def Gemm(onnx_node, ng_inputs): # type: (NodeWrapper, List[NgraphNode]) -> NgraphNode
"""Calculate general matrix multiplication Y = alpha * (A @ B) + beta * C."""
input_a, input_b, input_c = ng_inputs
alpha = onnx_node.get_attribute_value('alpha', 1) # Scalar multiplier for A @ B
beta = onnx_node.get_attribute_value('beta', 1) # Scalar multiplier for input tensor C
broadcast = onnx_node.get_attribute_value('broadcast', 1) # Should C be broadcast?
trans_a = onnx_node.get_attribute_value('transA', False) # Should A be transposed?
trans_b = onnx_node.get_attribute_value('transB', False) # Should B be transposed?
if trans_a:
input_a = transpose(input_a)
if trans_b:
input_b = transpose(input_b)
# onnx-tensorflow: https://github.com/onnx/onnx-tensorflow/
# blob/17075f44c9071600beccfc62c92b22d1cd957bfd/onnx_tf/backend.py#L711
# They have hardcoded flatten input `A` before transposition.
#
# Firstly, we check whether input data have incompatible shapes and then try flatten input data.
if not has_matmul_compatible_shapes(input_a.shape, input_b.shape):
input_a = flatten_innermost_empty_dims(input_a)
input_b = flatten_innermost_empty_dims(input_b)
if not has_matmul_compatible_shapes(input_a.shape, input_b.shape):
raise ValueError('Gemm node (%s): input "A" and "B" data shapes are incompatible to '
'multiply with each other.', onnx_node.name)
a_dot_b = ng.dot(input_a, input_b)
if not broadcast and input_c.shape != a_dot_b.shape:
raise ValueError('Gemm node (%s): input data shapes are incompatible and broadcast '
' was not requested!', onnx_node.name)
return alpha * a_dot_b + beta * input_c
def Gemm(onnx_node,
ng_inputs): # type: (NodeWrapper, List[NgraphNode]) -> NgraphNode
"""Calculate general matrix multiplication Y = alpha * (A @ B) + beta * C.
Support is currently limited to 2D matrices only. Higher dimensional tensors will
be flattened to 2D before multiplication.
"""
input_a, input_b, input_c = ng_inputs
alpha = onnx_node.get_attribute_value('alpha',
1) # Scalar multiplier for A @ B
beta = onnx_node.get_attribute_value(
'beta', 1) # Scalar multiplier for input tensor C
trans_a = onnx_node.get_attribute_value('transA',
False) # Should A be transposed?
trans_b = onnx_node.get_attribute_value('transB',
False) # Should B be transposed?
if trans_a:
input_a = transpose(input_a)
if trans_b:
input_b = transpose(input_b)
input_a, input_b = reshape_for_matmul(onnx_node, input_a, input_b)
a_dot_b = ng.dot(input_a, input_b)
if alpha != 1:
a_dot_b = alpha * a_dot_b
if beta != 1:
input_c = beta * input_c
_, input_c = numpy_style_broadcast_for_binary_operation(
onnx_node, [a_dot_b, input_c])
return a_dot_b + input_c
def Transpose(onnx_node, ng_inputs): # type: (NodeWrapper, List[NgraphNode]) -> NgraphNode
"""Transpose the input tensor similar to numpy.transpose.
By default, reverse the dimensions, but if `perm` attribute is specified
permute the axes according to the values given.
"""
input_node = ng_inputs[0]
permute_axes = onnx_node.get_attribute_value('perm')
if permute_axes is None:
return transpose(input_node)
else:
return reorder_axes(input_node, permute_axes)