index
int64 | repo_name
string | branch_name
string | path
string | content
string | import_graph
string |
|---|---|---|---|---|---|
33,559
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/tokens/fast/mbart.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
import os
from contextlib import contextmanager
from shutil import copyfile
from tokenizers import processors
from ....tokens.utils import AddedToken
from ....tokens.fast import PreTrainedTokenizerFast
from ..mbart import Tokenizer as MBart
VOCAB_FS = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}
VOCAB_MAP = {
"vocab_file": {
"facebook/mbart-large-en-ro": "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model",
"facebook/mbart-large-cc25": "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model",
},
"tokenizer_file": {
"facebook/mbart-large-en-ro": "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json",
"facebook/mbart-large-cc25": "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json",
},
}
INPUT_CAPS = {
"facebook/mbart-large-en-ro": 1024,
"facebook/mbart-large-cc25": 1024,
}
# fmt: off
FAIRSEQ_LANGUAGE_CODES = ["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN"]
# fmt: on
class MBartTokenizerFast(PreTrainedTokenizerFast):
vocab_fs = VOCAB_FS
input_caps = INPUT_CAPS
vocab_map = VOCAB_MAP
model_input_names = ["input_ids", "mask"]
slow_tokenizer_class = MBart
prefix_tokens = []
suffix_tokens = []
def __init__(
self,
vocab_file=None,
tokenizer_file=None,
bos="<s>",
eos="</s>",
sep="</s>",
cls="<s>",
unk="<unk>",
pad="<pad>",
msk="<mask>",
src_lang=None,
tgt_lang=None,
additional_special_tokens=None,
**kw,
):
msk = AddedToken(msk, lstrip=True, rstrip=False) if isinstance(msk, str) else msk
super().__init__(
vocab_file=vocab_file,
tokenizer_file=tokenizer_file,
bos=bos,
eos=eos,
sep=sep,
cls=cls,
unk=unk,
pad=pad,
msk=msk,
src_lang=src_lang,
tgt_lang=tgt_lang,
additional_special_tokens=additional_special_tokens,
**kw,
)
self.vocab_file = vocab_file
self.can_save_slow_tokenizer = False if not self.vocab_file else True
_additional_special_tokens = FAIRSEQ_LANGUAGE_CODES.copy()
if additional_special_tokens is not None:
_additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in _additional_special_tokens]
)
self.add_special_tokens({"additional_special_tokens": _additional_special_tokens})
self.lang_code_to_id = {
lang_code: self.convert_tokens_to_ids(lang_code) for lang_code in FAIRSEQ_LANGUAGE_CODES
}
self._src_lang = src_lang if src_lang is not None else "en_XX"
self.cur_lang_code = self.convert_tokens_to_ids(self._src_lang)
self.tgt_lang = tgt_lang
self.set_src_lang_special_tokens(self._src_lang)
@property
def src_lang(self):
return self._src_lang
@src_lang.setter
def src_lang(self, new_src_lang):
self._src_lang = new_src_lang
self.set_src_lang_special_tokens(self._src_lang)
def build_inputs_with_special_tokens(self, toks_0, toks_1=None):
if toks_1 is None:
return self.prefix_tokens + toks_0 + self.suffix_tokens
return self.prefix_tokens + toks_0 + toks_1 + self.suffix_tokens
def create_token_type_ids_from_sequences(self, toks_0, toks_1=None):
sep = [self.SEP]
cls = [self.cls_token_id]
if toks_1 is None:
return len(cls + toks_0 + sep) * [0]
return len(cls + toks_0 + sep + sep + toks_1 + sep) * [0]
def _build_translation_inputs(
self,
raw_inputs,
return_tensors,
src_lang,
tgt_lang,
**extra_kw,
):
if src_lang is None or tgt_lang is None:
raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model")
self.src_lang = src_lang
inputs = self(
raw_inputs, add_special_tokens=True, return_tensors=return_tensors, **extra_kw
)
tgt_lang_id = self.convert_tokens_to_ids(tgt_lang)
inputs["forced_BOS"] = tgt_lang_id
return inputs
def prepare_seq2seq_batch(
self,
src_texts,
src_lang="en_XX",
tgt_texts=None,
tgt_lang="ro_RO",
**kw,
):
self.src_lang = src_lang
self.tgt_lang = tgt_lang
return super().prepare_seq2seq_batch(src_texts, tgt_texts, **kw)
@contextmanager
def as_target_tokenizer(self):
self.set_tgt_lang_special_tokens(self.tgt_lang)
yield
self.set_src_lang_special_tokens(self.src_lang)
def set_src_lang_special_tokens(self, src_lang):
self.cur_lang_code = self.convert_tokens_to_ids(src_lang)
self.prefix_tokens = []
self.suffix_tokens = [self.EOS, self.cur_lang_code]
prefix_tokens_str = self.convert_ids_to_tokens(self.prefix_tokens)
suffix_tokens_str = self.convert_ids_to_tokens(self.suffix_tokens)
self._tokenizer.post_processor = processors.TemplateProcessing(
single=prefix_tokens_str + ["$A"] + suffix_tokens_str,
pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str,
special_tokens=list(
zip(prefix_tokens_str + suffix_tokens_str, self.prefix_tokens + self.suffix_tokens)
),
)
def set_tgt_lang_special_tokens(self, lang):
self.cur_lang_code = self.convert_tokens_to_ids(lang)
self.prefix_tokens = []
self.suffix_tokens = [self.EOS, self.cur_lang_code]
prefix_tokens_str = self.convert_ids_to_tokens(self.prefix_tokens)
suffix_tokens_str = self.convert_ids_to_tokens(self.suffix_tokens)
self._tokenizer.post_processor = processors.TemplateProcessing(
single=prefix_tokens_str + ["$A"] + suffix_tokens_str,
pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str,
special_tokens=list(
zip(prefix_tokens_str + suffix_tokens_str, self.prefix_tokens + self.suffix_tokens)
),
)
def save_vocabulary(self, dir, pre=None):
assert self.can_save_slow_tokenizer
path = os.path.join(dir, (pre + "-" if pre else "") + VOCAB_FS["vocab_file"])
if os.path.abspath(self.vocab_file) != os.path.abspath(path):
copyfile(self.vocab_file, path)
return (path,)
|
{"/qnarre/prep/convert/xlnet.py": ["/qnarre/prep/config/xlnet.py", "/qnarre/models/xlnet.py"], "/qnarre/prep/convert/bert.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/base/doc/patcher.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/nominals.py"], "/qnarre/prep/convert/funnel.py": ["/qnarre/prep/config/funnel.py", "/qnarre/models/funnel.py"], "/qnarre/prep/tokens/fast/realm.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py", "/qnarre/tokens/utils.py"], "/qnarre/models/decision_transfo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/decision_transfo.py"], "/qnarre/models/fsmt.py": ["/qnarre/core/embed.py"], "/qnarre/models/roformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/xlnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc.py": ["/qnarre/base/author.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], "/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], 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["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/deberta.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/fast/splinter.py": ["/qnarre/tokens/fast.py"], "/qnarre/models/old/trafo.py": ["/qnarre/core/attention.py", "/qnarre/core/base.py", "/qnarre/core/mlp.py", "/qnarre/core/deduce.py", "/qnarre/core/norm.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/led.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/realm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/convbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/data2vec.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/tools/triton/python/triton/language/math.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/run/beam.py": ["/qnarre/run/qa.py"], "/tools/triton/python/triton/compiler/compiler.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/tools/disasm.py", "/tools/triton/python/triton/compiler/code_generator.py", "/tools/triton/python/triton/compiler/make_launcher.py"], "/qnarre/base/doc/graph.py": ["/qnarre/base/doc/base.py"], "/qnarre/base/activism.