mirror of
https://github.com/zebrajr/pytorch.git
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16c72a5a6b
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/46978 Refactored and added type annotations to the most part of the file. Some top-level codegen functions are called by other codegen scripts. Will migrate them in subsequent PRs. Test Plan: Imported from OSS Reviewed By: ezyang Differential Revision: D24589210 Pulled By: ljk53 fbshipit-source-id: e0c7e5b3672b41983f321400c2e2330d1462e76e
968 lines
35 KiB
Python
968 lines
35 KiB
Python
# Generates Python bindings for ATen functions
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#
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# The bindings are generated as methods on python_variable or functions on the
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# torch._C._nn. torch._C._fft, or torch._C._linalg objects.
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#
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# Code tries to stick to the following rules:
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#
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# - templates should be colocated with the functions that use them.
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# no templates are currently shared between functions, but if that
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# happens, maybe put the template with the first one
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#
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# - don't use environment dictionaries when calling template.substitute().
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# pass named arguments directly for everything, otherwise it's much too
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# hard to track what's actually being used and by who
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#
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# - colocate any new hacks/adjustments with existing ones of the same kind.
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# ideally in a data structure rather than code if possible. See e.g.
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# SCHEMA_DEFAULT_CONVERSION_HACKS, etc.
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#
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# - similarly, conversions from one format to another should ideally happen
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# all at once in a single place.
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#
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# - no nontrivial nested functions. couple-liners are ok but please no more.
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# especially avoid functions that read/write outer variables defined far away.
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#
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# - raise RuntimeError instead of asserting, and put as much
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# information as is available into the message. I.e. no need to
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# plumb in new params whose only purpose is to fill out an error
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# message, but use what's there
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#
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from collections import defaultdict
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import itertools
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import re
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from .gen_variable_type import DONT_RECORD_TRACE
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from .utils import write, is_tensor_method
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from tools.codegen.code_template import CodeTemplate
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from tools.codegen.api.python import *
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from tools.codegen.gen import cpp_string, with_native_function
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from tools.codegen.model import *
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from typing import Dict, Optional, List, Any, Tuple, Set
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#
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# declarations blocklist
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# We skip codegen for these functions, for various reasons.
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# Future PRs will categorize this list and eliminate or hoist
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# them out of eager-only codegen.
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# See https://github.com/pytorch/pytorch/issues/30788
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#
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# These functions require manual Python bindings or are not exposed to Python
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SKIP_PYTHON_BINDINGS = [
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'alias', 'contiguous', 'is_cuda', 'is_sparse', 'size', 'stride',
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'.*_backward', '.*_backward_(out|input|weight|bias)', '.*_forward',
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'.*_forward_out', '_unsafe_view', 'tensor', '_?sparse_coo_tensor.*',
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'_arange.*', '_range.*', '_linspace.*', '_logspace.*',
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'_sparse_add_out', '_sparse_div.*', '_sparse_mul.*', '_sparse_sub.*', '_sparse_dense_add_out',
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'index', 'unique_dim_consecutive',
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'_indexCopy_', '_cumsum.*', '_cumprod.*', '_sum.*', '_prod.*',
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'_th_.*', '_thnn_.*',
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'arange.*', 'range.*', '_solve.*', '_inverse.*',
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'full(_out)?',
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'_cholesky.*', '_triangular_solve.*', '_qr.*', '_symeig.*', '_svd.*',
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'slice', 'randint(_out)?',
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'item', '_local_scalar_dense', 'to',
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'copy_sparse_to_sparse_', 'copy_',
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'numpy_T', # this needs to be an attribute in Python, not a function
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'nonzero(_(out|numpy))?',
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'set_quantizer_', # return types not supported yet
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'set_data',
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'.*_overrideable', # overrideable functions for backend extension
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'data', 'is_leaf', 'output_nr', '_version', 'requires_grad_', 'retain_grad', 'set_'
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]
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# These function signatures are not exposed to Python. Note that this signature
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# list does not support regex.
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SKIP_PYTHON_BINDINGS_SIGNATURES = [
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'add(Tensor, Scalar, Scalar)', 'add_(Tensor, Scalar, Scalar)',
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'sub(Tensor, Scalar, Scalar)', 'sub_(Tensor, Scalar, Scalar)',
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'mul(Tensor, Scalar)', 'mul_(Tensor, Scalar)',
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'div(Tensor, Scalar)', 'div_(Tensor, Scalar)',
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]
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def should_generate_python_binding(declaration):
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name = declaration['name']
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for pattern in SKIP_PYTHON_BINDINGS:
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if re.match('^' + pattern + '$', name):
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return False
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simple_types = [arg['simple_type'] for arg in declaration['arguments']]
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signature = '{}({})'.format(name, ', '.join(simple_types))
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for pattern in SKIP_PYTHON_BINDINGS_SIGNATURES:
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if pattern == signature:
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return False
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return True
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#
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# top-level codegen functions, called from gen_autograd
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#
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def get_py_variable_methods(declarations):
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"""
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Get declarations (grouped by name) which should be generated
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as methods on Tensor.
