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Commit ff3bd8e6 authored by Khalique Ahmed's avatar Khalique Ahmed
Browse files

manual merge

parents 32b69ceb c310bc5c
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Showing with 285 additions and 474 deletions
CMakeLists.txt
View file @ ff3bd8e6
......@@ -60,7 +60,11 @@ endif()
set(MIGRAPHX_ENABLE_CPU Off CACHE BOOL "")
set(CMAKE_CXX_STANDARD_DEFAULT "")
add_compile_options(-std=c++14)
if(CMAKE_CXX_COMPILER_ID MATCHES "Clang")
add_compile_options(-std=c++17)
else()
add_compile_options(-std=c++14)
endif()
list(APPEND CMAKE_MODULE_PATH ${CMAKE_CURRENT_SOURCE_DIR}/cmake)
include(EnableCompilerWarnings)
......@@ -187,6 +191,8 @@ rocm_enable_cppcheck(
definePrefix:*test/include/test.hpp
useSmartPointer:*src/api/api.cpp
useSmartPointer:*make_shared_array.hpp
constParameter:*src/targets/gpu/*.cpp
constParameter:*src/targets/gpu/*.hpp
FORCE
INCONCLUSIVE
RULE_FILE
......
Dockerfile
View file @ ff3bd8e6
......@@ -74,7 +74,7 @@ RUN cget -p $PREFIX install facebook/zstd@v1.4.5 -X subdir -DCMAKE_DIR=build/cma
RUN cget -p $PREFIX install ccache@v4.1
# Install newer cmake for onnx runtime
RUN cget -p /opt/cmake install kitware/cmake@v3.13.0
RUN cget -p /opt/cmake install kitware/cmake@v3.13.4
ARG ONNXRUNTIME_REPO=https://github.com/Microsoft/onnxruntime
ARG ONNXRUNTIME_BRANCH=master
......@@ -86,6 +86,8 @@ RUN git clone --single-branch --branch ${ONNXRUNTIME_BRANCH} --recursive ${ONNXR
ADD tools/build_and_test_onnxrt.sh /onnxruntime/build_and_test_onnxrt.sh
RUN PATH=/opt/cmake/bin:$PATH cget -p /usr/local install ROCmSoftwarePlatform/llvm-project-mlir@02078ce236ad90e3aec04c0c770ef5bfc99e49c2
ENV MIOPEN_FIND_DB_PATH=/tmp/miopen/find-db
ENV MIOPEN_USER_DB_PATH=/tmp/miopen/user-db
ENV LD_LIBRARY_PATH=$PREFIX/lib
......
Jenkinsfile
View file @ ff3bd8e6
......@@ -94,6 +94,12 @@ rocmtest clang_debug: rocmnode('vega') { cmake_build ->
cmake_build("/opt/rocm/llvm/bin/clang++", "-DCMAKE_BUILD_TYPE=release")
stash includes: 'build/*.deb', name: 'migraphx-package'
}
}, mlir_debug: rocmnode('vega') { cmake_build ->
stage('MLIR Debug') {
def sanitizers = "undefined"
def debug_flags = "-g -O2 -fsanitize=${sanitizers} -fno-sanitize-recover=${sanitizers}"
cmake_build("/opt/rocm/llvm/bin/clang++", "-DCMAKE_BUILD_TYPE=debug -DMIGRAPHX_ENABLE_PYTHON=Off -DMIGRAPHX_ENABLE_MLIR=On -DCMAKE_CXX_FLAGS_DEBUG='${debug_flags}'")
}
}
def onnxnode(name, body) {
......
examples/python_bert_squad_example/BERT-Squad.ipynb
View file @ ff3bd8e6
......@@ -43,7 +43,7 @@
"from os import path\n",
"import sys\n",
"\n",
"import tokenization\n",
"import tokenizers\n",
"from run_onnx_squad import *\n",
"\n",
"import migraphx"
......@@ -137,8 +137,7 @@
"outputs": [],
"source": [
"vocab_file = os.path.join('uncased_L-12_H-768_A-12', 'vocab.txt')\n",
"tokenizer = tokenization.FullTokenizer(vocab_file=vocab_file,\n",
" do_lower_case=True)"
"tokenizer = tokenizers.BertWordPieceTokenizer(vocab_file)"
]
},
{
......