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py", "/qnarre/base/judgment.py"], "/qnarre/base/doc/exporter.py": ["/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/fnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/roformer.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/megatron.py": ["/qnarre/prep/config/megatron.py", "/qnarre/models/megatron.py"], "/qnarre/prep/tokens/fast/roformer.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/roformer.py"], "/qnarre/core/runner.py": ["/qnarre/core/params.py"], "/qnarre/run/seq2seq.py": ["/qnarre/run/qa.py"], "/qnarre/prep/tokens/luke.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/util/table.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/tree.py", "/qnarre/base/doc/util/utils.py"], "/tools/triton/python/triton/language/standard.py": ["/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/language/__init__.py"], "/qnarre/base/doc/filters.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/args.py"], "/qnarre/prep/convert/gpt_neo.py": ["/qnarre/prep/config/gpt_neo.py", "/qnarre/models/gpt_neo.py"], "/qnarre/base/doc/section.py": ["/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/message.py"], "/qnarre/models/funnel.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/funnel.py"], "/tools/triton/python/triton/common/__init__.py": ["/tools/triton/python/triton/common/build.py"], "/qnarre/base/doc/qnn.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/models/plbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/reformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/images.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/base.py"], "/qnarre/models/yoso.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/canine.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/blog.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/counter.py"], "/qnarre/models/longformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/fast/pegasus.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/pegasus.py"], "/qnarre/base/judgment.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py", "/qnarre/base/conflict.py", "/qnarre/base/conjecture.py"], "/qnarre/models/splinter.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/rag.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/convert/albert.py": ["/qnarre/prep/config/albert.py", "/qnarre/models/albert.py"], "/qnarre/prep/tokens/fast/gpt2.py": ["/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/fast/mbart.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/roformer.py": ["/qnarre/models/roformer.py"], "/qnarre/base/doc/util/tree.py": ["/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py", "/qnarre/base/doc/util/utils.py"], "/qnarre/models/rembert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/t5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/xlm.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/bert_tf2.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/tools/triton/python/triton/ops/__init__.py": ["/tools/triton/python/triton/ops/cross_entropy.py", "/tools/triton/python/triton/ops/matmul.py"], "/qnarre/core/norm.py": ["/qnarre/core/base.py"], "/qnarre/models/mpnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/author.py": ["/qnarre/base/named.py"], "/qnarre/base/doc/args.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/log.py"], "/qnarre/prep/tokens/gpt.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/distilbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/proof.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py"], "/qnarre/prep/tokens/byt5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/electra.py": ["/qnarre/prep/config/electra.py", "/qnarre/models/electra.py"], "/qnarre/models/luke.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", 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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,560
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/tokens/distilbert.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
from .bert import Tokenizer as Bert
VOCAB_FS = {"vocab_file": "vocab.txt"}
VOCAB_MAP = {
"vocab_file": {
"distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt",
"distilbert-base-uncased-distilled-squad": "https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt",
"distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt",
"distilbert-base-cased-distilled-squad": "https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt",
"distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt",
"distilbert-base-multilingual-cased": "https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt",
}
}
INPUT_CAPS = {
"distilbert-base-uncased": 512,
"distilbert-base-uncased-distilled-squad": 512,
"distilbert-base-cased": 512,
"distilbert-base-cased-distilled-squad": 512,
"distilbert-base-german-cased": 512,
"distilbert-base-multilingual-cased": 512,
}
PRETRAINED_INIT_CONFIGURATION = {
"distilbert-base-uncased": {"do_lower_case": True},
"distilbert-base-uncased-distilled-squad": {"do_lower_case": True},
"distilbert-base-cased": {"do_lower_case": False},
"distilbert-base-cased-distilled-squad": {"do_lower_case": False},
"distilbert-base-german-cased": {"do_lower_case": False},
"distilbert-base-multilingual-cased": {"do_lower_case": False},
}
class Tokenizer(Bert):
vocab_fs = VOCAB_FS
vocab_map = VOCAB_MAP
input_caps = INPUT_CAPS
pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
model_input_names = ["input_ids", "mask"]
|
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"/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], "/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/dispatch.py": ["/qnarre/base/doc/blog.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/context.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/analyzer.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/images.py"], "/qnarre/models/big_bird.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/big_bird.py"], "/tools/triton/python/triton/runtime/jit.py": ["/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/fast/roberta.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/bigbird.py": ["/qnarre/prep/config/big_bird.py", "/qnarre/models/big_bird.py"], "/qnarre/models/gpt_neo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/gpt_neo.py"], "/qnarre/prep/convert/t5.py": ["/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/prep/tokens/fsmt.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/deberta.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/fast/splinter.py": ["/qnarre/tokens/fast.py"], "/qnarre/models/old/trafo.py": ["/qnarre/core/attention.py", "/qnarre/core/base.py", "/qnarre/core/mlp.py", "/qnarre/core/deduce.py", "/qnarre/core/norm.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/led.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/realm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/convbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/data2vec.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/tools/triton/python/triton/language/math.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/run/beam.py": ["/qnarre/run/qa.py"], "/tools/triton/python/triton/compiler/compiler.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/tools/disasm.py", "/tools/triton/python/triton/compiler/code_generator.py", "/tools/triton/python/triton/compiler/make_launcher.py"], "/qnarre/base/doc/graph.py": ["/qnarre/base/doc/base.py"], "/qnarre/base/activism.