def testLSTMBlockCell(self):
with self.session(use_gpu=True, graph=ops.Graph()) as sess:
with variable_scope.variable_scope(
"root", initializer=init_ops.constant_initializer(0.5)):
x = array_ops.zeros([1, 2])
m0 = array_ops.zeros([1, 2])
m1 = array_ops.zeros([1, 2])
m2 = array_ops.zeros([1, 2])
m3 = array_ops.zeros([1, 2])
g, ((out_m0, out_m1),
(out_m2, out_m3)) = rnn_cell.MultiRNNCell(
[lstm_ops.LSTMBlockCell(2) for _ in range(2)],
state_is_tuple=True)(x, ((m0, m1), (m2, m3)))
sess.run([variables.global_variables_initializer()])
res = sess.run(
[g, out_m0, out_m1, out_m2, out_m3], {
x.name: np.array([[1., 1.]]),
m0.name: 0.1 * np.ones([1, 2]),
m1.name: 0.1 * np.ones([1, 2]),
m2.name: 0.1 * np.ones([1, 2]),
m3.name: 0.1 * np.ones([1, 2])
})
self.assertEqual(len(res), 5)
self.assertAllClose(res[0], [[0.24024698, 0.24024698]])
# These numbers are from testBasicLSTMCell and only test c/h.
self.assertAllClose(res[1], [[0.68967271, 0.68967271]])
self.assertAllClose(res[2], [[0.44848421, 0.44848421]])
self.assertAllClose(res[3], [[0.39897051, 0.39897051]])
self.assertAllClose(res[4], [[0.24024698, 0.24024698]])
def benchmarkTfRNNLSTMBlockCellTraining(self):
test_configs = self._GetTestConfig()
for config_name, config in test_configs.items():
num_layers = config["num_layers"]
num_units = config["num_units"]
batch_size = config["batch_size"]
seq_length = config["seq_length"]
with ops.Graph().as_default(), ops.device("/device:GPU:0"):
inputs = array_ops.zeros([batch_size, seq_length, num_units],
dtypes.float32)
multi_cell = contrib_rnn.MultiRNNCell([
lstm_ops.LSTMBlockCell(num_units)
for _ in range(num_layers)
])
outputs, final_state = rnn.dynamic_rnn(multi_cell,
inputs,
dtype=dtypes.float32)
trainable_variables = ops.get_collection(
ops.GraphKeys.TRAINABLE_VARIABLES)
gradients = gradients_impl.gradients([outputs, final_state],
trainable_variables)
training_op = control_flow_ops.group(*gradients)
self._BenchmarkOp(
training_op, "tf_rnn_lstm_block_cell %s %s" %
(config_name, self._GetConfigDesc(config)))
def testLSTMBasicToBlockCell(self):
with self.session(use_gpu=True) as sess:
x = array_ops.zeros([1, 2])
x_values = np.random.randn(1, 2)
m0_val = 0.1 * np.ones([1, 2])
m1_val = -0.1 * np.ones([1, 2])
m2_val = -0.2 * np.ones([1, 2])
m3_val = 0.2 * np.ones([1, 2])
initializer = init_ops.random_uniform_initializer(-0.01,
0.01,
seed=19890212)
with variable_scope.variable_scope("basic",
initializer=initializer):
m0 = array_ops.zeros([1, 2])
m1 = array_ops.zeros([1, 2])
m2 = array_ops.zeros([1, 2])
m3 = array_ops.zeros([1, 2])
g, ((out_m0, out_m1),
(out_m2, out_m3)) = rnn_cell.MultiRNNCell(
[
rnn_cell.BasicLSTMCell(2, state_is_tuple=True)
for _ in range(2)
],
state_is_tuple=True)(x, ((m0, m1), (m2, m3)))
sess.run([variables.global_variables_initializer()])
basic_res = sess.run(
[g, out_m0, out_m1, out_m2, out_m3], {
x.name: x_values,
m0.name: m0_val,
m1.name: m1_val,
m2.name: m2_val,
m3.name: m3_val
})
with variable_scope.variable_scope("block",
initializer=initializer):
m0 = array_ops.zeros([1, 2])
m1 = array_ops.zeros([1, 2])
m2 = array_ops.zeros([1, 2])
m3 = array_ops.zeros([1, 2])
g, ((out_m0, out_m1),
(out_m2, out_m3)) = rnn_cell.MultiRNNCell(
[lstm_ops.LSTMBlockCell(2) for _ in range(2)],
state_is_tuple=True)(x, ((m0, m1), (m2, m3)))
sess.run([variables.global_variables_initializer()])
block_res = sess.run(
[g, out_m0, out_m1, out_m2, out_m3], {
x.name: x_values,
m0.name: m0_val,
m1.name: m1_val,
m2.name: m2_val,
m3.name: m3_val
})
self.assertEqual(len(basic_res), len(block_res))
for basic, block in zip(basic_res, block_res):
self.assertAllClose(basic, block)
def benchmarkLSTMBlockCellFpropWithDynamicRNN(self):
print("BlockLSTMCell forward propagation via dynamic_rnn().")
