#######################################################################
# Generic run functions for TVM & tensorflow
# ------------------------------------------
+
+
def convert_to_list(x):
if not isinstance(x, list):
x = [x]
return x
+
def vmobj_to_list(o):
if isinstance(o, tvm.relay.backend.vmobj.Tensor):
return [o.asnumpy().tolist()]
elif 'tensor' in o.constructor.name_hint:
return [o.fields[0].asnumpy()]
else:
- raise RuntimeError("Unknown object type: %s" % o.constructor.name_hint)
+ raise RuntimeError("Unknown object type: %s" %
+ o.constructor.name_hint)
elif isinstance(o, tvm.relay.backend.interpreter.TensorValue):
return [o.data.asnumpy()]
else:
raise RuntimeError("Unknown object type: %s" % type(o))
+
def run_tvm_graph(graph_def, input_data, input_node, num_output=1,
target='llvm', out_names=None, opt_level=3, mode='graph_runtime'):
""" Generic function to compile on relay and execute on tvm """
# get outputs
assert out_names is None or num_output == len(out_names), (
"out_names: {} num_output: {}".format(out_names, num_output))
- tvm_output_list = [m.get_output(i).asnumpy() for i in range(num_output)]
+ tvm_output_list = [m.get_output(i).asnumpy()
+ for i in range(num_output)]
return tvm_output_list
+
def run_tf_graph(sess, input_data, input_node, output_node):
""" Generic function to execute tensorflow """
input_data = convert_to_list(input_data)
input_node = convert_to_list(input_node)
output_node = convert_to_list(output_node)
- tensor = [sess.graph.get_tensor_by_name(output_name) for output_name in output_node]
+ tensor = [sess.graph.get_tensor_by_name(
+ output_name) for output_name in output_node]
input_dict = {e: input_data[i] for i, e in enumerate(input_node)}
sess,
sess.graph.as_graph_def(add_shapes=True),
out_node,
- )
+ )
tf_output = run_tf_graph(sess, in_data, in_name, out_name)
for device in ["llvm", "cuda"]:
# since the names from tensorflow and relay runs are not exactly same,
# first len(tf_output) will be compared
for i in range(len(tf_output)):
- tvm.testing.assert_allclose(tf_output[i], tvm_output[i], atol=1e-5, rtol=1e-5)
+ tvm.testing.assert_allclose(
+ tf_output[i], tvm_output[i], atol=1e-5, rtol=1e-5)
sess.close()
+
def is_gpu_available():
from tensorflow.python.client import device_lib
local_device_protos = device_lib.list_local_devices()
#######################################################################
# Pooling
# -------
+
+
def _test_pooling_iteration(input_shape, **kwargs):
""" One iteration of pool operation with given shapes and attributes """
compare_tf_with_tvm(x, 'Placeholder:0', out_name)
+
def _test_pooling(input_shape, **kwargs):
_test_pooling_iteration(input_shape, **kwargs)
kwargs['data_format'] = 'NCHW'
_test_pooling_iteration(input_shape, **kwargs)
+
def test_forward_pooling():
""" Pooling """
# Convolution
# -----------
+
def _test_convolution(opname, tensor_in_sizes, filter_in_sizes,
dilations, strides, padding, data_format):
""" One iteration of convolution with given shapes and attributes """
with tf.Graph().as_default():
in_data = array_ops.placeholder(shape=tensor_in_sizes, dtype='float32')
- in_filter = constant_op.constant(filter_array, shape=filter_in_sizes, dtype='float32')
+ in_filter = constant_op.constant(
+ filter_array, shape=filter_in_sizes, dtype='float32')
if data_format == 'NHWC':
strides = [1] + strides + [1]
dilations = [1] + dilations + [1]
'Placeholder:0', 'Conv2D:0')
else:
nn_ops.depthwise_conv2d_native(in_data,
- in_filter,
- strides=strides,
- dilations=dilations,
- padding=padding,
- data_format=data_format)
+ in_filter,
+ strides=strides,
+ dilations=dilations,
+ padding=padding,
+ data_format=data_format)
compare_tf_with_tvm(np.reshape(data_array, tensor_in_sizes).astype('float32'),
'Placeholder:0', 'DepthwiseConv2dNative:0')
+
def test_forward_convolution():
if is_gpu_available():
_test_convolution('conv', [4, 176, 8, 8], [1, 1, 176, 32], [1, 1], [1, 1], 'SAME', 'NCHW')
#######################################################################
# BiasAdd
# -----------
+
+
def _test_biasadd(tensor_in_sizes, data_format):
""" One iteration of biasadd with given shapes and attributes """
total_size_1 = 1
for s in tensor_in_sizes:
total_size_1 *= s
- tensor_bias_sizes = [tensor_in_sizes[1]] if data_format == 'NCHW' else [tensor_in_sizes[3]]
+ tensor_bias_sizes = [tensor_in_sizes[1]
+ ] if data_format == 'NCHW' else [tensor_in_sizes[3]]
total_size_2 = tensor_bias_sizes[0]
# Initializes the input tensor with array containing incrementing
# numbers from 1.
with tf.Graph().as_default():
in_data = array_ops.placeholder(shape=tensor_in_sizes, dtype='float32')
- in_bias = constant_op.constant(bias_array, shape=tensor_bias_sizes, dtype='float32')
+ in_bias = constant_op.constant(
+ bias_array, shape=tensor_bias_sizes, dtype='float32')
nn_ops.bias_add(in_data,
in_bias,
data_format=data_format)
compare_tf_with_tvm(np.reshape(data_array, tensor_in_sizes).astype('float32'),
'Placeholder:0', 'BiasAdd:0')
+
def test_forward_biasadd():
if is_gpu_available():
_test_biasadd([4, 176, 8, 8], 'NCHW')
_test_biasadd([4, 17, 17, 19], 'NHWC')
_test_biasadd([4, 3, 3, 124], 'NHWC')
+
def _test_forward_where(input_shape):
with tf.Graph().as_default():
- dtype = tf.float32
+ dtype = tf.float32
t = tf.constant(np.random.choice([0, 1, -2, 3, -1, 0.1, -0.2],
size=input_shape).astype(dtype.name))
out = tf.where(t)
compare_tf_with_tvm([], [], out.name, mode='debug')
compare_tf_with_tvm([], [], out.name, mode='vm')
+
def test_forward_argwhere():
_test_forward_where((5,))
_test_forward_where((5, 5))
#######################################################################
# SpaceToBatchND
# --------------
+
+
def _test_space_to_batch_nd(input_shape, block_shape, paddings, dtype='int32'):
data = np.random.uniform(0, 5, size=input_shape).astype(dtype)
compare_tf_with_tvm(data, in_data.name, out.name)
+
def test_forward_space_to_batch_nd():
# test cases: https://www.tensorflow.org/api_docs/cc/class/tensorflow/ops/space-to-batch-n-d
_test_space_to_batch_nd(
#######################################################################
# BatchToSpaceND
# --------------
+
+
def _test_batch_to_space_nd(input_shape, block_shape, crops, dtype='int32'):
data = np.random.uniform(0, 5, size=input_shape).astype(dtype)
compare_tf_with_tvm(data, in_data.name, out.name)
+
def test_forward_batch_to_space_nd():
# test cases: https://www.tensorflow.org/api_docs/cc/class/tensorflow/ops/batch-to-space-n-d
_test_batch_to_space_nd(
# Reshape
# -------
+
def _test_reshape(data, out_shape):
""" One iteration of reshape operation with given data and out shape """
compare_tf_with_tvm(data, 'Placeholder:0', 'Reshape:0')
+
def test_forward_reshape():
_test_reshape(np.arange(6.0), [2, 3])
_test_reshape(np.arange(6), [-1, 2])
# DepthToSpace
# ------------
+
def _test_depthtospace(data, block_size):
""" One iteration of depth_to_space operation with given data and block size """
compare_tf_with_tvm(data, 'Placeholder:0', 'DepthToSpace:0')
+
def test_forward_depthtospace():
_test_depthtospace(np.random.normal(size=[1, 32, 32, 4]), 2)
