mirror of
https://github.com/pytorch/pytorch.git
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375a83e6c1
Summary: Fixes https://github.com/pytorch/pytorch/issues/44215 Pull Request resolved: https://github.com/pytorch/pytorch/pull/44216 Reviewed By: gchanan Differential Revision: D23963216 Pulled By: ezyang fbshipit-source-id: b3fed51b2a1cbd05e3cd0222c89c38d61d8968c1
867 lines
31 KiB
Python
Executable File
867 lines
31 KiB
Python
Executable File
#!/usr/bin/env python
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""" The Python Hipify script.
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##
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# Copyright (c) 2015-2016 Advanced Micro Devices, Inc. All rights reserved.
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# 2017-2018 Advanced Micro Devices, Inc. and
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# Facebook Inc. All rights reserved.
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#
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# Permission is hereby granted, free of charge, to any person obtaining a copy
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# of this software and associated documentation files (the "Software"), to deal
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# in the Software without restriction, including without limitation the rights
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# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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# copies of the Software, and to permit persons to whom the Software is
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# furnished to do so, subject to the following conditions:
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#
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# The above copyright notice and this permission notice shall be included in
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# all copies or substantial portions of the Software.
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#
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
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# THE SOFTWARE.
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"""
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import argparse
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import fnmatch
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import re
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import shutil
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import sys
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import os
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from . import constants
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from .cuda_to_hip_mappings import CUDA_TO_HIP_MAPPINGS
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from .cuda_to_hip_mappings import MATH_TRANSPILATIONS
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from typing import Dict, List
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from collections.abc import Mapping
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# Hardcode the PyTorch template map
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"""This dictionary provides the mapping from PyTorch kernel template types
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to their actual types."""
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PYTORCH_TEMPLATE_MAP = {"Dtype": "scalar_t", "T": "scalar_t"}
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class InputError(Exception):
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# Exception raised for errors in the input.
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def __init__(self, message):
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super(InputError, self).__init__(message)
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self.message = message
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def __str__(self):
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return "{}: {}".format("Input error", self.message)
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def openf(filename, mode):
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return open(filename, mode, errors='ignore')
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# Color coding for printing
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class bcolors:
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HEADER = '\033[95m'
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OKBLUE = '\033[94m'
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OKGREEN = '\033[92m'
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WARNING = '\033[93m'
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FAIL = '\033[91m'
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ENDC = '\033[0m'
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BOLD = '\033[1m'
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UNDERLINE = '\033[4m'
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# To the programmer, the output of hipify most likely are intermediates.
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# This class allows users of hipify to ask for a cleanup by running the
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# hipify and compilation in a with instantiating this context manager class
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# with keep_intermediates=False.
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# The main usecase is the cpp_extensions, specifically the load method.
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# It is a good idea to keep intermediates (in case of errors or to
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# not recompile unchanged files), but in cases where you don't want to
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# keep them (e.g. in the CI), this can be used to remove files.
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class GeneratedFileCleaner:
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"""Context Manager to clean up generated files"""
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def __init__(self, keep_intermediates=False):
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self.keep_intermediates = keep_intermediates
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self.files_to_clean = set()
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self.dirs_to_clean = []
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def __enter__(self):
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return self
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def open(self, fn, *args, **kwargs):
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if not os.path.exists(fn):
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self.files_to_clean.add(os.path.abspath(fn))
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return open(fn, *args, **kwargs)
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def makedirs(self, dn, exist_ok=False):
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parent, n = os.path.split(dn)
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if not n:
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parent, n = os.path.split(parent)
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if parent and n and not os.path.exists(parent):
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self.makedirs(parent, exist_ok=True)
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if not os.path.isdir(dn) or not exist_ok:
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os.mkdir(dn)
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self.dirs_to_clean.append(os.path.abspath(dn))
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def __exit__(self, type, value, traceback):
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if not self.keep_intermediates:
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for f in self.files_to_clean:
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os.unlink(f)
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for d in self.dirs_to_clean[::-1]:
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os.rmdir(d)
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def matched_files_iter(root_path, includes=('*',), ignores=(), extensions=(), out_of_place_only=False, is_pytorch_extension=False):
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def _fnmatch(filepath, patterns):
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return any(fnmatch.fnmatch(filepath, pattern) for pattern in patterns)
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def match_extensions(filename):
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"""Helper method to see if filename ends with certain extension"""
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return any(filename.endswith(e) for e in extensions)
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exact_matches = set(includes)
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# This is a very rough heuristic; really, we want to avoid scanning
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# any file which is not checked into source control, but this script
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# needs to work even if you're in a Git or Hg checkout, so easier to
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# just block the biggest time sinks that won't matter in the
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# end.
