From 48f63759f5814c1160a0eb7b0aa748a505a162a3 Mon Sep 17 00:00:00 2001 From: Marek Kolodziej Date: Mon, 1 May 2017 15:49:17 +0000 Subject: [PATCH 01/58] CLI option to use inference unrolling during training --- seq2seq/model_infer_gpu.sh | 4 +++- seq2seq/model_train_gpu.sh | 4 +++- seq2seq/seq2seq_bucketing.py | 20 +++++++++++++++----- 3 files changed, 21 insertions(+), 7 deletions(-) diff --git a/seq2seq/model_infer_gpu.sh b/seq2seq/model_infer_gpu.sh index 1755ec1..05c41c1 100755 --- a/seq2seq/model_infer_gpu.sh +++ b/seq2seq/model_infer_gpu.sh @@ -1,3 +1,5 @@ #!/bin/bash -python seq2seq_bucketing.py --num-hidden 512 --num-embed 512 --num-layers 1 --gpus 0,1 --batch-size 256 --disp-batches 1 --num-epochs 1 --model-prefix trained_model --dropout 0.5 --infer --load-epoch 1 +python seq2seq_bucketing.py --num-hidden 512 --num-embed 512 --num-layers 2 --gpus 0 --batch-size 128 \ + --disp-batches 1 --num-epochs 1 --model-prefix trained_model --dropout 0.0 \ + --infer --load-epoch 1 # --use-cudnn-cells diff --git a/seq2seq/model_train_gpu.sh b/seq2seq/model_train_gpu.sh index 3f4e987..e8d383e 100755 --- a/seq2seq/model_train_gpu.sh +++ b/seq2seq/model_train_gpu.sh @@ -1,3 +1,5 @@ #!/bin/bash -python seq2seq_bucketing.py --num-hidden 512 --num-embed 512 --num-layers 22 --gpus 0,1 --batch-size 256 --optimizer adagrad --lr 0.1 --disp-batches 1 --num-epochs 1 --model-prefix trained_model --dropout 0.5 +python seq2seq_bucketing.py --num-hidden 512 --num-embed 512 --num-layers 2 --gpus 0 --batch-size 128 \ + --optimizer adagrad --lr 0.1 --disp-batches 1 --num-epochs 1 --model-prefix trained_model --dropout 0.0 \ + --inference-unrolling-for-training # --use-cudnn-cells diff --git a/seq2seq/seq2seq_bucketing.py b/seq2seq/seq2seq_bucketing.py index ef7b471..cfe55b3 100755 --- a/seq2seq/seq2seq_bucketing.py +++ b/seq2seq/seq2seq_bucketing.py @@ -70,6 +70,9 @@ parser.add_argument('--use-cudnn-cells', action='store_true', help='Use CUDNN LSTM (mx.rnn.FusedRNNCell) for training instead of in-graph LSTM cells (mx.rnn.LSTMCell)') +parser.add_argument('--inference-unrolling-for-training', action='store_true', + help='Feed previous prediction (instead of previous ground truth) into the decoder input during training') + #buckets = [32] # buckets = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100] @@ -258,8 +261,10 @@ def sym_gen(seq_len): # This should be based on EOS or max seq len for inference, but here we unroll to the target length # TODO: fix symbol - outputs, _ = decoder.unroll(dec_seq_len, targ_embed, begin_state=states, layout=layout, merge_outputs=True) -# outputs, _ = decoder_unroll(decoder, targ_embed, targ_vocab, dec_seq_len, 0, begin_state=states, layout='TNC', merge_outputs=True) + if args.inference_unrolling_for_training: + outputs, _ = decoder_unroll(decoder, targ_embed, targ_vocab, dec_seq_len, 0, begin_state=states, layout='TNC', merge_outputs=True) + else: + outputs, _ = decoder.unroll(dec_seq_len, targ_embed, begin_state=states, layout=layout, merge_outputs=True) # NEW rs = mx.sym.Reshape(outputs, shape=(-1, args.num_hidden), name='sym_gen_reshape1') @@ -423,8 +428,8 @@ def sym_gen(seq_len): # This should be based on EOS or max seq len for inference, but here we unroll to the target length # TODO: fix symbol -# outputs, _ = decoder.unroll(dec_seq_len, targ_embed, begin_state=states, layout=layout, merge_outputs=True) - outputs, _ = decoder_unroll(decoder, targ_embed, targ_vocab, dec_seq_len, 0, begin_state=states, layout='TNC', merge_outputs=True) + outputs, _ = decoder.unroll(dec_seq_len, targ_embed, begin_state=states, layout=layout, merge_outputs=True) +# outputs, _ = decoder_unroll(decoder, targ_embed, targ_vocab, dec_seq_len, 0, begin_state=states, layout='TNC', merge_outputs=True) # NEW rs = mx.sym.Reshape(outputs, shape=(-1, args.num_hidden), name='sym_gen_reshape1') @@ -485,7 +490,8 @@ def sym_gen(seq_len): contexts = [mx.gpu(int(i)) for i in args.gpus.split(',')] else: contexts = mx.cpu(0) - + + print("\n") if args.num_layers >= 4 and len(args.gpus.split(',')) >= 4 and not args.stack_rnn: print('WARNING: stack-rnn is recommended to train complex model on multiple GPUs') @@ -495,4 +501,8 @@ def sym_gen(seq_len): # with non-fused MXNet symbol infer(args) else: + if args.inference_unrolling_for_training: + print("INFO: Using inference decoder unrolling for training") + else: + print("INFO: Using regular decoder unrolling for training") train(args) From 4608c6741d71ec31d5a85b9079b132a856109696 Mon Sep 17 00:00:00 2001 From: Marek Kolodziej Date: Mon, 1 May 2017 18:53:17 +0000 Subject: [PATCH 02/58] Checkpoint --- seq2seq/model_train_gpu.sh | 6 ++-- seq2seq/seq2seq_bucketing.py | 61 +++++++++++++++++++++++------------- 2 files changed, 43 insertions(+), 24 deletions(-) diff --git a/seq2seq/model_train_gpu.sh b/seq2seq/model_train_gpu.sh index e8d383e..0cca9ce 100755 --- a/seq2seq/model_train_gpu.sh +++ b/seq2seq/model_train_gpu.sh @@ -1,5 +1,5 @@ #!/bin/bash -python seq2seq_bucketing.py --num-hidden 512 --num-embed 512 --num-layers 2 --gpus 0 --batch-size 128 \ - --optimizer adagrad --lr 0.1 --disp-batches 1 --num-epochs 1 --model-prefix trained_model --dropout 0.0 \ - --inference-unrolling-for-training +python seq2seq_bucketing.py --num-hidden 512 --num-embed 512 --num-layers 2 --gpus 0 --batch-size 64 \ + --optimizer adagrad --lr 0.1 --disp-batches 1 --num-epochs 1 --model-prefix trained_model --dropout 0.0 +# --inference-unrolling-for-training # --use-cudnn-cells diff --git a/seq2seq/seq2seq_bucketing.py b/seq2seq/seq2seq_bucketing.py index cfe55b3..5a02e25 100755 --- a/seq2seq/seq2seq_bucketing.py +++ b/seq2seq/seq2seq_bucketing.py @@ -174,7 +174,10 @@ def get_data(layout): return train_iter, valid_iter, train_iter.src_vocab, train_iter.targ_vocab # WORK IN PROGRESS !!! -def decoder_unroll(decoder, target_embed, targ_vocab, unroll_length, go_symbol, begin_state=None, layout='TNC', merge_outputs=None): +def decoder_unroll(decoder, target_embed, targ_vocab, unroll_length, + go_symbol, + fc_weight, fc_bias, targ_em_weight, + begin_state=None, layout='TNC', merge_outputs=None): decoder.reset() @@ -192,19 +195,19 @@ def decoder_unroll(decoder, target_embed, targ_vocab, unroll_length, go_symbol, # NEW 1 # fc_weight = mx.sym.Variable('fc_weight') # fc_bias = mx.sym.Variable('fc_bias') -# em_weight = mx.sym.Variable('em_weight') -# for i in range(0, unroll_length): -# output, states = decoder(embed, states) -# outputs.append(embed) -# fc = mx.sym.FullyConnected(data=output, weight=fc_weight, bias=fc_bias, num_hidden=len(targ_vocab), name='decoder_fc%d_'%i) -# am = mx.sym.argmax(data=fc, axis=1) -# embed = mx.sym.Embedding(data=am, weight=em_weight, input_dim=len(targ_vocab), -# output_dim=args.num_embed, name='decoder_embed%d_'%i) - - # NEW 2 +# targ_em_weight = mx.sym.Variable('targ_em_weight') for i in range(0, unroll_length): - embed, states = decoder(embed, states) + output, states = decoder(embed, states) outputs.append(embed) + fc = mx.sym.FullyConnected(data=output, weight=fc_weight, bias=fc_bias, num_hidden=len(targ_vocab), name='decoder_fc%d_'%i) + am = mx.sym.argmax(data=fc, axis=1) + embed = mx.sym.Embedding(data=am, weight=targ_em_weight, input_dim=len(targ_vocab), + output_dim=args.num_embed, name='decoder_embed%d_'%i) + + # NEW 2 +# for i in range(0, unroll_length): +# embed, states = decoder(embed, states) +# outputs.append(embed) outputs, _ = _normalize_sequence(unroll_length, outputs, layout, merge_outputs) @@ -217,6 +220,10 @@ def train(args): data_train, data_val, src_vocab, targ_vocab = get_data('TN') print "len(src_vocab) len(targ_vocab)", len(src_vocab), len(targ_vocab) + fc_weight = mx.sym.Variable('fc_weight') + fc_bias = mx.sym.Variable('fc_bias') + targ_em_weight = mx.sym.Variable('targ_em_weight') + encoder = SequentialRNNCell() if args.use_cudnn_cells: @@ -248,7 +255,7 @@ def sym_gen(seq_len): src_embed = mx.sym.Embedding(data=src_data, input_dim=len(src_vocab), output_dim=args.num_embed, name='src_embed') - targ_embed = mx.sym.Embedding(data=targ_data, input_dim=len(targ_vocab), # data=data + targ_embed = mx.sym.Embedding(data=targ_data, weight=targ_em_weight, input_dim=len(targ_vocab), # data=data output_dim=args.num_embed, name='targ_embed') encoder.reset() @@ -262,13 +269,13 @@ def sym_gen(seq_len): # This should be based on EOS or max seq len for inference, but here we unroll to the target length # TODO: fix symbol if args.inference_unrolling_for_training: - outputs, _ = decoder_unroll(decoder, targ_embed, targ_vocab, dec_seq_len, 0, begin_state=states, layout='TNC', merge_outputs=True) + outputs, _ = decoder_unroll(decoder, targ_embed, targ_vocab, dec_seq_len, 0, fc_weight, fc_bias, targ_em_weight, begin_state=states, layout='TNC', merge_outputs=True) else: outputs, _ = decoder.unroll(dec_seq_len, targ_embed, begin_state=states, layout=layout, merge_outputs=True) # NEW rs = mx.sym.Reshape(outputs, shape=(-1, args.num_hidden), name='sym_gen_reshape1') - fc = mx.sym.FullyConnected(data=rs, num_hidden=len(targ_vocab), name='sym_gen_fc') + fc = mx.sym.FullyConnected(data=rs, weight=fc_weight, bias=fc_bias, num_hidden=len(targ_vocab), name='sym_gen_fc') label_rs = mx.sym.Reshape(data=label, shape=(-1,), name='sym_gen_reshape2') pred = mx.sym.SoftmaxOutput(data=fc, label=label_rs, name='sym_gen_softmax') @@ -292,7 +299,7 @@ def sym_gen(seq_len): if args.load_epoch: _, arg_params, aux_params = mx.rnn.load_rnn_checkpoint( - cell, args.model_prefix, args.load_epoch) + [encoder, decoder], args.model_prefix, args.load_epoch) else: arg_params = None aux_params = None @@ -322,7 +329,7 @@ def sym_gen(seq_len): begin_epoch = args.load_epoch, num_epoch = args.num_epochs, batch_end_callback = mx.callback.Speedometer(batch_size=args.batch_size, frequent=args.disp_batches, auto_reset=True), - epoch_end_callback = mx.rnn.do_rnn_checkpoint(decoder, args.model_prefix, 1) + epoch_end_callback = mx.rnn.do_rnn_checkpoint([encoder, decoder], args.model_prefix, 1) if args.model_prefix else None) train_duration = time() - start @@ -380,6 +387,10 @@ def infer(args): print "len(src_vocab) len(targ_vocab)", len(src_vocab), len(targ_vocab) + fc_weight = mx.sym.Variable('fc_weight') + fc_bias = mx.sym.Variable('fc_bias') + targ_em_weight = mx.sym.Variable('targ_em_weight') + if args.use_cudnn_cells: encoder = mx.rnn.FusedRNNCell(args.num_hidden, num_layers=args.num_layers, dropout=args.dropout, mode='lstm', prefix='lstm_encoder', bidirectional=args.bidirectional, get_next_state=True).unfuse() @@ -428,15 +439,23 @@ def sym_gen(seq_len): # This should be based on EOS or max seq len for inference, but here we unroll to the target length # TODO: fix symbol - outputs, _ = decoder.unroll(dec_seq_len, targ_embed, begin_state=states, layout=layout, merge_outputs=True) -# outputs, _ = decoder_unroll(decoder, targ_embed, targ_vocab, dec_seq_len, 0, begin_state=states, layout='TNC', merge_outputs=True) +# outputs, _ = decoder.unroll(dec_seq_len, targ_embed, begin_state=states, layout=layout, merge_outputs=True) + outputs, _ = decoder_unroll(decoder, targ_embed, targ_vocab, dec_seq_len, 0, + fc_weight, fc_bias, targ_em_weight, + begin_state=states, layout='TNC', merge_outputs=True) # NEW + rs = mx.sym.Reshape(outputs, shape=(-1, args.num_hidden), name='sym_gen_reshape1') - fc = mx.sym.FullyConnected(data=rs, num_hidden=len(targ_vocab), name='sym_gen_fc') + fc = mx.sym.FullyConnected(data=rs, weight=fc_weight, bias=fc_bias, num_hidden=len(targ_vocab), name='sym_gen_fc') label_rs = mx.sym.Reshape(data=label, shape=(-1,), name='sym_gen_reshape2') pred = mx.sym.SoftmaxOutput(data=fc, label=label_rs, name='sym_gen_softmax') +# rs = mx.sym.Reshape(outputs, shape=(-1, args.num_hidden), name='sym_gen_reshape1') +# fc = mx.sym.FullyConnected(data=rs, num_hidden=len(targ_vocab), name='sym_gen_fc') +# label_rs = mx.sym.Reshape(data=label, shape=(-1,), name='sym_gen_reshape2') +# pred = mx.sym.SoftmaxOutput(data=fc, label=label_rs, name='sym_gen_softmax') + return pred, ('src_data', 'targ_data',), ('softmax_label',) if args.gpus: @@ -453,7 +472,7 @@ def sym_gen(seq_len): if args.load_epoch: _, arg_params, aux_params = mx.rnn.load_rnn_checkpoint( - decoder, args.model_prefix, args.load_epoch) + [encoder, decoder], args.model_prefix, args.load_epoch) model.set_params(arg_params, aux_params) else: From c5a1dfc714c1f92fcda8a98b499a35fa09691e59 Mon Sep 17 00:00:00 2001 From: Marek Kolodziej Date: Mon, 1 May 2017 20:17:27 +0000 Subject: [PATCH 03/58] Inference rewrite --- seq2seq/seq2seq_bucketing.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/seq2seq/seq2seq_bucketing.py b/seq2seq/seq2seq_bucketing.py index 5a02e25..a5e0227 100755 --- a/seq2seq/seq2seq_bucketing.py +++ b/seq2seq/seq2seq_bucketing.py @@ -222,7 +222,7 @@ def train(args): fc_weight = mx.sym.Variable('fc_weight') fc_bias = mx.sym.Variable('fc_bias') - targ_em_weight = mx.sym.Variable('targ_em_weight') + targ_em_weight = mx.sym.Variable('targ_embed_weight') encoder = SequentialRNNCell() @@ -389,7 +389,7 @@ def infer(args): fc_weight = mx.sym.Variable('fc_weight') fc_bias = mx.sym.Variable('fc_bias') - targ_em_weight = mx.sym.Variable('targ_em_weight') + targ_em_weight = mx.sym.Variable('targ_embed_weight') if args.use_cudnn_cells: encoder = mx.rnn.FusedRNNCell(args.num_hidden, num_layers=args.num_layers, dropout=args.dropout, @@ -473,12 +473,14 @@ def sym_gen(seq_len): if args.load_epoch: _, arg_params, aux_params = mx.rnn.load_rnn_checkpoint( [encoder, decoder], args.model_prefix, args.load_epoch) + print(arg_params) model.set_params(arg_params, aux_params) else: arg_params = None aux_params = None + opt_params = { 'learning_rate': args.lr, 'wd': args.wd From c88ea075de6fedd042d9b15be49103f8f516464a Mon Sep 17 00:00:00 2001 From: Marek Kolodziej Date: Mon, 1 May 2017 20:39:19 +0000 Subject: [PATCH 04/58] Inference fix WIP --- seq2seq/model_train_gpu.sh | 2 +- seq2seq/seq2seq_bucketing.py | 5 +++-- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/seq2seq/model_train_gpu.sh b/seq2seq/model_train_gpu.sh index 0cca9ce..8e7cad2 100755 --- a/seq2seq/model_train_gpu.sh +++ b/seq2seq/model_train_gpu.sh @@ -1,5 +1,5 @@ #!/bin/bash -python seq2seq_bucketing.py --num-hidden 512 --num-embed 512 --num-layers 2 --gpus 0 --batch-size 64 \ +python seq2seq_bucketing.py --num-hidden 512 --num-embed 512 --num-layers 2 --gpus 0,1 --batch-size 128 \ --optimizer adagrad --lr 0.1 --disp-batches 1 --num-epochs 1 --model-prefix trained_model --dropout 0.0 # --inference-unrolling-for-training # --use-cudnn-cells diff --git a/seq2seq/seq2seq_bucketing.py b/seq2seq/seq2seq_bucketing.py index a5e0227..723b1a5 100755 --- a/seq2seq/seq2seq_bucketing.py +++ b/seq2seq/seq2seq_bucketing.py @@ -426,7 +426,8 @@ def sym_gen(seq_len): src_embed = mx.sym.Embedding(data=src_data, input_dim=len(src_vocab), output_dim=args.num_embed, name='src_embed') - targ_embed = mx.sym.Embedding(data=targ_data, input_dim=len(targ_vocab), # data=data + targ_embed = mx.sym.Embedding(data=targ_data, input_dim=len(targ_vocab), + weight = targ_em_weight, # data=data output_dim=args.num_embed, name='targ_embed') encoder.reset() @@ -473,7 +474,7 @@ def sym_gen(seq_len): if args.load_epoch: _, arg_params, aux_params = mx.rnn.load_rnn_checkpoint( [encoder, decoder], args.model_prefix, args.load_epoch) - print(arg_params) +# print(arg_params) model.set_params(arg_params, aux_params) else: From 8db59804112bafdba6bf98dd65fda5ea77922ba0 Mon Sep 17 00:00:00 2001 From: Marek Kolodziej Date: Tue, 2 May 2017 04:39:25 +0000 Subject: [PATCH 05/58] WIP --- seq2seq/model_infer_gpu.sh | 4 ++-- seq2seq/model_train_gpu.sh | 4 ++-- seq2seq/seq2seq_bucketing.py | 4 ++-- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/seq2seq/model_infer_gpu.sh b/seq2seq/model_infer_gpu.sh index 05c41c1..7cb385e 100755 --- a/seq2seq/model_infer_gpu.sh +++ b/seq2seq/model_infer_gpu.sh @@ -1,5 +1,5 @@ #!/bin/bash -python seq2seq_bucketing.py --num-hidden 512 --num-embed 512 --num-layers 2 --gpus 0 --batch-size 128 \ +python seq2seq_bucketing.py --num-hidden 512 --num-embed 512 --num-layers 2 --gpus 0,1 --batch-size 256 \ --disp-batches 1 --num-epochs 1 --model-prefix trained_model --dropout 0.0 \ - --infer --load-epoch 1 + --infer --load-epoch 6 # --use-cudnn-cells diff --git a/seq2seq/model_train_gpu.sh b/seq2seq/model_train_gpu.sh index 8e7cad2..bb62d40 100755 --- a/seq2seq/model_train_gpu.sh +++ b/seq2seq/model_train_gpu.sh @@ -1,5 +1,5 @@ #!/bin/bash -python seq2seq_bucketing.py --num-hidden 512 --num-embed 512 --num-layers 2 --gpus 0,1 --batch-size 128 \ - --optimizer adagrad --lr 0.1 --disp-batches 1 --num-epochs 1 --model-prefix trained_model --dropout 0.0 +python seq2seq_bucketing.py --num-hidden 512 --num-embed 512 --num-layers 2 --gpus 0,1,2,3 --batch-size 256 \ + --optimizer adagrad --lr 0.1 --disp-batches 1 --num-epochs 10 --model-prefix trained_model --dropout 0.0 # --inference-unrolling-for-training # --use-cudnn-cells diff --git a/seq2seq/seq2seq_bucketing.py b/seq2seq/seq2seq_bucketing.py index 723b1a5..e739697 100755 --- a/seq2seq/seq2seq_bucketing.py +++ b/seq2seq/seq2seq_bucketing.py @@ -198,7 +198,7 @@ def decoder_unroll(decoder, target_embed, targ_vocab, unroll_length, # targ_em_weight = mx.sym.Variable('targ_em_weight') for i in range(0, unroll_length): output, states = decoder(embed, states) - outputs.append(embed) + outputs.append(output) fc = mx.sym.FullyConnected(data=output, weight=fc_weight, bias=fc_bias, num_hidden=len(targ_vocab), name='decoder_fc%d_'%i) am = mx.sym.argmax(data=fc, axis=1) embed = mx.sym.Embedding(data=am, weight=targ_em_weight, input_dim=len(targ_vocab), @@ -495,7 +495,7 @@ def sym_gen(seq_len): start = time() # mx.metric.Perplexity - model.score(data_val, BleuScore(invalid_label), #PPL(invalid_label), + model.score(data_val, BleuScore(invalid_label), #mx.metric.Perplexity(invalid_label), batch_end_callback=mx.callback.Speedometer(batch_size=args.batch_size, frequent=5, auto_reset=True)) infer_duration = time() - start From af657a200ad9f13ce85b54f495833d12c3d39542 Mon Sep 17 00:00:00 2001 From: Marek Kolodziej Date: Tue, 2 May 2017 05:34:36 +0000 Subject: [PATCH 06/58] WIP --- seq2seq/seq2seq_bucketing.py | 42 ++++++++++++++++++++++++++++++++++-- 1 file changed, 40 insertions(+), 2 deletions(-) diff --git a/seq2seq/seq2seq_bucketing.py b/seq2seq/seq2seq_bucketing.py index e739697..9468279 100755 --- a/seq2seq/seq2seq_bucketing.py +++ b/seq2seq/seq2seq_bucketing.py @@ -495,8 +495,46 @@ def sym_gen(seq_len): start = time() # mx.metric.Perplexity - model.score(data_val, BleuScore(invalid_label), #mx.metric.Perplexity(invalid_label), - batch_end_callback=mx.callback.Speedometer(batch_size=args.batch_size, frequent=5, auto_reset=True)) +# model.score(data_val, BleuScore(invalid_label), #mx.metric.Perplexity(invalid_label), +# batch_end_callback=mx.callback.Speedometer(batch_size=args.batch_size, frequent=5, auto_reset=True)) + + for bkt in range(10): + data_val.reset() + data_batch = data_val.next() + + model.forward(data_batch, is_train=None) + source = data_batch.data[0] + preds = model.get_outputs()[0] + labels = data_batch.label[0] + + maxed = mx.ndarray.argmax(data=preds, axis=1) + pred_nparr = maxed.asnumpy() + src_nparr = source.asnumpy() + label_nparr = labels.asnumpy().astype(np.int32) + sent_len, batch_size = np.shape(label_nparr) + pred_nparr = pred_nparr.reshape(sent_len, batch_size).astype(np.int32) + + + # range should be number of examples in the bucket + for i in range(10): + src_lst = list(reversed(drop_sentinels(src_nparr[:, i].tolist()))) + exp_lst = drop_sentinels(label_nparr[:, i].tolist()) + act_lst = drop_sentinels(pred_nparr[:, i].tolist()) + + # convert source to text + src_txt = array_to_text(src_lst, inv_src_vocab) + + # convert expected translation to text + exp_txt = array_to_text(exp_lst, inv_targ_vocab) + + # convert actual translation to text + act_txt = array_to_text(act_lst, inv_targ_vocab) + + print("\n") + print("Source text: %s" % src_txt) + print("Expected translation: %s" % exp_txt) + print("Actual translation: %s" % act_txt) + infer_duration = time() - start time_per_epoch = infer_duration / args.num_epochs From b4d32340f655f0b150927f6238e69d550f976ad8 Mon Sep 17 00:00:00 2001 From: Marek Kolodziej Date: Tue, 2 May 2017 05:40:48 +0000 Subject: [PATCH 07/58] WIP --- seq2seq/seq2seq_bucketing.py | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/seq2seq/seq2seq_bucketing.py b/seq2seq/seq2seq_bucketing.py index 9468279..d0ca57d 100755 --- a/seq2seq/seq2seq_bucketing.py +++ b/seq2seq/seq2seq_bucketing.py @@ -336,6 +336,14 @@ def sym_gen(seq_len): time_per_epoch = train_duration / args.num_epochs print("\n\nTime per epoch: %.2f seconds\n\n" % time_per_epoch) + +def drop_sentinels(text_lst): + sentinels = lambda x: x == reserved_tokens[''] or x == reserved_tokens[''] + text_lst = dropwhile(lambda x: sentinels(x), text_lst) + text_lst = takewhile(lambda x: not sentinels(x) and x != reserved_tokens[''], text_lst) + return list(text_lst) + + class BleuScore(mx.metric.EvalMetric): def __init__(self, ignore_label, axis=-1): super(BleuScore, self).__init__('BleuScore') @@ -345,12 +353,6 @@ def __init__(self, ignore_label, axis=-1): def update(self, labels, preds): assert len(labels) == len(preds) - def drop_sentinels(text_lst): - sentinels = lambda x: x == reserved_tokens[''] or x == reserved_tokens[''] - text_lst = dropwhile(lambda x: sentinels(x), text_lst) - text_lst = takewhile(lambda x: not sentinels(x) and x != reserved_tokens[''], text_lst) - return list(text_lst) - smoothing_fn = nltk.translate.bleu_score.SmoothingFunction().method3 for label, pred in zip(labels, preds): From a513a9834090df794e63cdc870168b9533d2cd59 Mon Sep 17 00:00:00 2001 From: Marek Kolodziej Date: Tue, 2 May 2017 05:43:04 +0000 Subject: [PATCH 08/58] WIP --- seq2seq/seq2seq_bucketing.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/seq2seq/seq2seq_bucketing.py b/seq2seq/seq2seq_bucketing.py index d0ca57d..b918a0b 100755 --- a/seq2seq/seq2seq_bucketing.py +++ b/seq2seq/seq2seq_bucketing.py @@ -171,7 +171,7 @@ def get_data(layout): print("\nDataset deserialization time: %.2f seconds\n" % duration) - return train_iter, valid_iter, train_iter.src_vocab, train_iter.targ_vocab + return train_iter, valid_iter, train_iter.src_vocab, train_iter.targ_vocab, train_iter.inv_src_vocab, train_iter.inv_targ_vocab # WORK IN PROGRESS !!! def decoder_unroll(decoder, target_embed, targ_vocab, unroll_length, @@ -217,7 +217,7 @@ def train(args): from time import time - data_train, data_val, src_vocab, targ_vocab = get_data('TN') + data_train, data_val, src_vocab, targ_vocab, inv_src_vocab, inv_targ_vocab = get_data('TN') print "len(src_vocab) len(targ_vocab)", len(src_vocab), len(targ_vocab) fc_weight = mx.sym.Variable('fc_weight') @@ -385,7 +385,7 @@ def get(self): def infer(args): assert args.model_prefix, "Must specifiy path to load from" - data_train, data_val, src_vocab, targ_vocab = get_data('TN') + data_train, data_val, src_vocab, targ_vocab, inv_src_vocab, inv_targ_vocab = get_data('TN') print "len(src_vocab) len(targ_vocab)", len(src_vocab), len(targ_vocab) From 98763c19cecc0b75a0c084258764f31de5ba8d8d Mon Sep 17 00:00:00 2001 From: Marek Kolodziej Date: Wed, 3 May 2017 04:10:00 +0000 Subject: [PATCH 09/58] Re-enable BLEU score before printing translations for inference --- seq2seq/seq2seq_bucketing.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/seq2seq/seq2seq_bucketing.py b/seq2seq/seq2seq_bucketing.py index b918a0b..208dead 100755 --- a/seq2seq/seq2seq_bucketing.py +++ b/seq2seq/seq2seq_bucketing.py @@ -497,8 +497,8 @@ def sym_gen(seq_len): start = time() # mx.metric.Perplexity -# model.score(data_val, BleuScore(invalid_label), #mx.metric.Perplexity(invalid_label), -# batch_end_callback=mx.callback.Speedometer(batch_size=args.batch_size, frequent=5, auto_reset=True)) + model.score(data_val, BleuScore(invalid_label), #mx.metric.Perplexity(invalid_label), + batch_end_callback=mx.callback.Speedometer(batch_size=args.batch_size, frequent=5, auto_reset=True)) for bkt in range(10): data_val.reset() From 70a52430419ec6b7995ab8c83935925249b0b902 Mon Sep 17 00:00:00 2001 From: Marek Kolodziej Date: Wed, 3 May 2017 19:45:05 +0000 Subject: [PATCH 10/58] WIP test set BLEU score --- seq2seq/preprocess_data.py | 31 +++++++++++++++++++++++++------ seq2seq/utils.py | 18 ++++++++++++------ 2 files changed, 37 insertions(+), 12 deletions(-) diff --git a/seq2seq/preprocess_data.py b/seq2seq/preprocess_data.py index 274b7f1..8ed58b0 100644 --- a/seq2seq/preprocess_data.py +++ b/seq2seq/preprocess_data.py @@ -28,10 +28,12 @@ start = time() dataset = get_s2s_data( - src_train_path= './data/wmt15-de-en/train.en', - src_valid_path= './data/wmt15-de-en/valid.en', - targ_train_path= './data/wmt15-de-en/train.de', - targ_valid_path= './data/wmt15-de-en/valid.de' + src_train_path= './data/wmt15-de-en/train.de', + src_valid_path= './data/wmt15-de-en/valid.de', + src_test_path = './data/wmt15-de-en/test.de', + targ_train_path= './data/wmt15-de-en/train.en', + targ_valid_path= './data/wmt15-de-en/valid.en', + targ_test_path= './data/wmt15-de-en/test.en', ) # dataset = get_s2s_data( @@ -59,11 +61,12 @@ # all_pairs = [(5, 5), (15,15), (20, 20)] # max_sent_len = 25 + batch_size=64 print("Constructing train iterator") start = time() train_iter = Seq2SeqIter(dataset.src_train_sent, dataset.targ_train_sent, dataset.src_vocab, dataset.inv_src_vocab, - dataset.targ_vocab, dataset.inv_targ_vocab, layout='TN', batch_size=64, buckets=all_pairs, max_sent_len=max_len) + dataset.targ_vocab, dataset.inv_targ_vocab, layout='TN', batch_size=batch_size, buckets=all_pairs, max_sent_len=max_len) train_iter.bucketize() train_iter_duration = time() - start @@ -80,7 +83,7 @@ print("Constructing valid iterator") valid_iter_duration = time() valid_iter = Seq2SeqIter(dataset.src_valid_sent, dataset.targ_valid_sent, dataset.src_vocab, dataset.inv_src_vocab, - dataset.targ_vocab, dataset.inv_targ_vocab, layout='TN', batch_size=64, buckets=all_pairs, max_sent_len=50) + dataset.targ_vocab, dataset.inv_targ_vocab, layout='TN', batch_size=batch_size, buckets=all_pairs, max_sent_len=max_len) valid_iter.bucketize() valid_iter_duration = time() - start @@ -93,3 +96,19 @@ valid_ser_duration = time() - start print("\nSerializing validation set iterator took %.4f seconds\n" % valid_ser_duration) + print("Constructing test iterator") + test_iter_duration = time() + test_iter = Seq2SeqIter(dataset.src_test_sent, dataset.targ_test_sent, dataset.src_vocab, dataset.inv_src_vocab, + dataset.targ_vocab, dataset.inv_targ_vocab, layout='TN', batch_size=batch_size, buckets=all_pairs, max_sent_len=max_len) + + test_iter.bucketize() + test_iter_duration = time() - start + print("\nBucketizing data for test set iterator took %.4f seconds\n" % test_iter_duration) + + print("Serializing test set iterator.") + start = time() + with open('./data/test_iterator.pkl', 'wb') as f: + pickle.dump(test_iter, f, pickle.HIGHEST_PROTOCOL) + test_ser_duration = time() - start + print("\nSerializing test set iterator took %.4f seconds\n" % test_ser_duration) + diff --git a/seq2seq/utils.py b/seq2seq/utils.py index 237547d..b5f848b 100644 --- a/seq2seq/utils.py +++ b/seq2seq/utils.py @@ -15,8 +15,8 @@ Dataset = namedtuple( 'Dataset', - ['src_train_sent', 'src_valid_sent', 'src_vocab', 'inv_src_vocab', - 'targ_train_sent', 'targ_valid_sent', 'targ_vocab', 'inv_targ_vocab']) + ['src_train_sent', 'src_valid_sent', 'src_test_sent', 'src_vocab', 'inv_src_vocab', + 'targ_train_sent', 'targ_valid_sent', 'targ_test_sent', 'targ_vocab', 'inv_targ_vocab']) def invert_dict(d): return {v: k for k, v in d.iteritems()} @@ -91,7 +91,8 @@ def array_to_text(array, inv_vocab): sent.append(inv_vocab[token]) return " ".join(sent) -def get_s2s_data(src_train_path, src_valid_path, targ_train_path, targ_valid_path, +def get_s2s_data(src_train_path, src_valid_path, targ_train_path, + targ_valid_path, src_test_path, targ_test_path, reserved_tokens=['', '', '', '']): print("Creating joint source dictionary") @@ -99,8 +100,10 @@ def get_s2s_data(src_train_path, src_valid_path, targ_train_path, targ_valid_pat print("Tokenizing src_train_path") src_train_sent = tokenize_text(src_train_path, vocab=src_dict) - print("Tokenizing targ_train_path") + print("Tokenizing src_valid_path") src_valid_sent = tokenize_text(src_valid_path, vocab=src_dict) + print("Tokenizing src_test_path") + src_test_sent = tokenize_text(src_test_path, vocab=src_dict) print("Creating joint target dictionary") targ_dict, inv_targ_dict = top_words_train_valid(targ_train_path, targ_valid_path) @@ -109,6 +112,8 @@ def get_s2s_data(src_train_path, src_valid_path, targ_train_path, targ_valid_pat targ_train_sent = tokenize_text(targ_train_path, vocab=targ_dict) print("Tokenizing