RNN f32 inference example

Intel® oneAPI Deep Neural Network Developer Guide and Reference

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ID 768875

Date 2/28/2024

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RNN f32 inference example

This C++ API example demonstrates how to build GNMT model inference.

Example code: cpu_rnn_inference_f32.cpp

For the encoder we use:

one primitive for the bidirectional layer of the encoder
one primitive for all remaining unidirectional layers in the encoder For the decoder we use:
one primitive for the first iteration
one primitive for all subsequent iterations in the decoder. Note that in this example, this primitive computes the states in place.
the attention mechanism is implemented separately as there is no support for the context vectors in oneDNN yet

Initialize a CPU engine and stream. The last parameter in the call represents the index of the engine.

auto cpu_engine = engine(engine::kind::cpu, 0);
stream s(cpu_engine);

Declare encoder net and decoder net

std::vector<primitive> encoder_net, decoder_net;
std::vector<std::unordered_map<int, memory>> encoder_net_args,
        decoder_net_args;

std::vector<float> net_src(batch * src_seq_length_max * feature_size, 1.0f);
std::vector<float> net_dst(batch * tgt_seq_length_max * feature_size, 1.0f);

Encoder

Initialize Encoder Memory

memory::dims enc_bidir_src_layer_tz
        = {src_seq_length_max, batch, feature_size};
memory::dims enc_bidir_weights_layer_tz
        = {enc_bidir_n_layers, 2, feature_size, lstm_n_gates, feature_size};
memory::dims enc_bidir_weights_iter_tz
        = {enc_bidir_n_layers, 2, feature_size, lstm_n_gates, feature_size};
memory::dims enc_bidir_bias_tz
        = {enc_bidir_n_layers, 2, lstm_n_gates, feature_size};
memory::dims enc_bidir_dst_layer_tz
        = {src_seq_length_max, batch, 2 * feature_size};

Encoder: 1 bidirectional layer and 7 unidirectional layers

Create the memory for user data

auto user_enc_bidir_src_layer_md = dnnl::memory::desc(
        {enc_bidir_src_layer_tz}, dnnl::memory::data_type::f32,
        dnnl::memory::format_tag::tnc);

auto user_enc_bidir_wei_layer_md = dnnl::memory::desc(
        {enc_bidir_weights_layer_tz}, dnnl::memory::data_type::f32,
        dnnl::memory::format_tag::ldigo);

auto user_enc_bidir_wei_iter_md = dnnl::memory::desc(
        {enc_bidir_weights_iter_tz}, dnnl::memory::data_type::f32,
        dnnl::memory::format_tag::ldigo);

auto user_enc_bidir_bias_md = dnnl::memory::desc({enc_bidir_bias_tz},
        dnnl::memory::data_type::f32, dnnl::memory::format_tag::ldgo);

auto user_enc_bidir_src_layer_memory = dnnl::memory(
        user_enc_bidir_src_layer_md, cpu_engine, net_src.data());
auto user_enc_bidir_wei_layer_memory
        = dnnl::memory(user_enc_bidir_wei_layer_md, cpu_engine,
                user_enc_bidir_wei_layer.data());
auto user_enc_bidir_wei_iter_memory
        = dnnl::memory(user_enc_bidir_wei_iter_md, cpu_engine,
                user_enc_bidir_wei_iter.data());
auto user_enc_bidir_bias_memory = dnnl::memory(
        user_enc_bidir_bias_md, cpu_engine, user_enc_bidir_bias.data());

Create memory descriptors for RNN data w/o specified layout

auto enc_bidir_wei_layer_md = memory::desc({enc_bidir_weights_layer_tz},
        memory::data_type::f32, memory::format_tag::any);

auto enc_bidir_wei_iter_md = memory::desc({enc_bidir_weights_iter_tz},
        memory::data_type::f32, memory::format_tag::any);

auto enc_bidir_dst_layer_md = memory::desc({enc_bidir_dst_layer_tz},
        memory::data_type::f32, memory::format_tag::any);

Create bidirectional RNN

auto enc_bidir_prim_desc = lstm_forward::primitive_desc(cpu_engine,
        prop_kind::forward_inference, rnn_direction::bidirectional_concat,
        user_enc_bidir_src_layer_md, memory::desc(), memory::desc(),
        enc_bidir_wei_layer_md, enc_bidir_wei_iter_md,
        user_enc_bidir_bias_md, enc_bidir_dst_layer_md, memory::desc(),
        memory::desc());