examples/python_bert_squad_example/README.md
View file @ ff3bd8e6
......@@ -7,21 +7,25 @@ There are two ways to run the example:
# Steps
1) Install MIGraphX to your environment. Please follow the steps to build MIGraphX given at https://github.com/ROCmSoftwarePlatform/AMDMIGraphX
2) Install the requirements file
2) Upgrade your pip3 to latest version
```
pip3 install -r requirements_migraphx.txt
pip3 install --upgrade pip
```
3) Install `unzip` and fetch the uncased file (vocabulary):
3) Install the requirements file
```
pip3 install -r requirements_bertsquad.txt
```
4) Install `unzip` and fetch the uncased file (vocabulary):
```
apt-get install unzip
wget -q https://storage.googleapis.com/bert_models/2018_10_18/uncased_L-12_H-768_A-12.zip
unzip uncased_L-12_H-768_A-12.zip
```
4) Get BERT ONNX model (bertsquad-10.onnx):
5) Get BERT ONNX model (bertsquad-10.onnx):
```
wget https://github.com/onnx/models/raw/master/text/machine_comprehension/bert-squad/model/bertsquad-10.onnx
```
5) Run the inference, it will compile and run the model on three questions and small data provided in `inputs.json`:
6) Run the inference, it will compile and run the model on three questions and small data provided in `inputs.json`:
```
python3 bert-squad-migraphx.py
```
......
examples/python_bert_squad_example/bert-squad-migraphx.py
View file @ ff3bd8e6
......@@ -5,7 +5,7 @@ import os.path
from os import path
import sys
import tokenization
import tokenizers
from run_onnx_squad import *
import migraphx
......@@ -30,8 +30,7 @@ n_best_size = 20
max_answer_length = 30
vocab_file = os.path.join('uncased_L-12_H-768_A-12', 'vocab.txt')
tokenizer = tokenization.FullTokenizer(vocab_file=vocab_file,
do_lower_case=True)
tokenizer = tokenizers.BertWordPieceTokenizer(vocab_file)
# Use convert_examples_to_features method from run_onnx_squad to get parameters from the input
input_ids, input_mask, segment_ids, extra_data = convert_examples_to_features(
......
examples/python_bert_squad_example/requirements_bertsquad.txt 100644 → 100755
View file @ ff3bd8e6
tensorflow==1.14
onnxruntime
\ No newline at end of file
tensorflow==2.4.0
onnxruntime
tokenizers
\ No newline at end of file
examples/python_bert_squad_example/run_onnx_squad.py 100644 → 100755
View file @ ff3bd8e6
......@@ -38,7 +38,8 @@ from timeit import default_timer as timer
import numpy as np
import onnxruntime as onnxrt
import six
import tokenization
from tokenizers import BertWordPieceTokenizer
from tokenizers import pre_tokenizers
RawResult = collections.namedtuple("RawResult",
["unique_id", "start_logits", "end_logits"])
......@@ -70,9 +71,8 @@ class SquadExample(object):
def __repr__(self):
s = []
s.append("qas_id: %s" % (tokenization.printable_text(self.qas_id)))
s.append("question_text: %s" %
(tokenization.printable_text(self.question_text)))
s.append("qas_id: %s" % (self.qas_id))
s.append("question_text: %s" % (self.question_text))
s.append("doc_tokens: [%s]" % (" ".join(self.doc_tokens)))
if self.start_position:
s.append("start_position: %d" % (self.start_position))
......@@ -130,7 +130,7 @@ def convert_examples_to_features(examples, tokenizer, max_seq_length,
unique_id = 0
for (example_index, example) in enumerate(examples):
query_tokens = tokenizer.tokenize(example.question_text)
query_tokens = tokenizer.encode(example.question_text)
if len(query_tokens) > max_query_length:
query_tokens = query_tokens[0:max_query_length]
......@@ -140,8 +140,8 @@ def convert_examples_to_features(examples, tokenizer, max_seq_length,
all_doc_tokens = []
for (i, token) in enumerate(example.doc_tokens):
orig_to_tok_index.append(len(all_doc_tokens))
sub_tokens = tokenizer.tokenize(token)
for sub_token in sub_tokens:
sub_tokens = tokenizer.encode(token, add_special_tokens=False)
for sub_token in sub_tokens.tokens:
tok_to_orig_index.append(i)
all_doc_tokens.append(sub_token)
......@@ -172,7 +172,7 @@ def convert_examples_to_features(examples, tokenizer, max_seq_length,
segment_ids = []
tokens.append("[CLS]")
segment_ids.append(0)
for token in query_tokens:
for token in query_tokens.tokens:
tokens.append(token)
segment_ids.append(0)
tokens.append("[SEP]")
......@@ -192,7 +192,9 @@ def convert_examples_to_features(examples, tokenizer, max_seq_length,
tokens.append("[SEP]")
segment_ids.append(1)
input_ids = tokenizer.convert_tokens_to_ids(tokens)
input_ids = []
for token in tokens:
input_ids.append(tokenizer.token_to_id(token))
