tensorflow GPU crashes for 0 batch size CUDNN_STATUS_BAD_PARAM

Question

This issue seem to be existing for a long time and lots of users are facing the issue.

stream_executor/cuda/cuda_dnn.cc:444] could not convert BatchDescriptor {count: 0 feature_map_count: 64 spatial: 7 264 value_min: 0.000000 value_max: 0.000000 layout: BatchDepthYX} to cudnn tensor descriptor: CUDNN_STATUS_BAD_PARAM

The message is so mysterious that I do not know what happened in my code, however, my code works fine on CPU tensorflow.

I heard that we can use tf.cond to get around this, but I'm new to tensorflow-gpu, so can someone please help me? My code uses Keras and takes generator like input, this is to avoid any out-of-memory issue. The generator is built by a while True loop that spits out data by some batch size.

def resnet_model(bin_multiple):
    #input and reshape
    inputs = Input(shape=input_shape)
    reshape = Reshape(input_shape_channels)(inputs)
    #normal convnet layer (have to do one initially to get 64 channels)
    conv = Conv2D(64,(1,bin_multiple*note_range),padding="same",activation='relu')(reshape)
    pool = MaxPooling2D(pool_size=(1,2))(conv)
    for i in range(int(np.log2(bin_multiple))-1):
        print( i)
        #residual block
        bn = BatchNormalization()(pool)
        re = Activation('relu')(bn)
        freq_range = int((bin_multiple/(2**(i+1)))*note_range)
        print(freq_range)
        conv = Conv2D(64,(1,freq_range),padding="same",activation='relu')(re)
        #add and downsample
        ad = add([pool,conv])
        pool = MaxPooling2D(pool_size=(1,2))(ad)
    flattened = Flatten()(pool)
    fc = Dense(1024, activation='relu')(flattened)
    do = Dropout(0.5)(fc)
    fc = Dense(512, activation='relu')(do)
    do = Dropout(0.5)(fc)
    outputs = Dense(note_range, activation='sigmoid')(do)
    model = Model(inputs=inputs, outputs=outputs)
    return model

model = resnet_model(bin_multiple)
init_lr = float(args['init_lr'])
    model.compile(loss='binary_crossentropy',
              optimizer=SGD(lr=init_lr,momentum=0.9), metrics=['accuracy', 'mae', 'categorical_accuracy'])
model.summary()
history = model.fit_generator(trainGen.next(),trainGen.steps(), epochs=epochs,     
verbose=1,validation_data=valGen.next(),validation_steps=valGen.steps(),callbacks=callbacks, workers=8, use_multiprocessing=True)

Answer 1

The problem is when you model received 0 batch size. For me I had the error because I have 1000 example and I run it on multiple GPus ( 2 GPU) with batch size equal to 32 .And in My graph I divided the batch size to mini batch size to so each GPU take 16 example. At step 31 ( 31 * 32) I will finished 992 examples , so there is only 8 example left, it will go to GPU 1 and GPU2 will end with zero batch size that's why I received your error above.

Still couldn't solve it and still searching about proper solution. I hope this help you to discover when in your code you received zero batch size.