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for (abs_dirpath, dirs, filenames) in os.walk(root_path, topdown=True):
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rel_dirpath = os.path.relpath(abs_dirpath, root_path)
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if rel_dirpath == '.':
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# Blah blah blah O(n) blah blah
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if ".git" in dirs:
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dirs.remove(".git")
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if "build" in dirs:
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dirs.remove("build")
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if "third_party" in dirs:
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dirs.remove("third_party")
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for filename in filenames:
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filepath = os.path.join(rel_dirpath, filename)
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# We respect extensions, UNLESS you wrote the entire
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# filename verbatim, in which case we always accept it
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if (
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_fnmatch(filepath, includes)
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and (not _fnmatch(filepath, ignores))
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and (match_extensions(filepath) or filepath in exact_matches)
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):
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if not is_pytorch_extension: # for pytorch extensions, consider all files
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if not is_pytorch_file(filepath) and not is_caffe2_gpu_file(filepath):
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continue
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if out_of_place_only and not is_out_of_place(filepath):
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continue
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yield filepath
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def preprocess(
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output_directory,
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all_files,
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show_detailed=False,
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show_progress=True,
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hip_clang_launch=False,
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is_pytorch_extension=False,
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clean_ctx=None):
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"""
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Call preprocessor on selected files.
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Arguments)
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show_detailed - Show a detailed summary of the transpilation process.
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"""
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if clean_ctx is None:
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clean_ctx = GeneratedFileCleaner(keep_intermediates=True)
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# Preprocessing statistics.
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stats: Dict[str, List] = {"unsupported_calls": [], "kernel_launches": []}
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for filepath in all_files:
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result = preprocessor(output_directory, filepath, stats, hip_clang_launch, is_pytorch_extension, clean_ctx)
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# Show what happened
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if show_progress:
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print(
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filepath, "->",
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get_hip_file_path(filepath), result)
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print(bcolors.OKGREEN + "Successfully preprocessed all matching files." + bcolors.ENDC, file=sys.stderr)
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# Show detailed summary
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if show_detailed:
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compute_stats(stats)
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def compute_stats(stats):
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unsupported_calls = {cuda_call for (cuda_call, _filepath) in stats["unsupported_calls"]}
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# Print the number of unsupported calls
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print("Total number of unsupported CUDA function calls: {0:d}".format(len(unsupported_calls)))
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# Print the list of unsupported calls
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print(", ".join(unsupported_calls))
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# Print the number of kernel launches
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print("\nTotal number of replaced kernel launches: {0:d}".format(len(stats["kernel_launches"])))
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def add_dim3(kernel_string, cuda_kernel):
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'''adds dim3() to the second and third arguments in the kernel launch'''
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count = 0
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closure = 0
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kernel_string = kernel_string.replace("<<<", "").replace(">>>", "")
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arg_locs: List[Dict[str, int]] = [{} for _ in range(2)]
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arg_locs[count]['start'] = 0
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for ind, c in enumerate(kernel_string):
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if count > 1:
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break
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if c == "(":
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closure += 1
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elif c == ")":
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closure -= 1
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elif (c == "," or ind == len(kernel_string) - 1) and closure == 0:
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arg_locs[count]['end'] = ind + (c != ",")
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count += 1
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if count < 2:
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arg_locs[count]['start'] = ind + 1
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first_arg_raw = kernel_string[arg_locs[0]['start']:arg_locs[0]['end'] + 1]
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second_arg_raw = kernel_string[arg_locs[1]['start']:arg_locs[1]['end']]
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first_arg_clean = kernel_string[arg_locs[0]['start']:arg_locs[0]['end']].replace("\n", "").strip(" ")
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second_arg_clean = kernel_string[arg_locs[1]['start']:arg_locs[1]['end']].replace("\n", "").strip(" ")
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first_arg_dim3 = "dim3({})".format(first_arg_clean)
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second_arg_dim3 = "dim3({})".format(second_arg_clean)
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first_arg_raw_dim3 = first_arg_raw.replace(first_arg_clean, first_arg_dim3)
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second_arg_raw_dim3 = second_arg_raw.replace(second_arg_clean, second_arg_dim3)
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cuda_kernel = cuda_kernel.replace(first_arg_raw + second_arg_raw, first_arg_raw_dim3 + second_arg_raw_dim3)
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return cuda_kernel
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RE_KERNEL_LAUNCH = re.compile(r'([ ]+)(detail?)::[ ]+\\\n[ ]+')
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def processKernelLaunches(string, stats):
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""" Replace the CUDA style Kernel launches with the HIP style kernel launches."""