_test_depthtospace(np.random.normal(size=[1, 16, 8, 32]), 4)
# SpaceToDepth
# ------------
+
def _test_spacetodepth(data, block_size):
""" One iteration of space_to_depth operation with given data and block size """
compare_tf_with_tvm(data, 'Placeholder:0', 'SpaceToDepth:0')
+
def test_forward_spacetodepth():
_test_spacetodepth(np.random.normal(size=[1, 32, 32, 4]), 2)
_test_spacetodepth(np.random.normal(size=[1, 16, 8, 32]), 4)
# Squeeze
# -------
+
def _test_squeeze(data, squeeze_dims=None):
""" One iteration of squeeze """
compare_tf_with_tvm(data, 'Placeholder:0', 'Squeeze:0')
+
def test_forward_squeeze():
""" Squeeze """
_test_squeeze(np.arange(6).reshape((1, 2, 1, 3, 1)), [-3, -5])
_test_squeeze(np.arange(6).reshape((1, 2, 1, 3, 1)), [-3, -5, -1])
+
def test_tensor_array_constructor():
def run(dtype_str):
with tf.Graph().as_default():
- dtype = {
+ dtype = {
'float32': tf.float32,
- 'int32' : tf.int32
+ 'int32': tf.int32
}[dtype_str]
- t = tf.constant(np.array([[1.0, 2.0], [3.0, 4.0]]).astype(dtype_str), dtype=dtype)
- t2 = tf.constant(np.array([[1.0, 2.0], [3.0, 4.0]]).astype(dtype_str), dtype=dtype)
- ta1 = tf.TensorArray(dtype=dtype, size=2, infer_shape=False, dynamic_size=False)
+ t = tf.constant(np.array([[1.0, 2.0], [3.0, 4.0]]).astype(
+ dtype_str), dtype=dtype)
+ t2 = tf.constant(np.array([[1.0, 2.0], [3.0, 4.0]]).astype(
+ dtype_str), dtype=dtype)
+ ta1 = tf.TensorArray(dtype=dtype, size=2,
+ infer_shape=False, dynamic_size=False)
ta2 = ta1.write(0, t)
ta3 = ta2.write(1, t2)
out = ta3.read(0)
run('float32')
run('int32')
+
def test_tensor_array_scatter():
def run(dtype_str):
with tf.Graph().as_default():
- dtype = {
+ dtype = {
'float32': tf.float32,
- 'int32' : tf.int32
+ 'int32': tf.int32
}[dtype_str]
- t = tf.constant(np.array([[1.0], [2.0], [3.0]]).astype(dtype_str), dtype=dtype)
+ t = tf.constant(np.array([[1.0], [2.0], [3.0]]).astype(
+ dtype_str), dtype=dtype)
indices = tf.constant([2, 1, 0])
- ta1 = tf.TensorArray(dtype=dtype, size=3, infer_shape=False, dynamic_size=False)
+ ta1 = tf.TensorArray(dtype=dtype, size=3,
+ infer_shape=False, dynamic_size=False)
ta2 = ta1.scatter(indices, t)
out0 = ta2.read(0)
out1 = ta2.read(1)
out2 = ta2.read(2)
g = tf.get_default_graph()
compare_tf_with_tvm([], [], ['TensorArrayReadV3:0'], mode='debug')
- compare_tf_with_tvm([], [], ['TensorArrayReadV3_1:0'], mode='debug')
- compare_tf_with_tvm([], [], ['TensorArrayReadV3_2:0'], mode='debug')
+ compare_tf_with_tvm(
+ [], [], ['TensorArrayReadV3_1:0'], mode='debug')
+ compare_tf_with_tvm(
+ [], [], ['TensorArrayReadV3_2:0'], mode='debug')
run('float32')
run('int32')
# g = tf.get_default_graph()
# compare_tf_with_tvm([], [], ['TensorArrayGatherV3:0'], mode='debug')
+
def test_tensor_array_split():
def run(dtype_str):
with tf.Graph().as_default():
- dtype = {
+ dtype = {
'float32': tf.float32,
- 'int32' : tf.int32
+ 'int32': tf.int32
}[dtype_str]
- t = tf.constant(np.array([[1.0], [2.0], [3.0], [4.0], [5.0], [6.0], [7.0], [8.0]]).astype(dtype_str), dtype=dtype)
+ t = tf.constant(np.array([[1.0], [2.0], [3.0], [4.0], [5.0], [
+ 6.0], [7.0], [8.0]]).astype(dtype_str), dtype=dtype)
split_length = tf.constant([2, 2, 2, 2], dtype=tf.int32)
- ta1 = tf.TensorArray(dtype=dtype, size=4, infer_shape=False, dynamic_size=False)
+ ta1 = tf.TensorArray(dtype=dtype, size=4,
+ infer_shape=False, dynamic_size=False)
ta2 = ta1.split(t, split_length)
out0 = ta2.read(0)
out1 = ta2.read(1)
out3 = ta2.read(3)
g = tf.get_default_graph()
compare_tf_with_tvm([], [], ['TensorArrayReadV3:0'], mode='debug')
- compare_tf_with_tvm([], [], ['TensorArrayReadV3_1:0'], mode='debug')
- compare_tf_with_tvm([], [], ['TensorArrayReadV3_2:0'], mode='debug')
- compare_tf_with_tvm([], [], ['TensorArrayReadV3_3:0'], mode='debug')
+ compare_tf_with_tvm(
+ [], [], ['TensorArrayReadV3_1:0'], mode='debug')
+ compare_tf_with_tvm(
+ [], [], ['TensorArrayReadV3_2:0'], mode='debug')
+ compare_tf_with_tvm(
+ [], [], ['TensorArrayReadV3_3:0'], mode='debug')
run('float32')
run('int32')
+
def test_tensor_array_concat():
def run(dtype_str):
with tf.Graph().as_default():
dtype = {
'float32': tf.float32,
- 'int32' : tf.int32
+ 'int32': tf.int32
}[dtype_str]
- t = tf.constant(np.array([[1.0], [2.0], [3.0], [4.0], [5.0], [6.0], [7.0], [8.0]]).astype(dtype_str), dtype=dtype)
+ t = tf.constant(np.array([[1.0], [2.0], [3.0], [4.0], [5.0], [
+ 6.0], [7.0], [8.0]]).astype(dtype_str), dtype=dtype)
split_length = tf.constant([2, 2, 2, 2], dtype=tf.int32)
- ta1 = tf.TensorArray(dtype=dtype, size=4, infer_shape=False, dynamic_size=False)
+ ta1 = tf.TensorArray(dtype=dtype, size=4,
+ infer_shape=False, dynamic_size=False)
ta2 = ta1.split(t, split_length)
t = ta2.concat()
- compare_tf_with_tvm([], [], ['TensorArrayConcatV3:0'], mode='debug')
+ compare_tf_with_tvm(
+ [], [], ['TensorArrayConcatV3:0'], mode='debug')
run('float32')
run('int32')
+
def test_tensor_array_size():
def run(dtype_str):
with tf.Graph().as_default():
- dtype = {
+ dtype = {
'float32': tf.float32,
- 'int32' : tf.int32
+ 'int32': tf.int32
}[dtype_str]
- ta1 = tf.TensorArray(dtype=dtype, size=2, infer_shape=False, dynamic_size=False)
+ ta1 = tf.TensorArray(dtype=dtype, size=2,
+ infer_shape=False, dynamic_size=False)
out = ta1.size()
g = tf.get_default_graph()
compare_tf_with_tvm([], [], 'TensorArraySizeV3:0', mode='debug')
# ConcatV2
# --------
+
def _test_concat_v2(shape1, shape2, dim):
""" One iteration of ConcatV2 """
np_data1 = np.random.uniform(size=shape1).astype(dtype)
np_data2 = np.random.uniform(size=shape2).astype(dtype)
- compare_tf_with_tvm([np_data1, np_data2], ['in1:0', 'in2:0'], 'ConcatV2:0')
+ compare_tf_with_tvm([np_data1, np_data2], [
+ 'in1:0', 'in2:0'], 'ConcatV2:0')
+
def test_forward_concat_v2():
if tf.__version__ < LooseVersion('1.4.1'):
# Sigmoid
# -------
+
def _test_sigmoid(data):
""" One iteration of sigmoid """
compare_tf_with_tvm(data, 'Placeholder:0', 'Sigmoid:0')
+
def test_forward_sigmoid():
""" Sigmoid """
# Argmin/Argmax
# -------------
+
def _test_argx(func, data, **kwargs):
with tf.Graph().as_default():
- inp = array_ops.placeholder(shape=data.shape, dtype=data.dtype, name="c0")
+ inp = array_ops.placeholder(
+ shape=data.shape, dtype=data.dtype, name="c0")
func(inp, name="argx0", output_type=tf.int32, **kwargs)
compare_tf_with_tvm(data, 'c0:0', 'argx0:0')
+
def test_forward_argminmax():
for axis in [None, 0, 1, 2]:
data = np.random.uniform(size=(8, 4, 9)).astype('float32')
# Reduce
# ------
+
def _test_reduce(func, data, **kwargs):
""" One iteration of a reduce operation"""
with tf.Graph().as_default():
- inp = array_ops.placeholder(shape=data.shape, dtype=data.dtype, name="c0")
+ inp = array_ops.placeholder(
+ shape=data.shape, dtype=data.dtype, name="c0")
func(inp, name="reducex0", **kwargs)
compare_tf_with_tvm(data, 'c0:0', 'reducex0:0')
+
def test_forward_reduce():
data = np.random.uniform(size=(8, 4, 9)).astype('float32')
_test_reduce(tf.reduce_sum, data=data)
"w", shape=[size, size], dtype=input_tensor.dtype)
math_ops.matmul(input_tensor, w)
- compare_tf_with_tvm(data, 'Placeholder:0', 'MatMul:0', init_global_variables=True)
