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@ -50,6 +50,7 @@ __all__ = [
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'sequence_last_step',
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'dropout',
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'split',
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'matmul',
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]
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@ -1597,3 +1598,73 @@ def split(input, num_or_sections, dim=-1):
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'axis': dim
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})
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return outs
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def matmul(x, y, transpose_x=False, transpose_y=False, name=None):
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"""
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Applies matrix multipication to two tensors. Currently only rank 1 to rank
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3 input tensors are supported.
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The actual behavior depends on the shapes of :math:`x`, :math:`y` and the
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flag values of :attr:`transpose_x`, :attr:`transpose_y`. Specifically:
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- If a transpose flag is specified, the last two dimensions of the tensor
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are transposed. If the tensor is rank-1 of shape :math:`[D]`, then for
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:math:`x` it is treated as :math:`[1, D]` in nontransposed form and as
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:math:`[D, 1]` in transposed form, whereas for :math:`y` it is the
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opposite: It is treated as :math:`[D, 1]` in nontransposed form and as
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:math:`[1, D]` in transposed form.
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- After transpose, the two tensors are 2-D or 3-D and matrix multipication
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performs in the following way.
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- If both are 2-D, they are multiplied like conventional matrices.
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- If either is 3-D, it is treated as a stack of matrices residing in the
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last two dimensions and a batched matrix multiply supporting broadcast
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applies on the two tensors.
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Also note that if the raw tensor :math:`x` or :math:`y` is rank-1 and
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nontransposed, the prepended or appended dimension :math:`1` will be
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removed after matrix multipication.
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Args:
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x (Variable): The input variable which is a Tensor or LoDTensor.
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y (Variable): The input variable which is a Tensor or LoDTensor.
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transpose_x (bool): Whether to transpose :math:`x` before multiplication.
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transpose_y (bool): Whether to transpose :math:`y` before multiplication.
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name(str|None): A name for this layer(optional). If set None, the layer
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will be named automatically.
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Returns:
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Variable: The product Tensor variable.
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Examples:
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.. code-block:: python
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# Examples to clarify shapes of the inputs and output
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# x: [B, M, K], y: [B, K, N]
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fluid.layers.matmul(x, y) # out: [B, M, N]
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# x: [B, M, K], y: [K, N]
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fluid.layers.matmul(x, y) # out: [B, M, N]
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# x: [B, M, K], y: [K]
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fluid.layers.matmul(x, y) # out: [B, M]
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# x: [M, K], y: [K, N]
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fluid.layers.matmul(x, y) # out: [M, N]
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# x: [K], y: [K]
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fluid.layers.matmul(x, y) # out: [1]
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# x: [M], y: [N]
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fluid.layers.matmul(x, y, True, True) # out: [M, N]
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"""
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helper = LayerHelper('matmul', **locals())
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assert max(
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len(x.shape), len(y.shape)
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) <= 3, 'Currently only rank 1 to rank 3 input tensors are supported.'
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out = helper.create_tmp_variable(dtype=helper.input_dtype())
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helper.append_op(
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type='matmul',
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inputs={'X': x,
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'Y': y},
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outputs={'Out': out},
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attrs={'transpose_X': transpose_x,
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'transpose_Y': transpose_y})
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return out
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fengjiayi
7 years ago