print("--------------------------------------------------------------")
print("LSTMBlockCell Seconds per inference.")
print("batch_size,cell_size,input_size,time_steps,use_gpu,wall_time")
iters = 10
for config in benchmarking.dict_product({
"batch_size": [1, 8, 13, 32, 67, 128],
"cell_size": [128, 250, 512, 650, 1024, 1350],
"time_steps": [40],
"use_gpu": [True, False],
"dtype": ["float32", "float16"],
}):
dtype = dtypes.float32 if config[
"dtype"] == "float32" else dtypes.float16
with ops.Graph().as_default():
with benchmarking.device(use_gpu=config["use_gpu"]):
inputs = variable_scope.get_variable(
"x",
dtype=dtype,
shape=[
config["time_steps"], config["batch_size"],
config["cell_size"]
])
cell = lstm_ops.LSTMBlockCell(config["cell_size"],
dtype=dtype)
outputs = rnn.dynamic_rnn(cell,
inputs,
time_major=True,
dtype=dtype)
init_op = variables.global_variables_initializer()
with session.Session() as sess:
sess.run(init_op)
wall_time = benchmarking.seconds_per_run(
outputs, sess, iters)
# Print to stdout. If the TEST_REPORT_FILE_PREFIX environment variable
# is set, this will produce a copy-paste-able CSV file.
print(",".join(
map(str, [
config["dtype"], config["batch_size"],
config["cell_size"], config["cell_size"],
config["time_steps"], config["use_gpu"], wall_time
])))
benchmark_name_template = "_".join([
"LSTMBlockCell_fprop", "DT_%(dtype)s", "BS%(batch_size)i",
"CS%(cell_size)i", "IS%(cell_size)i", "TS%(time_steps)i",
"gpu_%(use_gpu)s"
])
self.report_benchmark(name=benchmark_name_template % config,
iters=iters,
wall_time=wall_time,
extras=config)
def testCompatibleNames(self):
with self.session(use_gpu=True, graph=ops.Graph()):
cell = rnn_cell.LSTMCell(10)
pcell = rnn_cell.LSTMCell(10, use_peepholes=True)
inputs = [array_ops.zeros([4, 5])] * 6
rnn.static_rnn(cell, inputs, dtype=dtypes.float32, scope="basic")
rnn.static_rnn(pcell,
inputs,
dtype=dtypes.float32,
scope="peephole")
basic_names = {
v.name: v.get_shape()
for v in variables.trainable_variables()
}
with self.session(use_gpu=True, graph=ops.Graph()):
cell = lstm_ops.LSTMBlockCell(10)
pcell = lstm_ops.LSTMBlockCell(10, use_peephole=True)
inputs = [array_ops.zeros([4, 5])] * 6
rnn.static_rnn(cell, inputs, dtype=dtypes.float32, scope="basic")
rnn.static_rnn(pcell,
inputs,
dtype=dtypes.float32,
scope="peephole")
block_names = {
v.name: v.get_shape()
for v in variables.trainable_variables()
}
with self.session(use_gpu=True, graph=ops.Graph()):
cell = lstm_ops.LSTMBlockFusedCell(10)
pcell = lstm_ops.LSTMBlockFusedCell(10, use_peephole=True)
inputs = array_ops.stack([array_ops.zeros([4, 5])] * 6)
cell(inputs, dtype=dtypes.float32, scope="basic/lstm_cell")
pcell(inputs, dtype=dtypes.float32, scope="peephole/lstm_cell")
fused_names = {
v.name: v.get_shape()
for v in variables.trainable_variables()
}
self.assertEqual(basic_names, block_names)
self.assertEqual(basic_names, fused_names)
def testNoneDimsWithDynamicRNN(self):
with self.session(use_gpu=True, graph=ops.Graph()) as sess:
batch_size = 4
num_steps = 5
input_dim = 6
cell_size = 7
cell = lstm_ops.LSTMBlockCell(cell_size)
x = array_ops.placeholder(dtypes.float32,
shape=(None, None, input_dim))
output, _ = rnn.dynamic_rnn(cell,
x,
time_major=True,
dtype=dtypes.float32)
sess.run(variables.global_variables_initializer())
feed = {}
feed[x] = np.random.randn(num_steps, batch_size, input_dim)
sess.run(output, feed)