+ compare_tf_with_tvm(data, 'Placeholder:0', 'MatMul:0',
+ init_global_variables=True)
+
def test_forward_variable():
"""Variable type op test"""
for transpose_a in [False, True]:
for transpose_b in [False, True]:
outer = outer or []
- A_shape = outer + (A_shape_init[::-1] if transpose_a else A_shape_init)
- B_shape = outer + (B_shape_init[::-1] if transpose_b else B_shape_init)
+ A_shape = outer + \
+ (A_shape_init[::-1] if transpose_a else A_shape_init)
+ B_shape = outer + \
+ (B_shape_init[::-1] if transpose_b else B_shape_init)
with tf.Graph().as_default():
A = tf.placeholder(shape=A_shape, dtype=dtype, name='A')
B = tf.placeholder(shape=B_shape, dtype=dtype, name='B')
- result = tf.matmul(A, B, transpose_a=transpose_a, transpose_b=transpose_b)
+ result = tf.matmul(
+ A, B, transpose_a=transpose_a, transpose_b=transpose_b)
A_np = np.random.uniform(high=5.0, size=A_shape).astype(dtype)
B_np = np.random.uniform(high=5.0, size=B_shape).astype(dtype)
- compare_tf_with_tvm([A_np, B_np], [A.name, B.name], result.name)
+ compare_tf_with_tvm(
+ [A_np, B_np], [A.name, B.name], result.name)
+
def test_forward_matmul():
""" MatMul op test"""
_test_matmul(1, 3, 6, 'int32')
_test_matmul(5, 3, 1, 'float64')
+
def _test_batch_matmul(A_shape, B_shape, dtype, adjoint_a=False, adjoint_b=False):
with tf.Graph().as_default():
B_np = np.random.uniform(high=5.0, size=B_shape).astype(dtype)
compare_tf_with_tvm([A_np, B_np], [A.name, B.name], result.name)
+
def test_forward_batch_matmul():
""" TF op BatchMatMul, BatchMatMulV2 test"""
_test_batch_matmul((3, 5, 4), (3, 4, 5), 'int32')
_test_batch_matmul((3, 5, 4), (3, 5, 4), 'int32', True, False)
_test_batch_matmul((3, 5, 4), (3, 5, 4), 'float32', False, True)
_test_batch_matmul((2, 3, 4, 5, 6), (2, 3, 4, 6, 5), 'int32')
- _test_batch_matmul((1, 2, 3, 4, 5, 6), (1, 2, 3, 4, 6, 5), 'float32', True, True)
+ _test_batch_matmul((1, 2, 3, 4, 5, 6),
+ (1, 2, 3, 4, 6, 5), 'float32', True, True)
_test_batch_matmul((3, 4, 5, 6), (3, 4, 5, 6), 'int32', True, False)
- _test_batch_matmul((2, 3, 4, 2, 3, 4, 5, 6), (2, 3, 4, 2, 3, 4, 5, 6), 'float32', False, True)
+ _test_batch_matmul((2, 3, 4, 2, 3, 4, 5, 6),
+ (2, 3, 4, 2, 3, 4, 5, 6), 'float32', False, True)
#######################################################################
compare_tf_with_tvm(np_data, 'in_data:0', 'strided_slice:0')
+
def test_forward_stridedslice():
'''test StridedSlice'''
_test_stridedslice((2), [1], [1], [1], 'float32', shrink_axis_mask=1)
- _test_stridedslice((3, 4, 3), [1, -1, 0], [4, -5, 3], [2, -1, 1], 'float32')
- _test_stridedslice((3, 4, 3), [1, 0], [4, 3], [2, 1], 'float32', ellipsis_mask=8)
- _test_stridedslice((3, 4, 3), [1, 0], [4, 2], [2, 1], 'float32', ellipsis_mask=2)
- _test_stridedslice((3, 4, 5, 3), [1, 0], [4, 2], [2, 1], 'float32', ellipsis_mask=2)
- _test_stridedslice((3, 4, 5, 3), [1, 0, 1], [4, 2, 2], [2, 1, 1], 'float32', ellipsis_mask=2)
- _test_stridedslice((3, 4, 3), [1, 1, 0], [4, 4, 2], [2, 1, 1], 'float32', new_axis_mask=5)
+ _test_stridedslice((3, 4, 3), [1, -1, 0],
+ [4, -5, 3], [2, -1, 1], 'float32')
+ _test_stridedslice((3, 4, 3), [1, 0], [4, 3], [
+ 2, 1], 'float32', ellipsis_mask=8)
+ _test_stridedslice((3, 4, 3), [1, 0], [4, 2], [
+ 2, 1], 'float32', ellipsis_mask=2)
+ _test_stridedslice((3, 4, 5, 3), [1, 0], [4, 2], [
+ 2, 1], 'float32', ellipsis_mask=2)
+ _test_stridedslice((3, 4, 5, 3), [1, 0, 1], [4, 2, 2], [
+ 2, 1, 1], 'float32', ellipsis_mask=2)
+ _test_stridedslice((3, 4, 3), [1, 1, 0], [4, 4, 2], [
+ 2, 1, 1], 'float32', new_axis_mask=5)
_test_stridedslice((3, 4, 3), [1, 1, 1], [4, 4, 1], [2, 1, 1], 'float32', ellipsis_mask=2,
new_axis_mask=4)
_test_stridedslice((6, 4, 5), [1, 1, 1], [6, 3, 4], [2, 1, 1], 'float32', ellipsis_mask=2,
new_axis_mask=3)
_test_stridedslice((3, 4, 3), [1, 1, 2], [4, 4, 3], [2, 1, 1], 'float32', ellipsis_mask=2,
new_axis_mask=2)
- _test_stridedslice((3, 4), [1, 0], [4, 4], [1, 1], 'float32', shrink_axis_mask=2)
+ _test_stridedslice((3, 4), [1, 0], [4, 4], [
+ 1, 1], 'float32', shrink_axis_mask=2)
_test_stridedslice((3, 4, 3), [1, 1, 0], [4, 4, 3], [2, 1, 1], 'float32', shrink_axis_mask=2,
new_axis_mask=2)
_test_stridedslice((3, 4, 3), [1, 1, 0], [4, 4, 3], [2, 1, 1], 'float32', shrink_axis_mask=1,
# FloorDiv, RealDiv
# -----------------
+
def _test_forward_divide(ip_shape, dtype):
np_numer = np.random.uniform(-100, 100, size=ip_shape).astype(dtype)
np_denomin = np.random.uniform(1, 100, size=ip_shape).astype(dtype)
numerator = tf.placeholder(dtype, ip_shape, name="numer")
denominator = tf.placeholder(dtype, ip_shape, name="denomin")
tf.math.divide(numerator, denominator, name='RealDiv')
- compare_tf_with_tvm([np_numer, np_denomin], ['numer:0', 'denomin:0'], 'RealDiv:0')
+ compare_tf_with_tvm([np_numer, np_denomin], [
+ 'numer:0', 'denomin:0'], 'RealDiv:0')
+
def _test_forward_floordiv(ip_shape, dtype):
np_numer = np.random.uniform(-100, 100, size=ip_shape).astype(dtype)
tf.math.floordiv(numerator, tf.constant(5, dtype=dtype), name='FloorDiv')
compare_tf_with_tvm([np_numer], ['numer:0'], 'FloorDiv:0')
+
def test_forward_divide():
'''test FloorDiv, RealDiv'''
_test_forward_divide((4,), 'int32')
in_data_1 = tf.placeholder(dtype, ip_shape, name="in_data_1")
in_data_2 = tf.placeholder(dtype, ip_shape, name="in_data_2")
tf.truncatemod(in_data_1, in_data_2, name='truncatemod')
- compare_tf_with_tvm([np_data_1, np_data_2], ['in_data_1:0', 'in_data_2:0'], 'truncatemod:0')
+ compare_tf_with_tvm([np_data_1, np_data_2], [
+ 'in_data_1:0', 'in_data_2:0'], 'truncatemod:0')
+
def test_forward_truncatemod():
'''test TruncateMod'''
return indices
np_indices = _fill_indices(indice_value)
- compare_tf_with_tvm([np_data, np_indices], ['in_data:0', 'indices:0'], out.name)
+ compare_tf_with_tvm([np_data, np_indices], [
+ 'in_data:0', 'indices:0'], out.name)
+
def test_forward_gather():
'''test Gather/GatherV2 layer'''
_test_gather((3, 3, 3), (1, 1, 2), [[[1, 0]]], 2, 'int32')
_test_gather((4, 3, 5, 6), (1, 4), [[2, 1, 0, 0]], 0, 'float32')
+
def test_forward_gather_nd():
"""test operator GatherNd"""
np_data = np.random.uniform(1, 100, size=(2, 2)).astype(np.float32)
lft_data = tf.placeholder(dtype, name="lft_data")
rgt_data = tf.placeholder(dtype, name="rgt_data")
tf.nn.bias_add(lft_data, rgt_data, name="BiasAdd")
- compare_tf_with_tvm([lh_data, rh_data], ['lft_data:0', 'rgt_data:0'], 'BiasAdd:0')
+ compare_tf_with_tvm([lh_data, rh_data], [
+ 'lft_data:0', 'rgt_data:0'], 'BiasAdd:0')
check_bias_add((10, 8, 16, 32), (32,), dtype="int32")
check_bias_add((10, 20), (20,), dtype="float32")
tf.reset_default_graph()
in_data = tf.placeholder(dtype, in_shape, name="in_data")
num_split = len(num_or_size_splits) if isinstance(num_or_size_splits, list)\
- else num_or_size_splits
+ else num_or_size_splits
split = tf.split(in_data, num_or_size_splits, axis=axis)
relu = [tf.nn.relu(i) for i in split]
compare_tf_with_tvm([np_data], 'in_data:0', 'concat:0')
+
def test_forward_split():
'''test split layer'''
# rank 1
tf.math.top_k(in_data, k, name='TopK')
compare_tf_with_tvm([np_data], ['in_data:0'], 'TopK:0')
+
def test_forward_top_k_v2():
