| from optparse import OptionParser |
| import yaml |
| from collections import OrderedDict |
| |
| import cwrap_parser |
| import nn_parse |
| import native_parse |
| import preprocess_declarations |
| import function_wrapper |
| import copy_wrapper |
| |
| from code_template import CodeTemplate |
| |
| |
| # This file is the top-level entry point for code generation in ATen. |
| # It takes an arbitrary number of arguments specifying metadata files to |
| # process (.cwrap, .yaml and .h) and outputs a number generated header |
| # and cpp files in ATen/ (see invocations of 'write' for each file that |
| # is written.) It is invoked from cmake; look for the 'cwrap_files' |
| # variable for an up-to-date list of files which are passed. |
| |
| |
| parser = OptionParser() |
| parser.add_option('-s', '--source-path', help='path to source directory for ATen', |
| action='store', default='.') |
| parser.add_option('-o', '--output-dependencies', |
| help='only output a list of dependencies', action='store') |
| parser.add_option('-n', '--no-cuda', action='store_true') |
| |
| options, files = parser.parse_args() |
| |
| |
| class FileManager(object): |
| def __init__(self): |
| self.filenames = set() |
| self.outputs_written = False |
| self.undeclared_files = [] |
| |
| def will_write(self, filename): |
| filename = "ATen/" + filename |
| if self.outputs_written: |
| raise Exception("'will_write' can only be called before " + |
| "the call to write_outputs, refactor so outputs are registered " + |
| "before running the generators") |
| self.filenames.add(filename) |
| |
| def _write_if_changed(self, filename, contents): |
| try: |
| with open(filename, 'r') as f: |
| old_contents = f.read() |
| except IOError: |
| old_contents = None |
| if contents != old_contents: |
| with open(filename, 'w') as f: |
| f.write(contents) |
| |
| def write_outputs(self, filename): |
| """Write a file containing the list of all outputs which are |
| generated by this script.""" |
| self._write_if_changed( |
| filename, |
| ''.join(name + ";" for name in sorted(self.filenames))) |
| self.outputs_written = True |
| |
| def write(self, filename, s): |
| filename = "ATen/" + filename |
| self._write_if_changed(filename, s) |
| if filename not in self.filenames: |
| self.undeclared_files.append(filename) |
| else: |
| self.filenames.remove(filename) |
| |
| def check_all_files_written(self): |
| if len(self.undeclared_files) > 0: |
| raise Exception( |
| "trying to write files {} which are not ".format(self.undeclared_files) + |
| "in the list of outputs this script produces. " + |
| "use will_write to add them.") |
| if len(self.filenames) > 0: |
| raise Exception("Outputs declared with 'will_write' were " + |
| "never written: {}".format(self.filenames)) |
| |
| |
| TEMPLATE_PATH = options.source_path + "/templates" |
| GENERATOR_DERIVED = CodeTemplate.from_file( |
| TEMPLATE_PATH + "/GeneratorDerived.h") |
| STORAGE_DERIVED_CPP = CodeTemplate.from_file( |
| TEMPLATE_PATH + "/StorageDerived.cpp") |
| STORAGE_DERIVED_H = CodeTemplate.from_file(TEMPLATE_PATH + "/StorageDerived.h") |
| |
| TYPE_DERIVED_CPP = CodeTemplate.from_file(TEMPLATE_PATH + "/TypeDerived.cpp") |
| TYPE_DERIVED_H = CodeTemplate.from_file(TEMPLATE_PATH + "/TypeDerived.h") |
| TYPE_H = CodeTemplate.from_file(TEMPLATE_PATH + "/Type.h") |
| TYPE_CPP = CodeTemplate.from_file(TEMPLATE_PATH + "/Type.cpp") |
| |
| TENSOR_DERIVED_CPP = CodeTemplate.from_file( |
| TEMPLATE_PATH + "/TensorDerived.cpp") |
| TENSOR_SPARSE_CPP = CodeTemplate.from_file( |
| TEMPLATE_PATH + "/TensorSparse.cpp") |
