Unsupported layer type: SlicingOpLambda

Created by: rkovachfuentes

Prerequisites

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• Test that the bug appears on the current version of the master branch. Make sure to include the commit hash of the commit you checked out.
• Check that the issue hasn't already been reported, by checking the currently open issues.
• If there are steps to reproduce the problem, make sure to write them down below.
• If relevant, please include the hls4ml project files, which were created directly before and/or after the bug.

Quick summary

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Unsupported Layer Type error is raised when trying to configure qkeras model (using hls4ml.utils.config_from_keras_model).

Details

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Steps to Reproduce

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1. Clone the hls4ml repository
2. Checkout the master branch, with commit hash: commit dd18adb1d3fb1ac3bf18c2b7feb37f44c10b6262
3. Reload qkeras model from h5, using: model = qkeras.utils.load_qmodel('/home/rkovachf/hls4ml-tutorial/hls4mltest.h5',custom_objects)
4. Model architecture is described below:

def CreateQModel(shape):
    x = x_in = Input(shape)
    x = QConv2D(5,3, activation="quantized_relu(bits = 16, integer = 4, use_sigmoid = 1)", name="conv2d1")(x)
    '''QConv2D(5,3,
        kernel_quantizer="stochastic_ternary",
        bias_quantizer="ternary", name="first_conv2d")(x)'''
    x = QActivation(activation="quantized_relu(bits = 16, integer = 4, use_sigmoid = 1)", 
                    name="relu1")(x)
    
    x_conv = QConv2D(5,3, activation="quantized_relu(bits = 16, integer = 4, use_sigmoid = 1)", name="conv2d2")(x)
    x = QAveragePooling2D(pool_size=(9, 3), strides=None, padding="valid", data_format=None)(x_conv[...,:1])
    y = QAveragePooling2D(pool_size=(3, 17), strides=None, padding="valid", data_format=None)(x_conv[...,1:2])
    cota = QAveragePooling2D(pool_size=(2,2), strides=None, padding="valid", data_format=None)(x_conv[...,2:3])
    cotb = QAveragePooling2D(pool_size=(2,2), strides=None, padding="valid", data_format=None)(x_conv[...,3:4])
    cov = QAveragePooling2D(pool_size=(2,2), strides=None, padding="valid", data_format=None)(x_conv[...,4:5])
    cota = QActivation(activation="quantized_relu(bits = 16, integer = 4, use_sigmoid = 1)", 
                    name="relucota")(cota)
    
    cotb = QActivation(activation="quantized_relu(bits = 16, integer = 4, use_sigmoid = 1)", 
                    name="relucotb")(cotb)
    
    cota = Flatten()(cota)
    cotb = Flatten()(cotb)
    x = QActivation(activation="quantized_relu(bits = 16, integer = 4, use_sigmoid = 1)", 
                    name="relux")(x)
    
    y = QActivation(activation="quantized_relu(bits = 16, integer = 4, use_sigmoid = 1)", 
                    name="reluy")(y)
    
    x = Flatten()(x)
    y = Flatten()(y)
    x = QDense(units = 10, 
               kernel_quantizer="quantized_bits(bits = 8,integer = 4,symmetric=0)",
               use_bias= True,
               bias_quantizer= "quantized_bits(bits = 8,integer = 4,symmetric=0)",
               name = "dense10x")(x)
    y = QDense(units = 10, 
               kernel_quantizer="quantized_bits(bits = 8,integer = 4,symmetric=0)",
               use_bias= True,
               bias_quantizer= "quantized_bits(bits = 8,integer = 4,symmetric=0)",
               name = "dense10y")(y)
    cota = QDense(units = 10, 
               kernel_quantizer="quantized_bits(bits = 8,integer = 4,symmetric=0)",
               use_bias= True,
               bias_quantizer= "quantized_bits(bits = 8,integer = 4,symmetric=0)",
               name = "dense10cota")(cota)
    cotb = QDense(units = 10, 
               kernel_quantizer="quantized_bits(bits = 8,integer = 4,symmetric=0)",
               use_bias= True,
               bias_quantizer= "quantized_bits(bits = 8,integer = 4,symmetric=0)",
               name = "dense10cotb")(cotb)
    x = QActivation(activation="quantized_relu(bits = 16, integer = 4, use_sigmoid = 1)", 
                    name="relu2x")(x)
    