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py", "/qnarre/base/judgment.py"], "/qnarre/base/doc/exporter.py": ["/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/fnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/roformer.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/megatron.py": ["/qnarre/prep/config/megatron.py", "/qnarre/models/megatron.py"], "/qnarre/prep/tokens/fast/roformer.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/roformer.py"], "/qnarre/core/runner.py": ["/qnarre/core/params.py"], "/qnarre/run/seq2seq.py": ["/qnarre/run/qa.py"], "/qnarre/prep/tokens/luke.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/util/table.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/tree.py", "/qnarre/base/doc/util/utils.py"], "/tools/triton/python/triton/language/standard.py": ["/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/language/__init__.py"], "/qnarre/base/doc/filters.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/args.py"], "/qnarre/prep/convert/gpt_neo.py": ["/qnarre/prep/config/gpt_neo.py", "/qnarre/models/gpt_neo.py"], "/qnarre/base/doc/section.py": ["/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/message.py"], "/qnarre/models/funnel.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/funnel.py"], "/tools/triton/python/triton/common/__init__.py": ["/tools/triton/python/triton/common/build.py"], "/qnarre/base/doc/qnn.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/models/plbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/reformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/images.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/base.py"], "/qnarre/models/yoso.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/canine.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/blog.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/counter.py"], "/qnarre/models/longformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/fast/pegasus.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/pegasus.py"], "/qnarre/base/judgment.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py", "/qnarre/base/conflict.py", "/qnarre/base/conjecture.py"], "/qnarre/models/splinter.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/rag.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/convert/albert.py": ["/qnarre/prep/config/albert.py", "/qnarre/models/albert.py"], "/qnarre/prep/tokens/fast/gpt2.py": ["/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/fast/mbart.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/roformer.py": ["/qnarre/models/roformer.py"], "/qnarre/base/doc/util/tree.py": ["/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py", "/qnarre/base/doc/util/utils.py"], "/qnarre/models/rembert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/t5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/xlm.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/bert_tf2.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/tools/triton/python/triton/ops/__init__.py": ["/tools/triton/python/triton/ops/cross_entropy.py", "/tools/triton/python/triton/ops/matmul.py"], "/qnarre/core/norm.py": ["/qnarre/core/base.py"], "/qnarre/models/mpnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/author.py": ["/qnarre/base/named.py"], "/qnarre/base/doc/args.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/log.py"], "/qnarre/prep/tokens/gpt.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/distilbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/proof.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py"], "/qnarre/prep/tokens/byt5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/electra.py": ["/qnarre/prep/config/electra.py", "/qnarre/models/electra.py"], "/qnarre/models/luke.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": ["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", 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["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,561
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/convert/roformer.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
import numpy as np
import re
import tensorflow as tf
import torch
from argparse import ArgumentParser
from os.path import abspath
from transformers.utils import logging
from ..config.roformer import PreTrained
from ...models.roformer import ForMasked
logging.set_verbosity_info()
log = logging.get_logger(__name__)
def load_src_weights(model, config, tf_checkpoint_path):
tf_path = abspath(tf_checkpoint_path)
log.info(f"Converting TensorFlow checkpoint from {tf_path}")
init_vars = tf.train.list_variables(tf_path)
names = []
arrays = []
for name, shape in init_vars:
log.info(f"Loading TF weight {name} with shape {shape}")
array = tf.train.load_variable(tf_path, name)
names.append(name.replace("bert", "roformer"))
arrays.append(array)
for name, array in zip(names, arrays):
name = name.split("/")
if any(
n
in [
"adam_v",
"adam_m",
"AdamWeightDecayOptimizer",
"AdamWeightDecayOptimizer_1",
"global_step",
]
for n in name
):
log.info(f"Skipping {'/'.join(name)}")
continue
pointer = model
for m_name in name:
if re.fullmatch(r"[A-Za-z]+_\d+", m_name):
scope_names = re.split(r"_(\d+)", m_name)
else:
scope_names = [m_name]
if scope_names[0] == "kernel" or scope_names[0] == "gamma":
pointer = getattr(pointer, "weight")
elif scope_names[0] == "output_bias" or scope_names[0] == "beta":
pointer = getattr(pointer, "bias")
elif scope_names[0] == "output_weights":
pointer = getattr(pointer, "weight")
elif scope_names[0] == "squad":
pointer = getattr(pointer, "classifier")
else:
try:
pointer = getattr(pointer, scope_names[0])
except AttributeError:
log.info(f"Skipping {'/'.join(name)}")
continue
if len(scope_names) >= 2:
num = int(scope_names[1])
pointer = pointer[num]
if m_name[-11:] == "_embeddings":
pointer = getattr(pointer, "weight")
elif m_name == "kernel":
array = np.transpose(array)
try:
if not pointer.shape == array.shape:
raise ValueError(
f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched"
)
except AssertionError as e:
e.args += (pointer.shape, array.shape)
raise
log.info(f"Initialize PyTorch weight {name}")
pointer.data = torch.from_numpy(array)
return model
def to_pytorch(src_path, cfg_path, save_path):
cfg = PreTrained.from_json_file(cfg_path)
print(f"Building from config: {cfg}")
m = ForMasked(cfg)
load_src_weights(m, cfg, src_path)
print(f"Saving to: {save_path}")
torch.save(m.state_dict(), save_path, _use_new_zipfile_serialization=False)
if __name__ == "__main__":
x = ArgumentParser()
x.add_argument("--src_path", default=None, type=str, required=True)
x.add_argument("--cfg_path", default=None, type=str, required=True)
x.add_argument("--save_path", default=None, type=str, required=True)
y = x.parse_args()
to_pytorch(y.src_path, y.cfg_path, y.save_path)
|
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"/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,562
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/base/doc/util/tree.py
|
# Copyright 2019 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
import pathlib as pth
import collections as co
import multiprocessing as mp
from .log import Logger
from .row import Row
from .node import Node
from .utils import sinker, Sinks
from .error import ExtractWarning
log = Logger(__name__)
Entry = co.namedtuple('Entry', 'path row')
class Tree:
def __init__(self, root, **kw):
super().__init__()
if isinstance(root, Node):
self.name = root.name
self._root = root
else:
self.name = root
self._root = Node(self.name, **kw)