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# Concat the namespace with the kernel names. (Find cleaner way of doing this later).
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string = RE_KERNEL_LAUNCH.sub(lambda inp: "{0}{1}::".format(inp.group(1), inp.group(2)), string)
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def grab_method_and_template(in_kernel):
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# The positions for relevant kernel components.
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pos = {
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"kernel_launch": {"start": in_kernel["start"], "end": in_kernel["end"]},
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"kernel_name": {"start": -1, "end": -1},
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"template": {"start": -1, "end": -1}
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}
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# Count for balancing template
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count = {"<>": 0}
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# Status for whether we are parsing a certain item.
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START = 0
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AT_TEMPLATE = 1
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AFTER_TEMPLATE = 2
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AT_KERNEL_NAME = 3
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status = START
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# Parse the string character by character
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for i in range(pos["kernel_launch"]["start"] - 1, -1, -1):
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char = string[i]
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# Handle Templating Arguments
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if status == START or status == AT_TEMPLATE:
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if char == ">":
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if status == START:
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status = AT_TEMPLATE
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pos["template"]["end"] = i
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count["<>"] += 1
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if char == "<":
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count["<>"] -= 1
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if count["<>"] == 0 and (status == AT_TEMPLATE):
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pos["template"]["start"] = i
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status = AFTER_TEMPLATE
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# Handle Kernel Name
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if status != AT_TEMPLATE:
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if string[i].isalnum() or string[i] in {'(', ')', '_', ':', '#'}:
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if status != AT_KERNEL_NAME:
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status = AT_KERNEL_NAME
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pos["kernel_name"]["end"] = i
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# Case: Kernel name starts the string.
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if i == 0:
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pos["kernel_name"]["start"] = 0
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# Finished
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return [(pos["kernel_name"]), (pos["template"]), (pos["kernel_launch"])]
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else:
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# Potential ending point if we're already traversing a kernel's name.
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if status == AT_KERNEL_NAME:
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pos["kernel_name"]["start"] = i
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# Finished
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return [(pos["kernel_name"]), (pos["template"]), (pos["kernel_launch"])]
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def find_kernel_bounds(string):
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"""Finds the starting and ending points for all kernel launches in the string."""
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kernel_end = 0
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kernel_positions = []
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# Continue until we cannot find any more kernels anymore.
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while string.find("<<<", kernel_end) != -1:
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# Get kernel starting position (starting from the previous ending point)
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kernel_start = string.find("<<<", kernel_end)
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# Get kernel ending position (adjust end point past the >>>)
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kernel_end = string.find(">>>", kernel_start) + 3
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if kernel_end <= 0:
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raise InputError("no kernel end found")
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# Add to list of traversed kernels
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kernel_positions.append({"start": kernel_start, "end": kernel_end,
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"group": string[kernel_start: kernel_end]})
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return kernel_positions
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# Grab positional ranges of all kernel launches
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get_kernel_positions = list(find_kernel_bounds(string))
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output_string = string
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# Replace each CUDA kernel with a HIP kernel.