targ_valid_path") targ_valid_sent = tokenize_text(targ_valid_path, vocab=targ_dict) + print("Tokenizing targ_test_path") + targ_test_sent = tokenize_text(targ_test_path, vocab=targ_dict) print("\nEncoded source language sentences:\n") for i in range(5): @@ -120,6 +125,7 @@ def get_s2s_data(src_train_path, src_valid_path, targ_train_path, targ_valid_pat return Dataset( - src_train_sent=src_train_sent, src_valid_sent=src_valid_sent, src_vocab=src_dict, inv_src_vocab=inv_src_dict, - targ_train_sent=targ_train_sent, targ_valid_sent=targ_valid_sent, targ_vocab=targ_dict, inv_targ_vocab=inv_targ_dict) + src_train_sent=src_train_sent, src_valid_sent=src_valid_sent, src_test_sent=src_test_sent, + src_vocab=src_dict, inv_src_vocab=inv_src_dict, targ_train_sent=targ_train_sent, + targ_valid_sent=targ_valid_sent, targ_test_sent=targ_test_sent, targ_vocab=targ_dict, inv_targ_vocab=inv_targ_dict) From b44a215f1636d9b18a7f352f4f4d8b0522c71fca Mon Sep 17 00:00:00 2001 From: Marek Kolodziej Date: Wed, 3 May 2017 19:49:05 +0000 Subject: [PATCH 11/58] WIP test set BLEU score --- seq2seq/seq2seq_bucketing.py | 18 ++++++++++++------ 1 file changed, 12 insertions(+), 6 deletions(-) diff --git a/seq2seq/seq2seq_bucketing.py b/seq2seq/seq2seq_bucketing.py index 208dead..5a6449e 100755 --- a/seq2seq/seq2seq_bucketing.py +++ b/seq2seq/seq2seq_bucketing.py @@ -159,6 +159,12 @@ def get_data(layout): valid_iter.initialize() valid_iter.batch_size = args.batch_size + with open('./data/test_iterator.pkl', 'rb') as f: + test_iter = pickle.load(f) + + test_iter.initialize() + test_iter.batch_size = args.batch_size + print("\nEncoded source language sentences:\n") for i in range(5): print(array_to_text(train_iter.src_sent[i], train_iter.inv_src_vocab)) @@ -171,7 +177,7 @@ def get_data(layout): print("\nDataset deserialization time: %.2f seconds\n" % duration) - return train_iter, valid_iter, train_iter.src_vocab, train_iter.targ_vocab, train_iter.inv_src_vocab, train_iter.inv_targ_vocab + return train_iter, valid_iter, test_iter, train_iter.src_vocab, train_iter.targ_vocab, train_iter.inv_src_vocab, train_iter.inv_targ_vocab # WORK IN PROGRESS !!! def decoder_unroll(decoder, target_embed, targ_vocab, unroll_length, @@ -217,7 +223,7 @@ def train(args): from time import time - data_train, data_val, src_vocab, targ_vocab, inv_src_vocab, inv_targ_vocab = get_data('TN') + data_train, data_val, _, src_vocab, targ_vocab, inv_src_vocab, inv_targ_vocab = get_data('TN') print "len(src_vocab) len(targ_vocab)", len(src_vocab), len(targ_vocab) fc_weight = mx.sym.Variable('fc_weight') @@ -385,7 +391,7 @@ def get(self): def infer(args): assert args.model_prefix, "Must specifiy path to load from" - data_train, data_val, src_vocab, targ_vocab, inv_src_vocab, inv_targ_vocab = get_data('TN') + _, _, data_test, src_vocab, targ_vocab, inv_src_vocab, inv_targ_vocab = get_data('TN') print "len(src_vocab) len(targ_vocab)", len(src_vocab), len(targ_vocab) @@ -471,7 +477,7 @@ def sym_gen(seq_len): default_bucket_key = data_train.default_bucket_key, context = contexts) - model.bind(data_val.provide_data, data_val.provide_label, for_training=False) + model.bind(data_test.provide_data, data_test.provide_label, for_training=False) if args.load_epoch: _, arg_params, aux_params = mx.rnn.load_rnn_checkpoint( @@ -497,8 +503,8 @@ def sym_gen(seq_len): start = time() # mx.metric.Perplexity - model.score(data_val, BleuScore(invalid_label), #mx.metric.Perplexity(invalid_label), - batch_end_callback=mx.callback.Speedometer(batch_size=args.batch_size, frequent=5, auto_reset=True)) + model.score(data_test, BleuScore(invalid_label), #mx.metric.Perplexity(invalid_label), + batch_end_callback=mx.callback.Speedometer(batch_size=args.batch_size, frequent=1, auto_reset=True)) for bkt in range(10): data_val.reset() From 1fab8ce7cd166c1e9d3374d1977a3d83d09034e3 Mon Sep 17 00:00:00 2001 From: Marek Kolodziej Date: Wed, 3 May 2017 19:59:27 +0000 Subject: [PATCH 12/58] WIP --- seq2seq/model_infer_gpu.sh | 4 ++-- seq2seq/model_train_gpu.sh | 4 ++-- seq2seq/preprocess_data.py | 19 ++++++------------- seq2seq/seq2seq_bucketing.py | 10 +++++----- 4 files changed, 15 insertions(+), 22 deletions(-) diff --git a/seq2seq/model_infer_gpu.sh b/seq2seq/model_infer_gpu.sh index 7cb385e..0b01710 100755 --- a/seq2seq/model_infer_gpu.sh +++ b/seq2seq/model_infer_gpu.sh @@ -1,5 +1,5 @@ #!/bin/bash -python seq2seq_bucketing.py --num-hidden 512 --num-embed 512 --num-layers 2 --gpus 0,1 --batch-size 256 \ +python seq2seq_bucketing.py --num-hidden 512 --num-embed 512 --num-layers 2 --gpus 0 --batch-size 128 \ --disp-batches 1 --num-epochs 1 --model-prefix trained_model --dropout 0.0 \ - --infer --load-epoch 6 + --infer --load-epoch 1 --use-cudnn-cells # --use-cudnn-cells diff --git a/seq2seq/model_train_gpu.sh b/seq2seq/model_train_gpu.sh index bb62d40..f51ce41 100755 --- a/seq2seq/model_train_gpu.sh +++ b/seq2seq/model_train_gpu.sh @@ -1,5 +1,5 @@ #!/bin/bash -python seq2seq_bucketing.py --num-hidden 512 --num-embed 512 --num-layers 2 --gpus 0,1,2,3 --batch-size 256 \ - --optimizer adagrad --lr 0.1 --disp-batches 1 --num-epochs 10 --model-prefix trained_model --dropout 0.0 +python seq2seq_bucketing.py --num-hidden 512 --num-embed 512 --num-layers 2 --gpus 0 --batch-size 128 \ + --optimizer adagrad --lr 0.1 --disp-batches 1 --num-epochs 1 --model-prefix trained_model --dropout 0.0 --use-cudnn-cells # --inference-unrolling-for-training # --use-cudnn-cells diff --git a/seq2seq/preprocess_data.py b/seq2seq/preprocess_data.py index 8ed58b0..b010c0a 100644 --- a/seq2seq/preprocess_data.py +++ b/seq2seq/preprocess_data.py @@ -28,21 +28,14 @@ start = time() dataset = get_s2s_data( - src_train_path= './data/wmt15-de-en/train.de', - src_valid_path= './data/wmt15-de-en/valid.de', - src_test_path = './data/wmt15-de-en/test.de', - targ_train_path= './data/wmt15-de-en/train.en', - targ_valid_path= './data/wmt15-de-en/valid.en', - targ_test_path= './data/wmt15-de-en/test.en', + src_train_path= './data/europarl-v7.es-en.en_train_small', + src_valid_path= './data/europarl-v7.es-en.en_valid_small', + src_test_path = './data/europarl-v7.es-en.en_test_small', + targ_train_path= './data/europarl-v7.es-en.es_train_small', + targ_valid_path= './data/europarl-v7.es-en.en_valid_small', + targ_test_path = './data/europarl-v7.es-en.es_test_small' ) -# dataset = get_s2s_data( -# src_train_path= './data/europarl-v7.es-en.en_train_small', -# src_valid_path= './data/europarl-v7.es-en.en_valid_small', -# targ_train_path= './data/europarl-v7.es-en.es_train_small', -# targ_valid_path= './data/europarl-v7.es-en.en_valid_small' -# ) - preproc_duration = time() - start print("\nPreprocessing data took %.4f seconds\n" % preproc_duration) diff --git a/seq2seq/seq2seq_bucketing.py b/seq2seq/seq2seq_bucketing.py index 5a6449e..35211bd 100755 --- a/seq2seq/seq2seq_bucketing.py +++ b/seq2seq/seq2seq_bucketing.py @@ -474,7 +474,7 @@ def sym_gen(seq_len): model = mx.mod.BucketingModule( sym_gen = sym_gen, - default_bucket_key = data_train.default_bucket_key, + default_bucket_key = data_test.default_bucket_key, context = contexts) model.bind(data_test.provide_data, data_test.provide_label, for_training=False) @@ -505,10 +505,10 @@ def sym_gen(seq_len): # mx.metric.Perplexity model.score(data_test, BleuScore(invalid_label), #mx.metric.Perplexity(invalid_label), batch_end_callback=mx.callback.Speedometer(batch_size=args.batch_size, frequent=1, auto_reset=True)) - - for bkt in range(10): - data_val.reset() - data_batch = data_val.next() + # 10 + for bkt in range(0): + data_test.reset() + data_batch = data_test.next() model.forward(data_batch, is_train=None) source = data_batch.data[0] From 99b6cd7a50afdbfeab54371c023d03dc608a0fe9 Mon Sep 17 00:00:00 2001 From: Marek 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z_531JHq7Tq)8Ge@#Jvnm*8x(T;k(Q<`9UPH0&y&4$qyo_Ll*oXl6Zr`r^4+sxNBJ? zeOBPPr3IQUKZqp$THIOigAjjmJ#j73rml{K%m&|wJ06~e(edFk$S*+V*>a}M4`M3ZV)jfW#>*hjhRNsGj_-{y zsmC5tpGSTdCg;LE2={!T4BNY_X?SjKowDWqU;06)(-O%3H<1zKH2Xm$?F-a#AuE0mwK@cQ zr_B$d7Uwu)Q^<-RM6Djlc^UQ>=cVVt>GFf9#aL@=%Sqf>@qhh?>mRI`avZ1 z@b^yI{UDNfwYA|<=xNBax8K&J$q%9)ULeNd65_{{2-a1sO@0s)>5&CLh=cl7p@3;o4Xxj$;BvMb-Rqj*qGXK%iaQF6drO;a`N5#ru zROlV(>Fq8LMW3O4OLPhG;?5>~DQ0w@R?g4bQQWz+cVO#K)Deq3t9v+#3f69(PNf)aE%X#C#lk>wM>)D2-u#(kmC;(uGj|qy a3cWqi6%;y~@-1N<^0MekD!YbD?f(JfT0h Date: Wed, 3 May 2017 22:16:41 +0000 Subject: [PATCH 14/58] Change preprocess to DE-EN --- seq2seq/preprocess_data.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/seq2seq/preprocess_data.py b/seq2seq/preprocess_data.py index b010c0a..fa88c14 100644 --- a/seq2seq/preprocess_data.py +++ b/seq2seq/preprocess_data.py @@ -28,12 +28,12 @@ start = time() dataset = get_s2s_data( - src_train_path= './data/europarl-v7.es-en.en_train_small', - src_valid_path= './data/europarl-v7.es-en.en_valid_small', - src_test_path = './data/europarl-v7.es-en.en_test_small', - targ_train_path= './data/europarl-v7.es-en.es_train_small', - targ_valid_path= './data/europarl-v7.es-en.en_valid_small', - targ_test_path = './data/europarl-v7.es-en.es_test_small' + src_train_path= './data/train.de', + src_valid_path= './data/valid.de', + src_test_path = './data/test.de', + targ_train_path= './data/train.en', + targ_valid_path= './data/valid.en', + targ_test_path = './data/test.en' ) From 604502c3a963f4adb641b16ca6ee3b47f1e2e4e9 Mon Sep 17 00:00:00 2001 From: Marek Kolodziej Date: Wed, 3 May 2017 22:22:04 +0000 Subject: [PATCH 15/58] WMT15-DE-EN --- seq2seq/preprocess_data.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/seq2seq/preprocess_data.py b/seq2seq/preprocess_data.py index fa88c14..42aaafb 100644 --- a/seq2seq/preprocess_data.py +++ b/seq2seq/preprocess_data.py @@ -28,12 +28,12 @@ start = time() dataset = get_s2s_data( - src_train_path= './data/train.de', - src_valid_path= './data/valid.de', - src_test_path = './data/test.de', - targ_train_path= './data/train.en', - targ_valid_path= './data/valid.en', - targ_test_path = './data/test.en' + src_train_path= './data/wmt15-de-en/train.de', + src_valid_path= './data/wmt15-de-en/valid.de', + src_test_path = './data/wmt15-de-en/test.de', + targ_train_path= './data/wmt15-de-en/train.en', + targ_valid_path= './data/wmt15-de-en/valid.en', + targ_test_path = './data/wmt15-de-en/test.en' ) From 7fc4e07548fb9b7514f3bc3eb739a8b7e7c7499c Mon Sep 17 00:00:00 2001 From: Marek Kolodziej Date: Wed, 3 May 2017 17:27:54 -0700 Subject: [PATCH 16/58] Fixed validation iterator --- seq2seq/preprocess_data.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/seq2seq/preprocess_data.py b/seq2seq/preprocess_data.py index 42aaafb..7836dea 100644 --- a/seq2seq/preprocess_data.py +++ b/seq2seq/preprocess_data.py @@ -85,7 +85,7 @@ print("Serializing validation set iterator.") start = time() with open('./data/valid_iterator.pkl', 'wb') as f: - pickle.dump(train_iter, f, pickle.HIGHEST_PROTOCOL) + pickle.dump(valid_iter, f, pickle.HIGHEST_PROTOCOL) valid_ser_duration = time() - start print("\nSerializing validation set iterator took %.4f seconds\n" % valid_ser_duration) From 29f12dad5c6dd8f3aad938714ec4d7dbec5aeebc Mon Sep 17 00:00:00 2001 From: Marek Kolodziej Date: Thu, 4 May 2017 01:24:02 +0000 Subject: [PATCH 17/58] Fixed dataset fetching --- seq2seq/data/test_iterator.pkl | Bin 859434 -> 0 bytes seq2seq/get_opennmt_data.sh | 38 ++++++++------------------------- 2 files changed, 9 insertions(+), 29 deletions(-) delete mode 100644 seq2seq/data/test_iterator.pkl diff --git a/seq2seq/data/test_iterator.pkl b/seq2seq/data/test_iterator.pkl deleted file mode 100644 index c70312552d9a980b019ee657c4b16fbd823a2aa4..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 859434 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z_531JHq7Tq)8Ge@#Jvnm*8x(T;k(Q<`9UPH0&y&4$qyo_Ll*oXl6Zr`r^4+sxNBJ? zeOBPPr3IQUKZqp$THIOigAjjmJ#j73rml{K%m&|wJ06~e(edFk$S*+V*>a}M4`M3ZV)jfW#>*hjhRNsGj_-{y zsmC5tpGSTdCg;LE2={!T4BNY_X?SjKowDWqU;06)(-O%3H<1zKH2Xm$?F-a#AuE0mwK@cQ zr_B$d7Uwu)Q^<-RM6Djlc^UQ>=cVVt>GFf9#aL@=%Sqf>@qhh?>mRI`avZ1 z@b^yI{UDNfwYA|<=xNBax8K&J$q%9)ULeNd65_{{2-a1sO@0s)>5&CLh=cl7p@3;o4Xxj$;BvMb-Rqj*qGXK%iaQF6drO;a`N5#ru zROlV(>Fq8LMW3O4OLPhG;?5>~DQ0w@R?g4bQQWz+cVO#K)Deq3t9v+#3f69(PNf)aE%X#C#lk>wM>)D2-u#(kmC;(uGj|qy a3cWqi6%;y~@-1N<^0MekD!YbD?f(JfT0h all.de +cat commoncrawl.de-en.de europarl-v7.de-en.de news-commentary-v10.de-en.de > train.de # the test set already officially exists -mv newstest2013.de test.de +mv newstest2013.de valid.de -# do the same thing to English corpora - -cat commoncrawl.de-en.en europarl-v7.de-en.en news-commentary-v10.de-en.en > all.en -mv newstest2013.en test.en - -popd > /dev/null - -echo -e "\nShuffling examples and splitting into training and validation samples" +wget -O test.de https://nlp.stanford.edu/projects/nmt/data/wmt14.en-de/newstest2014.de +# do the same thing to English corpora -# shuffle examples so validation data isn't completely from one -# corpus while training is from another +cat commoncrawl.de-en.en europarl-v7.de-en.en news-commentary-v10.de-en.en > train.en +mv newstest2013.en valid.en -python split_train_valid.py \ - --in-src-data-path ${IN_SRC_DATA_PATH} \ - --in-targ-data-path ${IN_TARG_DATA_PATH} \ - --out-src-train-path ${OUT_SRC_TRAIN_PATH} \ - --out-targ-train-path ${OUT_TARG_TRAIN_PATH} \ - --out-src-valid-path ${OUT_SRC_VALID_PATH} \ - --out-targ-valid-path ${OUT_TARG_VALID_PATH} \ - --validation-fraction 0.2 \ - --shuffle-seed 42 +wget -O test.en https://nlp.stanford.edu/projects/nmt/data/wmt14.en-de/newstest2014.en +popd > /dev/null +echo -e "\nData download complete\n" From 179313a4f5252cdec4cb8c87a3f22c2cb67db19e Mon Sep 17 00:00:00 2001 From: Marek Kolodziej Date: Thu, 4 May 2017 06:59:22 +0000 Subject: [PATCH 18/58] Correction of s2s preprocess data method arg order --- seq2seq/utils.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/seq2seq/utils.py b/seq2seq/utils.py index b5f848b..28be52f 100644 --- a/seq2seq/utils.py +++ b/seq2seq/utils.py @@ -91,8 +91,8 @@ def array_to_text(array, inv_vocab): sent.append(inv_vocab[token]) return " ".join(sent) -def get_s2s_data(src_train_path, src_valid_path, targ_train_path, - targ_valid_path, src_test_path, targ_test_path, +def get_s2s_data(src_train_path, src_valid_path, src_test_path, targ_train_path, + targ_valid_path, targ_test_path, reserved_tokens=['', '', '', '']): print("Creating joint source dictionary") From f8d063167ceb13a94265554f954b7ddc09ee1b6c Mon Sep 17 00:00:00 2001 From: Marek Kolodziej Date: Thu, 4 May 2017 14:28:57 +0000 Subject: [PATCH 19/58] Sanity check printing fix --- seq2seq/seq2seq_bucketing.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/seq2seq/seq2seq_bucketing.py b/seq2seq/seq2seq_bucketing.py index 35211bd..3ba23e1 100755 --- a/seq2seq/seq2seq_bucketing.py +++ b/seq2seq/seq2seq_bucketing.py @@ -171,7 +171,7 @@ def get_data(layout): print("\nEncoded target language sentences:\n") for i in range(5): - print(array_to_text(valid_iter.targ_sent[i], train_iter.inv_targ_vocab)) + print(array_to_text(train_iter.targ_sent[i], train_iter.inv_targ_vocab)) duration = time() - start @@ -506,7 +506,7 @@ def sym_gen(seq_len): model.score(data_test, BleuScore(invalid_label), #mx.metric.Perplexity(invalid_label), batch_end_callback=mx.callback.Speedometer(batch_size=args.batch_size, frequent=1, auto_reset=True)) # 10 - for bkt in range(0): + for bkt in range(10): data_test.reset() data_batch = data_test.next() From 5dc86a124fada88cd54859562b419ff4b8bed4df Mon Sep 17 00:00:00 2001 From: Marek Kolodziej Date: Thu, 4 May 2017 20:13:27 +0000 Subject: [PATCH 20/58] Prevent mem. reallocations --- seq2seq/seq2seq_bucketing.py | 9 +++------ seq2seq/seq2seq_iterator.py | 4 +++- 2 files changed, 6 insertions(+), 7 deletions(-) diff --git a/seq2seq/seq2seq_bucketing.py b/seq2seq/seq2seq_bucketing.py index 3ba23e1..3a21682 100755 --- a/seq2seq/seq2seq_bucketing.py +++ b/seq2seq/seq2seq_bucketing.py @@ -148,22 +148,19 @@ def get_data(layout): with open('./data/train_iterator.pkl', 'rb') as f: # _en_de.pkl train_iter = pickle.load(f) - train_iter.initialize() - train_iter.batch_size = args.batch_size + train_iter.initialize(curr_batch_size=args.batch_size) print("\nUnpickling validation iterator") with open('./data/valid_iterator.pkl', 'rb') as f: # _en_de.pkl valid_iter = pickle.load(f) - valid_iter.initialize() - valid_iter.batch_size = args.batch_size + valid_iter.initialize(curr_batch_size=args.batch_size) with open('./data/test_iterator.pkl', 'rb') as f: test_iter = pickle.load(f) - test_iter.initialize() - test_iter.batch_size = args.batch_size + test_iter.initialize(curr_batch_size=args.batch_size) print("\nEncoded source language sentences:\n") for i in range(5): diff --git a/seq2seq/seq2seq_iterator.py b/seq2seq/seq2seq_iterator.py index 3fd096b..b254565 100644 --- a/seq2seq/seq2seq_iterator.py +++ b/seq2seq/seq2seq_iterator.py @@ -102,7 +102,9 @@ def __init__( # else: # raise ValueError("Invalid layout %s: Must by NT (batch major) or TN (time major)") - def initialize(self): + def initialize(self, curr_batch_size=None): + if curr_batch_size: + self.batch_size = curr_batch_size if self.layout == 'TN': self.provide_data = [ mx.io.DataDesc(self.src_data_name, (self.default_bucket_key[0], self.batch_size), layout='TN'), From c26c1c90c0395c2b68e23f211907fff9039cba28 Mon Sep 17 00:00:00 2001 From: Marek Kolodziej Date: Thu, 4 May 2017 20:48:12 +0000 Subject: [PATCH 21/58] More realloc fixes --- seq2seq/seq2seq_iterator.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/seq2seq/seq2seq_iterator.py b/seq2seq/seq2seq_iterator.py index b254565..d851f32 100644 --- a/seq2seq/seq2seq_iterator.py +++ b/seq2seq/seq2seq_iterator.py @@ -105,16 +105,17 @@ def __init__( def initialize(self, curr_batch_size=None): if curr_batch_size: self.batch_size = curr_batch_size + self.default_bucket_key = (self.default_bucket_key[0]+1, self.default_bucket_key[1]+1) if self.layout == 'TN': self.provide_data = [ mx.io.DataDesc(self.src_data_name, (self.default_bucket_key[0], self.batch_size), layout='TN'), - mx.io.DataDesc(self.targ_data_name, (self.default_bucket_key[0], self.batch_size), layout='TN') + mx.io.DataDesc(self.targ_data_name, (self.default_bucket_key[1], self.batch_size), layout='TN') ] self.provide_label = [mx.io.DataDesc(self.label_name, (self.default_bucket_key[1], self.batch_size), layout='TN')] elif self.layout == 'NT': self.provide_data = [ (self.src_data_name, (self.batch_size, self.default_bucket_key[0])), - (self.targ_data_name, (self.batch_size, self.default_bucket_key[0]))] + (self.targ_data_name, (self.batch_size, self.default_bucket_key[1]))] self.provide_label = [(self.label_name, (self.batch_size, self.default_bucket_key[1]))] else: raise ValueError("Invalid layout %s: Must by NT (batch major) or TN (time major)") From e36fd44a1e36e6aeed105f6372207092948defcc Mon Sep 17 00:00:00 2001 From: Marek Kolodziej Date: Thu, 4 May 2017 21:56:07 +0000 Subject: [PATCH 22/58] Set all seeds (Python, NumPy and MxNet) --- seq2seq/model_train_gpu.sh | 4 ++-- seq2seq/seq2seq_bucketing.py | 11 +++++++++++ 2 files changed, 13 insertions(+), 2 deletions(-) diff --git a/seq2seq/model_train_gpu.sh b/seq2seq/model_train_gpu.sh index f51ce41..233bc34 100755 --- a/seq2seq/model_train_gpu.sh +++ b/seq2seq/model_train_gpu.sh @@ -1,5 +1,5 @@ #!/bin/bash python seq2seq_bucketing.py --num-hidden 512 --num-embed 512 --num-layers 2 --gpus 0 --batch-size 128 \ - --optimizer adagrad --lr 0.1 --disp-batches 1 --num-epochs 1 --model-prefix trained_model --dropout 0.0 --use-cudnn-cells + --optimizer adagrad --lr 0.141 --disp-batches 100 --num-epochs 1 --model-prefix trained_model \ + --dropout 0.3 --seed 1234 --use-cudnn-cells # --inference-unrolling-for-training -# --use-cudnn-cells diff --git a/seq2seq/seq2seq_bucketing.py b/seq2seq/seq2seq_bucketing.py index 3a21682..414f518 100755 --- a/seq2seq/seq2seq_bucketing.py +++ b/seq2seq/seq2seq_bucketing.py @@ -72,6 +72,8 @@ parser.add_argument('--inference-unrolling-for-training', action='store_true', help='Feed previous prediction (instead of previous ground truth) into the decoder input during training') +parser.add_argument('--seed', type=int, default=1234, + help='Set random seed for Python, NumPy and MxNet RNGs') #buckets = [32] # buckets = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100] @@ -551,6 +553,15 @@ def sym_gen(seq_len): logging.basicConfig(level=logging.DEBUG, format=head) args = parser.parse_args() + + # set random seeds for Python, NumPy and MxNet + import random + seed = args.seed + np.random.seed(seed) + random.seed(seed) + mx.random.seed(seed) + print("Using seed: %d" % seed) + if args.gpus: contexts = [mx.gpu(int(i)) for i in args.gpus.split(',')] else: From b8447cfbb1ab638642734b3544babe1583b39cad Mon Sep 17 00:00:00 2001 From: Marek Kolodziej Date: Fri, 5 May 2017 16:40:02 +0000 Subject: [PATCH 23/58] Report only final average BLEU --- seq2seq/seq2seq_bucketing.py | 112 +++++++++++++++++++---------------- 1 file changed, 61 insertions(+), 51 deletions(-) diff --git a/seq2seq/seq2seq_bucketing.py b/seq2seq/seq2seq_bucketing.py index 414f518..46befd5 100755 --- a/seq2seq/seq2seq_bucketing.py +++ b/seq2seq/seq2seq_bucketing.py @@ -141,42 +141,46 @@ def _normalize_sequence(length, inputs, layout, merge, in_layout=None): return inputs, axis -def get_data(layout): +def get_data(layout, infer=False): start = time() print("\nUnpickling training iterator") - with open('./data/train_iterator.pkl', 'rb') as f: # _en_de.pkl - train_iter = pickle.load(f) + if not infer: + with open('./data/train_iterator.pkl', 'rb') as f: # _en_de.pkl + train_iter = pickle.load(f) - train_iter.initialize(curr_batch_size=args.batch_size) + train_iter.initialize(curr_batch_size=args.batch_size) - print("\nUnpickling validation iterator") + print("\nUnpickling validation iterator") - with open('./data/valid_iterator.pkl', 'rb') as f: # _en_de.pkl - valid_iter = pickle.load(f) + with open('./data/valid_iterator.pkl', 'rb') as f: # _en_de.pkl + valid_iter = pickle.load(f) - valid_iter.initialize(curr_batch_size=args.batch_size) + valid_iter.initialize(curr_batch_size=args.batch_size) with open('./data/test_iterator.pkl', 'rb') as f: test_iter = pickle.load(f) test_iter.initialize(curr_batch_size=args.batch_size) - print("\nEncoded source language sentences:\n") - for i in range(5): - print(array_to_text(train_iter.src_sent[i], train_iter.inv_src_vocab)) +# print("\nEncoded source language sentences:\n") +# for i in range(5): +# print(array_to_text(train_iter.src_sent[i], train_iter.inv_src_vocab)) - print("\nEncoded target language sentences:\n") - for i in range(5): - print(array_to_text(train_iter.targ_sent[i], train_iter.inv_targ_vocab)) +# print("\nEncoded target language sentences:\n") +# for i in range(5): +# print(array_to_text(train_iter.targ_sent[i], train_iter.inv_targ_vocab)) duration = time() - start print("\nDataset deserialization time: %.2f seconds\n" % duration) - - return train_iter, valid_iter, test_iter, train_iter.src_vocab, train_iter.targ_vocab, train_iter.inv_src_vocab, train_iter.inv_targ_vocab + + if not infer: + return train_iter, valid_iter, test_iter, train_iter.src_vocab, train_iter.targ_vocab, train_iter.inv_src_vocab, train_iter.inv_targ_vocab + else: + return test_iter, test_iter.src_vocab, test_iter.inv_src_vocab, test_iter.targ_vocab, test_iter.inv_targ_vocab # WORK IN PROGRESS !!! def decoder_unroll(decoder, target_embed, targ_vocab, unroll_length, @@ -390,7 +394,7 @@ def get(self): def infer(args): assert args.model_prefix, "Must specifiy path to load from" - _, _, data_test, src_vocab, targ_vocab, inv_src_vocab, inv_targ_vocab = get_data('TN') + data_test, src_vocab, inv_src_vocab, targ_vocab, inv_targ_vocab = get_data('TN', infer=True) print "len(src_vocab) len(targ_vocab)", len(src_vocab), len(targ_vocab) @@ -502,50 +506,56 @@ def sym_gen(seq_len): start = time() # mx.metric.Perplexity - model.score(data_test, BleuScore(invalid_label), #mx.metric.Perplexity(invalid_label), - batch_end_callback=mx.callback.Speedometer(batch_size=args.batch_size, frequent=1, auto_reset=True)) - # 10 - for bkt in range(10): - data_test.reset() - data_batch = data_test.next() +# model.score(data_test, BleuScore(invalid_label), #mx.metric.Perplexity(invalid_label), +# batch_end_callback=mx.callback.Speedometer(batch_size=args.batch_size, frequent=1, auto_reset=True)) - model.forward(data_batch, is_train=None) - source = data_batch.data[0] - preds = model.get_outputs()[0] - labels = data_batch.label[0] + examples = [] + bleu_acc = 0.0 + num_inst = 0 - maxed = mx.ndarray.argmax(data=preds, axis=1) - pred_nparr = maxed.asnumpy() - src_nparr = source.asnumpy() - label_nparr = labels.asnumpy().astype(np.int32) - sent_len, batch_size = np.shape(label_nparr) - pred_nparr = pred_nparr.reshape(sent_len, batch_size).astype(np.int32) + try: + data_test.reset() + smoothing_fn = nltk.translate.bleu_score.SmoothingFunction().method3 - # range should be number of examples in the bucket - for i in range(10): - src_lst = list(reversed(drop_sentinels(src_nparr[:, i].tolist()))) - exp_lst = drop_sentinels(label_nparr[:, i].tolist()) - act_lst = drop_sentinels(pred_nparr[:, i].tolist()) + while True: - # convert source to text - src_txt = array_to_text(src_lst, inv_src_vocab) + data_batch = data_test.next() + model.forward(data_batch, is_train=None) + source = data_batch.data[0] + preds = model.get_outputs()[0] + labels = data_batch.label[0] - # convert expected translation to text - exp_txt = array_to_text(exp_lst, inv_targ_vocab) + maxed = mx.ndarray.argmax(data=preds, axis=1) + pred_nparr = maxed.asnumpy() + src_nparr = source.asnumpy() + label_nparr = labels.asnumpy().astype(np.int32) + sent_len, batch_size = np.shape(label_nparr) + pred_nparr = pred_nparr.reshape(sent_len, batch_size).astype(np.int32) + + for i in range(batch_size): - # convert actual translation to text - act_txt = array_to_text(act_lst, inv_targ_vocab) + src_lst = list(reversed(drop_sentinels(src_nparr[:, i].tolist()))) + exp_lst = drop_sentinels(label_nparr[:, i].tolist()) + act_lst = drop_sentinels(pred_nparr[:, i].tolist()) - print("\n") - print("Source text: %s" % src_txt) - print("Expected translation: %s" % exp_txt) - print("Actual translation: %s" % act_txt) + expected = exp_lst + actual = act_lst + bleu = nltk.translate.bleu_score.sentence_bleu( + references=[expected], hypothesis=actual, weights=(0.25, 0.25, 0.25, 0.25), + smoothing_function = smoothing_fn + ) + bleu_acc += bleu + num_inst += 1 + examples.append((src_lst, exp_lst, act_lst, bleu)) + except StopIteration as se: + pass + + bleu_acc /= num_inst - infer_duration = time() - start - time_per_epoch = infer_duration / args.num_epochs - print("\n\nTime per epoch: %.2f seconds\n\n" % time_per_epoch) + print("\nTest set BLEU score (averaged over all examples): %.3f\n" % bleu_acc) + if __name__ == '__main__': import logging From c140bccf557cc61ca2962ff68ecdc311a513859c Mon Sep 17 00:00:00 2001 From: Marek Kolodziej Date: Fri, 5 May 2017 18:33:42 +0000 Subject: [PATCH 24/58] Print final BLEU and translations --- seq2seq/seq2seq_bucketing.py | 18 +++++++++++++++++- 1 file changed, 17 insertions(+), 1 deletion(-) diff --git a/seq2seq/seq2seq_bucketing.py b/seq2seq/seq2seq_bucketing.py index 46befd5..9146a26 100755 --- a/seq2seq/seq2seq_bucketing.py +++ b/seq2seq/seq2seq_bucketing.py @@ -9,6 +9,7 @@ from mxnet.rnn import LSTMCell, SequentialRNNCell, FusedRNNCell #from rnn_cell import LSTMCell, SequentialRNNCell from itertools import takewhile, dropwhile +from operator import itemgetter from time import time import re @@ -554,8 +555,23 @@ def sym_gen(seq_len): bleu_acc /= num_inst + # Find the top K best translations + examples = sorted(examples, key=itemgetter(3), reverse=True) + + num_examples = 20 + + print("\nSample translations:\n") + for i in range(min(num_examples, len(examples))): + src_lst, exp_lst, act_lst, bleu = examples[i] + src_txt = array_to_text(src_lst, data_test.inv_src_vocab) + exp_txt = array_to_text(exp_lst, data_test.inv_targ_vocab) + act_txt = array_to_text(act_lst, data_test.inv_targ_vocab) + print("\n") + print("Source text: %s" % src_txt) + print("Expected translation: %s" % exp_txt) + print("Actual translation: %s" % act_txt) + print("\n") print("\nTest set BLEU score (averaged over all examples): %.3f\n" % bleu_acc) - if __name__ == '__main__': import logging From 1070919a0ac9bc0e6c1c4e2077d14eced0466814 Mon Sep 17 00:00:00 2001 From: Marek Kolodziej Date: Tue, 16 May 2017 19:50:25 +0000 Subject: [PATCH 25/58] Repo cleanup --- Dockerfile | 76 --- LICENSE.txt | 202 ++++++ seq2seq/README.md => README.md | 6 +- seq2seq/__init__.py => __init__.py | 0 .../attention_cell.py => attention_cell.py | 0 ...en_es_nmt_data.sh => get_en_es_nmt_data.sh | 0 ...get_opennmt_data.sh => get_opennmt_data.sh | 0 jupyter_notebook_config.py | 584 ------------------ .../model_infer_gpu.sh => model_infer_gpu.sh | 0 .../model_train_cpu.sh => model_train_cpu.sh | 0 .../model_train_gpu.sh => model_train_gpu.sh | 0 nan.patch | 46 -- .../preprocess_data.py => preprocess_data.py | 0 seq2seq/rnn_cell.py => rnn_cell.py | 0 ...q2seq_bucketing.py => seq2seq_bucketing.py | 0 ...seq2seq_iterator.py => seq2seq_iterator.py | 0 speedometer_reset.patch | 32 - ...lit_train_valid.py => split_train_valid.py | 0 start_container.sh | 3 - train_ptb/README.md | 15 - train_ptb/cudnn_lstm_bucketing.py | 218 ------- train_ptb/get_ptb_data.sh | 14 - train_ptb/lstm_bucketing.py | 107 ---- seq2seq/utils.py => utils.py | 0 24 files changed, 207 insertions(+), 1096 deletions(-) delete mode 100755 Dockerfile create mode 100644 LICENSE.txt rename seq2seq/README.md => README.md (84%) rename seq2seq/__init__.py => __init__.py (100%) rename seq2seq/attention_cell.py => attention_cell.py (100%) rename seq2seq/get_en_es_nmt_data.sh => get_en_es_nmt_data.sh (100%) rename seq2seq/get_opennmt_data.sh => get_opennmt_data.sh (100%) delete mode 100755 jupyter_notebook_config.py rename seq2seq/model_infer_gpu.sh => model_infer_gpu.sh (100%) rename seq2seq/model_train_cpu.sh => model_train_cpu.sh (100%) rename seq2seq/model_train_gpu.sh => model_train_gpu.sh (100%) delete mode 100644 nan.patch rename seq2seq/preprocess_data.py => preprocess_data.py (100%) rename seq2seq/rnn_cell.py => rnn_cell.py (100%) rename seq2seq/seq2seq_bucketing.py => seq2seq_bucketing.py (100%) rename seq2seq/seq2seq_iterator.py => seq2seq_iterator.py (100%) delete mode 100644 speedometer_reset.patch rename seq2seq/split_train_valid.py => split_train_valid.py (100%) delete mode 100755 start_container.sh delete mode 100644 train_ptb/README.md delete mode 100644 train_ptb/cudnn_lstm_bucketing.py delete mode 100755 train_ptb/get_ptb_data.sh delete mode 100644 train_ptb/lstm_bucketing.py rename seq2seq/utils.py => utils.py (100%) diff --git a/Dockerfile b/Dockerfile deleted file mode 100755 index 0624d87..0000000 --- a/Dockerfile +++ /dev/null @@ -1,76 +0,0 @@ -FROM nvidia/cuda:8.0-cudnn5-devel - -RUN apt-get update && apt-get -y upgrade && \ - apt-get install -y \ - build-essential \ - ca-certificates \ - git \ - libopenblas-dev \ - libatlas-base-dev \ - libglib2.0-dev \ - libopencv-dev \ - python-dev \ - python-numpy \ - python-setuptools \ - wget \ - cmake \ - curl \ - python-pip \ - python-dev \ - unzip \ - sudo \ - vim \ - libglib2.0-dev \ - libtiff5-dev \ - libjpeg8-dev \ - zlib1g-dev - -RUN pip install --upgrade numpy scipy matplotlib scikit-learn sympy nltk setuptools requests - -COPY nan.patch /root - -# Build MxNet for Python -RUN cd /root && git clone --recursive https://github.com/dmlc/mxnet.git && cp nan.patch /root/mxnet/ && \ - cd mxnet && git checkout 955f6be6977ca1a27d3e912fd62a08f019dd1f76 && git apply nan.patch && \ - cp make/config.mk . && \ - echo "USE_CUDA=1" >> config.mk && \ - echo "USE_CUDNN=1" >> config.mk && \ - echo "CUDA_ARCH :=" \ - "-gencode arch=compute_35,code=sm_35" \ - "-gencode arch=compute_52,code=sm_52" \ - "-gencode arch=compute_60,code=sm_60" \ - "-gencode arch=compute_61,code=sm_61" \ - "-gencode arch=compute_61,code=compute_61" >> config.mk && \ - echo "USE_CUDA_PATH=/usr/local/cuda" >> config.mk - -ENV LD_LIBRARY_PATH $LD_LIBRARY_PATH:/usr/local/lib -RUN cd /root/mxnet && make -j$(nproc) && \ - mv lib/libmxnet.so /usr/local/lib && \ - ldconfig && \ - make clean && \ - cd python && \ - pip install -e . - -# Python3 support -RUN apt-get -y install python3-pip -RUN pip3 install numpy - -# Jupyter notebook support -COPY jupyter_notebook_config.py /root/.jupyter/jupyter_notebook_config.py -EXPOSE 8888 - -ENV PYTHONPATH /root/mxnet/python - -# Build MxNet for Scala -#RUN apt-get -y install maven openjdk-8-jdk scala -#RUN cd /root/mxnet && make scalapkg && make scalainstall - -# Build MxNet for R - WIP !!! -#RUN apt-get -y install r-base r-base-dev - -RUN pip install unidecode dill tqdm - -WORKDIR /root/mxnet - - - diff --git a/LICENSE.txt b/LICENSE.txt new file mode 100644 index 0000000..d645695 --- /dev/null +++ b/LICENSE.txt @@ -0,0 +1,202 @@ + + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/seq2seq/README.md b/README.md similarity index 84% rename from seq2seq/README.md rename to README.md index 618e0bb..f0c92b6 100644 --- a/seq2seq/README.md +++ b/README.md @@ -38,8 +38,12 @@ How to run the scripts? Credits ------- -Thanks to Eric Xie (@piiswrong) for the attention cell implementation. +Many thanks to Eric Xie (@piiswrong), Sheng Zha (@szha) and Antti-Pekka Hynninen (@ap-hynninen) for valuable input. +License +------- + +This project is licensed under the Apache 2.0 license. See the text of the license [here](https://github.com/mkolod/mxnet_seq2seq/blob/master/LICENSE.txt). > **Note:** diff --git a/seq2seq/__init__.py b/__init__.py similarity index 100% rename from seq2seq/__init__.py rename to __init__.py diff --git a/seq2seq/attention_cell.py b/attention_cell.py similarity index 100% rename from seq2seq/attention_cell.py rename to attention_cell.py diff --git a/seq2seq/get_en_es_nmt_data.sh b/get_en_es_nmt_data.sh similarity index 100% rename from seq2seq/get_en_es_nmt_data.sh rename to get_en_es_nmt_data.sh diff --git a/seq2seq/get_opennmt_data.sh b/get_opennmt_data.sh similarity index 100% rename from seq2seq/get_opennmt_data.sh rename to get_opennmt_data.sh diff --git a/jupyter_notebook_config.py b/jupyter_notebook_config.py deleted file mode 100755 index 646be37..0000000 --- a/jupyter_notebook_config.py +++ /dev/null @@ -1,584 +0,0 @@ -# Configuration file for jupyter-notebook. - -#------------------------------------------------------------------------------ -# Application(SingletonConfigurable) configuration -#------------------------------------------------------------------------------ - -## This is an application. - -## The date format used by logging formatters for %(asctime)s -#c.Application.log_datefmt = '%Y-%m-%d %H:%M:%S' - -## The Logging format template -#c.Application.log_format = '[%(name)s]%(highlevel)s %(message)s' - -## Set the log level by value or name. -#c.Application.log_level = 30 - -#------------------------------------------------------------------------------ -# JupyterApp(Application) configuration -#------------------------------------------------------------------------------ - -## Base class for Jupyter applications - -## Answer yes to any prompts. -#c.JupyterApp.answer_yes = False - -## Full path of a config file. -#c.JupyterApp.config_file = u'' - -## Specify a config file to load. -#c.JupyterApp.config_file_name = u'' - -## Generate default config file. -#c.JupyterApp.generate_config = False - -#------------------------------------------------------------------------------ -# NotebookApp(JupyterApp) configuration -#------------------------------------------------------------------------------ - -## Set the Access-Control-Allow-Credentials: true header -#c.NotebookApp.allow_credentials = False - -## Set the Access-Control-Allow-Origin header -# -# Use '*' to allow any origin to access your server. -# -# Takes precedence over allow_origin_pat. -#c.NotebookApp.allow_origin = '' - -## Use a regular expression for the Access-Control-Allow-Origin header -# -# Requests from an origin matching the expression will get replies with: -# -# Access-Control-Allow-Origin: origin -# -# where `origin` is the origin of the request. -# -# Ignored if allow_origin is set. -#c.NotebookApp.allow_origin_pat = '' - -## DEPRECATED use base_url -#c.NotebookApp.base_project_url = '/' - -## The base URL for the notebook server. -# -# Leading and trailing slashes can be omitted, and will automatically be added. -#c.NotebookApp.base_url = '/' - -## Specify what command to use to invoke a web browser when opening the notebook. -# If not specified, the default browser will be determined by the `webbrowser` -# standard library module, which allows setting of the BROWSER environment -# variable to override it. -#c.NotebookApp.browser = u'' - -## The full path to an SSL/TLS certificate file. -#c.NotebookApp.certfile = u'' - -## The full path to a certificate authority certificate for SSL/TLS client -# authentication. -#c.NotebookApp.client_ca = u'' - -## The config manager class to use -#c.NotebookApp.config_manager_class = 'notebook.services.config.manager.ConfigManager' - -## The notebook manager class to use. -#c.NotebookApp.contents_manager_class = 'notebook.services.contents.filemanager.FileContentsManager' - -## Extra keyword arguments to pass to `set_secure_cookie`. See tornado's -# set_secure_cookie docs for details. -#c.NotebookApp.cookie_options = {} - -## The random bytes used to secure cookies. By default this is a new random -# number every time you start the Notebook. Set it to a value in a config file -# to enable logins to persist across server sessions. -# -# Note: Cookie secrets should be kept private, do not share config files with -# cookie_secret stored in plaintext (you can read the value from a file). -#c.NotebookApp.cookie_secret = '' - -## The file where the cookie secret is stored. -#c.NotebookApp.cookie_secret_file = u'' - -## The default URL to redirect to from `/` -#c.NotebookApp.default_url = '/tree' - -## Disable cross-site-request-forgery protection -# -# Jupyter notebook 4.3.1 introduces protection from cross-site request -# forgeries, requiring API requests to either: -# -# - originate from pages served by this server (validated with XSRF cookie and -# token), or - authenticate with a token -# -# Some anonymous compute resources still desire the ability to run code, -# completely without authentication. These services can disable all -# authentication and security checks, with the full knowledge of what that -# implies. -#c.NotebookApp.disable_check_xsrf = False - -## Whether to enable MathJax for typesetting math/TeX -# -# MathJax is the javascript library Jupyter uses to render math/LaTeX. It is -# very large, so you may want to disable it if you have a slow internet -# connection, or for offline use of the notebook. -# -# When disabled, equations etc. will appear as their untransformed TeX source. -#c.NotebookApp.enable_mathjax = True - -## extra paths to look for Javascript notebook extensions -#c.NotebookApp.extra_nbextensions_path = [] - -## Extra paths to search for serving static files. -# -# This allows adding javascript/css to be available from the notebook server -# machine, or overriding individual files in the IPython -#c.NotebookApp.extra_static_paths = [] - -## Extra paths to search for serving jinja templates. -# -# Can be used to override templates from notebook.templates. -#c.NotebookApp.extra_template_paths = [] - -## -#c.NotebookApp.file_to_run = '' - -## Use minified JS file or not, mainly use during dev to avoid JS recompilation -#c.NotebookApp.ignore_minified_js = False - -## (bytes/sec) Maximum rate at which messages can be sent on iopub before they -# are limited. -#c.NotebookApp.iopub_data_rate_limit = 0 - -## (msg/sec) Maximum rate at which messages can be sent on iopub before they are -# limited. -#c.NotebookApp.iopub_msg_rate_limit = 0 - -## The IP address the notebook server will listen on. -c.NotebookApp.ip = '*' - -## Supply extra arguments that will be passed to Jinja environment. -#c.NotebookApp.jinja_environment_options = {} - -## Extra variables to supply to jinja templates when rendering. -#c.NotebookApp.jinja_template_vars = {} - -## The kernel manager class to use. -#c.NotebookApp.kernel_manager_class = 'notebook.services.kernels.kernelmanager.MappingKernelManager' - -## The kernel spec manager class to use. Should be a subclass of -# `jupyter_client.kernelspec.KernelSpecManager`. -# -# The Api of KernelSpecManager is provisional and might change without warning -# between this version of Jupyter and the next stable one. -#c.NotebookApp.kernel_spec_manager_class = 'jupyter_client.kernelspec.KernelSpecManager' - -## The full path to a private key file for usage with SSL/TLS. -#c.NotebookApp.keyfile = u'' - -## The login handler class to use. -#c.NotebookApp.login_handler_class = 'notebook.auth.login.LoginHandler' - -## The logout handler class to use. -#c.NotebookApp.logout_handler_class = 'notebook.auth.logout.LogoutHandler' - -## A custom url for MathJax.js. Should be in the form of a case-sensitive url to -# MathJax, for example: /static/components/MathJax/MathJax.js -#c.NotebookApp.mathjax_url = '' - -## Dict of Python modules to load as notebook server extensions.Entry values can -# be used to enable and disable the loading ofthe extensions. The extensions -# will be loaded in alphabetical order. -#c.NotebookApp.nbserver_extensions = {} - -## The directory to use for notebooks and kernels. -#c.NotebookApp.notebook_dir = u'' - -## Whether to open in a browser after starting. The specific browser used is -# platform dependent and determined by the python standard library `webbrowser` -# module, unless it is overridden using the --browser (NotebookApp.browser) -# configuration option. -c.NotebookApp.open_browser = False - -## Hashed password to use for web authentication. -# -# To generate, type in a python/IPython shell: -# -# from notebook.auth import passwd; passwd() -# -# The string should be of the form type:salt:hashed-password. -#c.NotebookApp.password = u'' - -## The port the notebook server will listen on. -c.NotebookApp.port = 8888 - -## The number of additional ports to try if the specified port is not available. -#c.NotebookApp.port_retries = 50 - -## DISABLED: use %pylab or %matplotlib in the notebook to enable matplotlib. -#c.NotebookApp.pylab = 'disabled' - -## (sec) Time window used to check the message and data rate limits. -#c.NotebookApp.rate_limit_window = 1.0 - -## Reraise exceptions encountered loading server extensions? -#c.NotebookApp.reraise_server_extension_failures = False - -## DEPRECATED use the nbserver_extensions dict instead -#c.NotebookApp.server_extensions = [] - -## The session manager class to use. -#c.NotebookApp.session_manager_class = 'notebook.services.sessions.sessionmanager.SessionManager' - -## Supply SSL options for the tornado HTTPServer. See the tornado docs for -# details. -#c.NotebookApp.ssl_options = {} - -## Token used for authenticating first-time connections to the server. -# -# When no password is enabled, the default is to generate a new, random token. -# -# Setting to an empty string disables authentication altogether, which is NOT -# RECOMMENDED. -#c.NotebookApp.token = '' - -## Supply overrides for the tornado.web.Application that the Jupyter notebook -# uses. -#c.NotebookApp.tornado_settings = {} - -## Whether to trust or not X-Scheme/X-Forwarded-Proto and X-Real-Ip/X-Forwarded- -# For headerssent by the upstream reverse proxy. Necessary if the proxy handles -# SSL -#c.NotebookApp.trust_xheaders = False - -## DEPRECATED, use tornado_settings -#c.NotebookApp.webapp_settings = {} - -## The base URL for websockets, if it differs from the HTTP server (hint: it -# almost certainly doesn't). -# -# Should be in the form of an HTTP origin: ws[s]://hostname[:port] -#c.NotebookApp.websocket_url = '' - -#------------------------------------------------------------------------------ -# ConnectionFileMixin(LoggingConfigurable) configuration -#------------------------------------------------------------------------------ - -## Mixin for configurable classes that work with connection files - -## JSON file in which to store connection info [default: kernel-.json] -# -# This file will contain the IP, ports, and authentication key needed to connect -# clients to this kernel. By default, this file will be created in the security -# dir of the current profile, but can be specified by absolute path. -#c.ConnectionFileMixin.connection_file = '' - -## set the control (ROUTER) port [default: random] -#c.ConnectionFileMixin.control_port = 0 - -## set the heartbeat port [default: random] -#c.ConnectionFileMixin.hb_port = 0 - -## set the iopub (PUB) port [default: random] -#c.ConnectionFileMixin.iopub_port = 0 - -## Set the kernel's IP address [default localhost]. If the IP address is -# something other than localhost, then Consoles on other machines will be able -# to connect to the Kernel, so be careful! -#c.ConnectionFileMixin.ip = u'' - -## set the shell (ROUTER) port [default: random] -#c.ConnectionFileMixin.shell_port = 0 - -## set the stdin (ROUTER) port [default: random] -#c.ConnectionFileMixin.stdin_port = 0 - -## -#c.ConnectionFileMixin.transport = 'tcp' - -#------------------------------------------------------------------------------ -# KernelManager(ConnectionFileMixin) configuration -#------------------------------------------------------------------------------ - -## Manages a single kernel in a subprocess on this host. -# -# This version starts kernels with Popen. - -## Should we autorestart the kernel if it dies. -#c.KernelManager.autorestart = True - -## DEPRECATED: Use kernel_name instead. -# -# The Popen Command to launch the kernel. Override this if you have a custom -# kernel. If kernel_cmd is specified in a configuration file, Jupyter does not -# pass any arguments to the kernel, because it cannot make any assumptions about -# the arguments that the kernel understands. In particular, this means that the -# kernel does not receive the option --debug if it given on the Jupyter command -# line. -#c.KernelManager.kernel_cmd = [] - -## Time to wait for a kernel to terminate before killing it, in seconds. -#c.KernelManager.shutdown_wait_time = 5.0 - -#------------------------------------------------------------------------------ -# Session(Configurable) configuration -#------------------------------------------------------------------------------ - -## Object for handling serialization and sending of messages. -# -# The Session object handles building messages and sending them with ZMQ sockets -# or ZMQStream objects. Objects can communicate with each other over the -# network via Session objects, and only need to work with the dict-based IPython -# message spec. The Session will handle serialization/deserialization, security, -# and metadata. -# -# Sessions support configurable serialization via packer/unpacker traits, and -# signing with HMAC digests via the key/keyfile traits. -# -# Parameters ---------- -# -# debug : bool -# whether to trigger extra debugging statements -# packer/unpacker : str : 'json', 'pickle' or import_string -# importstrings for methods to serialize message parts. If just -# 'json' or 'pickle', predefined JSON and pickle packers will be used. -# Otherwise, the entire importstring must be used. -# -# The functions must accept at least valid JSON input, and output *bytes*. -# -# For example, to use msgpack: -# packer = 'msgpack.packb', unpacker='msgpack.unpackb' -# pack/unpack : callables -# You can also set the pack/unpack callables for serialization directly. -# session : bytes -# the ID of this Session object. The default is to generate a new UUID. -# username : unicode -# username added to message headers. The default is to ask the OS. -# key : bytes -# The key used to initialize an HMAC signature. If unset, messages -# will not be signed or checked. -# keyfile : filepath -# The file containing a key. If this is set, `key` will be initialized -# to the contents of the file. - -## Threshold (in bytes) beyond which an object's buffer should be extracted to -# avoid pickling. -#c.Session.buffer_threshold = 1024 - -## Whether to check PID to protect against calls after fork. -# -# This check can be disabled if fork-safety is handled elsewhere. -#c.Session.check_pid = True - -## Threshold (in bytes) beyond which a buffer should be sent without copying. -#c.Session.copy_threshold = 65536 - -## Debug output in the Session -#c.Session.debug = False - -## The maximum number of digests to remember. -# -# The digest history will be culled when it exceeds this value. -#c.Session.digest_history_size = 65536 - -## The maximum number of items for a container to be introspected for custom -# serialization. Containers larger than this are pickled outright. -#c.Session.item_threshold = 64 - -## execution key, for signing messages. -#c.Session.key = '' - -## path to file containing execution key. -#c.Session.keyfile = '' - -## Metadata dictionary, which serves as the default top-level metadata dict for -# each message. -#c.Session.metadata = {} - -## The name of the packer for serializing messages. Should be one of 'json', -# 'pickle', or an import name for a custom callable serializer. -#c.Session.packer = 'json' - -## The UUID identifying this session. -#c.Session.session = u'' - -## The digest scheme used to construct the message signatures. Must have the form -# 'hmac-HASH'. -#c.Session.signature_scheme = 'hmac-sha256' - -## The name of the unpacker for unserializing messages. Only used with custom -# functions for `packer`. -#c.Session.unpacker = 'json' - -## Username for the Session. Default is your system username. -#c.Session.username = u'username' - -#------------------------------------------------------------------------------ -# MultiKernelManager(LoggingConfigurable) configuration -#------------------------------------------------------------------------------ - -## A class for managing multiple kernels. - -## The name of the default kernel to start -#c.MultiKernelManager.default_kernel_name = 'python2' - -## The kernel manager class. This is configurable to allow subclassing of the -# KernelManager for customized behavior. -#c.MultiKernelManager.kernel_manager_class = 'jupyter_client.ioloop.IOLoopKernelManager' - -#------------------------------------------------------------------------------ -# MappingKernelManager(MultiKernelManager) configuration -#------------------------------------------------------------------------------ - -## A KernelManager that handles notebook mapping and HTTP error handling - -## -#c.MappingKernelManager.root_dir = u'' - -#------------------------------------------------------------------------------ -# ContentsManager(LoggingConfigurable) configuration -#------------------------------------------------------------------------------ - -## Base class for serving files and directories. -# -# This serves any text or binary file, as well as directories, with special -# handling for JSON notebook documents. -# -# Most APIs take a path argument, which is always an API-style unicode path, and -# always refers to a directory. -# -# - unicode, not url-escaped -# - '/'-separated -# - leading and trailing '/' will be stripped -# - if unspecified, path defaults to '', -# indicating the root path. - -## -#c.ContentsManager.checkpoints = None - -## -#c.ContentsManager.checkpoints_class = 'notebook.services.contents.checkpoints.Checkpoints' - -## -#c.ContentsManager.checkpoints_kwargs = {} - -## Glob patterns to hide in file and directory listings. -#c.ContentsManager.hide_globs = [u'__pycache__', '*.pyc', '*.pyo', '.DS_Store', '*.so', '*.dylib', '*~'] - -## Python callable or importstring thereof -# -# To be called on a contents model prior to save. -# -# This can be used to process the structure, such as removing notebook outputs -# or other side effects that should not be saved. -# -# It will be called as (all arguments passed by keyword):: -# -# hook(path=path, model=model, contents_manager=self) -# -# - model: the model to be saved. Includes file contents. -# Modifying this dict will affect the file that is stored. -# - path: the API path of the save destination -# - contents_manager: this ContentsManager instance -#c.ContentsManager.pre_save_hook = None - -## The base name used when creating untitled directories. -#c.ContentsManager.untitled_directory = 'Untitled Folder' - -## The base name used when creating untitled files. -#c.ContentsManager.untitled_file = 'untitled' - -## The base name used when creating untitled notebooks. -#c.ContentsManager.untitled_notebook = 'Untitled' - -#------------------------------------------------------------------------------ -# FileManagerMixin(Configurable) configuration -#------------------------------------------------------------------------------ - -## Mixin for ContentsAPI classes that interact with the filesystem. -# -# Provides facilities for reading, writing, and copying both notebooks and -# generic files. -# -# Shared by FileContentsManager and FileCheckpoints. -# -# Note ---- Classes using this mixin must provide the following attributes: -# -# root_dir : unicode -# A directory against against which API-style paths are to be resolved. -# -# log : logging.Logger - -## By default notebooks are saved on disk on a temporary file and then if -# succefully written, it replaces the old ones. This procedure, namely -# 'atomic_writing', causes some bugs on file system whitout operation order -# enforcement (like some networked fs). If set to False, the new notebook is -# written directly on the old one which could fail (eg: full filesystem or quota -# ) -#c.FileManagerMixin.use_atomic_writing = True - -#------------------------------------------------------------------------------ -# FileContentsManager(FileManagerMixin,ContentsManager) configuration -#------------------------------------------------------------------------------ - -## Python callable or importstring thereof -# -# to be called on the path of a file just saved. -# -# This can be used to process the file on disk, such as converting the notebook -# to a script or HTML via nbconvert. -# -# It will be called as (all arguments passed by keyword):: -# -# hook(os_path=os_path, model=model, contents_manager=instance) -# -# - path: the filesystem path to the file just written - model: the model -# representing the file - contents_manager: this ContentsManager instance -#c.FileContentsManager.post_save_hook = None - -## -#c.FileContentsManager.root_dir = u'' - -## DEPRECATED, use post_save_hook. Will be removed in Notebook 5.0 -#c.FileContentsManager.save_script = False - -#------------------------------------------------------------------------------ -# NotebookNotary(LoggingConfigurable) configuration -#------------------------------------------------------------------------------ - -## A class for computing and verifying notebook signatures. - -## The hashing algorithm used to sign notebooks. -#c.NotebookNotary.algorithm = 'sha256' - -## The sqlite file in which to store notebook signatures. By default, this will -# be in your Jupyter data directory. You can set it to ':memory:' to disable -# sqlite writing to the filesystem. -#c.NotebookNotary.db_file = u'' - -## The secret key with which notebooks are signed. -#c.NotebookNotary.secret = '' - -## The file where the secret key is stored. -#c.NotebookNotary.secret_file = u'' - -## A callable returning the storage backend for notebook signatures. The default -# uses an SQLite database. -#c.NotebookNotary.store_factory = traitlets.Undefined - -#------------------------------------------------------------------------------ -# KernelSpecManager(LoggingConfigurable) configuration -#------------------------------------------------------------------------------ - -## If there is no Python kernelspec registered and the IPython kernel is -# available, ensure it is added to the spec list. -#c.KernelSpecManager.ensure_native_kernel = True - -## The kernel spec class. This is configurable to allow subclassing of the -# KernelSpecManager for customized behavior. -#c.KernelSpecManager.kernel_spec_class = 'jupyter_client.kernelspec.KernelSpec' - -## Whitelist of allowed kernel names. -# -# By default, all installed kernels are allowed. -#c.KernelSpecManager.whitelist = set([]) diff --git a/seq2seq/model_infer_gpu.sh b/model_infer_gpu.sh similarity index 100% rename from seq2seq/model_infer_gpu.sh rename to model_infer_gpu.sh diff --git a/seq2seq/model_train_cpu.sh b/model_train_cpu.sh similarity index 100% rename from seq2seq/model_train_cpu.sh rename to model_train_cpu.sh diff --git a/seq2seq/model_train_gpu.sh b/model_train_gpu.sh similarity index 100% rename from seq2seq/model_train_gpu.sh rename to model_train_gpu.sh diff --git a/nan.patch b/nan.patch deleted file mode 100644 index e858c1e..0000000 --- a/nan.patch +++ /dev/null @@ -1,46 +0,0 @@ -diff --git a/python/mxnet/callback.py b/python/mxnet/callback.py -index 396f5a1..544eab2 100644 ---- a/python/mxnet/callback.py -+++ b/python/mxnet/callback.py -@@ -96,13 +96,16 @@ class Speedometer(object): - frequent: int - How many batches between calculations. - Defaults to calculating & logging every 50 batches. -+ auto_reset : bool -+ Reset the metric after each log. - """ -- def __init__(self, batch_size, frequent=50): -+ def __init__(self, batch_size, frequent=50, auto_reset=False): - self.batch_size = batch_size - self.frequent = frequent - self.init = False - self.tic = 0 - self.last_count = 0 -+ self.auto_reset = auto_reset - - def __call__(self, param): - """Callback to Show speed.""" -@@ -116,7 +119,8 @@ class Speedometer(object): - speed = self.frequent * self.batch_size / (time.time() - self.tic) - if param.eval_metric is not None: - name_value = param.eval_metric.get_name_value() -- param.eval_metric.reset() -+ if self.auto_reset: -+ param.eval_metric.reset() - for name, value in name_value: - logging.info('Epoch[%d] Batch [%d]\tSpeed: %.2f samples/sec\tTrain-%s=%f', - param.epoch, count, speed, name, value) -diff --git a/python/mxnet/metric.py b/python/mxnet/metric.py -index 2870fab..8a8b974 100644 ---- a/python/mxnet/metric.py -+++ b/python/mxnet/metric.py -@@ -265,7 +265,8 @@ class Perplexity(EvalMetric): - self.num_inst += num - - def get(self): -- return (self.name, math.exp(self.sum_metric/self.num_inst)) -+ num = self.num_inst if self.num_inst > 0 else float('nan') -+ return (self.name, math.exp(self.sum_metric/num)) - - #################### - # REGRESSION METRICS diff --git a/seq2seq/preprocess_data.py b/preprocess_data.py similarity index 100% rename from seq2seq/preprocess_data.py rename to preprocess_data.py diff --git a/seq2seq/rnn_cell.py b/rnn_cell.py similarity index 100% rename from seq2seq/rnn_cell.py rename to rnn_cell.py diff --git a/seq2seq/seq2seq_bucketing.py b/seq2seq_bucketing.py similarity index 100% rename from seq2seq/seq2seq_bucketing.py rename to seq2seq_bucketing.py diff --git a/seq2seq/seq2seq_iterator.py b/seq2seq_iterator.py similarity index 100% rename from seq2seq/seq2seq_iterator.py rename to seq2seq_iterator.py diff --git a/speedometer_reset.patch b/speedometer_reset.patch deleted file mode 100644 index f284fd2..0000000 --- a/speedometer_reset.patch +++ /dev/null @@ -1,32 +0,0 @@ -diff --git a/python/mxnet/callback.py b/python/mxnet/callback.py -index 396f5a1..544eab2 100644 ---- a/python/mxnet/callback.py -+++ b/python/mxnet/callback.py -@@ -96,13 +96,16 @@ class Speedometer(object): - frequent: int - How many batches between calculations. - Defaults to calculating & logging every 50 batches. -+ auto_reset : bool -+ Reset the metric after each log. - """ -- def __init__(self, batch_size, frequent=50): -+ def __init__(self, batch_size, frequent=50, auto_reset=False): - self.batch_size = batch_size - self.frequent = frequent - self.init = False - self.tic = 0 - self.last_count = 0 -+ self.auto_reset = auto_reset - - def __call__(self, param): - """Callback to Show speed.""" -@@ -116,7 +119,8 @@ class Speedometer(object): - speed = self.frequent * self.batch_size / (time.time() - self.tic) - if param.eval_metric is not None: - name_value = param.eval_metric.get_name_value() -- param.eval_metric.reset() -+ if self.auto_reset: -+ param.eval_metric.reset() - for name, value in name_value: - logging.info('Epoch[%d] Batch [%d]\tSpeed: %.2f samples/sec\tTrain-%s=%f', - param.epoch, count, speed, name, value) diff --git a/seq2seq/split_train_valid.py b/split_train_valid.py similarity index 100% rename from seq2seq/split_train_valid.py rename to split_train_valid.py diff --git a/start_container.sh b/start_container.sh deleted file mode 100755 index 685abfc..0000000 --- a/start_container.sh +++ /dev/null @@ -1,3 +0,0 @@ -#!