[Relax][NN] Add batch_flatten operator

[guan404ming]

Why

The batch_flatten operator was needed for CoreML backend support but was not implemented in Relax, causing the CoreML pattern and converter to be commented out.

How

• Add C++ operator relax.nn.batch_flatten with struct info inference
• Add Python wrapper in relax.op.nn
• Add legalization via topi.reshape
• Enable CoreML pattern and converter for nn.batch_flatten
• Add unit tests for operator, struct info inference, and legalization

[gemini-code-assist]

Summary of Changes

Hello @guan404ming, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request implements the batch_flatten operator within the Relax framework, addressing a critical dependency for the CoreML backend. The new operator flattens all dimensions of a tensor except the batch dimension, which was previously a missing feature preventing full CoreML integration. The changes encompass the entire lifecycle of the operator, from its low-level C++ definition and Python bindings to its shape inference, legalization, and thorough unit testing, ensuring its correct and robust operation within the system.

Highlights

• New batch_flatten Operator: Introduced relax.nn.batch_flatten to flatten all dimensions except the batch dimension, crucial for neural network architectures.
• CoreML Backend Support Enabled: The absence of batch_flatten previously caused CoreML patterns and converters to be commented out; this PR enables them.
• Comprehensive Implementation: The operator includes a C++ definition, Python wrapper, struct info inference, and legalization via topi.reshape.
• Extensive Unit Testing: New unit tests cover operator correctness, struct info inference (including symbolic shapes), and legalization, ensuring robust functionality.

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[gemini-code-assist]

Code Review

This pull request introduces the relax.nn.batch_flatten operator, which is a great addition for CoreML backend support. The implementation is clean and follows the existing structure for Relax operators well. You've included comprehensive changes across the C++ implementation, Python bindings, legalization, and enabled it for the CoreML backend. The unit tests are thorough, covering correctness, struct info inference with various static and symbolic shapes, and legalization.

I found one potential issue in the legalization logic that could lead to a crash when dealing with inputs of unknown shape. I've left a specific comment with a suggested fix. Other than that, the changes look excellent.

[gemini-code-assist]

The legalization for relax.nn.batch_flatten assumes that call.struct_info.shape is always available. However, if the input tensor to batch_flatten has an unknown number of dimensions or unknown shape values, the struct info inference for batch_flatten will correctly produce a TensorStructInfo without a concrete shape. In this scenario, call.struct_info.shape will be None, and attempting to access call.struct_info.shape.values will result in an AttributeError, crashing the compiler.

To make the legalization more robust, you should add a check to ensure call.struct_info.shape is defined before attempting to use it. If it's not defined, the operator should not be legalized, and the original call should be returned. This is a common pattern in this file for other operators.

It would also be great to add a new test case in tests/python/relax/test_transform_legalize_ops_nn.py to cover this scenario, for example with an input tensor of R.Tensor(ndim=4, dtype="float32"), and assert that batch_flatten is not legalized in this case.

Suggested change

	def _nn_batch_flatten(bb: BlockBuilder, call: Call) -> Expr:
	return bb.call_te(topi.reshape, call.args[0], call.struct_info.shape.values)
	def _nn_batch_flatten(bb: BlockBuilder, call: Call) -> Expr:
	if call.struct_info.shape is None:
	return call
	return bb.call_te(topi.reshape, call.args[0], call.struct_info.shape.values)