## Summary
This PR adds support for the TFLite `RANDOM_UNIFORM`,
`RANDOM_STANDARD_NORMAL`, and `MULTINOMIAL` operators in the Relax
TFLite frontend, covering the items I claimed in #19412.
`RANDOM_UNIFORM` and `RANDOM_STANDARD_NORMAL` are lowered to seeded
`tvm.contrib.random` calls with dynamic shape support. `MULTINOMIAL` is
lowered by composing existing Relax ops around
`relax.op.multinomial_from_uniform`.
## Changes
### Frontend
1. Add converter registrations for `RANDOM_UNIFORM`,
`RANDOM_STANDARD_NORMAL`, and `MULTINOMIAL`.
2. Add shared helpers to:
- parse TFLite `RandomOptions` seeds
- convert shape tensors to Relax shape expressions for dynamic-shape
random ops
3. Lower `RANDOM_UNIFORM` to `tvm.contrib.random.uniform`.
4. Lower `RANDOM_STANDARD_NORMAL` to `tvm.contrib.random.normal`.
5. Lower `MULTINOMIAL` by:
- applying `R.nn.softmax` to logits
- generating seeded uniform samples
- calling `R.multinomial_from_uniform`
- reshaping the result to `[batch_size, num_samples]`
### Runtime
1. Extend `src/runtime/contrib/random/random.cc` to accept seeded calls
for `tvm.contrib.random.uniform` and `tvm.contrib.random.normal`.
2. Preserve compatibility with the existing unseeded calling convention.
## Testing
All tests pass:
```bash
pytest
tests/python/relax/test_frontend_tflite.py::test_random_uniform_dynamic_shape
\
tests/python/relax/test_frontend_tflite.py::test_random_standard_normal_dynamic_shape
\
tests/python/relax/test_frontend_tflite.py::test_multinomial_dynamic_num_samples
-v
```
## References
- #19412
- Claimed items: MULTINOMIAL, RANDOM_STANDARD_NORMAL, RANDOM_UNIFORM
HoYi
71c634f8b1