wehub-resource-sync
367 lines
13 KiB
Python
367 lines
13 KiB
Python
# 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, in_dynamic_mode, pir
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from paddle.utils import unique_name
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from ... import base
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from ...base import core, framework
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from ...base.core import VarDesc
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from ...base.data_feeder import check_variable_and_dtype
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from ...base.framework import _current_expected_place
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from .initializer import Initializer
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__all__ = []
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class Dirac(Initializer):
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r"""Initialize the 3D/4D/5D Tensor with Dirac delta function.
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It can reserve the feature of convolution layer input, which means that
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as many channels are reserved as possible.
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In this initialize method, elements in the middle of convolution kernels will
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be set to 1 . The formula can be described as follow.
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.. math::
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X[d, d, shape[2]//2, shape[3]//2, ...]=1, \ d=0,1...N
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where, ``N`` is the minimum value of ``in_channels`` and ``out_channels``
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Args:
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groups(int|None, optional): 0-dimension of the Tensor will be divided by groups,
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each group has the same value. Default: 1.
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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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Dirac initializer instance objects.
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Examples:
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.. code-block:: pycon
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>>> import paddle
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>>> # 1. For kernel_size is uneven number:
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>>> attr = paddle.ParamAttr(initializer=paddle.nn.initializer.Dirac())
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>>> conv = paddle.nn.Conv1D(3, 2, 3, weight_attr=attr)
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>>> print(conv.weight)
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Parameter containing:
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Tensor(shape=[2, 3, 3], dtype=float32, place=CPUPlace, stop_gradient=False,
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[[[0., 1., 0.],
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[0., 0., 0.],
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[0., 0., 0.]],
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[[0., 0., 0.],
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[0., 1., 0.],
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[0., 0., 0.]]])
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>>> input = paddle.rand([8, 3, 10])
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>>> output = conv(input)
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>>> output == input[:, 0:2, 1:9]
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>>> print(output.shape)
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paddle.Size([8, 2, 8])
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>>> # It means output is almost the same with input, 2 channels are reserved
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>>> # 2. For kernel_size is even number:
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>>> attr = paddle.ParamAttr(initializer=paddle.nn.initializer.Dirac())
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>>> conv = paddle.nn.Conv1D(3, 2, 4, weight_attr=attr)
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>>> print(conv.weight)
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Parameter containing:
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Tensor(shape=[2, 3, 4], dtype=float32, place=CPUPlace, stop_gradient=False,
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[[[0., 0., 1., 0.],
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[0., 0., 0., 0.],
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[0., 0., 0., 0.]],
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[[0., 0., 0., 0.],
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[0., 0., 1., 0.],
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[0., 0., 0., 0.]]])
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"""
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def __init__(self, groups: int = 1, name: str | None = None) -> None:
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assert groups > 0 and isinstance(groups, int), (
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" 'groups' must be a positive integer. "
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)
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super().__init__()
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self._groups = groups
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def __call__(
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self, var: paddle.Tensor, block: pir.Block | None = None
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) -> paddle.Tensor:
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"""Initialize the input tensor with dirac 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 most critical OP(scatter) in this initializer, which contains 7~8 ops in total.
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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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), "Currently, dirac initializer not support lazy init for dist param."
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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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check_variable_and_dtype(
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var, "Out", ['float16', 'bfloat16', 'float32', 'float64'], 'Dirac'
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)
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assert len(var.shape) in [
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3,
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4,
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5,
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], "Only Tensor with 3/4/5 dimensions can be initialized by Dirac"
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assert (var.shape[0] % self._groups) == 0, (
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"Tensor 0-dimension must be divisible by groups"
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)
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if framework.in_pir_mode():
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if var.dtype != core.DataType.FLOAT32:
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out_dtype = core.DataType.FLOAT32
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out_var = var
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else:
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out_dtype = var.dtype
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out_var = var
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else:
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if var.dtype != VarDesc.VarType.FP32:
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out_dtype = VarDesc.VarType.FP32
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out_var = block.create_var(
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name=unique_name.generate(
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".".join(['dirac', var.name, 'tmp'])
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),
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shape=var.shape,
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dtype=out_dtype,
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type=VarDesc.VarType.DENSE_TENSOR,
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persistable=False,
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)
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else:
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out_dtype = var.dtype
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out_var = var
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op = None
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if framework.in_dygraph_mode():
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with base.dygraph.no_grad():
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place = _current_expected_place()
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_C_ops.full_(
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out_var, out_var.shape, str(float(0)), out_dtype, place
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)
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elif framework.in_pir_mode():
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place = _current_expected_place()
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out_var = _C_ops.full(out_var.shape, float(0), out_dtype, place)
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else:
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block.append_op(
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type='fill_constant',
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inputs={},
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outputs={'Out': out_var},
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attrs={
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'value': float(0),
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'dtype': out_var.dtype,
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'shape': out_var.shape,
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},
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stop_gradient=True,
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)
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origin_shape = var.shape
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num_per_group = origin_shape[0] // self._groups
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min_shape = min(num_per_group, origin_shape[1])
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idx_list = []
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value_list = []
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strides = []
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prod = 1
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for dim in reversed(origin_shape):
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strides.insert(0, prod)
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prod *= dim
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for i in range(self._groups):
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for j in range(min_shape):
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value_list.append(1.0)
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offset = 0
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for k, stride in enumerate(strides):
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if k == 0:
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offset += (j + i * num_per_group) * stride