Create memory for input data and use reorders to reorder user data to internal representation

auto enc_bidir_wei_layer_memory
        = memory(enc_bidir_prim_desc.weights_layer_desc(), cpu_engine);
auto enc_bidir_wei_layer_reorder_pd = reorder::primitive_desc(
        user_enc_bidir_wei_layer_memory, enc_bidir_wei_layer_memory);
reorder(enc_bidir_wei_layer_reorder_pd)
        .execute(s, user_enc_bidir_wei_layer_memory,
                enc_bidir_wei_layer_memory);

Encoder : add the bidirectional rnn primitive with related arguments into encoder_net

encoder_net.push_back(lstm_forward(enc_bidir_prim_desc));
encoder_net_args.push_back(
        {{DNNL_ARG_SRC_LAYER, user_enc_bidir_src_layer_memory},
                {DNNL_ARG_WEIGHTS_LAYER, enc_bidir_wei_layer_memory},
                {DNNL_ARG_WEIGHTS_ITER, enc_bidir_wei_iter_memory},
                {DNNL_ARG_BIAS, user_enc_bidir_bias_memory},
                {DNNL_ARG_DST_LAYER, enc_bidir_dst_layer_memory}});

Encoder: unidirectional layers

First unidirectinal layer scales 2 * feature_size output of bidirectional layer to feature_size output

std::vector<float> user_enc_uni_first_wei_layer(
        1 * 1 * 2 * feature_size * lstm_n_gates * feature_size, 1.0f);
std::vector<float> user_enc_uni_first_wei_iter(
        1 * 1 * feature_size * lstm_n_gates * feature_size, 1.0f);
std::vector<float> user_enc_uni_first_bias(
        1 * 1 * lstm_n_gates * feature_size, 1.0f);

Encoder : Create unidirection RNN for first cell

auto enc_uni_first_prim_desc = lstm_forward::primitive_desc(cpu_engine,
        prop_kind::forward_inference,
        rnn_direction::unidirectional_left2right, enc_bidir_dst_layer_md,
        memory::desc(), memory::desc(), enc_uni_first_wei_layer_md,
        enc_uni_first_wei_iter_md, user_enc_uni_first_bias_md,
        enc_uni_first_dst_layer_md, memory::desc(), memory::desc());

Encoder : add the first unidirectional rnn primitive with related arguments into encoder_net

// TODO: add a reorder when they will be available
encoder_net.push_back(lstm_forward(enc_uni_first_prim_desc));
encoder_net_args.push_back(
        {{DNNL_ARG_SRC_LAYER, enc_bidir_dst_layer_memory},
                {DNNL_ARG_WEIGHTS_LAYER, enc_uni_first_wei_layer_memory},
                {DNNL_ARG_WEIGHTS_ITER, enc_uni_first_wei_iter_memory},
                {DNNL_ARG_BIAS, user_enc_uni_first_bias_memory},
                {DNNL_ARG_DST_LAYER, enc_uni_first_dst_layer_memory}});

Encoder : Remaining unidirectional layers

std::vector<float> user_enc_uni_wei_layer((enc_unidir_n_layers - 1) * 1
                * feature_size * lstm_n_gates * feature_size,
        1.0f);
std::vector<float> user_enc_uni_wei_iter((enc_unidir_n_layers - 1) * 1
                * feature_size * lstm_n_gates * feature_size,
        1.0f);
std::vector<float> user_enc_uni_bias(
        (enc_unidir_n_layers - 1) * 1 * lstm_n_gates * feature_size, 1.0f);

Encoder : Create unidirection RNN cell

auto enc_uni_prim_desc = lstm_forward::primitive_desc(cpu_engine,
        prop_kind::forward_inference,
        rnn_direction::unidirectional_left2right,
        enc_uni_first_dst_layer_md, memory::desc(), memory::desc(),
        enc_uni_wei_layer_md, enc_uni_wei_iter_md, user_enc_uni_bias_md,
        enc_dst_layer_md, memory::desc(), memory::desc());

Encoder : add the unidirectional rnn primitive with related arguments into encoder_net

encoder_net.push_back(lstm_forward(enc_uni_prim_desc));
encoder_net_args.push_back(
        {{DNNL_ARG_SRC_LAYER, enc_uni_first_dst_layer_memory},
                {DNNL_ARG_WEIGHTS_LAYER, enc_uni_wei_layer_memory},
                {DNNL_ARG_WEIGHTS_ITER, enc_uni_wei_iter_memory},
                {DNNL_ARG_BIAS, user_enc_uni_bias_memory},
                {DNNL_ARG_DST_LAYER, enc_dst_layer_memory}});