| TENSOR_DENSE_CPP = CodeTemplate.from_file( |
| TEMPLATE_PATH + "/TensorDense.cpp") |
| |
| TENSOR_DERIVED_H = CodeTemplate.from_file(TEMPLATE_PATH + "/TensorDerived.h") |
| TENSOR_H = CodeTemplate.from_file(TEMPLATE_PATH + "/Tensor.h") |
| TENSOR_METHODS_H = CodeTemplate.from_file(TEMPLATE_PATH + "/TensorMethods.h") |
| |
| FUNCTIONS_H = CodeTemplate.from_file(TEMPLATE_PATH + "/Functions.h") |
| |
| NATIVE_FUNCTIONS_H = CodeTemplate.from_file(TEMPLATE_PATH + "/NativeFunctions.h") |
| |
| file_manager = FileManager() |
| |
| generators = { |
| 'CPUGenerator.h': { |
| 'name': 'CPU', |
| 'th_generator': 'THGenerator * generator;', |
| 'header': 'TH/TH.h', |
| }, |
| 'CUDAGenerator.h': { |
| 'name': 'CUDA', |
| 'th_generator': 'THCGenerator * generator;', |
| 'header': 'THC/THC.h' |
| }, |
| } |
| |
| backends = ['CPU'] |
| if not options.no_cuda: |
| backends.append('CUDA') |
| |
| densities = ['Dense', 'Sparse'] |
| |
| # scalar_name, c_type, accreal, th_scalar_type, is_floating_type |
| scalar_types = [ |
| ('Byte', 'uint8_t', 'Long', 'uint8_t', False), |
| ('Char', 'int8_t', 'Long', 'int8_t', False), |
| ('Double', 'double', 'Double', 'double', True), |
| ('Float', 'float', 'Double', 'float', True), |
| ('Int', 'int', 'Long', 'int32_t', False), |
| ('Long', 'int64_t', 'Long', 'int64_t', False), |
| ('Short', 'int16_t', 'Long', 'int16_t', False), |
| ('Half', 'Half', 'Double', 'THHalf', True), |
| ] |
| |
| # shared environment for non-derived base classes Type.h Tensor.h Storage.h |
| top_env = { |
| 'type_registrations': [], |
| 'type_headers': [], |
| 'type_method_declarations': [], |
| 'type_method_definitions': [], |
| 'type_method_inline_definitions': [], |
| 'tensor_method_declarations': [], |
| 'tensor_method_definitions': [], |
| 'function_declarations': [], |
| 'function_definitions': [], |
| 'type_ids': [], |
| 'native_function_declarations': [], |
| } |
| |
| |
| def dict_representer(dumper, data): |
| return dumper.represent_dict(data.items()) |
| |
| |
| def postprocess_output_declarations(output_declarations): |
| # ensure each return has a name associated with it |
| for decl in output_declarations: |
| has_named_ret = False |
| for n, ret in enumerate(decl['returns']): |
| if 'name' not in ret: |
| assert not has_named_ret |
| if decl['inplace']: |
| ret['name'] = 'self' |
| elif len(decl['returns']) == 1: |
| ret['name'] = 'result' |
| else: |
| ret['name'] = 'result' + str(n) |
| else: |
| has_named_ret = True |
| |
| return output_declarations |
| |
| |
| def format_yaml(data): |
| if options.output_dependencies: |
| # yaml formatting is slow so don't do it if we will ditch it. |
| return "" |
| noalias_dumper = yaml.dumper.SafeDumper |
| noalias_dumper.ignore_aliases = lambda self, data: True |
| # Support serializing OrderedDict |
| noalias_dumper.add_representer(OrderedDict, dict_representer) |
| return yaml.dump(data, default_flow_style=False, Dumper=noalias_dumper) |
| |
| |
| def generate_storage_type_and_tensor(backend, density, scalar_type, declarations): |
| scalar_name, c_type, accreal, th_scalar_type, is_floating_type = scalar_type |
| env = {} |
| density_tag = 'Sparse' if density == 'Sparse' else '' |
| th_density_tag = 'S' if density == 'Sparse' else '' |
| env['Density'] = density |
| env['ScalarName'] = scalar_name |
| env['ScalarType'] = c_type |
| env['THScalarType'] = th_scalar_type |
| env['AccScalarName'] = accreal |
| env['isFloatingType'] = is_floating_type |
| env['isIntegralType'] = not is_floating_type |
| env['Storage'] = "{}{}Storage".format(backend, scalar_name) |
