RNN int8 inference example

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

Example code: cpu_rnn_inference_int8.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, 0.1f);
std::vector<float> net_dst(batch * tgt_seq_length_max * feature_size, 0.1f);

Quantization factors for f32 data

std::vector<float> weights_scales(lstm_n_gates * feature_size);
// assign halves of vector with arbitrary values
const dim_t scales_half = lstm_n_gates * feature_size / 2;
std::fill(
        weights_scales.begin(), weights_scales.begin() + scales_half, 30.f);
std::fill(
        weights_scales.begin() + scales_half, weights_scales.end(), 65.5f);

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

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

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

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

auto user_enc_bidir_src_layer_memory
        = memory(user_enc_bidir_src_layer_md, cpu_engine, net_src.data());
auto user_enc_bidir_wei_layer_memory = memory(user_enc_bidir_wei_layer_md,
        cpu_engine, user_enc_bidir_wei_layer.data());
auto user_enc_bidir_wei_iter_memory = memory(user_enc_bidir_wei_iter_md,
        cpu_engine, user_enc_bidir_wei_iter.data());
auto user_enc_bidir_bias_memory = 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_src_layer_md = memory::desc({enc_bidir_src_layer_tz},
        memory::data_type::u8, memory::format_tag::any);

auto enc_bidir_wei_layer_md = memory::desc({enc_bidir_weights_layer_tz},
        memory::data_type::s8, memory::format_tag::any);

auto enc_bidir_wei_iter_md = memory::desc({enc_bidir_weights_iter_tz},
        memory::data_type::s8, memory::format_tag::any);

auto enc_bidir_dst_layer_md = memory::desc({enc_bidir_dst_layer_tz},
        memory::data_type::u8, memory::format_tag::any);

Create bidirectional RNN

lstm_forward::desc bi_layer_desc(prop_kind::forward_inference,
        rnn_direction::bidirectional_concat, 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());

Define RNN attributes that store quantization parameters

primitive_attr attr;
attr.set_rnn_data_qparams(data_scale, data_shift);
attr.set_rnn_weights_qparams(weights_scale_mask, weights_scales);

// check if int8 LSTM is supported
lstm_forward::primitive_desc enc_bidir_prim_desc;
try {
    enc_bidir_prim_desc
            = lstm_forward::primitive_desc(bi_layer_desc, attr, cpu_engine);
} catch (error &e) {
    if (e.status == dnnl_unimplemented)
        throw example_allows_unimplemented {
                "No int8 LSTM implementation is available for this "
                "platform.\n"
                "Please refer to the developer guide for details."};

    // on any other error just re-throw
    throw;
}

Create memory for input data and use reorders to quantize values to int8 NOTE: same attributes are used when creating RNN primitive and reorders

auto enc_bidir_src_layer_memory
        = memory(enc_bidir_prim_desc.src_layer_desc(), cpu_engine);
auto enc_bidir_src_layer_reorder_pd = reorder::primitive_desc(
        user_enc_bidir_src_layer_memory, enc_bidir_src_layer_memory, attr);
encoder_net.push_back(reorder(enc_bidir_src_layer_reorder_pd));
encoder_net_args.push_back(
        {{DNNL_ARG_FROM, user_enc_bidir_src_layer_memory},
                {DNNL_ARG_TO, enc_bidir_src_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, 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, 0.3f);
std::vector<float> user_enc_uni_first_wei_iter(
        1 * 1 * feature_size * lstm_n_gates * feature_size, 0.2f);
std::vector<float> user_enc_uni_first_bias(
        1 * 1 * lstm_n_gates * feature_size, 1.0f);

Encoder : Create unidirection RNN for first cell

lstm_forward::desc enc_uni_first_layer_desc(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());

auto enc_uni_first_prim_desc = lstm_forward::primitive_desc(
        enc_uni_first_layer_desc, attr, cpu_engine);

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

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,
        0.3f);
std::vector<float> user_enc_uni_wei_iter((enc_unidir_n_layers - 1) * 1
                * feature_size * lstm_n_gates * feature_size,
        0.2f);
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

lstm_forward::desc enc_uni_layer_desc(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());
auto enc_uni_prim_desc = lstm_forward::primitive_desc(
        enc_uni_layer_desc, attr, cpu_engine);

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,
        0.2f);
std::vector<float> user_dec_wei_iter(dec_n_layers * 1
                * (feature_size + feature_size) * lstm_n_gates
                * feature_size,
        0.3f);
std::vector<float> user_dec_bias(
        dec_n_layers * 1 * lstm_n_gates * feature_size, 1.0f);
std::vector<int8_t> user_weights_attention_src_layer(
        feature_size * feature_size, 1);
float weights_attention_scale = 127.;
std::vector<float> user_weights_annotation(
        feature_size * feature_size, 1.0f);
std::vector<float> user_weights_alignments(feature_size, 1.0f);
// Buffer to store decoder output for all iterations
std::vector<uint8_t> dec_dst(tgt_seq_length_max * batch * feature_size, 0);

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};
std::vector<float> dec_dst_iter(
        dec_n_layers * batch * 2 * feature_size, 1.0f);

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 Create memory descriptors for RNN data w/o specified layout

auto user_dec_wei_layer_md = memory::desc({user_dec_wei_layer_dims},
        memory::data_type::f32, memory::format_tag::ldigo);
auto user_dec_wei_iter_md = memory::desc({user_dec_wei_iter_dims},
        memory::data_type::f32, memory::format_tag::ldigo);
auto user_dec_bias_md = memory::desc({user_dec_bias_dims},
        memory::data_type::f32, memory::format_tag::ldgo);
auto dec_src_layer_md = memory::desc({dec_src_layer_dims},
        memory::data_type::u8, memory::format_tag::tnc);
auto dec_dst_layer_md = memory::desc({dec_dst_layer_dims},
        memory::data_type::u8, memory::format_tag::tnc);
auto dec_dst_iter_md = memory::desc({dec_dst_iter_dims},
        memory::data_type::f32, memory::format_tag::ldnc);
auto dec_dst_iter_c_md = memory::desc({dec_dst_iter_c_dims},
        memory::data_type::f32, memory::format_tag::ldnc);

Decoder : Create memory

auto user_dec_wei_layer_memory = memory(
        user_dec_wei_layer_md, cpu_engine, user_dec_wei_layer.data());
auto user_dec_wei_iter_memory = memory(
        user_dec_wei_iter_md, cpu_engine, user_dec_wei_iter.data());
auto user_dec_bias_memory
        = memory(user_dec_bias_md, cpu_engine, user_dec_bias.data());
auto dec_src_layer_memory = memory(dec_src_layer_md, cpu_engine);
auto dec_dst_layer_memory
        = memory(dec_dst_layer_md, cpu_engine, dec_dst.data());
auto dec_dst_iter_c_memory = 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
        = memory(dec_dst_iter_md, cpu_engine, dec_dst_iter.data());
auto dec_dst_iter_noctx_md = dec_dst_iter_md.submemory_desc(
        dec_dst_iter_noctx_dims, {0, 0, 0, 0, 0});

Decoder : Create memory for input data and use reorders to quantize values to int8

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, attr);
reorder(dec_wei_layer_reorder_pd)
        .execute(s, user_dec_wei_layer_memory, dec_wei_layer_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());

precompute compensation for s8u8s32 gemm in compute attention

compute_sum_of_rows(user_weights_attention_src_layer.data(),
        feature_size, feature_size, weights_attention_sum_rows.data());

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(),
        weights_attention_scale, weights_attention_sum_rows.data(),
        src_att_layer_handle, data_scale, data_shift,
        (uint8_t *)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
        = (uint8_t *)dec_dst_layer_memory.get_data_handle();
dec_src_layer_memory.set_data_handle(dst_layer_handle);
dec_dst_layer_memory.set_data_handle(
        dst_layer_handle + batch * feature_size);