_test_forward_top_k_v2((3,), 1)
_test_forward_top_k_v2((3,), 3)
compare_tf_with_tvm([np_data], ['in_data:0'], 'stack:0')
+
def test_forward_unstack():
'''test unstack layer'''
_test_unstack((6,), 0, 'int32')
tf.tile(in_data, multiples=multiples, name="tile")
compare_tf_with_tvm([np_data], ['in_data:0'], 'tile:0')
+
def test_forward_tile():
'''test Tile'''
_test_tile((2, ), (3, ), "int32")
def _test_forward_clip_by_value(ip_shape, clip_value_min, clip_value_max, dtype):
tf.reset_default_graph()
in_data = tf.placeholder(dtype, ip_shape, name="in_data")
- tf.clip_by_value(in_data, clip_value_min, clip_value_max, name="ClipByValue")
+ tf.clip_by_value(in_data, clip_value_min,
+ clip_value_max, name="ClipByValue")
np_data = np.random.uniform(-100, 100, size=ip_shape).astype(dtype)
compare_tf_with_tvm([np_data], ['in_data:0'], 'ClipByValue:0')
+
def test_forward_clip_by_value():
'''test ClipByValue op'''
if tf.__version__ < LooseVersion('1.9'):
# Multi Input to graph
# --------------------
+
def test_forward_multi_input():
with tf.Graph().as_default():
in1 = tf.placeholder(tf.int32, shape=[3, 3], name='in1')
# Multi Output to Graph
# ---------------------
+
def test_forward_multi_output():
with tf.Graph().as_default():
in1 = tf.placeholder(tf.int32, shape=[3, 3], name='in1')
tvm_output = run_tvm_graph(final_graph_def, in_data, in_node, target='llvm',
out_names=out_node, num_output=2)
for i in range(len(tf_output)):
- tvm.testing.assert_allclose(tf_output[i], tvm_output[i], atol=1e-5, rtol=1e-5)
+ tvm.testing.assert_allclose(
+ tf_output[i], tvm_output[i], atol=1e-5, rtol=1e-5)
#######################################################################
# Resize Bilinear, Nearest_Neighbor
# ---------------------------------
+
def _test_resize_bilinear(in_shape, to_shape, align_corners):
""" One iteration of resize bilinear """
in_data = array_ops.placeholder(shape=data.shape, dtype=data.dtype)
shape_data = constant_op.constant(
shape_data, shape=shape_data.shape, dtype=shape_data.dtype)
- tf.image.resize_bilinear(in_data, shape_data, align_corners=align_corners)
+ tf.image.resize_bilinear(
+ in_data, shape_data, align_corners=align_corners)
compare_tf_with_tvm(data, 'Placeholder:0', 'ResizeBilinear:0')
+
def _test_resize_bilinear_from_tensor(in_shape, align_corners):
""" One iteration of resize bilinear with non-constant output shape, requires
value inference to get proper output shape."""
in_data = array_ops.placeholder(
shape=[in_shape[0], in_shape[1], None, None], dtype=data.dtype)
to_shape = tf.shape(in_data)[2:]
- tf.image.resize_bilinear(in_data, to_shape, align_corners=align_corners)
+ tf.image.resize_bilinear(
+ in_data, to_shape, align_corners=align_corners)
compare_tf_with_tvm(data, 'Placeholder:0', 'ResizeBilinear:0')
in_data = array_ops.placeholder(shape=data.shape, dtype=data.dtype)
shape_data = constant_op.constant(
shape_data, shape=shape_data.shape, dtype=shape_data.dtype)
- tf.image.resize_nearest_neighbor(in_data, shape_data, name='resize_nearest_neighbor')
+ tf.image.resize_nearest_neighbor(
+ in_data, shape_data, name='resize_nearest_neighbor')
compare_tf_with_tvm(data, 'Placeholder:0', 'resize_nearest_neighbor:0')
shape_data, shape=shape_data.shape, dtype=shape_data.dtype)
tf.broadcast_to(in_data, shape_data)
- compare_tf_with_tvm(data, 'Placeholder:0', 'BroadcastTo:0', opt_level=0)
+ compare_tf_with_tvm(data, 'Placeholder:0',
+ 'BroadcastTo:0', opt_level=0)
def _test_broadcast_to_from_tensor(in_shape):
tf.ones(shape=in_shape, dtype='float32')
compare_tf_with_tvm(in_shape, [], 'ones:0', opt_level=1)
+
def _test_fill_from_tensor(in_shape):
""" Use the fill op to create a tensor of ones with non-constant shape.
Some extra ops need to be added here to prevent the graph from
y = tf.math.add(in_data, tf.reduce_mean(x), name='out1')
compare_tf_with_tvm(data, 'Placeholder:0', 'out1:0')
+
def test_forward_fill():
""" Resize Bilinear """
# Crop to bounding box
# --------------------
+
def _test_crop(in_shape, off_h, off_w, tar_h, tar_w):
""" Crop to bounding box """
data = np.random.uniform(size=in_shape).astype('float32')
with tf.Graph().as_default():
in_data = array_ops.placeholder(shape=data.shape, dtype=data.dtype)
tf.image.crop_to_bounding_box(in_data, off_h, off_w, tar_h, tar_w)
- compare_tf_with_tvm(data, 'Placeholder:0', 'crop_to_bounding_box/Slice:0')
+ compare_tf_with_tvm(data, 'Placeholder:0',
+ 'crop_to_bounding_box/Slice:0')
+
def test_forward_crop():
""" Crop to bounding box """
method=method, name="crop_and_resize")
compare_tf_with_tvm([image], ['in_data:0'], 'crop_and_resize:0')
+
def test_forward_crop_and_resize():
""" CropAndResize """
_test_forward_crop_and_resize([1, 11, 11, 3], [[0, 0, 1, 1]], [0], [5, 5])
- _test_forward_crop_and_resize([1, 11, 11, 3], [[0, 0, .9, .9]], [0], [5, 5])
- _test_forward_crop_and_resize([1, 11, 11, 3], [[.1, .2, 1, 1]], [0], [5, 5])
- _test_forward_crop_and_resize([1, 21, 21, 3], [[.2, .3, .7, .9]], [0], [3, 4])
- _test_forward_crop_and_resize([1, 41, 41, 3], [[0.2, 0.4, 0.8, 0.8]], [0], [3, 3])
+ _test_forward_crop_and_resize(
+ [1, 11, 11, 3], [[0, 0, .9, .9]], [0], [5, 5])
+ _test_forward_crop_and_resize(
+ [1, 11, 11, 3], [[.1, .2, 1, 1]], [0], [5, 5])
+ _test_forward_crop_and_resize(
+ [1, 21, 21, 3], [[.2, .3, .7, .9]], [0], [3, 4])
+ _test_forward_crop_and_resize(
+ [1, 41, 41, 3], [[0.2, 0.4, 0.8, 0.8]], [0], [3, 3])
_test_forward_crop_and_resize([10, 11, 11, 3],
[[0, 0, 0.9, 0.9], [0.2, 0.2, 0.8, 0.8]],
[0, 1],
[5, 5])
_test_forward_crop_and_resize([3, 11, 11, 3],
- [[0, 0, 0.9, 0.9], [0.2, 0.2, 0.8, 0.8],[0, 0, 1, 1]],
+ [[0, 0, 0.9, 0.9], [
+ 0.2, 0.2, 0.8, 0.8], [0, 0, 1, 1]],
[0, 1, 2],
[3, 3])
_test_forward_crop_and_resize([3, 11, 11, 3],
tf.reset_default_graph()
input_size = num_hidden
input_data = np.full((batch_size, input_size), 1., dtype=dtype)
- in_state_c = np.full((num_layers, batch_size, num_hidden), 0.1, dtype=dtype)
- in_state_h = np.full((num_layers, batch_size, num_hidden), 0.1, dtype=dtype)
+ in_state_c = np.full(
+ (num_layers, batch_size, num_hidden), 0.1, dtype=dtype)
+ in_state_h = np.full(
+ (num_layers, batch_size, num_hidden), 0.1, dtype=dtype)
def _get_tensorflow_output():
with tf.Session() as sess:
m1 = array_ops.zeros([batch_size, num_hidden])
x = tf.placeholder(shape=(batch_size, input_size), dtype=dtype)
g, ((out_m0, out_m1)) = \
- tf.contrib.rnn.LSTMBlockCell(num_hidden,
- forget_bias=forget_bias)(x, ((m0, m1)))
+ tf.contrib.rnn.LSTMBlockCell(num_hidden,
+ forget_bias=forget_bias)(x, ((m0, m1)))
sess.run([variables.global_variables_initializer()])
res = sess.run([g, out_m0, out_m1], {
x.name: np.array([[1., 1.]]),
tvm_out = [out, out_state_c, out_state_h]
tvm.testing.assert_allclose(tf_out[0], tvm_out[0], rtol=1e-3, atol=1e-3)
+
def test_forward_lstm():
'''test LSTM block cell'''
_test_lstm_cell(1, 2, 1, 0.5, 'float32')
-
#######################################################################
# Pack
# ---
compare_tf_with_tvm([a, b], ['pl_a:0', 'pl_b:0'], 'stack:0')
+
def test_forward_pack():
for axis in range(-3, 3):
_test_pack(axis, [3, 2, 1])
tf.unstack(in_data, axis=axis, name="Unpack")
