Category : quantization

I tried to apply INT8bit quantization before FloatingPoint32bit Matrix Multiplication, then requantize accumulated INT32bit output to INT8bit. After all, I guess there’s a couple of mix-ups somewhere in the process. I feel stuck in spotting those trouble spots. data flow [Affine Quantization]: input(fp32) -> quant(int8) ____ matmul(int32) -> requant(int8) ->deq(fp32) input(fp32) -> quant(int8) —-/ My ..

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I was able to successfully quantise a pytorch model for huggingface text classification with intel lpot(neural compressor) I now have the fp32 model and quantised int8 models in my machine. For inference I loaded the quantised lpot model with the below code model = AutoModelForSequenceClassification.from_pretrained(‘fp32/model/path’) from lpot.utils.pytorch import load modellpot = load("path/to/lpotmodel/", model) I am ..

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I’ve tried QAT implementation in my model training script. I’m using functional API for the model creation. Steps that I followed to implement QAT API, Build the model acrhitecture Inserted the appropriate quantize_model function Train the model Let me provide you the code snippet for more clearance words_input = Input(shape=(None,),dtype=’int32′,name=’words_input’) words = Embedding(input_dim=wordEmbeddings.shape[0], output_dim=wordEmbeddings.shape[1], weights=[wordEmbeddings], ..

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So I have the following code snipped which works till the last line where i enter the interpreter.invoke() input_data10 = np.expand_dims(input_text[1:1001], axis=1) interpreter.resize_tensor_input(input_details[0][‘index’], [1000, 1, 100]) interpreter.allocate_tensors() interpreter.set_tensor(input_details[0][‘index’], input_data10) interpreter.allocate_tensors() interpreter.invoke() The error I am getting is this one: ————————————————————————— RuntimeError Traceback (most recent call last) <ipython-input-51-7d35ed1dfe14> in <module> —-> 1 interpreter.invoke() /usr/local/lib/python3.6/dist-packages/tensorflow/lite/python/interpreter.py in invoke(self) ..

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