Connecting Conv1d layer with LSTM Layer.

[svs11]

I’m afraid that hls4ml is not properly flattening the tensor between a conv1d layer and an LSTM layer. For the network generated from the following Keras code:

import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Conv1D, MaxPooling1D, LSTM, Flatten, Dense,TimeDistributed def create_model(): model = Sequential() model.add(Conv1D(8, 3, padding='same', activation='relu', input_shape=(16, 1))) model.add(MaxPooling1D(pool_size=2, strides=2, padding='same')) model.add(Conv1D(16, 3, padding='same', activation='relu')) model.add(MaxPooling1D(pool_size=2, strides=2, padding='same')) model.add(Conv1D(32, 3, padding='same', activation='relu')) model.add(MaxPooling1D(pool_size=2, strides=2, padding='same'))

model.add(LSTM(8, return_sequences=True))
model.add(LSTM(8,return_sequences=True))

model.add(Dense(1)) 

model_json = model.to_json()
with open("lstm_CNN_model.json", "w") as json_file:
    json_file.write(model_json)
model.save("lstm_CNN.h5")
print("Model saved to lstm_CNN_model.json and lstm_CNN.h5.")
return model

Create the model

model = create_model()

I see the following generated CPP code:

include

include "network_64_4_64_2_32_2_32_ru.h"

include "parameters.h"

void network_64_4_64_2_32_2_32_ru( hls::stream &conv1d_input, hls::stream &layer15_out ) {

// hls-fpga-machine-learning insert IO
#pragma HLS INTERFACE axis port=conv1d_input,layer15_out 
#pragma HLS DATAFLOW 

ifndef SYNTHESIS

static bool loaded_weights = false;
if (!loaded_weights) {
    // hls-fpga-machine-learning insert load weights
    nnet::load_weights_from_txt<model_default_t, 24>(w2, "w2.txt");
    nnet::load_weights_from_txt<model_default_t, 8>(b2, "b2.txt");
    nnet::load_weights_from_txt<model_default_t, 384>(w5, "w5.txt");
    nnet::load_weights_from_txt<model_default_t, 16>(b5, "b5.txt");
    nnet::load_weights_from_txt<model_default_t, 1536>(w8, "w8.txt");
    nnet::load_weights_from_txt<model_default_t, 32>(b8, "b8.txt");
    nnet::load_weights_from_txt<model_default_t, 1024>(w11, "w11.txt");
    nnet::load_weights_from_txt<model_default_t, 256>(wr11, "wr11.txt");
    nnet::load_weights_from_txt<model_default_t, 32>(b11, "b11.txt");
    nnet::load_weights_from_txt<model_default_t, 32>(br11, "br11.txt");
    nnet::load_weights_from_txt<model_default_t, 256>(w12, "w12.txt");
    nnet::load_weights_from_txt<model_default_t, 256>(wr12, "wr12.txt");
    nnet::load_weights_from_txt<model_default_t, 32>(b12, "b12.txt");
    nnet::load_weights_from_txt<model_default_t, 32>(br12, "br12.txt");
    nnet::load_weights_from_txt<model_default_t, 8>(w15, "w15.txt");
    nnet::load_weights_from_txt<model_default_t, 1>(b15, "b15.txt");
    loaded_weights = true;
}

endif

// ****************************************
// NETWORK INSTANTIATION
// ****************************************

// hls-fpga-machine-learning insert layers

hls::stream<layer16_t> layer16_out("layer16_out");
#pragma HLS STREAM variable=layer16_out depth=18
nnet::zeropad1d_cl<input_t, layer16_t, config16>(conv1d_input, layer16_out); // zp1d_conv1d

hls::stream<layer2_t> layer2_out("layer2_out");
#pragma HLS STREAM variable=layer2_out depth=16
nnet::conv_1d_cl<layer16_t, layer2_t, config2>(layer16_out, layer2_out, w2, b2); // conv1d

hls::stream<layer3_t> layer3_out("layer3_out");
#pragma HLS STREAM variable=layer3_out depth=16
nnet::relu<layer2_t, layer3_t, relu_config3>(layer2_out, layer3_out); // conv1d_relu

hls::stream<layer4_t> layer4_out("layer4_out");
#pragma HLS STREAM variable=layer4_out depth=8
nnet::pooling1d_cl<layer3_t, layer4_t, config4>(layer3_out, layer4_out); // max_pooling1d

hls::stream<layer17_t> layer17_out("layer17_out");
#pragma HLS STREAM variable=layer17_out depth=10
nnet::zeropad1d_cl<layer4_t, layer17_t, config17>(layer4_out, layer17_out); // zp1d_conv1d_1

hls::stream<layer5_t> layer5_out("layer5_out");
#pragma HLS STREAM variable=layer5_out depth=8
nnet::conv_1d_cl<layer17_t, layer5_t, config5>(layer17_out, layer5_out, w5, b5); // conv1d_1

