* coreml : skip model load in convert-whisper-to-coreml.py
This commit updates the conversion process for Whisper models to use the
"mlprogram" format instead of "neuralnetwork".
The motivation for this change is that when using the "neuralnetwork"
format the underlying model produced is based on protobuf and my
understanding is that there are limitations to this format, such as
sizes of strings and the complexity of the model.
Currently when trying to convert larger models such as large-v3 the
conversion fails but succeeds for smaller models.
The "mlprogram" format is a more recent addition to CoreML and is
designed to be more flexible and powerful, allowing for more complex
models and larger data types. This seems to work for larger and smaller
models alike and unless I'm there are considerations that I'm not aware
of I think this is what we should be using moving forward.
The error that is generated for large models is the following:
```console
Running MIL backend_neuralnetwork pipeline: 100%|█████████| 9/9 [00:00<00:00, 35.44 passes/s]
Translating MIL ==> NeuralNetwork Ops: 100%|███████████| 5641/5641 [03:31<00:00, 26.65 ops/s]
Traceback (most recent call last):
File "/Users/danbev/work/ai/whisper-work/models/convert-whisper-to-coreml.py", line 322, in <module>
encoder = convert_encoder(hparams, encoder, quantize=args.quantize)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/danbev/work/ai/whisper-work/models/convert-whisper-to-coreml.py", line 255, in convert_encoder
model = ct.convert(
^^^^^^^^^^^
File "/Users/danbev/work/ai/whisper-work/venv/lib/python3.11/site-packages/coremltools/converters/_converters_entry.py", line 635, in convert
mlmodel = mil_convert(
^^^^^^^^^^^^
File "/Users/danbev/work/ai/whisper-work/venv/lib/python3.11/site-packages/coremltools/converters/mil/converter.py", line 186, in mil_convert
return _mil_convert(
^^^^^^^^^^^^^
File "/Users/danbev/work/ai/whisper-work/venv/lib/python3.11/site-packages/coremltools/converters/mil/converter.py", line 245, in _mil_convert
return modelClass(
^^^^^^^^^^^
File "/Users/danbev/work/ai/whisper-work/venv/lib/python3.11/site-packages/coremltools/models/model.py", line 489, in __init__
self.__proxy__, self._spec, self._framework_error = self._get_proxy_and_spec(
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/danbev/work/ai/whisper-work/venv/lib/python3.11/site-packages/coremltools/models/model.py", line 550, in _get_proxy_and_spec
_MLModelProxy(
ValueError: basic_string
```
Refs: https://github.com/ggml-org/whisper.cpp/issues/3012
* coreml: fix Whisper to CoreML conversion by disabling SDPA
This commit disables the use of PyTorch's
`scaled_dot_product_attention` in the Whisper model to avoid
compatibility issues during CoreML conversion.
The issue occurs because coremltools requires PyTorch 2.5.0, but the
Whisper implementation may expect behavior from newer PyTorch versions.
By setting `MultiHeadAttention.use_sdpa = False`, we force Whisper to
use its fallback manual attention implementation, which works correctly
with PyTorch 2.5.0 during the tracing process.
Refs: https://github.com/ggerganov/whisper.cpp/issues/2783
* coreml: fix audio shape in whisper decoder conversion
This commit fixes the audio shape in the whisper decoder conversion
script.
The motivation for this is that the audio shape was incorrect and
was causing the conversion to fail.
* coreml : set -e in generate-coreml-interface.sh
The commit sets the -e flag in the generate-coreml-interface.sh script
to make sure the script fails if any command fails.
* coreml : update generated encoder/decoder interfaces
This commit updates the generated encoder/decoder interfaces for the
whisper model which is the result of running the
generate-coreml-interface.sh script.