__hash__ = None
def __eq__(self, other):
if isinstance(other, type(self)):
return (self.name == other.name and self._root == other._root)
return NotImplemented
def __repr__(self):
s = type(self).__name__
s += "({})".format(repr(self._root))
return s
def stringer(self, indent=0, **kw):
yield from self._root.stringer(indent=indent + 2, **kw)
def walker(self, src=None, path=None, col=None, **kw):
path = path or pth.PurePath()
if col is not None:
path /= col
src = src or self._root.walker(**kw)
itr = iter(src)
r = False
ps = []
while True:
r = r or next(itr)
reject = yield Entry(path, r)
if reject is True and isinstance(r, Node):
r = itr.send(reject)
continue
if isinstance(r, Node):
ps.append(path)
path = path / r.name
elif r is None:
path = ps.pop()
r = False
def filterer(self, src=None, rejects=None, **kw):
rejects = rejects or {}
with sinker(rejects) as sink:
src = src or self.walker(**kw)
def _entries(include=None, exclude=None, **kw):
itr = iter(src)
e = False
while True:
e = e or next(itr)
_, row = e
if row is not None:
if include is None or not include(e, **kw):
if exclude and exclude(e, **kw):
sink.send((Sinks.excluded, e))
e = itr.send(True)
continue
yield e
e = False
for e in _entries(**kw):
yield e
def appender(self, src, **kw):
src = self.walker(self._root.appender(src), **kw)
for e in self.filterer(**kw, src=src):
yield e
def extractor(self, col, **kw):
gen = self.filterer(**kw)
next(gen)
for p, r in gen:
if r is None:
yield r
elif isinstance(r, Node):
yield Node(r.name)
else:
try:
i = r.extract(p / r.name, **kw)
r = Row(r.name, **{col: i})
yield r
except ExtractWarning:
log.warning('Extract failed for {}', r.name)
def separator(self, src=None, duplicates=None, **kw):
duplicates = duplicates or {}
with sinker(duplicates) as sink:
src = src or self.filterer(**kw)
def _entries(uniques=None, **kw):
uniques = uniques or {}
for e in src:
path, row = e
if row is not None:
r = uniques.setdefault(path / row.name, row)
if r is row:
yield e
else:
sink.send((Sinks.duplicate, e))
for e in _entries(**kw):
yield e
async def apply(self, meth, *args, src=None, path=None, **kw):
fs = []
with mp.Pool() as pool:
for p, r in src or self.filterer(**kw, path=path):
if r is not None and not isinstance(r, Node):
p /= r.name
f = r.schedule(meth, *args, pool=pool, path=p, **kw)
if f is not None:
fs.append(f)
for f in fs:
await f
return all(f.result() for f in fs)
def normalize(self, col_of=None, **kw):
col, cols = col_of or (None, ())
self._root.consolidate(col)
self._root.normalize(bool(cols))
for c in cols:
self.rename_items(c, **kw)
def rower(self, **kw):
for e in self.filterer(**kw):
_, r = e
if r is not None and not isinstance(r, Node):
yield e
def rename_items(self, col, *, base, **kw):
for p, r in self.rower(**kw, col=col):
r.rename_item(col, pth.Path(base / p), True)
for p, r in self.rower(**kw, col=col):
r.rename_item(col, pth.Path(base / p))
def copy_items(self, col, **kw):
for p, r in self.rower(**kw):
r.copy_item(col, p, **kw)
def clear_col(self, col, **kw):
for _, r in self.rower(**kw):
r.clear_col(col, **kw)
def stats(self, **kw):
dirs, files = 0
for e in self.filterer(**kw):
_, r = e
if isinstance(r, Node):
dirs += 1
elif isinstance(r, Row):
files += 1
return (dirs, files)
|
{"/qnarre/prep/convert/xlnet.py": ["/qnarre/prep/config/xlnet.py", "/qnarre/models/xlnet.py"], "/qnarre/prep/convert/bert.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/base/doc/patcher.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/nominals.py"], "/qnarre/prep/convert/funnel.py": ["/qnarre/prep/config/funnel.py", "/qnarre/models/funnel.py"], "/qnarre/prep/tokens/fast/realm.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py", "/qnarre/tokens/utils.py"], "/qnarre/models/decision_transfo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/decision_transfo.py"], "/qnarre/models/fsmt.py": ["/qnarre/core/embed.py"], "/qnarre/models/roformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/xlnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc.py": ["/qnarre/base/author.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], "/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/dispatch.py": ["/qnarre/base/doc/blog.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/context.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/analyzer.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/images.py"], "/qnarre/models/big_bird.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/big_bird.py"], "/tools/triton/python/triton/runtime/jit.py": ["/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/fast/roberta.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/bigbird.py": ["/qnarre/prep/config/big_bird.py", "/qnarre/models/big_bird.py"], "/qnarre/models/gpt_neo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/gpt_neo.py"], "/qnarre/prep/convert/t5.py": ["/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/prep/tokens/fsmt.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/deberta.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/fast/splinter.py": ["/qnarre/tokens/fast.py"], "/qnarre/models/old/trafo.py": ["/qnarre/core/attention.py", "/qnarre/core/base.py", "/qnarre/core/mlp.py", "/qnarre/core/deduce.py", "/qnarre/core/norm.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/led.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/realm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/convbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/data2vec.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/tools/triton/python/triton/language/math.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/run/beam.py": ["/qnarre/run/qa.py"], "/tools/triton/python/triton/compiler/compiler.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/tools/disasm.py", "/tools/triton/python/triton/compiler/code_generator.py", "/tools/triton/python/triton/compiler/make_launcher.py"], "/qnarre/base/doc/graph.py": ["/qnarre/base/doc/base.py"], "/qnarre/base/activism.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py", "/qnarre/base/judgment.py"], "/qnarre/base/doc/exporter.py": ["/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/fnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/roformer.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/megatron.py": ["/qnarre/prep/config/megatron.py", "/qnarre/models/megatron.py"], "/qnarre/prep/tokens/fast/roformer.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/roformer.py"], "/qnarre/core/runner.py": ["/qnarre/core/params.py"], "/qnarre/run/seq2seq.py": ["/qnarre/run/qa.py"], "/qnarre/prep/tokens/luke.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/util/table.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/tree.py", "/qnarre/base/doc/util/utils.py"], "/tools/triton/python/triton/language/standard.py": ["/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/language/__init__.py"], "/qnarre/base/doc/filters.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/args.py"], "/qnarre/prep/convert/gpt_neo.py": ["/qnarre/prep/config/gpt_neo.py", "/qnarre/models/gpt_neo.py"], "/qnarre/base/doc/section.py": ["/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/message.py"], "/qnarre/models/funnel.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/funnel.py"], "/tools/triton/python/triton/common/__init__.py": ["/tools/triton/python/triton/common/build.py"], "/qnarre/base/doc/qnn.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/models/plbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/reformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/images.