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);
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);
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) {
throw example_allows_unimplemented {
"No int8 LSTM implementation is available for this "
"platform.\n"
"Please refer to the developer guide for details."};
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(
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, 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(
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(
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);
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);
