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286 lines
10 KiB
286 lines
10 KiB
# Copyright (c) 2018 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 print_function
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from .. import core
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from ..framework import Variable, unique_name
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from .layer_function_generator import OpProtoHolder
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from ..initializer import force_init_on_cpu
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_supported_int_dtype_ = [
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core.VarDesc.VarType.UINT8,
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core.VarDesc.VarType.INT8,
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core.VarDesc.VarType.INT16,
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core.VarDesc.VarType.INT32,
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core.VarDesc.VarType.INT64,
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]
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def monkey_patch_variable():
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def unique_tmp_name():
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return unique_name.generate("tmp")
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def safe_get_dtype(var):
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try:
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dtype = var.dtype
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except:
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raise ValueError("Cannot get data type from %s", var.name)
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return dtype
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def current_block(var):
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return var.block
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def create_new_tmp_var(block, dtype):
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tmp_name = unique_tmp_name()
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return block.create_var(name=tmp_name, dtype=dtype)
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def create_tensor(block, value, dtype, shape):
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value = float(value)
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var = create_new_tmp_var(block, dtype)
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block.append_op(
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type="fill_constant",
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outputs={'Out': [var]},
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attrs={
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'dtype': var.dtype,
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'shape': shape,
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'value': value,
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'force_cpu': force_init_on_cpu()
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},
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stop_gradient=True)
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var.stop_gradient = True
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return var
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def create_scalar(block, value, dtype):
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return create_tensor(block, value, dtype, shape=[1])
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def create_tensor_with_batchsize(ref_var, value, dtype):
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assert isinstance(ref_var, Variable)
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value = float(value)
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block = current_block(ref_var)
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var = create_new_tmp_var(block, dtype)
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batch_dim = -1
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for i, d in enumerate(ref_var.shape):
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if d < 0:
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batch_dim = i
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break
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assert batch_dim != -1
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block.append_op(
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type='fill_constant_batch_size_like',
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outputs={'Out': [var]},
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inputs={'Input': [ref_var]},
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attrs={
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'shape': ref_var.shape,
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'value': value,
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'input_dim_idx': batch_dim,
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'output_dim_idx': batch_dim
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},
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stop_gradient=True)
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var.stop_gradient = True
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return var
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def astype(self, dtype):
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"""
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**Notes**:
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**The variable must be a** :ref:`api_fluid_Tensor`
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Cast a variable to a specified data type.
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Args:
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self(Variable): The source variable
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dtype: The target data type
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Returns:
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Variable: Variable with new dtype
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Examples:
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In Static Graph Mode:
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.. code-block:: python
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import paddle.fluid as fluid
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startup_prog = fluid.Program()
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main_prog = fluid.Program()
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with fluid.program_guard(startup_prog, main_prog):
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original_variable = fluid.data(name = "new_variable", shape=[2,2], dtype='float32')
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new_variable = original_variable.astype('int64')
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print("new var's dtype is: {}".format(new_variable.dtype))
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In Dygraph Mode:
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.. code-block:: python
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import paddle.fluid as fluid
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import numpy as np
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x = np.ones([2, 2], np.float32)
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with fluid.dygraph.guard():
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original_variable = fluid.dygraph.to_variable(x)
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print("original var's dtype is: {}, numpy dtype is {}".format(original_variable.dtype, original_variable.numpy().dtype))
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new_variable = original_variable.astype('int64')
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print("new var's dtype is: {}, numpy dtype is {}".format(new_variable.dtype, new_variable.numpy().dtype))
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"""
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block = current_block(self)
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out = create_new_tmp_var(block, dtype)
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block.append_op(
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type="cast",
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inputs={"X": [self]},
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outputs={"Out": [out]},
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attrs={"in_dtype": self.dtype,
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"out_dtype": out.dtype})
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return out
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def _scalar_elementwise_op_(var, scale, bias):
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block = current_block(var)
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out = create_new_tmp_var(block, var.dtype)
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block.append_op(
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type="scale",
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inputs={"X": [var]},
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outputs={"Out": [out]},
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attrs={"scale": scale,
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"bias": bias})
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return out
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def _scalar_elementwise_add_(var, value):
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return _scalar_elementwise_op_(var, 1.0, value)
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def _scalar_elementwise_sub_(var, value):
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return _scalar_elementwise_op_(var, 1.0, -value)
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def _scalar_elementwise_rsub_(var, value):
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return _scalar_elementwise_op_(var, -1.0, value)
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def _scalar_elementwise_mul_(var, value):
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return _scalar_elementwise_op_(var, value, 0.0)
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def _scalar_elementwise_div_(var, value):
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return _scalar_elementwise_op_(var, 1.0 / value, 0.0)
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def _elemwise_method_creator_(method_name,
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op_type,
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reverse=False,
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scalar_method=None):
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def __impl__(self, other_var):
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# FIXME(zjl): elementwise_div between integers cannot be converted to scale,
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# which may lose accuracy. This is a hot fix for release 1.6.
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if scalar_method is not None and not (
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op_type == 'elementwise_div' and
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self.dtype in _supported_int_dtype_):
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if isinstance(other_var, float):
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if self.dtype in _supported_int_dtype_:
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assert other_var == int(other_var), \
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"float value {} cannot convert to integer".format(other_var)
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return scalar_method(self, other_var)
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elif isinstance(other_var, int):
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return scalar_method(self, float(other_var))
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lhs_dtype = safe_get_dtype(self)
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if not isinstance(other_var, Variable):
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if reverse:
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has_batch_size = False
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for elem in self.shape:
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if elem < 0:
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has_batch_size = True
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break
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if not has_batch_size:
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other_var = create_tensor(
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current_block(self),
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other_var,
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dtype=lhs_dtype,
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shape=self.shape)
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else:
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other_var = create_tensor_with_batchsize(
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self, other_var, lhs_dtype)
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else:
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# add fill_op to current_block
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other_var = create_scalar(
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current_block(self), value=other_var, dtype=lhs_dtype)
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rhs_dtype = safe_get_dtype(other_var)
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if lhs_dtype != rhs_dtype:
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other_var = astype(other_var, lhs_dtype)
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if reverse:
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tmp = self
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self = other_var
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other_var = tmp
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out = create_new_tmp_var(current_block(self), dtype=lhs_dtype)
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axis = -1
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if other_var.shape[0] == -1:
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axis = 0
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assert len(self.shape) >= len(other_var.shape), (
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"The rank of the first argument of an binary operator cannot "
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"be smaller than the rank of its second argument: %s vs %s" %
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(len(self.shape), len(other_var.shape)))
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current_block(self).append_op(
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type=op_type,
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inputs={'X': [self],
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'Y': [other_var]},
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outputs={'Out': out},
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attrs={'axis': axis})
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return out
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comment = OpProtoHolder.instance().get_op_proto(op_type).comment
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__impl__.__doc__ = """
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{0}
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Args:
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self(Variable): left hand variable
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other_var(Variable|float|int): right hand variable
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Returns:
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Variable
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""".format(comment)
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__impl__.__name__ = method_name
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return __impl__
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# inject methods
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for method_name, op_type, reverse, scalar_method in (
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("__add__", "elementwise_add", False, _scalar_elementwise_add_),
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# a+b == b+a. Do not need to reverse explicitly
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("__radd__", "elementwise_add", False, _scalar_elementwise_add_),
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("__sub__", "elementwise_sub", False, _scalar_elementwise_sub_),
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("__rsub__", "elementwise_sub", True, _scalar_elementwise_rsub_),
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("__mul__", "elementwise_mul", False, _scalar_elementwise_mul_),
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# a*b == b*a. Do not need to reverse explicitly
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("__rmul__", "elementwise_mul", False, _scalar_elementwise_mul_),
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("__div__", "elementwise_div", False, _scalar_elementwise_div_),
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("__truediv__", "elementwise_div", False, _scalar_elementwise_div_),
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("__rdiv__", "elementwise_div", True, None),
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("__rtruediv__", "elementwise_div", True, None),
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("__pow__", "elementwise_pow", False, None),
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("__rpow__", "elementwise_pow", True, None),
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("__floordiv__", "elementwise_floordiv", False, None),
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("__mod__", "elementwise_mod", False, None),
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# for logical compare
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("__eq__", "equal", False, None),
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("__ne__", "not_equal", False, None),
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("__lt__", "less_than", False, None),
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("__le__", "less_equal", False, None),
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("__gt__", "greater_than", False, None),
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("__ge__", "greater_equal", False, None)):
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setattr(Variable, method_name,
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_elemwise_method_creator_(method_name, op_type, reverse,
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scalar_method))
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Variable.astype = astype
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