coreml : fix ANE optimized encoder (#1716)
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@ -24,9 +24,9 @@ struct whisper_coreml_context * whisper_coreml_init(const char * path_model) {
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// select which device to run the Core ML model on
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// select which device to run the Core ML model on
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MLModelConfiguration *config = [[MLModelConfiguration alloc] init];
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MLModelConfiguration *config = [[MLModelConfiguration alloc] init];
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config.computeUnits = MLComputeUnitsCPUAndGPU;
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// config.computeUnits = MLComputeUnitsCPUAndGPU;
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//config.computeUnits = MLComputeUnitsCPUAndNeuralEngine;
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//config.computeUnits = MLComputeUnitsCPUAndNeuralEngine;
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//config.computeUnits = MLComputeUnitsAll;
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config.computeUnits = MLComputeUnitsAll;
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const void * data = CFBridgingRetain([[whisper_encoder_impl alloc] initWithContentsOfURL:url_model configuration:config error:nil]);
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const void * data = CFBridgingRetain([[whisper_encoder_impl alloc] initWithContentsOfURL:url_model configuration:config error:nil]);
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@ -143,20 +143,7 @@ class AudioEncoderANE(AudioEncoder):
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x = block(x)
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x = block(x)
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x = self.ln_post(x)
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x = self.ln_post(x)
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x = x.squeeze(2).transpose(1, 2)
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# """
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# TODO:
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# I think we need to transpose the result here to make it fit whisper.cpp memory order.
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# However, even doing this, the results are still wrong. Kind of less wrong compared to
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# not transposing, but still wrong.
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# Also, I don't know why the original OpenAI implementation does not need to transpose
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# transpose to (batch_size, n_ctx, n_state)
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# x : torch.Tensor, shape = (batch_size, n_state, 1, n_ctx)
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# """
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# x = x.transpose(1,3)
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return x
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return x
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@ -23,7 +23,7 @@ if [[ $mname == "-h5" ]]; then
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echo $mpath
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echo $mpath
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python3 models/convert-h5-to-coreml.py --model-name $mname --model-path $mpath --encoder-only True
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python3 models/convert-h5-to-coreml.py --model-name $mname --model-path $mpath --encoder-only True
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else
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else
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python3 models/convert-whisper-to-coreml.py --model $mname --encoder-only True
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python3 models/convert-whisper-to-coreml.py --model $mname --encoder-only True --optimize-ane True
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fi
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fi
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xcrun coremlc compile models/coreml-encoder-${mname}.mlpackage models/
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xcrun coremlc compile models/coreml-encoder-${mname}.mlpackage models/
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