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"""
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def should_bind(declaration):
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return (should_generate_python_binding(declaration) and
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not is_nn_module_function(declaration) and
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is_tensor_method(declaration))
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return group_declarations_by_op_name([d for d in declarations if should_bind(d)])
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def gen_py_variable_methods(out, declarations, template_path):
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"""
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Generate Tensor methods.
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"""
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PY_VARIABLE_METHODS_CPP = CodeTemplate.from_file(template_path + '/python_variable_methods.cpp')
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py_variable_methods = get_py_variable_methods(declarations)
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env = create_python_bindings(py_variable_methods, is_python_method=True, module=None)
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write(out, 'python_variable_methods.cpp', PY_VARIABLE_METHODS_CPP, env)
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def get_py_nn_functions(declarations):
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"""
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Get declarations (grouped by name) which should be generated
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as functions in the "nn" module.
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"""
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def should_bind(declaration):
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return (should_generate_python_binding(declaration) and
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is_nn_module_function(declaration))
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return group_declarations_by_op_name([d for d in declarations if should_bind(d)])
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def gen_py_nn_functions(out, declarations, template_path):
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"""
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Generate functions in the "nn" module.
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"""
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PY_NN_FUNCTIONS_CPP = CodeTemplate.from_file(template_path + '/python_nn_functions.cpp')
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py_nn_functions = get_py_nn_functions(declarations)
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env = create_python_bindings(py_nn_functions, is_python_method=False, module="torch.nn")
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write(out, 'python_nn_functions.cpp', PY_NN_FUNCTIONS_CPP, env)
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def get_py_fft_functions(declarations):
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"""
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Get declarations (grouped by name) which should be generated
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as functions in the "fft" module.
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"""
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def should_bind(declaration):
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return (should_generate_python_binding(declaration) and
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is_fft_module_function(declaration))
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return group_declarations_by_op_name([d for d in declarations if should_bind(d)])
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def gen_py_fft_functions(out, declarations, template_path):
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"""
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Generate functions in the "fft" module.
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"""
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PY_FFT_FUNCTIONS_CPP = CodeTemplate.from_file(template_path + '/python_fft_functions.cpp')
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py_fft_functions = get_py_fft_functions(declarations)
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env = create_python_bindings(py_fft_functions, is_python_method=False, module="torch.fft")
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write(out, 'python_fft_functions.cpp', PY_FFT_FUNCTIONS_CPP, env)
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def get_py_linalg_functions(declarations):
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"""
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Get declarations (grouped by name) which should be generated
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as functions in the "linalg" module.
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"""
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def should_bind(declaration):
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return (should_generate_python_binding(declaration) and
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is_linalg_module_function(declaration))
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return group_declarations_by_op_name([d for d in declarations if should_bind(d)])
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def gen_py_linalg_functions(out, declarations, template_path):
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"""
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Generate functions in the "linalg" module.
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"""
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PY_LINALG_FUNCTIONS_CPP = CodeTemplate.from_file(template_path + '/python_linalg_functions.cpp')
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py_linalg_functions = get_py_linalg_functions(declarations)
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env = create_python_bindings(py_linalg_functions, is_python_method=False, module="torch.linalg")
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write(out, 'python_linalg_functions.cpp', PY_LINALG_FUNCTIONS_CPP, env)
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def get_py_torch_functions(declarations):
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"""
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Get declarations (grouped by name) which should be generated
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as functions in the "torch" module.
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"""
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def should_bind(declaration):
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return (should_generate_python_binding(declaration) and
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not is_nn_module_function(declaration) and
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not is_fft_module_function(declaration) and
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not is_linalg_module_function(declaration) and
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is_torch_function(declaration))
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return group_declarations_by_op_name([d for d in declarations if should_bind(d)])
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def gen_py_torch_functions(out, declarations, template_path):
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"""
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Generate functions in the "torch" module.
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"""
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PY_TORCH_FUNCTIONS_CPP = CodeTemplate.from_file(template_path + '/python_torch_functions.cpp')
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py_torch_functions = get_py_torch_functions(declarations)
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env = create_python_bindings(py_torch_functions, is_python_method=False, module="torch")
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write(out, 'python_torch_functions.cpp', PY_TORCH_FUNCTIONS_CPP, env)
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def group_declarations_by_op_name(declarations):
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groups = defaultdict(list)
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for d in declarations:
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groups[op_name(d)].append(d)
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return groups
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def create_python_bindings(python_functions, is_python_method, module):
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"""Generates Python bindings to ATen functions"""
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py_methods: List[str] = []
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py_method_defs: List[str] = []
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py_forwards: List[str] = []
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for name in sorted(python_functions.keys()):
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overloads = list(decl_to_signature_function_pair(decl, method=is_python_method)
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for decl in python_functions[name])
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py_methods.append(method_impl(name, module, overloads, method=is_python_method))
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py_method_defs.append(method_def(name, module, overloads, method=is_python_method))
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py_forwards.extend(forward_decls(name, overloads, method=is_python_method))
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return {
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'py_forwards': py_forwards,
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'py_methods': py_methods,
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'py_method_defs': py_method_defs,
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}
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#
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# declaration derived props, utils, etc.