| env['Type'] = "{}{}{}Type".format(density_tag, backend, scalar_name) |
| env['Tensor'] = "{}{}{}Tensor".format(density_tag, backend, scalar_name) |
| env['DenseTensor'] = "{}{}Tensor".format(backend, scalar_name) |
| env['SparseTensor'] = "Sparse{}{}Tensor".format(backend, scalar_name) |
| env['Backend'] = density_tag + backend |
| env['DenseBackend'] = backend |
| |
| # used for generating switch logic for external functions |
| tag = density_tag + backend + scalar_name |
| env['TypeID'] = 'TypeID::' + tag |
| top_env['type_ids'].append(tag + ',') |
| |
| if backend == 'CUDA': |
| env['th_headers'] = [ |
| '#include <THC/THC.h>', |
| '#include <THCUNN/THCUNN.h>', |
| '#undef THNN_', |
| '#undef THCIndexTensor_', |
| '#include <THCS/THCS.h>', |
| '#undef THCIndexTensor_', |
| ] |
| env['extra_cuda_headers'] = ['#include <ATen/cuda/CUDAHalf.h>'] |
| sname = '' if scalar_name == "Float" else scalar_name |
| env['THType'] = 'Cuda{}'.format(sname) |
| env['THStorage'] = 'THCuda{}Storage'.format(sname) |
| if density == 'Dense': |
| env['THTensor'] = 'THCuda{}Tensor'.format(sname) |
| else: |
| env['THTensor'] = 'THCS{}Tensor'.format(scalar_name) |
| env['THIndexTensor'] = 'THCudaLongTensor' |
| env['state'] = ['context->thc_state'] |
| env['isCUDA'] = 'true' |
| env['storage_device'] = 'return storage->device;' |
| env['Generator'] = 'CUDAGenerator' |
| else: |
| env['th_headers'] = [ |
| '#include <TH/TH.h>', |
| '#include <THNN/THNN.h>', |
| '#undef THNN_', |
| '#include <THS/THS.h>', |
| ] |
| env['extra_cuda_headers'] = [] |
| env['THType'] = scalar_name |
| env['THStorage'] = "TH{}Storage".format(scalar_name) |
| env['THTensor'] = 'TH{}{}Tensor'.format(th_density_tag, scalar_name) |
| env['THIndexTensor'] = 'THLongTensor' |
| env['state'] = [] |
| env['isCUDA'] = 'false' |
| env['storage_device'] = 'throw std::runtime_error("CPU storage has no device");' |
| env['Generator'] = 'CPUGenerator' |
| env['AS_REAL'] = env['ScalarType'] |
| if scalar_name == "Half": |
| env['SparseTensor'] = 'Tensor' |
| if backend == "CUDA": |
| env['to_th_type'] = 'HalfFix<__half,Half>' |
| env['to_at_type'] = 'HalfFix<Half,__half>' |
| env['AS_REAL'] = 'convert<half,double>' |
| env['THScalarType'] = 'half' |
| else: |
| env['to_th_type'] = 'HalfFix<THHalf,Half>' |
| env['to_at_type'] = 'HalfFix<Half,THHalf>' |
| elif scalar_name == 'Long': |
| env['to_th_type'] = 'long' |
| env['to_at_type'] = 'int64_t' |
| else: |
| env['to_th_type'] = '' |
| env['to_at_type'] = '' |
| |
| declarations, definitions = function_wrapper.create_derived( |
| env, declarations) |
| env['type_derived_method_declarations'] = declarations |
| env['type_derived_method_definitions'] = definitions |
| |
| if density != 'Sparse': |
| # there are no special storage types for Sparse, they are composed |
| # of Dense tensors |
| file_manager.write(env['Storage'] + ".cpp", STORAGE_DERIVED_CPP.substitute(env)) |
| file_manager.write(env['Storage'] + ".h", STORAGE_DERIVED_H.substitute(env)) |
| env['TensorDenseOrSparse'] = TENSOR_DENSE_CPP.substitute(env) |
| env['THTensor_nDimension'] = 'tensor->nDimension' |
| else: |
| env['TensorDenseOrSparse'] = TENSOR_SPARSE_CPP.substitute(env) |
| env['THTensor_nDimension'] = 'tensor->nDimensionI + tensor->nDimensionV' |
| |
| file_manager.write(env['Type'] + ".cpp", TYPE_DERIVED_CPP.substitute(env)) |
| file_manager.write(env['Type'] + ".h", TYPE_DERIVED_H.substitute(env)) |
| |
| file_manager.write(env['Tensor'] + ".cpp", TENSOR_DERIVED_CPP.substitute(env)) |
| file_manager.write(env['Tensor'] + ".h", TENSOR_DERIVED_H.substitute(env)) |
| |