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for kernel in get_kernel_positions:
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# Get kernel components
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params = grab_method_and_template(kernel)
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# Find parenthesis after kernel launch
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parenthesis = string.find("(", kernel["end"])
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# Extract cuda kernel
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cuda_kernel = string[params[0]["start"]:parenthesis + 1]
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kernel_string = string[kernel['start']:kernel['end']]
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end_param_index = 0 if params[1]['end'] == -1 else 1
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kernel_name_with_template = string[params[0]['start']:params[end_param_index]['end'] + 1]
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cuda_kernel_dim3 = add_dim3(kernel_string, cuda_kernel)
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# Keep number of kernel launch params consistent (grid dims, group dims, stream, dynamic shared size)
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num_klp = len(extract_arguments(0, kernel["group"].replace("<<<", "(").replace(">>>", ")")))
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hip_kernel = "hipLaunchKernelGGL(" + cuda_kernel_dim3[0:-1].replace(
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">>>", ", 0" * (4 - num_klp) + ">>>").replace("<<<", ", ").replace(
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">>>", ", ").replace(kernel_name_with_template, "(" + kernel_name_with_template + ")")
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# Replace cuda kernel with hip kernel
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output_string = output_string.replace(cuda_kernel, hip_kernel)
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# Update the statistics
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stats["kernel_launches"].append(hip_kernel)
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return output_string
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def find_closure_group(input_string, start, group):
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"""Generalization for finding a balancing closure group
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if group = ["(", ")"], then finds the first balanced parentheses.
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if group = ["{", "}"], then finds the first balanced bracket.
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Given an input string, a starting position in the input string, and the group type,
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find_closure_group returns the positions of group[0] and group[1] as a tuple.
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Example:
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find_closure_group("(hi)", 0, ["(", ")"])
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Returns:
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0, 3
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"""
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inside_parenthesis = False
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parens = 0
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pos = start
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p_start, p_end = -1, -1
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while pos < len(input_string):
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if input_string[pos] == group[0]:
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if inside_parenthesis is False:
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inside_parenthesis = True
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parens = 1
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p_start = pos
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else:
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parens += 1
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elif input_string[pos] == group[1] and inside_parenthesis:
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parens -= 1
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if parens == 0:
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p_end = pos
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return p_start, p_end
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pos += 1
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return None, None
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def find_bracket_group(input_string, start):
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"""Finds the first balanced parantheses."""
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return find_closure_group(input_string, start, group=["{", "}"])
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def find_parentheses_group(input_string, start):
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"""Finds the first balanced bracket."""
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return find_closure_group(input_string, start, group=["(", ")"])
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RE_ASSERT = re.compile(r"\bassert[ ]*\(")
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def replace_math_functions(input_string):
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"""FIXME: Temporarily replace std:: invocations of math functions
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with non-std:: versions to prevent linker errors NOTE: This
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can lead to correctness issues when running tests, since the
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correct version of the math function (exp/expf) might not get
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called. Plan is to remove this function once HIP supports
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std:: math function calls inside device code
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"""
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output_string = input_string
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for func in MATH_TRANSPILATIONS:
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output_string = output_string.replace(r'{}('.format(func), '{}('.format(MATH_TRANSPILATIONS[func]))
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return output_string
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RE_SYNCTHREADS = re.compile(r"[:]?[:]?\b(__syncthreads)\b(\w*\()")
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def hip_header_magic(input_string):
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"""If the file makes kernel builtin calls and does not include the cuda_runtime.h header,
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then automatically add an #include to match the "magic" includes provided by NVCC.
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TODO:
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Update logic to ignore cases where the cuda_runtime.h is included by another file.
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"""
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# Copy the input.
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output_string = input_string
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# Check if one of the following headers is already included.
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headers = ["hip/hip_runtime.h", "hip/hip_runtime_api.h"]
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if any(re.search(r'#include ("{0}"|<{0}>)'.format(ext), output_string) for ext in headers):
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return output_string
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# Rough logic to detect if we're inside device code
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hasDeviceLogic: int
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hasDeviceLogic = "hipLaunchKernelGGL" in output_string
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hasDeviceLogic += "__global__" in output_string
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hasDeviceLogic += "__shared__" in output_string
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hasDeviceLogic += RE_SYNCTHREADS.search(output_string) is not None
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# If device logic found, provide the necessary header.
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if hasDeviceLogic:
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output_string = '#include "hip/hip_runtime.h"\n' + input_string
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return output_string
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RE_EXTERN_SHARED = re.compile(r"extern\s+([\w\(\)]+)?\s*__shared__\s+([\w:<>\s]+)\s+(\w+)\s*\[\s*\]\s*;")
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def replace_extern_shared(input_string):
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"""Match extern __shared__ type foo[]; syntax and use HIP_DYNAMIC_SHARED() MACRO instead.