# The mask has 1 for real tokens and 0 for padding tokens. Only real
# tokens are attended to.
......@@ -437,9 +439,15 @@ def get_final_text(pred_text, orig_text, do_lower_case):
# and `pred_text`, and check if they are the same length. If they are
# NOT the same length, the heuristic has failed. If they are the same
# length, we assume the characters are one-to-one aligned.
tokenizer = tokenization.BasicTokenizer(do_lower_case=do_lower_case)
tokenizer = pre_tokenizers.Sequence(
[pre_tokenizers.Whitespace(),
pre_tokenizers.Punctuation()])
tok_text = " ".join(tokenizer.tokenize(orig_text))
tok_text = []
for item in tokenizer.pre_tokenize_str(orig_text):
tok_text.append(item[0])
tok_text = " ".join(tok_text)
start_position = tok_text.find(pred_text)
if start_position == -1:
......@@ -559,8 +567,7 @@ def main():
sess_options = onnxrt.SessionOptions()
sess_options.session_log_verbosity_level = args.log
tokenizer = tokenization.FullTokenizer(vocab_file=args.vocab_file,
do_lower_case=True)
tokenizer = BertWordPieceTokenizer(vocab_file)
eval_examples = read_squad_examples(input_file=args.predict_file)
input_ids, input_mask, segment_ids, extra_data = \
......
examples/python_bert_squad_example/tokenization.py deleted 100644 → 0
View file @ 32b69ceb
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tokenization classes."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import re
import unicodedata
import six
import tensorflow as tf
def validate_case_matches_checkpoint(do_lower_case, init_checkpoint):
"""Checks whether the casing config is consistent with the checkpoint name."""
# The casing has to be passed in by the user and there is no explicit check
# as to whether it matches the checkpoint. The casing information probably
# should have been stored in the bert_config.json file, but it's not, so
# we have to heuristically detect it to validate.
if not init_checkpoint:
return
m = re.match("^.*?([A-Za-z0-9_-]+)/bert_model.ckpt", init_checkpoint)
if m is None:
return
model_name = m.group(1)
lower_models = [
"uncased_L-24_H-1024_A-16", "uncased_L-12_H-768_A-12",
"multilingual_L-12_H-768_A-12", "chinese_L-12_H-768_A-12"
]
cased_models = [
"cased_L-12_H-768_A-12", "cased_L-24_H-1024_A-16",
"multi_cased_L-12_H-768_A-12"
]
is_bad_config = False
if model_name in lower_models and not do_lower_case:
is_bad_config = True
actual_flag = "False"
case_name = "lowercased"
opposite_flag = "True"
if model_name in cased_models and do_lower_case:
is_bad_config = True
actual_flag = "True"
case_name = "cased"
opposite_flag = "False"
if is_bad_config:
raise ValueError(
"You passed in `--do_lower_case=%s` with `--init_checkpoint=%s`. "
"However, `%s` seems to be a %s model, so you "
"should pass in `--do_lower_case=%s` so that the fine-tuning matches "
"how the model was pre-training. If this error is wrong, please "
"just comment out this check." %
(actual_flag, init_checkpoint, model_name, case_name,
opposite_flag))
def convert_to_unicode(text):
"""Converts `text` to Unicode (if it's not already), assuming utf-8 input."""
if six.PY3:
if isinstance(text, str):
return text
elif isinstance(text, bytes):
return text.decode("utf-8", "ignore")
else:
raise ValueError("Unsupported string type: %s" % (type(text)))
elif six.PY2:
if isinstance(text, str):
return text.decode("utf-8", "ignore")
elif isinstance(text, unicode):
return text
else:
raise ValueError("Unsupported string type: %s" % (type(text)))
else:
raise ValueError("Not running on Python2 or Python 3?")
def printable_text(text):
"""Returns text encoded in a way suitable for print or `tf.logging`."""
# These functions want `str` for both Python2 and Python3, but in one case
# it's a Unicode string and in the other it's a byte string.
if six.PY3:
if isinstance(text, str):
return text
elif isinstance(text, bytes):
return text.decode("utf-8", "ignore")
else:
raise ValueError("Unsupported string type: %s" % (type(text)))
elif six.PY2:
if isinstance(text, str):
return text
elif isinstance(text, unicode):
return text.encode("utf-8")
else:
raise ValueError("Unsupported string type: %s" % (type(text)))
else:
raise ValueError("Not running on Python2 or Python 3?")
def load_vocab(vocab_file):
"""Loads a vocabulary file into a dictionary."""
vocab = collections.OrderedDict()
index = 0
with tf.gfile.GFile(vocab_file, "r") as reader:
while True:
token = convert_to_unicode(reader.readline())
if not token:
break
token = token.strip()
vocab[token] = index
index += 1
return vocab
def convert_by_vocab(vocab, items):
"""Converts a sequence of [tokens|ids] using the vocab."""
