def _create_params_savable(self, params, model):
"""Create a RNNParamsSaveable for the weight and bias parameters.
Args:
params: a Variable for weight and bias parameters.
model: a CudnnRNN model.
"""
params_saveable = cudnn_rnn_ops.RNNParamsSaveable(
model.params_to_canonical, model.canonical_to_params, params)
ops.add_to_collection(ops.GraphKeys.SAVEABLE_OBJECTS, params_saveable)
def _CreateParamsSavable(params,
model,
base_variable_scope="rnn",
name="params_canonical"):
"""Create a RNNParamsSaveable for the weight and bias parameters.
Args:
params: a Variable for weight and bias parameters.
model: a CudnnRNN model.
base_variable_scope: a string, prefix of names of saved variables.
name: a string, name of the RNNParamsSaveable object.
"""
params_saveable = cudnn_rnn_ops.RNNParamsSaveable(
model,
model.params_to_canonical,
model.canonical_to_params, [params],
base_variable_scope=base_variable_scope,
name=name)
ops.add_to_collection(ops.GraphKeys.SAVEABLE_OBJECTS, params_saveable)
def convert(model_dir, output_dir, best_weights=False):
print("Load model")
md = ModelDir(model_dir)
model = md.get_model()
dim = model.embed_mapper.layers[1].n_units
global_step = tf.get_variable('global_step',
shape=[],
dtype='int32',
initializer=tf.constant_initializer(0),
trainable=False)
print("Setting up cudnn version")
#global_step = tf.get_variable('global_step', shape=[], dtype='int32', trainable=False)
sess = tf.Session()
sess.run(global_step.assign(0))
with sess.as_default():
model.set_input_spec(
ParagraphAndQuestionSpec(1, None, None, 14), {"the"},
ResourceLoader(lambda a, b: {"the": np.zeros(300, np.float32)}))
print("Buiding graph")
pred = model.get_prediction()
test_questions = ParagraphAndQuestion(
["Harry", "Potter", "was", "written", "by", "JK"],
["Who", "wrote", "Harry", "Potter", "?"], None, "test_questions")
print("Load vars")
md.restore_checkpoint(sess)
print("Restore finished")
feed = model.encode([test_questions], False)
cuddn_out = sess.run([pred.start_logits, pred.end_logits], feed_dict=feed)
print("Done, copying files...")
if not exists(output_dir):
mkdir(output_dir)
for file in listdir(model_dir):
if isfile(file) and file != "model.npy":
copyfile(join(model_dir, file), join(output_dir, file))
print("Done, mapping tensors...")
to_save = []
to_init = []
for x in tf.trainable_variables():
if x.name.endswith("/gru_parameters:0"):
key = x.name[:-len("/gru_parameters:0")]
fw_params = x
if "map_embed" in x.name:
c = cudnn_rnn_ops.CudnnGRU(1, dim, 400)
elif "chained-out" in x.name:
c = cudnn_rnn_ops.CudnnGRU(1, dim, dim * 4)
else:
c = cudnn_rnn_ops.CudnnGRU(1, dim, dim * 2)
params_saveable = cudnn_rnn_ops.RNNParamsSaveable(
c, c.params_to_canonical, c.canonical_to_params, [fw_params],
key)
for spec in params_saveable.specs:
if spec.name.endswith("bias_cudnn 0") or \
spec.name.endswith("bias_cudnn 1"):
# ??? What do these even do?
continue
name = spec.name.split("/")
name.remove("cell_0")
if "forward" in name:
ix = name.index("forward")
name.insert(ix + 2, "fw")
else:
ix = name.index("backward")
name.insert(ix + 2, "bw")
del name[ix]
ix = name.index("multi_rnn_cell")
name[ix] = "bidirectional_rnn"
name = "/".join(name)
v = tf.Variable(sess.run(spec.tensor), name=name)
to_init.append(v)
to_save.append(v)
else:
to_save.append(x)
other = [
x for x in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
if x not in tf.trainable_variables()
]
print(other)
sess.run(tf.initialize_variables(to_init))
saver = tf.train.Saver(to_save + other)
save_dir = join(output_dir, "save")
if not exists(save_dir):
mkdir(save_dir)
saver.save(sess, join(save_dir, "checkpoint"), sess.run(global_step))
sess.close()
tf.reset_default_graph()
print("Updating model...")
model.embed_mapper.layers = [
model.embed_mapper.layers[0],
BiRecurrentMapper(CompatGruCellSpec(dim))
]
model.match_encoder.layers = list(model.match_encoder.layers)
other = model.match_encoder.layers[1].other
other.layers = list(other.layers)
other.layers[1] = BiRecurrentMapper(CompatGruCellSpec(dim))
pred = model.predictor.predictor
pred.first_layer = BiRecurrentMapper(CompatGruCellSpec(dim))
pred.second_layer = BiRecurrentMapper(CompatGruCellSpec(dim))
with open(join(output_dir, "model.pkl"), "wb") as f:
pickle.dump(model, f)
print("Testing...")
with open(join(output_dir, "model.pkl"), "rb") as f:
model = pickle.load(f)
sess = tf.Session()
model.set_input_spec(
ParagraphAndQuestionSpec(1, None, None, 14), {"the"},
ResourceLoader(lambda a, b: {"the": np.zeros(300, np.float32)}))
pred = model.get_prediction()
print("Rebuilding")
saver = tf.train.Saver()
saver.restore(sess, tf.train.latest_checkpoint(save_dir))
feed = model.encode([test_questions], False)
cpu_out = sess.run([pred.start_logits, pred.end_logits], feed_dict=feed)
print("These should be close:")
print([np.allclose(a, b) for a, b in zip(cpu_out, cuddn_out)])
print(cpu_out)
print(cuddn_out)