/bin/bash - -nvidia-docker run --rm -it -v `pwd`:/mxnet_seq2seq -p 8888:8888 mxnet_seq2seq diff --git a/train_ptb/README.md b/train_ptb/README.md deleted file mode 100644 index 8a6f29d..0000000 --- a/train_ptb/README.md +++ /dev/null @@ -1,15 +0,0 @@ -RNN Example -=========== -This folder contains RNN examples using high level mxnet.rnn interface. - -Examples using low level symbol interface have been deprecated and moved to old/ - -## Data -Run `get_ptb_data.sh` to download PenTreeBank data. - -## Python - -- [lstm_bucketing.py](lstm_bucketing.py) PennTreeBank language model by using LSTM - -Performance Note: -More ```MXNET_GPU_WORKER_NTHREADS``` may lead to better performance. For setting ```MXNET_GPU_WORKER_NTHREADS```, please refer to [Environment Variables](https://mxnet.readthedocs.org/en/latest/how_to/env_var.html). diff --git a/train_ptb/cudnn_lstm_bucketing.py b/train_ptb/cudnn_lstm_bucketing.py deleted file mode 100644 index 8e0ad9d..0000000 --- a/train_ptb/cudnn_lstm_bucketing.py +++ /dev/null @@ -1,218 +0,0 @@ -import numpy as np -import mxnet as mx -import argparse - -parser = argparse.ArgumentParser(description="Train RNN on Penn Tree Bank", - formatter_class=argparse.ArgumentDefaultsHelpFormatter) -parser.add_argument('--test', default=False, action='store_true', - help='whether to do testing instead of training') -parser.add_argument('--model-prefix', type=str, default=None, - help='path to save/load model') -parser.add_argument('--load-epoch', type=int, default=0, - help='load from epoch') -parser.add_argument('--num-layers', type=int, default=2, - help='number of stacked RNN layers') -parser.add_argument('--num-hidden', type=int, default=200, - help='hidden layer size') -parser.add_argument('--num-embed', type=int, default=200, - help='embedding layer size') -parser.add_argument('--bidirectional', type=bool, default=False, - help='whether to use bidirectional layers') -parser.add_argument('--gpus', type=str, - help='list of gpus to run, e.g. 0 or 0,2,5. empty means using cpu. ' \ - 'Increase batch size when using multiple gpus for best performance.') -parser.add_argument('--kv-store', type=str, default='device', - help='key-value store type') -parser.add_argument('--num-epochs', type=int, default=25, - help='max num of epochs') -parser.add_argument('--lr', type=float, default=0.01, - help='initial learning rate') -parser.add_argument('--optimizer', type=str, default='sgd', - help='the optimizer type') -parser.add_argument('--mom', type=float, default=0.0, - help='momentum for sgd') -parser.add_argument('--wd', type=float, default=0.00001, - help='weight decay for sgd') -parser.add_argument('--batch-size', type=int, default=32, - help='the batch size.') -parser.add_argument('--disp-batches', type=int, default=50, - help='show progress for every n batches') -# When training a deep, complex model, it's recommended to stack fused RNN cells (one -# layer per cell) together instead of one with all layers. The reason is that fused RNN -# cells doesn't set gradients to be ready until the computation for the entire layer is -# completed. Breaking a multi-layer fused RNN cell into several one-layer ones allows -# gradients to be processed ealier. This reduces communication overhead, especially with -# multiple GPUs. -parser.add_argument('--stack-rnn', default=False, - help='stack fused RNN cells to reduce communication overhead') -parser.add_argument('--dropout', type=float, default='0.0', - help='dropout keep probability') - -#buckets = [32] -buckets = [10, 20, 30, 40, 50, 60] - -start_label = 1 -invalid_label = 0 - -def tokenize_text(fname, vocab=None, invalid_label=-1, start_label=0): - lines = open(fname).readlines() - lines = [filter(None, i.split(' ')) for i in lines] - sentences, vocab = mx.rnn.encode_sentences(lines, vocab=vocab, invalid_label=invalid_label, start_label=start_label) - return sentences, vocab - -def get_data(layout): - train_sent, vocab = tokenize_text("./data/ptb.train.txt", start_label=start_label, - invalid_label=invalid_label) - val_sent, _ = tokenize_text("./data/ptb.test.txt", vocab=vocab, start_label=start_label, - invalid_label=invalid_label) - - data_train = mx.rnn.BucketSentenceIter(train_sent, args.batch_size, buckets=buckets, - invalid_label=invalid_label, layout=layout) - data_val = mx.rnn.BucketSentenceIter(val_sent, args.batch_size, buckets=buckets, - invalid_label=invalid_label, layout=layout) - return data_train, data_val, vocab - - -def train(args): - data_train, data_val, vocab = get_data('TN') - if args.stack_rnn: - stack = mx.rnn.SequentialRNNCell() - for layer in range(args.num_layers): - dropout = 0.0 - if layer < (args.num_layers - 1): - dropout = args.dropout - stack.add(mx.rnn.FusedRNNCell(args.num_hidden, num_layers=1, - mode='lstm', prefix='lstm_%d'%layer, dropout=dropout, - bidirectional=args.bidirectional)) - cell = stack - else: - cell = mx.rnn.FusedRNNCell(args.num_hidden, num_layers=args.num_layers, dropout=args.dropout, - mode='lstm', bidirectional=args.bidirectional) - - def sym_gen(seq_len): - data = mx.sym.Variable('data') - label = mx.sym.Variable('softmax_label') - embed = mx.sym.Embedding(data=data, input_dim=len(vocab), output_dim=args.num_embed,name='embed') - - output, _ = cell.unroll(seq_len, inputs=embed, merge_outputs=True, layout='TNC') - - pred = mx.sym.Reshape(output, - shape=(-1, args.num_hidden*(1+args.bidirectional))) - pred = mx.sym.FullyConnected(data=pred, num_hidden=len(vocab), name='pred') - - label = mx.sym.Reshape(label, shape=(-1,)) - pred = mx.sym.SoftmaxOutput(data=pred, label=label, name='softmax') - - return pred, ('data',), ('softmax_label',) - - if args.gpus: - contexts = [mx.gpu(int(i)) for i in args.gpus.split(',')] - else: - contexts = mx.cpu(0) - - model = mx.mod.BucketingModule( - sym_gen = sym_gen, - default_bucket_key = data_train.default_bucket_key, - context = contexts) - - if args.load_epoch: - _, arg_params, aux_params = mx.rnn.load_rnn_checkpoint( - cell, args.model_prefix, args.load_epoch) - else: - arg_params = None - aux_params = None - - opt_params = { - 'learning_rate': args.lr, - 'wd': args.wd - } - - if args.optimizer not in ['adadelta', 'adagrad', 'adam', 'rmsprop']: - opt_params['momentum'] = args.mom - - model.fit( - train_data = data_train, - eval_data = data_val, - eval_metric = mx.metric.Perplexity(invalid_label), - kvstore = args.kv_store, - optimizer = args.optimizer, - optimizer_params = opt_params, - initializer = mx.init.Xavier(factor_type="in", magnitude=2.34), - arg_params = arg_params, - aux_params = aux_params, - begin_epoch = args.load_epoch, - num_epoch = args.num_epochs, - batch_end_callback = mx.callback.Speedometer(args.batch_size, args.disp_batches), - epoch_end_callback = mx.rnn.do_rnn_checkpoint(cell, args.model_prefix, 1) - if args.model_prefix else None) - -def test(args): - assert args.model_prefix, "Must specifiy path to load from" - _, data_val, vocab = get_data('NT') - - if not args.stack_rnn: - stack = mx.rnn.FusedRNNCell(args.num_hidden, num_layers=args.num_layers, - mode='lstm', bidirectional=args.bidirectional).unfuse() - else: - stack = mx.rnn.SequentialRNNCell() - for i in range(args.num_layers): - cell = mx.rnn.LSTMCell(num_hidden=args.num_hidden, prefix='lstm_%dl0_'%i) - if args.bidirectional: - cell = mx.rnn.BidirectionalCell( - cell, - mx.rnn.LSTMCell(num_hidden=args.num_hidden, prefix='lstm_%dr0_'%i), - output_prefix='bi_lstm_%d'%i) - stack.add(cell) - - def sym_gen(seq_len): - data = mx.sym.Variable('data') - label = mx.sym.Variable('softmax_label') - embed = mx.sym.Embedding(data=data, input_dim=len(vocab), - output_dim=args.num_embed, name='embed') - - stack.reset() - outputs, states = stack.unroll(seq_len, inputs=embed, merge_outputs=True) - - pred = mx.sym.Reshape(outputs, - shape=(-1, args.num_hidden*(1+args.bidirectional))) - pred = mx.sym.FullyConnected(data=pred, num_hidden=len(vocab), name='pred') - - label = mx.sym.Reshape(label, shape=(-1,)) - pred = mx.sym.SoftmaxOutput(data=pred, label=label, name='softmax') - - return pred, ('data',), ('softmax_label',) - - if args.gpus: - contexts = [mx.gpu(int(i)) for i in args.gpus.split(',')] - else: - contexts = mx.cpu(0) - - model = mx.mod.BucketingModule( - sym_gen = sym_gen, - default_bucket_key = data_val.default_bucket_key, - context = contexts) - model.bind(data_val.provide_data, data_val.provide_label, for_training=False) - - # note here we load using SequentialRNNCell instead of FusedRNNCell. - _, arg_params, aux_params = mx.rnn.load_rnn_checkpoint(stack, args.model_prefix, args.load_epoch) - model.set_params(arg_params, aux_params) - - model.score(data_val, mx.metric.Perplexity(invalid_label), - batch_end_callback=mx.callback.Speedometer(args.batch_size, 5)) - -if __name__ == '__main__': - import logging - head = '%(asctime)-15s %(message)s' - logging.basicConfig(level=logging.DEBUG, format=head) - - args = parser.parse_args() - - if args.num_layers >= 4 and len(args.gpus.split(',')) >= 4 and not args.stack_rnn: - print('WARNING: stack-rnn is recommended to train complex model on multiple GPUs') - - if args.test: - # Demonstrates how to load a model trained with CuDNN RNN and predict - # with non-fused MXNet symbol - test(args) - else: - train(args) diff --git a/train_ptb/get_ptb_data.sh b/train_ptb/get_ptb_data.sh deleted file mode 100755 index 1ec009a..0000000 --- a/train_ptb/get_ptb_data.sh +++ /dev/null @@ -1,14 +0,0 @@ -#!/usr/bin/env bash - -RNN_DIR=$(cd `dirname $0`; pwd) -DATA_DIR="${RNN_DIR}/data/" - -if [[ ! -d "${DATA_DIR}" ]]; then - echo "${DATA_DIR} doesn't exist, will create one"; - mkdir -p ${DATA_DIR} -fi - -wget -P ${DATA_DIR} https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/ptb/ptb.train.txt; -wget -P ${DATA_DIR} https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/ptb/ptb.valid.txt; -wget -P ${DATA_DIR} https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/ptb/ptb.test.txt; -wget -P ${DATA_DIR} https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/tinyshakespeare/input.txt; diff --git a/train_ptb/lstm_bucketing.py b/train_ptb/lstm_bucketing.py deleted file mode 100644 index 4bc934a..0000000 --- a/train_ptb/lstm_bucketing.py +++ /dev/null @@ -1,107 +0,0 @@ -import numpy as np -import mxnet as mx -import argparse - -parser = argparse.ArgumentParser(description="Train RNN on Penn Tree Bank", - formatter_class=argparse.ArgumentDefaultsHelpFormatter) -parser.add_argument('--num-layers', type=int, default=2, - help='number of stacked RNN layers') -parser.add_argument('--num-hidden', type=int, default=200, - help='hidden layer size') -parser.add_argument('--num-embed', type=int, default=200, - help='embedding layer size') -parser.add_argument('--gpus', type=str, - help='list of gpus to run, e.g. 0 or 0,2,5. empty means using cpu. ' \ - 'Increase batch size when using multiple gpus for best performance.') -parser.add_argument('--kv-store', type=str, default='device', - help='key-value store type') -parser.add_argument('--num-epochs', type=int, default=25, - help='max num of epochs') -parser.add_argument('--lr', type=float, default=0.01, - help='initial learning rate') -parser.add_argument('--optimizer', type=str, default='sgd', - help='the optimizer type') -parser.add_argument('--mom', type=float, default=0.0, - help='momentum for sgd') -parser.add_argument('--wd', type=float, default=0.00001, - help='weight decay for sgd') -parser.add_argument('--batch-size', type=int, default=32, - help='the batch size.') -parser.add_argument('--disp-batches', type=int, default=50, - help='show progress for every n batches') - - -def tokenize_text(fname, vocab=None, invalid_label=-1, start_label=0): - lines = open(fname).readlines() - lines = [filter(None, i.split(' ')) for i in lines] - sentences, vocab = mx.rnn.encode_sentences(lines, vocab=vocab, invalid_label=invalid_label, - start_label=start_label) - return sentences, vocab - - -if __name__ == '__main__': - import logging - head = '%(asctime)-15s %(message)s' - logging.basicConfig(level=logging.DEBUG, format=head) - - args = parser.parse_args() - - #buckets = [] - buckets = [10, 20, 30, 40, 50, 60] - - start_label = 1 - invalid_label = 0 - - train_sent, vocab = tokenize_text("./data/ptb.train.txt", start_label=start_label, - invalid_label=invalid_label) - val_sent, _ = tokenize_text("./data/ptb.test.txt", vocab=vocab, start_label=start_label, - invalid_label=invalid_label) - - data_train = mx.rnn.BucketSentenceIter(train_sent, args.batch_size, buckets=buckets, - invalid_label=invalid_label) - data_val = mx.rnn.BucketSentenceIter(val_sent, args.batch_size, buckets=buckets, - invalid_label=invalid_label) - - stack = mx.rnn.SequentialRNNCell() - for i in range(args.num_layers): - stack.add(mx.rnn.LSTMCell(num_hidden=args.num_hidden, prefix='lstm_l%d_'%i)) - - def sym_gen(seq_len): - data = mx.sym.Variable('data') - label = mx.sym.Variable('softmax_label') - embed = mx.sym.Embedding(data=data, input_dim=len(vocab), - output_dim=args.num_embed, name='embed') - - stack.reset() - outputs, states = stack.unroll(seq_len, inputs=embed, merge_outputs=True) - - pred = mx.sym.Reshape(outputs, shape=(-1, args.num_hidden)) - pred = mx.sym.FullyConnected(data=pred, num_hidden=len(vocab), name='pred') - - label = mx.sym.Reshape(label, shape=(-1,)) - pred = mx.sym.SoftmaxOutput(data=pred, label=label, name='softmax') - - return pred, ('data',), ('softmax_label',) - - if args.gpus: - contexts = [mx.gpu(int(i)) for i in args.gpus.split(',')] - else: - contexts = mx.cpu(0) - - model = mx.mod.BucketingModule( - sym_gen = sym_gen, - default_bucket_key = data_train.default_bucket_key, - context = contexts) - - model.fit( - train_data = data_train, - eval_data = data_val, - eval_metric = mx.metric.Perplexity(invalid_label), - kvstore = args.kv_store, - optimizer = args.optimizer, - optimizer_params = { 'learning_rate': args.lr, - 'momentum': args.mom, - 'wd': args.wd }, - initializer = mx.init.Xavier(factor_type="in", magnitude=2.34), - num_epoch = args.num_epochs, - batch_end_callback = mx.callback.Speedometer(args.batch_size, args.disp_batches)) diff --git a/seq2seq/utils.py b/utils.py similarity index 100% rename from seq2seq/utils.py rename to utils.py From c57d8920e2d7a07faa6c518f427b3dd2d90ef7a3 Mon Sep 17 00:00:00 2001 From: Marek Kolodziej Date: Tue, 16 May 2017 19:52:38 +0000 Subject: [PATCH 26/58] Remove rnn_cell --- rnn_cell.py | 951 ---------------------------------------------------- 1 file changed, 951 deletions(-) delete mode 100644 rnn_cell.py diff --git a/rnn_cell.py b/rnn_cell.py deleted file mode 100644 index f93072f..0000000 --- a/rnn_cell.py +++ /dev/null @@ -1,951 +0,0 @@ -# coding: utf-8 -# pylint: disable=no-member, invalid-name, protected-access, no-self-use -# pylint: disable=too-many-branches, too-many-arguments, no-self-use -# pylint: disable=too-many-lines -"""Definition of various recurrent neural network cells.""" -from __future__ import print_function - -import warnings - -import mxnet as mx -from mxnet import symbol, init, ndarray, _symbol_internal -from mxnet.base import string_types, numeric_types - - -def _cells_state_shape(cells): - return sum([c.state_shape for c in cells], []) - -def _cells_begin_state(cells, **kwargs): - return sum([c.begin_state(**kwargs) for c in cells], []) - -def _cells_unpack_weights(cells, args): - for cell in cells: - args = cell.unpack_weights(args) - return args - -def _cells_pack_weights(cells, args): - for cell in cells: - args = cell.pack_weights(args) - return args - -def _normalize_sequence(length, inputs, layout, merge, in_layout=None): - assert inputs is not None, \ - "unroll(inputs=None) has been deprecated. " \ - "Please create input variables outside unroll." - - axis = layout.find('T') - in_axis = in_layout.find('T') if in_layout is not None else axis - if isinstance(inputs, symbol.Symbol): - if merge is False: - assert len(inputs.list_outputs()) == 1, \ - "unroll doesn't allow grouped symbol as input. Please convert " \ - "to list with list(inputs) first or let unroll handle splitting." - inputs = list(symbol.split(inputs, axis=in_axis, num_outputs=length, - squeeze_axis=1)) - else: - assert length is None or len(inputs) == length - if merge is True: - inputs = [symbol.expand_dims(i, axis=axis) for i in inputs] - inputs = symbol.Concat(*inputs, dim=axis) - in_axis = axis - - if isinstance(inputs, symbol.Symbol) and axis != in_axis: - inputs = symbol.swapaxes(inputs, dim0=axis, dim1=in_axis) - - return inputs, axis - - -class RNNParams(object): - """Container for holding variables. - Used by RNN cells for parameter sharing between cells. - - Parameters - ---------- - prefix : str - All variables' name created by this container will - be prepended with prefix - """ - def __init__(self, prefix=''): - self._prefix = prefix - self._params = {} - - def get(self, name, **kwargs): - """Get a variable with name or create a new one if missing. - - Parameters - ---------- - name : str - name of the variable - **kwargs : - more arguments that's passed to symbol.Variable - """ - name = self._prefix + name - if name not in self._params: - self._params[name] = symbol.Variable(name, **kwargs) - return self._params[name] - - -class BaseRNNCell(object): - """Abstract base class for RNN cells - - Parameters - ---------- - prefix : str - prefix for name of layers - (and name of weight if params is None) - params : RNNParams or None - container for weight sharing between cells. - created if None. - """ - def __init__(self, prefix='', params=None): - if params is None: - params = RNNParams(prefix) - self._own_params = True - else: - self._own_params = False - self._prefix = prefix - self._params = params - self._modified = False - - self.reset() - - def reset(self): - """Reset before re-using the cell for another graph""" - self._init_counter = -1 - self._counter = -1 - - def __call__(self, inputs, states): - """Construct symbol for one step of RNN. - - Parameters - ---------- - inputs : sym.Variable - input symbol, 2D, batch * num_units - states : sym.Variable - state from previous step or begin_state(). - - Returns - ------- - output : Symbol - output symbol - states : Symbol - state to next step of RNN. - """ - raise NotImplementedError() - - @property - def params(self): - """Parameters of this cell""" - self._own_params = False - return self._params - - @property - def state_shape(self): - """shape(s) of states""" - raise NotImplementedError() - - @property - def _gate_names(self): - """name(s) of gates""" - return () - - def begin_state(self, func=symbol.zeros, **kwargs): - """Initial state for this cell. - - Parameters - ---------- - func : callable, default symbol.zeros - Function for creating initial state. Can be symbol.zeros, - symbol.uniform, symbol.Variable etc. - Use symbol.Variable if you want to directly - feed input as states. - **kwargs : - more keyword arguments passed to func. For example - mean, std, dtype, etc. - - Returns - ------- - states : nested list of Symbol - starting states for first RNN step - """ - assert not self._modified, \ - "After applying modifier cells (e.g. DropoutCell) the base " \ - "cell cannot be called directly. Call the modifier cell instead." - states = [] - for shape in self.state_shape: - self._init_counter += 1 - if shape is None: - state = func(name='%sbegin_state_%d'%(self._prefix, self._init_counter), - **kwargs) - else: - state = func(name='%sbegin_state_%d'%(self._prefix, self._init_counter), - shape=shape, **kwargs) - states.append(state) - return states - - def unpack_weights(self, args): - """Unpack fused weight matrices into separate - weight matrices - - Parameters - ---------- - args : dict of str -> NDArray - dictionary containing packed weights. - usually from Module.get_output() - - Returns - ------- - args : dict of str -> NDArray - dictionary with weights associated to - this cell unpacked. - """ - args = args.copy() - if not self._gate_names: - return args - h = self._num_hidden - for group_name in ['i2h', 'h2h']: - weight = args.pop('%s%s_weight'%(self._prefix, group_name)) - bias = args.pop('%s%s_bias' % (self._prefix, group_name)) - for j, gate in enumerate(self._gate_names): - wname = '%s%s%s_weight' % (self._prefix, group_name, gate) - args[wname] = weight[j*h:(j+1)*h].copy() - bname = '%s%s%s_bias' % (self._prefix, group_name, gate) - args[bname] = bias[j*h:(j+1)*h].copy() - return args - - def pack_weights(self, args): - """Pack separate weight matrices into fused - weight. - - Parameters - ---------- - args : dict of str -> NDArray - dictionary containing unpacked weights. - - Returns - ------- - args : dict of str -> NDArray - dictionary with weights associated to - this cell packed. - """ - args = args.copy() - if not self._gate_names: - return args - for group_name in ['i2h', 'h2h']: - weight = [] - bias = [] - for gate in self._gate_names: - wname = '%s%s%s_weight'%(self._prefix, group_name, gate) - weight.append(args.pop(wname)) - bname = '%s%s%s_bias'%(self._prefix, group_name, gate) - bias.append(args.pop(bname)) - args['%s%s_weight'%(self._prefix, group_name)] = ndarray.concatenate(weight) - args['%s%s_bias'%(self._prefix, group_name)] = ndarray.concatenate(bias) - return args - - def unroll(self, length, inputs, begin_state=None, layout='NTC', merge_outputs=None): - """Unroll an RNN cell across time steps. - - Parameters - ---------- - length : int - number of steps to unroll - inputs : Symbol, list of Symbol, or None - if inputs is a single Symbol (usually the output - of Embedding symbol), it should have shape - (batch_size, length, ...) if layout == 'NTC', - or (length, batch_size, ...) if layout == 'TNC'. - - If inputs is a list of symbols (usually output of - previous unroll), they should all have shape - (batch_size, ...). - begin_state : nested list of Symbol - input states. Created by begin_state() - or output state of another cell. Created - from begin_state() if None. - layout : str - layout of input symbol. Only used if inputs - is a single Symbol. - merge_outputs : bool - If False, return outputs as a list of Symbols. - If True, concatenate output across time steps - and return a single symbol with shape - (batch_size, length, ...) if layout == 'NTC', - or (length, batch_size, ...) if layout == 'TNC'. - If None, output whatever is faster - - Returns - ------- - outputs : list of Symbol - output symbols. - states : Symbol or nested list of Symbol - has the same structure as begin_state() - """ - self.reset() - - inputs, _ = _normalize_sequence(length, inputs, layout, False) - if begin_state is None: - begin_state = self.begin_state() - - states = begin_state - outputs = [] - for i in range(length): - output, states = self(inputs[i], states) - outputs.append(output) - - outputs, _ = _normalize_sequence(length, outputs, layout, merge_outputs) - - return outputs, states - - #pylint: disable=no-self-use - def _get_activation(self, inputs, activation, **kwargs): - """Get activation function. Convert if is string""" - if isinstance(activation, string_types): - return symbol.Activation(inputs, act_type=activation, **kwargs) - else: - return activation(inputs, **kwargs) - - -class RNNCell(BaseRNNCell): - """Simple recurrent neural network cell - - Parameters - ---------- - num_hidden : int - number of units in output symbol - activation : str or Symbol, default 'tanh' - type of activation function - prefix : str, default 'rnn_' - prefix for name of layers - (and name of weight if params is None) - params : RNNParams or None - container for weight sharing between cells. - created if None. - """ - def __init__(self, num_hidden, activation='tanh', prefix='rnn_', params=None): - super(RNNCell, self).__init__(prefix=prefix, params=params) - self._num_hidden = num_hidden - self._activation = activation - self._iW = self.params.get('i2h_weight') - self._iB = self.params.get('i2h_bias') - self._hW = self.params.get('h2h_weight') - self._hB = self.params.get('h2h_bias') - - @property - def state_shape(self): - return [(0, self._num_hidden)] - - @property - def _gate_names(self): - return ('',) - - def __call__(self, inputs, states): - self._counter += 1 - name = '%st%d_'%(self._prefix, self._counter) - i2h = symbol.FullyConnected(data=inputs, weight=self._iW, bias=self._iB, - num_hidden=self._num_hidden, - name='%si2h'%name) - h2h = symbol.FullyConnected(data=states[0], weight=self._hW, bias=self._hB, - num_hidden=self._num_hidden, - name='%sh2h'%name) - output = self._get_activation(i2h + h2h, self._activation, - name='%sout'%name) - - return output, [output] - - -class LSTMCell(BaseRNNCell): - """Long-Short Term Memory (LSTM) network cell. - - Parameters - ---------- - num_hidden : int - number of units in output symbol - prefix : str, default 'rnn_' - prefix for name of layers - (and name of weight if params is None) - params : RNNParams or None - container for weight sharing between cells. - created if None. - forget_bias : bias added to forget gate, default 1.0. - Jozefowicz et al. 2015 recommends setting this to 1.0 - """ - def __init__(self, num_hidden, prefix='lstm_', params=None, forget_bias=1.0): - super(LSTMCell, self).