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elif k == 1:
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offset += j * stride
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else:
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offset += origin_shape[k] // 2 * stride
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idx_list.append(offset)
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if framework.in_dygraph_mode():
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with base.dygraph.no_grad():
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tmp_out = _C_ops.reshape(out_var, [-1])
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tmp_out._share_underline_tensor_to(out_var)
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elif framework.in_pir_mode():
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out_var = _C_ops.reshape(out_var, [-1])
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else:
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x_shape = block.create_var(
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name=unique_name.generate(".".join([out_var.name, "XShape"])),
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dtype=out_dtype,
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shape=out_var.shape,
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type=VarDesc.VarType.DENSE_TENSOR,
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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="reshape2",
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inputs={"X": out_var},
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attrs={'shape': [-1]},
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outputs={"Out": out_var, "XShape": x_shape},
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stop_gradient=True,
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)
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if framework.in_pir_mode():
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index_tensor = paddle.zeros(
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[len(idx_list)], dtype=core.DataType.INT64
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)
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index_tensor.stop_gradient = True
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else:
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index_tensor = block.create_var(
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name=unique_name.generate('scatter_index'),
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persistable=False,
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stop_gradient=True,
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)
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if framework.in_dygraph_mode():
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with base.dygraph.no_grad():
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tmp_tensor = framework._create_tensor()
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_C_ops.assign_value_(
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tmp_tensor,
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[len(idx_list)],
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VarDesc.VarType.INT64,
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idx_list,
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_current_expected_place(),
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)
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tmp_tensor._share_underline_tensor_to(index_tensor)
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elif framework.in_pir_mode():
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_C_ops.assign_value_(
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index_tensor,
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[len(idx_list)],
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core.DataType.INT64,
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idx_list,
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_current_expected_place(),
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)
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else:
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block.append_op(
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type='assign_value',
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outputs={'Out': index_tensor},
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attrs={
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'dtype': VarDesc.VarType.INT64,
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'shape': [len(idx_list)],
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'values': idx_list,
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},
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stop_gradient=True,
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)
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if framework.in_pir_mode():
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value_tensor = paddle.zeros(
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[len(value_list)], dtype=core.DataType.FLOAT32
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)
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value_tensor.stop_gradient = True
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else:
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value_tensor = block.create_var(
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name=unique_name.generate('scatter_value'),
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persistable=False,
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stop_gradient=True,
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)
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if framework.in_dygraph_mode():
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with base.dygraph.no_grad():
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tmp_tensor = framework._create_tensor()
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_C_ops.assign_value_(
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tmp_tensor,
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[len(value_list)],
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VarDesc.VarType.FP32,
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value_list,
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_current_expected_place(),
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)
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tmp_tensor._share_underline_tensor_to(value_tensor)
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elif framework.in_pir_mode():
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_C_ops.assign_value_(
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value_tensor,
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[len(value_list)],
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core.DataType.FLOAT32,
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value_list,
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_current_expected_place(),
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)
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else:
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block.append_op(
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type='assign_value',
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outputs={'Out': value_tensor},
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attrs={
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'dtype': VarDesc.VarType.FP32,
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'shape': [len(value_list)],
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'values': value_list,
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},
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stop_gradient=True,
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)
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if framework.in_dygraph_mode():
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with base.dygraph.no_grad():
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tmp_out = _C_ops.scatter(
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out_var, index_tensor, value_tensor, True
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)
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tmp_out._share_underline_tensor_to(out_var)
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tmp_reshape_out = _C_ops.reshape(out_var, origin_shape)
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tmp_reshape_out._share_underline_tensor_to(out_var)
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if var.dtype != VarDesc.VarType.FP32:
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tmp_cast_out = _C_ops.cast(out_var, var.dtype)
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tmp_cast_out._share_underline_tensor_to(var)
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elif framework.in_pir_mode():
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out_var = _C_ops.scatter(out_var, index_tensor, value_tensor, True)
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out_var = _C_ops.reshape(out_var, origin_shape)
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if var.dtype != core.DataType.FLOAT32:
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return _C_ops.cast(out_var, var.dtype)
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return out_var
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else:
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op = block.append_op(
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type="scatter",
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inputs={
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"X": out_var,
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"Ids": index_tensor,
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"Updates": value_tensor,
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},
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attrs={'overwrite': True},
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outputs={"Out": out_var},
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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([out_var.name, "XShape"])),
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dtype=out_dtype,
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shape=out_var.shape,
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type=VarDesc.VarType.DENSE_TENSOR,
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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="reshape2",
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inputs={"X": out_var},
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attrs={'shape': origin_shape},
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outputs={"Out": out_var, "XShape": x_shape},
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stop_gradient=True,
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)
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if var.dtype != VarDesc.VarType.FP32:
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block.append_op(
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type="cast",
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inputs={"X": out_var},
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outputs={"Out": var},
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attrs={"in_dtype": out_var.dtype, "out_dtype": var.dtype},
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stop_gradient=True,
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)
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if not in_dynamic_mode():
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var.op = op
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return op
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