chore: import upstream snapshot with attribution
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wehub-resource-sync
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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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 __future__ import annotations
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import paddle
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from paddle import _C_ops, pir
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from paddle.utils import unique_name
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from ...base import framework
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from ...base.data_feeder import check_variable_and_dtype
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from ...base.dygraph import no_grad
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from .initializer import Initializer
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__all__ = []
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class Orthogonal(Initializer):
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"""The orthogonal initializer. The initialized tensor is (semi) orthogonal.
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It's only applied to Tensor whose dimension is greater than or equal to 2.
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For the Tensor whose dimension is greater than 2, the 0 dimension is seen as ``rows`` ,
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and the >=1 dimension are flattened as ``cols`` .
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Which can be describe as:
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.. code-block:: text
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rows = shape[0]
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cols = shape[1]·shape[2]···shape[N]
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if rows < cols:
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The rows are orthogonal vectors
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elif rows > cols:
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The columns are orthogonal vectors
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else rows = cols:
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Both rows and columns are orthogonal vectors
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Args:
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gain(float, optional): The multiplication coefficient for initialized tensor. Default: 1.0.
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name(str|None, optional): The default value is None. Normally there is no need for user to set this
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property. For more information, please refer to :ref:`api_guide_Name`.
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Returns:
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A parameter initialized by orthogonal initialized.
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Examples:
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.. code-block:: pycon
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>>> import paddle
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>>> weight_attr = paddle.ParamAttr(initializer=paddle.nn.initializer.Orthogonal())
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>>> linear = paddle.nn.Linear(10, 15, weight_attr=weight_attr)
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>>> # linear.weight: X * X' = I
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>>> linear = paddle.nn.Linear(15, 10, weight_attr=weight_attr)
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>>> # linear.weight: X' * X = I
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"""
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def __init__(self, gain: float = 1.0, name: str | None = None) -> None:
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assert gain is not None, 'gain should not be None'
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super().__init__()
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self._gain = gain
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def __call__(self, var: paddle.Tensor, block: pir.Block | None = None):
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"""Initialize the input tensor with orthogonal initializer.
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Args:
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var(Tensor): Tensor that needs to be initialized.
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block(Block|None, optional): The block in which initialization ops
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should be added. Used in static graph only, default None.
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Returns:
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The last initialization op, it contain 8 ops in orthogonal initializer.
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"""
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assert not (
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isinstance(var, framework.EagerParamBase) and var.is_dist()
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), (
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"Currently, orthogonal initializer not support lazy init for dist param."
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)
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block = self._check_block(block)
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assert isinstance(
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var, (framework.Variable, paddle.pir.Value, pir.core.ParameterMeta)
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)
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assert isinstance(block, (framework.Block, pir.Block))
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self._seed = block.program.random_seed
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shape = var.shape
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assert len(shape) >= 2, (
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"Only Tensor with 2 or more dimensions can be initialized by Orthogonal"
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)
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row = shape[0]
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col = 1
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for i in shape[1:]:
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col *= i
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flatten_shape = [max(row, col), min(row, col)]
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if framework.in_dygraph_mode():
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with no_grad():
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place = framework._current_expected_place()
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normal_var = _C_ops.gaussian(
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flatten_shape, 0.0, 1.0, self._seed, var.dtype, place
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)
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q, r = _C_ops.qr(normal_var, 'reduced')
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r_diag = _C_ops.diag(r, 0, 0)
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r_sign = _C_ops.sign(r_diag)
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q = _C_ops.multiply(q, r_sign)
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if row < col:
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q = _C_ops.transpose(q, [1, 0])
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q = _C_ops.reshape(q, var.shape)
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tmp = _C_ops.scale(q, self._gain, 0.0, True)
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tmp._share_underline_tensor_to(var)
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return None
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elif framework.in_pir_mode():
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place = framework._current_expected_place()
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normal_var = _C_ops.gaussian(
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flatten_shape, 0.0, 1.0, self._seed, var.dtype, place
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)
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q, r = _C_ops.qr(normal_var, 'reduced')
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r_diag = _C_ops.diag(r, 0, 0)
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r_sign = _C_ops.sign(r_diag)
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q = _C_ops.multiply(q, r_sign)
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if row < col:
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q = _C_ops.transpose(q, [1, 0])
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q = _C_ops.reshape(q, var.shape)
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tmp = _C_ops.scale(q, self._gain, 0.0, True)
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return tmp
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# 'qr' op only support float32/float64 now
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check_variable_and_dtype(
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var, "Out", ["float32", "float64"], "Orthogonal"
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)
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normal_var = block.create_var(
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name=unique_name.generate('.'.join(['gaussian_random', 'tmp'])),
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dtype=var.dtype,
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persistable=False,
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stop_gradient=True,
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)
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block.append_op(
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type='gaussian_random',
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inputs={},
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outputs={'Out': normal_var},
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attrs={
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'mean': 0.0,
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'std': 1.0,
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'shape': flatten_shape,
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'seed': self._seed,
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'dtype': var.dtype,
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},
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stop_gradient=True,
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)
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q = block.create_var(
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name=unique_name.generate('.'.join(['qr', 'q', 'tmp'])),
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dtype=normal_var.dtype,
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persistable=False,
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stop_gradient=True,
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)
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r = block.create_var(
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name=unique_name.generate('.'.join(['qr', 'r', 'tmp'])),
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dtype=normal_var.dtype,
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persistable=False,
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stop_gradient=True,
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)
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block.append_op(
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type='qr',
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inputs={'X': [normal_var]},
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outputs={
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'Q': q,
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'R': r,
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},
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attrs={'mode': 'reduced'},
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stop_gradient=True,
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)
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r_diag = block.create_var(
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name=unique_name.generate('.'.join(['diag', 'tmp'])),
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dtype=r.dtype,
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persistable=False,
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stop_gradient=True,
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)
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block.append_op(
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type='diag_v2',
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inputs={'X': r},
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outputs={'Out': r_diag},
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attrs={'offset': 0, 'padding_value': 0},
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stop_gradient=True,
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)
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r_sign = r_diag
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block.append_op(
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type='sign',
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inputs={'X': [r_diag]},
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outputs={'Out': r_sign},
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stop_gradient=True,
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)
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block.append_op(
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type='elementwise_mul',
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inputs={'X': q, 'Y': r_sign},
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outputs={'Out': q},
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attrs={},
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stop_gradient=True,
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)
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x_shape = block.create_var(
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name=unique_name.generate('.'.join(['transpose', 'shape', 'tmp'])),
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dtype=q.dtype,
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persistable=False,
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stop_gradient=True,
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)
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if row < col:
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q_transpose = block.create_var(
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name=unique_name.generate('.'.join(['transpose', 'tmp'])),
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dtype=q.dtype,
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persistable=False,
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stop_gradient=True,
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)
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block.append_op(
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type='transpose2',
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inputs={'X': q},
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outputs={'Out': q_transpose, 'XShape': x_shape},
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attrs={'axis': [1, 0]},
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stop_gradient=True,
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)
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q = q_transpose
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block.append_op(
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type='reshape2',
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inputs={'X': q},
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outputs={'Out': q, "XShape": x_shape},
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attrs={'shape': var.shape},
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stop_gradient=True,
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)
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op = block.append_op(
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type='scale',
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inputs={'X': q},
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outputs={'Out': var},
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attrs={'scale': self._gain, 'bias': 0.0},
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)
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return op
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