__init__(prefix=prefix, params=params) - - self._num_hidden = num_hidden - self._iW = self.params.get('i2h_weight') - self._hW = self.params.get('h2h_weight') - # we add the forget_bias to i2h_bias, this adds the bias to the forget gate activation - self._iB = self.params.get('i2h_bias', init=init.LSTMBias(forget_bias=forget_bias)) - self._hB = self.params.get('h2h_bias') - - @property - def state_shape(self): - return [(0, self._num_hidden), (0, self._num_hidden)] - - @property - def _gate_names(self): - return ['_i', '_f', '_c', '_o'] - - def __call__(self, inputs, states): - self._counter += 1 - name = '%st%d_'%(self._prefix, self._counter) - i2h = symbol.FullyConnected(data=inputs, weight=self._iW, bias=self._iB, - num_hidden=self._num_hidden*4, - name='%si2h'%name) - h2h = symbol.FullyConnected(data=states[0], weight=self._hW, bias=self._hB, - num_hidden=self._num_hidden*4, - name='%sh2h'%name) - gates = i2h + h2h - slice_gates = symbol.SliceChannel(gates, num_outputs=4, - name="%sslice"%name) - in_gate = symbol.Activation(slice_gates[0], act_type="sigmoid", - name='%si'%name) - forget_gate = symbol.Activation(slice_gates[1], act_type="sigmoid", - name='%sf'%name) - in_transform = symbol.Activation(slice_gates[2], act_type="tanh", - name='%sc'%name) - out_gate = symbol.Activation(slice_gates[3], act_type="sigmoid", - name='%so'%name) - next_c = symbol._internal._plus(forget_gate * states[1], in_gate * in_transform, - name='%sstate'%name) - next_h = symbol._internal._mul(out_gate, symbol.Activation(next_c, act_type="tanh"), - name='%sout'%name) - - return next_h, [next_h, next_c] - - -class GRUCell(BaseRNNCell): - """Gated Rectified Unit (GRU) network cell. - Note: this is an implementation of the cuDNN version of GRUs - (slight modification compared to Cho et al. 2014). - - Parameters - ---------- - num_hidden : int - number of units in output symbol - prefix : str, default 'gru_' - prefix for name of layers - (and name of weight if params is None) - params : RNNParams or None - container for weight sharing between cells. - created if None. - """ - def __init__(self, num_hidden, prefix='gru_', params=None): - super(GRUCell, self).__init__(prefix=prefix, params=params) - self._num_hidden = num_hidden - self._iW = self.params.get("i2h_weight") - self._iB = self.params.get("i2h_bias") - self._hW = self.params.get("h2h_weight") - self._hB = self.params.get("h2h_bias") - - @property - def state_shape(self): - return [(0, self._num_hidden)] - - @property - def _gate_names(self): - return ['_r', '_z', '_o'] - - def __call__(self, inputs, states): - # pylint: disable=too-many-locals - self._counter += 1 - - seq_idx = self._counter - name = '%st%d_' % (self._prefix, seq_idx) - prev_state_h = states[0] - - i2h = symbol.FullyConnected(data=inputs, - weight=self._iW, - bias=self._iB, - num_hidden=self._num_hidden * 3, - name="%s_i2h" % name) - h2h = symbol.FullyConnected(data=prev_state_h, - weight=self._hW, - bias=self._hB, - num_hidden=self._num_hidden * 3, - name="%s_h2h" % name) - - i2h_r, i2h_z, i2h = symbol.SliceChannel(i2h, num_outputs=3, name="%s_i2h_slice" % name) - h2h_r, h2h_z, h2h = symbol.SliceChannel(h2h, num_outputs=3, name="%s_h2h_slice" % name) - - reset_gate = symbol.Activation(i2h_r + h2h_r, act_type="sigmoid", - name="%s_r_act" % name) - update_gate = symbol.Activation(i2h_z + h2h_z, act_type="sigmoid", - name="%s_z_act" % name) - - next_h_tmp = symbol.Activation(i2h + reset_gate * h2h, act_type="tanh", - name="%s_h_act" % name) - - next_h = symbol._internal._plus((1. - update_gate) * next_h_tmp, update_gate * prev_state_h, - name='%sout' % name) - - return next_h, [next_h] - - -class FusedRNNCell(BaseRNNCell): - """Fusing RNN layers across time step into one kernel. - Improves speed but is less flexible. Currently only - supported if using cuDNN on GPU. - - Parameters - ---------- - """ - def __init__(self, num_hidden, num_layers=1, mode='lstm', bidirectional=False, - dropout=0., get_next_state=False, forget_bias=1.0, - prefix=None, params=None): - if prefix is None: - prefix = '%s_'%mode - super(FusedRNNCell, self).__init__(prefix=prefix, params=params) - self._num_hidden = num_hidden - self._num_layers = num_layers - self._mode = mode - self._bidirectional = bidirectional - self._dropout = dropout - self._get_next_state = get_next_state - self._directions = ['l', 'r'] if bidirectional else ['l'] - - initializer = init.FusedRNN(None, num_hidden, num_layers, mode, - bidirectional, forget_bias) - self._parameter = self.params.get('parameters', init=initializer) - - @property - def state_shape(self): - b = self._bidirectional + 1 - n = (self._mode == 'lstm') + 1 - return [(b*self._num_layers, 0, self._num_hidden)]*n - - @property - def _gate_names(self): - return {'rnn_relu': [''], - 'rnn_tanh': [''], - 'lstm': ['_i', '_f', '_c', '_o'], - 'gru': ['_r', '_z', '_o']}[self._mode] - - @property - def _num_gates(self): - return len(self._gate_names) - - def _slice_weights(self, arr, li, lh): - """slice fused rnn weights""" - args = {} - gate_names = self._gate_names - directions = self._directions - - b = len(directions) - p = 0 - for layer in range(self._num_layers): - for direction in directions: - for gate in gate_names: - name = '%s%s%d_i2h%s_weight'%(self._prefix, direction, layer, gate) - if layer > 0: - size = b*lh*lh - args[name] = arr[p:p+size].reshape((lh, b*lh)) - else: - size = li*lh - args[name] = arr[p:p+size].reshape((lh, li)) - p += size - for gate in gate_names: - name = '%s%s%d_h2h%s_weight'%(self._prefix, direction, layer, gate) - size = lh**2 - args[name] = arr[p:p+size].reshape((lh, lh)) - p += size - - for layer in range(self._num_layers): - for direction in directions: - for gate in gate_names: - name = '%s%s%d_i2h%s_bias'%(self._prefix, direction, layer, gate) - args[name] = arr[p:p+lh] - p += lh - for gate in gate_names: - name = '%s%s%d_h2h%s_bias'%(self._prefix, direction, layer, gate) - args[name] = arr[p:p+lh] - p += lh - - assert p == arr.size, "Invalid parameters size for FusedRNNCell" - return args - - def unpack_weights(self, args): - args = args.copy() - arr = args.pop(self._parameter.name) - b = len(self._directions) - m = self._num_gates - h = self._num_hidden - num_input = arr.size//b//h//m - (self._num_layers - 1)*(h+b*h+2) - h - 2 - - nargs = self._slice_weights(arr, num_input, self._num_hidden) - args.update({name: nd.copy() for name, nd in nargs.items()}) - return args - - def pack_weights(self, args): - args = args.copy() - b = self._bidirectional + 1 - m = self._num_gates - c = self._gate_names - h = self._num_hidden - w0 = args['%sl0_i2h%s_weight'%(self._prefix, c[0])] - num_input = w0.shape[1] - total = (num_input+h+2)*h*m*b + (self._num_layers-1)*m*h*(h+b*h+2)*b - - arr = ndarray.zeros((total,), ctx=w0.context, dtype=w0.dtype) - for name, nd in self._slice_weights(arr, num_input, h).items(): - nd[:] = args.pop(name) - args[self._parameter.name] = arr - return args - - def __call__(self, inputs, states): - raise NotImplementedError("FusedRNNCell cannot be stepped. Please use unroll") - - def unroll(self, length, inputs, begin_state=None, layout='NTC', merge_outputs=None): - self.reset() - - inputs, axis = _normalize_sequence(length, inputs, layout, True) - if axis == 1: - warnings.warn("NTC layout detected. Consider using " - "TNC for FusedRNNCell for faster speed") - inputs = symbol.swapaxes(inputs, dim1=0, dim2=1) - else: - assert axis == 0, "Unsupported layout %s"%layout - if begin_state is None: - begin_state = self.begin_state() - - states = begin_state - if self._mode == 'lstm': - states = {'state': states[0], 'state_cell': states[1]} # pylint: disable=redefined-variable-type - else: - states = {'state': states[0]} - - rnn = symbol.RNN(data=inputs, parameters=self._parameter, - state_size=self._num_hidden, num_layers=self._num_layers, - bidirectional=self._bidirectional, p=self._dropout, - state_outputs=self._get_next_state, - mode=self._mode, name=self._prefix+'rnn', - **states) - - if not self._get_next_state: - outputs, states = rnn, [] - elif self._mode == 'lstm': - outputs, states = rnn[0], [rnn[1], rnn[2]] - else: - outputs, states = rnn[0], [rnn[1]] - - if axis == 1: - outputs = symbol.swapaxes(outputs, dim1=0, dim2=1) - - outputs, _ = _normalize_sequence(length, outputs, layout, merge_outputs) - - return outputs, states - - def unfuse(self): - """Unfuse the fused RNN in to a stack of rnn cells. - - Returns - ------- - cell : SequentialRNNCell - unfused cell that can be used for stepping, and can run on CPU. - """ - stack = SequentialRNNCell() - get_cell = {'rnn_relu': lambda cell_prefix: RNNCell(self._num_hidden, - activation='relu', - prefix=cell_prefix), - 'rnn_tanh': lambda cell_prefix: RNNCell(self._num_hidden, - activation='tanh', - prefix=cell_prefix), - 'lstm': lambda cell_prefix: LSTMCell(self._num_hidden, - prefix=cell_prefix), - 'gru': lambda cell_prefix: GRUCell(self._num_hidden, - prefix=cell_prefix)}[self._mode] - for i in range(self._num_layers): - if self._bidirectional: - stack.add(BidirectionalCell( - get_cell('%sl%d_'%(self._prefix, i)), - get_cell('%sr%d_'%(self._prefix, i)), - output_prefix='%sbi_l%d_'%(self._prefix, i))) - else: - stack.add(get_cell('%sl%d_'%(self._prefix, i))) - - if self._dropout > 0 and i != self._num_layers - 1: - stack.add(DropoutCell(self._dropout, prefix='%s_dropout%d_'%(self._prefix, i))) - - return stack - - -class SequentialRNNCell(BaseRNNCell): - """Sequantially stacking multiple RNN cells - - Parameters - ---------- - params : RNNParams or None - container for weight sharing between cells. - created if None. - """ - def __init__(self, params=None): - super(SequentialRNNCell, self).__init__(prefix='', params=params) - self._override_cell_params = params is not None - self._cells = [] - - def add(self, cell): - """Append a cell into the stack. - - Parameters - ---------- - cell : rnn cell - """ - self._cells.append(cell) - if self._override_cell_params: - assert cell._own_params, \ - "Either specify params for SequentialRNNCell " \ - "or child cells, not both." - cell.params._params.update(self.params._params) - self.params._params.update(cell.params._params) - - @property - def state_shape(self): - return _cells_state_shape(self._cells) - - def begin_state(self, **kwargs): - assert not self._modified, \ - "After applying modifier cells (e.g. ZoneoutCell) the base " \ - "cell cannot be called directly. Call the modifier cell instead." - return _cells_begin_state(self._cells, **kwargs) - - def unpack_weights(self, args): - return _cells_unpack_weights(self._cells, args) - - def pack_weights(self, args): - return _cells_pack_weights(self._cells, args) - - def __call__(self, inputs, states): - self._counter += 1 - next_states = [] - p = 0 - for cell in self._cells: - assert not isinstance(cell, BidirectionalCell) - n = len(cell.state_shape) - state = states[p:p+n] - p += n - inputs, state = cell(inputs, state) - next_states.append(state) - return inputs, sum(next_states, []) - - def unroll(self, length, inputs, begin_state=None, layout='NTC', merge_outputs=None): - self.reset() - - num_cells = len(self._cells) - if begin_state is None: - begin_state = self.begin_state() - - p = 0 - next_states = [] - for i, cell in enumerate(self._cells): - n = len(cell.state_shape) - states = begin_state[p:p+n] - p += n - inputs, states = cell.unroll(length, inputs=inputs, begin_state=states, layout=layout, - merge_outputs=None if i < num_cells-1 else merge_outputs) - next_states.extend(states) - - return inputs, next_states - - -class DropoutCell(BaseRNNCell): - """Apply dropout on input. - - Parameters - ---------- - dropout : float - percentage of elements to drop out, which - is 1 - percentage to retain. - """ - def __init__(self, dropout, prefix='dropout_', params=None): - super(DropoutCell, self).__init__(prefix, params) - assert isinstance(dropout, numeric_types), "dropout probability must be a number" - self.dropout = dropout - - @property - def state_shape(self): - return [] - - def __call__(self, inputs, states): - if self.dropout > 0: - inputs = symbol.Dropout(data=inputs, p=self.dropout) - return inputs, states - - def unroll(self, length, inputs, begin_state=None, layout='NTC', merge_outputs=None): - self.reset() - inputs, _ = _normalize_sequence(length, inputs, layout, merge_outputs) - if isinstance(inputs, symbol.Symbol): - return self(inputs, []) - else: - return super(DropoutCell, self).unroll( - length, inputs, begin_state=begin_state, layout=layout, - merge_outputs=merge_outputs) - - -class ModifierCell(BaseRNNCell): - """Base class for modifier cells. A modifier - cell takes a base cell, apply modifications - on it (e.g. Zoneout), and returns a new cell. - - After applying modifiers the base cell should - no longer be called directly. The modifer cell - should be used instead. - """ - def __init__(self, base_cell): - super(ModifierCell, self).__init__() - base_cell._modified = True - self.base_cell = base_cell - - @property - def params(self): - self._own_params = False - return self.base_cell.params - - @property - def state_shape(self): - return self.base_cell.state_shape - - def begin_state(self, init_sym=symbol.zeros, **kwargs): - assert not self._modified, \ - "After applying modifier cells (e.g. DropoutCell) the base " \ - "cell cannot be called directly. Call the modifier cell instead." - self.base_cell._modified = False - begin = self.base_cell.begin_state(init_sym, **kwargs) - self.base_cell._modified = True - return begin - - def unpack_weights(self, args): - return self.base_cell.unpack_weights(args) - - def pack_weights(self, args): - return self.base_cell.pack_weights(args) - - def __call__(self, inputs, states): - raise NotImplementedError - - -class ZoneoutCell(ModifierCell): - """Apply Zoneout on base cell""" - def __init__(self, base_cell, zoneout_outputs=0., zoneout_states=0.): - assert not isinstance(base_cell, FusedRNNCell), \ - "FusedRNNCell doesn't support zoneout. " \ - "Please unfuse first." - assert not isinstance(base_cell, BidirectionalCell), \ - "BidirectionalCell doesn't support zoneout since it doesn't support step. " \ - "Please add ZoneoutCell to the cells underneath instead." - assert not isinstance(base_cell, SequentialRNNCell) or not base_cell._bidirectional, \ - "Bidirectional SequentialRNNCell doesn't support zoneout. " \ - "Please add ZoneoutCell to the cells underneath instead." - super(ZoneoutCell, self).__init__(base_cell) - self.zoneout_outputs = zoneout_outputs - self.zoneout_states = zoneout_states - self.prev_output = None - - def reset(self): - super(ZoneoutCell, self).reset() - self.prev_output = None - - def __call__(self, inputs, states): - cell, p_outputs, p_states = self.base_cell, self.zoneout_outputs, self.zoneout_states - next_output, next_states = cell(inputs, states) - mask = (lambda p, like: - symbol.Dropout(_symbol_internal._identity_with_attr_like_rhs(symbol.ones((0, 0)), - like), - p=p)) - - prev_output = self.prev_output if self.prev_output else symbol.zeros((0, 0)) - - output = (symbol.where(mask(p_outputs, next_output), next_output, prev_output) - if p_outputs != 0. else next_output) - states = ([symbol.where(mask(p_states, new_s), new_s, old_s) for new_s, old_s in - zip(next_states, states)] if p_states != 0. else next_states) - - self.prev_output = output - - return output, states - - - -class BidirectionalCell(BaseRNNCell): - """Bidirectional RNN cell - - Parameters - ---------- - l_cell : BaseRNNCell - cell for forward unrolling - r_cell : BaseRNNCell - cell for backward unrolling - output_prefix : str, default 'bi_' - prefix for name of output - """ - def __init__(self, l_cell, r_cell, params=None, output_prefix='bi_'): - super(BidirectionalCell, self).__init__('', params=params) - self._override_cell_params = params is not None - self._cells = [l_cell, r_cell] - self._output_prefix = output_prefix - - def unpack_weights(self, args): - return _cells_unpack_weights(self._cells, args) - - def pack_weights(self, args): - return _cells_pack_weights(self._cells, args) - - def __call__(self, inputs, states): - raise NotImplementedError("Bidirectional cannot be stepped. Please use unroll") - - @property - def state_shape(self): - return _cells_state_shape(self._cells) - - def begin_state(self, **kwargs): - assert not self._modified, \ - "After applying modifier cells (e.g. DropoutCell) the base " \ - "cell cannot be called directly. Call the modifier cell instead." - return _cells_begin_state(self._cells, **kwargs) - - def unroll(self, length, inputs, begin_state=None, layout='NTC', merge_outputs=None): - self.reset() - - inputs, axis = _normalize_sequence(length, inputs, layout, False) - if begin_state is None: - begin_state = self.begin_state() - - states = begin_state - l_cell, r_cell = self._cells - l_outputs, l_states = l_cell.unroll(length, inputs=inputs, - begin_state=states[:len(l_cell.state_shape)], - layout=layout, merge_outputs=merge_outputs) - r_outputs, r_states = r_cell.unroll(length, - inputs=list(reversed(inputs)), - begin_state=states[len(l_cell.state_shape):], - layout=layout, merge_outputs=merge_outputs) - - if merge_outputs is None: - merge_outputs = (isinstance(l_outputs, symbol.Symbol) - and isinstance(r_outputs, symbol.Symbol)) - if not merge_outputs: - if isinstance(l_outputs, symbol.Symbol): - l_outputs = list(symbol.SliceChannel(l_outputs, axis=axis, - num_outputs=length, squeeze_axis=1)) - if isinstance(r_outputs, symbol.Symbol): - r_outputs = list(symbol.SliceChannel(r_outputs, axis=axis, - num_outputs=length, squeeze_axis=1)) - - if merge_outputs: - l_outputs = [l_outputs] - r_outputs = [symbol.reverse(r_outputs, axis=axis)] - else: - r_outputs = list(reversed(r_outputs)) - - outputs = [symbol.Concat(l_o, r_o, dim=1+merge_outputs, - name=('%sout'%(self._output_prefix) if merge_outputs - else '%st%d'%(self._output_prefix, i))) - for i, l_o, r_o in - zip(range(len(l_outputs)), l_outputs, r_outputs)] - - if merge_outputs: - outputs = outputs[0] - - states = [l_states, r_states] - return outputs, states From 00c13e411e26f303eaadb6cb0e96b97f7618642d Mon Sep 17 00:00:00 2001 From: Marek Kolodziej Date: Tue, 16 May 2017 22:20:34 +0000 Subject: [PATCH 27/58] WIP --- model_train_gpu_alt_unrolling.sh | 4 +++ seq2seq_bucketing.py | 48 ++++++++++++++++---------------- 2 files changed, 28 insertions(+), 24 deletions(-) create mode 100755 model_train_gpu_alt_unrolling.sh diff --git a/model_train_gpu_alt_unrolling.sh b/model_train_gpu_alt_unrolling.sh new file mode 100755 index 0000000..c6be5b1 --- /dev/null +++ b/model_train_gpu_alt_unrolling.sh @@ -0,0 +1,4 @@ +#!/bin/bash +python seq2seq_bucketing.py --num-hidden 512 --num-embed 512 --num-layers 2 --gpus 0 --batch-size 128 \ + --optimizer adagrad --lr 0.141 --disp-batches 100 --num-epochs 1 --model-prefix trained_model \ + --dropout 0.3 --seed 1234 --inference-unrolling-for-training diff --git a/seq2seq_bucketing.py b/seq2seq_bucketing.py index 9146a26..587b756 100755 --- a/seq2seq_bucketing.py +++ b/seq2seq_bucketing.py @@ -183,10 +183,9 @@ def get_data(layout, infer=False): else: return test_iter, test_iter.src_vocab, test_iter.inv_src_vocab, test_iter.targ_vocab, test_iter.inv_targ_vocab -# WORK IN PROGRESS !!! -def decoder_unroll(decoder, target_embed, targ_vocab, unroll_length, - go_symbol, - fc_weight, fc_bias, targ_em_weight, + +def infer_decoder_unroll(decoder, encoder_outputs, target_embed, targ_vocab, unroll_length, + go_symbol, fc_weight, fc_bias, attention_fc_weight, attention_fc_bias, targ_em_weight, begin_state=None, layout='TNC', merge_outputs=None): decoder.reset() @@ -202,10 +201,6 @@ def decoder_unroll(decoder, target_embed, targ_vocab, unroll_length, embed = inputs[0] - # NEW 1 -# fc_weight = mx.sym.Variable('fc_weight') -# fc_bias = mx.sym.Variable('fc_bias') -# targ_em_weight = mx.sym.Variable('targ_em_weight') for i in range(0, unroll_length): output, states = decoder(embed, states) outputs.append(output) @@ -214,11 +209,6 @@ def decoder_unroll(decoder, target_embed, targ_vocab, unroll_length, embed = mx.sym.Embedding(data=am, weight=targ_em_weight, input_dim=len(targ_vocab), output_dim=args.num_embed, name='decoder_embed%d_'%i) - # NEW 2 -# for i in range(0, unroll_length): -# embed, states = decoder(embed, states) -# outputs.append(embed) - outputs, _ = _normalize_sequence(unroll_length, outputs, layout, merge_outputs) return outputs, states @@ -230,6 +220,9 @@ def train(args): data_train, data_val, _, src_vocab, targ_vocab, inv_src_vocab, inv_targ_vocab = get_data('TN') print "len(src_vocab) len(targ_vocab)", len(src_vocab), len(targ_vocab) + attention_fc_weight = mx.sym.Variable('attention_fc_weight') + attention_fc_bias = mx.sym.Variable('attention_fc_bias') + fc_weight = mx.sym.Variable('fc_weight') fc_bias = mx.sym.Variable('fc_bias') targ_em_weight = mx.sym.Variable('targ_embed_weight') @@ -244,7 +237,7 @@ def train(args): encoder.add(LSTMCell(args.num_hidden, prefix='rnn_encoder%d_' % i)) if i < args.num_layers - 1 and args.dropout > 0.0: encoder.add(mx.rnn.DropoutCell(args.dropout, prefix='rnn_encoder%d_' % i)) - encoder.add(AttentionEncoderCell()) +# encoder.add(AttentionEncoderCell()) decoder = mx.rnn.SequentialRNNCell() @@ -256,7 +249,7 @@ def train(args): decoder.add(LSTMCell(args.num_hidden, prefix=('rnn_decoder%d_' % i))) if i < args.num_layers - 1 and args.dropout > 0.0: decoder.add(mx.rnn.DropoutCell(args.dropout, prefix='rnn_decoder%d_' % i)) - decoder.add(DotAttentionCell()) +# decoder.add(DotAttentionCell()) def sym_gen(seq_len): src_data = mx.sym.Variable('src_data') @@ -274,14 +267,16 @@ def sym_gen(seq_len): enc_seq_len, dec_seq_len = seq_len layout = 'TNC' - _, states = encoder.unroll(enc_seq_len, inputs=src_embed, layout=layout) + encoder_outputs, encoder_states = encoder.unroll(enc_seq_len, inputs=src_embed, layout=layout) # This should be based on EOS or max seq len for inference, but here we unroll to the target length # TODO: fix symbol if args.inference_unrolling_for_training: - outputs, _ = decoder_unroll(decoder, targ_embed, targ_vocab, dec_seq_len, 0, fc_weight, fc_bias, targ_em_weight, begin_state=states, layout='TNC', merge_outputs=True) + outputs, _ = infer_decoder_unroll(decoder, encoder_outputs, targ_embed, targ_vocab, dec_seq_len, 0, fc_weight, fc_bias, + attention_fc_weight, attention_fc_bias, + targ_em_weight, begin_state=encoder_states, layout='TNC', merge_outputs=True) else: - outputs, _ = decoder.unroll(dec_seq_len, targ_embed, begin_state=states, layout=layout, merge_outputs=True) + outputs, _ = decoder.unroll(dec_seq_len, targ_embed, begin_state=encoder_states, layout=layout, merge_outputs=True) # NEW rs = mx.sym.Reshape(outputs, shape=(-1, args.num_hidden), name='sym_gen_reshape1') @@ -399,6 +394,9 @@ def infer(args): print "len(src_vocab) len(targ_vocab)", len(src_vocab), len(targ_vocab) + attention_fc_weight = mx.sym.Variable('attention_fc_weight') + attention_fc_bias = mx.sym.Variable('attention_fc_bias') + fc_weight = mx.sym.Variable('fc_weight') fc_bias = mx.sym.Variable('fc_bias') targ_em_weight = mx.sym.Variable('targ_embed_weight') @@ -415,7 +413,7 @@ def infer(args): if i < args.num_layers - 1 and args.dropout > 0.0: encoder.add(mx.rnn.DropoutCell(args.dropout, prefix='rnn_encoder%d_' % i)) - encoder.add(AttentionEncoderCell()) +# encoder.add(AttentionEncoderCell()) if args.use_cudnn_cells: decoder = mx.rnn.FusedRNNCell(args.num_hidden, num_layers=args.num_layers, @@ -429,7 +427,7 @@ def infer(args): if i < args.num_layers - 1 and args.dropout > 0.0: decoder.add(mx.rnn.DropoutCell(args.dropout, prefix='rnn_decoder%d_' % i)) - decoder.add(DotAttentionCell()) +# decoder.add(DotAttentionCell()) def sym_gen(seq_len): src_data = mx.sym.Variable('src_data') @@ -448,14 +446,16 @@ def sym_gen(seq_len): enc_seq_len, dec_seq_len = seq_len layout = 'TNC' - _, states = encoder.unroll(enc_seq_len, inputs=src_embed, layout=layout) + encoder_outputs, encoder_states = encoder.unroll(enc_seq_len, inputs=src_embed, layout=layout) # This should be based on EOS or max seq len for inference, but here we unroll to the target length # TODO: fix symbol # outputs, _ = decoder.unroll(dec_seq_len, targ_embed, begin_state=states, layout=layout, merge_outputs=True) - outputs, _ = decoder_unroll(decoder, targ_embed, targ_vocab, dec_seq_len, 0, - fc_weight, fc_bias, targ_em_weight, - begin_state=states, layout='TNC', merge_outputs=True) + outputs, _ = infer_decoder_unroll(decoder, encoder_outputs, targ_embed, targ_vocab, dec_seq_len, 0, + fc_weight, fc_bias, + attention_fc_weight, attention_fc_bias, + targ_em_weight, + begin_state=encoder_states, layout='TNC', merge_outputs=True) # NEW From 624fad8f652bc34ad04e3d4ed94993cf0ae2feba Mon Sep 17 00:00:00 2001 From: Marek Kolodziej Date: Wed, 17 May 2017 20:59:34 +0000 Subject: [PATCH 28/58] WIP --- seq2seq_bucketing.py | 46 ++++++++++++++++++++++++++++++++++++-------- 1 file changed, 38 insertions(+), 8 deletions(-) diff --git a/seq2seq_bucketing.py b/seq2seq_bucketing.py index 587b756..4afe327 100755 --- a/seq2seq_bucketing.py +++ b/seq2seq_bucketing.py @@ -202,14 +202,44 @@ def infer_decoder_unroll(decoder, encoder_outputs, target_embed, targ_vocab, unr embed = inputs[0] for i in range(0, unroll_length): - output, states = decoder(embed, states) - outputs.append(output) - fc = mx.sym.FullyConnected(data=output, weight=fc_weight, bias=fc_bias, num_hidden=len(targ_vocab), name='decoder_fc%d_'%i) - am = mx.sym.argmax(data=fc, axis=1) - embed = mx.sym.Embedding(data=am, weight=targ_em_weight, input_dim=len(targ_vocab), - output_dim=args.num_embed, name='decoder_embed%d_'%i) - - outputs, _ = _normalize_sequence(unroll_length, outputs, layout, merge_outputs) + + output, states = decoder(inputs[j], states) + transposed = mx.sym.transpose(output, axes=(0, 2, 1)) + + alignments = [] + + for j in range(len(encoder_outputs)): + + alignments.append(mx.sym.batch_dot(transposed, encoder_outputs[j])) + + alignments = mx.sym.softmax(mx.sym.Group(alignments)) + + + + +# concatted = mx.sym.concat(inputs, decoder_outputs[j]) +# att_fc = mx.sym.FullyConnected( +# data=concatted, weight=attention_fc_weight, bias=attention_fc_bias, num_hidden=args.num_hidden, name='attention_fc%d_' % j +# ) +# att_tanh = mx.sym.Activation(data=att_fc, act_type="tanh", name='attention_tanh%d_' % j) + + +# dots.append(concatted) +# dots = mx.sym.Group(dots) + +# if len(decoder_outputs) > 1: +# attention_states = [symbol.concat(*decoder_outputs, dim=1)] + +# concatted = decoder_outputs * + +# output, states = decoder(embed, states) +# outputs.append(output) +# fc = mx.sym.FullyConnected(data=output, weight=fc_weight, bias=fc_bias, num_hidden=len(targ_vocab), name='decoder_fc%d_'%i) +# am = mx.sym.argmax(data=fc, axis=1) +# embed = mx.sym.Embedding(data=am, weight=targ_em_weight, input_dim=len(targ_vocab), +# output_dim=args.num_embed, name='decoder_embed%d_'%i) +# +# outputs, _ = _normalize_sequence(unroll_length, outputs, layout, merge_outputs) return outputs, states From 40112a36aad66da20435e71e76bd127b8910ff51 Mon Sep 17 00:00:00 2001 From: Marek Kolodziej Date: Wed, 17 May 2017 21:14:26 +0000 Subject: [PATCH 29/58] WIP --- seq2seq_bucketing.py | 56 +++++++++++++++++++------------------------- 1 file changed, 24 insertions(+), 32 deletions(-) diff --git a/seq2seq_bucketing.py b/seq2seq_bucketing.py index 4afe327..c80e9bc 100755 --- a/seq2seq_bucketing.py +++ b/seq2seq_bucketing.py @@ -184,7 +184,7 @@ def get_data(layout, infer=False): return test_iter, test_iter.src_vocab, test_iter.inv_src_vocab, test_iter.targ_vocab, test_iter.inv_targ_vocab -def infer_decoder_unroll(decoder, encoder_outputs, target_embed, targ_vocab, unroll_length, +def train_decoder_unroll(decoder, encoder_outputs, target_embed, targ_vocab, unroll_length, go_symbol, fc_weight, fc_bias, attention_fc_weight, attention_fc_bias, targ_em_weight, begin_state=None, layout='TNC', merge_outputs=None): @@ -203,43 +203,31 @@ def infer_decoder_unroll(decoder, encoder_outputs, target_embed, targ_vocab, unr for i in range(0, unroll_length): - output, states = decoder(inputs[j], states) - transposed = mx.sym.transpose(output, axes=(0, 2, 1)) + output, states = decoder(inputs[j], states) + transposed = mx.sym.transpose(output, axes=(0, 2, 1)) - alignments = [] + alignments = [] - for j in range(len(encoder_outputs)): + for j in range(len(encoder_outputs)): - alignments.append(mx.sym.batch_dot(transposed, encoder_outputs[j])) + alignments.append(mx.sym.batch_dot(transposed, encoder_outputs[j])) - alignments = mx.sym.softmax(mx.sym.Group(alignments)) - - - + alignments = mx.sym.softmax(mx.sym.Group(alignments)) -# concatted = mx.sym.concat(inputs, decoder_outputs[j]) -# att_fc = mx.sym.FullyConnected( -# data=concatted, weight=attention_fc_weight, bias=attention_fc_bias, num_hidden=args.num_hidden, name='attention_fc%d_' % j -# ) -# att_tanh = mx.sym.Activation(data=att_fc, act_type="tanh", name='attention_tanh%d_' % j) + weighted = encoder_outputs[0] * alignments[0] - -# dots.append(concatted) -# dots = mx.sym.Group(dots) - -# if len(decoder_outputs) > 1: -# attention_states = [symbol.concat(*decoder_outputs, dim=1)] + for j in range(1, len(encoder_outputs)): + weighted += encoder_outputs[j] * alignments[j] + + concatenated = mx.sym.concat(inputs[i], weighted) -# concatted = decoder_outputs * - -# output, states = decoder(embed, states) -# outputs.append(output) -# fc = mx.sym.FullyConnected(data=output, weight=fc_weight, bias=fc_bias, num_hidden=len(targ_vocab), name='decoder_fc%d_'%i) -# am = mx.sym.argmax(data=fc, axis=1) -# embed = mx.sym.Embedding(data=am, weight=targ_em_weight, input_dim=len(targ_vocab), -# output_dim=args.num_embed, name='decoder_embed%d_'%i) -# -# outputs, _ = _normalize_sequence(unroll_length, outputs, layout, merge_outputs) + attention_fc = mx.sym.FullyConnected( + data=concatenated, weight=attention_fc_weight, bias=attention_fc_bias, num_hidden=args.num_hidden, name='attention_fc%d_' % i + ) + + att_tanh = mx.sym.Activation(data = attention_fc, act_type='tanh', name = 'attention_tanh%d_' % i) + + outputs.append(att_tanh) return outputs, states @@ -306,7 +294,11 @@ def sym_gen(seq_len): attention_fc_weight, attention_fc_bias, targ_em_weight, begin_state=encoder_states, layout='TNC', merge_outputs=True) else: - outputs, _ = decoder.unroll(dec_seq_len, targ_embed, begin_state=encoder_states, layout=layout, merge_outputs=True) + outputs, _ = train_decoder_unroll(decoder, encoder_outputs, targ_embed, targ_vocab, dec_seq_len, 0, fc_weight, fc_bias, + attention_fc_weight, attention_fc_bias, + targ_em_weight, begin_state=encoder_states, layout='TNC', merge_outputs=True) + +# outputs, _ = decoder.unroll(dec_seq_len, targ_embed, begin_state=encoder_states, layout=layout, merge_outputs=True) # NEW rs = mx.sym.Reshape(outputs, shape=(-1, args.num_hidden), name='sym_gen_reshape1') From 05dd934f530290277207063a7c291c5581ec15f6 Mon Sep 17 00:00:00 2001 From: Marek Kolodziej Date: Wed, 17 May 2017 21:32:31 +0000 Subject: [PATCH 30/58] WIP --- model_train_gpu_alt_unrolling.sh | 5 +++- seq2seq_bucketing.py | 51 +++++++++++++++++++++++++++++++- 2 files changed, 54 insertions(+), 2 deletions(-) diff --git a/model_train_gpu_alt_unrolling.sh b/model_train_gpu_alt_unrolling.sh index c6be5b1..9d7f816 100755 --- a/model_train_gpu_alt_unrolling.sh +++ b/model_train_gpu_alt_unrolling.sh @@ -1,4 +1,7 @@ #!