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# declarations are dicts loaded from Declarations.yaml,
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# passed to our codegen methods by callers in gen_autograd
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#
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def get_pycname(name: str) -> str:
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return f'THPVariable_{name}'
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def is_noarg(overloads: Sequence[PythonSignatureNativeFunctionPair]) -> bool:
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return len(overloads) == 1 and overloads[0].signature.arguments_count() == 0
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def is_output(arg):
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return arg.get('output', False)
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def has_outputs(declaration):
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return any([is_output(arg) for arg in declaration['arguments']])
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def is_torch_function(declaration):
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return 'namespace' in declaration['method_of']
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def is_nn_module_function(declaration):
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return declaration.get('python_module') == 'nn'
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def is_fft_module_function(declaration):
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return declaration.get('python_module') == 'fft'
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def is_linalg_module_function(declaration):
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return declaration.get('python_module') == 'linalg'
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def op_name(declaration):
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name = declaration['name']
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if has_outputs(declaration):
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if not name.endswith("_out"):
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raise RuntimeError(
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'{} has output params, expecting name ending with \'_out\''.
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format(declaration['name']))
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return name[:-4]
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else:
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if name.endswith("_out"):
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raise RuntimeError(
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'{}: name ends with \'_out\', expecting output params'.
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format(declaration['name']))
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return name
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
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#
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# Named Tuple Codegen
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#
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
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# TODO: remove the copy of this method in 'tools/pyi/gen_pyi.py'.
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@with_native_function
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def namedtuple_fieldnames(f: NativeFunction) -> List[str]:
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returns = f.func.returns
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if len(returns) <= 1 or all(map(lambda r: r.name is None, returns)):
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return []
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else:
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if any(map(lambda r: r.name is None, returns)):
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# When building on Windows, `PyStructSequence_UnnamedField` could not be
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# resolved by the linker for some reason, which cause error in building:
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#
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# python_nn_functions.cpp.obj : error LNK2001: unresolved external symbol
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# PyStructSequence_UnnamedField
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#
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# Thus, at this point in time, we do not support unnamed
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# fields in namedtuple; you must either name all fields,
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# or none of them.
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raise ValueError("Unnamed field is not supported by codegen")
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return list(map(lambda r: str(r.name), returns))
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@with_native_function
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def gen_namedtuple_typename_key(f: NativeFunction) -> str:
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name = cpp.name(f.func)
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fieldnames = namedtuple_fieldnames(f)
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return '_'.join([name] + fieldnames)
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def emit_namedtuple_typedefs(
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overloads: Sequence[PythonSignatureNativeFunctionPair]
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) -> Tuple[List[str], Dict[str, str]]:
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"""
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Generate block of named tuple type def inits, and add typeref snippets
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to declarations that use them
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"""
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flddefnames: Dict[str, str] = {} # map from unique field name lists to field def name
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flddefs: List[str] = [] # field def declarations
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typenames: Dict[str, str] = {} # map from unique name + field name lists to typedef name
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typedefs: List[str] = [] # typedef declarations and init code
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for overload in overloads:
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fieldnames = namedtuple_fieldnames(overload.function)
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if not fieldnames:
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continue
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fn_key = '_'.join(fieldnames)
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fieldsname = flddefnames.get(fn_key)
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if fieldsname is None:
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fieldsname = f'NamedTuple_fields{"" if not flddefs else len(flddefs)}'
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flddefnames[fn_key] = fieldsname
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fields = ', '.join(f'{{"{fn}", ""}}' for fn in fieldnames)
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flddefs.append(f"""\
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static PyStructSequence_Field {fieldsname}[] = {{ {fields}, {{nullptr}} }};
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""")
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name = cpp.name(overload.function.func) # use @with_native_function?