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https://github.com/ROCm-Developer-Tools/HIP/blob/master/docs/markdown/hip_kernel_language.md#__shared__
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Example:
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"extern __shared__ char smemChar[];" => "HIP_DYNAMIC_SHARED( char, smemChar)"
|
|
"extern __shared__ unsigned char smem[];" => "HIP_DYNAMIC_SHARED( unsigned char, my_smem)"
|
|
"""
|
|
output_string = input_string
|
|
output_string = RE_EXTERN_SHARED.sub(
|
|
lambda inp: "HIP_DYNAMIC_SHARED({0} {1}, {2})".format(
|
|
inp.group(1) or "", inp.group(2), inp.group(3)), output_string)
|
|
|
|
return output_string
|
|
|
|
|
|
def get_hip_file_path(filepath):
|
|
"""
|
|
Returns the new name of the hipified file
|
|
"""
|
|
# At the moment, some files are HIPified in place. The predicate
|
|
# is_out_of_place tells us if this is the case or not.
|
|
if not is_out_of_place(filepath):
|
|
return filepath
|
|
|
|
dirpath, filename = os.path.split(filepath)
|
|
root, ext = os.path.splitext(filename)
|
|
|
|
# Here's the plan:
|
|
#
|
|
# In general, we need to disambiguate the HIPified filename so that
|
|
# it gets a different name from the original Caffe2 filename, so
|
|
# that we don't overwrite the original file. (Additionally,
|
|
# hcc historically had a bug where if you had two files with
|
|
# the same basename, they would clobber each other.)
|
|
#
|
|
# There's a lot of different naming conventions across PyTorch
|
|
# and Caffe2, but the general recipe is to convert occurrences
|
|
# of cuda/gpu to hip, and add hip if there are no occurrences
|
|
# of cuda/gpu anywhere.
|
|
#
|
|
# Concretely, we do the following:
|
|
#
|
|
# - If there is a directory component named "cuda", replace
|
|
# it with "hip", AND
|
|
#
|
|
# - If the file name contains "CUDA", replace it with "HIP", AND
|
|
#
|
|
# If NONE of the above occurred, then insert "hip" in the file path
|
|
# as the direct parent folder of the file
|
|
#
|
|
# Furthermore, ALWAYS replace '.cu' with '.hip', because those files
|
|
# contain CUDA kernels that needs to be hipified and processed with
|
|
# hcc compiler
|
|
#
|
|
# This isn't set in stone; we might adjust this to support other
|
|
# naming conventions.
|
|
|
|
if ext == '.cu':
|
|
ext = '.hip'
|
|
|
|
orig_dirpath = dirpath
|
|
|
|
dirpath = dirpath.replace('cuda', 'hip')
|
|
dirpath = dirpath.replace('THC', 'THH')
|
|
|
|
root = root.replace('cuda', 'hip')
|
|
root = root.replace('CUDA', 'HIP')
|
|
# Special case to handle caffe2/core/THCCachingAllocator
|
|
if dirpath != "caffe2/core":
|
|
root = root.replace('THC', 'THH')
|
|
|
|
if dirpath == orig_dirpath:
|
|
dirpath = os.path.join(dirpath, 'hip')
|
|
|
|
return os.path.join(dirpath, root + ext)
|
|
|
|
|
|
def is_out_of_place(filepath):
|
|
if filepath.startswith("torch/"):
|
|
return False
|
|
if filepath.startswith("tools/autograd/templates/"):
|
|
return False
|
|
return True
|
|
|
|
|
|
# Keep this synchronized with includes/ignores in build_amd.py
|
|
def is_pytorch_file(filepath):
|
|
if filepath.startswith("aten/"):
|
|
if filepath.startswith("aten/src/ATen/core/"):
|
|
return False
|
|
return True
|
|
if filepath.startswith("torch/"):
|
|
return True
|
|
if filepath.startswith("tools/autograd/templates/"):
|
|
return True
|
|
return False
|
|
|
|
|
|
def is_caffe2_gpu_file(filepath):
|
|
if filepath.startswith("c10/cuda"):
|
|
return True
|
|
filename = os.path.basename(filepath)
|
|
_, ext = os.path.splitext(filename)
|
|
return ('gpu' in filename or ext in ['.cu', '.cuh']) and ('cudnn' not in filename)
|
|
|
|
|
|
# Cribbed from https://stackoverflow.com/questions/42742810/speed-up-millions-of-regex-replacements-in-python-3/42789508#42789508
|
|
class Trie():
|
|
"""Regex::Trie in Python. Creates a Trie out of a list of words. The trie can be exported to a Regex pattern.
