def GraphFn(self, x1, x2):
x = x1
q = math_ops.abs(x)
q = q + 1.0
q = gen_math_ops.exp(q)
q = gen_math_ops.log(q)
q = array_ops.squeeze(q, axis=-2)
q = math_ops.abs(q)
q = q + 2.2
q = gen_math_ops.sqrt(q)
q = gen_math_ops.rsqrt(q)
q = math_ops.negative(q)
q = array_ops.squeeze(q, axis=3)
q = math_ops.abs(q)
q = q + 3.0
a = gen_math_ops.reciprocal(q)
# this chain of operations has a batch size of 5, which is different from
# the batch size for the other operations.
x = constant_op.constant(np.random.randn(5, 8, 12), dtype=x.dtype)
q = math_ops.abs(x)
q = q + 2.0
q = gen_math_ops.exp(q)
q = gen_math_ops.log(q)
q = math_ops.abs(q)
q = q + 2.1
q = gen_math_ops.sqrt(q)
q = gen_math_ops.rsqrt(q)
q = math_ops.negative(q)
q = math_ops.abs(q)
q = q + 4.0
b = gen_math_ops.reciprocal(q)
# TODO(jie): this one will break, broadcasting on batch.
x = x2
q = math_ops.abs(x)
q = q + 5.0
q = gen_math_ops.exp(q)
q = array_ops.squeeze(q, axis=[-1, -2, 3])
q = gen_math_ops.log(q)
q = math_ops.abs(q)
q = q + 5.1
q = gen_array_ops.reshape(q, [12, 5, 1, 1, 8, 1, 12])
q = array_ops.squeeze(q, axis=[5, 2, 3])
q = gen_math_ops.sqrt(q)
q = math_ops.abs(q)
q = q + 5.2
q = gen_math_ops.rsqrt(q)
q = math_ops.negative(q)
q = math_ops.abs(q)
q = q + 5.3
c = gen_math_ops.reciprocal(q)
q = a * b
q = q / c
return array_ops.squeeze(q, name="output_0")
def minimize_clipped(optimizer,
loss,
clip_value,
return_gvs=False,
soft=False,
**kwargs):
"""Computes a train_op with clipped gradients in the range [-clip_value, clip_value]
:param optimizer: Tensorflow optimizer object
:param loss: tensor
:param clip_value: scalar value
:param return_gvs: returns list of tuples of (gradient, parameter) for trainable variables
:param kwargs: kwargs for optimizer.compute_gradients function
:return: train_step
"""
gvs = optimizer.compute_gradients(loss, **kwargs)
clipped_gvs = [(g, v) for (g, v) in gvs if g is not None]
if not soft:
clipped_gvs = [(tf.clip_by_value(g, -clip_value, clip_value), v)
for (g, v) in clipped_gvs]
else:
n_elems = 0
norm_squared = 0.
for g, v in gvs:
n_elems += tf.reduce_prod(tf.shape(g))
norm_squared += tf.reduce_sum(g**2)
norm_squared /= tf.to_float(n_elems)
inv_norm = gen_math_ops.rsqrt(norm_squared)
cond = tf.greater(norm_squared, clip_value**2)
def clip(x):
return tf.cond(cond, lambda: clip_value * x * inv_norm, lambda: x)
clipped_gvs = [(clip(g), v) for (g, v) in clipped_gvs]
train_step = optimizer.apply_gradients(clipped_gvs)
if return_gvs:
train_step = (train_step, gvs)
return train_step
def GetParams(self):
"""Test for unary operations in TF-TRT."""
dtype = dtypes.float32
input_name = "input"
input_dims = [12, 5, 8, 1, 1, 12]
output_name = "output"
input2_name = "input_2"
input2_dims = [12, 5, 8, 1, 12, 1, 1]
g = ops.Graph()
with g.as_default():
x = array_ops.placeholder(dtype=dtype, shape=input_dims, name=input_name)
q = math_ops.abs(x)
q = q + 1.0
q = gen_math_ops.exp(q)
q = gen_math_ops.log(q)
q = array_ops.squeeze(q, axis=-2)
q = math_ops.abs(q)
q = q + 2.2
q = gen_math_ops.sqrt(q)
q = gen_math_ops.rsqrt(q)
q = math_ops.negative(q)
q = array_ops.squeeze(q, axis=3)
q = math_ops.abs(q)
q = q + 3.0
a = gen_math_ops.reciprocal(q)
x = constant_op.constant(np.random.randn(5, 8, 12), dtype=dtype)
q = math_ops.abs(x)
q = q + 2.0
q = gen_math_ops.exp(q)
q = gen_math_ops.log(q)
q = math_ops.abs(q)
q = q + 2.1
q = gen_math_ops.sqrt(q)
q = gen_math_ops.rsqrt(q)
q = math_ops.negative(q)
q = math_ops.abs(q)
q = q + 4.0
b = gen_math_ops.reciprocal(q)