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/base.py"], "/qnarre/models/yoso.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/canine.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/blog.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/counter.py"], "/qnarre/models/longformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/fast/pegasus.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/pegasus.py"], "/qnarre/base/judgment.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py", "/qnarre/base/conflict.py", "/qnarre/base/conjecture.py"], "/qnarre/models/splinter.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/rag.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/convert/albert.py": ["/qnarre/prep/config/albert.py", "/qnarre/models/albert.py"], "/qnarre/prep/tokens/fast/gpt2.py": ["/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/fast/mbart.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/roformer.py": ["/qnarre/models/roformer.py"], "/qnarre/base/doc/util/tree.py": ["/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py", "/qnarre/base/doc/util/utils.py"], "/qnarre/models/rembert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/t5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/xlm.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/bert_tf2.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/tools/triton/python/triton/ops/__init__.py": ["/tools/triton/python/triton/ops/cross_entropy.py", "/tools/triton/python/triton/ops/matmul.py"], "/qnarre/core/norm.py": ["/qnarre/core/base.py"], "/qnarre/models/mpnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/author.py": ["/qnarre/base/named.py"], "/qnarre/base/doc/args.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/log.py"], "/qnarre/prep/tokens/gpt.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/distilbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/proof.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py"], "/qnarre/prep/tokens/byt5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/electra.py": ["/qnarre/prep/config/electra.py", "/qnarre/models/electra.py"], "/qnarre/models/luke.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": ["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,563
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/models/rembert.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
import math
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import functional as F
from transformers.utils import logging
from .. import core as qc
from ..core import utils as qu
from ..core import output as qo
from ..core import forward as qf
from ..core import attention as qa
from ..core.embed import Embed
from ..core.mlp import Classifier, MLP, Predictor, Pool
from ..prep.config.bert import PreTrained
from torch.nn import CrossEntropyLoss
from ...pytorch_utils import (
apply_chunking_to_forward,
)
log = logging.get_logger(__name__)
LIST = [
"google/rembert",
]
class RemBertEmbeddings(qc.Module):
def __init__(self, config):
super().__init__()
self.word_embeddings = qc.Embed(
config.s_vocab, config.input_embedding_size, padding_idx=config.PAD
)
self.position_embeddings = qc.Embed(config.n_pos, config.input_embedding_size)
self.token_type_embeddings = qc.Embed(config.n_typ, config.input_embedding_size)
self.norm = qc.LayerNorm(config.input_embedding_size, eps=config.eps)
self.drop = qc.Dropout(config.drop)
self.register_buffer("position_ids", torch.arange(config.n_pos).expand((1, -1)))
def forward(
self,
input_ids=None,
token_type_ids=None,
position_ids=None,
inputs_embeds=None,
past_key_values_length=0,
):
if input_ids is not None:
input_shape = input_ids.size()
else:
input_shape = inputs_embeds.size()[:-1]
seq_length = input_shape[1]
if position_ids is None:
position_ids = self.position_ids[
:, past_key_values_length : seq_length + past_key_values_length
]
if token_type_ids is None:
token_type_ids = torch.zeros(
input_shape, dtype=torch.long, device=self.position_ids.device
)
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
token_type_embeddings = self.token_type_embeddings(token_type_ids)
embeddings = inputs_embeds + token_type_embeddings
position_embeddings = self.position_embeddings(position_ids)
embeddings += position_embeddings
embeddings = self.norm(embeddings)
embeddings = self.drop(embeddings)
return embeddings
class RemBertSelfAttention(qc.Module):
def __init__(self, config):
super().__init__()
if config.d_model % config.n_heads != 0 and not hasattr(config, "d_embed"):
raise ValueError(
f"The hidden size ({config.d_model}) is not a multiple of the number of attention "
f"heads ({config.n_heads})"
)
self.n_heads = config.n_heads
self.attention_head_size = int(config.d_model / config.n_heads)
self.all_head_size = self.n_heads * self.attention_head_size
self.query = qc.Linear(config.d_model, self.all_head_size)
self.key = qc.Linear(config.d_model, self.all_head_size)
self.value = qc.Linear(config.d_model, self.all_head_size)
self.drop = qc.Dropout(config.drop_attn)
self.is_decoder = config.is_decoder
def transpose_for_scores(self, x):
new_x_shape = x.size()[:-1] + (self.n_heads, self.attention_head_size)
x = x.view(*new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(
self,
hiddens,
attention_mask=None,
head_mask=None,
enc_hiddens=None,
encoder_attention_mask=None,
past_key_value=None,
output_attentions=False,
):
mixed_query_layer = self.query(hiddens)
is_cross_attention = enc_hiddens is not None
if is_cross_attention and past_key_value is not None:
# reuse k,v, crosses
key_layer = past_key_value[0]
value_layer = past_key_value[1]
attention_mask = encoder_attention_mask
elif is_cross_attention:
key_layer = self.transpose_for_scores(self.key(enc_hiddens))
value_layer = self.transpose_for_scores(self.value(enc_hiddens))
attention_mask = encoder_attention_mask
elif past_key_value is not None:
key_layer = self.transpose_for_scores(self.key(hiddens))
value_layer = self.transpose_for_scores(self.value(hiddens))
key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
else:
key_layer = self.transpose_for_scores(self.key(hiddens))
value_layer = self.transpose_for_scores(self.value(hiddens))
query_layer = self.transpose_for_scores(mixed_query_layer)
if self.is_decoder:
past_key_value = (key_layer, value_layer)
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in RemBertModel forward() function)
attention_scores = attention_scores + attention_mask
# Normalize the attention scores to probabilities.
attention_probs = F.softmax(attention_scores, dim=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.drop(attention_probs)
# Mask heads if we want to
if head_mask is not None:
attention_probs = attention_probs * head_mask
context_layer = torch.matmul(attention_probs, value_layer)
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(*new_context_layer_shape)
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
if self.is_decoder:
outputs = outputs + (past_key_value,)
return outputs
# Copied from transformers.models.bert.modeling_bert.BertSelfOutput with Bert->RemBert
class RemBertSelfOutput(qc.Module):
def __init__(self, config):
super().__init__()
self.dense = qc.Linear(config.d_model, config.d_model)
self.norm = qc.LayerNorm(config.d_model, eps=config.eps)
self.drop = qc.Dropout(config.drop)
def forward(self, hiddens, input_tensor):
hiddens = self.dense(hiddens)
hiddens = self.drop(hiddens)
hiddens = self.norm(hiddens + input_tensor)