output = []
for item in items:
output.append(vocab[item])
return output
def convert_tokens_to_ids(vocab, tokens):
return convert_by_vocab(vocab, tokens)
def convert_ids_to_tokens(inv_vocab, ids):
return convert_by_vocab(inv_vocab, ids)
def whitespace_tokenize(text):
"""Runs basic whitespace cleaning and splitting on a piece of text."""
text = text.strip()
if not text:
return []
tokens = text.split()
return tokens
class FullTokenizer(object):
"""Runs end-to-end tokenziation."""
def __init__(self, vocab_file, do_lower_case=True):
self.vocab = load_vocab(vocab_file)
self.inv_vocab = {v: k for k, v in self.vocab.items()}
self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case)
self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
def tokenize(self, text):
split_tokens = []
for token in self.basic_tokenizer.tokenize(text):
for sub_token in self.wordpiece_tokenizer.tokenize(token):
split_tokens.append(sub_token)
return split_tokens
def convert_tokens_to_ids(self, tokens):
return convert_by_vocab(self.vocab, tokens)
def convert_ids_to_tokens(self, ids):
return convert_by_vocab(self.inv_vocab, ids)
class BasicTokenizer(object):
"""Runs basic tokenization (punctuation splitting, lower casing, etc.)."""
def __init__(self, do_lower_case=True):
"""Constructs a BasicTokenizer.
Args:
do_lower_case: Whether to lower case the input.
"""
self.do_lower_case = do_lower_case
def tokenize(self, text):
"""Tokenizes a piece of text."""
text = convert_to_unicode(text)
text = self._clean_text(text)
# This was added on November 1st, 2018 for the multilingual and Chinese
# models. This is also applied to the English models now, but it doesn't
# matter since the English models were not trained on any Chinese data
# and generally don't have any Chinese data in them (there are Chinese
# characters in the vocabulary because Wikipedia does have some Chinese
# words in the English Wikipedia.).
text = self._tokenize_chinese_chars(text)
orig_tokens = whitespace_tokenize(text)
split_tokens = []
for token in orig_tokens:
if self.do_lower_case:
token = token.lower()
token = self._run_strip_accents(token)
split_tokens.extend(self._run_split_on_punc(token))
output_tokens = whitespace_tokenize(" ".join(split_tokens))
return output_tokens
def _run_strip_accents(self, text):
"""Strips accents from a piece of text."""
text = unicodedata.normalize("NFD", text)
output = []
for char in text:
cat = unicodedata.category(char)
if cat == "Mn":
continue
output.append(char)
return "".join(output)
def _run_split_on_punc(self, text):
"""Splits punctuation on a piece of text."""
chars = list(text)
i = 0
start_new_word = True
output = []
while i < len(chars):
char = chars[i]
if _is_punctuation(char):
output.append([char])
start_new_word = True
else:
if start_new_word:
output.append([])
start_new_word = False
output[-1].append(char)
i += 1
return ["".join(x) for x in output]
def _tokenize_chinese_chars(self, text):
"""Adds whitespace around any CJK character."""
output = []
for char in text:
cp = ord(char)
if self._is_chinese_char(cp):
output.append(" ")
output.append(char)
output.append(" ")
else:
output.append(char)
return "".join(output)
def _is_chinese_char(self, cp):
"""Checks whether CP is the codepoint of a CJK character."""
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
# despite its name. The modern Korean Hangul alphabet is a different block,
# as is Japanese Hiragana and Katakana. Those alphabets are used to write
# space-separated words, so they are not treated specially and handled
# like the all of the other languages.
if ((cp >= 0x4E00 and cp <= 0x9FFF) or #
(cp >= 0x3400 and cp <= 0x4DBF) or #
(cp >= 0x20000 and cp <= 0x2A6DF) or #
(cp >= 0x2A700 and cp <= 0x2B73F) or #
(cp >= 0x2B740 and cp <= 0x2B81F) or #
(cp >= 0x2B820 and cp <= 0x2CEAF) or
(cp >= 0xF900 and cp <= 0xFAFF) or #
(cp >= 0x2F800 and cp <= 0x2FA1F)): #
return True
return False
def _clean_text(self, text):
"""Performs invalid character removal and whitespace cleanup on text."""
output = []
for char in text:
cp = ord(char)
if cp == 0 or cp == 0xfffd or _is_control(char):
continue
if _is_whitespace(char):
output.append(" ")
else:
output.append(char)
return "".join(output)
class WordpieceTokenizer(object):
"""Runs WordPiece tokenziation."""
def __init__(self, vocab, unk_token="[UNK]", max_input_chars_per_word=200):
self.vocab = vocab
self.unk_token = unk_token
self.max_input_chars_per_word = max_input_chars_per_word
def tokenize(self, text):
"""Tokenizes a piece of text into its word pieces.
