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[TENSORFLOW]reduce ops updated (apache#5180)
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@siju-samuel @dpankratz
siju-samuel authored and dpankratz committed Apr 24, 2020
1 parent 3b94aed commit 3058d15
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45 changes: 27 additions & 18 deletions python/tvm/relay/frontend/tensorflow.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1094,6 +1094,14 @@ def _impl(inputs, attr, params, mod):
ignores=['name', 'Tidx'])([inputs[0]], attr)
return _impl

def _euclidean_norm():
def _impl(inputs, attr, params, mod):
axis = tuple(_get_list_param(params, inputs[1]))
keep_dims = bool(attr.get('keep_dims', False))
return _op.sqrt(_op.cast(_op.reduce.sum(_op.multiply(inputs[0], inputs[0]),
axis, keep_dims), "float32"))
return _impl

def _square():
def _impl(inputs, attr, params, mod):
return _op.multiply(inputs[0], inputs[0])
Expand Down Expand Up @@ -1686,8 +1694,8 @@ def _impl(inputs, attr, params, mod):
_convert_map = {
'Abs' : AttrCvt('abs'),
'Add' : _elemwise('add'),
'AddV2' : _elemwise('add'),
'AddN' : _add_n(),
'AddV2' : _elemwise('add'),
'All' : _reduce('all'),
'Any' : _reduce('any'),
'ArgMax' : _argx(_op.argmax, 'argmax'),
Expand All @@ -1710,16 +1718,18 @@ def _impl(inputs, attr, params, mod):
'Concat' : _concat(),
'ConcatV2' : _concatV2(),
'Conv2D' : _conv('conv'),
'Conv3D' : _conv3d('conv'),
'Conv2DBackpropInput' : _conv('conv_transpose'),
'Conv3D' : _conv3d('conv'),
'Cos' : AttrCvt('cos'),
'CropAndResize' : _crop_and_resize(),
'DecodeJpeg' : _decode_image(),
'DepthwiseConv2dNative' : _conv('depthwise'),
'DepthToSpace' : _depth_to_space(),
'DepthwiseConv2dNative' : _conv('depthwise'),
'Dilation2D' : _dilation2d(),
'Equal' : _broadcast('equal'),
'Elu' : _elu(),
'Equal' : _broadcast('equal'),
'Erf' : AttrCvt('erf'),
'EuclideanNorm' : _euclidean_norm(),
'Exp' : AttrCvt('exp'),
'ExpandDims' : _expand_dims(),
'Fill' : _fill(),
Expand All @@ -1743,19 +1753,16 @@ def _impl(inputs, attr, params, mod):
'LessEqual' : _broadcast('less_equal'),
'Log' : AttrCvt('log'),
'Log1p' : _log1p(),
'Tan' : AttrCvt('tan'),
'Cos' : AttrCvt('cos'),
'Sin' : AttrCvt('sin'),
'LogicalAnd' : _logical('logical_and'),
'LogicalOr' : _logical('logical_or'),
'LogicalNot' : _logical('logical_not'),
'LogicalOr' : _logical('logical_or'),
'LogSoftmax' : AttrCvt('log_softmax'),
'LRN' : _lrn(),
'MatMul' : _matmul(),
'Max' : _reduce('max'),
'Maximum' : _elemwise('maximum'),
'MaxPool' : _pooling('max_pool'),
'MaxPool3D' : _pool3d('max_pool3d'),
'Maximum' : _elemwise('maximum'),
'Mean' : _mean(),
'Min' : _reduce('min'),
'Minimum' : _elemwise('minimum'),
Expand All @@ -1767,14 +1774,6 @@ def _impl(inputs, attr, params, mod):
'NotEqual' : _broadcast('not_equal'),
'OneHot' : _one_hot(),
'Pack' : _pack(),
'TensorArrayV3' : _tensor_array(),
'TensorArrayScatterV3' : _tensor_array_scatter(),
'TensorArrayGatherV3' : _tensor_array_gather(),
'TensorArraySizeV3' : _tensor_array_size(),
'TensorArrayWriteV3' : _tensor_array_write(),
'TensorArrayReadV3' : _tensor_array_read(),
'TensorArraySplitV3' : _tensor_array_split(),
'TensorArrayConcatV3' : _tensor_array_concat(),
'Pad' : _pad('Pad'),
'PadV2' : _pad('PadV2'),
'Pow' : _elemwise('power'),
Expand All @@ -1785,8 +1784,8 @@ def _impl(inputs, attr, params, mod):
'Relu' : AttrCvt('relu'),
'Relu6' : _relu6(),
'Reshape' : _reshape(),
'ResizeBilinear' : _resize('bilinear'),
'ResizeBicubic' : _resize('bilinear'),
'ResizeBilinear' : _resize('bilinear'),
'ResizeNearestNeighbor' : _resize('nearest_neighbor'),
'ReverseV2' : _reverse_v2(),
'RightShift' : AttrCvt('right_shift'),
Expand All @@ -1797,6 +1796,7 @@ def _impl(inputs, attr, params, mod):
'Shape' : _shape(),
'Sigmoid' : AttrCvt('sigmoid'),
'Sign' : AttrCvt('sign'),
'Sin' : AttrCvt('sin'),
'Size' : _size(),
'Slice' : _slice(),
'Softmax' : _softmax(),
Expand All @@ -1813,7 +1813,16 @@ def _impl(inputs, attr, params, mod):
'StridedSlice' : _stridedSlice(),
'Sub' : _elemwise('subtract'),
'Sum' : _sum(),
'Tan' : AttrCvt('tan'),
'Tanh' : AttrCvt('tanh'),
'TensorArrayConcatV3' : _tensor_array_concat(),
'TensorArrayGatherV3' : _tensor_array_gather(),
'TensorArrayReadV3' : _tensor_array_read(),
'TensorArrayScatterV3' : _tensor_array_scatter(),
'TensorArraySizeV3' : _tensor_array_size(),
'TensorArraySplitV3' : _tensor_array_split(),
'TensorArrayV3' : _tensor_array(),
'TensorArrayWriteV3' : _tensor_array_write(),
'Tile' : _tile(),
'TopKV2' : _topk(),
'Transpose' : _transpose(),
Expand Down
123 changes: 32 additions & 91 deletions tests/python/frontend/tensorflow/test_forward.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -1029,28 +1029,6 @@ def test_forward_argminmax():
_test_argx(tf.argmax, data=data, axis=axis)
_test_argx(tf.argmin, data=data, axis=axis)

