Convert TensorFlow GNMT Model¶
This tutorial explains how to convert Google* Neural Machine Translation (GNMT) model to the Intermediate Representation (IR).
On GitHub*, you can find several public versions of TensorFlow* GNMT model implementation. This tutorial explains how to convert the GNMT model from the TensorFlow* Neural Machine Translation (NMT) repository to the IR.
Create a Patch File¶
Before converting the model, you need to create a patch file for the repository. The patch modifies the framework code by adding a special command-line argument to the framework options that enables inference graph dumping:
Go to a writable directory and create a
GNMT_inference.patch
file.Copy the following diff code to the file:
diff --git a/nmt/inference.py b/nmt/inference.py index 2cbef07..e185490 100644 --- a/nmt/inference.py +++ b/nmt/inference.py @@ -17,9 +17,11 @@ from __future__ import print_function import codecs +import os import time import tensorflow as tf +from tensorflow.python.framework import graph_io from . import attention_model from . import gnmt_model @@ -105,6 +107,29 @@ def start_sess_and_load_model(infer_model, ckpt_path): return sess, loaded_infer_model +def inference_dump_graph(ckpt_path, path_to_dump, hparams, scope=None): + model_creator = get_model_creator(hparams) + infer_model = model_helper.create_infer_model(model_creator, hparams, scope) + sess = tf.Session( + graph=infer_model.graph, config=utils.get_config_proto()) + with infer_model.graph.as_default(): + loaded_infer_model = model_helper.load_model( + infer_model.model, ckpt_path, sess, "infer") + utils.print_out("Dumping inference graph to {}".format(path_to_dump)) + loaded_infer_model.saver.save( + sess, + os.path.join(path_to_dump + 'inference_GNMT_graph') + ) + utils.print_out("Dumping done!") + + output_node_name = 'index_to_string_Lookup' + utils.print_out("Freezing GNMT graph with output node {}...".format(output_node_name)) + frozen = tf.graph_util.convert_variables_to_constants(sess, sess.graph_def, + [output_node_name]) + graph_io.write_graph(frozen, '.', os.path.join(path_to_dump, 'frozen_GNMT_inference_graph.pb'), as_text=False) + utils.print_out("Freezing done. Freezed model frozen_GNMT_inference_graph.pb saved to {}".format(path_to_dump)) + + def inference(ckpt_path, inference_input_file, inference_output_file, diff --git a/nmt/nmt.py b/nmt/nmt.py index f5823d8..a733748 100644 --- a/nmt/nmt.py +++ b/nmt/nmt.py @@ -310,6 +310,13 @@ def add_arguments(parser): parser.add_argument("--num_intra_threads", type=int, default=0, help="number of intra_op_parallelism_threads") + # Special argument for inference model dumping without inference + parser.add_argument("--dump_inference_model", type="bool", nargs="?", + const=True, default=False, + help="Argument for dump inference graph for specified trained ckpt") + + parser.add_argument("--path_to_dump", type=str, default="", + help="Path to dump inference graph.") def create_hparams(flags): """Create training hparams.""" @@ -396,6 +403,9 @@ def create_hparams(flags): language_model=flags.language_model, num_intra_threads=flags.num_intra_threads, num_inter_threads=flags.num_inter_threads, + + dump_inference_model=flags.dump_inference_model, + path_to_dump=flags.path_to_dump, ) @@ -613,7 +623,7 @@ def create_or_load_hparams( return hparams -def run_main(flags, default_hparams, train_fn, inference_fn, target_session=""): +def run_main(flags, default_hparams, train_fn, inference_fn, inference_dump, target_session=""): """Run main.""" # Job jobid = flags.jobid @@ -653,8 +663,26 @@ def run_main(flags, default_hparams, train_fn, inference_fn, target_session=""): out_dir, default_hparams, flags.hparams_path, save_hparams=(jobid == 0)) - ## Train / Decode - if flags.inference_input_file: + # Dumping inference model + if flags.dump_inference_model: + # Inference indices + hparams.inference_indices = None + if flags.inference_list: + (hparams.inference_indices) = ( + [int(token) for token in flags.inference_list.split(",")]) + + # Ckpt + ckpt = flags.ckpt + if not ckpt: + ckpt = tf.train.latest_checkpoint(out_dir) + + # Path to dump graph + assert flags.path_to_dump != "", "Please, specify path_to_dump model." + path_to_dump = flags.path_to_dump + if not tf.gfile.Exists(path_to_dump): tf.gfile.MakeDirs(path_to_dump) + + inference_dump(ckpt, path_to_dump, hparams) + elif flags.inference_input_file: # Inference output directory trans_file = flags.inference_output_file assert trans_file @@ -693,7 +721,8 @@ def main(unused_argv): default_hparams = create_hparams(FLAGS) train_fn = train.train inference_fn = inference.inference - run_main(FLAGS, default_hparams, train_fn, inference_fn) + inference_dump = inference.inference_dump_graph + run_main(FLAGS, default_hparams, train_fn, inference_fn, inference_dump) if __name__ == "__main__":
Save and close the file.
Convert GNMT Model to IR¶
Note
Please, use TensorFlow version 1.13 or lower.
Step 1. Clone the GitHub repository and check out the commit:
Clone the NMT reposirory:
git clone https://github.com/tensorflow/nmt.git
Check out the necessary commit:
git checkout b278487980832417ad8ac701c672b5c3dc7fa553
Step 2. Get a trained model. You have two options:
Train the model with the GNMT
wmt16_gnmt_4_layer.json
orwmt16_gnmt_8_layer.json
configuration file using the NMT framework.Do not use the pre-trained checkpoints provided in the NMT repository, as they are outdated and can be incompatible with the current repository version.