This uses a greedy longest-match-first algorithm to perform tokenization
using the given vocabulary.
For example:
input = "unaffable"
output = ["un", "##aff", "##able"]
Args:
text: A single token or whitespace separated tokens. This should have
already been passed through `BasicTokenizer.
Returns:
A list of wordpiece tokens.
"""
text = convert_to_unicode(text)
output_tokens = []
for token in whitespace_tokenize(text):
chars = list(token)
if len(chars) > self.max_input_chars_per_word:
output_tokens.append(self.unk_token)
continue
is_bad = False
start = 0
sub_tokens = []
while start < len(chars):
end = len(chars)
cur_substr = None
while start < end:
substr = "".join(chars[start:end])
if start > 0:
substr = "##" + substr
if substr in self.vocab:
cur_substr = substr
break
end -= 1
if cur_substr is None:
is_bad = True
break
sub_tokens.append(cur_substr)
start = end
if is_bad:
output_tokens.append(self.unk_token)
else:
output_tokens.extend(sub_tokens)
return output_tokens
def _is_whitespace(char):
"""Checks whether `chars` is a whitespace character."""
# \t, \n, and \r are technically contorl characters but we treat them
# as whitespace since they are generally considered as such.
if char == " " or char == "\t" or char == "\n" or char == "\r":
return True
cat = unicodedata.category(char)
if cat == "Zs":
return True
return False
def _is_control(char):
"""Checks whether `chars` is a control character."""
# These are technically control characters but we count them as whitespace
# characters.
if char == "\t" or char == "\n" or char == "\r":
return False
cat = unicodedata.category(char)
if cat in ("Cc", "Cf"):
return True
return False
def _is_punctuation(char):
"""Checks whether `chars` is a punctuation character."""
cp = ord(char)
# We treat all non-letter/number ASCII as punctuation.
# Characters such as "^", "$", and "`" are not in the Unicode
# Punctuation class but we treat them as punctuation anyways, for
# consistency.
if ((cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64)
or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126)):
return True
cat = unicodedata.category(char)
if cat.startswith("P"):
return True
return False
requirements.txt
View file @ ff3bd8e6
......@@ -6,4 +6,4 @@ nlohmann/json@v3.8.0
blaze,https://bitbucket.org/blaze-lib/blaze/get/f0755dea0e03.tar.gz -X header -DHEADER_DIR=blaze
half,https://github.com/pfultz2/half/archive/1.12.0.tar.gz -X header -H sha256:0a08660b68abb176ebc2a0cdf8de46e3182a7f46c66443bb80dbfaaec98cf969
pybind/pybind11@d159a563383d10c821ba7b2a71905d1207db6de4 --build
msgpack/msgpack-c@cpp-3.3.0 -DMSGPACK_BUILD_TESTS=Off
\ No newline at end of file
msgpack/msgpack-c@cpp-3.3.0 -DMSGPACK_BUILD_TESTS=Off
src/CMakeLists.txt
View file @ ff3bd8e6
......@@ -7,6 +7,7 @@ include(CheckCXXLinkerFlag)
add_library(migraphx
adjust_allocation.cpp
analyze_streams.cpp
argument.cpp
auto_contiguous.cpp
eliminate_common_subexpression.cpp
decompose.cpp
......@@ -121,6 +122,7 @@ register_migraphx_ops(
pad
pooling
pow
prefix_scan_sum
prelu
quant_convolution
quant_dot
......
src/api/api.cpp
View file @ ff3bd8e6
......@@ -49,6 +49,7 @@ shape::type_t to_shape_type(migraphx_shape_datatype_t t)
{
switch(t)
{
case migraphx_shape_tuple_type: return shape::tuple_type;
#define MIGRAPHX_DETAIL_SHAPE_CASE_CONVERT(x, y) \
case migraphx_shape_##x: return shape::x;
MIGRAPHX_SHAPE_VISIT_TYPES(MIGRAPHX_DETAIL_SHAPE_CASE_CONVERT)
......@@ -61,6 +62,7 @@ migraphx_shape_datatype_t to_shape_type(shape::type_t t)
{
switch(t)
{
case shape::tuple_type: return migraphx_shape_tuple_type;
#define MIGRAPHX_DETAIL_SHAPE_CASE_CONVERT(x, y) \
case shape::x: return migraphx_shape_##x;
MIGRAPHX_SHAPE_VISIT_TYPES(MIGRAPHX_DETAIL_SHAPE_CASE_CONVERT)
......