return hiddens
class Attention(qc.Module):
def __init__(self, config):
super().__init__()
self.self = RemBertSelfAttention(config)
self.output = RemBertSelfOutput(config)
# Copied from transformers.models.bert.modeling_bert.BertAttention.forward
def forward(
self,
hiddens,
attention_mask=None,
head_mask=None,
enc_hiddens=None,
encoder_attention_mask=None,
past_key_value=None,
output_attentions=False,
):
self_outputs = self.self(
hiddens,
attention_mask,
head_mask,
enc_hiddens,
encoder_attention_mask,
past_key_value,
output_attentions,
)
attention_output = self.output(self_outputs[0], hiddens)
outputs = (attention_output,) + self_outputs[1:] # add attns if we output them
return outputs
# Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->RemBert
class RemBertIntermediate(qc.Module):
def __init__(self, cfg):
super().__init__()
self.dense = qc.Linear(cfg.d_model, cfg.d_ff)
self.act = qu.activation(cfg.act)
def forward(self, x):
y = self.dense(x)
y = self.act(y)
return y
# Copied from transformers.models.bert.modeling_bert.BertOutput with Bert->RemBert
class RemBertOutput(qc.Module):
def __init__(self, config):
super().__init__()
self.dense = qc.Linear(config.d_ff, config.d_model)
self.norm = qc.LayerNorm(config.d_model, eps=config.eps)
self.drop = qc.Dropout(config.drop)
def forward(self, y, input_tensor):
y = self.dense(y)
y = self.drop(y)
y = self.norm(y + input_tensor)
return y
class Layer(qc.Module):
def __init__(self, config):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = Attention(config)
self.is_decoder = config.is_decoder
self.add_cross_attention = config.add_cross_attention
if self.add_cross_attention:
if not self.is_decoder:
raise ValueError(
f"{self} should be used as a decoder model if cross attention is added"
)
self.crossattention = Attention(config)
self.intermediate = RemBertIntermediate(config)
self.output = RemBertOutput(config)
# Copied from transformers.models.bert.modeling_bert.BertLayer.forward
def forward(
self,
hiddens,
attention_mask=None,
head_mask=None,
enc_hiddens=None,
encoder_attention_mask=None,
past_key_value=None,
output_attentions=False,
):
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
self_attention_outputs = self.attention(
hiddens,
attention_mask,
head_mask,
output_attentions=output_attentions,
past_key_value=self_attn_past_key_value,
)
attention_output = self_attention_outputs[0]
# if decoder, the last output is tuple of self-attn cache
if self.is_decoder:
outputs = self_attention_outputs[1:-1]
present_key_value = self_attention_outputs[-1]
else:
outputs = self_attention_outputs[1:] # add self attns if we output attention weights
cross_attn_present_key_value = None
if self.is_decoder and enc_hiddens is not None:
if not hasattr(self, "crossattention"):
raise ValueError(
f"If `enc_hiddens` are passed, {self} has to be instantiated with cross-attention layers by setting `config.add_cross_attention=True`"
)
# cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
cross_attention_outputs = self.crossattention(
attention_output,
attention_mask,
head_mask,
enc_hiddens,
encoder_attention_mask,
cross_attn_past_key_value,
output_attentions,
)
attention_output = cross_attention_outputs[0]
outputs = (
outputs + cross_attention_outputs[1:-1]
) # add cross attns if we output attention weights
# add cross-attn cache to positions 3,4 of present_key_value tuple
cross_attn_present_key_value = cross_attention_outputs[-1]
present_key_value = present_key_value + cross_attn_present_key_value
layer_output = apply_chunking_to_forward(
self.feed_forward_chunk,
self.chunk_size_feed_forward,
self.seq_len_dim,
attention_output,
)
outputs = (layer_output,) + outputs
# if decoder, return the attn key/values as the last output
if self.is_decoder:
outputs = outputs + (present_key_value,)
return outputs
# Copied from transformers.models.bert.modeling_bert.BertLayer.feed_forward_chunk
def feed_forward_chunk(self, attention_output):
intermediate_output = self.intermediate(attention_output)
layer_output = self.output(intermediate_output, attention_output)
return layer_output
class Encoder(qc.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.embedding_hidden_mapping_in = qc.Linear(config.input_embedding_size, config.d_model)
self.layer = nn.ModuleList([Layer(config) for _ in range(config.n_lays)])
self.gradient_checkpointing = False
def forward(
self,
hiddens,
attention_mask=None,
head_mask=None,
enc_hiddens=None,
encoder_attention_mask=None,
caches=None,
y_cache=None,
output_attentions=False,
output_hidden_states=False,
return_dict=True,
):
hiddens = self.embedding_hidden_mapping_in(hiddens)
all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
next_decoder_cache = () if y_cache else None
for i, layer_module in enumerate(self.layer):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hiddens,)
layer_head_mask = head_mask[i] if head_mask is not None else None
past_key_value = caches[i] if caches is not None else None
if self.gradient_checkpointing and self.training:
if y_cache:
log.warning(
"`y_cache=True` is incompatible with gradient checkpointing. Setting `y_cache=False`..."
)
y_cache = False
def create_custom_forward(module):
def custom_forward(*inputs):
return module(*inputs, past_key_value, output_attentions)
return custom_forward
layer_outputs = torch.utils.checkpoint.checkpoint(
create_custom_forward(layer_module),
hiddens,
attention_mask,
layer_head_mask,
enc_hiddens,
encoder_attention_mask,
)
else:
layer_outputs = layer_module(
hiddens,
attention_mask,
layer_head_mask,
enc_hiddens,
encoder_attention_mask,
past_key_value,
output_attentions,
)
hiddens = layer_outputs[0]
if y_cache:
next_decoder_cache += (layer_outputs[-1],)
if output_attentions:
all_self_attentions = all_self_attentions + (layer_outputs[1],)
if self.config.add_cross_attention:
all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hiddens,)
if not return_dict:
return tuple(
v
for v in [
hiddens,
next_decoder_cache,
all_hidden_states,
all_self_attentions,
all_cross_attentions,
]
if v is not None
)
return qo.CachesCrosses(
y=hiddens,
caches=next_decoder_cache,
hiddens=all_hidden_states,
attns=all_self_attentions,
crosses=all_cross_attentions,
)
# Copied from transformers.models.bert.modeling_bert.BertPredictionHeadTransform with Bert->RemBert
class RemBertPredictionHeadTransform(qc.Module):
def __init__(self, cfg):
super().__init__()
self.dense = qc.Linear(cfg.d_model, cfg.d_model)
self.act = qu.activation(cfg.act)
self.norm = qc.LayerNorm(cfg.d_model, eps=cfg.eps)
def forward(self, x):
y = self.dense(x)
y = self.act(y)
y = self.norm(y)
return y
class Model(PreTrained):
def __init__(self, config, add_pooling_layer=True):
super().__init__(config)
self.config = config
self.embeddings = RemBertEmbeddings(config)
self.encoder = Encoder(config)
self.pool = Pool(config) if add_pooling_layer else None
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
enc_hiddens=None,
encoder_attention_mask=None,
caches=None,
y_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
output_attentions = (
output_attentions if output_attentions is not None else self.config.output_attentions
)
output_hidden_states = (
output_hidden_states
if output_hidden_states is not None
else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if self.config.is_decoder:
y_cache = y_cache if y_cache is not None else self.config.y_cache
else:
y_cache = False
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
input_shape = input_ids.size()
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
batch_size, seq_length = input_shape