This tutorial assumes the use of the trained GNMT model from wmt16_gnmt_4_layer.json
config, German to English translation.
Step 3. Create an inference graph:
The OpenVINO assumes that a model is used for inference only. Hence, before converting the model into the IR, you need to transform the training graph into the inference graph. For the GNMT model, the training graph and the inference graph have different decoders: the training graph uses a greedy search decoding algorithm, while the inference graph uses a beam search decoding algorithm.
Apply the
GNMT_inference.patch
patch to the repository. Refer to the Create a Patch File instructions if you do not have it:git apply /path/to/patch/GNMT_inference.patch
Run the NMT framework to dump the inference model:
python -m nmt.nmt
--src=de
--tgt=en
--ckpt=/path/to/ckpt/translate.ckpt
--hparams_path=/path/to/repository/nmt/nmt/standard_hparams/wmt16_gnmt_4_layer.json
--vocab_prefix=/path/to/vocab/vocab.bpe.32000
--out_dir=""
--dump_inference_model
--infer_mode beam_search
--path_to_dump /path/to/dump/model/
If you use different checkpoints, use the corresponding values for the src
, tgt
, ckpt
, hparams_path
, and vocab_prefix
parameters. Inference checkpoint inference_GNMT_graph
and frozen inference graph frozen_GNMT_inference_graph.pb
will appear in the /path/to/dump/model/
folder.
To generate vocab.bpe.32000
, execute the nmt/scripts/wmt16_en_de.sh
script. If you face an issue of a size mismatch between the checkpoint graph’s embedding layer and vocabulary (both src and target), we recommend you to add the following code to the nmt.py
file to the extend_hparams
function after the line 508 (after initialization of the src_vocab_size
and tgt_vocab_size
variables):
src_vocab_size -= 1
tgt_vocab_size -= 1
Step 4. Convert the model to the IR:
mo
--input_model /path/to/dump/model/frozen_GNMT_inference_graph.pb
--input "IteratorGetNext:1{i32}[1],IteratorGetNext:0{i32}[1 50],dynamic_seq2seq/hash_table_Lookup_1:0[1]->[2],dynamic_seq2seq/hash_table_Lookup:0[1]->[1]"
--output dynamic_seq2seq/decoder/decoder/GatherTree
--output_dir /path/to/output/IR/
Input and output cutting with the --input
and --output
options is required since OpenVINO does not support IteratorGetNext
and LookupTableFindV2
operations.
Input cutting:
IteratorGetNext
operation iterates over a dataset. It is cut by output ports: port 0 contains data tensor with shape[batch_size, max_sequence_length]
, port 1 containssequence_length
for every batch with shape[batch_size]
.LookupTableFindV2
operations (dynamic_seq2seq/hash_table_Lookup_1
anddynamic_seq2seq/hash_table_Lookup
nodes in the graph) are cut with constant values).
Output cutting:
LookupTableFindV2
operation is cut from the output and thedynamic_seq2seq/decoder/decoder/GatherTree
node is treated as a new exit point.
For more information about model cutting, refer to Cutting Off Parts of a Model.
How to Use GNMT Model¶
Note
This step assumes you have converted a model to the Intermediate Representation.
Inputs of the model:
IteratorGetNext/placeholder_out_port_0
input with shape[batch_size, max_sequence_length]
containsbatch_size
decoded input sentences. Every sentence is decoded the same way as indices of sentence elements in vocabulary and padded with index ofeos
(end of sentence symbol). If the length of the sentence is less thanmax_sequence_length
, remaining elements are filled with index ofeos
token.IteratorGetNext/placeholder_out_port_1
input with shape[batch_size]
contains sequence lengths for every sentence from the first input. For example, ifmax_sequence_length = 50
,batch_size = 1
and the sentence has only 30 elements, then the input tensor forIteratorGetNext/placeholder_out_port_1
should be[30]
.
Outputs of the model:
dynamic_seq2seq/decoder/decoder/GatherTree
tensor with shape[max_sequence_length \* 2, batch, beam_size]
, that containsbeam_size
best translations for every sentence from input (also decoded as indices of words in vocabulary).Note
Shape of this tensor in TensorFlow* can be different: instead of
max_sequence_length \* 2
, it can be any value less than that, because OpenVINO does not support dynamic shapes of outputs, while TensorFlow can stop decoding iterations wheneos
symbol is generated.*
With benchmark app:
benchmark_app -m <path to the generated GNMT IR> -d CPU
With OpenVINO Runtime Python API:
Note
Before running the example, insert a path to your GNMT .xml
and .bin
files into MODEL_PATH
and WEIGHTS_PATH
, and fill input_data_tensor
and seq_lengths
tensors according to your input data.
from openvino.inference_engine import IENetwork, IECore
MODEL_PATH = '/path/to/IR/frozen_GNMT_inference_graph.xml'
WEIGHTS_PATH = '/path/to/IR/frozen_GNMT_inference_graph.bin'
# Creating network
net = IENetwork(
model=MODEL_PATH,
weights=WEIGHTS_PATH)
# Creating input data
input_data = {'IteratorGetNext/placeholder_out_port_0': input_data_tensor,
'IteratorGetNext/placeholder_out_port_1': seq_lengths}
# Creating plugin and loading extensions
ie = IECore()
ie.add_extension(extension_path="libcpu_extension.so", device_name="CPU")
# Loading network
exec_net = ie.load_network(network=net, device_name="CPU")
# Run inference
result_ie = exec_net.infer(input_data)
For more information about Python API, refer to OpenVINO Runtime Python API.