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@ -11,15 +11,148 @@
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from paddle.common_ops_import import *
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# TODO: define functions of linear algebra
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# __all__ = ['matmul',
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# 'dot',
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# 'einsum',
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# 'morm',
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# 'transpose',
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# 'dist',
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# 't',
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# 'cross',
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# 'cholesky',
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# 'tensordot']
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__all__ = [
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'matmul',
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# 'dot',
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# 'einsum',
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# 'morm',
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# 'transpose',
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# 'dist',
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# 't',
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# 'cross',
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# 'cholesky',
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# 'tensordot'
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]
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def matmul(x, y, transpose_x=False, transpose_y=False, alpha=1.0, name=None):
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"""
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Applies matrix multiplication to two tensors.
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Currently, the input tensors' rank can be any, but when the rank of any
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inputs is bigger than 3, this two inputs' rank should be equal.
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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 n-D and matrix multiplication
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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 n-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 multiplication.
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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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alpha (float): The scale of output. Default 1.0.
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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 (or LoDTensor) 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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# paddle.matmul(x, y) # out: [B, ..., M, N]
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# x: [B, M, K], y: [B, K, N]
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# paddle.matmul(x, y) # out: [B, M, N]
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# x: [B, M, K], y: [K, N]
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# paddle.matmul(x, y) # out: [B, M, N]
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# x: [M, K], y: [K, N]
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# paddle.matmul(x, y) # out: [M, N]
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# x: [B, M, K], y: [K]
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# paddle.matmul(x, y) # out: [B, M]
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# x: [K], y: [K]
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# paddle.matmul(x, y) # out: [1]
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# x: [M], y: [N]
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# paddle.matmul(x, y, True, True) # out: [M, N]
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import paddle.fluid as fluid
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x = fluid.data(name='x', shape=[2, 3], dtype='float32')
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y = fluid.data(name='y', shape=[3, 2], dtype='float32')
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out = paddle.matmul(x, y, True, True)
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"""
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attrs = {
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'transpose_X': transpose_x,
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'transpose_Y': transpose_y,
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'alpha': float(alpha),
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}
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if in_dygraph_mode():
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return core.ops.matmul(x, y, 'transpose_X', transpose_x, 'transpose_Y',
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transpose_y, 'alpha', float(alpha))
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def __check_input(x, y):
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var_names = {'x': x, 'y': y}
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for name, val in var_names.items():
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check_variable_and_dtype(
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val, name, ['float16', 'float32', 'float64'], 'matmul')
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x_shape = list(x.shape)
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y_shape = list(y.shape)
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if len(x_shape) == 1:
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x_shape = [1] + x_shape
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if len(y_shape) == 1:
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y_shape = y_shape + [1]
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# check the inner 2 dimensions
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if transpose_x:
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x_shape[-2], x_shape[-1] = x_shape[-1], x_shape[-2]
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if transpose_y:
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y_shape[-2], y_shape[-1] = y_shape[-1], y_shape[-2]
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if x_shape[-1] != y_shape[-2]:
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assert (x_shape[-1] == -1) or (y_shape[-2] == -1), \
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"After performing an optional transpose, Input X's width should be " \
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"equal to Y's width for multiplication " \
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"prerequisites. But received X's shape: %s, Y's shape: %s\n" % \
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(x_shape, y_shape)
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if len(y_shape) > 2 and len(x_shape) > 2:
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for i, dim_x in enumerate(x_shape[:-2]):
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# don't check neg shape
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if dim_x < 0 or y_shape[i] < 0:
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continue
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if dim_x != y_shape[i]:
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raise ValueError(
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"When the matrix is larger than 2 dimensions, the higher "
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"dimensional values of the two matrices need to be equal. "
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"But received x_shape[%d] != y_shape[%d]. X's shape: %s, "
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"Y's shape: %s.\n" % (i, i, x_shape, y_shape))
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__check_input(x, y)
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helper = LayerHelper('matmul', **locals())
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out = helper.create_variable_for_type_inference(dtype=x.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=attrs)
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return out
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David Lin
6 years ago
committed by
GitHub