hls::stream<layer6_t> layer6_out("layer6_out");
#pragma HLS STREAM variable=layer6_out depth=8
nnet::relu<layer5_t, layer6_t, relu_config6>(layer5_out, layer6_out); // conv1d_1_relu

hls::stream<layer7_t> layer7_out("layer7_out");
#pragma HLS STREAM variable=layer7_out depth=4
nnet::pooling1d_cl<layer6_t, layer7_t, config7>(layer6_out, layer7_out); // max_pooling1d_1

hls::stream<layer18_t> layer18_out("layer18_out");
#pragma HLS STREAM variable=layer18_out depth=6
nnet::zeropad1d_cl<layer7_t, layer18_t, config18>(layer7_out, layer18_out); // zp1d_conv1d_2

hls::stream<layer8_t> layer8_out("layer8_out");
#pragma HLS STREAM variable=layer8_out depth=4
nnet::conv_1d_cl<layer18_t, layer8_t, config8>(layer18_out, layer8_out, w8, b8); // conv1d_2

hls::stream<layer9_t> layer9_out("layer9_out");
#pragma HLS STREAM variable=layer9_out depth=4
nnet::relu<layer8_t, layer9_t, relu_config9>(layer8_out, layer9_out); // conv1d_2_relu

hls::stream<layer10_t> layer10_out("layer10_out");
#pragma HLS STREAM variable=layer10_out depth=2
nnet::pooling1d_cl<layer9_t, layer10_t, config10>(layer9_out, layer10_out); // max_pooling1d_2

hls::stream<layer11_t> layer11_out("layer11_out");
#pragma HLS STREAM variable=layer11_out depth=2
nnet::lstm_stack<layer10_t, layer11_t, config11>(layer10_out, layer11_out, w11, wr11, b11, br11); // lstm

hls::stream<layer12_t> layer12_out("layer12_out");
#pragma HLS STREAM variable=layer12_out depth=2
nnet::lstm_stack<layer11_t, layer12_t, config12>(layer11_out, layer12_out, w12, wr12, b12, br12); // lstm_1

nnet::pointwise_conv_1d_cl<layer12_t, result_t, config15>(layer12_out, layer15_out, w15, b15); // dense

}

The number of weights on the first LSTM layer is expected to be the number of outputs from the last conv1d+pooling layer, which is 2 samples 32 channels x 4 gates 8 states = 2048, but is instead shown in the generated CPP as 1024.

How should I be connecting a pooling layer to an LSTM layer to guarantee that all outputs are conveyed?

[JanFSchulte]

Hi!

I had a look at your model, and just printing the trainable weights for the first LSTM layer, I see

[<tf.Variable 'lstm/lstm_cell/kernel:0' shape=(32, 32) dtype=float32, numpy=
array([[ 0.01692209, -0.22750317, -0.17461008, ..., -0.1579345 ,
        -0.21596879, -0.18585742],
       [-0.00184596,  0.1575419 ,  0.20252898, ...,  0.14971459,
         0.17116585, -0.04111549],
       [-0.24567568,  0.01723912, -0.15928173, ..., -0.20553797,
         0.22376046, -0.24837291],
       ...,
       [-0.20332155,  0.06006312,  0.06557494, ...,  0.10808015,
        -0.2113491 , -0.05491558],
       [-0.27010858,  0.10658553, -0.13689941, ...,  0.2040728 ,
        -0.14297459,  0.2779071 ],
       [-0.29793295, -0.13058276,  0.01223576, ..., -0.02761602,
        -0.27836597,  0.1290856 ]], dtype=float32)>,

for the kernel weights, which are of size 32 x 32 = 1024. So hls4ml is correctly inferring the size of the weight tensor. I think there is a misunderstanding of the expected size here, the number of samples does not impact the size of the weight tensors, see for example https://medium.com/analytics-vidhya/demystifying-lstm-weights-and-biases-dimensions-c47dbd39b30a.

[svs11]

Thank you for your reply!

After reading the page linked in your response, I see that my terminology might be incorrect. I'm assuming--or rather I desire--that the CNN+pooling layer is providing a "sequence length" of 1 and an embedded dimension of samples * channels. We're building latency-constrained models and we can't afford to invoke the LSTM equations multiple times per forward pass. In other words, we want to flatten the tensor feeding the LSTM layer into a single embedding vector. Is this possible?

Thank you! -Suyash

[JanFSchulte]

Hi Suyash,

I don't think something like this is supported in hls4ml at the moment. AFAIK, our implementation keeps the structure of iterating over the time steps to calculate the results. I presume it would be possible to add an optional version that flattens the inputs (Flatten layers are supported) and processes the full calculation in one go. People that are more expert on the implementation on LSTM in hls4ml can correct me, but I think this would require some development.