coreml : fix --quantize crash for mlprogram format; fix --optimize-ane label (#3868)

commit 8b92060 switched ct.convert() to mlprogram, but did not update
the --quantize path.  quantize_weights() from
neural_network.quantization_utils only works with the legacy
neuralnetwork format.  Running with --quantize crashed with:

  Exception: MLModel of type mlProgram cannot be loaded just from the
  model spec object. It also needs the path to the weights file.

Fix: pass compute_precision=ct.precision.FLOAT16 into ct.convert() when
--quantize is set.  This matches the original intent of nbits=16 (F16
storage) without changing the quantization scheme or model accuracy.

Also fix the three boolean CLI flags (--encoder-only, --quantize,
--optimize-ane) to use a _str_to_bool helper so that both
  --flag True
and
  --flag False
parse correctly.  The type=bool form accepted "False" as True because
bool("False") == True.

Remove the "currently broken" label from --optimize-ane: the ANE path
(WhisperANE with Conv2d attention and LayerNormANE) converts and loads
correctly with both PyTorch 2.x and coremltools 9.x.

Author: krystophny

models/convert-whisper-to-coreml.py

@@ -8,10 +8,19 @@
 from typing import Dict
 from typing import Optional
 from ane_transformers.reference.layer_norm import LayerNormANE as LayerNormANEBase
-from coremltools.models.neural_network.quantization_utils import quantize_weights
 from whisper.model import Whisper, AudioEncoder, TextDecoder, ResidualAttentionBlock, MultiHeadAttention, ModelDimensions
 from whisper import load_model
 
+
+def _str_to_bool(v):
+    if isinstance(v, bool):
+        return v
+    if v.lower() in ("true", "1", "yes"):
+        return True
+    if v.lower() in ("false", "0", "no"):
+        return False
+    raise argparse.ArgumentTypeError(f"boolean value expected, got '{v}'")
+
 # Disable PyTorch Scaled Dot-Product Attention (SDPA) to avoid compatibility issues.
 # The Whisper implementation expects a specific behavior from
 # torch.nn.functional.scaled_dot_product_attention that differs between PyTorch
@@ -258,11 +267,9 @@ def convert_encoder(hparams, model, quantize=False):
         inputs=[ct.TensorType(name="logmel_data", shape=input_shape)],
         outputs=[ct.TensorType(name="output")],
         compute_units=ct.ComputeUnit.ALL,
+        compute_precision=ct.precision.FLOAT16 if quantize else ct.precision.FLOAT32,
     )
 
-    if quantize:
-        model = quantize_weights(model, nbits=16)
-
     return model
 
 def convert_decoder(hparams, model, quantize=False):
@@ -283,20 +290,18 @@ def convert_decoder(hparams, model, quantize=False):
             ct.TensorType(name="token_data", shape=tokens_shape, dtype=int),
             ct.TensorType(name="audio_data", shape=audio_shape)
         ],
+        compute_precision=ct.precision.FLOAT16 if quantize else ct.precision.FLOAT32,
     )
 
-    if quantize:
-        model = quantize_weights(model, nbits=16)
-
     return model
 
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument("--model", type=str, help="model to convert (e.g. tiny, tiny.en, base, base.en, small, small.en, medium, medium.en, large-v1, large-v2, large-v3, large-v3-turbo)", required=True)
-    parser.add_argument("--encoder-only", type=bool, help="only convert encoder", default=False)
-    parser.add_argument("--quantize",     type=bool, help="quantize weights to F16", default=False)
-    parser.add_argument("--optimize-ane", type=bool, help="optimize for ANE execution (currently broken)", default=False)
+    parser.add_argument("--encoder-only", type=_str_to_bool, help="only convert encoder", default=False)
+    parser.add_argument("--quantize",     type=_str_to_bool, help="quantize weights to F16", default=False)
+    parser.add_argument("--optimize-ane", type=_str_to_bool, help="optimize for ANE execution", default=False)
     args = parser.parse_args()
 
     if args.model not in ["tiny", "tiny.en", "base", "base.en", "small", "small.en", "small.en-tdrz", "medium", "medium.en", "large-v1", "large-v2", "large-v3", "large-v3-turbo"]: