bug in Leaky_relu instantiation

[AnouarITI]

I have a model that includes a leaky_relu layer. The leaky relu function requires three input parameters (input, alpha, output) however, during instantiation, it only generates a function with two input parameters (input, output).

This is the Leaky Relu function in activation.h:

template <class data_T, class param_T, class res_T, typename CONFIG_T>
void leaky_relu(data_T data[CONFIG_T::n_in], param_T alpha, res_T res[CONFIG_T::n_in]) {
    #pragma HLS PIPELINE

    data_T datareg;
    for (int ii = 0; ii < CONFIG_T::n_in; ii++) {
        datareg = data[ii];
        if (datareg > 0)
            res[ii] = datareg;
        else
            res[ii] = alpha * datareg;
    }
}

in myproject() function Leaky relu is instantiated as follow: nnet::leaky_relu<layer12_t, layer15_t, leaky_relu_config15>(layer12_out, layer15_out)

Could this be fixed please?

[JanFSchulte]

Hi,

can you please be more specific about your setup and model? Which version of hls4ml are you using? Which io_type and backend are you trying to use? I tried to replicate this issue but for me it seems to be working correctly with the current state of the master branch.

[AnouarITI]

Thank you for your reply. I was trying to convert a keras ResNet model with 24k parameters using hls4ml version 0.6.0. I used the following config:

• Precison: ap_fixed<16,6>
• Strategy: resource
• Reuse factor: 32
• io_type: io_stream
• Backend: VivadoAccelerator
• part: xczu7ev-ffvc1156-2-e (zcu104)

[bo3z]

There have been some changes in hls4ml on the data types of activations. Can you please confirm you have the latest version of hls4ml. You can either install it via pip or clone it from github and install it using pip install -e . . I suspect what's going on here is that you have some mismatch of files from different versions.

[JanFSchulte]

Version 0.6.0 is certainly a bit old. As I said, it seems to be working fine in the current master, so I would indeed encourage you to test with either the master or install version 0.8. via pip.

[zsrabbani]

I have the same problem. I already have the updated version of 0.8.1 but still have the same error.

[jmitrevs]

Can you give an example that we can run that shows the problem?

[zsrabbani]

### Model: def resnet_block(input_data, filters, conv_size): shortcut = input_data # Store the original input for the shortcut connection

# Pre-activation (Batch Normalization and Activation before Convolution)
x = BatchNormalization()(input_data)
x = Activation('leaky_relu')(x)

# Bottleneck 1x1 Convolution (reduce channels)
x = Conv2D(filters // 4, 1, activation=None, padding='same', kernel_initializer='lecun_uniform', kernel_regularizer=l1(0.0001))(x)

x = BatchNormalization()(x)
x = Activation('leaky_relu')(x)

# 3x3 Convolution
x = Conv2D(filters // 4, conv_size, activation=None, padding='same',kernel_initializer='lecun_uniform', kernel_regularizer=l1(0.0001))(x)

x = BatchNormalization()(x)
x = Activation('leaky_relu')(x)

# Bottleneck 1x1 Convolution (increase channels back to original)
x = Conv2D(filters, 1, activation=None, padding='same',kernel_initializer='lecun_uniform', kernel_regularizer=l1(0.0001))(x)

# Shortcut connection (with 1x1 Convolution if needed to match dimensions)
if input_data.shape[-1] != filters:  # Check if channel dimensions match
    shortcut = Conv2D(filters, 1, activation=None, padding='same',kernel_initializer='lecun_uniform', kernel_regularizer=l1(0.0001))(shortcut)

# Add the shortcut connection
x = Add()([x, shortcut])

return x

num_resnet_blocks = 6 num_filters = 16 kernel_size = 3,3

rf_in = Input(shape=(32, 32, 2), name = 'rf_input')

x = Conv2D(num_filters, (kernel_size), activation=None, padding='same', kernel_initializer='lecun_uniform', kernel_regularizer=l1(0.0001))(rf_in) x = BatchNormalization()(x) x = Activation('leaky_relu')(x)

for i in range(num_resnet_blocks): x = resnet_block(x, num_filters, (kernel_size))

x = Conv2D(num_filters, (kernel_size), activation=None, padding = 'same')(x) x = BatchNormalization()(x) x = Activation('leaky_relu')(x)

x = GlobalAveragePooling2D()(x) x = Flatten()(x)

dense_1 = Dense(128, activation='leaky_relu', kernel_initializer='lecun_uniform', kernel_regularizer=l1(0.0001))(x) dropout_1 = Dropout(0.7)(dense_1) dense_2 = Dense(128, activation='leaky_relu', kernel_initializer='lecun_uniform', kernel_regularizer=l1(0.0001))(dropout_1) dropout_2 = Dropout(0.7)(dense_2) softmax = Dense(7, activation='softmax')(dropout_2)

model = keras.Model(rf_in, softmax) model.summary()

### Hls4ml setup:

Precison: ap_fixed<16,6>
Strategy: resource
Reuse factor: 32
io_type: io_stream
Backend: VivadoAccelerator
part: xczu7ev-ffvc1156-2-e (zcu104)

hls_config = hls4ml.utils.config_from_keras_model(model, granularity='name', default_reuse_factor=32) hls_config['Model']['Strategy']='Resource'

hls_config['LayerName']['dense_2']['exp_table_t'] = 'ap_fixed<16,6>' hls_config['LayerName']['dense_2']['inv_table_t'] = 'ap_fixed<16,6>' hls_config['LayerName']['dense_2']['Strategy'] = 'Stable' hls_model = hls4ml.converters.convert_from_keras_model( model, hls_config=hls_config, output_dir='Res32', io_type='io_stream', backend='VivadoAccelerator', part='xczu7ev-ffvc1156-2-e')

[jmitrevs]

I confirmed the bug in the main branch and will try to fix it. In the meantime, this works (though you should check if the alpha is the same default).

[jmitrevs]

#!/usr/bin/env python

import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import (
    Activation,
    Conv2D,
    Dense,
    BatchNormalization,
    Add,
    Input,
    GlobalAveragePooling2D,
    Dropout,
    Flatten,
    LeakyReLU,
)

import hls4ml


### Model:

def resnet_block(input_data, filters, conv_size):
    shortcut = input_data  # Store the original input for the shortcut connection

    # Pre-activation (Batch Normalization and Activation before Convolution)
    x = BatchNormalization()(input_data)
    x = LeakyReLU()(x)

    # Bottleneck 1x1 Convolution (reduce channels)
    x = Conv2D(filters // 4, 1, activation=None, padding='same', kernel_initializer='lecun_uniform', kernel_regularizer=tf.keras.regularizers.L1(0.0001))(x)

    x = BatchNormalization()(x)
    x = LeakyReLU()(x)

    # 3x3 Convolution
    x = Conv2D(filters // 4, conv_size, activation=None, padding='same',kernel_initializer='lecun_uniform', kernel_regularizer=tf.keras.regularizers.L1(0.0001))(x)

    x = BatchNormalization()(x)
    x = LeakyReLU()(x)

    # Bottleneck 1x1 Convolution (increase channels back to original)
    x = Conv2D(filters, 1, activation=None, padding='same',kernel_initializer='lecun_uniform', kernel_regularizer=tf.keras.regularizers.L1(0.0001))(x)

    # Shortcut connection (with 1x1 Convolution if needed to match dimensions)
    if input_data.shape[-1] != filters:  # Check if channel dimensions match
        shortcut = Conv2D(filters, 1, activation=None, padding='same',kernel_initializer='lecun_uniform', kernel_regularizer=tf.keras.regularizers.L1(0.0001))(shortcut)

    # Add the shortcut connection
    x = Add()([x, shortcut])

    return x


num_resnet_blocks = 6
num_filters = 16
kernel_size = 3,3

rf_in = Input(shape=(32, 32, 2), name = 'rf_input')

x = Conv2D(num_filters, (kernel_size), activation=None, padding='same', kernel_initializer='lecun_uniform', kernel_regularizer=tf.keras.regularizers.L1(0.0001))(rf_in)
x = BatchNormalization()(x)
x = LeakyReLU()(x)

for i in range(num_resnet_blocks):
    x = resnet_block(x, num_filters, (kernel_size))

x = Conv2D(num_filters, (kernel_size), activation=None, padding = 'same')(x)
x = BatchNormalization()(x)
x = LeakyReLU()(x)

x = GlobalAveragePooling2D()(x)
x = Flatten()(x)

dense_1 = Dense(128, kernel_initializer='lecun_uniform', kernel_regularizer=tf.keras.regularizers.L1(0.0001))(x)
dense_act_1 = LeakyReLU()(dense_1)
dropout_1 = Dropout(0.7)(dense_act_1)
dense_2 = Dense(128, kernel_initializer='lecun_uniform', kernel_regularizer=tf.keras.regularizers.L1(0.0001))(dropout_1)
dense_act_2 = LeakyReLU()(dense_2)
dropout_2 = Dropout(0.7)(dense_act_2)
softmax = Dense(7, activation='softmax')(dropout_2)

model = tf.keras.Model(rf_in, softmax)
model.summary()

hls_config = hls4ml.utils.config_from_keras_model(model, granularity='name', default_reuse_factor=32, backend='Vivado')
hls_config['Model']['Strategy']='Resource'

hls_config['LayerName']['dense_2']['exp_table_t'] = 'ap_fixed<16,6>'
hls_config['LayerName']['dense_2']['inv_table_t'] = 'ap_fixed<16,6>'
hls_config['LayerName']['dense_2']['Strategy'] = 'Stable'
hls_model = hls4ml.converters.convert_from_keras_model(
    model,
    hls_config=hls_config,
    output_dir='Res32_v2',
    io_type='io_stream',
    backend='Vivado',
    part='xczu7ev-ffvc1156-2-e')

hls_model.compile()

[bo3z]

Hi @AnouarITI, PR #1085 by @jmitrevs should fix this issue. Can you check out the branch and confirm it works?