/bin/bash python seq2seq_bucketing.py --num-hidden 512 --num-embed 512 --num-layers 2 --gpus 0 --batch-size 128 \ --optimizer adagrad --lr 0.141 --disp-batches 100 --num-epochs 1 --model-prefix trained_model \ - --dropout 0.3 --seed 1234 --inference-unrolling-for-training + --dropout 0.3 --seed 1234 + +# --inference-unrolling-for-training + diff --git a/seq2seq_bucketing.py b/seq2seq_bucketing.py index c80e9bc..332b98b 100755 --- a/seq2seq_bucketing.py +++ b/seq2seq_bucketing.py @@ -203,7 +203,7 @@ def train_decoder_unroll(decoder, encoder_outputs, target_embed, targ_vocab, unr for i in range(0, unroll_length): - output, states = decoder(inputs[j], states) + output, states = decoder(inputs[i], states) transposed = mx.sym.transpose(output, axes=(0, 2, 1)) alignments = [] @@ -231,6 +231,55 @@ def train_decoder_unroll(decoder, encoder_outputs, target_embed, targ_vocab, unr return outputs, states +def infer_decoder_unroll(decoder, encoder_outputs, target_embed, targ_vocab, unroll_length, + go_symbol, fc_weight, fc_bias, attention_fc_weight, attention_fc_bias, targ_em_weight, + begin_state=None, layout='TNC', merge_outputs=None): + + decoder.reset() + + if begin_state is None: + begin_state = decoder.begin_state() + + inputs, _ = _normalize_sequence(unroll_length, target_embed, layout, False) + + # Need to use hidden state from attention model, but as input + states = begin_state + outputs = [] + + embed = inputs[0] + + for i in range(0, unroll_length): + + output, states = decoder(inputs[j], states) + transposed = mx.sym.transpose(output, axes=(0, 2, 1)) + + alignments = [] + + for j in range(len(encoder_outputs)): + + alignments.append(mx.sym.batch_dot(transposed, encoder_outputs[j])) + + alignments = mx.sym.softmax(mx.sym.Group(alignments)) + + weighted = encoder_outputs[0] * alignments[0] + + for j in range(1, len(encoder_outputs)): + weighted += encoder_outputs[j] * alignments[j] + + concatenated = mx.sym.concat(inputs[i], weighted) + + attention_fc = mx.sym.FullyConnected( + data=concatenated, weight=attention_fc_weight, bias=attention_fc_bias, num_hidden=args.num_hidden, name='attention_fc%d_' % i + ) + + att_tanh = mx.sym.Activation(data = attention_fc, act_type='tanh', name = 'attention_tanh%d_' % i) + + outputs.append(att_tanh) + + return outputs, states + + + def train(args): from time import time From 3bbc66934a09a160883c232f828d5c3177de5407 Mon Sep 17 00:00:00 2001 From: Marek Kolodziej Date: Wed, 17 May 2017 21:51:07 +0000 Subject: [PATCH 31/58] WIP attention for training --- seq2seq_bucketing.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/seq2seq_bucketing.py b/seq2seq_bucketing.py index 332b98b..9355344 100755 --- a/seq2seq_bucketing.py +++ b/seq2seq_bucketing.py @@ -231,6 +231,8 @@ def train_decoder_unroll(decoder, encoder_outputs, target_embed, targ_vocab, unr return outputs, states +# This requires a rewrite now that we're adding attention. +# See the pre-attention version for hints. def infer_decoder_unroll(decoder, encoder_outputs, target_embed, targ_vocab, unroll_length, go_symbol, fc_weight, fc_bias, attention_fc_weight, attention_fc_bias, targ_em_weight, begin_state=None, layout='TNC', merge_outputs=None): From 5486c0e9c285af870d2a6665b51278e6401782eb Mon Sep 17 00:00:00 2001 From: Marek Kolodziej Date: Thu, 18 May 2017 21:47:26 +0000 Subject: [PATCH 32/58] WIP --- seq2seq_bucketing.py | 32 +++++++++++++++++++++++++------- 1 file changed, 25 insertions(+), 7 deletions(-) diff --git a/seq2seq_bucketing.py b/seq2seq_bucketing.py index 9355344..f85dbf8 100755 --- a/seq2seq_bucketing.py +++ b/seq2seq_bucketing.py @@ -188,6 +188,8 @@ def train_decoder_unroll(decoder, encoder_outputs, target_embed, targ_vocab, unr go_symbol, fc_weight, fc_bias, attention_fc_weight, attention_fc_bias, targ_em_weight, begin_state=None, layout='TNC', merge_outputs=None): + print("EEFE#F#RF$F") + decoder.reset() if begin_state is None: @@ -209,13 +211,18 @@ def train_decoder_unroll(decoder, encoder_outputs, target_embed, targ_vocab, unr alignments = [] for j in range(len(encoder_outputs)): - - alignments.append(mx.sym.batch_dot(transposed, encoder_outputs[j])) - alignments = mx.sym.softmax(mx.sym.Group(alignments)) + dot = mx.sym.batch_dot(transposed, encoder_outputs[j]) + sm = mx.sym.softmax(dot) + alignments.append(sm) +# alignments = mx.sym.Group(alignments) + weighted = encoder_outputs[0] * alignments[0] + print(weighted) + print(type(weighted)) + for j in range(1, len(encoder_outputs)): weighted += encoder_outputs[j] * alignments[j] @@ -227,7 +234,10 @@ def train_decoder_unroll(decoder, encoder_outputs, target_embed, targ_vocab, unr att_tanh = mx.sym.Activation(data = attention_fc, act_type='tanh', name = 'attention_tanh%d_' % i) - outputs.append(att_tanh) + outputs.append(att_tanh) + + + outputs, _ = _normalize_sequence(unroll_length, outputs, layout, merge_outputs) return outputs, states @@ -252,16 +262,19 @@ def infer_decoder_unroll(decoder, encoder_outputs, target_embed, targ_vocab, unr for i in range(0, unroll_length): - output, states = decoder(inputs[j], states) + output, states = decoder(inputs[i], states) transposed = mx.sym.transpose(output, axes=(0, 2, 1)) alignments = [] for j in range(len(encoder_outputs)): - alignments.append(mx.sym.batch_dot(transposed, encoder_outputs[j])) + dot = mx.sym.batch_dot(transposed, encoder_outputs[j]) + sm = mx.sym.softmax(dot) + + alignments.append(sm) - alignments = mx.sym.softmax(mx.sym.Group(alignments)) +# alignments = mx.sym.Group(alignments) weighted = encoder_outputs[0] * alignments[0] @@ -351,6 +364,11 @@ def sym_gen(seq_len): # outputs, _ = decoder.unroll(dec_seq_len, targ_embed, begin_state=encoder_states, layout=layout, merge_outputs=True) + print(outputs) + print(type(outputs)) + + print("\n") + # NEW rs = mx.sym.Reshape(outputs, shape=(-1, args.num_hidden), name='sym_gen_reshape1') fc = mx.sym.FullyConnected(data=rs, weight=fc_weight, bias=fc_bias, num_hidden=len(targ_vocab), name='sym_gen_fc') From d81b4d3414892120e1186bcb3e04fd959f5ae4f8 Mon Sep 17 00:00:00 2001 From: Marek Kolodziej Date: Thu, 18 May 2017 22:27:09 +0000 Subject: [PATCH 33/58] WIP --- seq2seq_bucketing.py | 15 ++++++++++----- 1 file changed, 10 insertions(+), 5 deletions(-) diff --git a/seq2seq_bucketing.py b/seq2seq_bucketing.py index f85dbf8..1c13bfc 100755 --- a/seq2seq_bucketing.py +++ b/seq2seq_bucketing.py @@ -188,8 +188,6 @@ def train_decoder_unroll(decoder, encoder_outputs, target_embed, targ_vocab, unr go_symbol, fc_weight, fc_bias, attention_fc_weight, attention_fc_bias, targ_em_weight, begin_state=None, layout='TNC', merge_outputs=None): - print("EEFE#F#RF$F") - decoder.reset() if begin_state is None: @@ -205,14 +203,21 @@ def train_decoder_unroll(decoder, encoder_outputs, target_embed, targ_vocab, unr for i in range(0, unroll_length): - output, states = decoder(inputs[i], states) - transposed = mx.sym.transpose(output, axes=(0, 2, 1)) + output, states = decoder(inputs[i], states) + # axes=(0, 2, 1) +# transposed = mx.sym.transpose(output, axes=(1, 0), name='train_decoder_transpose%d_' % i) + transposed = mx.sym.expand_dims(output, axis=2) + transposed = mx.sym.transpose(transposed, axes=(0, 2, 1), name='train_decoder_transpose%d_' % i) alignments = [] for j in range(len(encoder_outputs)): - dot = mx.sym.batch_dot(transposed, encoder_outputs[j]) + enc_out = encoder_outputs[j] + enc_out = mx.sym.expand_dims(enc_out, axis=2) + +# dot = mx.sym.batch_dot(transposed, enc_out) + dot = mx.sym.broadcast_mul(transposed, enc_out, name='train_decoder_broadcast_mul%d_' % j) sm = mx.sym.softmax(dot) alignments.append(sm) From 1c688107c7645c23e70e4670847778f17b51b89f Mon Sep 17 00:00:00 2001 From: Marek Kolodziej Date: Thu, 18 May 2017 22:30:16 +0000 Subject: [PATCH 34/58] WIP --- seq2seq_bucketing.py | 13 ++++++------- 1 file changed, 6 insertions(+), 7 deletions(-) diff --git a/seq2seq_bucketing.py b/seq2seq_bucketing.py index 1c13bfc..5593976 100755 --- a/seq2seq_bucketing.py +++ b/seq2seq_bucketing.py @@ -206,18 +206,20 @@ def train_decoder_unroll(decoder, encoder_outputs, target_embed, targ_vocab, unr output, states = decoder(inputs[i], states) # axes=(0, 2, 1) # transposed = mx.sym.transpose(output, axes=(1, 0), name='train_decoder_transpose%d_' % i) - transposed = mx.sym.expand_dims(output, axis=2) - transposed = mx.sym.transpose(transposed, axes=(0, 2, 1), name='train_decoder_transpose%d_' % i) +# transposed = mx.sym.expand_dims(output, axis=2) +# transposed = mx.sym.transpose(transposed, axes=(0, 2, 1), name='train_decoder_transpose%d_' % i) + transposed = output alignments = [] for j in range(len(encoder_outputs)): enc_out = encoder_outputs[j] - enc_out = mx.sym.expand_dims(enc_out, axis=2) +# enc_out = mx.sym.expand_dims(enc_out, axis=2) # dot = mx.sym.batch_dot(transposed, enc_out) - dot = mx.sym.broadcast_mul(transposed, enc_out, name='train_decoder_broadcast_mul%d_' % j) + dot = transposed * enc_out +# dot = mx.sym.broadcast_mul(transposed, enc_out, name='train_decoder_broadcast_mul%d_' % j) sm = mx.sym.softmax(dot) alignments.append(sm) @@ -225,9 +227,6 @@ def train_decoder_unroll(decoder, encoder_outputs, target_embed, targ_vocab, unr weighted = encoder_outputs[0] * alignments[0] - print(weighted) - print(type(weighted)) - for j in range(1, len(encoder_outputs)): weighted += encoder_outputs[j] * alignments[j] From f54822208da0a00375ee11d84c1bfd735cb0b808 Mon Sep 17 00:00:00 2001 From: Marek Kolodziej Date: Thu, 18 May 2017 22:31:25 +0000 Subject: [PATCH 35/58] WIP --- seq2seq_bucketing.py | 5 ----- 1 file changed, 5 deletions(-) diff --git a/seq2seq_bucketing.py b/seq2seq_bucketing.py index 5593976..b6b8c71 100755 --- a/seq2seq_bucketing.py +++ b/seq2seq_bucketing.py @@ -368,11 +368,6 @@ def sym_gen(seq_len): # outputs, _ = decoder.unroll(dec_seq_len, targ_embed, begin_state=encoder_states, layout=layout, merge_outputs=True) - print(outputs) - print(type(outputs)) - - print("\n") - # NEW rs = mx.sym.Reshape(outputs, shape=(-1, args.num_hidden), name='sym_gen_reshape1') fc = mx.sym.FullyConnected(data=rs, weight=fc_weight, bias=fc_bias, num_hidden=len(targ_vocab), name='sym_gen_fc') From 4285117f5ece8bbd7376df13753dd17081847bf0 Mon Sep 17 00:00:00 2001 From: Marek Kolodziej Date: Thu, 18 May 2017 23:20:21 +0000 Subject: [PATCH 36/58] Attention works? --- model_train_gpu.sh | 5 +++-- seq2seq_bucketing.py | 7 +++++++ 2 files changed, 10 insertions(+), 2 deletions(-) diff --git a/model_train_gpu.sh b/model_train_gpu.sh index 233bc34..6c67644 100755 --- a/model_train_gpu.sh +++ b/model_train_gpu.sh @@ -1,5 +1,6 @@ #!/bin/bash python seq2seq_bucketing.py --num-hidden 512 --num-embed 512 --num-layers 2 --gpus 0 --batch-size 128 \ - --optimizer adagrad --lr 0.141 --disp-batches 100 --num-epochs 1 --model-prefix trained_model \ - --dropout 0.3 --seed 1234 --use-cudnn-cells + --optimizer adagrad --lr 0.141 --disp-batches 10 --num-epochs 10 --model-prefix trained_model \ + --dropout 0.3 --seed 1234 --remove-state-feed +# --use-cudnn-cells # --inference-unrolling-for-training diff --git a/seq2seq_bucketing.py b/seq2seq_bucketing.py index b6b8c71..1581409 100755 --- a/seq2seq_bucketing.py +++ b/seq2seq_bucketing.py @@ -58,6 +58,7 @@ help='show progress for every n batches') parser.add_argument('--max-grad-norm', type=float, default=5.0, help='maximum gradient norm (larger values will be clipped') + # When training a deep, complex model, it's recommended to stack fused RNN cells (one # layer per cell) together instead of one with all layers. The reason is that fused RNN # cells doesn't set gradients to be ready until the computation for the entire layer is @@ -76,6 +77,9 @@ parser.add_argument('--seed', type=int, default=1234, help='Set random seed for Python, NumPy and MxNet RNGs') +parser.add_argument('--remove-state-feed', action='store_true', + help='Remove direct state feeding from encoder to decoder (use when using attention)') + #buckets = [32] # buckets = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100] @@ -355,6 +359,9 @@ def sym_gen(seq_len): layout = 'TNC' encoder_outputs, encoder_states = encoder.unroll(enc_seq_len, inputs=src_embed, layout=layout) + if args.remove_state_feed: + encoder_states = None + # This should be based on EOS or max seq len for inference, but here we unroll to the target length # TODO: fix symbol if args.inference_unrolling_for_training: From b987b88a0f963d51284f9e05b63375b8d22b5954 Mon Sep 17 00:00:00 2001 From: Marek Kolodziej Date: Mon, 22 May 2017 17:12:53 +0000 Subject: [PATCH 37/58] Correct attention WIP --- seq2seq_bucketing.py | 68 +++++++++++++++++++++++++++----------------- 1 file changed, 42 insertions(+), 26 deletions(-) diff --git a/seq2seq_bucketing.py b/seq2seq_bucketing.py index 1581409..a3ce033 100755 --- a/seq2seq_bucketing.py +++ b/seq2seq_bucketing.py @@ -203,41 +203,59 @@ def train_decoder_unroll(decoder, encoder_outputs, target_embed, targ_vocab, unr states = begin_state outputs = [] - embed = inputs[0] + attention_state = mx.sym.zeros_like(encoder_outputs[-1], name='train_dec_unroll_attention_state') - for i in range(0, unroll_length): + enc_len = len(encoder_outputs) - output, states = decoder(inputs[i], states) - # axes=(0, 2, 1) -# transposed = mx.sym.transpose(output, axes=(1, 0), name='train_decoder_transpose%d_' % i) -# transposed = mx.sym.expand_dims(output, axis=2) -# transposed = mx.sym.transpose(transposed, axes=(0, 2, 1), name='train_decoder_transpose%d_' % i) - transposed = output + for i in range(unroll_length): - alignments = [] + weighted_state = mx.sym.zeros_like(encoder_outputs[-1], name='train_dec_unroll_weighted_state_%d_' % i) + +# align_weights = None - for j in range(len(encoder_outputs)): + curr_input = inputs[i] + curr_input = mx.sym.expand_dims(curr_input, axis=2, name='train_dec_unroll_expand_dims_%d_' % i) + + dots = [] + concat_dots = None - enc_out = encoder_outputs[j] -# enc_out = mx.sym.expand_dims(enc_out, axis=2) + # loop over all the encoder periods to create weights for weighted state + for j in range(enc_len): -# dot = mx.sym.batch_dot(transposed, enc_out) - dot = transposed * enc_out -# dot = mx.sym.broadcast_mul(transposed, enc_out, name='train_decoder_broadcast_mul%d_' % j) - sm = mx.sym.softmax(dot) - alignments.append(sm) +# transposed = mx.sym.transpose(encoder_outputs[j], axes=(1, 0), name='train_decoder_transpose%d_' % i) +# transposed = encoder_outputs[j] + transposed = mx.sym.expand_dims(encoder_outputs[j], axis=2) + transposed = mx.sym.transpose(transposed, axes=(0, 2, 1), name='train_decoder_transpose%d_' % i) + + dot = mx.sym.batch_dot(transposed, curr_input, name='train_decoder_batch_dot_%d_%d_' % (i, j)) + dot = mx.sym.exp(dot) + dot = mx.sym.split(dot, axis=2, num_outputs=1) + + dots.append(dot) + if not concat_dots: + concat_dots = dot + else: + concat_dots = mx.sym.concat(concat_dots, dot) + + dot_sum = mx.sym.sum(concat_dots, axis = 1) + + for j in range(enc_len): + curr_dot = mx.sym.transpose(dots[j]) + + attention_state += mx.sym.broadcast_mul(curr_dot, encoder_outputs[j], name='train_encoder_acc_attention_%d_%d_' % (i, j)) # / dot_sum + attention_state = mx.sym.broadcast_div(attention_state, dot_sum) -# alignments = mx.sym.Group(alignments) - weighted = encoder_outputs[0] * alignments[0] +# arg_shapes, aux_shapes, out_shapes = attention_state.infer_shape() +# print_inferred_shapes(attention_state, arg_shapes, aux_shape, out_shapes) - for j in range(1, len(encoder_outputs)): - weighted += encoder_outputs[j] * alignments[j] - - concatenated = mx.sym.concat(inputs[i], weighted) +# def print_inferred_shapes(node, arg_shapes, aux_shapes, out_shapes): + + + concatenatted = mx.sym.concat(inputs[i], attention_state, name = 'train_decoder_concat_%d_' % i) attention_fc = mx.sym.FullyConnected( - data=concatenated, weight=attention_fc_weight, bias=attention_fc_bias, num_hidden=args.num_hidden, name='attention_fc%d_' % i + data=concatenatted, weight=attention_fc_weight, bias=attention_fc_bias, num_hidden=args.num_hidden, name='attention_fc%d_' % i ) att_tanh = mx.sym.Activation(data = attention_fc, act_type='tanh', name = 'attention_tanh%d_' % i) @@ -282,8 +300,6 @@ def infer_decoder_unroll(decoder, encoder_outputs, target_embed, targ_vocab, unr alignments.append(sm) -# alignments = mx.sym.Group(alignments) - weighted = encoder_outputs[0] * alignments[0] for j in range(1, len(encoder_outputs)): From 13d1cd6591165f1371d145ffad28eb3743e57a23 Mon Sep 17 00:00:00 2001 From: Marek Kolodziej Date: Mon, 22 May 2017 19:54:38 +0000 Subject: [PATCH 38/58] New attention works? --- model_train_gpu.sh | 2 +- seq2seq_bucketing.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/model_train_gpu.sh b/model_train_gpu.sh index 6c67644..01de685 100755 --- a/model_train_gpu.sh +++ b/model_train_gpu.sh @@ -1,6 +1,6 @@ #!/bin/bash python seq2seq_bucketing.py --num-hidden 512 --num-embed 512 --num-layers 2 --gpus 0 --batch-size 128 \ - --optimizer adagrad --lr 0.141 --disp-batches 10 --num-epochs 10 --model-prefix trained_model \ + --optimizer adagrad --lr 0.0141 --disp-batches 1 --num-epochs 10 --model-prefix trained_model \ --dropout 0.3 --seed 1234 --remove-state-feed # --use-cudnn-cells # --inference-unrolling-for-training diff --git a/seq2seq_bucketing.py b/seq2seq_bucketing.py index a3ce033..56bf1c4 100755 --- a/seq2seq_bucketing.py +++ b/seq2seq_bucketing.py @@ -229,7 +229,7 @@ def train_decoder_unroll(decoder, encoder_outputs, target_embed, targ_vocab, unr dot = mx.sym.batch_dot(transposed, curr_input, name='train_decoder_batch_dot_%d_%d_' % (i, j)) dot = mx.sym.exp(dot) - dot = mx.sym.split(dot, axis=2, num_outputs=1) + dot = mx.sym.reshape(dot, shape=(1, args.batch_size)) dots.append(dot) if not concat_dots: From 27bf57233a36a659a1c015b28c954f6e7e31e2ec Mon Sep 17 00:00:00 2001 From: Marek Kolodziej Date: Mon, 22 May 2017 20:32:43 +0000 Subject: [PATCH 39/58] New attention --- model_train_gpu.sh | 3 ++- seq2seq_bucketing.py | 13 +------------ 2 files changed, 3 insertions(+), 13 deletions(-) diff --git a/model_train_gpu.sh b/model_train_gpu.sh index 01de685..38ad422 100755 --- a/model_train_gpu.sh +++ b/model_train_gpu.sh @@ -1,6 +1,7 @@ #!/bin/bash python seq2seq_bucketing.py --num-hidden 512 --num-embed 512 --num-layers 2 --gpus 0 --batch-size 128 \ - --optimizer adagrad --lr 0.0141 --disp-batches 1 --num-epochs 10 --model-prefix trained_model \ + --optimizer adagrad --lr 0.0141 --disp-batches 1 --num-epochs 10 \ --dropout 0.3 --seed 1234 --remove-state-feed +# --model-prefix trained_model # --use-cudnn-cells # --inference-unrolling-for-training diff --git a/seq2seq_bucketing.py b/seq2seq_bucketing.py index 56bf1c4..6c7d1ee 100755 --- a/seq2seq_bucketing.py +++ b/seq2seq_bucketing.py @@ -211,8 +211,6 @@ def train_decoder_unroll(decoder, encoder_outputs, target_embed, targ_vocab, unr weighted_state = mx.sym.zeros_like(encoder_outputs[-1], name='train_dec_unroll_weighted_state_%d_' % i) -# align_weights = None - curr_input = inputs[i] curr_input = mx.sym.expand_dims(curr_input, axis=2, name='train_dec_unroll_expand_dims_%d_' % i) @@ -222,8 +220,6 @@ def train_decoder_unroll(decoder, encoder_outputs, target_embed, targ_vocab, unr # loop over all the encoder periods to create weights for weighted state for j in range(enc_len): -# transposed = mx.sym.transpose(encoder_outputs[j], axes=(1, 0), name='train_decoder_transpose%d_' % i) -# transposed = encoder_outputs[j] transposed = mx.sym.expand_dims(encoder_outputs[j], axis=2) transposed = mx.sym.transpose(transposed, axes=(0, 2, 1), name='train_decoder_transpose%d_' % i) @@ -242,16 +238,9 @@ def train_decoder_unroll(decoder, encoder_outputs, target_embed, targ_vocab, unr for j in range(enc_len): curr_dot = mx.sym.transpose(dots[j]) - attention_state += mx.sym.broadcast_mul(curr_dot, encoder_outputs[j], name='train_encoder_acc_attention_%d_%d_' % (i, j)) # / dot_sum + attention_state += mx.sym.broadcast_mul(curr_dot, encoder_outputs[j], name='train_encoder_acc_attention_%d_%d_' % (i, j)) attention_state = mx.sym.broadcast_div(attention_state, dot_sum) - -# arg_shapes, aux_shapes, out_shapes = attention_state.infer_shape() -# print_inferred_shapes(attention_state, arg_shapes, aux_shape, out_shapes) - -# def print_inferred_shapes(node, arg_shapes, aux_shapes, out_shapes): - - concatenatted = mx.sym.concat(inputs[i], attention_state, name = 'train_decoder_concat_%d_' % i) attention_fc = mx.sym.FullyConnected( From d52fa23c658bb2ec7343fe85468040add5e5e261 Mon Sep 17 00:00:00 2001 From: Marek Kolodziej Date: Mon, 22 May 2017 23:49:58 +0000 Subject: [PATCH 40/58] scale attention only once --- seq2seq_bucketing.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/seq2seq_bucketing.py b/seq2seq_bucketing.py index 6c7d1ee..fe05e86 100755 --- a/seq2seq_bucketing.py +++ b/seq2seq_bucketing.py @@ -237,14 +237,14 @@ def train_decoder_unroll(decoder, encoder_outputs, target_embed, targ_vocab, unr for j in range(enc_len): curr_dot = mx.sym.transpose(dots[j]) - attention_state += mx.sym.broadcast_mul(curr_dot, encoder_outputs[j], name='train_encoder_acc_attention_%d_%d_' % (i, j)) - attention_state = mx.sym.broadcast_div(attention_state, dot_sum) - concatenatted = mx.sym.concat(inputs[i], attention_state, name = 'train_decoder_concat_%d_' % i) + attention_state = mx.sym.broadcast_div(attention_state, dot_sum) + + concatenated = mx.sym.concat(inputs[i], attention_state, name = 'train_decoder_concat_%d_' % i) attention_fc = mx.sym.FullyConnected( - data=concatenatted, weight=attention_fc_weight, bias=attention_fc_bias, num_hidden=args.num_hidden, name='attention_fc%d_' % i + data=concatenated, weight=attention_fc_weight, bias=attention_fc_bias, num_hidden=args.num_hidden, name='attention_fc%d_' % i ) att_tanh = mx.sym.Activation(data = attention_fc, act_type='tanh', name = 'attention_tanh%d_' % i) From 7a35432f05fdfcb4df2799dba962c78e7675d963 Mon Sep 17 00:00:00 2001 From: Marek Kolodziej Date: Tue, 23 May 2017 00:06:40 +0000 Subject: [PATCH 41/58] Fixed decoder bug --- model_train_gpu.sh | 2 +- seq2seq_bucketing.py | 5 +++-- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/model_train_gpu.sh b/model_train_gpu.sh index 38ad422..a7027a2 100755 --- a/model_train_gpu.sh +++ b/model_train_gpu.sh @@ -1,6 +1,6 @@ #!/bin/bash python seq2seq_bucketing.py --num-hidden 512 --num-embed 512 --num-layers 2 --gpus 0 --batch-size 128 \ - --optimizer adagrad --lr 0.0141 --disp-batches 1 --num-epochs 10 \ + --optimizer adagrad --lr 0.0141 --disp-batches 10 --num-epochs 12 \ --dropout 0.3 --seed 1234 --remove-state-feed # --model-prefix trained_model # --use-cudnn-cells diff --git a/seq2seq_bucketing.py b/seq2seq_bucketing.py index fe05e86..ddd36b0 100755 --- a/seq2seq_bucketing.py +++ b/seq2seq_bucketing.py @@ -248,9 +248,10 @@ def train_decoder_unroll(decoder, encoder_outputs, target_embed, targ_vocab, unr ) att_tanh = mx.sym.Activation(data = attention_fc, act_type='tanh', name = 'attention_tanh%d_' % i) - - outputs.append(att_tanh) + output, states = decoder(att_tanh, states) + + outputs.append(output) outputs, _ = _normalize_sequence(unroll_length, outputs, layout, merge_outputs) From 0d6a307f55598d2a57a79a8d9a9c1f926648cd4e Mon Sep 17 00:00:00 2001 From: Marek Kolodziej Date: Tue, 23 May 2017 00:56:31 +0000 Subject: [PATCH 42/58] Fixed place for attention concat --- seq2seq_bucketing.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/seq2seq_bucketing.py b/seq2seq_bucketing.py index ddd36b0..e2fd0ef 100755 --- a/seq2seq_bucketing.py +++ b/seq2seq_bucketing.py @@ -241,7 +241,9 @@ def train_decoder_unroll(decoder, encoder_outputs, target_embed, targ_vocab, unr attention_state = mx.sym.broadcast_div(attention_state, dot_sum) - concatenated = mx.sym.concat(inputs[i], attention_state, name = 'train_decoder_concat_%d_' % i) + dec_out, states = decoder(inputs[i], states) + + concatenated = mx.sym.concat(dec_out, attention_state, name = 'train_decoder_concat_%d_' % i) attention_fc = mx.sym.FullyConnected( data=concatenated, weight=attention_fc_weight, bias=attention_fc_bias, num_hidden=args.num_hidden, name='attention_fc%d_' % i @@ -249,9 +251,7 @@ def train_decoder_unroll(decoder, encoder_outputs, target_embed, targ_vocab, unr att_tanh = mx.sym.Activation(data = attention_fc, act_type='tanh', name = 'attention_tanh%d_' % i) - output, states = decoder(att_tanh, states) - - outputs.append(output) + outputs.append(att_tanh) outputs, _ = _normalize_sequence(unroll_length, outputs, layout, merge_outputs) From eb952f02d8f6a78d3f597bc11fcc1b6ef6eb0610 Mon Sep 17 00:00:00 2001 From: Marek Kolodziej Date: Wed, 24 May 2017 20:31:39 +0000 Subject: [PATCH 43/58] Input feed WIP --- model_train_gpu.sh | 7 ++++--- seq2seq_bucketing.py | 31 ++++++++++++++++++++++++------- 2 files changed, 28 insertions(+), 10 deletions(-) diff --git a/model_train_gpu.sh b/model_train_gpu.sh index a7027a2..8c41920 100755 --- a/model_train_gpu.sh +++ b/model_train_gpu.sh @@ -1,7 +1,8 @@ #!/bin/bash -python seq2seq_bucketing.py --num-hidden 512 --num-embed 512 --num-layers 2 --gpus 0 --batch-size 128 \ - --optimizer adagrad --lr 0.0141 --disp-batches 10 --num-epochs 12 \ - --dropout 0.3 --seed 1234 --remove-state-feed +python seq2seq_bucketing.py --num-hidden 512 --num-embed 512 --num-layers 2 --gpus 1 --batch-size 128 \ + --optimizer adagrad --lr 0.01 --disp-batches 10 --num-epochs 12 \ + --dropout 0.3 --seed 1234 --input-feed --remove-state-feed +# --remove-state-feed # --model-prefix trained_model # --use-cudnn-cells # --inference-unrolling-for-training diff --git a/seq2seq_bucketing.py b/seq2seq_bucketing.py index e2fd0ef..738c857 100755 --- a/seq2seq_bucketing.py +++ b/seq2seq_bucketing.py @@ -80,6 +80,10 @@ parser.add_argument('--remove-state-feed', action='store_true', help='Remove direct state feeding from encoder to decoder (use when using attention)') + +parser.add_argument('--input-feed', action='store_true', + help='Enable input feed (attention is fed into the decoder as input, rather than concatenated with output)') + #buckets = [32] # buckets = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100] @@ -241,17 +245,30 @@ def train_decoder_unroll(decoder, encoder_outputs, target_embed, targ_vocab, unr attention_state = mx.sym.broadcast_div(attention_state, dot_sum) - dec_out, states = decoder(inputs[i], states) - concatenated = mx.sym.concat(dec_out, attention_state, name = 'train_decoder_concat_%d_' % i) + if args.input_feed: - attention_fc = mx.sym.FullyConnected( - data=concatenated, weight=attention_fc_weight, bias=attention_fc_bias, num_hidden=args.num_hidden, name='attention_fc%d_' % i - ) + concatenated = mx.sym.concat(inputs[i], attention_state, name = 'train_decoder_concatt_%d_' % i) + attention_fc = mx.sym.FullyConnected( + data = concatenated, weight=attention_fc_weight, bias=attention_fc_bias, num_hidden=args.num_hidden, name='attention_fc%d_' % i + ) + att_tanh = mx.sym.Activation(data = attention_fc, act_type='tanh', name = 'attention_tanh%d_' % i) + dec_out, states = decoder(att_tanh, states) + outputs.append(dec_out) + + else: + dec_out, states = decoder(inputs[i], states) + + # Should this be dec_out or states as the first argument? + concatenated = mx.sym.concat(dec_out, attention_state, name = 'train_decoder_concat_%d_' % i) + + attention_fc = mx.sym.FullyConnected( + data=concatenated, weight=attention_fc_weight, bias=attention_fc_bias, num_hidden=args.num_hidden, name='attention_fc%d_' % i + ) - att_tanh = mx.sym.Activation(data = attention_fc, act_type='tanh', name = 'attention_tanh%d_' % i) + att_tanh = mx.sym.Activation(data = attention_fc, act_type='tanh', name = 'attention_tanh%d_' % i) - outputs.append(att_tanh) + outputs.append(att_tanh) outputs, _ = _normalize_sequence(unroll_length, outputs, layout, merge_outputs) From e9bef4abcf51c68d6f46577e2f5cbd2e40a4a798 Mon Sep 17 00:00:00 2001 From: Marek Kolodziej Date: Wed, 24 May 2017 21:33:01 -0700 Subject: [PATCH 44/58] Note on how to fix input feed --- seq2seq_bucketing.