Decoder with attention mechanism

Decoder : declare memory dimensions

std::vector<float> user_dec_wei_layer(
        dec_n_layers * 1 * feature_size * lstm_n_gates * feature_size,
        1.0f);
std::vector<float> user_dec_wei_iter(dec_n_layers * 1
                * (feature_size + feature_size) * lstm_n_gates
                * feature_size,
        1.0f);
std::vector<float> user_dec_bias(
        dec_n_layers * 1 * lstm_n_gates * feature_size, 1.0f);
std::vector<float> user_dec_dst(
        tgt_seq_length_max * batch * feature_size, 1.0f);
std::vector<float> user_weights_attention_src_layer(
        feature_size * feature_size, 1.0f);
std::vector<float> user_weights_annotation(
        feature_size * feature_size, 1.0f);
std::vector<float> user_weights_alignments(feature_size, 1.0f);

memory::dims user_dec_wei_layer_dims
        = {dec_n_layers, 1, feature_size, lstm_n_gates, feature_size};
memory::dims user_dec_wei_iter_dims = {dec_n_layers, 1,
        feature_size + feature_size, lstm_n_gates, feature_size};
memory::dims user_dec_bias_dims
        = {dec_n_layers, 1, lstm_n_gates, feature_size};

memory::dims dec_src_layer_dims = {1, batch, feature_size};
memory::dims dec_dst_layer_dims = {1, batch, feature_size};
memory::dims dec_dst_iter_c_dims = {dec_n_layers, 1, batch, feature_size};

We will use the same memory for dec_src_iter and dec_dst_iter However, dec_src_iter has a context vector but not dec_dst_iter. To resolve this we will create one memory that holds the context vector as well as the both the hidden and cell states. The dst_iter will be a sub-memory of this memory. Note that the cell state will be padded by feature_size values. However, we do not compute or access those.

memory::dims dec_dst_iter_dims
        = {dec_n_layers, 1, batch, feature_size + feature_size};
memory::dims dec_dst_iter_noctx_dims
        = {dec_n_layers, 1, batch, feature_size};

Decoder : create memory description

auto user_dec_wei_layer_md = dnnl::memory::desc({user_dec_wei_layer_dims},
        dnnl::memory::data_type::f32, dnnl::memory::format_tag::ldigo);
auto user_dec_wei_iter_md = dnnl::memory::desc({user_dec_wei_iter_dims},
        dnnl::memory::data_type::f32, dnnl::memory::format_tag::ldigo);
auto user_dec_bias_md = dnnl::memory::desc({user_dec_bias_dims},
        dnnl::memory::data_type::f32, dnnl::memory::format_tag::ldgo);
auto dec_dst_layer_md = dnnl::memory::desc({dec_dst_layer_dims},
        dnnl::memory::data_type::f32, dnnl::memory::format_tag::tnc);
auto dec_src_layer_md = dnnl::memory::desc({dec_src_layer_dims},
        dnnl::memory::data_type::f32, dnnl::memory::format_tag::tnc);
auto dec_dst_iter_md = dnnl::memory::desc({dec_dst_iter_dims},
        dnnl::memory::data_type::f32, dnnl::memory::format_tag::ldnc);
auto dec_dst_iter_c_md = dnnl::memory::desc({dec_dst_iter_c_dims},
        dnnl::memory::data_type::f32, dnnl::memory::format_tag::ldnc);

Decoder : Create memory

auto user_dec_wei_layer_memory = dnnl::memory(
        user_dec_wei_layer_md, cpu_engine, user_dec_wei_layer.data());
auto user_dec_wei_iter_memory = dnnl::memory(
        user_dec_wei_iter_md, cpu_engine, user_dec_wei_iter.data());
auto user_dec_bias_memory
        = dnnl::memory(user_dec_bias_md, cpu_engine, user_dec_bias.data());
auto user_dec_dst_layer_memory
        = dnnl::memory(dec_dst_layer_md, cpu_engine, user_dec_dst.data());
auto dec_src_layer_memory = dnnl::memory(dec_src_layer_md, cpu_engine);
auto dec_dst_iter_c_memory = dnnl::memory(dec_dst_iter_c_md, cpu_engine);