compare_tf_with_tvm([np_data], ['in_data:0'], 'Unpack:0')
+
def test_forward_unpack():
_test_forward_unpack((3,), 0, 'int32')
_test_forward_unpack((3,), -1, 'int16')
#######################################################################
# Range
# -----
+
+
def test_forward_range():
"""test operator Range"""
tf.reset_default_graph()
#######################################################################
# Pad
# ---
+
+
def _test_pad(input_shape, paddings, mode, **kwargs):
""" One iteration of pad operation with given shape"""
compare_tf_with_tvm(x, 'Placeholder:0', out_name)
+
def test_forward_pad():
""" Pad """
_test_pad((2, 3), [[1, 1], [2, 2]], mode="CONSTANT")
#######################################################################
# Logical operators
# --------------------
+
+
def test_logical_and():
with tf.Graph().as_default():
in1 = tf.placeholder(tf.bool, shape=[1, 4, 4, 3], name='in1')
in2 = tf.placeholder(tf.bool, shape=[1, 4, 4, 3], name='in2')
out = tf.logical_and(in1, in2, name='out')
- in_data1 = np.random.choice(a=[False, True], size=(1, 4, 4, 3)).astype('bool')
- in_data2 = np.random.choice(a=[False, True], size=(1, 4, 4, 3)).astype('bool')
+ in_data1 = np.random.choice(
+ a=[False, True], size=(1, 4, 4, 3)).astype('bool')
+ in_data2 = np.random.choice(
+ a=[False, True], size=(1, 4, 4, 3)).astype('bool')
compare_tf_with_tvm([in_data1, in_data2], ['in1:0', 'in2:0'], 'out:0')
+
def test_logical_or():
with tf.Graph().as_default():
in1 = tf.placeholder(tf.bool, shape=[1, 4, 4, 3], name='in1')
in2 = tf.placeholder(tf.bool, shape=[1, 4, 4, 3], name='in2')
out = tf.logical_or(in1, in2, name='out')
- in_data1 = np.random.choice(a=[False, True], size=(1, 4, 4, 3)).astype('bool')
- in_data2 = np.random.choice(a=[False, True], size=(1, 4, 4, 3)).astype('bool')
+ in_data1 = np.random.choice(
+ a=[False, True], size=(1, 4, 4, 3)).astype('bool')
+ in_data2 = np.random.choice(
+ a=[False, True], size=(1, 4, 4, 3)).astype('bool')
compare_tf_with_tvm([in_data1, in_data2], ['in1:0', 'in2:0'], 'out:0')
+
def test_logical_xor():
with tf.Graph().as_default():
in1 = tf.placeholder(tf.bool, shape=[1, 4, 4, 3], name='in1')
in2 = tf.placeholder(tf.bool, shape=[1, 4, 4, 3], name='in2')
out = tf.logical_xor(in1, in2, name='out')
- in_data1 = np.random.choice(a=[False, True], size=(1, 4, 4, 3)).astype('bool')
- in_data2 = np.random.choice(a=[False, True], size=(1, 4, 4, 3)).astype('bool')
+ in_data1 = np.random.choice(
+ a=[False, True], size=(1, 4, 4, 3)).astype('bool')
+ in_data2 = np.random.choice(
+ a=[False, True], size=(1, 4, 4, 3)).astype('bool')
compare_tf_with_tvm([in_data1, in_data2], ['in1:0', 'in2:0'], 'out:0')
+
def test_logical_not():
with tf.Graph().as_default():
in1 = tf.placeholder(tf.bool, shape=[1, 4, 4, 3], name='in1')
out = tf.logical_not(in1, name='out')
- in_data1 = np.random.choice(a=[False, True], size=(1, 4, 4, 3)).astype('bool')
+ in_data1 = np.random.choice(
+ a=[False, True], size=(1, 4, 4, 3)).astype('bool')
compare_tf_with_tvm(in_data1, 'in1:0', 'out:0')
+
def test_forward_logical():
test_logical_and()
test_logical_or()
''' Where: return elements depending on conditions'''
with tf.Graph().as_default():
with tf.Session() as sess:
- input1 = tf.placeholder(tf.int32, shape=[1, 4, 4, 3], name='input1')
- input2 = tf.placeholder(tf.int32, shape=[1, 4, 4, 3], name='input2')
+ input1 = tf.placeholder(
+ tf.int32, shape=[1, 4, 4, 3], name='input1')
+ input2 = tf.placeholder(
+ tf.int32, shape=[1, 4, 4, 3], name='input2')
mask = input1 > input2
tf.where(mask, input1 + 1, input2 * 2)
- in_data1 = np.random.uniform(0, 10, size=(1, 4, 4, 3)).astype("uint32")
- in_data2 = np.random.uniform(0, 10, size=(1, 4, 4, 3)).astype("uint32")
- compare_tf_with_tvm([in_data1, in_data2], ['input1:0', 'input2:0'], 'Select:0')
+ in_data1 = np.random.uniform(
+ 0, 10, size=(1, 4, 4, 3)).astype("uint32")
+ in_data2 = np.random.uniform(
+ 0, 10, size=(1, 4, 4, 3)).astype("uint32")
+ compare_tf_with_tvm([in_data1, in_data2], [
+ 'input1:0', 'input2:0'], 'Select:0')
#######################################################################
data = np.random.uniform(size=(1, 299, 299, 3)).astype('float32')
with tf.Session() as sess:
- tf_output = run_tf_graph(sess, data, 'input:0', 'InceptionV3/Predictions/Reshape_1:0')
+ tf_output = run_tf_graph(
+ sess, data, 'input:0', 'InceptionV3/Predictions/Reshape_1:0')
tvm_output = run_tvm_graph(graph_def, data, 'input')
- tvm.testing.assert_allclose(tf_output[0], tvm_output[0], rtol=1e-5, atol=1e-5)
+ tvm.testing.assert_allclose(
+ tf_output[0], tvm_output[0], rtol=1e-5, atol=1e-5)
#######################################################################
# Inception V1
# ------------
+
+
def test_forward_inception_v1():
'''test inception V1 model'''
with tf.Graph().as_default():
- graph_def = tf_testing.get_workload("InceptionV1/classify_image_graph_def-with_shapes.pb")
+ graph_def = tf_testing.get_workload(
+ "InceptionV1/classify_image_graph_def-with_shapes.pb")
# Call the utility to import the graph definition into default graph.
graph_def = tf_testing.ProcessGraphDefParam(graph_def)
from tvm.contrib import util
img_array = np.random.uniform(size=(1, 600, 600, 3)).astype("uint8")
- img = Image.frombuffer('RGB', (600, 600), img_array.tostring(), 'raw', 'RGB', 0, 1)
+ img = Image.frombuffer(
+ 'RGB', (600, 600), img_array.tostring(), 'raw', 'RGB', 0, 1)
temp = util.tempdir()
img_path = temp.relpath("tf-test.jpg")
img.save(img_path)
# Extract tensorflow decoded image frame for tvm input
with tf.Session() as sess:
- tvm_data = run_tf_graph(sess, data, 'DecodeJpeg/contents:0', 'DecodeJpeg:0')
+ tvm_data = run_tf_graph(
+ sess, data, 'DecodeJpeg/contents:0', 'DecodeJpeg:0')
with tf.Session() as sess:
- tf_output = run_tf_graph(sess, data, 'DecodeJpeg/contents:0', 'softmax:0')
- tvm_output = run_tvm_graph(graph_def, tvm_data, 'DecodeJpeg/contents')
- tvm.testing.assert_allclose(tf_output[0], tvm_output[0], rtol=1e-5, atol=1e-5)
+ tf_output = run_tf_graph(
+ sess, data, 'DecodeJpeg/contents:0', 'softmax:0')
+ tvm_output = run_tvm_graph(
+ graph_def, tvm_data, 'DecodeJpeg/contents')
+ tvm.testing.assert_allclose(
+ tf_output[0], tvm_output[0], rtol=1e-5, atol=1e-5)
#######################################################################
# Mobilenet
# ---------
+
+
def test_forward_mobilenet():
'''test mobilenet model'''
# MobilenetV2
#######################################################################
# ResnetV2
# --------
+
+
def test_forward_resnetv2():
'''test resnet model'''
if is_gpu_available():
out_node = 'ArgMax'
with tf.Session() as sess:
- tf_output = run_tf_graph(sess, data, 'input_tensor:0', out_node + ':0')
+ tf_output = run_tf_graph(
+ sess, data, 'input_tensor:0', out_node + ':0')
for device in ["llvm", "cuda"]:
ctx = tvm.context(device, 0)
if not ctx.exist:
#######################################################################
# Placeholder
# -----------
+
+
def test_forward_placeholder():
'''test a simple pb with Placeholder node in the end of GraphDef'''
with tf.Graph().as_default():
with tf.Session() as sess:
