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
user_enc_bidir_src_layer_md, cpu_engine, net_src.data());
auto user_enc_bidir_wei_layer_memory
user_enc_bidir_wei_layer.data());
auto user_enc_bidir_wei_iter_memory
user_enc_bidir_wei_iter.data());
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
lstm_forward::desc bi_layer_desc(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());
auto enc_bidir_prim_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(
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
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());
enc_uni_first_layer_desc, 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(
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
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());
enc_uni_layer_desc, 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(
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
Decoder : Create memory
user_dec_wei_layer_md, cpu_engine, user_dec_wei_layer.data());
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);
dec_dst_iter_noctx_dims, {0, 0, 0, 0, 0});
Decoder : Create RNN decoder cell
lstm_forward::desc dec_ctx_desc(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);
auto dec_ctx_prim_desc
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
decoder_net.push_back(lstm_forward(dec_ctx_prim_desc));
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
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
dst_layer_handle + batch * feature_size);