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tn_key = gen_namedtuple_typename_key(overload.function)
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typename = typenames.get(tn_key)
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if typename is None:
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typename = f'NamedTuple{"" if not typedefs else len(typedefs)}'
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typenames[tn_key] = typename
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typedefs.append(f"""\
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static PyTypeObject {typename};
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static bool {typename}_initialized = false;
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if (!{typename}_initialized) {{
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{typename}_initialized = true;
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static PyStructSequence_Desc desc = {{ "torch.return_types.{name}", nullptr, {fieldsname}, {len(fieldnames)} }};
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PyStructSequence_InitType(&{typename}, &desc);
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{typename}.tp_repr = (reprfunc)torch::utils::returned_structseq_repr;
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}}
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""")
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return flddefs + typedefs, typenames
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
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#
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# Method Impl Codegen
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#
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
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# python binding for all overloads of a particular function/method
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PY_VARIABLE_METHOD_VARARGS = CodeTemplate(r"""\
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// ${name}
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static PyObject * ${pycname}(PyObject* self_, PyObject* args, PyObject* kwargs)
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{
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${method_header}
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static PythonArgParser parser({
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${signatures}
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}, /*traceable=*/${traceable});
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ParsedArgs<${max_args}> parsed_args;
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auto _r = parser.parse(${self_}, args, kwargs, parsed_args);
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${check_has_torch_function}
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switch (_r.idx) {
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${dispatch}
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}
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${method_footer}
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}
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""")
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# handler for a single parsed signature - may be a single overload or
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# a pair of overloads that whose signatures only differ in output params
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# (plugged into PY_VARIABLE_METHOD_VARARGS as an item in ${dispatch})
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PY_VARIABLE_CASE = CodeTemplate("""\
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case ${overload_index}: {
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${body}
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}
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""")
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# python binding for single-overload function/method
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PY_VARIABLE_METHOD_VARARGS_SINGLETON = CodeTemplate("""\
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// ${name}
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static PyObject * ${pycname}(PyObject* self_, PyObject* args, PyObject* kwargs)
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{
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${method_header}
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static PythonArgParser parser({
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${signatures}
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}, /*traceable=*/${traceable});
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ParsedArgs<${max_args}> parsed_args;
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auto _r = parser.parse(${self_}, args, kwargs, parsed_args);
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${check_has_torch_function}
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${dispatch}
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${method_footer}
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}
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""")
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# python binding for a method with no args, shortcuts parsing
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PY_VARIABLE_METHOD_NOARGS = CodeTemplate("""\
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// ${name}
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static PyObject * ${pycname}(PyObject* self_, PyObject* args)
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{
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${method_header}
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${check_has_torch_function}
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${dispatch}
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${method_footer}
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}
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""")
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def method_impl(
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name: str,
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module: str,
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overloads: Sequence[PythonSignatureNativeFunctionPair],
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*,
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method: bool
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) -> str:
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"""
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Generate a python binding for all overloads of an op.
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"""
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pycname = get_pycname(name)
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noarg = is_noarg(overloads)
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namedtuple_inits, namedtuple_typenames = emit_namedtuple_typedefs(overloads)
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method_header = ['HANDLE_TH_ERRORS']
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method_header += namedtuple_inits
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method_header += [
|
|
"Tensor& self = reinterpret_cast<THPVariable*>(self_)->cdata;"
|
|
] if method else []
|
|
|
|
method_footer = ([] if noarg else ['Py_RETURN_NONE;']) + ['END_HANDLE_TH_ERRORS']
|
|
|
|
traceable = 'true' if all(should_trace(o.function) for o in overloads) else 'false'
|
|
|
|
grouped_overloads: Sequence[PythonSignatureGroup] = group_overloads(overloads)
|
|
is_singleton = len(grouped_overloads) == 1
|
|
signatures: List[str] = []
|
|
dispatch: List[str] = []
|
|
for overload_index, overload in enumerate(grouped_overloads):
|
|
signature = overload.signature.signature_str()
|
|
signatures.append(f'{cpp_string(str(signature))},')
|
|
dispatch_body = emit_dispatch_case(overload, namedtuple_typenames)
|
|
dispatch.append(
|
|
PY_VARIABLE_CASE.substitute(overload_index=overload_index, body=dispatch_body)
|
|
if not is_singleton else dispatch_body)
|
|
|
|
if noarg:
|
|
template = PY_VARIABLE_METHOD_NOARGS
|
|
elif is_singleton:
|
|
template = PY_VARIABLE_METHOD_VARARGS_SINGLETON
|
|
else:
|
|
template = PY_VARIABLE_METHOD_VARARGS
|
|
|
|
return template.substitute(
|
|
name=name,
|
|
pycname=pycname,
|
|
method_header=method_header,
|
|
max_args=max(map(lambda o: o.signature.arguments_count(), overloads)),
|
|
signatures=signatures,
|
|
traceable=traceable,
|
|
check_has_torch_function=gen_has_torch_function_check(
|
|
name=name,
|
|
module=module,
|
|
noarg=noarg,
|
|
method=method,
|
|
),
|
|
dispatch=dispatch,
|
|
method_footer=method_footer,
|
|
self_="self_" if method else "nullptr",
|
|
)
|
|
|
|
def gen_has_torch_function_check(name: str, module: str, *, noarg: bool, method: bool) -> str:
|
|
if noarg:
|
|
if method:
|
|
return f"""\
|
|
if(check_has_torch_function(self_)) {{
|
|
return handle_torch_function(self_, "{name}");
|
|
}}
|
|
"""
|
|
else:
|
|
return ''
|
|
|
|
self_ = "self_" if method else "nullptr"
|
|
namespace = {
|
|
"torch": "THPVariableFunctionsModule",
|
|
"torch.nn": "THPNNVariableFunctionsModule",
|
|
"torch.fft": "THPFFTVariableFunctionsModule",
|
|
"torch.linalg": "THPLinalgVariableFunctionsModule",
|
|
}[module] if module else "THPVariableClass"
|
|
|
|
return f"""\
|
|
if(_r.has_torch_function()) {{
|
|
return handle_torch_function(_r, {self_}, args, kwargs, {namespace}, "{module or "torch.Tensor"}");
|
|
}}
|
|
"""
|
|
|
|
# handler for output/no-output overload pair
|
|
PY_VARIABLE_OUT = CodeTemplate("""\
|
|
if (_r.isNone(${out_idx})) {
|
|
${call_dispatch}
|
|
} else {
|
|
${call_dispatch_out}
|
|
}
|
|
""")
|
|
|
|
def emit_dispatch_case(
|
|
overload: PythonSignatureGroup,
|
|
namedtuple_typenames: Dict[str, str],
|
|
) -> str:
|
|
"""
|
|
Emit dispatch code for a single parsed signature. This corresponds to either
|
|
a single native function, or a pair that differ only in output params. In the
|
|
latter case, a single python signature is used for both and dispatching
|
|
switches on the presence/absence of passed output args.