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/seq2seq_bucketing.py b/seq2seq_bucketing.py index 738c857..093e433 100755 --- a/seq2seq_bucketing.py +++ b/seq2seq_bucketing.py @@ -248,6 +248,11 @@ def train_decoder_unroll(decoder, encoder_outputs, target_embed, targ_vocab, unr if args.input_feed: + # This is wrong !!! + # First produce the attentional output like in the else part (after tanh). + # Then feed this entire output together with input embedding as a concat state. + # See p. 5 here: https://arxiv.org/pdf/1508.04025.pdf + concatenated = mx.sym.concat(inputs[i], attention_state, name = 'train_decoder_concatt_%d_' % i) attention_fc = mx.sym.FullyConnected( data = concatenated, weight=attention_fc_weight, bias=attention_fc_bias, num_hidden=args.num_hidden, name='attention_fc%d_' % i From a5997cd81f2afedf004bc9ac591509fb4d454ec5 Mon Sep 17 00:00:00 2001 From: Marek Kolodziej Date: Thu, 25 May 2017 05:08:15 +0000 Subject: [PATCH 45/58] WIP input feed --- seq2seq_bucketing.py | 40 ++++++++++++++++++++++++---------------- 1 file changed, 24 insertions(+), 16 deletions(-) diff --git a/seq2seq_bucketing.py b/seq2seq_bucketing.py index 093e433..4c13f9d 100755 --- a/seq2seq_bucketing.py +++ b/seq2seq_bucketing.py @@ -246,34 +246,42 @@ def train_decoder_unroll(decoder, encoder_outputs, target_embed, targ_vocab, unr attention_state = mx.sym.broadcast_div(attention_state, dot_sum) - if args.input_feed: +# if args.input_feed: # This is wrong !!! # First produce the attentional output like in the else part (after tanh). # Then feed this entire output together with input embedding as a concat state. # See p. 5 here: https://arxiv.org/pdf/1508.04025.pdf - concatenated = mx.sym.concat(inputs[i], attention_state, name = 'train_decoder_concatt_%d_' % i) - attention_fc = mx.sym.FullyConnected( - data = concatenated, weight=attention_fc_weight, bias=attention_fc_bias, num_hidden=args.num_hidden, name='attention_fc%d_' % i - ) - att_tanh = mx.sym.Activation(data = attention_fc, act_type='tanh', name = 'attention_tanh%d_' % i) - dec_out, states = decoder(att_tanh, states) - outputs.append(dec_out) + +# concatenated = mx.sym.concat(inputs[i], attention_state, name = 'train_decoder_concatt_%d_' % i) +# attention_fc = mx.sym.FullyConnected( +# data = concatenated, weight=attention_fc_weight, bias=attention_fc_bias, num_hidden=args.num_hidden, name='attention_fc%d_' % i +# ) +# att_tanh = mx.sym.Activation(data = attention_fc, act_type='tanh', name = 'attention_tanh%d_' % i) +# dec_out, states = decoder(att_tanh, states) +# outputs.append(dec_out) + + if i == 0: + att_tanh = inputs[0] + if args.input_feed: + decoder_feed = mx.sym.concat(inputs[i], att_tanh, name = 'decoder_feed_concat_%d_' % i) else: - dec_out, states = decoder(inputs[i], states) + decoder_feed = inputs[i] + + dec_out, states = decoder(decoder_feed, states) - # Should this be dec_out or states as the first argument? - concatenated = mx.sym.concat(dec_out, attention_state, name = 'train_decoder_concat_%d_' % i) + # Should this be dec_out or states as the first argument? + concatenated = mx.sym.concat(dec_out, attention_state, name = 'train_decoder_concat_%d_' % i) - attention_fc = mx.sym.FullyConnected( - data=concatenated, weight=attention_fc_weight, bias=attention_fc_bias, num_hidden=args.num_hidden, name='attention_fc%d_' % i - ) + attention_fc = mx.sym.FullyConnected( + data=concatenated, weight=attention_fc_weight, bias=attention_fc_bias, num_hidden=args.num_hidden, name='attention_fc%d_' % i + ) - att_tanh = mx.sym.Activation(data = attention_fc, act_type='tanh', name = 'attention_tanh%d_' % i) + att_tanh = mx.sym.Activation(data = attention_fc, act_type='tanh', name = 'attention_tanh%d_' % i) - outputs.append(att_tanh) + outputs.append(att_tanh) outputs, _ = _normalize_sequence(unroll_length, outputs, layout, merge_outputs) From 77f215b9c53fa824391aba6491949af8d5600740 Mon Sep 17 00:00:00 2001 From: Marek Kolodziej Date: Thu, 25 May 2017 21:48:42 +0000 Subject: [PATCH 46/58] Inference works with input feed --- model_infer_gpu.sh | 7 ++- model_train_gpu.sh | 2 +- seq2seq_bucketing.py | 122 ++++++++++++++++++------------------------- 3 files changed, 58 insertions(+), 73 deletions(-) diff --git a/model_infer_gpu.sh b/model_infer_gpu.sh index 0b01710..b94b6a3 100755 --- a/model_infer_gpu.sh +++ b/model_infer_gpu.sh @@ -1,5 +1,8 @@ #!/bin/bash -python seq2seq_bucketing.py --num-hidden 512 --num-embed 512 --num-layers 2 --gpus 0 --batch-size 128 \ +#!/bin/bash +python seq2seq_bucketing.py --num-hidden 512 --num-embed 512 --num-layers 2 --gpus 0 --batch-size 8 \ --disp-batches 1 --num-epochs 1 --model-prefix trained_model --dropout 0.0 \ - --infer --load-epoch 1 --use-cudnn-cells + --infer --load-epoch 1 --input-feed --remove-state-feed # --use-cudnn-cells +# --use-cudnn-cells +# --inference-unrolling-for-training diff --git a/model_train_gpu.sh b/model_train_gpu.sh index 8c41920..f3540a6 100755 --- a/model_train_gpu.sh +++ b/model_train_gpu.sh @@ -1,7 +1,7 @@ #!/bin/bash python seq2seq_bucketing.py --num-hidden 512 --num-embed 512 --num-layers 2 --gpus 1 --batch-size 128 \ --optimizer adagrad --lr 0.01 --disp-batches 10 --num-epochs 12 \ - --dropout 0.3 --seed 1234 --input-feed --remove-state-feed + --dropout 0.3 --seed 1234 --model-prefix trained_model --input-feed --remove-state-feed # --remove-state-feed # --model-prefix trained_model # --use-cudnn-cells diff --git a/seq2seq_bucketing.py b/seq2seq_bucketing.py index 4c13f9d..3f45d30 100755 --- a/seq2seq_bucketing.py +++ b/seq2seq_bucketing.py @@ -195,96 +195,54 @@ def get_data(layout, infer=False): def train_decoder_unroll(decoder, encoder_outputs, target_embed, targ_vocab, unroll_length, go_symbol, fc_weight, fc_bias, attention_fc_weight, attention_fc_bias, targ_em_weight, begin_state=None, layout='TNC', merge_outputs=None): - decoder.reset() - if begin_state is None: begin_state = decoder.begin_state() - inputs, _ = _normalize_sequence(unroll_length, target_embed, layout, False) - # Need to use hidden state from attention model, but as input states = begin_state outputs = [] - attention_state = mx.sym.zeros_like(encoder_outputs[-1], name='train_dec_unroll_attention_state') - enc_len = len(encoder_outputs) - for i in range(unroll_length): - weighted_state = mx.sym.zeros_like(encoder_outputs[-1], name='train_dec_unroll_weighted_state_%d_' % i) - curr_input = inputs[i] curr_input = mx.sym.expand_dims(curr_input, axis=2, name='train_dec_unroll_expand_dims_%d_' % i) - dots = [] concat_dots = None - # loop over all the encoder periods to create weights for weighted state for j in range(enc_len): - transposed = mx.sym.expand_dims(encoder_outputs[j], axis=2) transposed = mx.sym.transpose(transposed, axes=(0, 2, 1), name='train_decoder_transpose%d_' % i) - dot = mx.sym.batch_dot(transposed, curr_input, name='train_decoder_batch_dot_%d_%d_' % (i, j)) dot = mx.sym.exp(dot) dot = mx.sym.reshape(dot, shape=(1, args.batch_size)) - dots.append(dot) if not concat_dots: concat_dots = dot else: concat_dots = mx.sym.concat(concat_dots, dot) - dot_sum = mx.sym.sum(concat_dots, axis = 1) - for j in range(enc_len): curr_dot = mx.sym.transpose(dots[j]) attention_state += mx.sym.broadcast_mul(curr_dot, encoder_outputs[j], name='train_encoder_acc_attention_%d_%d_' % (i, j)) attention_state = mx.sym.broadcast_div(attention_state, dot_sum) - - -# if args.input_feed: - - # This is wrong !!! - # First produce the attentional output like in the else part (after tanh). - # Then feed this entire output together with input embedding as a concat state. - # See p. 5 here: https://arxiv.org/pdf/1508.04025.pdf - - -# concatenated = mx.sym.concat(inputs[i], attention_state, name = 'train_decoder_concatt_%d_' % i) -# attention_fc = mx.sym.FullyConnected( -# data = concatenated, weight=attention_fc_weight, bias=attention_fc_bias, num_hidden=args.num_hidden, name='attention_fc%d_' % i -# ) -# att_tanh = mx.sym.Activation(data = attention_fc, act_type='tanh', name = 'attention_tanh%d_' % i) -# dec_out, states = decoder(att_tanh, states) -# outputs.append(dec_out) - if i == 0: att_tanh = inputs[0] - if args.input_feed: decoder_feed = mx.sym.concat(inputs[i], att_tanh, name = 'decoder_feed_concat_%d_' % i) else: decoder_feed = inputs[i] - dec_out, states = decoder(decoder_feed, states) - # Should this be dec_out or states as the first argument? concatenated = mx.sym.concat(dec_out, attention_state, name = 'train_decoder_concat_%d_' % i) - attention_fc = mx.sym.FullyConnected( data=concatenated, weight=attention_fc_weight, bias=attention_fc_bias, num_hidden=args.num_hidden, name='attention_fc%d_' % i ) - att_tanh = mx.sym.Activation(data = attention_fc, act_type='tanh', name = 'attention_tanh%d_' % i) - outputs.append(att_tanh) - outputs, _ = _normalize_sequence(unroll_length, outputs, layout, merge_outputs) - return outputs, states # This requires a rewrite now that we're adding attention. @@ -292,52 +250,77 @@ def train_decoder_unroll(decoder, encoder_outputs, target_embed, targ_vocab, unr def infer_decoder_unroll(decoder, encoder_outputs, target_embed, targ_vocab, unroll_length, go_symbol, fc_weight, fc_bias, attention_fc_weight, attention_fc_bias, targ_em_weight, begin_state=None, layout='TNC', merge_outputs=None): - decoder.reset() - if begin_state is None: begin_state = decoder.begin_state() - inputs, _ = _normalize_sequence(unroll_length, target_embed, layout, False) - # Need to use hidden state from attention model, but as input states = begin_state outputs = [] - embed = inputs[0] - for i in range(0, unroll_length): - - output, states = decoder(inputs[i], states) - transposed = mx.sym.transpose(output, axes=(0, 2, 1)) - - alignments = [] - - for j in range(len(encoder_outputs)): - - dot = mx.sym.batch_dot(transposed, encoder_outputs[j]) - sm = mx.sym.softmax(dot) - - alignments.append(sm) - - weighted = encoder_outputs[0] * alignments[0] - - for j in range(1, len(encoder_outputs)): - weighted += encoder_outputs[j] * alignments[j] - - concatenated = mx.sym.concat(inputs[i], weighted) +######################################################################################################################### + attention_state = mx.sym.zeros_like(encoder_outputs[-1], name='train_dec_unroll_attention_state') + enc_len = len(encoder_outputs) + for i in range(unroll_length): + weighted_state = mx.sym.zeros_like(encoder_outputs[-1], name='train_dec_unroll_weighted_state_%d_' % i) + curr_input = inputs[i] + curr_input = mx.sym.expand_dims(curr_input, axis=2, name='train_dec_unroll_expand_dims_%d_' % i) + dots = [] + concat_dots = None + # loop over all the encoder periods to create weights for weighted state + for j in range(enc_len): + transposed = mx.sym.expand_dims(encoder_outputs[j], axis=2) + transposed = mx.sym.transpose(transposed, axes=(0, 2, 1), name='train_decoder_transpose%d_' % i) + dot = mx.sym.batch_dot(transposed, curr_input, name='train_decoder_batch_dot_%d_%d_' % (i, j)) + dot = mx.sym.exp(dot) + dot = mx.sym.reshape(dot, shape=(1, args.batch_size)) + dots.append(dot) + if not concat_dots: + concat_dots = dot + else: + concat_dots = mx.sym.concat(concat_dots, dot) + dot_sum = mx.sym.sum(concat_dots, axis = 1) + for j in range(enc_len): + curr_dot = mx.sym.transpose(dots[j]) + attention_state += mx.sym.broadcast_mul(curr_dot, encoder_outputs[j], name='train_encoder_acc_attention_%d_%d_' % (i, j)) + attention_state = mx.sym.broadcast_div(attention_state, dot_sum) + if i == 0: + att_tanh = embed + if args.input_feed: + decoder_feed = mx.sym.concat(embed, att_tanh, name = 'decoder_feed_concat_%d_' % i) + else: + decoder_feed = inputs[i] + dec_out, states = decoder(decoder_feed, states) + # Should this be dec_out or states as the first argument? + concatenated = mx.sym.concat(dec_out, attention_state, name = 'train_decoder_concat_%d_' % i) attention_fc = mx.sym.FullyConnected( data=concatenated, weight=attention_fc_weight, bias=attention_fc_bias, num_hidden=args.num_hidden, name='attention_fc%d_' % i ) - att_tanh = mx.sym.Activation(data = attention_fc, act_type='tanh', name = 'attention_tanh%d_' % i) - outputs.append(att_tanh) + fc = mx.sym.FullyConnected(data=att_tanh, weight=fc_weight, bias=fc_bias, num_hidden=len(targ_vocab), name='decoder_fc%d_'%i) + am = mx.sym.argmax(data=fc, axis=1) + embed = mx.sym.Embedding(data=am, weight=targ_em_weight, input_dim=len(targ_vocab), output_dim=args.num_embed, name='decoder_embed%d_'%i) + + outputs, _ = _normalize_sequence(unroll_length, outputs, layout, merge_outputs) return outputs, states +######################################################################################################################### +# for i in range(0, unroll_length): +# output, states = decoder(embed, states) +# outputs.append(output) +# fc = mx.sym.FullyConnected(data=output, weight=fc_weight, bias=fc_bias, num_hidden=len(targ_vocab), name='decoder_fc%d_'%i) +# am = mx.sym.argmax(data=fc, axis=1) +# embed = mx.sym.Embedding(data=am, weight=targ_em_weight, input_dim=len(targ_vocab), +# output_dim=args.num_embed, name='decoder_embed%d_'%i) +# +# outputs, _ = _normalize_sequence(unroll_length, outputs, layout, merge_outputs) +# +# return outputs, states def train(args): @@ -703,7 +686,6 @@ def sym_gen(seq_len): print("Source text: %s" % src_txt) print("Expected translation: %s" % exp_txt) print("Actual translation: %s" % act_txt) - print("\n") print("\nTest set BLEU score (averaged over all examples): %.3f\n" % bleu_acc) if __name__ == '__main__': From 1d44d241c5dca95a0e68f7cda7d6dc76d40a963e Mon Sep 17 00:00:00 2001 From: Marek Kolodziej Date: Fri, 26 May 2017 16:41:46 +0000 Subject: [PATCH 47/58] Multi-GPU training working again --- model_train_gpu.sh | 8 ++++---- seq2seq_bucketing.py | 6 ++++-- 2 files changed, 8 insertions(+), 6 deletions(-) diff --git a/model_train_gpu.sh b/model_train_gpu.sh index f3540a6..ce03e52 100755 --- a/model_train_gpu.sh +++ b/model_train_gpu.sh @@ -1,8 +1,8 @@ #!/bin/bash -python seq2seq_bucketing.py --num-hidden 512 --num-embed 512 --num-layers 2 --gpus 1 --batch-size 128 \ - --optimizer adagrad --lr 0.01 --disp-batches 10 --num-epochs 12 \ - --dropout 0.3 --seed 1234 --model-prefix trained_model --input-feed --remove-state-feed -# --remove-state-feed +python seq2seq_bucketing.py --num-hidden 512 --num-embed 512 --num-layers 2 --gpus 0,1 --batch-size 256 \ + --optimizer adagrad --lr 0.01 --disp-batches 2 --num-epochs 1 \ + --dropout 0.3 --seed 1234 --model-prefix trained_model --model-prefix trained_model +# --input-feed --remove-state-feed # --model-prefix trained_model # --use-cudnn-cells # --inference-unrolling-for-training diff --git a/seq2seq_bucketing.py b/seq2seq_bucketing.py index 3f45d30..3e48794 100755 --- a/seq2seq_bucketing.py +++ b/seq2seq_bucketing.py @@ -215,8 +215,10 @@ def train_decoder_unroll(decoder, encoder_outputs, target_embed, targ_vocab, unr transposed = mx.sym.expand_dims(encoder_outputs[j], axis=2) transposed = mx.sym.transpose(transposed, axes=(0, 2, 1), name='train_decoder_transpose%d_' % i) dot = mx.sym.batch_dot(transposed, curr_input, name='train_decoder_batch_dot_%d_%d_' % (i, j)) - dot = mx.sym.exp(dot) - dot = mx.sym.reshape(dot, shape=(1, args.batch_size)) + dot = mx.sym.exp(dot, name='train_decoder_exp_%d_%d' % (i, j)) + # The batch size shouldn't be an arg here anyway. We should just remove extra dimensions + # and then transpose. + dot = mx.sym.reshape(dot, shape=(1, args.batch_size / len(contexts)), name='train_decoder_unroll_reshape_%d_%d' % (i, j)) dots.append(dot) if not concat_dots: concat_dots = dot From 95ace12541633aeb20dc9f51b2df813ecfc22302 Mon Sep 17 00:00:00 2001 From: Marek Kolodziej Date: Fri, 26 May 2017 16:47:31 +0000 Subject: [PATCH 48/58] Training and inference work with multi-GPU again --- model_infer_gpu.sh | 5 +++-- seq2seq_bucketing.py | 5 +---- 2 files changed, 4 insertions(+), 6 deletions(-) diff --git a/model_infer_gpu.sh b/model_infer_gpu.sh index b94b6a3..311b1d8 100755 --- a/model_infer_gpu.sh +++ b/model_infer_gpu.sh @@ -1,8 +1,9 @@ #!/bin/bash #!/bin/bash -python seq2seq_bucketing.py --num-hidden 512 --num-embed 512 --num-layers 2 --gpus 0 --batch-size 8 \ +python seq2seq_bucketing.py --num-hidden 512 --num-embed 512 --num-layers 2 --gpus 0,1 --batch-size 8 \ --disp-batches 1 --num-epochs 1 --model-prefix trained_model --dropout 0.0 \ - --infer --load-epoch 1 --input-feed --remove-state-feed + --infer --load-epoch 1 +# --input-feed --remove-state-feed # --use-cudnn-cells # --use-cudnn-cells # --inference-unrolling-for-training diff --git a/seq2seq_bucketing.py b/seq2seq_bucketing.py index 3e48794..7ba3eaa 100755 --- a/seq2seq_bucketing.py +++ b/seq2seq_bucketing.py @@ -261,7 +261,6 @@ def infer_decoder_unroll(decoder, encoder_outputs, target_embed, targ_vocab, unr outputs = [] embed = inputs[0] -######################################################################################################################### attention_state = mx.sym.zeros_like(encoder_outputs[-1], name='train_dec_unroll_attention_state') enc_len = len(encoder_outputs) for i in range(unroll_length): @@ -276,7 +275,7 @@ def infer_decoder_unroll(decoder, encoder_outputs, target_embed, targ_vocab, unr transposed = mx.sym.transpose(transposed, axes=(0, 2, 1), name='train_decoder_transpose%d_' % i) dot = mx.sym.batch_dot(transposed, curr_input, name='train_decoder_batch_dot_%d_%d_' % (i, j)) dot = mx.sym.exp(dot) - dot = mx.sym.reshape(dot, shape=(1, args.batch_size)) + dot = mx.sym.reshape(dot, shape=(1, args.batch_size / len(contexts))) dots.append(dot) if not concat_dots: concat_dots = dot @@ -310,8 +309,6 @@ def infer_decoder_unroll(decoder, encoder_outputs, target_embed, targ_vocab, unr outputs, _ = _normalize_sequence(unroll_length, outputs, layout, merge_outputs) return outputs, states -######################################################################################################################### - # for i in range(0, unroll_length): # output, states = decoder(embed, states) # outputs.append(output) From c216cd118f275ab52f6514b9b7ab1d2250e18047 Mon Sep 17 00:00:00 2001 From: Marek Kolodziej Date: Fri, 26 May 2017 17:38:21 +0000 Subject: [PATCH 49/58] Attention is optional now (need to use a CLI switch to enable it) --- model_infer_gpu.sh | 3 ++- model_train_gpu.sh | 3 ++- seq2seq_bucketing.py | 42 +++++++++++++++++++++++++----------------- 3 files changed, 29 insertions(+), 19 deletions(-) diff --git a/model_infer_gpu.sh b/model_infer_gpu.sh index 311b1d8..3049bcb 100755 --- a/model_infer_gpu.sh +++ b/model_infer_gpu.sh @@ -2,7 +2,8 @@ #!/bin/bash python seq2seq_bucketing.py --num-hidden 512 --num-embed 512 --num-layers 2 --gpus 0,1 --batch-size 8 \ --disp-batches 1 --num-epochs 1 --model-prefix trained_model --dropout 0.0 \ - --infer --load-epoch 1 + --infer --load-epoch 1 --attention +# --attention # --input-feed --remove-state-feed # --use-cudnn-cells # --use-cudnn-cells diff --git a/model_train_gpu.sh b/model_train_gpu.sh index ce03e52..5c7a066 100755 --- a/model_train_gpu.sh +++ b/model_train_gpu.sh @@ -1,7 +1,8 @@ #!/bin/bash python seq2seq_bucketing.py --num-hidden 512 --num-embed 512 --num-layers 2 --gpus 0,1 --batch-size 256 \ --optimizer adagrad --lr 0.01 --disp-batches 2 --num-epochs 1 \ - --dropout 0.3 --seed 1234 --model-prefix trained_model --model-prefix trained_model + --dropout 0.3 --seed 1234 --model-prefix trained_model --model-prefix trained_model --attention +# --attention # --input-feed --remove-state-feed # --model-prefix trained_model # --use-cudnn-cells diff --git a/seq2seq_bucketing.py b/seq2seq_bucketing.py index 7ba3eaa..fda88f7 100755 --- a/seq2seq_bucketing.py +++ b/seq2seq_bucketing.py @@ -19,7 +19,7 @@ from seq2seq_iterator import * -from attention_cell import AttentionEncoderCell, DotAttentionCell +# from attention_cell import AttentionEncoderCell, DotAttentionCell parser = argparse.ArgumentParser(description="Train RNN on Penn Tree Bank", formatter_class=argparse.ArgumentDefaultsHelpFormatter) @@ -84,6 +84,9 @@ parser.add_argument('--input-feed', action='store_true', help='Enable input feed (attention is fed into the decoder as input, rather than concatenated with output)') +parser.add_argument('--attention', action='store_true', + help='Use attention (dot attention is the currently implemented form') + #buckets = [32] # buckets = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100] @@ -237,13 +240,16 @@ def train_decoder_unroll(decoder, encoder_outputs, target_embed, targ_vocab, unr else: decoder_feed = inputs[i] dec_out, states = decoder(decoder_feed, states) - # Should this be dec_out or states as the first argument? - concatenated = mx.sym.concat(dec_out, attention_state, name = 'train_decoder_concat_%d_' % i) - attention_fc = mx.sym.FullyConnected( - data=concatenated, weight=attention_fc_weight, bias=attention_fc_bias, num_hidden=args.num_hidden, name='attention_fc%d_' % i - ) - att_tanh = mx.sym.Activation(data = attention_fc, act_type='tanh', name = 'attention_tanh%d_' % i) - outputs.append(att_tanh) + if args.attention: + # Should this be dec_out or states as the first argument? + concatenated = mx.sym.concat(dec_out, attention_state, name = 'train_decoder_concat_%d_' % i) + attention_fc = mx.sym.FullyConnected( + data=concatenated, weight=attention_fc_weight, bias=attention_fc_bias, num_hidden=args.num_hidden, name='attention_fc%d_' % i + ) + curr_out = mx.sym.Activation(data = attention_fc, act_type='tanh', name = 'attention_tanh%d_' % i) + else: + curr_out = dec_out + outputs.append(curr_out) outputs, _ = _normalize_sequence(unroll_length, outputs, layout, merge_outputs) return outputs, states @@ -294,15 +300,17 @@ def infer_decoder_unroll(decoder, encoder_outputs, target_embed, targ_vocab, unr else: decoder_feed = inputs[i] dec_out, states = decoder(decoder_feed, states) - # Should this be dec_out or states as the first argument? - concatenated = mx.sym.concat(dec_out, attention_state, name = 'train_decoder_concat_%d_' % i) - attention_fc = mx.sym.FullyConnected( - data=concatenated, weight=attention_fc_weight, bias=attention_fc_bias, num_hidden=args.num_hidden, name='attention_fc%d_' % i - ) - att_tanh = mx.sym.Activation(data = attention_fc, act_type='tanh', name = 'attention_tanh%d_' % i) - outputs.append(att_tanh) - - fc = mx.sym.FullyConnected(data=att_tanh, weight=fc_weight, bias=fc_bias, num_hidden=len(targ_vocab), name='decoder_fc%d_'%i) + # Should this be dec_out or states as the first argument? + if args.attention: + concatenated = mx.sym.concat(dec_out, attention_state, name = 'train_decoder_concat_%d_' % i) + attention_fc = mx.sym.FullyConnected( + data=concatenated, weight=attention_fc_weight, bias=attention_fc_bias, num_hidden=args.num_hidden, name='attention_fc%d_' % i + ) + curr_out = mx.sym.Activation(data = attention_fc, act_type='tanh', name = 'attention_tanh%d_' % i) + else: + curr_out = dec_out + outputs.append(curr_out) + fc = mx.sym.FullyConnected(data=curr_out, weight=fc_weight, bias=fc_bias, num_hidden=len(targ_vocab), name='decoder_fc%d_'%i) am = mx.sym.argmax(data=fc, axis=1) embed = mx.sym.Embedding(data=am, weight=targ_em_weight, input_dim=len(targ_vocab), output_dim=args.num_embed, name='decoder_embed%d_'%i) From af53f2b48af3069549ef8adbc9fa6ef39f273c1a Mon Sep 17 00:00:00 2001 From: Marek Kolodziej Date: Fri, 26 May 2017 18:18:07 +0000 Subject: [PATCH 50/58] Fixed bug in attention feed? --- model_train_gpu.sh | 4 ++-- seq2seq_bucketing.py | 15 ++++++++++----- 2 files changed, 12 insertions(+), 7 deletions(-) diff --git a/model_train_gpu.sh b/model_train_gpu.sh index 5c7a066..e203ef6 100755 --- a/model_train_gpu.sh +++ b/model_train_gpu.sh @@ -1,7 +1,7 @@ #!/bin/bash python seq2seq_bucketing.py --num-hidden 512 --num-embed 512 --num-layers 2 --gpus 0,1 --batch-size 256 \ - --optimizer adagrad --lr 0.01 --disp-batches 2 --num-epochs 1 \ - --dropout 0.3 --seed 1234 --model-prefix trained_model --model-prefix trained_model --attention + --optimizer adagrad --lr 0.001 --disp-batches 1 --num-epochs 10 \ + --dropout 0.3 --seed 1234 --model-prefix trained_model --model-prefix trained_model --attention # --attention # --input-feed --remove-state-feed # --model-prefix trained_model diff --git a/seq2seq_bucketing.py b/seq2seq_bucketing.py index fda88f7..2fdc479 100755 --- a/seq2seq_bucketing.py +++ b/seq2seq_bucketing.py @@ -208,16 +208,22 @@ def train_decoder_unroll(decoder, encoder_outputs, target_embed, targ_vocab, unr attention_state = mx.sym.zeros_like(encoder_outputs[-1], name='train_dec_unroll_attention_state') enc_len = len(encoder_outputs) for i in range(unroll_length): - weighted_state = mx.sym.zeros_like(encoder_outputs[-1], name='train_dec_unroll_weighted_state_%d_' % i) - curr_input = inputs[i] - curr_input = mx.sym.expand_dims(curr_input, axis=2, name='train_dec_unroll_expand_dims_%d_' % i) +# curr_input = inputs[i] +# curr_input = mx.sym.expand_dims(curr_input, axis=2, name='train_dec_unroll_expand_dims_%d_' % i) + + if i == 0: + curr_att_input = mx.sym.zeros_like(states[-1]) + else: + curr_att_input = curr_out + curr_att_input = mx.sym.expand_dims(curr_att_input, axis=2, name='train_dec_unroll_expand_dims_%d_' % i) + dots = [] concat_dots = None # loop over all the encoder periods to create weights for weighted state for j in range(enc_len): transposed = mx.sym.expand_dims(encoder_outputs[j], axis=2) transposed = mx.sym.transpose(transposed, axes=(0, 2, 1), name='train_decoder_transpose%d_' % i) - dot = mx.sym.batch_dot(transposed, curr_input, name='train_decoder_batch_dot_%d_%d_' % (i, j)) + dot = mx.sym.batch_dot(transposed, curr_att_input, name='train_decoder_batch_dot_%d_%d_' % (i, j)) dot = mx.sym.exp(dot, name='train_decoder_exp_%d_%d' % (i, j)) # The batch size shouldn't be an arg here anyway. We should just remove extra dimensions # and then transpose. @@ -270,7 +276,6 @@ def infer_decoder_unroll(decoder, encoder_outputs, target_embed, targ_vocab, unr attention_state = mx.sym.zeros_like(encoder_outputs[-1], name='train_dec_unroll_attention_state') enc_len = len(encoder_outputs) for i in range(unroll_length): - weighted_state = mx.sym.zeros_like(encoder_outputs[-1], name='train_dec_unroll_weighted_state_%d_' % i) curr_input = inputs[i] curr_input = mx.sym.expand_dims(curr_input, axis=2, name='train_dec_unroll_expand_dims_%d_' % i) dots = [] From 3c498fd9adc83aeb956d742dd5451ae0a2ec458f Mon Sep 17 00:00:00 2001 From: Marek Kolodziej Date: Fri, 26 May 2017 20:21:07 +0000 Subject: [PATCH 51/58] WIP --- model_train_gpu.sh | 4 ++-- seq2seq_bucketing.py | 11 ++++------- 2 files changed, 6 insertions(+), 9 deletions(-) diff --git a/model_train_gpu.sh b/model_train_gpu.sh index e203ef6..4ca8e7c 100755 --- a/model_train_gpu.sh +++ b/model_train_gpu.sh @@ -1,7 +1,7 @@ #!