Decoder : As mentioned above, we create a view without context out of the memory with context.

auto dec_dst_iter_memory = dnnl::memory(dec_dst_iter_md, cpu_engine);
auto dec_dst_iter_noctx_md = dec_dst_iter_md.submemory_desc(
        dec_dst_iter_noctx_dims, {0, 0, 0, 0, 0});

Decoder : Create RNN decoder cell

auto dec_ctx_prim_desc = lstm_forward::primitive_desc(cpu_engine,
        prop_kind::forward_inference,
        rnn_direction::unidirectional_left2right, dec_src_layer_md,
        dec_dst_iter_md, dec_dst_iter_c_md, dec_wei_layer_md,
        dec_wei_iter_md, user_dec_bias_md, dec_dst_layer_md,
        dec_dst_iter_noctx_md, dec_dst_iter_c_md);

Decoder : reorder weight memory

auto dec_wei_layer_memory
        = memory(dec_ctx_prim_desc.weights_layer_desc(), cpu_engine);
auto dec_wei_layer_reorder_pd = reorder::primitive_desc(
        user_dec_wei_layer_memory, dec_wei_layer_memory);
reorder(dec_wei_layer_reorder_pd)
        .execute(s, user_dec_wei_layer_memory, dec_wei_layer_memory);

auto dec_wei_iter_memory
        = memory(dec_ctx_prim_desc.weights_iter_desc(), cpu_engine);
auto dec_wei_iter_reorder_pd = reorder::primitive_desc(
        user_dec_wei_iter_memory, dec_wei_iter_memory);
reorder(dec_wei_iter_reorder_pd)
        .execute(s, user_dec_wei_iter_memory, dec_wei_iter_memory);

Decoder : add the rnn primitive with related arguments into decoder_net

// TODO: add a reorder when they will be available
decoder_net.push_back(lstm_forward(dec_ctx_prim_desc));
decoder_net_args.push_back({{DNNL_ARG_SRC_LAYER, dec_src_layer_memory},
        {DNNL_ARG_SRC_ITER, dec_dst_iter_memory},
        {DNNL_ARG_SRC_ITER_C, dec_dst_iter_c_memory},
        {DNNL_ARG_WEIGHTS_LAYER, dec_wei_layer_memory},
        {DNNL_ARG_WEIGHTS_ITER, dec_wei_iter_memory},
        {DNNL_ARG_BIAS, user_dec_bias_memory},
        {DNNL_ARG_DST_LAYER, user_dec_dst_layer_memory},
        {DNNL_ARG_DST_ITER, dec_dst_iter_memory},
        {DNNL_ARG_DST_ITER_C, dec_dst_iter_c_memory}});

Execution

run encoder (1 stream)

for (size_t p = 0; p < encoder_net.size(); ++p)
    encoder_net.at(p).execute(s, encoder_net_args.at(p));

we compute the weighted annotations once before the decoder

compute_weighted_annotations(weighted_annotations.data(),
        src_seq_length_max, batch, feature_size,
        user_weights_annotation.data(),
        (float *)enc_dst_layer_memory.get_data_handle());

We initialize src_layer to the embedding of the end of sequence character, which are assumed to be 0 here

memset(dec_src_layer_memory.get_data_handle(), 0,
        dec_src_layer_memory.get_desc().get_size());

From now on, src points to the output of the last iteration

Compute attention context vector into the first layer src_iter

compute_attention(src_att_iter_handle, src_seq_length_max, batch,
        feature_size, user_weights_attention_src_layer.data(),
        src_att_layer_handle,
        (float *)enc_bidir_dst_layer_memory.get_data_handle(),
        weighted_annotations.data(),
        user_weights_alignments.data());

copy the context vectors to all layers of src_iter

copy_context(
        src_att_iter_handle, dec_n_layers, batch, feature_size);

run the decoder iteration

for (size_t p = 0; p < decoder_net.size(); ++p)
    decoder_net.at(p).execute(s, decoder_net_args.at(p));

Move the handle on the src/dst layer to the next iteration

auto dst_layer_handle
        = (float *)user_dec_dst_layer_memory.get_data_handle();
dec_src_layer_memory.set_data_handle(dst_layer_handle);
user_dec_dst_layer_memory.set_data_handle(
        dst_layer_handle + batch * feature_size);

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Intel® oneAPI Deep Neural Network Developer Guide and Reference

RNN f32 inference example