|
|
"""
|
|
if overload.outplace is not None:
|
|
# dispatch output and no-output variants, branch on _r.isNone(<out_idx>)
|
|
return PY_VARIABLE_OUT.substitute(
|
|
out_idx=overload.signature.output_idx(),
|
|
call_dispatch=emit_single_dispatch(
|
|
overload.signature, overload.base, namedtuple_typenames),
|
|
call_dispatch_out=emit_single_dispatch(
|
|
overload.signature, overload.outplace, namedtuple_typenames),
|
|
)
|
|
else:
|
|
# no-output version only
|
|
return emit_single_dispatch(
|
|
overload.signature, overload.base, namedtuple_typenames)
|
|
|
|
# Copied from 'gen_variable_type.should_trace()'.
|
|
# TODO: consolidate after migrating autograd codegen.
|
|
@with_native_function
|
|
def should_trace(f: NativeFunction) -> bool:
|
|
# Operations involving Storage or Type are not traceable at the moment
|
|
if any(str(arg.type) in {'Storage', 'Type', 'ConstQuantizerPtr'}
|
|
for arg in f.func.schema_order_arguments()):
|
|
return False
|
|
# We can't trace functions which don't have any Tensor or TensorList returns
|
|
if not any(r.type.is_tensor_like() for r in f.func.returns):
|
|
return False
|
|
name = cpp.name(f.func)
|
|
base_name = f.func.name.name.base
|
|
if base_name in DONT_RECORD_TRACE or name in DONT_RECORD_TRACE:
|
|
return False
|
|
return True
|
|
|
|
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
|
|
#
|
|
# Forward Declarations Codegen
|
|
#
|
|
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
|
|
|
|
def forward_decls(
|
|
name: str,
|
|
overloads: Sequence[PythonSignatureNativeFunctionPair],
|
|
*,
|
|
method: bool
|
|
) -> Tuple[str, ...]:
|
|
if method:
|
|
return ()
|
|
|
|
pycname = get_pycname(name)
|
|
if is_noarg(overloads):
|
|
return (f"""\
|
|
static PyObject * {pycname}(PyObject* self_, PyObject* args);
|
|
""",)
|
|
else:
|
|
return (f"""\
|
|
static PyObject * {pycname}(PyObject* self_, PyObject* args, PyObject* kwargs);
|
|
""",)
|
|
|
|
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
|
|
#
|
|
# Method Def (Binding Table Entry) Codegen
|
|
#
|
|
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
|
|
|
|
# Python binary operator dunder methods
|
|
BINARY_OP_NAMES = [
|
|
'__lt__', '__le__',
|
|
'__gt__', '__ge__',
|
|
'__eq__', '__ne__',
|
|
|
|
'__add__', '__radd__', '__iadd__',
|
|
'__sub__', '__rsub__', '__isub__',
|
|
'__mul__', '__rmul__', '__imul__',
|
|
'__matmul__', '__rmatmul__', '__imatmul__',
|
|
'__truediv__', '__rtruediv__', '__itruediv__',
|
|
'__floordiv__', '__rfloordiv__', '__ifloordiv__',
|
|
'__mod__', '__rmod__', '__imod__',
|
|
'__divmod__', '__rdivmod__', '__idivmod__',
|
|
'__pow__', '__rpow__', '__ipow__',
|
|
'__lshift__', '__rlshift__', '__ilshift__',
|
|
'__rshift__', '__rrshift__', '__irshift__',
|
|
'__and__', '__rand__', '__iand__',
|
|
'__xor__', '__rxor__', '__ixor__',
|
|
'__or__', '__ror__', '__ior__',
|
|
]
|
|
|
|
def method_def(
|
|
name: str,
|
|
module: str,
|
|
overloads: Sequence[PythonSignatureNativeFunctionPair],
|
|
*,
|
|
method: bool
|
|
) -> str:
|
|
"""
|
|
Generate method def entry.
