import os import numpy as np import paddle.v2 as paddle import paddle.fluid as fluid from model import transformer, position_encoding_init from optim import LearningRateScheduler from config import TrainTaskConfig, ModelHyperParams, pos_enc_param_names, \ encoder_input_data_names, decoder_input_data_names, label_data_names def pad_batch_data(insts, pad_idx, n_head, is_target=False, return_pos=True, return_attn_bias=True, return_max_len=True): """ Pad the instances to the max sequence length in batch, and generate the corresponding position data and attention bias. """ return_list = [] max_len = max(len(inst) for inst in insts) inst_data = np.array( [inst + [pad_idx] * (max_len - len(inst)) for inst in insts]) return_list += [inst_data.astype("int64").reshape([-1, 1])] if return_pos: inst_pos = np.array([[ pos_i + 1 if w_i != pad_idx else 0 for pos_i, w_i in enumerate(inst) ] for inst in inst_data]) return_list += [inst_pos.astype("int64").reshape([-1, 1])] if return_attn_bias: if is_target: # This is used to avoid attention on paddings and subsequent # words. slf_attn_bias_data = np.ones((inst_data.shape[0], max_len, max_len)) slf_attn_bias_data = np.triu(slf_attn_bias_data, 1).reshape( [-1, 1, max_len, max_len]) slf_attn_bias_data = np.tile(slf_attn_bias_data, [1, n_head, 1, 1]) * [-1e9] else: # This is used to avoid attention on paddings. slf_attn_bias_data = np.array([[0] * len(inst) + [-1e9] * (max_len - len(inst)) for inst in insts]) slf_attn_bias_data = np.tile( slf_attn_bias_data.reshape([-1, 1, 1, max_len]), [1, n_head, max_len, 1]) return_list += [slf_attn_bias_data.astype("float32")] if return_max_len: return_list += [max_len] return return_list if len(return_list) > 1 else return_list[0] def prepare_batch_input(insts, input_data_names, src_pad_idx, trg_pad_idx, n_head, d_model): """ Put all padded data needed by training into a dict. """ src_word, src_pos, src_slf_attn_bias, src_max_len = pad_batch_data( [inst[0] for inst in insts], src_pad_idx, n_head, is_target=False) trg_word, trg_pos, trg_slf_attn_bias, trg_max_len = pad_batch_data( [inst[1] for inst in insts], trg_pad_idx, n_head, is_target=True) trg_src_attn_bias = np.tile(src_slf_attn_bias[:, :, ::src_max_len, :], [1, 1, trg_max_len, 1]).astype("float32") lbl_word = pad_batch_data([inst[2] for inst in insts], trg_pad_idx, n_head, False, False, False, False) lbl_weight = (lbl_word != trg_pad_idx).astype("float32").reshape([-1, 1]) src_data_shape = np.array([len(insts), src_max_len, d_model], dtype="int32") trg_data_shape = np.array([len(insts), trg_max_len, d_model], dtype="int32") input_dict = dict( zip(input_data_names, [ src_word, src_pos, src_slf_attn_bias, src_data_shape, trg_word, trg_pos, trg_slf_attn_bias, trg_src_attn_bias, trg_data_shape, lbl_word, lbl_weight ])) return input_dict def main(): place = fluid.CUDAPlace(0) if TrainTaskConfig.use_gpu else fluid.CPUPlace() exe = fluid.Executor(place) cost, predict = transformer( ModelHyperParams.src_vocab_size + 1, ModelHyperParams.trg_vocab_size + 1, ModelHyperParams.max_length + 1, ModelHyperParams.n_layer, ModelHyperParams.n_head, ModelHyperParams.d_key, ModelHyperParams.d_value, ModelHyperParams.d_model, ModelHyperParams.d_inner_hid, ModelHyperParams.dropout, ModelHyperParams.src_pad_idx, ModelHyperParams.trg_pad_idx, ModelHyperParams.pos_pad_idx) lr_scheduler = LearningRateScheduler(ModelHyperParams.d_model, TrainTaskConfig.warmup_steps, place, TrainTaskConfig.learning_rate) optimizer = fluid.optimizer.Adam( learning_rate=lr_scheduler.learning_rate, beta1=TrainTaskConfig.beta1, beta2=TrainTaskConfig.beta2, epsilon=TrainTaskConfig.eps) optimizer.minimize(cost) train_data = paddle.batch( paddle.reader.shuffle( paddle.dataset.wmt16.train(ModelHyperParams.src_vocab_size, ModelHyperParams.trg_vocab_size), buf_size=100000), batch_size=TrainTaskConfig.batch_size) # Program to do validation. test_program = fluid.default_main_program().clone() with fluid.program_guard(test_program): test_program = fluid.io.get_inference_program([cost]) val_data = paddle.batch( paddle.dataset.wmt16.validation(ModelHyperParams.src_vocab_size, ModelHyperParams.trg_vocab_size), batch_size=TrainTaskConfig.batch_size) def test(exe): test_costs = [] for batch_id, data in enumerate(val_data()): data_input = prepare_batch_input( data, encoder_input_data_names + decoder_input_data_names[:-1] + label_data_names, ModelHyperParams.src_pad_idx, ModelHyperParams.trg_pad_idx, ModelHyperParams.n_head, ModelHyperParams.d_model) test_cost = exe.run(test_program, feed=data_input, fetch_list=[cost])[0] test_costs.append(test_cost) return np.mean(test_costs) # Initialize the parameters. exe.run(fluid.framework.default_startup_program()) for pos_enc_param_name in pos_enc_param_names: pos_enc_param = fluid.global_scope().find_var( pos_enc_param_name).get_tensor() pos_enc_param.set( position_encoding_init(ModelHyperParams.max_length + 1, ModelHyperParams.d_model), place) for pass_id in xrange(TrainTaskConfig.pass_num): for batch_id, data in enumerate(train_data()): data_input = prepare_batch_input( data, encoder_input_data_names + decoder_input_data_names[:-1] + label_data_names, ModelHyperParams.src_pad_idx, ModelHyperParams.trg_pad_idx, ModelHyperParams.n_head, ModelHyperParams.d_model) lr_scheduler.update_learning_rate(data_input) outs = exe.run(fluid.framework.default_main_program(), feed=data_input, fetch_list=[cost], use_program_cache=True) cost_val = np.array(outs[0]) print("pass_id = " + str(pass_id) + " batch = " + str(batch_id) + " cost = " + str(cost_val)) # Validate and save the model for inference. val_cost = test(exe) print("pass_id = " + str(pass_id) + " val_cost = " + str(val_cost)) fluid.io.save_inference_model( os.path.join(TrainTaskConfig.model_dir, "pass_" + str(pass_id) + ".infer.model"), encoder_input_data_names + decoder_input_data_names[:-1], [predict], exe) if __name__ == "__main__": main()