_tensor_schema_optional_fields PASSED                                                                                                                                   [ 12%]
tests/standalone_tests/test_tensor_schema.py::test_tensor_schema_constant_dim_failure PASSED                                                                                                                              [ 18%]
tests/standalone_tests/test_tensor_schema.py::test_tensor_schema_symbolic_dim_mismatch PASSED                                                                                                                             [ 25%]
tests/standalone_tests/test_tensor_schema.py::test_tensor_schema_list_tensor_valid PASSED                                                                                                                                 [ 31%]
tests/standalone_tests/test_tensor_schema.py::test_tensor_schema_variable_patch_counts_valid PASSED                                                                                                                       [ 37%]
tests/standalone_tests/test_tensor_schema.py::test_tensor_schema_tuple_tensor_valid PASSED                                                                                                                                [ 43%]
tests/standalone_tests/test_tensor_schema.py::test_tensor_schema_inconsistent_shapes_in_list PASSED                                                                                                                       [ 50%]
tests/standalone_tests/test_tensor_schema.py::test_tensor_schema_empty_list PASSED                                                                                                                                        [ 56%]
tests/standalone_tests/test_tensor_schema.py::test_tensor_schema_validation_disabled_skips_shape_check PASSED                                                                                                             [ 62%]
tests/standalone_tests/test_tensor_schema.py::test_tensor_schema_with_valid_resolve_binding_dims PASSED                                                                                                                   [ 68%]
tests/standalone_tests/test_tensor_schema.py::test_tensor_schema_with_invalid_resolve_binding_dims PASSED                                                                                                                 [ 75%]
tests/standalone_tests/test_tensor_schema.py::test_tensor_schema_with_list_of_symbolic_dim PASSED                                                                                                                         [ 81%]
tests/standalone_tests/test_tensor_schema.py::test_tensor_schema_with_list_of_symbolic_dim_mismatch_in_length PASSED                                                                                                      [ 87%]
tests/standalone_tests/test_tensor_schema.py::test_valid_tensor_schema_with_static_last_dim PASSED                                                                                                                        [ 93%]
tests/standalone_tests/test_tensor_schema.py::test_invalid_tensor_schema_with_static_last_dim PASSED                                                                                                                      [100%]

[github-actions]

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[gemini-code-assist]

Code Review

This PR migrates MiniCPMVImageInputs to TensorSchema. I've provided comments on the TensorShape definitions and a missing check that could cause regressions for variable-sized inputs.

[gemini-code-assist]

The TensorShape for pixel_values does not correctly handle variable image sizes, which could lead to validation failures for valid inputs. The height (h) and width (w) dimensions can differ between tensors in the pixel_values list. Without marking h and w as dynamic, TensorSchema validation will require all tensors in the list to have the same shape. This is a regression from the previous TypedDict-based implementation and would cause validation to fail for valid inputs with varying image sizes.

pixel_values: Annotated[
    list[torch.Tensor],
    TensorShape("bns", 3, "h", "w", dynamic_dims={"h", "w"}),
]

[bbeckca]

I think we're fine to leave as is since this wasn't enforced and values aren't used in any symbolic cross field checks.

[gemini-code-assist]

The TensorShape for image_embeds does not account for a variable number of slices, which could lead to validation failures. The TensorShape does not seem to account for a variable number of slices (ns) when image_embeds is a list of tensors. Each tensor in the list corresponds to an image and has a shape of (num_slices, hidden_size). If num_slices can vary per image, this will cause validation to fail. To support this, ns should be marked as a dynamic dimension.

image_embeds: Annotated[
    Union[torch.Tensor, list[torch.Tensor]],
    TensorShape("bn", "ns", "hs", dynamic_dims={"ns"}),
]

[bbeckca]

This wasn't enforced previously, so I haven't added any validations. Feel free to let me know if we should make ns dynamic dim.

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @bbeckca.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork