chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
@@ -0,0 +1,430 @@
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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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import argparse
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import re
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import yaml
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from api_base import BaseAPI
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class BackwardAPI(BaseAPI):
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def __init__(self, backward_item_yaml):
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super().__init__(backward_item_yaml)
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self.check_args(backward_item_yaml['forward'])
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self.no_need_buffer = self.parse_no_need_buffer(backward_item_yaml)
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def get_api_name(self, api_item_yaml):
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return api_item_yaml['backward_op']
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def parse_forward_config(self, forward_config):
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# api_name (const Tensor& input, ... , int attr, ...) -> Tensor(out)
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result = re.search(
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r"(?P<op>[a-z][a-z0-9_]+)\s*(?P<args>\([^\)]+\))\s*->\s*(?P<outputs>.+)",
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forward_config,
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)
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api = result.group('op')
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(
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_,
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outputs,
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_,
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) = self.parse_output(self.api, result.group('outputs'))
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outputs = [item.split('@')[0] for item in outputs]
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fw_inputs, fw_attrs = self.parse_input_and_attr(
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api, result.group('args')
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)
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return api, fw_inputs, fw_attrs, outputs
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def parse_no_need_buffer(self, api_item_yaml):
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no_need_buffer = []
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if 'no_need_buffer' in api_item_yaml:
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no_need_buffer = [
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item.strip()
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for item in api_item_yaml['no_need_buffer'].split(',')
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]
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return no_need_buffer
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def check_args(self, forward_config):
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# parse the forward and backward config
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_, fw_inputs, fw_attrs, fw_outputs = self.parse_forward_config(
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forward_config
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)
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# check the inputs of backward
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for input in self.inputs['names']:
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if input not in fw_inputs['names'] and input not in fw_outputs:
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if input.endswith('_grad'):
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original_name = input[:-5]
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assert original_name in fw_outputs, (
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f"{self.api} : Input Tensor error: the input tensor({input}) of backward should be an input or output or grad of output in forward api. \
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Please check the forward of {self.api} in yaml."
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)
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# check the attributes of backward
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for attr in self.attrs['names']:
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assert (
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attr in fw_attrs['names']
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and self.attrs['attr_info'][attr][0]
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== fw_attrs['attr_info'][attr][0]
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) or self.attrs['attr_info'][attr][1] is not None, (
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f"{self.api} : Attribute error: The attribute({attr}) of backward isn't consistent with forward api or doesn't have default value. \
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Please check the args of {self.api} in yaml."
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)
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# check the output of backward
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assert len(self.outputs['types']) <= len(fw_inputs['names']), (
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f"{self.api} : Output error: The number of outputs should be less then the number of inputs of forward api. \
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Please check the output of {self.api} in yaml."
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)
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def get_declare_args(
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self, inplace_flag=False, grad_flag=False, append_predefined_out=False
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):
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return self.get_define_args(
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grad_flag=grad_flag, append_predefined_out=append_predefined_out
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)
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def get_define_args(
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self, inplace_flag=False, grad_flag=False, append_predefined_out=False
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):
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out_type_map = {
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'Tensor': 'Tensor*',
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'std::vector<Tensor>': 'std::vector<Tensor*>',
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}
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inputs_and_attrs = super().get_define_args(
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grad_flag=grad_flag, append_predefined_out=False
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)
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outs = []
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for i, name in enumerate(self.outputs['names']):
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outs.append(
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out_type_map[self.outputs['types'][i]]
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+ ' '
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+ name.split('@')[0]
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)
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result = inputs_and_attrs + ', ' + ", ".join(outs)
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return result
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def gene_return_code(self):
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return ""
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def gene_api_declaration(
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self, grad_flag=False, append_predefined_out=False
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):
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if not self.is_base_api and not self.is_only_composite_api:
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invoke_func_name = self.invoke.split('(')[0]
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if (not invoke_func_name.endswith("_grad")) and (
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not invoke_func_name.endswith('_impl')
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):
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return ""
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if self.is_only_composite_api:
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return ""
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api_func_name = self.get_api_func_name()
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api_declaration = f"""
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PADDLE_API void {api_func_name}({self.get_declare_args()});
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"""
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return api_declaration
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def gene_kernel_backend_select(self):
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all_no_need_buffer = True
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for in_name in self.inputs['names']:
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if in_name not in self.no_need_buffer:
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all_no_need_buffer = False
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if all_no_need_buffer:
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return """
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kernel_backend = ParseBackend(egr::Controller::Instance().GetExpectedPlace());
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"""
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else:
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return super().gene_kernel_backend_select()
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def get_return_type(self, inplace_flag=False):
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return 'void'
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def gene_output(
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self,
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out_dtype_list,
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out_tensor_type_list=None,
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code_indent='',
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inplace_flag=False,
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):
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kernel_output = []
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output_names = []
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output_create = ""
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if len(out_dtype_list) == 1:
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kernel_output.append('kernel_out')
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output_names.append('kernel_out')
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inplace_assign = (
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" = " + self.inplace_map[self.outputs['names'][0]]
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if inplace_flag
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and self.inplace_map is not None
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and self.outputs['names'][0] in self.inplace_map
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else ""
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)
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output_create = ""
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set_out_func = (
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'SetKernelOutput'
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if out_tensor_type_list is None
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or out_tensor_type_list[0] == 'dense'
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else 'SetSelectedRowsKernelOutput'
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)
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if out_dtype_list[0] == 'std::vector<Tensor>':
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assert self.outputs['out_size_expr'] is not None, (
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f"{self.api}: The out size expr : '{{expr}}' should be set when output has Tensor[]. You can refer 'split' api."
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)
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output_create = (
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output_create
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+ f"""
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{code_indent} auto kernel_out = {set_out_func}(&{self.outputs['names'][0]});"""
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)
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else:
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output_create = (
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output_create
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+ f"""
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{code_indent} auto kernel_out = {set_out_func}({self.outputs['names'][0]});"""
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)
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elif len(out_dtype_list) > 1:
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output_create = ""
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for i, out_type_item in enumerate(out_dtype_list):
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kernel_output.append(f'kernel_out_{i}')
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output_names.append(f'kernel_out_{i}')
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set_out_func = (
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'SetKernelOutput'
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if out_tensor_type_list is None
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or out_tensor_type_list[i] == 'dense'
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else 'SetSelectedRowsKernelOutput'
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)
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if out_type_item == 'Tensor':
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if (
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inplace_flag
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and self.inplace_map is not None
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and self.outputs['names'][i] in self.inplace_map
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):
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output_create = (
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output_create
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+ f"""
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{code_indent} *{self.outputs['names'][i]} = {self.inplace_map[self.outputs['names'][i]]};"""
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)
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output_create = (
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output_create
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+ f"""
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{code_indent} auto kernel_out_{i} = {set_out_func}({self.outputs['names'][i]});"""
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)
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else:
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if (
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inplace_flag
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and self.inplace_map is not None
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and self.outputs['names'][i] in self.inplace_map
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):
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output_create = (
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output_create
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+ f"""
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{code_indent} *{self.outputs['names'][i]} = {self.inplace_map[self.outputs['names'][i]]};"""
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)
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assert self.outputs['out_size_expr'][i] is not None, (
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f"{self.api}: The out size expr : '{{expr}}' should be set when output has Tensor[]. You can refer 'split' api."
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)
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output_create = (
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output_create
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+ f"""
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{code_indent} auto kernel_out_{i} = {set_out_func}(&{self.outputs['names'][i]});"""
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)
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else:
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raise ValueError(
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f"{self.api} : Output error: the output should not be empty."