src/api/include/migraphx/migraphx.h
View file @ ff3bd8e6
......@@ -36,6 +36,7 @@ typedef enum {
#define MIGRAPHX_SHAPE_GENERATE_ENUM_TYPES(x, t) migraphx_shape_##x,
/// An enum to represent the different data type inputs
typedef enum {
migraphx_shape_tuple_type,
MIGRAPHX_SHAPE_VISIT_TYPES(MIGRAPHX_SHAPE_GENERATE_ENUM_TYPES)
} migraphx_shape_datatype_t;
#undef MIGRAPHX_SHAPE_GENERATE_ENUM_TYPES
......
src/argument.cpp 0 → 100755
View file @ ff3bd8e6
#include <migraphx/argument.hpp>
#include <migraphx/functional.hpp>
#include <unordered_map>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
argument::argument(const shape& s) : m_shape(s)
{
auto buffer = make_shared_array<char>(s.bytes());
m_data = {[=]() mutable { return buffer.get(); }};
}
argument::argument(shape s, std::nullptr_t)
: m_shape(std::move(s)), m_data({[] { return nullptr; }})
{
}
argument::argument(const shape& s, const argument::data_t& d) : m_shape(s), m_data(d) {}
argument argument::load(const shape& s, char* buffer)
{
if(s.type() != shape::tuple_type)
return argument{s, buffer};
// Collect all shapes
std::unordered_map<std::size_t, shape> shapes;
{
// cppcheck-suppress variableScope
std::size_t i = 0;
fix([&](auto self, auto ss) {
if(ss.sub_shapes().empty())
{
shapes[i] = ss;
i++;
}
else
{
for(auto&& child : ss.sub_shapes())
self(child);
}
})(s);
}
// Sort by type size
std::vector<std::size_t> order(shapes.size());
std::iota(order.begin(), order.end(), 0);
std::sort(order.begin(), order.end(), by(std::greater<>{}, [&](auto i) {
return shapes[i].type_size();
}));
// Compute offsets
std::unordered_map<std::size_t, std::size_t> offsets;
std::size_t offset = 0;
for(auto i : order)
{
offsets[i] = offset;
offset += shapes[i].bytes();
}
assert(offset == s.bytes());
// cppcheck-suppress variableScope
std::size_t i = 0;
return fix<argument>([&](auto self, auto ss) {
if(ss.sub_shapes().empty())
{
argument r{shapes[i], buffer + offsets[i]};
i++;
return r;
}
std::vector<argument> subs;
std::transform(ss.sub_shapes().begin(),
ss.sub_shapes().end(),
std::back_inserter(subs),
[&](auto child) { return self(child); });
return argument{subs};
})(s);
}
std::vector<shape> to_shapes(const std::vector<argument>& args)
{
std::vector<shape> shapes;
std::transform(args.begin(), args.end(), std::back_inserter(shapes), [](auto&& arg) {
return arg.get_shape();
});
return shapes;
}
argument::argument(const std::vector<argument>& args)
: m_shape(to_shapes(args)), m_data(data_t::from_args(args))
{
}
char* argument::data() const
{
assert(m_shape.type() != shape::tuple_type);
assert(not this->empty());
return m_data.get();
}
bool argument::empty() const { return not m_data.get and m_data.sub.empty(); }
const shape& argument::get_shape() const { return this->m_shape; }
argument argument::reshape(const shape& s) const { return {s, this->m_data}; }
argument::data_t argument::data_t::share() const
{
data_t result;
if(this->get)
{
auto self = std::make_shared<data_t>(*this);
result.get = [self]() mutable { return self->get(); };
}
std::transform(sub.begin(), sub.end(), std::back_inserter(result.sub), [](const auto& d) {
return d.share();
});
return result;
}
argument::data_t argument::data_t::from_args(const std::vector<argument>& args)
{
data_t result;
std::transform(args.begin(), args.end(), std::back_inserter(result.sub), [](auto&& arg) {
return arg.m_data;
});
return result;
}
argument argument::share() const { return {m_shape, m_data.share()}; }
std::vector<argument> argument::get_sub_objects() const
{
std::vector<argument> result;
assert(m_shape.sub_shapes().size() == m_data.sub.size());
std::transform(m_shape.sub_shapes().begin(),
m_shape.sub_shapes().end(),
m_data.sub.begin(),
std::back_inserter(result),
[](auto&& s, auto&& d) {
return argument{s, d};
});
return result;
}
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
src/dead_code_elimination.cpp
View file @ ff3bd8e6
......@@ -29,14 +29,16 @@ std::ptrdiff_t bidistance(const Range& r, Iterator start, Iterator last)
return -n;
}
void dead_code_elimination::apply(module& p) const
void dead_code_elimination::apply(program& p) const { p.remove_unused_modules(); }
void dead_code_elimination::apply(module& m) const
{
auto last = std::prev(p.end());