| type_register = (('context->type_registry[static_cast<int>(Backend::{})]' + |
| '[static_cast<int>(ScalarType::{})].reset(new {}(context));') |
| .format(env['Backend'], scalar_name, env['Type'])) |
| top_env['type_registrations'].append(type_register) |
| top_env['type_headers'].append( |
| '#include "ATen/{}.h"'.format(env['Type'])) |
| |
| return env |
| |
| |
| def iterate_types(): |
| for backend in backends: |
| for density in densities: |
| for scalar_type in scalar_types: |
| if density == 'Sparse' and scalar_type[0] == 'Half': |
| # THS does not do half type yet. |
| continue |
| yield (backend, density, scalar_type) |
| |
| |
| ################### |
| # declare what files will be output _before_ we do any work |
| # so that the script runs quickly when we are just querying the |
| # outputs |
| def declare_outputs(): |
| files = ['Declarations.yaml', 'Type.h', 'Type.cpp', 'Tensor.h', |
| 'TensorMethods.h', 'Functions.h', |
| 'Copy.cpp', 'NativeFunctions.h'] |
| for f in files: |
| file_manager.will_write(f) |
| for fname in sorted(generators.keys()): |
| file_manager.will_write(fname) |
| for backend, density, scalar_types in iterate_types(): |
| scalar_name = scalar_types[0] |
| full_backend = "Sparse" + backend if density == "Sparse" else backend |
| for kind in ["Storage", "Type", "Tensor"]: |
| if kind == 'Storage' and density == "Sparse": |
| continue |
| file_manager.will_write("{}{}{}.h".format(full_backend, scalar_name, kind)) |
| file_manager.will_write("{}{}{}.cpp".format(full_backend, scalar_name, kind)) |
| |
| |
| def generate_outputs(): |
| cwrap_files = [f for f in files if f.endswith('.cwrap')] |
| nn_files = [f for f in files if f.endswith('nn.yaml') or f.endswith('.h')] |
| native_files = [f for f in files if f.endswith('native_functions.yaml')] |
| |
| declarations = [d |
| for file in cwrap_files |
| for d in cwrap_parser.parse(file)] |
| |
| declarations += nn_parse.run(nn_files) |
| declarations += native_parse.run(native_files) |
| declarations = preprocess_declarations.run(declarations) |
| for fname, env in generators.items(): |
| file_manager.write(fname, GENERATOR_DERIVED.substitute(env)) |
| |
| # note: this will fill in top_env['type/tensor_method_declarations/definitions'] |
| # and modify the declarations to include any information that will all_backends |
| # be used by function_wrapper.create_derived |
| output_declarations = function_wrapper.create_generic(top_env, declarations) |
| output_declarations = postprocess_output_declarations(output_declarations) |
| file_manager.write("Declarations.yaml", format_yaml(output_declarations)) |
| |
| # populated by generate_storage_type_and_tensor |
| all_types = [] |
| |
| for backend, density, scalar_type in iterate_types(): |
| all_types.append(generate_storage_type_and_tensor( |
| backend, density, scalar_type, declarations)) |
| |
| file_manager.write('Type.h', TYPE_H.substitute(top_env)) |
| file_manager.write('Type.cpp', TYPE_CPP.substitute(top_env)) |
| |
| file_manager.write('Tensor.h', TENSOR_H.substitute(top_env)) |
| file_manager.write('TensorMethods.h', TENSOR_METHODS_H.substitute(top_env)) |
| file_manager.write('Functions.h', FUNCTIONS_H.substitute(top_env)) |
| |
| file_manager.write('Copy.cpp', copy_wrapper.create(all_types)) |
| file_manager.write('NativeFunctions.h', NATIVE_FUNCTIONS_H.substitute(top_env)) |
| |
| file_manager.check_all_files_written() |
| |
| |
| declare_outputs() |
| if options.output_dependencies is not None: |
| file_manager.write_outputs(options.output_dependencies) |
| else: |
| generate_outputs() |