|
|
"""
|
|
pycname = get_pycname(name)
|
|
|
|
if is_noarg(overloads):
|
|
pyfunc_cast = ''
|
|
flags = 'METH_NOARGS' if method else 'METH_VARARGS | METH_KEYWORDS'
|
|
else:
|
|
pyfunc_cast = 'castPyCFunctionWithKeywords'
|
|
flags = 'METH_VARARGS | METH_KEYWORDS'
|
|
|
|
if module == "torch":
|
|
flags += ' | METH_STATIC'
|
|
|
|
if name in BINARY_OP_NAMES:
|
|
# PyMethodDef entry for binary op, throws not implemented error
|
|
return f"""\
|
|
{{"{name}", {pyfunc_cast}(TypeError_to_NotImplemented_<{pycname}>), {flags}, NULL}},"""
|
|
else:
|
|
# PyMethodDef entry
|
|
return f"""\
|
|
{{"{name}", {pyfunc_cast}({pycname}), {flags}, NULL}},"""
|
|
|
|
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
|
|
#
|
|
# Overload Sorting and Grouping
|
|
#
|
|
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
|
|
|
|
def group_overloads(
|
|
overloads: Sequence[PythonSignatureNativeFunctionPair]
|
|
) -> Sequence[PythonSignatureGroup]:
|
|
bases: Dict[str, PythonSignatureNativeFunctionPair] = {}
|
|
outplaces: Dict[str, PythonSignatureNativeFunctionPair] = {}
|
|
|
|
# first group by signature ignoring out arguments
|
|
for overload in overloads:
|
|
sig = overload.signature.signature_str(skip_outputs=True)
|
|
if overload.function.func.is_out_fn():
|
|
if sig in outplaces:
|
|
raise RuntimeError(
|
|
f'Found duplicated function definition:\n- {overload.function.func}.\n'
|
|
f'Existing definition:\n- {outplaces[sig].function.func}.'
|
|
)
|
|
outplaces[sig] = overload
|
|
else:
|
|
if sig in bases:
|
|
raise RuntimeError(
|
|
f'Found duplicated function definition:\n- {overload.function.func}.\n'
|
|
f'Existing definition:\n- {bases[sig].function.func}.'
|
|
)
|
|
bases[sig] = overload
|
|
|
|
for sig, out in outplaces.items():
|
|
if sig not in bases:
|
|
candidates: List[str] = []
|
|
for overload in overloads:
|
|
if str(overload.function.func.name.name) == str(out.function.func.name.name) \
|
|
and not overload.function.func.is_out_fn() \
|
|
and not overload.signature.deprecated:
|
|
candidates.append(overload.signature.signature_str(skip_outputs=True))
|
|
out_sig = out.signature.signature_str()
|
|
raise RuntimeError(
|
|
f'While identifying overloads, we found an out schema {out_sig} without a corresponding non-out variant. '
|
|
f'We expected the non-out variant to have schema: \n- {sig}\nPlease check that you spelled the schema '
|
|
'correctly in native_functions.yaml. We discovered the following candidate(s): \n'
|
|
+ '\n'.join(f'- {candidate}' for candidate in candidates))
|
|
|
|
grouped: List[PythonSignatureGroup] = []
|
|
for sig, base in bases.items():
|
|
outplace = outplaces.get(sig)
|
|
grouped.append(PythonSignatureGroup(
|
|
# prefer the signature with optional out=... arguments because it's the
|
|
# superset that can be used to parse input for both base and outplace.
|
|
signature=outplace.signature if outplace is not None else base.signature,
|
|
base=base.function,
|
|
outplace=outplace.function if outplace is not None else None,
|
|
))
|
|
|
|
return sort_overloads(grouped)