for(auto ins : iterator_for(p))
auto last = std::prev(m.end());
for(auto ins : iterator_for(m))
{
// Skip the first instruction, since we always process the previous
// instruction
if(ins == p.begin())
if(ins == m.begin())
continue;
const auto i = std::prev(ins);
// Skip the last instruction
......@@ -46,9 +48,9 @@ void dead_code_elimination::apply(module& p) const
if(i->get_shape().elements() == 0 and i->name().front() != '@' and
i->name() != "undefined" and i->name() != "identity")
continue;
assert(bidistance(p, i, last) > 0);
assert(bidistance(m, i, last) > 0);
fix([&](auto self, auto leaf) {
if(not p.has_instruction(leaf))
if(not m.has_instruction(leaf))
return;
if(leaf->outputs().empty())
......@@ -56,15 +58,15 @@ void dead_code_elimination::apply(module& p) const
std::unordered_set<instruction_ref> args(leaf->inputs().begin(),
leaf->inputs().end());
leaf->clear_arguments();
assert(bidistance(p, last, leaf) < 0);
assert(bidistance(m, last, leaf) < 0);
assert(leaf != ins);
p.move_instruction(leaf, p.end());
m.move_instruction(leaf, m.end());
for(auto arg : args)
self(arg);
}
})(i);
}
p.remove_instructions(std::next(last), p.end());
m.remove_instructions(std::next(last), m.end());
}
} // namespace MIGRAPHX_INLINE_NS
......
src/decompose.cpp 100755 → 100644
View file @ ff3bd8e6
......@@ -12,35 +12,44 @@
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace {
struct alpha_beta
{
float alpha = 0.0;
float beta = 0.0;
};
alpha_beta get_alpha_beta(const operation& op)
{
auto v = op.to_value();
return {v.at("alpha").to<float>(), v.at("beta").to<float>()};
}
struct find_dot_add
{
auto matcher() const { return match::name("dot")(match::nargs(3)); }
auto matcher() const { return match::name("dot", "quant_dot")(match::nargs(3)); }
void apply(module& p, const match::matcher_result& r) const
{
auto ins = r.result;
auto dot = any_cast<op::dot>(ins->get_operator());
if(not float_equal(dot.beta, 1) and
not contains({shape::float_type, shape::half_type, shape::double_type},
ins->get_shape().type()))
return;
auto ins = r.result;
auto dot = get_alpha_beta(ins->get_operator());
auto a_ins = ins->inputs()[0];
auto b_ins = ins->inputs()[1];
if(not float_equal(dot.alpha, 1))
{
auto alpha = p.add_literal(literal{shape{ins->get_shape().type()}, {dot.alpha}});
auto alpha = p.add_literal(literal{shape{a_ins->get_shape().type()}, {dot.alpha}});
auto alpha_broadcast = p.insert_instruction(
ins,
make_op("multibroadcast", {{"output_lens", a_ins->get_shape().lens()}}),
alpha);
a_ins = p.insert_instruction(ins, make_op("mul"), a_ins, alpha_broadcast);
}
auto dot_ins = p.insert_instruction(ins, make_op("dot", {{"beta", 0}}), a_ins, b_ins);
auto dot_ins = p.insert_instruction(ins, make_op(ins->name(), {{"beta", 0}}), a_ins, b_ins);
auto c_ins = ins->inputs()[2];
if(not float_equal(dot.beta, 1))
{
auto beta = p.add_literal(literal{shape{ins->get_shape().type()}, {dot.beta}});
auto beta = p.add_literal(literal{shape{c_ins->get_shape().type()}, {dot.beta}});
auto beta_broadcast = p.insert_instruction(
ins, make_op("multibroadcast", {{"output_lens", ins->get_shape().lens()}}), beta);
c_ins = p.insert_instruction(ins, make_op("mul"), c_ins, beta_broadcast);
......@@ -51,24 +60,24 @@ struct find_dot_add
struct find_dot_alpha
{
auto matcher() const { return match::name("dot")(match::nargs(2)); }
auto matcher() const { return match::name("dot", "quant_dot")(match::nargs(2)); }
void apply(module& p, const match::matcher_result& r) const
{
auto ins = r.result;
auto dot = any_cast<op::dot>(ins->get_operator());
auto dot = get_alpha_beta(ins->get_operator());
auto a_ins = ins->inputs()[0];
auto b_ins = ins->inputs()[1];
if(not float_equal(dot.alpha, 1))
{
auto alpha = p.add_literal(literal{shape{ins->get_shape().type()}, {dot.alpha}});
auto alpha = p.add_literal(literal{shape{a_ins->get_shape().type()}, {dot.alpha}});
auto alpha_broadcast = p.insert_instruction(
ins,
make_op("multibroadcast", {{"output_lens", a_ins->get_shape().lens()}}),
alpha);
a_ins = p.insert_instruction(ins, make_op("mul"), a_ins, alpha_broadcast);
}
p.replace_instruction(ins, make_op("dot", {{"beta", 0}}), a_ins, b_ins);
p.replace_instruction(ins, make_op(ins->name(), {{"beta", 0}}), a_ins, b_ins);
}
};
......