# TODO(jie): this one will break, broadcasting on batch.
x = array_ops.placeholder(
dtype=dtype, shape=input2_dims, name=input2_name)
q = math_ops.abs(x)
q = q + 5.0
q = gen_math_ops.exp(q)
q = array_ops.squeeze(q, axis=[-1, -2, 3])
q = gen_math_ops.log(q)
q = math_ops.abs(q)
q = q + 5.1
q = gen_array_ops.reshape(q, [12, 5, 1, 1, 8, 1, 12])
q = array_ops.squeeze(q, axis=[5, 2, 3])
q = gen_math_ops.sqrt(q)
q = math_ops.abs(q)
q = q + 5.2
q = gen_math_ops.rsqrt(q)
q = math_ops.negative(q)
q = math_ops.abs(q)
q = q + 5.3
c = gen_math_ops.reciprocal(q)
q = a * b
q = q / c
array_ops.squeeze(q, name=output_name)
return trt_test.TfTrtIntegrationTestParams(
gdef=g.as_graph_def(),
input_names=[input_name, input2_name],
input_dims=[input_dims, input2_dims],
output_names=[output_name],
expected_output_dims=[(12, 5, 8, 12)])
def GetParams(self):
"""Test for unary operations in TF-TRT."""
dtype = dtypes.float32
input_name = "input"
input_dims = [12, 5, 8, 1, 1, 12]
input2_name = "input_2"
input2_dims = [12, 5, 8, 1, 12, 1, 1]
g = ops.Graph()
with g.as_default():
x = array_ops.placeholder(dtype=dtype, shape=input_dims, name=input_name)
q = math_ops.abs(x)
q = q + 1.0
q = gen_math_ops.exp(q)
q = gen_math_ops.log(q)
q = array_ops.squeeze(q, axis=-2)
q = math_ops.abs(q)
q = q + 2.2
q = gen_math_ops.sqrt(q)
q = gen_math_ops.rsqrt(q)
q = math_ops.negative(q)
q = array_ops.squeeze(q, axis=3)
q = math_ops.abs(q)
q = q + 3.0
a = gen_math_ops.reciprocal(q)
x = constant_op.constant(np.random.randn(5, 8, 12), dtype=dtype)
q = math_ops.abs(x)
q = q + 2.0
q = gen_math_ops.exp(q)
q = gen_math_ops.log(q)
q = math_ops.abs(q)
q = q + 2.1
q = gen_math_ops.sqrt(q)
q = gen_math_ops.rsqrt(q)
q = math_ops.negative(q)
q = math_ops.abs(q)
q = q + 4.0
b = gen_math_ops.reciprocal(q)
# TODO(jie): this one will break, broadcasting on batch.
x = array_ops.placeholder(
dtype=dtype, shape=input2_dims, name=input2_name)
q = math_ops.abs(x)
q = q + 5.0
q = gen_math_ops.exp(q)
q = array_ops.squeeze(q, axis=[-1, -2, 3])
q = gen_math_ops.log(q)
q = math_ops.abs(q)
q = q + 5.1
q = gen_array_ops.reshape(q, [12, 5, 1, 1, 8, 1, 12])
q = array_ops.squeeze(q, axis=[5, 2, 3])
q = gen_math_ops.sqrt(q)
q = math_ops.abs(q)
q = q + 5.2
q = gen_math_ops.rsqrt(q)
q = math_ops.negative(q)
q = math_ops.abs(q)
q = q + 5.3
c = gen_math_ops.reciprocal(q)
q = a * b
q = q / c
array_ops.squeeze(q, name=self.output_name)
return trt_test.TfTrtIntegrationTestParams(
gdef=g.as_graph_def(),
input_names=[input_name, input2_name],
input_dims=[input_dims, input2_dims],
num_expected_engines=5,
expected_output_dims=(12, 5, 8, 12),
allclose_atol=1.e-03,
allclose_rtol=1.e-03)