/bin/bash python seq2seq_bucketing.py --num-hidden 512 --num-embed 512 --num-layers 2 --gpus 0,1 --batch-size 256 \ - --optimizer adagrad --lr 0.001 --disp-batches 1 --num-epochs 10 \ - --dropout 0.3 --seed 1234 --model-prefix trained_model --model-prefix trained_model --attention + --optimizer adagrad --lr 0.01 --disp-batches 1 --num-epochs 10 \ + --dropout 0.3 --seed 1234 --model-prefix trained_model --model-prefix trained_model --attention # --input-feed --remove-state-feed # --attention # --input-feed --remove-state-feed # --model-prefix trained_model diff --git a/seq2seq_bucketing.py b/seq2seq_bucketing.py index 2fdc479..65a33be 100755 --- a/seq2seq_bucketing.py +++ b/seq2seq_bucketing.py @@ -208,11 +208,8 @@ def train_decoder_unroll(decoder, encoder_outputs, target_embed, targ_vocab, unr attention_state = mx.sym.zeros_like(encoder_outputs[-1], name='train_dec_unroll_attention_state') enc_len = len(encoder_outputs) for i in range(unroll_length): -# curr_input = inputs[i] -# curr_input = mx.sym.expand_dims(curr_input, axis=2, name='train_dec_unroll_expand_dims_%d_' % i) - if i == 0: - curr_att_input = mx.sym.zeros_like(states[-1]) + curr_att_input = mx.sym.zeros_like(states[-1]) # states[-1] else: curr_att_input = curr_out curr_att_input = mx.sym.expand_dims(curr_att_input, axis=2, name='train_dec_unroll_expand_dims_%d_' % i) @@ -239,10 +236,10 @@ def train_decoder_unroll(decoder, encoder_outputs, target_embed, targ_vocab, unr attention_state += mx.sym.broadcast_mul(curr_dot, encoder_outputs[j], name='train_encoder_acc_attention_%d_%d_' % (i, j)) attention_state = mx.sym.broadcast_div(attention_state, dot_sum) - if i == 0: - att_tanh = inputs[0] if args.input_feed: - decoder_feed = mx.sym.concat(inputs[i], att_tanh, name = 'decoder_feed_concat_%d_' % i) + if i == 0: + curr_out = inputs[0] + decoder_feed = mx.sym.concat(inputs[i], curr_out, name = 'decoder_feed_concat_%d_' % i) else: decoder_feed = inputs[i] dec_out, states = decoder(decoder_feed, states) From b296d1e25a1012103c1a9cc1ea655f8b55080ce2 Mon Sep 17 00:00:00 2001 From: Marek Kolodziej Date: Fri, 26 May 2017 14:54:03 -0700 Subject: [PATCH 52/58] WIP --- model_train_gpu.sh | 2 +- seq2seq_bucketing.py | 28 ++++++++++++++++++++++------ 2 files changed, 23 insertions(+), 7 deletions(-) diff --git a/model_train_gpu.sh b/model_train_gpu.sh index 4ca8e7c..20a81ca 100755 --- a/model_train_gpu.sh +++ b/model_train_gpu.sh @@ -1,7 +1,7 @@ #!/bin/bash python seq2seq_bucketing.py --num-hidden 512 --num-embed 512 --num-layers 2 --gpus 0,1 --batch-size 256 \ --optimizer adagrad --lr 0.01 --disp-batches 1 --num-epochs 10 \ - --dropout 0.3 --seed 1234 --model-prefix trained_model --model-prefix trained_model --attention # --input-feed --remove-state-feed + --dropout 0.3 --seed 1234 --model-prefix trained_model --model-prefix trained_model --attention --input-feed --remove-state-feed # --attention # --input-feed --remove-state-feed # --model-prefix trained_model diff --git a/seq2seq_bucketing.py b/seq2seq_bucketing.py index 65a33be..f1b7289 100755 --- a/seq2seq_bucketing.py +++ b/seq2seq_bucketing.py @@ -209,7 +209,10 @@ def train_decoder_unroll(decoder, encoder_outputs, target_embed, targ_vocab, unr enc_len = len(encoder_outputs) for i in range(unroll_length): if i == 0: - curr_att_input = mx.sym.zeros_like(states[-1]) # states[-1] +# if args.remove_state_feed: + curr_att_input = mx.sym.zeros_like(states[-1]) +# else: +# curr_att_input = states[-1] else: curr_att_input = curr_out curr_att_input = mx.sym.expand_dims(curr_att_input, axis=2, name='train_dec_unroll_expand_dims_%d_' % i) @@ -238,7 +241,7 @@ def train_decoder_unroll(decoder, encoder_outputs, target_embed, targ_vocab, unr attention_state = mx.sym.broadcast_div(attention_state, dot_sum) if args.input_feed: if i == 0: - curr_out = inputs[0] + curr_out = mx.sym.zeros_like(inputs[0]) decoder_feed = mx.sym.concat(inputs[i], curr_out, name = 'decoder_feed_concat_%d_' % i) else: decoder_feed = inputs[i] @@ -272,16 +275,29 @@ def infer_decoder_unroll(decoder, encoder_outputs, target_embed, targ_vocab, unr attention_state = mx.sym.zeros_like(encoder_outputs[-1], name='train_dec_unroll_attention_state') enc_len = len(encoder_outputs) + for i in range(unroll_length): - curr_input = inputs[i] - curr_input = mx.sym.expand_dims(curr_input, axis=2, name='train_dec_unroll_expand_dims_%d_' % i) + + if i == 0: +# if args.remove_state_feed: + curr_att_input = mx.sym.zeros_like(states[-1]) +# else: +# curr_att_input = states[-1] + else: + curr_att_input = curr_out + curr_att_input = mx.sym.expand_dims(curr_att_input, axis=2, name='train_dec_unroll_expand_dims_%d_' % i) + +# curr_input = inputs[i] +# curr_input = mx.sym.expand_dims(curr_input, axis=2, name='train_dec_unroll_expand_dims_%d_' % i) + dots = [] concat_dots = None + # loop over all the encoder periods to create weights for weighted state for j in range(enc_len): transposed = mx.sym.expand_dims(encoder_outputs[j], axis=2) transposed = mx.sym.transpose(transposed, axes=(0, 2, 1), name='train_decoder_transpose%d_' % i) - dot = mx.sym.batch_dot(transposed, curr_input, name='train_decoder_batch_dot_%d_%d_' % (i, j)) + dot = mx.sym.batch_dot(transposed, curr_att_input, name='train_decoder_batch_dot_%d_%d_' % (i, j)) dot = mx.sym.exp(dot) dot = mx.sym.reshape(dot, shape=(1, args.batch_size / len(contexts))) dots.append(dot) @@ -298,7 +314,7 @@ def infer_decoder_unroll(decoder, encoder_outputs, target_embed, targ_vocab, unr if i == 0: att_tanh = embed if args.input_feed: - decoder_feed = mx.sym.concat(embed, att_tanh, name = 'decoder_feed_concat_%d_' % i) + decoder_feed = mx.sym.concat(embed, curr_out, name = 'decoder_feed_concat_%d_' % i) else: decoder_feed = inputs[i] dec_out, states = decoder(decoder_feed, states) From f6adf225583f1ea5835146c2b835d9d8dbf9ad36 Mon Sep 17 00:00:00 2001 From: mattiadg Date: Mon, 29 May 2017 11:56:08 +0200 Subject: [PATCH 53/58] Fixed attention input with the decoder output, and input_feeding with the previous attention output --- seq2seq_bucketing.py | 25 ++++++++++--------------- 1 file changed, 10 insertions(+), 15 deletions(-) diff --git a/seq2seq_bucketing.py b/seq2seq_bucketing.py index f1b7289..c5e8d6d 100755 --- a/seq2seq_bucketing.py +++ b/seq2seq_bucketing.py @@ -205,17 +205,19 @@ def train_decoder_unroll(decoder, encoder_outputs, target_embed, targ_vocab, unr # Need to use hidden state from attention model, but as input states = begin_state outputs = [] - attention_state = mx.sym.zeros_like(encoder_outputs[-1], name='train_dec_unroll_attention_state') enc_len = len(encoder_outputs) + attention_state = None for i in range(unroll_length): - if i == 0: -# if args.remove_state_feed: - curr_att_input = mx.sym.zeros_like(states[-1]) -# else: -# curr_att_input = states[-1] + if args.input_feed: + prev_attention_state = attention_state if attention_state else mx.sym.zeros_like(encoder_outputs[-1], name='train_dec_unroll_prev_attention_state') + attention_state = mx.sym.zeros_like(encoder_outputs[-1], name='train_dec_unroll_attention_state') + if args.input_feed: + decoder_feed = mx.sym.concat(inputs[i], prev_attention_state, name = 'decoder_feed_concat_%d_' % i) else: - curr_att_input = curr_out - curr_att_input = mx.sym.expand_dims(curr_att_input, axis=2, name='train_dec_unroll_expand_dims_%d_' % i) + decoder_feed = inputs[i] + dec_out, states = decoder(decoder_feed, states) + + curr_att_input = mx.sym.expand_dims(dec_out, axis=2, name='train_dec_unroll_expand_dims_%d_' % i) dots = [] concat_dots = None @@ -239,13 +241,6 @@ def train_decoder_unroll(decoder, encoder_outputs, target_embed, targ_vocab, unr attention_state += mx.sym.broadcast_mul(curr_dot, encoder_outputs[j], name='train_encoder_acc_attention_%d_%d_' % (i, j)) attention_state = mx.sym.broadcast_div(attention_state, dot_sum) - if args.input_feed: - if i == 0: - curr_out = mx.sym.zeros_like(inputs[0]) - decoder_feed = mx.sym.concat(inputs[i], curr_out, name = 'decoder_feed_concat_%d_' % i) - else: - decoder_feed = inputs[i] - dec_out, states = decoder(decoder_feed, states) if args.attention: # Should this be dec_out or states as the first argument? concatenated = mx.sym.concat(dec_out, attention_state, name = 'train_decoder_concat_%d_' % i) From 6e9d6902c29d4382f0fe5aa7bea6055cd7c2d1d8 Mon Sep 17 00:00:00 2001 From: mattiadg Date: Mon, 29 May 2017 16:13:59 +0200 Subject: [PATCH 54/58] I refactored the code for the attention and I added it also to the infer function --- seq2seq_bucketing.py | 255 +++++++++++++++++++------------------------ 1 file changed, 113 insertions(+), 142 deletions(-) diff --git a/seq2seq_bucketing.py b/seq2seq_bucketing.py index c5e8d6d..8efbb35 100755 --- a/seq2seq_bucketing.py +++ b/seq2seq_bucketing.py @@ -194,153 +194,121 @@ def get_data(layout, infer=False): else: return test_iter, test_iter.src_vocab, test_iter.inv_src_vocab, test_iter.targ_vocab, test_iter.inv_targ_vocab +def attention_step(i, encoder_outputs, decoder_output): + + attention_state = mx.sym.zeros_like(encoder_outputs[-1], name='train_dec_unroll_attention_state') + curr_att_input = mx.sym.expand_dims(decoder_output, axis=2, name='train_dec_unroll_expand_dims_%d_' % i) + enc_len = len(encoder_outputs) + dots = [] + concat_dots = None + # loop over all the encoder periods to create weights for weighted state + for j in range(enc_len): + transposed = mx.sym.expand_dims(encoder_outputs[j], axis=2) + transposed = mx.sym.transpose(transposed, axes=(0, 2, 1), name='train_decoder_transpose%d_' % i) + dot = mx.sym.batch_dot(transposed, curr_att_input, name='train_decoder_batch_dot_%d_%d_' % (i, j)) + dot = mx.sym.exp(dot, name='train_decoder_exp_%d_%d' % (i, j)) + # The batch size shouldn't be an arg here anyway. We should just remove extra dimensions + # and then transpose. + dot = mx.sym.reshape(dot, shape=(1, args.batch_size / len(contexts)), + name='train_decoder_unroll_reshape_%d_%d' % (i, j)) + dots.append(dot) + if not concat_dots: + concat_dots = dot + else: + concat_dots = mx.sym.concat(concat_dots, dot) + dot_sum = mx.sym.sum(concat_dots, axis=1) + for j in range(enc_len): + curr_dot = mx.sym.transpose(dots[j]) + attention_state += mx.sym.broadcast_mul(curr_dot, encoder_outputs[j], + name='train_encoder_acc_attention_%d_%d_' % (i, j)) + + attention_state = mx.sym.broadcast_div(attention_state, dot_sum) + + return attention_state + def train_decoder_unroll(decoder, encoder_outputs, target_embed, targ_vocab, unroll_length, - go_symbol, fc_weight, fc_bias, attention_fc_weight, attention_fc_bias, targ_em_weight, - begin_state=None, layout='TNC', merge_outputs=None): - decoder.reset() - if begin_state is None: - begin_state = decoder.begin_state() - inputs, _ = _normalize_sequence(unroll_length, target_embed, layout, False) - # Need to use hidden state from attention model, but as input - states = begin_state - outputs = [] - enc_len = len(encoder_outputs) - attention_state = None - for i in range(unroll_length): - if args.input_feed: - prev_attention_state = attention_state if attention_state else mx.sym.zeros_like(encoder_outputs[-1], name='train_dec_unroll_prev_attention_state') - attention_state = mx.sym.zeros_like(encoder_outputs[-1], name='train_dec_unroll_attention_state') - if args.input_feed: - decoder_feed = mx.sym.concat(inputs[i], prev_attention_state, name = 'decoder_feed_concat_%d_' % i) - else: - decoder_feed = inputs[i] - dec_out, states = decoder(decoder_feed, states) - - curr_att_input = mx.sym.expand_dims(dec_out, axis=2, name='train_dec_unroll_expand_dims_%d_' % i) - - dots = [] - concat_dots = None - # loop over all the encoder periods to create weights for weighted state - for j in range(enc_len): - transposed = mx.sym.expand_dims(encoder_outputs[j], axis=2) - transposed = mx.sym.transpose(transposed, axes=(0, 2, 1), name='train_decoder_transpose%d_' % i) - dot = mx.sym.batch_dot(transposed, curr_att_input, name='train_decoder_batch_dot_%d_%d_' % (i, j)) - dot = mx.sym.exp(dot, name='train_decoder_exp_%d_%d' % (i, j)) - # The batch size shouldn't be an arg here anyway. We should just remove extra dimensions - # and then transpose. - dot = mx.sym.reshape(dot, shape=(1, args.batch_size / len(contexts)), name='train_decoder_unroll_reshape_%d_%d' % (i, j)) - dots.append(dot) - if not concat_dots: - concat_dots = dot - else: - concat_dots = mx.sym.concat(concat_dots, dot) - dot_sum = mx.sym.sum(concat_dots, axis = 1) - for j in range(enc_len): - curr_dot = mx.sym.transpose(dots[j]) - attention_state += mx.sym.broadcast_mul(curr_dot, encoder_outputs[j], name='train_encoder_acc_attention_%d_%d_' % (i, j)) - - attention_state = mx.sym.broadcast_div(attention_state, dot_sum) - if args.attention: - # Should this be dec_out or states as the first argument? - concatenated = mx.sym.concat(dec_out, attention_state, name = 'train_decoder_concat_%d_' % i) - attention_fc = mx.sym.FullyConnected( - data=concatenated, weight=attention_fc_weight, bias=attention_fc_bias, num_hidden=args.num_hidden, name='attention_fc%d_' % i - ) - curr_out = mx.sym.Activation(data = attention_fc, act_type='tanh', name = 'attention_tanh%d_' % i) - else: - curr_out = dec_out - outputs.append(curr_out) - outputs, _ = _normalize_sequence(unroll_length, outputs, layout, merge_outputs) - return outputs, states - -# This requires a rewrite now that we're adding attention. -# See the pre-attention version for hints. + go_symbol, fc_weight, fc_bias, attention_fc_weight, attention_fc_bias, targ_em_weight, + begin_state=None, layout='TNC', merge_outputs=None): + decoder.reset() + if begin_state is None: + begin_state = decoder.begin_state() + inputs, _ = _normalize_sequence(unroll_length, target_embed, layout, False) + # Need to use hidden state from attention model, but as input + states = begin_state + outputs = [] + + #At the first time step there is no previous attention + attention_state = None + + for i in range(unroll_length): + if args.input_feed: + # Copy previous attention output to concatenate with the embedding input + prev_attention_state = attention_state if attention_state else mx.sym.zeros_like(encoder_outputs[-1], name='train_dec_unroll_prev_attention_state') + decoder_feed = mx.sym.concat(inputs[i], prev_attention_state, name = 'decoder_feed_concat_%d_' % i) + else: + decoder_feed = inputs[i] + dec_out, states = decoder(decoder_feed, states) + + if args.attention: + # The attention receives as input all the encoder outputs and the current decoder output and return the vector + # for this time step + attention_state = attention_step(i, encoder_outputs, dec_out) + # The attention output is combined with the decoder output for computing the next word + concatenated = mx.sym.concat(dec_out, attention_state, name = 'train_decoder_concat_%d_' % i) + attention_fc = mx.sym.FullyConnected( + data=concatenated, weight=attention_fc_weight, bias=attention_fc_bias, num_hidden=args.num_hidden, name='attention_fc%d_' % i + ) + curr_out = mx.sym.Activation(data = attention_fc, act_type='tanh', name = 'attention_tanh%d_' % i) + else: + # We avoid all the attention computation + curr_out = dec_out + outputs.append(curr_out) + outputs, _ = _normalize_sequence(unroll_length, outputs, layout, merge_outputs) + return outputs, states + + def infer_decoder_unroll(decoder, encoder_outputs, target_embed, targ_vocab, unroll_length, go_symbol, fc_weight, fc_bias, attention_fc_weight, attention_fc_bias, targ_em_weight, begin_state=None, layout='TNC', merge_outputs=None): - decoder.reset() - if begin_state is None: - begin_state = decoder.begin_state() - inputs, _ = _normalize_sequence(unroll_length, target_embed, layout, False) - # Need to use hidden state from attention model, but as input - states = begin_state - outputs = [] - embed = inputs[0] - - attention_state = mx.sym.zeros_like(encoder_outputs[-1], name='train_dec_unroll_attention_state') - enc_len = len(encoder_outputs) - - for i in range(unroll_length): - - if i == 0: -# if args.remove_state_feed: - curr_att_input = mx.sym.zeros_like(states[-1]) -# else: -# curr_att_input = states[-1] - else: - curr_att_input = curr_out - curr_att_input = mx.sym.expand_dims(curr_att_input, axis=2, name='train_dec_unroll_expand_dims_%d_' % i) - -# curr_input = inputs[i] -# curr_input = mx.sym.expand_dims(curr_input, axis=2, name='train_dec_unroll_expand_dims_%d_' % i) - - dots = [] - concat_dots = None - - # loop over all the encoder periods to create weights for weighted state - for j in range(enc_len): - transposed = mx.sym.expand_dims(encoder_outputs[j], axis=2) - transposed = mx.sym.transpose(transposed, axes=(0, 2, 1), name='train_decoder_transpose%d_' % i) - dot = mx.sym.batch_dot(transposed, curr_att_input, name='train_decoder_batch_dot_%d_%d_' % (i, j)) - dot = mx.sym.exp(dot) - dot = mx.sym.reshape(dot, shape=(1, args.batch_size / len(contexts))) - dots.append(dot) - if not concat_dots: - concat_dots = dot - else: - concat_dots = mx.sym.concat(concat_dots, dot) - dot_sum = mx.sym.sum(concat_dots, axis = 1) - for j in range(enc_len): - curr_dot = mx.sym.transpose(dots[j]) - attention_state += mx.sym.broadcast_mul(curr_dot, encoder_outputs[j], name='train_encoder_acc_attention_%d_%d_' % (i, j)) - - attention_state = mx.sym.broadcast_div(attention_state, dot_sum) - if i == 0: - att_tanh = embed - if args.input_feed: - decoder_feed = mx.sym.concat(embed, curr_out, name = 'decoder_feed_concat_%d_' % i) - else: - decoder_feed = inputs[i] - dec_out, states = decoder(decoder_feed, states) - # Should this be dec_out or states as the first argument? - if args.attention: - concatenated = mx.sym.concat(dec_out, attention_state, name = 'train_decoder_concat_%d_' % i) - attention_fc = mx.sym.FullyConnected( - data=concatenated, weight=attention_fc_weight, bias=attention_fc_bias, num_hidden=args.num_hidden, name='attention_fc%d_' % i - ) - curr_out = mx.sym.Activation(data = attention_fc, act_type='tanh', name = 'attention_tanh%d_' % i) - else: - curr_out = dec_out - outputs.append(curr_out) - fc = mx.sym.FullyConnected(data=curr_out, weight=fc_weight, bias=fc_bias, num_hidden=len(targ_vocab), name='decoder_fc%d_'%i) - am = mx.sym.argmax(data=fc, axis=1) - embed = mx.sym.Embedding(data=am, weight=targ_em_weight, input_dim=len(targ_vocab), output_dim=args.num_embed, name='decoder_embed%d_'%i) - - outputs, _ = _normalize_sequence(unroll_length, outputs, layout, merge_outputs) - return outputs, states - -# for i in range(0, unroll_length): -# output, states = decoder(embed, states) -# outputs.append(output) -# fc = mx.sym.FullyConnected(data=output, weight=fc_weight, bias=fc_bias, num_hidden=len(targ_vocab), name='decoder_fc%d_'%i) -# am = mx.sym.argmax(data=fc, axis=1) -# embed = mx.sym.Embedding(data=am, weight=targ_em_weight, input_dim=len(targ_vocab), -# output_dim=args.num_embed, name='decoder_embed%d_'%i) -# -# outputs, _ = _normalize_sequence(unroll_length, outputs, layout, merge_outputs) -# -# return outputs, states + decoder.reset() + if begin_state is None: + begin_state = decoder.begin_state() + inputs, _ = _normalize_sequence(unroll_length, target_embed, layout, False) + # Need to use hidden state from attention model, but as input + states = begin_state + outputs = [] + embed = inputs[0] + + attention_state = None + + for i in range(unroll_length): + if args.input_feed: + # Copy previous attention output to concatenate with the embedding input + prev_attention_state = attention_state if attention_state else mx.sym.zeros_like(encoder_outputs[-1], + name='train_dec_unroll_prev_attention_state') + decoder_feed = mx.sym.concat(embed, prev_attention_state, name='decoder_feed_concat_%d_' % i) + else: + decoder_feed = embed + dec_out, states = decoder(decoder_feed, states) + + # Should this be dec_out or states as the first argument? + if args.attention: + attention_state = attention_step(i, encoder_outputs, dec_out) + concatenated = mx.sym.concat(dec_out, attention_state, name = 'train_decoder_concat_%d_' % i) + attention_fc = mx.sym.FullyConnected( + data=concatenated, weight=attention_fc_weight, bias=attention_fc_bias, num_hidden=args.num_hidden, name='attention_fc%d_' % i + ) + curr_out = mx.sym.Activation(data = attention_fc, act_type='tanh', name = 'attention_tanh%d_' % i) + else: + curr_out = dec_out + outputs.append(curr_out) + fc = mx.sym.FullyConnected(data=curr_out, weight=fc_weight, bias=fc_bias, num_hidden=len(targ_vocab), name='decoder_fc%d_'%i) + am = mx.sym.argmax(data=fc, axis=1) + embed = mx.sym.Embedding(data=am, weight=targ_em_weight, input_dim=len(targ_vocab), output_dim=args.num_embed, name='decoder_embed%d_'%i) + + outputs, _ = _normalize_sequence(unroll_length, outputs, layout, merge_outputs) + return outputs, states def train(args): @@ -715,6 +683,9 @@ def sym_gen(seq_len): args = parser.parse_args() + if args.input_feed: + assert (args.attention == True), "--input-feed is legal only with --attention!" + # set random seeds for Python, NumPy and MxNet import random seed = args.seed From c7cf714d082914509573d231a97bce8383a13dfa Mon Sep 17 00:00:00 2001 From: mattiadg Date: Mon, 29 May 2017 18:05:52 +0200 Subject: [PATCH 55/58] Added a working bidirectional encoder --- seq2seq_bucketing.py | 42 ++++++++++++++++++++++++++---------------- 1 file changed, 26 insertions(+), 16 deletions(-) diff --git a/seq2seq_bucketing.py b/seq2seq_bucketing.py index 8efbb35..3a9e5bf 100755 --- a/seq2seq_bucketing.py +++ b/seq2seq_bucketing.py @@ -6,7 +6,7 @@ import math import nltk -from mxnet.rnn import LSTMCell, SequentialRNNCell, FusedRNNCell +from mxnet.rnn import LSTMCell, SequentialRNNCell, FusedRNNCell, BidirectionalCell #from rnn_cell import LSTMCell, SequentialRNNCell from itertools import takewhile, dropwhile from operator import itemgetter @@ -35,7 +35,7 @@ help='hidden layer size') parser.add_argument('--num-embed', type=int, default=200, help='embedding layer size') -parser.add_argument('--bidirectional', type=bool, default=False, +parser.add_argument('--bidirectional', action='store_true', help='whether to use bidirectional layers') parser.add_argument('--gpus', type=str, help='list of gpus to run, e.g. 0 or 0,2,5. empty means using cpu. ' \ @@ -331,10 +331,18 @@ def train(args): mode='lstm', prefix='lstm_encoder', bidirectional=args.bidirectional, get_next_state=True)) else: for i in range(args.num_layers): - encoder.add(LSTMCell(args.num_hidden, prefix='rnn_encoder%d_' % i)) - if i < args.num_layers - 1 and args.dropout > 0.0: - encoder.add(mx.rnn.DropoutCell(args.dropout, prefix='rnn_encoder%d_' % i)) -# encoder.add(AttentionEncoderCell()) + if args.bidirectional: + encoder.add( + BidirectionalCell( + LSTMCell(args.num_hidden // 2, prefix='rnn_encoder_f%d_' % i), + LSTMCell(args.num_hidden // 2, prefix='rnn_encoder_b%d_' % i))) + if i < args.num_layers - 1 and args.dropout > 0.0: + encoder.add(mx.rnn.DropoutCell(args.dropout, prefix='rnn_encoder%d_' % i)) + else: + encoder.add( + LSTMCell(args.num_hidden, prefix='rnn_encoder%d_' % i)) + if i < args.num_layers - 1 and args.dropout > 0.0: + encoder.add(mx.rnn.DropoutCell(args.dropout, prefix='rnn_encoder%d_' % i)) decoder = mx.rnn.SequentialRNNCell() @@ -346,7 +354,6 @@ def train(args): decoder.add(LSTMCell(args.num_hidden, prefix=('rnn_decoder%d_' % i))) if i < args.num_layers - 1 and args.dropout > 0.0: decoder.add(mx.rnn.DropoutCell(args.dropout, prefix='rnn_decoder%d_' % i)) -# decoder.add(DotAttentionCell()) def sym_gen(seq_len): src_data = mx.sym.Variable('src_data') @@ -380,8 +387,6 @@ def sym_gen(seq_len): attention_fc_weight, attention_fc_bias, targ_em_weight, begin_state=encoder_states, layout='TNC', merge_outputs=True) -# outputs, _ = decoder.unroll(dec_seq_len, targ_embed, begin_state=encoder_states, layout=layout, merge_outputs=True) - # NEW rs = mx.sym.Reshape(outputs, shape=(-1, args.num_hidden), name='sym_gen_reshape1') fc = mx.sym.FullyConnected(data=rs, weight=fc_weight, bias=fc_bias, num_hidden=len(targ_vocab), name='sym_gen_fc') @@ -513,11 +518,18 @@ def infer(args): encoder = SequentialRNNCell() for i in range(args.num_layers): - encoder.add(LSTMCell(args.num_hidden, prefix='rnn_encoder%d_' % i)) - if i < args.num_layers - 1 and args.dropout > 0.0: - encoder.add(mx.rnn.DropoutCell(args.dropout, prefix='rnn_encoder%d_' % i)) - -# encoder.add(AttentionEncoderCell()) + if args.bidirectional: + encoder.add( + BidirectionalCell( + LSTMCell(args.num_hidden // 2, prefix='rnn_encoder_f%d_' % i), + LSTMCell(args.num_hidden // 2, prefix='rnn_encoder_b%d_' % i))) + if i < args.num_layers - 1 and args.dropout > 0.0: + encoder.add(mx.rnn.DropoutCell(args.dropout, prefix='rnn_encoder%d_' % i)) + else: + encoder.add( + LSTMCell(args.num_hidden, prefix='rnn_encoder%d_' % i)) + if i < args.num_layers - 1 and args.dropout > 0.0: + encoder.add(mx.rnn.DropoutCell(args.dropout, prefix='rnn_encoder%d_' % i)) if args.use_cudnn_cells: decoder = mx.rnn.FusedRNNCell(args.num_hidden, num_layers=args.num_layers, @@ -531,8 +543,6 @@ def infer(args): if i < args.num_layers - 1 and args.dropout > 0.0: decoder.add(mx.rnn.DropoutCell(args.dropout, prefix='rnn_decoder%d_' % i)) -# decoder.add(DotAttentionCell()) - def sym_gen(seq_len): src_data = mx.sym.Variable('src_data') targ_data = mx.sym.Variable('targ_data') From c1c11c14db3b615c0c83894c2885f60e18283041 Mon Sep 17 00:00:00 2001 From: Marek Kolodziej Date: Wed, 7 Jun 2017 20:27:16 +0000 Subject: [PATCH 56/58] Attention based on previous state --- model_train_gpu.sh | 2 +- seq2seq_bucketing.py | 5 ++++- 2 files changed, 5 insertions(+), 2 deletions(-) diff --git a/model_train_gpu.sh b/model_train_gpu.sh index 20a81ca..6449087 100755 --- a/model_train_gpu.sh +++ b/model_train_gpu.sh @@ -1,6 +1,6 @@ #!/bin/bash python seq2seq_bucketing.py --num-hidden 512 --num-embed 512 --num-layers 2 --gpus 0,1 --batch-size 256 \ - --optimizer adagrad --lr 0.01 --disp-batches 1 --num-epochs 10 \ + --optimizer adagrad --lr 0.01 --disp-batches 10 --num-epochs 10 \ --dropout 0.3 --seed 1234 --model-prefix trained_model --model-prefix trained_model --attention --input-feed --remove-state-feed # --attention # --input-feed --remove-state-feed diff --git a/seq2seq_bucketing.py b/seq2seq_bucketing.py index 3a9e5bf..243833a 100755 --- a/seq2seq_bucketing.py +++ b/seq2seq_bucketing.py @@ -240,6 +240,7 @@ def train_decoder_unroll(decoder, encoder_outputs, target_embed, targ_vocab, unr #At the first time step there is no previous attention attention_state = None + dec_out = None for i in range(unroll_length): if args.input_feed: @@ -248,12 +249,14 @@ def train_decoder_unroll(decoder, encoder_outputs, target_embed, targ_vocab, unr decoder_feed = mx.sym.concat(inputs[i], prev_attention_state, name = 'decoder_feed_concat_%d_' % i) else: decoder_feed = inputs[i] + + prev_dec_out = dec_out if dec_out else mx.sym.zeros_like(encoder_outputs[-1], name='train_dec_unroll_prev_dec_out') # begin_state dec_out, states = decoder(decoder_feed, states) if args.attention: # The attention receives as input all the encoder outputs and the current decoder output and return the vector # for this time step - attention_state = attention_step(i, encoder_outputs, dec_out) + attention_state = attention_step(i, encoder_outputs, prev_dec_out) # The attention output is combined with the decoder output for computing the next word concatenated = mx.sym.concat(dec_out, attention_state, name = 'train_decoder_concat_%d_' % i) attention_fc = mx.sym.FullyConnected( From 8929b81bce0ae8e6d89c6ec216eafb4892de7527 Mon Sep 17 00:00:00 2001 From: Marek Kolodziej Date: Mon, 7 Aug 2017 14:37:51 -0700 Subject: [PATCH 57/58] Update README.md --- README.md | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index f0c92b6..bbf30ab 100644 --- a/README.md +++ b/README.md @@ -1,11 +1,10 @@ -OpenNMT seq2seq model -========================== +LSTM encoder-decoder seq2seq model +================================== -This project is an implementation of the [OpenNMT sequence-to-sequence model](http://opennmt.net/Models/) in MxNet. The OpenNMT model is based on: +This project is an implementation of a simple encoder-decoder seq2seq model in MxNet. The OpenNMT model is based on: - a stacked LSTM encoder - a stacked LSTM decoder -- an attention model The reference model configuration is: From d53289eeea575650bd3693c5cd3afb63095fb9ca Mon Sep 17 00:00:00 2001 From: xuerenlv Date: Fri, 11 Aug 2017 17:28:30 +0800 Subject: [PATCH 58/58] fix bug: bidirectional --- seq2seq_bucketing.py | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/seq2seq_bucketing.py b/seq2seq_bucketing.py index 243833a..e7bbd1a 100755 --- a/seq2seq_bucketing.py +++ b/seq2seq_bucketing.py @@ -376,6 +376,10 @@ def sym_gen(seq_len): layout = 'TNC' encoder_outputs, encoder_states = encoder.unroll(enc_seq_len, inputs=src_embed, layout=layout) + if args.bidirectional: + encoder_states = [mx.sym.concat(encoder_states[0][0], encoder_states[0][1]), + mx.sym.concat(encoder_states[0][1], encoder_states[1][1])] + if args.remove_state_feed: encoder_states = None @@ -565,6 +569,10 @@ def sym_gen(seq_len): layout = 'TNC' encoder_outputs, encoder_states = encoder.unroll(enc_seq_len, inputs=src_embed, layout=layout) + if args.bidirectional: + encoder_states = [mx.sym.concat(encoder_states[0][0], encoder_states[0][1]), + mx.sym.concat(encoder_states[0][1], encoder_states[1][1])] + # This should be based on EOS or max seq len for inference, but here we unroll to the target length # TODO: fix symbol # outputs, _ = decoder.unroll(dec_seq_len, targ_embed, begin_state=states, layout=layout, merge_outputs=True)