src/eliminate_data_type.cpp
View file @ ff3bd8e6
......@@ -13,6 +13,8 @@ void eliminate_data_type::apply(module& m) const
{
if(ins->name()[0] == '@')
continue;
if(ins->name() == "convert")
continue;
auto inputs = ins->inputs();
std::transform(inputs.begin(), inputs.end(), inputs.begin(), [&](auto i) {
if(types.count(i->get_shape().type()) == 0)
......
src/include/migraphx/algorithm.hpp 100755 → 100644
View file @ ff3bd8e6
......@@ -7,6 +7,20 @@
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
template <class Iterator, class Output, class Predicate, class F>
void transform_if(Iterator start, Iterator last, Output out, Predicate pred, F f)
{
while(start != last)
{
if(pred(*start))
{
*out = f(*start);
++out;
}
++start;
}
}
template <class Iterator, class Output, class Predicate>
void group_by(Iterator start, Iterator last, Output out, Predicate pred)
{
......
src/include/migraphx/argument.hpp
View file @ ff3bd8e6
......@@ -8,6 +8,7 @@
#include <functional>
#include <utility>
// clang-format off
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
......@@ -20,57 +21,61 @@ inline namespace MIGRAPHX_INLINE_NS {
*/
struct argument : raw_data<argument>
{
argument() {}
argument() = default;
argument(const shape& s) : m_shape(s)
{
auto buffer = make_shared_array<char>(s.bytes());
data = [=]() mutable { return buffer.get(); };
}
argument(const shape& s);
template <class F, MIGRAPHX_REQUIRES(std::is_pointer<decltype(std::declval<F>()())>{})>
argument(shape s, F d)
: data([f = std::move(d)]() mutable { return reinterpret_cast<char*>(f()); }),
m_shape(std::move(s))
: m_shape(std::move(s)),
m_data({[f = std::move(d)]() mutable { return reinterpret_cast<char*>(f()); }})
{
}
template <class T>
argument(shape s, T* d)
: data([d] { return reinterpret_cast<char*>(d); }), m_shape(std::move(s))
: m_shape(std::move(s)), m_data({[d] { return reinterpret_cast<char*>(d); }})
{
}
template <class T>
argument(shape s, std::shared_ptr<T> d)
: data([d] { return reinterpret_cast<char*>(d.get()); }), m_shape(std::move(s))
: m_shape(std::move(s)), m_data({[d] { return reinterpret_cast<char*>(d.get()); }})
{
}
argument(shape s, std::nullptr_t) : data([] { return nullptr; }), m_shape(std::move(s)) {}
argument(shape s, std::nullptr_t);
argument(const std::vector<argument>& args);
static argument load(const shape& s, char* buffer);
/// Provides a raw pointer to the data
std::function<char*()> data = nullptr;
char* data() const;
/// Whether data is available
bool empty() const { return not data; }
bool empty() const;
const shape& get_shape() const { return this->m_shape; }
const shape& get_shape() const;
argument reshape(const shape& s) const
{
argument self = *this;
return {s, [=]() mutable { return self.data(); }};
}
argument reshape(const shape& s) const;
/// Make copy of the argument that is always sharing the data
argument share() const
{
auto self = std::make_shared<argument>(*this);
return {m_shape, [self]() mutable { return self->data(); }};
}
argument share() const;
std::vector<argument> get_sub_objects() const;
private:
struct data_t
{
std::function<char*()> get = nullptr;
std::vector<data_t> sub = {};
data_t share() const;
static data_t from_args(const std::vector<argument>& args);
};
argument(const shape& s, const data_t& d);
shape m_shape;
data_t m_data{};
};
void migraphx_to_value(value& v, const argument& a);
......@@ -78,5 +83,6 @@ void migraphx_from_value(const value& v, argument& a);
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
// clang-format on
#endif
src/include/migraphx/dead_code_elimination.hpp
View file @ ff3bd8e6
......@@ -9,6 +9,7 @@ namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
struct module;
struct program;
/**
* Remove instructions where the output is not used.
......@@ -16,7 +17,8 @@ struct module;
struct dead_code_elimination
{
std::string name() const { return "dead_code_elimination"; }
void apply(module& p) const;
void apply(module& m) const;
void apply(program& p) const;
};
} // namespace MIGRAPHX_INLINE_NS
......
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