device = input_ids.device if input_ids is not None else inputs_embeds.device
# past_key_values_length
past_key_values_length = caches[0][0].shape[2] if caches is not None else 0
if attention_mask is None:
attention_mask = torch.ones(
((batch_size, seq_length + past_key_values_length)), device=device
)
if token_type_ids is None:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
extended_attention_mask = self.get_extended_attention_mask(
attention_mask, input_shape, device
)
# If a 2D or 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, n_heads, seq_length, seq_length]
if self.config.is_decoder and enc_hiddens is not None:
encoder_batch_size, encoder_sequence_length, _ = enc_hiddens.size()
encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
if encoder_attention_mask is None:
encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
else:
encoder_extended_attention_mask = None
head_mask = self.get_head_mask(head_mask, self.config.n_lays)
embedding_output = self.embeddings(
input_ids=input_ids,
position_ids=position_ids,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
past_key_values_length=past_key_values_length,
)
encoder_outputs = self.encoder(
embedding_output,
attention_mask=extended_attention_mask,
head_mask=head_mask,
enc_hiddens=enc_hiddens,
encoder_attention_mask=encoder_extended_attention_mask,
caches=caches,
y_cache=y_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = encoder_outputs[0]
pooled_output = self.pool(sequence_output) if self.pool is not None else None
if not return_dict:
return (sequence_output, pooled_output) + encoder_outputs[1:]
return qo.BaseWithPoolingAndCrossAttentions(
y=sequence_output,
pools=pooled_output,
caches=encoder_outputs.caches,
hiddens=encoder_outputs.hiddens,
attns=encoder_outputs.attns,
crosses=encoder_outputs.crosses,
)
class ForMasked(PreTrained):
def __init__(self, **kw):
super().__init__(**kw)
cfg = self.get_cfg(kw)
self.model = Model(add_pool=False, **kw)
self.proj = Predictor(cfg.output_embedding_size, **kw)
forward = qf.forward_masked
class ForCausal(PreTrained):
def __init__(self, **kw):
super().__init__(**kw)
cfg = self.get_cfg(kw)
self.model = Model(add_pool=False, **kw)
self.proj = Predictor(cfg.output_embedding_size, **kw)
forward = qf.forward_causal
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
enc_hiddens=None,
encoder_attention_mask=None,
caches=None,
labels=None,
y_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.rembert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
enc_hiddens=enc_hiddens,
encoder_attention_mask=encoder_attention_mask,
caches=caches,
y_cache=y_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
prediction_scores = self.cls(sequence_output)
lm_loss = None
if labels is not None:
# we are doing next-token prediction; shift prediction scores and input ids by one
shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous()
labels = labels[:, 1:].contiguous()
loss_fct = CrossEntropyLoss()
lm_loss = loss_fct(
shifted_prediction_scores.view(-1, self.config.s_vocab), labels.view(-1)
)
if not return_dict:
output = (prediction_scores,) + outputs[2:]
return ((lm_loss,) + output) if lm_loss is not None else output
return CausalLMOutputWithCrossAttentions(
loss=lm_loss,
logits=prediction_scores,
caches=outputs.caches,
hiddens=outputs.hiddens,
attns=outputs.attns,
crosses=outputs.crosses,
)
class ForChoice(PreTrained):
def __init__(self, config):
super().__init__(config)
self.rembert = Model(config)
self.drop = qc.Dropout(config.classifier_dropout_prob)
self.classifier = qc.Linear(config.d_model, 1)
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
attention_mask = (
attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
)
token_type_ids = (
token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
)
position_ids = (
position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
)
inputs_embeds = (
inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
if inputs_embeds is not None
else None
)
outputs = self.rembert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = outputs[1]
pooled_output = self.drop(pooled_output)
logits = self.classifier(pooled_output)
reshaped_logits = logits.view(-1, num_choices)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(reshaped_logits, labels)
if not return_dict:
output = (reshaped_logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return qo.WithLoss(
loss=loss,
logits=reshaped_logits,
hiddens=outputs.hiddens,
attns=outputs.attns,
)
class ForSeqClass(PreTrained):
def __init__(self, **kw):
super().__init__(**kw)
self.get_cfg(kw)
self.model = Model(**kw)
self.proj = Classifier(**kw)
forward = qf.forward_seq
class ForTokClass(PreTrained):
def __init__(self, **kw):
super().__init__(**kw)
self.get_cfg(kw)
self.model = Model(add_pool=False, **kw)
self.proj = Classifier(**kw)
forward = qf.forward_tok
class ForQA(PreTrained):
def __init__(self, **kw):
super().__init__(**kw)
cfg = self.get_cfg(kw)
self.model = Model(add_pool=False, **kw)
self.proj = qc.Linear(cfg.d_model, cfg.n_labels, **kw)
forward = qf.forward_qa
|
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"/qnarre/base/doc/util/table.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/tree.py", "/qnarre/base/doc/util/utils.py"], "/tools/triton/python/triton/language/standard.py": ["/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/language/__init__.py"], "/qnarre/base/doc/filters.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/args.py"], "/qnarre/prep/convert/gpt_neo.py": ["/qnarre/prep/config/gpt_neo.py", "/qnarre/models/gpt_neo.py"], "/qnarre/base/doc/section.py": ["/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/message.py"], "/qnarre/models/funnel.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/funnel.py"], "/tools/triton/python/triton/common/__init__.py": ["/tools/triton/python/triton/common/build.py"], "/qnarre/base/doc/qnn.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/models/plbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/reformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/images.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/base.py"], "/qnarre/models/yoso.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/canine.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/blog.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/counter.py"], "/qnarre/models/longformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/fast/pegasus.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/pegasus.py"], "/qnarre/base/judgment.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py", "/qnarre/base/conflict.py", "/qnarre/base/conjecture.py"], "/qnarre/models/splinter.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/rag.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/convert/albert.py": ["/qnarre/prep/config/albert.py", "/qnarre/models/albert.py"], "/qnarre/prep/tokens/fast/gpt2.py": ["/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/fast/mbart.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/roformer.py": ["/qnarre/models/roformer.py"], "/qnarre/base/doc/util/tree.py": ["/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py", "/qnarre/base/doc/util/utils.py"], "/qnarre/models/rembert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/t5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/xlm.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/bert_tf2.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/tools/triton/python/triton/ops/__init__.py": ["/tools/triton/python/triton/ops/cross_entropy.py", "/tools/triton/python/triton/ops/matmul.py"], "/qnarre/core/norm.py": ["/qnarre/core/base.py"], "/qnarre/models/mpnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/author.py": ["/qnarre/base/named.py"], "/qnarre/base/doc/args.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/log.py"], "/qnarre/prep/tokens/gpt.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/distilbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/proof.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py"], "/qnarre/prep/tokens/byt5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/electra.py": ["/qnarre/prep/config/electra.py", "/qnarre/models/electra.py"], "/qnarre/models/luke.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": ["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,564
