## Summary
This PR adds Relax TFLite frontend support for
`UNIDIRECTIONAL_SEQUENCE_RNN` (BuiltinOperator 35), claimed in
[#19519](https://github.com/apache/tvm/issues/19519) Group A.
The op executes a simple RNN cell over a time sequence. The converter
unrolls the time steps at graph-construction time using Relax
primitives.
Cell equation:
```
h_t = fused_activation(x_t @ W.T + h_{t-1} @ Wr.T + b)
```
## Changes
- **Handler**: `convert_unidirectional_sequence_rnn` registered in
`convert_map` (alphabetical, U-region after `UNPACK`)
- **Inputs** (5): `input [batch, time, input_size]`, `input_weights
[num_units, input_size]`, `recurrent_weights [num_units, num_units]`,
`bias [num_units]`, `hidden_state [batch, num_units]` (variable,
zero-initialised)
- **Output**: `[batch, time, num_units]` (always batch-major)
- **time_major=True**: input is transposed to batch-major before
unrolling
- **Activations**: NONE, RELU, RELU6, TANH, SIGMOID (via
`convert_fused_activation_function`)
- **Quantized**: raises `OpNotImplemented` (not yet supported)
## Testing
Modern TF/Keras (2.x, Keras 3) no longer emits
`UNIDIRECTIONAL_SEQUENCE_RNN`; `SimpleRNN` with `unroll=False` lowers to
`WHILE`+TensorList ops, and `unroll=True` expands to elementwise ops.
Tests therefore follow the same flatbuffer-construction pattern used by
the StableHLO op PRs (#19536, #19587).
Three tests added to `tests/python/relax/test_frontend_tflite.py`:
- `test_unidirectional_sequence_rnn_none_activation` —
`tvm.ir.assert_structural_equal` with identity weights / zero bias, NONE
activation, time=1
- `test_unidirectional_sequence_rnn_relu_activation` — shape check,
random weights, RELU activation, time=3
- `test_unidirectional_sequence_rnn_time_major` — shape check,
`time_major=True` input layout
```bash
python -m pytest tests/python/relax/test_frontend_tflite.py -k unidirectional_sequence_rnn -v
```
All 3 tests pass. pre-commit (ASF header, ruff check, ruff format) all
pass.
## References
- Issue [#19519](https://github.com/apache/tvm/issues/19519) Group A:
Sequence / recurrent model operators
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
YinHanke
dcbebe7bfd