# Add shapes to the graph.
graph_def = tf_testing.AddShapesToGraphDef(sess, out_node)
- tf_output = run_tf_graph(sess, data, 'Placeholder:0', out_node + ':0')
+ tf_output = run_tf_graph(
+ sess, data, 'Placeholder:0', out_node + ':0')
tvm_output = run_tvm_graph(graph_def, data, 'Placeholder')
tvm.testing.assert_allclose(np.squeeze(tvm_output[0]), np.squeeze(tf_output[0]),
rtol=1e-5, atol=1e-5)
+
#######################################################################
# PTB
# ---
dir(tf.contrib)
+
+
def test_forward_ptb():
'''test ptb model'''
config = tf_testing.get_config()
vocab_size = config.vocab_size
out_sample_shape = (batch_size, vocab_size)
out_state_shape = (num_layers, 2, batch_size, num_hidden)
- #Sample input
+ # Sample input
inpt = "we have no useful information on"
cnt_sample = 20
return ''.join([id2word[x] for x in items]).replace('_', ' ')
def _get_tvm_graph_module(graph_def):
- #Cell inputs 'c and 'h' consist of all layers values
+ # Cell inputs 'c and 'h' consist of all layers values
shape_dict = {'Model/Placeholder': (batch_size, num_steps),
'Model/RNN/RNN/multi_rnn_cell/cell_0/lstm_cell/LSTMBlockCell_c':
(num_layers, batch_size, num_hidden),
'Model/RNN/RNN/multi_rnn_cell/cell_0/lstm_cell/LSTMBlockCell_h':
(num_layers, batch_size, num_hidden)}
- mod, params = relay.frontend.from_tensorflow(graph_def, shape=shape_dict)
+ mod, params = relay.frontend.from_tensorflow(
+ graph_def, shape=shape_dict)
dtype_dict = {'Model/Placeholder': 'int32',
- 'Model/RNN/RNN/multi_rnn_cell/cell_0/lstm_cell/LSTMBlockCell_c':'float32',
- 'Model/RNN/RNN/multi_rnn_cell/cell_0/lstm_cell/LSTMBlockCell_h':'float32'}
+ 'Model/RNN/RNN/multi_rnn_cell/cell_0/lstm_cell/LSTMBlockCell_c': 'float32',
+ 'Model/RNN/RNN/multi_rnn_cell/cell_0/lstm_cell/LSTMBlockCell_h': 'float32'}
target = 'llvm'
with relay.build_config(opt_level=0):
graph, lib, params = relay.build(mod,
samples = []
state = in_states
sample = None
+
def _get_sample(data, state):
input_data = np.full((batch_size, num_steps), data, dtype="int32")
in_state_tup = np.split(state, indices_or_sections=2, axis=1)
- in_state_c = np.reshape(in_state_tup[0], (num_layers, batch_size, num_hidden))
- in_state_h = np.reshape(in_state_tup[1], (num_layers, batch_size, num_hidden))
+ in_state_c = np.reshape(
+ in_state_tup[0], (num_layers, batch_size, num_hidden))
+ in_state_h = np.reshape(
+ in_state_tup[1], (num_layers, batch_size, num_hidden))
- model.set_input('Model/Placeholder', tvm.nd.array(input_data.astype("int32")))
+ model.set_input('Model/Placeholder',
+ tvm.nd.array(input_data.astype("int32")))
model.set_input('Model/RNN/RNN/multi_rnn_cell/cell_0/lstm_cell/LSTMBlockCell_c',
tvm.nd.array(in_state_c.astype("float32")))
model.set_input('Model/RNN/RNN/multi_rnn_cell/cell_0/lstm_cell/LSTMBlockCell_h',
graph_def = tf_testing.ProcessGraphDefParam(graph_def)
sess = tf.Session()
- #TVM graph module creation
+ # TVM graph module creation
params, m = _get_tvm_graph_module(graph_def)
# Create 10 predicted statments of 20 words
cnt_stm = 0
while cnt_stm < 10:
cnt_stm += 1
- in_state = np.full((num_layers, 2, batch_size, num_hidden), 0, dtype="float32")
+ in_state = np.full(
+ (num_layers, 2, batch_size, num_hidden), 0, dtype="float32")
seed_for_sample = inpt.split()
- tvm_samples, tvm_state = _do_tvm_sample(m, [word_to_id[word] \
+ tvm_samples, tvm_state = _do_tvm_sample(m, [word_to_id[word]
for word in seed_for_sample],
in_state, params, cnt_sample)
tvm_sample_str = _pretty_print(tvm_samples, False, id_to_word)
in_state, cnt_sample)
tf_sample_str = _pretty_print(tf_samples, False, id_to_word)
inpt = tvm_sample_str
- tvm.testing.assert_allclose(tf_samples, tvm_samples, rtol=1e-5, atol=1e-5)
+ tvm.testing.assert_allclose(
+ tf_samples, tvm_samples, rtol=1e-5, atol=1e-5)
assert tvm_sample_str == tf_sample_str
#######################################################################
# LRN (Local Response Normalization)
# ----------------------------------
+
def _test_lrn(ishape, size, axis, bias, alpha, beta):
""" testing local response normalization """
lrn_depth_radius = size / 2
inp_array = np.random.uniform(size=ishape).astype(np.float32)
with tf.Graph().as_default():
- in1 = tf.placeholder(shape=inp_array.shape, dtype=inp_array.dtype, name="lrn0_data")
+ in1 = tf.placeholder(shape=inp_array.shape,
+ dtype=inp_array.dtype, name="lrn0_data")
nn_ops.local_response_normalization(in1,
name="lrn",
depth_radius=lrn_depth_radius,
compare_tf_with_tvm(inp_array, 'lrn0_data:0', 'lrn:0')
+
def test_forward_lrn():
_test_lrn((1, 3, 20, 20), 3, 1, 1.0, 1.0, 0.5)
# l2_normalize
# ------------
+
def _test_l2_normalize(ishape, eps, axis):
""" testing l2 normalize (uses max, sum, square, sqrt frontend operators)"""
compare_tf_with_tvm(inp_array, 'Placeholder:0', 'l2_normalize:0')
+
def test_forward_l2_normalize():
_test_l2_normalize((1, 3, 20, 20), 0.001, (0,))
#######################################################################
# transpose
# ---------
+
+
def _test_forward_transpose(ishape, axes=None):
data = np.random.uniform(size=ishape).astype(np.float32)
with tf.Graph().as_default():
- in1 = tf.placeholder(shape=data.shape, dtype=data.dtype, name="transpose_data")
+ in1 = tf.placeholder(
+ shape=data.shape, dtype=data.dtype, name="transpose_data")
if axes is None:
tf.transpose(in1)
compare_tf_with_tvm(data, 'transpose_data:0', 'transpose:0')
+
def test_forward_transpose():
_test_forward_transpose((2, 3, 4), (1, 2, 0))
_test_forward_transpose((2, 3, 4))
tf.ceil(in1)
compare_tf_with_tvm(inp_array, 'Placeholder:0', 'Ceil:0')
+
def test_forward_floor():
ishape = (1, 3, 10, 10)
inp_array = np.random.uniform(size=ishape).astype(np.float32)
tf.floor(in1)
compare_tf_with_tvm(inp_array, 'Placeholder:0', 'Floor:0')
+
def test_forward_relu():
ishape = (1, 3, 10, 10)
inp_array = np.random.uniform(-5, 5, size=ishape).astype(np.float32)
tf.nn.relu(in1)
compare_tf_with_tvm(inp_array, 'Placeholder:0', 'Relu:0')
+
def test_forward_leaky_relu():
ishape = (1, 3, 10, 10)
inp_array = np.random.uniform(-5, 5, size=ishape).astype(np.float32)
tf.nn.leaky_relu(in1, alpha=0.4)
compare_tf_with_tvm(inp_array, 'Placeholder:0', 'LeakyRelu:0')
+
def test_forward_elu():
ishape = (1, 3, 10, 10)
inp_array = np.random.uniform(-5, 5, size=ishape).astype(np.float32)
tf.nn.elu(in1)
compare_tf_with_tvm(inp_array, 'Placeholder:0', 'Elu:0')
+
def test_forward_selu():
ishape = (1, 3, 10, 10)
inp_array = np.random.uniform(-5, 5, size=ishape).astype(np.float32)
tf.nn.selu(in1)
compare_tf_with_tvm(inp_array, 'Placeholder:0', 'Selu:0')
+
def test_forward_tanh():
ishape = (1, 3, 10, 10)
inp_array = np.random.uniform(-5, 5, size=ishape).astype(np.float32)
tf.round(in_data, name="round")
compare_tf_with_tvm([np_data], ['in_data:0'], 'round:0')
+
def test_forward_abs():
"""test operator Abs"""
np_data = np.random.uniform(1, 100, size=(9, 11)).astype(np.float32)
tf.math.abs(in_data, name="abs")
compare_tf_with_tvm([np_data], ['in_data:0'], 'abs:0')
+
def _test_forward_zeros_like(in_shape, dtype):
np_data = np.random.uniform(-10, 10, size=in_shape).astype(dtype)
tf.reset_default_graph()
tf.zeros_like(in_data, name="zeros_like")