#######################################################################
# 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")
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)
_test_reduce(tf.reduce_sum, data=data, axis=0)
_test_reduce(tf.reduce_sum, data=data, axis=(0, 1))


#######################################################################
# Variable
Expand Down Expand Up @@ -2845,55 +2823,42 @@ def check_size(ishape):
check_size((10,))

#######################################################################
# 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.reset_default_graph()
with tf.Graph().as_default():
in_data = tf.placeholder(tf.bool, (5, 7, 11), name="in_data")
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()
with tf.Graph().as_default():
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')
# All, Any, Max, Min, Prod, variance, std, logsumexp, euclidean_norm
# ------------------------------------------------------------------

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)
with tf.Graph().as_default():
in_data = tf.placeholder(dtype, name="in_data")
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):
def test_forward_reduce():
def _check_op(tf_op, ishape, axis, keepdims, dtype="float32"):
tf.reset_default_graph()
np_data = np.random.uniform(size=ishape).astype(dtype)
if dtype == "bool":
np_data = np.random.choice([True, False], size=ishape)
else:
np_data = np.random.uniform(size=ishape).astype(dtype)
if tf_op == tf.math.reduce_prod:
axis = 1
np_data = np_data.reshape(1, -1)
with tf.Graph().as_default():
in_data = tf.placeholder(dtype, name="in_data")
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")
check_min((10, 8, 16, 32), axis=(2, 3), keepdims=True, dtype="float32")
check_min((10, 8, 16, 32), axis=(1, 2), keepdims=True, dtype='float32')
reduce_op = tf_op(in_data, axis=axis,
keepdims=keepdims, name="reduce_std")
compare_tf_with_tvm([np_data], ['in_data:0'], reduce_op.name)

def _test_math_op(op, dtypes=["int32", "float32"]):
for dtype in dtypes:
_check_op(op, (3, 10), axis=(-1), keepdims=False, dtype=dtype)
_check_op(op, (8, 16, 32), axis=(-1), keepdims=False, dtype=dtype)
_check_op(op, (1, 8, 8, 3), axis=(2, 3), keepdims=True, dtype=dtype)
_check_op(op, (2, 3, 10, 10), axis=(1, 2), keepdims=True, dtype=dtype)

_test_math_op(tf.math.reduce_all, dtypes=["bool"])
_test_math_op(tf.math.reduce_any, dtypes=["bool"])
_test_math_op(tf.math.reduce_max)
_test_math_op(tf.math.reduce_min)
_test_math_op(tf.math.reduce_prod)
_test_math_op(tf.math.reduce_variance)
_test_math_op(tf.math.reduce_std, dtypes=["float32"])
_test_math_op(tf.math.reduce_logsumexp, dtypes=["float32"])
if package_version.parse(tf.VERSION) >= package_version.parse('1.15.0'):
_test_math_op(tf.math.reduce_euclidean_norm)

#######################################################################
# Relational operators
Expand Down Expand Up @@ -2942,26 +2907,6 @@ def test_forward_expand_dims():
_test_forward_expand_dims(np.array([[1], [2]]), -1)


#######################################################################
# Prod
# ----
def _test_forward_reduce_prod(shape, axis, keepdims):
inp_array1 = np.random.uniform(-5, 5, size=shape).astype(np.float32)
with tf.Graph().as_default():
in1 = tf.placeholder(shape=inp_array1.shape, dtype=inp_array1.dtype)
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)
_test_forward_reduce_prod((5, 5), 1, False)
_test_forward_reduce_prod((5,), 0, True)
_test_forward_reduce_prod((5, 5), 0, True)
_test_forward_reduce_prod((5, 5), 1, True)


#######################################################################
# Maximum, Minimum
# ----------------
Expand Down Expand Up @@ -3295,10 +3240,6 @@ def test_forward_isfinite():
test_forward_argminmax()
test_forward_reduce()
test_forward_mean()
test_forward_reduce_prod()
test_forward_reduce_all()
test_forward_reduce_any()
test_forward_reduce_min()

# General
test_forward_multi_input()
Expand Down

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