|
|
The corresponding Regex should match much faster than a simple Regex union."""
|
|
|
|
def __init__(self):
|
|
self.data = {}
|
|
|
|
def add(self, word):
|
|
ref = self.data
|
|
for char in word:
|
|
ref[char] = char in ref and ref[char] or {}
|
|
ref = ref[char]
|
|
ref[''] = 1
|
|
|
|
def dump(self):
|
|
return self.data
|
|
|
|
def quote(self, char):
|
|
return re.escape(char)
|
|
|
|
def _pattern(self, pData):
|
|
data = pData
|
|
if "" in data and len(data.keys()) == 1:
|
|
return None
|
|
|
|
alt = []
|
|
cc = []
|
|
q = 0
|
|
for char in sorted(data.keys()):
|
|
if isinstance(data[char], dict):
|
|
try:
|
|
recurse = self._pattern(data[char])
|
|
alt.append(self.quote(char) + recurse)
|
|
except Exception:
|
|
cc.append(self.quote(char))
|
|
else:
|
|
q = 1
|
|
cconly = not len(alt) > 0
|
|
|
|
if len(cc) > 0:
|
|
if len(cc) == 1:
|
|
alt.append(cc[0])
|
|
else:
|
|
alt.append('[' + ''.join(cc) + ']')
|
|
|
|
if len(alt) == 1:
|
|
result = alt[0]
|
|
else:
|
|
result = "(?:" + "|".join(alt) + ")"
|
|
|
|
if q:
|
|
if cconly:
|
|
result += "?"
|
|
else:
|
|
result = "(?:%s)?" % result
|
|
return result
|
|
|
|
def pattern(self):
|
|
return self._pattern(self.dump())
|
|
|
|
|
|
CAFFE2_TRIE = Trie()
|
|
CAFFE2_MAP = {}
|
|
PYTORCH_TRIE = Trie()
|
|
PYTORCH_MAP = {}
|
|
for mapping in CUDA_TO_HIP_MAPPINGS:
|
|
assert isinstance(mapping, Mapping)
|
|
for src, value in mapping.items():
|
|
dst = value[0]
|
|
meta_data = value[1:]
|
|
if constants.API_CAFFE2 not in meta_data:
|
|
PYTORCH_TRIE.add(src)
|
|
PYTORCH_MAP[src] = dst
|
|
if constants.API_PYTORCH not in meta_data:
|
|
CAFFE2_TRIE.add(src)
|
|
CAFFE2_MAP[src] = dst
|
|
RE_CAFFE2_PREPROCESSOR = re.compile(CAFFE2_TRIE.pattern())
|
|
RE_PYTORCH_PREPROCESSOR = re.compile(r'(?<=\W)({0})(?=\W)'.format(PYTORCH_TRIE.pattern()))
|
|
|
|
RE_QUOTE_HEADER = re.compile(r'#include "([^"]+)"')
|
|
RE_ANGLE_HEADER = re.compile(r'#include <([^>]+)>')
|
|
RE_THC_GENERIC_FILE = re.compile(r'#define THC_GENERIC_FILE "([^"]+)"')
|
|
RE_CU_SUFFIX = re.compile(r'\.cu\b') # be careful not to pick up .cuh
|
|
|
|
def preprocessor(output_directory, filepath, stats, hip_clang_launch, is_pytorch_extension, clean_ctx):
|
|
""" Executes the CUDA -> HIP conversion on the specified file. """
|
|
fin_path = os.path.join(output_directory, filepath)
|
|
with open(fin_path, 'r', encoding='utf-8') as fin:
|
|
output_source = fin.read()
|
|
|
|
fout_path = os.path.join(output_directory, get_hip_file_path(filepath))
|
|
if not os.path.exists(os.path.dirname(fout_path)):
|
|
clean_ctx.makedirs(os.path.dirname(fout_path))
|
|
|
|
# unsupported_calls statistics reporting is broken atm
|
|
def pt_repl(m):
|
|
return PYTORCH_MAP[m.group(0)]
|
|
|
|
if is_pytorch_extension:
|
|
output_source = RE_PYTORCH_PREPROCESSOR.sub(pt_repl, output_source)
|
|
else:
|
|
if is_pytorch_file(filepath):
|
|