|
|
|
|
# This function declares a partial order on declarations, and sorts them according
|
|
# to its linear extension. This is necessary, because there's some ambiguity in the
|
|
# choice of overload, and we want a different order.
|
|
#
|
|
# See Note[Order of overloads matters]
|
|
#
|
|
# A few examples of ambiguous python signature pairs.
|
|
#
|
|
# All parameters have the same type, except one taking Tensor the other taking
|
|
# Scalar. A numeric PyObject can be casted into Tensor, and a zero-dim Tensor
|
|
# object can be accepted as Scalar type parameter (see python_arg_parser.cpp).
|
|
# Therefore, same input arguments might be accepted by either python signature.
|
|
# We want to always parse the one taking Tensor first.
|
|
#
|
|
# bitwise_and(Tensor input, Tensor other, *, Tensor out=None)
|
|
# bitwise_and(Tensor input, Scalar other, *, Tensor out=None)
|
|
#
|
|
# If they have different number of parameters then they are not ambiguous - but
|
|
# the difference on output param can be ignored as it's optional.
|
|
#
|
|
# multiply(Tensor input, Tensor other, *, Tensor out=None)
|
|
# multiply(Tensor input, Scalar other)
|
|
#
|
|
# Both positional args and keyword-only args are considered together.
|
|
#
|
|
# subtract(Tensor other, *, Scalar alpha=1)
|
|
# subtract(Scalar other, Scalar alpha=1)
|
|
#
|
|
# A few ambiguous cases which it does NOT handle yet.
|
|
#
|
|
# If there is any difference in other parameters besides the Tensor/Scalar
|
|
# difference, then they are not considered ambiguous by this method anymore.
|
|
# However, the difference could be too trivial to disambiguate.
|
|
#
|
|
# foo(Tensor input, Scalar other, Scalar bar)
|
|
# foo(Tensor input, Tensor other, double bar)
|
|
#
|
|
# If they are taking different number of parameters then they are not considered
|
|
# ambiguous anymore, even if the difference is only on optional kwargs.
|
|
#
|
|
# foo(Scalar other, Scalar alpha=1)
|
|
# foo(Tensor other, *, Scalar alpha=1, Scalar beta=1)
|
|
#
|
|
|
|
def sort_overloads(
|
|
grouped_overloads: Sequence[PythonSignatureGroup]
|
|
) -> Sequence[PythonSignatureGroup]:
|
|
|
|
def is_arg_smaller(t1: Type, t2: Type) -> bool:
|
|
return str(t1) == 'Scalar' and str(t2) == 'Tensor'
|
|
|
|
def is_smaller(s1: PythonSignature, s2: PythonSignature) -> bool:
|
|
"""Returns True if s1 < s2 in the partial order."""
|
|
args1, args2 = s1.arguments(skip_outputs=True), s2.arguments(skip_outputs=True)
|
|
if len(args1) != len(args2):
|
|
return False
|
|
# TODO: should use some canonical form instead of 'str(arg.type)' - see comments
|
|
# above. The old codegen used the deprecated 'dynamic_type(arg.type)', which
|
|
# ignores the optional annotation, i.e. 'Scalar' and 'Scalar?'.
|
|
equal = all(arg1.type == arg2.type for arg1, arg2 in zip(args1, args2))
|
|
smaller_or_equal = all(str(arg1.type) == str(arg2.type)
|
|
or is_arg_smaller(arg1.type, arg2.type)
|
|
for arg1, arg2 in zip(args1, args2))
|
|
return smaller_or_equal and not equal
|
|
|
|
# First sort by signature
|
|
grouped_overloads = sorted(grouped_overloads, key=lambda x: x.signature.signature_str())
|
|
|
|
# Construct the relation graph
|
|
larger_than: Dict[int, Set[int]] = defaultdict(set)
|
|
for i1, overload1 in enumerate(grouped_overloads):
|
|
for i2, overload2 in enumerate(grouped_overloads):
|
|
if is_smaller(overload1.signature, overload2.signature):
|
|
larger_than[i1].add(i2)
|
|
|
|
if not larger_than:
|
|
return list(grouped_overloads)
|
|
|
|
# Use a topological sort to sort overloads according to the partial order.
|
|
N = len(grouped_overloads)
|
|
sorted_ids: List[int] = list(filter(lambda x: x not in larger_than, range(N)))
|
|
|
|
for idx in range(N):
|
|
# The size of sorted_ids will grow to N eventually.
|
|
i = sorted_ids[idx]
|
|
for j in sorted(larger_than.keys()):
|
|
larger = larger_than[j]
|
|
larger.discard(i)
|
|
if not larger:
|
|
del larger_than[j]
|
|
sorted_ids.append(j)
|
|
|
|
return list(map(lambda x: grouped_overloads[x], sorted_ids))
|
|
|
|
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
|
|
#
|
|
# Codegen API Integration
|
|
#
|
|
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
|
|
|
|
# These helper functions allow us to call the new codegen API from the
|
|
# old codegen script (which operates on Declarations.yaml).
|
|
|
|
# TODO: remove all these HACKs after migration is completed!
|
|
|
|
# function schema str -> NativeFunction
|
|
NF_TABLE: Optional[Dict[str, NativeFunction]] = None
|
|
|
|
def init(native_yaml_path: str) -> None:
|
|
from tools.codegen.gen import parse_native_yaml
|
|
global NF_TABLE
|
|
NF_TABLE = {str(f.func): f for f in parse_native_yaml(native_yaml_path)}
|
|
|
|
# Multiple decl entries can map to the same native function (because of deprecated decl).
|
|
def decl_to_native_function(decl: Dict[str, Any]) -> NativeFunction:
|
|
assert NF_TABLE is not None, 'need to initialize codegen.api.python with init()'
|
|
function_schema_str = decl['schema_string']
|
|
assert function_schema_str.startswith('aten::'), f'unknown namespace: {function_schema_str}'
|
|
function_schema_str = function_schema_str[len('aten::'):]
|
|
assert function_schema_str in NF_TABLE, f'cannot find func: {function_schema_str}'
|
|
return NF_TABLE[function_schema_str]
|
|
|
|
# Each decl entry has unique (python signature, native function) pair.
|
|
def decl_to_signature_function_pair(
|
|
decl: Dict[str, Any], *, method: bool
|
|
) -> PythonSignatureNativeFunctionPair:
|
|
f = decl_to_native_function(decl)
|
|
|
|
@with_native_function
|
|
def go(f: NativeFunction) -> PythonSignature:
|
|
return signature(f, method=method)
|
|
|
|
python_sig = go(f)
|
|
|
|
if decl.get('deprecated', False):