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)
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return kernel_output, output_names, output_create
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def gene_invoke_code(self, invoke_code, params_code):
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invoke_func_name = invoke_code.split('(')[0].strip()
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if invoke_func_name.endswith('_grad') or invoke_func_name.endswith(
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'_impl'
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):
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return f"""
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PADDLE_API {self.get_return_type()} {self.api}({params_code}) {{
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{invoke_code};
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}}"""
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else:
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return ""
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def header_include():
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return """
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#include <tuple>
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#include "paddle/phi/api/include/tensor.h"
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#include "paddle/phi/common/scalar.h"
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#include "paddle/phi/common/int_array.h"
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#include "paddle/utils/optional.h"
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"""
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def source_include(header_file_path, fw_header_file_path):
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return f"""
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#include "{header_file_path}"
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#include <memory>
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#include "glog/logging.h"
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#include "paddle/common/flags.h"
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#include "paddle/phi/api/lib/api_custom_impl.h"
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#include "paddle/phi/api/lib/api_gen_utils.h"
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#include "paddle/phi/api/lib/data_transform.h"
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#include "paddle/phi/api/lib/kernel_dispatch.h"
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#include "paddle/phi/common/type_traits.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "{fw_header_file_path}"
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#include "paddle/phi/infermeta/backward.h"
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#include "paddle/phi/infermeta/unary.h"
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#include "paddle/phi/infermeta/fusion.h"
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#include "paddle/phi/api/profiler/event_tracing.h"
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#include "paddle/phi/api/profiler/supplement_tracing.h"
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PD_DECLARE_bool(conv2d_disable_cudnn);
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COMMON_DECLARE_int32(low_precision_op_list);
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COMMON_DECLARE_bool(benchmark);
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"""
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def backward_api_namespace():
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return (
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"""
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namespace paddle {
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namespace experimental {
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""",
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"""
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} // namespace experimental
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} // namespace paddle
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""",
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)
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def generate_backward_api(
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backward_yaml_path,
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is_fused_backward_yaml,
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header_file_path,
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source_file_path,
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):
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bw_apis = []
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for each_api_yaml in backward_yaml_path:
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with open(each_api_yaml, 'r') as f:
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api_list = yaml.load(f, Loader=yaml.FullLoader)
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if api_list:
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bw_apis.extend(api_list)
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header_file = open(header_file_path, 'w')
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source_file = open(source_file_path, 'w')
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namespace = backward_api_namespace()
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header_file.write("#pragma once\n")
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header_file.write(header_include())
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header_file.write(namespace[0])
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include_header_file = (
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"paddle/phi/api/backward/fused_backward_api_base.h"
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if is_fused_backward_yaml
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else "paddle/phi/api/backward/backward_api_base.h"
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)
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include_fw_header_file = (
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"paddle/phi/api/include/fused_api.h"
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if is_fused_backward_yaml
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else "paddle/phi/api/include/api.h"
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)
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source_file.write(
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source_include(include_header_file, include_fw_header_file)
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)
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source_file.write(namespace[0])
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# not all fused ops support dygraph
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if is_fused_backward_yaml is True:
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new_bw_apis = [
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bw_api
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for bw_api in bw_apis
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if "support_dygraph_mode" in bw_api
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and bw_api["support_dygraph_mode"] is True
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]
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bw_apis = new_bw_apis
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for bw_api in bw_apis:
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bw_api = BackwardAPI(bw_api)
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header_file.write(bw_api.gene_api_declaration())
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source_file.write(bw_api.gene_api_code())
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header_file.write(namespace[1])
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source_file.write(namespace[1])
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header_file.close()
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source_file.close()
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def main():
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parser = argparse.ArgumentParser(
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description='Generate PaddlePaddle C++ backward API files'
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)
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parser.add_argument(
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'--backward_yaml_path',
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help='path to backward yaml file',
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nargs='+',
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default=['paddle/phi/ops/yaml/backward.yaml'],
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)
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parser.add_argument(
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'--is_fused_backward_yaml',
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help='flag of fused backward yaml',
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action='store_true',
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)
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parser.add_argument(
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'--backward_header_path',
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help='output of generated backward header code file',
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default='paddle/phi/api/backward/backward_api_base.h',
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)
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parser.add_argument(
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'--backward_source_path',
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help='output of generated backward source code file',
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default='paddle/phi/api/lib/backward_api_base.cc',
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)
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options = parser.parse_args()
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backward_yaml_path = options.backward_yaml_path
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is_fused_backward_yaml = options.is_fused_backward_yaml
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header_file_path = options.backward_header_path
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source_file_path = options.backward_source_path
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generate_backward_api(
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backward_yaml_path,
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is_fused_backward_yaml,
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header_file_path,
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source_file_path,
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)
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if __name__ == '__main__':
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main()
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Reference in New Issue
Block a user