output_source = RE_PYTORCH_PREPROCESSOR.sub(pt_repl, output_source)
|
|
else:
|
|
def c2_repl(m):
|
|
return CAFFE2_MAP[m.group(0)]
|
|
output_source = RE_CAFFE2_PREPROCESSOR.sub(c2_repl, output_source)
|
|
|
|
# Header rewrites
|
|
def mk_repl(templ):
|
|
def repl(m):
|
|
f = m.group(1)
|
|
if (
|
|
f.startswith("ATen/cuda")
|
|
or f.startswith("ATen/native/cuda")
|
|
or f.startswith("ATen/native/quantized/cuda")
|
|
or f.startswith("ATen/native/sparse/cuda")
|
|
or f.startswith("THC/")
|
|
or f.startswith("THCUNN/")
|
|
or (f.startswith("THC") and not f.startswith("THCP"))
|
|
):
|
|
return templ.format(get_hip_file_path(m.group(1)))
|
|
return m.group(0)
|
|
return repl
|
|
output_source = RE_QUOTE_HEADER.sub(mk_repl('#include "{0}"'), output_source)
|
|
output_source = RE_ANGLE_HEADER.sub(mk_repl('#include <{0}>'), output_source)
|
|
output_source = RE_THC_GENERIC_FILE.sub(mk_repl('#define THC_GENERIC_FILE "{0}"'), output_source)
|
|
|
|
# CMakeLists.txt rewrites
|
|
if filepath.endswith('CMakeLists.txt'):
|
|
output_source = output_source.replace('CUDA', 'HIP')
|
|
output_source = output_source.replace('THC', 'THH')
|
|
output_source = RE_CU_SUFFIX.sub('.hip', output_source)
|
|
|
|
# Perform Kernel Launch Replacements
|
|
if not hip_clang_launch:
|
|
output_source = processKernelLaunches(output_source, stats)
|
|
|
|
# Replace std:: with non-std:: versions
|
|
if (filepath.endswith(".cu") or filepath.endswith(".cuh")) and "PowKernel" not in filepath:
|
|
output_source = replace_math_functions(output_source)
|
|
|
|
# Include header if device code is contained.
|
|
output_source = hip_header_magic(output_source)
|
|
|
|
# Replace the extern __shared__
|
|
output_source = replace_extern_shared(output_source)
|
|
|
|
do_write = True
|
|
if os.path.exists(fout_path):
|
|
with open(fout_path, 'r', encoding='utf-8') as fout_old:
|
|
do_write = fout_old.read() != output_source
|
|
if do_write:
|
|
with clean_ctx.open(fout_path, 'w', encoding='utf-8') as fout:
|
|
fout.write(output_source)
|
|
return "ok"
|
|
else:
|
|
return "skipped"
|
|
|
|
def file_specific_replacement(filepath, search_string, replace_string, strict=False):
|
|
with openf(filepath, "r+") as f:
|
|
contents = f.read()
|
|
if strict:
|
|
contents = re.sub(r'\b({0})\b'.format(re.escape(search_string)), lambda x: replace_string, contents)
|
|
else:
|
|
contents = contents.replace(search_string, replace_string)
|
|
f.seek(0)
|
|
f.write(contents)
|
|
f.truncate()
|
|
|
|
|
|
def file_add_header(filepath, header):
|
|
with openf(filepath, "r+") as f:
|
|
contents = f.read()
|
|
if header[0] != "<" and header[-1] != ">":
|
|
header = '"{0}"'.format(header)
|
|
contents = ('#include {0} \n'.format(header)) + contents
|
|
f.seek(0)
|
|
f.write(contents)
|
|
f.truncate()
|
|
|
|
|
|
def fix_static_global_kernels(in_txt):
|
|
"""Static global kernels in HIP results in a compilation error."""