|
|
# TODO: directly load 'deprecated.yaml'.
|
|
# deprecated.yaml doesn't have complete type information, we need
|
|
# leverage the source signature (to which it delegates the call).
|
|
# Deprecated signature might reorder input_args and input_kwargs,
|
|
# but never changes output_args nor python_binding_args (if any?),
|
|
# so here we only look into these two types of args.
|
|
src_args: Dict[str, PythonArgument] = {a.name: PythonArgument(
|
|
name=a.name,
|
|
type=a.type,
|
|
default=None,
|
|
default_init=None,
|
|
) for a in itertools.chain(python_sig.input_args, python_sig.input_kwargs)}
|
|
args: List[Dict[str, Any]] = decl['arguments']
|
|
input_arg_names: List[str] = \
|
|
list(str(a['name']) for a in args if not a['kwarg_only'] and not a['output'])
|
|
input_kwarg_names: List[str] = \
|
|
list(str(a['name']) for a in args if a['kwarg_only'] and not a['output'])
|
|
python_sig = PythonSignatureDeprecated(
|
|
name=python_sig.name,
|
|
input_args=tuple(src_args[n] for n in input_arg_names if not method or n != 'self'),
|
|
input_kwargs=tuple(src_args[n] for n in input_kwarg_names),
|
|
output_args=python_sig.output_args,
|
|
tensor_options_args=python_sig.tensor_options_args,
|
|
method=python_sig.method,
|
|
deprecated_args_names=tuple(str(a['name']) for a in args),
|
|
deprecated_args_exprs=tuple(decl.get('call_args')),
|
|
)
|
|
return PythonSignatureNativeFunctionPair(
|
|
signature=python_sig,
|
|
function=f,
|
|
)
|
|
|
|
def emit_single_dispatch(
|
|
ps: PythonSignature, f: NativeFunction, namedtuple_typenames: Dict[str, str]
|
|
) -> str:
|
|
"""
|
|
Emit dispatch code for a single native function.
|
|
"""
|
|
@with_native_function
|
|
def go(f: NativeFunction) -> str:
|
|
# header comments
|
|
deprecated = '[deprecated] ' if ps.deprecated else ''
|
|
schema_comment = f'// {deprecated}aten::{f.func}'
|
|
|
|
# dispatch lambda signature
|
|
name = cpp.name(f.func)
|
|
lambda_formals = ', '.join(map(lambda a: f"{a.type_str} {a.name}",
|
|
dispatch_lambda_args(ps, f)))
|
|
lambda_return = dispatch_lambda_return_str(f)
|
|
|
|
# dispatch lambda body
|
|
dispatch_callee = cpp_dispatch_target(f)
|
|
dispatch_args = ', '.join(cpp_dispatch_exprs(f, python_signature=ps))
|
|
|
|
# from arg parser outputs to dispatch lambda arguments
|
|
parser_outputs = arg_parser_output_exprs(ps, f)
|
|
lambda_arg_exprs = dispatch_lambda_exprs(ps, f)
|
|
inits = '\n'.join(lambda_arg_exprs.inits)
|
|
lambda_args = ', '.join(lambda_arg_exprs.exprs)
|
|
|
|
# scatter fields
|
|
# TODO: Checking `ps.method and ('requires_grad' in parser_outputs)` is a hacky
|
|
# solution for enabling the 'requires_grad' argument for tensor methods
|
|
# new_full, new_empty, and new_zeros. A much better but more difficult to
|
|
# implement solution involves refactoring according to Ed's description here:
|
|
# https://github.com/pytorch/pytorch/issues/36455#issuecomment-614767589
|
|
need_set_requires_grad = ps.tensor_options_args and (not has_tensor_options(f) or (
|
|
ps.method and ('requires_grad' in parser_outputs)))
|
|
set_requires_grad = f'.set_requires_grad({parser_outputs["requires_grad"].expr})' \
|
|
if need_set_requires_grad else ''
|
|
|
|
if lambda_return == 'void':
|
|
return f"""\
|
|
{schema_comment}
|
|
{inits}
|
|
auto dispatch_{name} = []({lambda_formals}) -> {lambda_return} {{
|
|
pybind11::gil_scoped_release no_gil;
|
|
{dispatch_callee}({dispatch_args});
|
|
}};
|
|
dispatch_{name}({lambda_args}){set_requires_grad};
|
|
Py_RETURN_NONE;
|
|
"""
|
|
else:
|
|
typename = namedtuple_typenames.get(gen_namedtuple_typename_key(f))
|
|
namedtuple_typeref = f'&{typename}, ' if typename is not None else ''
|
|
return f"""\
|
|
{schema_comment}
|
|
{inits}
|
|
auto dispatch_{name} = []({lambda_formals}) -> {lambda_return} {{
|
|
pybind11::gil_scoped_release no_gil;
|
|
return {dispatch_callee}({dispatch_args});
|
|
}};
|
|
return wrap({namedtuple_typeref}dispatch_{name}({lambda_args}){set_requires_grad});
|
|
"""
|
|
|
|
return go(f)
|