compare_tf_with_tvm([np_data], ['in_data:0'], 'zeros_like:0')
+
def test_forward_zeros_like():
if tf.__version__ < LooseVersion('1.2'):
_test_forward_zeros_like((2, 3), "int32")
_test_forward_zeros_like((2, 3, 11), "float32")
_test_forward_zeros_like((2, 3, 11), "float64")
+
def test_forward_erf():
ishape = (1, 3, 10, 10)
inp_array = np.random.uniform(-5, 5, size=ishape).astype(np.float32)
tf.math.erf(in1)
compare_tf_with_tvm(inp_array, 'Placeholder:0', 'Erf:0')
+
def test_forward_squared_difference():
ishape = (1, 3, 10, 14)
inp_array_a = np.random.uniform(-5, 5, size=ishape).astype(np.float32)
inp_array_b = np.random.uniform(-5, 5, size=ishape).astype(np.float32)
with tf.Graph().as_default():
- in1 = tf.placeholder(shape=inp_array_a.shape, dtype=inp_array_a.dtype, name="in1")
- in2 = tf.placeholder(shape=inp_array_b.shape, dtype=inp_array_b.dtype, name="in2")
+ in1 = tf.placeholder(shape=inp_array_a.shape,
+ dtype=inp_array_a.dtype, name="in1")
+ in2 = tf.placeholder(shape=inp_array_b.shape,
+ dtype=inp_array_b.dtype, name="in2")
out = tf.math.squared_difference(in1, in2)
- compare_tf_with_tvm([inp_array_a, inp_array_b], [in1.name, in2.name], out.name)
+ compare_tf_with_tvm([inp_array_a, inp_array_b], [
+ in1.name, in2.name], out.name)
+
def _test_forward_reverse_v2(in_shape, axis, dtype):
np_data = np.random.uniform(-10, 10, size=in_shape).astype(dtype)
tf.reverse(in_data, axis=[axis], name="reverse")
compare_tf_with_tvm([np_data], ['in_data:0'], 'reverse:0')
+
def test_forward_reverse_v2():
"""test ReverseV2"""
_test_forward_reverse_v2((2, 3), 0, "int32")
_test_forward_reverse_v2((2, 3, 5), -1, "float64")
_test_forward_reverse_v2((2, 3, 5), -3, "float64")
+
def test_forward_sign():
"""test Sign"""
np_data = np.random.uniform(-10, 10, size=(5, 7, 11)).astype(np.float32)
tf.sign(in_data, name="sign")
compare_tf_with_tvm([np_data], ['in_data:0'], 'sign:0')
+
def test_forward_square():
"""test operator Square """
np_data = np.random.uniform(1, 100, size=(2, 3, 5)).astype(np.float32)
tf.square(in_data, name="square")
compare_tf_with_tvm([np_data], ['in_data:0'], 'square:0')
+
def test_forward_pow_exp():
"""test Pow and Exp """
np_in1 = np.random.uniform(-2, 2, size=(5, 7, 11)).astype(np.float32)
compare_tf_with_tvm([np_in1, np_in2], ['in1:0', 'in2:0'], 'pow:0')
compare_tf_with_tvm([np_in1], ['in1:0'], 'exp:0')
+
def test_forward_log():
"""test operator Log """
np_data = np.random.uniform(1, 100, size=(2, 3, 5)).astype(np.float32)
tf.log(in_data, name="log")
compare_tf_with_tvm([np_data], ['in_data:0'], 'log:0')
+
def test_forward_log1p():
"""test operator Log1p """
np_data = np.random.uniform(1, 100, size=(2, 3, 5)).astype(np.float32)
tf.log1p(in_data, name="log1p")
compare_tf_with_tvm([np_data], ['in_data:0'], 'log1p:0')
+
def test_forward_cos():
"""test operator cos """
np_data = np.random.uniform(1, 100, size=(2, 3, 5)).astype(np.float32)
tf.cos(in_data, name="cos")
compare_tf_with_tvm([np_data], ['in_data:0'], 'cos:0')
+
def test_forward_sin():
"""test operator sin """
np_data = np.random.uniform(1, 100, size=(2, 3, 5)).astype(np.float32)
tf.sin(in_data, name="sin")
compare_tf_with_tvm([np_data], ['in_data:0'], 'sin:0')
+
def test_forward_negative():
"""test tf operator Neg """
- np_data = np.random.uniform(-100, 255, size=(224, 224, 3)).astype(np.float32)
+ np_data = np.random.uniform(-100, 255,
+ size=(224, 224, 3)).astype(np.float32)
tf.reset_default_graph()
in_data = tf.placeholder(tf.float32, (224, 224, 3), name="in_data")
tf.negative(in_data, name="negative")
compare_tf_with_tvm([np_data], ['in_data:0'], 'negative:0')
+
def test_forward_log_softmax():
"""test operator LogSoftmax"""
np_data = np.random.uniform(1, 100, size=(9, 11)).astype(np.float32)
tf.math.log_softmax(in_data, name="LogSoftmax")
compare_tf_with_tvm([np_data], ['in_data:0'], 'LogSoftmax:0')
+
def test_forward_softplus():
"""test operator Softplus"""
np_data = np.random.uniform(1, 10, size=(2, 3, 5)).astype(np.float32)
tf.nn.softplus(in_data, name="softplus")
compare_tf_with_tvm([np_data], ['in_data:0'], 'softplus:0')
+
def test_forward_rsqrt():
"""test Rsqrt """
np_data = np.random.uniform(1, 100, size=(5, 7, 11)).astype(np.float32)
tf.rsqrt(in_data, name="rsqrt")
compare_tf_with_tvm([np_data], ['in_data:0'], 'rsqrt:0')
+
def test_forward_sqrt():
"""test Sqrt """
np_data = np.random.uniform(1, 100, size=(5, 7, 11)).astype(np.float32)
tf.sqrt(in_data, name="sqrt")
compare_tf_with_tvm([np_data], ['in_data:0'], 'sqrt:0')
+
def _test_forward_right_shift(in_shape, dtype):
"""test operator RightShift"""
lh_data = np.random.randint(1, 3, size=in_shape).astype(dtype)
lft_data = tf.placeholder(dtype, in_shape, name="lft_data")
rgt_data = tf.placeholder(dtype, in_shape, name="rgt_data")
tf.bitwise.right_shift(lft_data, rgt_data, name="RightShift")
- compare_tf_with_tvm([lh_data, rh_data], ['lft_data:0', 'rgt_data:0'], 'RightShift:0')
+ compare_tf_with_tvm([lh_data, rh_data], [
+ 'lft_data:0', 'rgt_data:0'], 'RightShift:0')
+
def test_forward_right_shift():
_test_forward_right_shift((7,), 'int32')
_test_forward_right_shift((3, 11), 'int16')
+
def _test_forward_left_shift(in_shape, dtype):
"""test operator LeftShift"""
lh_data = np.random.randint(100, 1000000, size=in_shape).astype(dtype)
lft_data = tf.placeholder(dtype, in_shape, name="lft_data")
rgt_data = tf.placeholder(dtype, in_shape, name="rgt_data")
tf.bitwise.left_shift(lft_data, rgt_data, name="LeftShift")
- compare_tf_with_tvm([lh_data, rh_data], ['lft_data:0', 'rgt_data:0'], 'LeftShift:0')
+ compare_tf_with_tvm([lh_data, rh_data], [
+ 'lft_data:0', 'rgt_data:0'], 'LeftShift:0')
+
def test_forward_left_shift():
_test_forward_left_shift((10,), 'int32')
#######################################################################
# Mean
# ----
+
+
def test_forward_mean():
def check_mean(ishape, **kwargs):
inp_array = np.random.uniform(size=ishape).astype(np.float32)
with tf.Graph().as_default():
in1 = tf.placeholder(shape=inp_array.shape, dtype=inp_array.dtype)
tf.keras.backend.mean(in1, **kwargs)
- compare_tf_with_tvm(inp_array, 'Placeholder:0', 'Mean:0', no_gpu=True)
+ compare_tf_with_tvm(inp_array, 'Placeholder:0',
+ 'Mean:0', no_gpu=True)
check_mean((10, 8, 16, 32))
check_mean((10, 8, 16, 32), axis=(2, 3))
#######################################################################
# Size
# ----
+
+
def test_forward_size():
def check_size(ishape):
np_input = np.random.uniform(size=ishape).astype(np.float32)
tf_input_shape[0] = None
with tf.Graph().as_default():
- input = tf.placeholder(shape=tf_input_shape, dtype=np_input.dtype, name='input')
+ input = tf.placeholder(shape=tf_input_shape,
+ dtype=np_input.dtype, name='input')
tf.size(input, name='size')
compare_tf_with_tvm([np_input], ['input:0'], 'size:0')
#######################################################################
# All, Any, Max, Min
# -------------
+
+
def test_forward_reduce_all():
"""Test the All operator."""
np_data = np.random.choice([True, False], size=(5, 7, 11))
tf.reduce_all(in_data, name="all")
compare_tf_with_tvm([np_data], ['in_data:0'], 'all:0')
-def test_forward_reduce_any():
- """Test the Any operator."""