    y = QActivation(activation="quantized_relu(bits = 16, integer = 4, use_sigmoid = 1)", 
                    name="relu2y")(y)
    
    cota = QActivation(activation="quantized_relu(bits = 16, integer = 4, use_sigmoid = 1)", 
                    name="relu2cota")(cota)
    
    cotb = QActivation(activation="quantized_relu(bits = 16, integer = 4, use_sigmoid = 1)", 
                    name="relu2cotb")(cotb)
    
    x = QDense(units = 2, 
               kernel_quantizer="quantized_bits(bits = 8,integer = 4,symmetric=0)",
               use_bias= True,
               bias_quantizer= "quantized_bits(bits = 8,integer = 4,symmetric=0)",
               name = "dense2x")(x)
    y = QDense(units = 2, 
               kernel_quantizer="quantized_bits(bits = 8,integer = 4,symmetric=0)",
               use_bias= True,
               bias_quantizer= "quantized_bits(bits = 8,integer = 4,symmetric=0)",
               name = "dense2y")(y)
    cota = QDense(units = 2, 
               kernel_quantizer="quantized_bits(bits = 8,integer = 4,symmetric=0)",
               use_bias= True,
               bias_quantizer= "quantized_bits(bits = 8,integer = 4,symmetric=0)",
               name = "dense2cota")(cota)
    cotb = QDense(units = 2, 
               kernel_quantizer="quantized_bits(bits = 8,integer = 4,symmetric=0)",
               use_bias= True,
               bias_quantizer= "quantized_bits(bits = 8,integer = 4,symmetric=0)",
               name = "dense2cotb")(cotb)
    cov = Flatten()(cov)
    cov = QDense(units = 6, 
               kernel_quantizer="quantized_bits(bits = 8,integer = 4,symmetric=0)",
               use_bias= True,
               bias_quantizer= "quantized_bits(bits = 8,integer = 4,symmetric=0)",
               name = "densecov")(cov)
    xy = tf.concat([x[...,:1],y[...,:1],cota[...,:1],cotb[...,:1],
                    x[...,1:2],y[...,1:2],cota[...,1:2],cotb[...,1:2],
                   cov], axis=1)
    model = Model(inputs=x_in, outputs=xy)
    return model

5. Error occurs:

Exception                                 Traceback (most recent call last)
Cell In[23], line 4
      1 import hls4ml
      2 import plotting
----> 4 config = hls4ml.utils.config_from_keras_model(model, granularity='name')
      5 config['LayerName']['softmax']['exp_table_t'] = 'ap_fixed<18,8>'
      6 config['LayerName']['softmax']['inv_table_t'] = 'ap_fixed<18,4>'

File ~/.conda/envs/hls4ml-tutorial/lib/python3.10/site-packages/hls4ml/utils/config.py:138, in config_from_keras_model(model, granularity, backend, default_precision, default_reuse_factor)
    134     model_arch = json.loads(model.to_json())
    136 reader = hls4ml.converters.KerasModelReader(model)
--> 138 layer_list, _, _ = hls4ml.converters.parse_keras_model(model_arch, reader)
    140 def make_layer_config(layer):
    141     cls_name = layer['class_name']

File ~/.conda/envs/hls4ml-tutorial/lib/python3.10/site-packages/hls4ml/converters/keras_to_hls.py:226, in parse_keras_model(model_arch, reader)
    224 for keras_layer in layer_config:
    225     if keras_layer['class_name'] not in supported_layers:
--> 226         raise Exception('ERROR: Unsupported layer type: {}'.format(keras_layer['class_name']))
    228 output_shapes = {}
    229 output_shape = None

Exception: ERROR: Unsupported layer type: SlicingOpLambda

Optional

Possible fix

If you already know where the issue stems from, or you have a hint please let us know.

Please add support for SlicingOpLambda.

Additional context

Add any other context about the problem here.