|
|
in_txt = in_txt.replace(" __global__ static", "__global__")
|
|
return in_txt
|
|
|
|
|
|
RE_INCLUDE = re.compile(r"#include .*\n")
|
|
|
|
|
|
def extract_arguments(start, string):
|
|
""" Return the list of arguments in the upcoming function parameter closure.
|
|
Example:
|
|
string (input): '(blocks, threads, 0, THCState_getCurrentStream(state))'
|
|
arguments (output):
|
|
'[{'start': 1, 'end': 7},
|
|
{'start': 8, 'end': 16},
|
|
{'start': 17, 'end': 19},
|
|
{'start': 20, 'end': 53}]'
|
|
"""
|
|
|
|
arguments = []
|
|
closures = {
|
|
"<": 0,
|
|
"(": 0
|
|
}
|
|
current_position = start
|
|
argument_start_pos = current_position + 1
|
|
|
|
# Search for final parenthesis
|
|
while current_position < len(string):
|
|
if string[current_position] == "(":
|
|
closures["("] += 1
|
|
elif string[current_position] == ")":
|
|
closures["("] -= 1
|
|
elif string[current_position] == "<":
|
|
closures["<"] += 1
|
|
elif string[current_position] == ">" and string[current_position - 1] != "-" and closures["<"] > 0:
|
|
closures["<"] -= 1
|
|
|
|
# Finished all arguments
|
|
if closures["("] == 0 and closures["<"] == 0:
|
|
# Add final argument
|
|
arguments.append({"start": argument_start_pos, "end": current_position})
|
|
break
|
|
|
|
# Finished current argument
|
|
if closures["("] == 1 and closures["<"] == 0 and string[current_position] == ",":
|
|
arguments.append({"start": argument_start_pos, "end": current_position})
|
|
argument_start_pos = current_position + 1
|
|
|
|
current_position += 1
|
|
|
|
return arguments
|
|
|
|
|
|
def str2bool(v):
|
|
"""ArgumentParser doesn't support type=bool. Thus, this helper method will convert
|
|
from possible string types to True / False."""
|
|
if v.lower() in ('yes', 'true', 't', 'y', '1'):
|
|
return True
|
|
elif v.lower() in ('no', 'false', 'f', 'n', '0'):
|
|
return False
|
|
else:
|
|
raise argparse.ArgumentTypeError('Boolean value expected.')
|
|
|
|
|
|
def hipify(
|
|
project_directory,
|
|
show_detailed=False,
|
|
extensions=(".cu", ".cuh", ".c", ".cc", ".cpp", ".h", ".in", ".hpp"),
|
|
output_directory="",
|
|
includes=(),
|
|
extra_files=(),
|
|
out_of_place_only=False,
|
|
ignores=(),
|
|
show_progress=True,
|
|
hip_clang_launch=False,
|
|
is_pytorch_extension=False,
|
|
clean_ctx=None
|
|
):
|
|
if project_directory == "":
|
|
project_directory = os.getcwd()
|
|
|
|
# Verify the project directory exists.
|
|
if not os.path.exists(project_directory):
|
|
print("The project folder specified does not exist.")
|
|
sys.exit(1)
|
|
|
|
# If no output directory, provide a default one.
|
|
if not output_directory:
|
|
project_directory.rstrip("/")
|
|
output_directory = project_directory + "_amd"
|
|
|
|
# Copy from project directory to output directory if not done already.
|
|
if not os.path.exists(output_directory):
|
|
shutil.copytree(project_directory, output_directory)
|
|
|
|
all_files = list(matched_files_iter(output_directory, includes=includes,
|
|
ignores=ignores, extensions=extensions,
|
|
out_of_place_only=out_of_place_only,
|
|
is_pytorch_extension=is_pytorch_extension))
|
|
all_files_set = set(all_files)
|
|
all_files += [f for f in extra_files if f not in all_files_set]
|
|
|
|
# Start Preprocessor
|
|
preprocess(
|
|
output_directory,
|
|
all_files,
|
|
show_detailed=show_detailed,
|
|
show_progress=show_progress,
|
|
hip_clang_launch=hip_clang_launch,
|
|
is_pytorch_extension=is_pytorch_extension,
|
|
clean_ctx=clean_ctx)
|