- np_data = np.random.choice([True, False], size=(5, 7, 11))
- tf.reset_default_graph()
- in_data = tf.placeholder(tf.bool, (5, 7, 11), name="in_data")
- tf.reduce_any(in_data, name="any")
- compare_tf_with_tvm([np_data], ['in_data:0'], 'any:0')
def test_forward_reduce_max():
def check_max(ishape, axis, keepdims, dtype):
tf.reset_default_graph()
np_data = np.random.uniform(size=ishape).astype(dtype)
in_data = tf.placeholder(dtype, name="in_data")
- tf.math.reduce_max(in_data, axis=axis, keepdims=keepdims, name="reduce_max")
+ tf.math.reduce_max(in_data, axis=axis,
+ keepdims=keepdims, name="reduce_max")
compare_tf_with_tvm([np_data], ['in_data:0'], 'reduce_max:0')
check_max((10, 8, 16, 32), axis=(-1), keepdims=True, dtype="int32")
check_max((10, 8, 16, 32), axis=(2, 3), keepdims=True, dtype="float32")
check_max((10, 8, 16, 32), axis=(1, 2), keepdims=True, dtype='float32')
+
def test_forward_reduce_min():
def check_min(ishape, axis, keepdims, dtype):
tf.reset_default_graph()
np_data = np.random.uniform(size=ishape).astype(dtype)
in_data = tf.placeholder(dtype, name="in_data")
- tf.math.reduce_min(in_data, axis=axis, keepdims=keepdims, name="reduce_max")
+ tf.math.reduce_min(in_data, axis=axis,
+ keepdims=keepdims, name="reduce_max")
compare_tf_with_tvm([np_data], ['in_data:0'], 'reduce_max:0')
check_min((10, 8, 16, 32), axis=(-1), keepdims=True, dtype="int32")
#######################################################################
# Relational operators
# --------------------
+
+
def _test_forward_rel_op(data, func):
with tf.Graph().as_default():
- in1 = tf.placeholder(shape=data[0].shape, dtype=data[0].dtype, name='in1')
- in2 = tf.placeholder(shape=data[1].shape, dtype=data[1].dtype, name='in2')
+ in1 = tf.placeholder(
+ shape=data[0].shape, dtype=data[0].dtype, name='in1')
+ in2 = tf.placeholder(
+ shape=data[1].shape, dtype=data[1].dtype, name='in2')
op = func(in1, in2, name='op')
out = tf.cast(op, tf.int32, name='out1')
compare_tf_with_tvm([data[0], data[1]], ['in1:0', 'in2:0'], 'out1:0')
+
def test_forward_rel_ops():
t1 = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
t2 = np.array([[9, 8, 7], [6, 5, 4], [3, 2, 1]])
#######################################################################
# ExpandDims
# ----------
+
+
def _test_forward_expand_dims(data, axis):
in1 = tf.placeholder(shape=data.shape, dtype=data.dtype, name='in1')
out = tf.expand_dims(in1, axis)
compare_tf_with_tvm([data], [in1.name], out.name)
+
def test_forward_expand_dims():
_test_forward_expand_dims(np.int32(1), 0)
_test_forward_expand_dims(np.array([1]), 0)
out = tf.math.reduce_prod(in1, axis, keepdims)
compare_tf_with_tvm(inp_array1, in1.name, out.name)
+
def test_forward_reduce_prod():
_test_forward_reduce_prod((5,), 0, False)
_test_forward_reduce_prod((5, 5), 0, False)
lft_data = tf.placeholder(dtype, name="lft_data")
rgt_data = tf.placeholder(dtype, name="rgt_data")
tf.math.maximum(lft_data, rgt_data, name="maximum")
- compare_tf_with_tvm([lh_data, rh_data], ['lft_data:0', 'rgt_data:0'], 'maximum:0')
+ compare_tf_with_tvm([lh_data, rh_data], [
+ 'lft_data:0', 'rgt_data:0'], 'maximum:0')
check_maximum((10, 8, 16, 32), (1,), dtype="int32")
check_maximum((10, 8, 16, 32), (10, 8, 16, 32), dtype="float32")
+
def test_forward_minimum():
"""test Op Minimum"""
def check_minimum(lh_shape, rh_shape, dtype):
lft_data = tf.placeholder(dtype, name="lft_data")
rgt_data = tf.placeholder(dtype, name="rgt_data")
tf.math.minimum(lft_data, rgt_data, name="minimum")
- compare_tf_with_tvm([lh_data, rh_data], ['lft_data:0', 'rgt_data:0'], 'minimum:0')
+ compare_tf_with_tvm([lh_data, rh_data], [
+ 'lft_data:0', 'rgt_data:0'], 'minimum:0')
check_minimum((10, 8, 16, 32), (1,), dtype="int32")
check_minimum((10, 8, 16, 32), (10, 8, 16, 32), dtype="float32")
var2 = array_ops.placeholder_with_default(var1, None, name='place1')
in_data2 = np.random.uniform(-5, 5, size=(3, 4, 5)).astype(np.float32)
- place1 = array_ops.placeholder(shape=in_data1.shape, dtype=in_data1.dtype, name='in2')
+ place1 = array_ops.placeholder(
+ shape=in_data1.shape, dtype=in_data1.dtype, name='in2')
out1 = tf.math.add(var1, var2, name='out1')
out2 = tf.math.add(out1, place1, name='out2')
#######################################################################
# OneHot
# ----------------------
+
+
def _test_forward_one_hot(indices_shape, depth, on_value, off_value, axis, out_dtype):
inp_array1 = np.random.randint(0, 5, size=indices_shape)
with tf.Graph().as_default():
in1 = tf.placeholder(shape=inp_array1.shape, dtype=inp_array1.dtype)
- out = tf.one_hot(in1, depth, on_value, off_value, axis, dtype=out_dtype)
+ out = tf.one_hot(in1, depth, on_value, off_value,
+ axis, dtype=out_dtype)
compare_tf_with_tvm(inp_array1, in1.name, out.name)
+
def test_forward_one_hot():
_test_forward_one_hot((3,), 3, 1, 0, -1, "int32")
_test_forward_one_hot((3,), 3, 1.0, 0.0, -1, "float32")
_test_forward_one_hot((3, 2, 4, 5), 6, 1, 0, 1, "int32")
_test_forward_one_hot((3, 2, 4, 5), 6, 1.0, 0.0, 0, "float32")
+#######################################################################
+# AddN
+# ----------------------
+
+
+def _test_forward_add_n(inputs):
+ tf.reset_default_graph()
+ with tf.Graph().as_default():
+ temp = []
+ for each in inputs:
+ temp.append(tf.placeholder(shape=each.shape, dtype=each.dtype))
+ output = tf.add_n(temp)
+ compare_tf_with_tvm([each for each in inputs], [
+ each.name for each in temp], output.name)
+
+
+def test_forward_add_n():
+ x = np.random.randint(1, 100, size=(3, 3, 3), dtype=np.int32)
+ y = np.random.randint(1, 100, size=(3, 3, 3), dtype=np.int32)
+ z = np.random.randint(1, 100, size=(3, 3, 3), dtype=np.int32)
+ m, n, o = x.astype(np.float32), y.astype(np.float32), z.astype(np.float32)
+ in0 = x
+ in1 = [x, y]
+ in2 = (x, y, z)
+ in3 = m
+ in4 = [m, n]
+ in5 = (m, n, o)
+ _test_forward_add_n(in0)
+ _test_forward_add_n(in1)
+ _test_forward_add_n(in2)
+ _test_forward_add_n(in3)
+ _test_forward_add_n(in4)
+ _test_forward_add_n(in5)
+
#######################################################################
# Main
test_forward_zeros_like()
test_forward_erf()
test_forward_squared_difference()
+ test_forward_add_n()
# Reductions
test_forward_argminmax()
projects / platform / upstream / tvm.git / commitdiff