def test_step(cell_type, context_gating, num_embed=2, encoder_num_hidden=5, decoder_num_hidden=5): vocab_size, batch_size, source_seq_len = 10, 10, 7, # (batch_size, source_seq_len, encoder_num_hidden) source = mx.sym.Variable("source") source_shape = (batch_size, source_seq_len, encoder_num_hidden) # (batch_size,) source_length = mx.sym.Variable("source_length") source_length_shape = (batch_size, ) # (batch_size, num_embed) word_vec_prev = mx.sym.Variable("word_vec_prev") word_vec_prev_shape = (batch_size, num_embed) # (batch_size, decoder_num_hidden) hidden_prev = mx.sym.Variable("hidden_prev") hidden_prev_shape = (batch_size, decoder_num_hidden) # List(mx.sym.Symbol(batch_size, decoder_num_hidden) states_shape = (batch_size, decoder_num_hidden) config_coverage = sockeye.coverage.CoverageConfig( type="tanh", num_hidden=2, layer_normalization=False) config_attention = sockeye.rnn_attention.AttentionConfig( type="coverage", num_hidden=2, input_previous_word=False, source_num_hidden=decoder_num_hidden, query_num_hidden=decoder_num_hidden, layer_normalization=False, config_coverage=config_coverage) attention = sockeye.rnn_attention.get_attention(config_attention, max_seq_len=source_seq_len) attention_state = attention.get_initial_state(source_length, source_seq_len) attention_func = attention.on(source, source_length, source_seq_len) config_rnn = sockeye.rnn.RNNConfig(cell_type=cell_type, num_hidden=decoder_num_hidden, num_layers=1, dropout_inputs=0., dropout_states=0., residual=False, forget_bias=0.) config_decoder = sockeye.decoder.RecurrentDecoderConfig( max_seq_len_source=source_seq_len, rnn_config=config_rnn, attention_config=config_attention, context_gating=context_gating) decoder = sockeye.decoder.RecurrentDecoder(config=config_decoder) if cell_type == C.GRU_TYPE: layer_states = [ gaussian_vector(shape=states_shape, return_symbol=True) for _ in range(config_rnn.num_layers) ] elif cell_type == C.LSTM_TYPE: layer_states = [ gaussian_vector(shape=states_shape, return_symbol=True) for _ in range(config_rnn.num_layers * 2) ] else: raise ValueError state, attention_state = decoder._step( word_vec_prev=word_vec_prev, state=sockeye.decoder.RecurrentDecoderState(hidden_prev, layer_states), attention_func=attention_func, attention_state=attention_state) sym = mx.sym.Group( [state.hidden, attention_state.probs, attention_state.dynamic_source]) executor = sym.simple_bind(ctx=mx.cpu(), source=source_shape, source_length=source_length_shape, word_vec_prev=word_vec_prev_shape, hidden_prev=hidden_prev_shape) executor.arg_dict["source"][:] = gaussian_vector(source_shape) executor.arg_dict["source_length"][:] = integer_vector( source_length_shape, source_seq_len) executor.arg_dict["word_vec_prev"][:] = gaussian_vector( word_vec_prev_shape) executor.arg_dict["hidden_prev"][:] = gaussian_vector(hidden_prev_shape) executor.arg_dict["states"] = layer_states hidden_result, attention_probs_result, attention_dynamic_source_result = executor.forward( ) assert hidden_result.shape == hidden_prev_shape assert attention_probs_result.shape == (batch_size, source_seq_len) assert attention_dynamic_source_result.shape == ( batch_size, source_seq_len, config_coverage.num_hidden)
def test_step(cell_type, context_gating, num_embed=2, encoder_num_hidden=5, decoder_num_hidden=5): vocab_size, batch_size, source_seq_len = 10, 10, 7, # (batch_size, source_seq_len, encoder_num_hidden) source = mx.sym.Variable("source") source_shape = (batch_size, source_seq_len, encoder_num_hidden) # (batch_size,) source_length = mx.sym.Variable("source_length") source_length_shape = (batch_size,) # (batch_size, num_embed) word_vec_prev = mx.sym.Variable("word_vec_prev") word_vec_prev_shape = (batch_size, num_embed) # (batch_size, decoder_num_hidden) hidden_prev = mx.sym.Variable("hidden_prev") hidden_prev_shape = (batch_size, decoder_num_hidden) # List(mx.sym.Symbol(batch_size, decoder_num_hidden) states_shape = (batch_size, decoder_num_hidden) config_coverage = sockeye.coverage.CoverageConfig(type="tanh", num_hidden=2, layer_normalization=False) config_attention = sockeye.rnn_attention.AttentionConfig(type="coverage", num_hidden=2, input_previous_word=False, source_num_hidden=decoder_num_hidden, query_num_hidden=decoder_num_hidden, layer_normalization=False, config_coverage=config_coverage) attention = sockeye.rnn_attention.get_attention(config_attention, max_seq_len=source_seq_len) attention_state = attention.get_initial_state(source_length, source_seq_len) attention_func = attention.on(source, source_length, source_seq_len) config_rnn = sockeye.rnn.RNNConfig(cell_type=cell_type, num_hidden=decoder_num_hidden, num_layers=1, dropout_inputs=0., dropout_states=0., residual=False, forget_bias=0.) config_decoder = sockeye.decoder.RecurrentDecoderConfig(max_seq_len_source=source_seq_len, rnn_config=config_rnn, attention_config=config_attention, context_gating=context_gating) decoder = sockeye.decoder.RecurrentDecoder(config=config_decoder) if cell_type == C.GRU_TYPE: layer_states = [gaussian_vector(shape=states_shape, return_symbol=True) for _ in range(config_rnn.num_layers)] elif cell_type == C.LSTM_TYPE: layer_states = [gaussian_vector(shape=states_shape, return_symbol=True) for _ in range(config_rnn.num_layers*2)] else: raise ValueError state, attention_state = decoder._step(word_vec_prev=word_vec_prev, state=sockeye.decoder.RecurrentDecoderState(hidden_prev, layer_states), attention_func=attention_func, attention_state=attention_state) sym = mx.sym.Group([state.hidden, attention_state.probs, attention_state.dynamic_source]) executor = sym.simple_bind(ctx=mx.cpu(), source=source_shape, source_length=source_length_shape, word_vec_prev=word_vec_prev_shape, hidden_prev=hidden_prev_shape) executor.arg_dict["source"][:] = gaussian_vector(source_shape) executor.arg_dict["source_length"][:] = integer_vector(source_length_shape, source_seq_len) executor.arg_dict["word_vec_prev"][:] = gaussian_vector(word_vec_prev_shape) executor.arg_dict["hidden_prev"][:] = gaussian_vector(hidden_prev_shape) executor.arg_dict["states"] = layer_states hidden_result, attention_probs_result, attention_dynamic_source_result = executor.forward() assert hidden_result.shape == hidden_prev_shape assert attention_probs_result.shape == (batch_size, source_seq_len) assert attention_dynamic_source_result.shape == (batch_size, source_seq_len, config_coverage.num_hidden)
def test_step(cell_type, context_gating, num_embed=2, encoder_num_hidden=5, decoder_num_hidden=5): attention_num_hidden, vocab_size, num_layers, \ batch_size, source_seq_len, coverage_num_hidden = 2, 10, 1, 10, 7, 2 # (batch_size, source_seq_len, encoder_num_hidden) source = mx.sym.Variable("source") source_shape = (batch_size, source_seq_len, encoder_num_hidden) # (batch_size,) source_length = mx.sym.Variable("source_length") source_length_shape = (batch_size, ) # (batch_size, num_embed) word_vec_prev = mx.sym.Variable("word_vec_prev") word_vec_prev_shape = (batch_size, num_embed) # (batch_size, decoder_num_hidden) hidden_prev = mx.sym.Variable("hidden_prev") hidden_prev_shape = (batch_size, decoder_num_hidden) # List(mx.sym.Symbol(batch_size, decoder_num_hidden) states_shape = (batch_size, decoder_num_hidden) attention = sockeye.attention.get_attention( input_previous_word=False, attention_type="coverage", attention_num_hidden=attention_num_hidden, rnn_num_hidden=decoder_num_hidden, max_seq_len=source_seq_len, attention_coverage_type="tanh", attention_coverage_num_hidden=coverage_num_hidden, layer_normalization=False) attention_state = attention.get_initial_state(source_length, source_seq_len) attention_func = attention.on(source, source_length, source_seq_len) decoder = sockeye.decoder.get_decoder(num_embed=num_embed, vocab_size=vocab_size, num_layers=num_layers, rnn_num_hidden=decoder_num_hidden, attention=attention, cell_type=cell_type, residual=False, forget_bias=0., dropout=0., weight_tying=False, lexicon=None, context_gating=context_gating) if cell_type == C.GRU_TYPE: layer_states = [ gaussian_vector(shape=states_shape, return_symbol=True) for _ in range(num_layers) ] elif cell_type == C.LSTM_TYPE: layer_states = [ gaussian_vector(shape=states_shape, return_symbol=True) for _ in range(num_layers * 2) ] state, attention_state = decoder._step(word_vec_prev=word_vec_prev, state=sockeye.decoder.DecoderState( hidden_prev, layer_states), attention_func=attention_func, attention_state=attention_state) sym = mx.sym.Group( [state.hidden, attention_state.probs, attention_state.dynamic_source]) executor = sym.simple_bind(ctx=mx.cpu(), source=source_shape, source_length=source_length_shape, word_vec_prev=word_vec_prev_shape, hidden_prev=hidden_prev_shape) executor.arg_dict["source"][:] = gaussian_vector(source_shape) executor.arg_dict["source_length"][:] = integer_vector( source_length_shape, source_seq_len) executor.arg_dict["word_vec_prev"][:] = gaussian_vector( word_vec_prev_shape) executor.arg_dict["hidden_prev"][:] = gaussian_vector(hidden_prev_shape) executor.arg_dict["states"] = layer_states hidden_result, attention_probs_result, attention_dynamic_source_result = executor.forward( ) assert hidden_result.shape == hidden_prev_shape assert attention_probs_result.shape == (batch_size, source_seq_len) assert attention_dynamic_source_result.shape == (batch_size, source_seq_len, coverage_num_hidden)