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/tokens/t5.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
import os
import re
import warnings
import sentencepiece as spm
from shutil import copyfile
from ...tokens.utils import PreTrainedTokenizer
VOCAB_FS = {"vocab_file": "spiece.model"}
VOCAB_MAP = {
"vocab_file": {
"t5-small": "https://huggingface.co/t5-small/resolve/main/spiece.model",
"t5-base": "https://huggingface.co/t5-base/resolve/main/spiece.model",
"t5-large": "https://huggingface.co/t5-large/resolve/main/spiece.model",
"t5-3b": "https://huggingface.co/t5-3b/resolve/main/spiece.model",
"t5-11b": "https://huggingface.co/t5-11b/resolve/main/spiece.model",
}
}
INPUT_CAPS = {
"t5-small": 512,
"t5-base": 512,
"t5-large": 512,
"t5-3b": 512,
"t5-11b": 512,
}
class Tokenizer(PreTrainedTokenizer):
vocab_fs = VOCAB_FS
vocab_map = VOCAB_MAP
input_caps = INPUT_CAPS
model_input_names = ["input_ids", "mask"]
def __init__(
self,
vocab_file,
eos="</s>",
unk="<unk>",
pad="<pad>",
extra_ids=100,
additional_special_tokens=None,
sp_model_kw=None,
**kw,
):
if extra_ids > 0 and additional_special_tokens is None:
additional_special_tokens = [f"<extra_id_{i}>" for i in range(extra_ids)]
elif extra_ids > 0 and additional_special_tokens is not None:
extra_tokens = len(
set(filter(lambda x: ("extra_id" in str(x)), additional_special_tokens))
)
if extra_tokens != extra_ids:
raise ValueError(
f"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are provided to T5Tokenizer. "
"In this case the additional_special_tokens must include the extra_ids tokens"
)
self.sp_model_kw = {} if sp_model_kw is None else sp_model_kw
super().__init__(
eos=eos,
unk=unk,
pad=pad,
extra_ids=extra_ids,
additional_special_tokens=additional_special_tokens,
sp_model_kw=self.sp_model_kw,
**kw,
)
self.vocab_file = vocab_file
self._extra_ids = extra_ids
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kw)
self.sp_model.Load(vocab_file)
@property
def s_vocab(self):
return self.sp_model.get_piece_size() + self._extra_ids
def get_vocab(self):
vocab = {self.convert_ids_to_tokens(i): i for i in range(self.s_vocab)}
vocab.update(self.added_tokens_encoder)
return vocab
def get_special_tokens_mask(
self,
toks_0,
toks_1=None,
has_specials=False,
):
if has_specials:
return super().get_special_tokens_mask(toks_0=toks_0, toks_1=toks_1, has_specials=True)
if toks_1 is None:
return ([0] * len(toks_0)) + [1]
return ([0] * len(toks_0)) + [1] + ([0] * len(toks_1)) + [1]
def _add_eos_if_not_present(self, token_ids):
if len(token_ids) > 0 and token_ids[-1] == self.EOS:
warnings.warn(
f"This sequence already has {self.eos}. In future versions this behavior may lead to duplicated eos tokens being added."
)
return token_ids
else:
return token_ids + [self.EOS]
def create_token_type_ids_from_sequences(self, toks_0, toks_1=None):
eos = [self.EOS]
if toks_1 is None:
return len(toks_0 + eos) * [0]
return len(toks_0 + eos + toks_1 + eos) * [0]
def build_inputs_with_special_tokens(self, toks_0, toks_1=None):
toks_0 = self._add_eos_if_not_present(toks_0)
if toks_1 is None:
return toks_0
else:
toks_1 = self._add_eos_if_not_present(toks_1)
return toks_0 + toks_1
def __getstate__(self):
state = self.__dict__.copy()
state["sp_model"] = None
return state
def __setstate__(self, d):
self.__dict__ = d
if not hasattr(self, "sp_model_kw"):
self.sp_model_kw = {}
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kw)
self.sp_model.Load(self.vocab_file)
def _tokenize(self, text):
return self.sp_model.encode(text, out_type=str)
def _convert_token_to_id(self, token):
if token.startswith("<extra_id_"):
match = re.match(r"<extra_id_(\d+)>", token)
num = int(match.group(1))
return self.s_vocab - num - 1
return self.sp_model.piece_to_id(token)
def _convert_id_to_token(self, index):
if index < self.sp_model.get_piece_size():
token = self.sp_model.IdToPiece(index)
else:
token = f"<extra_id_{self.s_vocab - 1 - index}>"
return token
def convert_tokens_to_string(self, tokens):
current_sub_tokens = []
out_string = ""
for token in tokens:
if token in self.all_special_tokens:
out_string += self.sp_model.decode_pieces(current_sub_tokens) + token + " "
current_sub_tokens = []
else:
current_sub_tokens.append(token)
out_string += self.sp_model.decode_pieces(current_sub_tokens)
return out_string.strip()
def save_vocabulary(self, dir, pre=None):
path = os.path.join(
dir,
(pre + "-" if pre else "") + VOCAB_FS["vocab_file"],
)
if os.path.abspath(self.vocab_file) != os.path.abspath(path) and os.path.isfile(
self.vocab_file
):
copyfile(self.vocab_file, path)
elif not os.path.isfile(self.vocab_file):
with open(path, "wb") as fi:
content_spiece_model = self.sp_model.serialized_model_proto()
fi.write(content_spiece_model)
return (path,)
|
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"/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,565
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/tokens/xlm.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
import json
import os
import re
import sys
import unicodedata
import sacremoses as sm
from ...tokens.utils import PreTrainedTokenizer
VOCAB_FS = {
"vocab_file": "vocab.json",
"merges_file": "merges.txt",
}
VOCAB_MAP = {
"vocab_file": {
"xlm-mlm-en-2048": "https://huggingface.co/xlm-mlm-en-2048/resolve/main/vocab.json",
"xlm-mlm-ende-1024": "https://huggingface.co/xlm-mlm-ende-1024/resolve/main/vocab.json",
"xlm-mlm-enfr-1024": "https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/vocab.json",
"xlm-mlm-enro-1024": "https://huggingface.co/xlm-mlm-enro-1024/resolve/main/vocab.json",
"xlm-mlm-tlm-xnli15-1024": "https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/vocab.json",
"xlm-mlm-xnli15-1024": "https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/vocab.json",
"xlm-clm-enfr-1024": "https://huggingface.co/xlm-clm-enfr-1024/resolve/main/vocab.json",
"xlm-clm-ende-1024": "https://huggingface.co/xlm-clm-ende-1024/resolve/main/vocab.json",
"xlm-mlm-17-1280": "https://huggingface.co/xlm-mlm-17-1280/resolve/main/vocab.json",
"xlm-mlm-100-1280": "https://huggingface.co/xlm-mlm-100-1280/resolve/main/vocab.json",
},
"merges_file": {
"xlm-mlm-en-2048": "https://huggingface.co/xlm-mlm-en-2048/resolve/main/merges.txt",
"xlm-mlm-ende-1024": "https://huggingface.co/xlm-mlm-ende-1024/resolve/main/merges.txt",
"xlm-mlm-enfr-1024": "https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/merges.txt",
"xlm-mlm-enro-1024": "https://huggingface.co/xlm-mlm-enro-1024/resolve/main/merges.txt",
"xlm-mlm-tlm-xnli15-1024": "https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/merges.txt",
"xlm-mlm-xnli15-1024": "https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/merges.txt",
"xlm-clm-enfr-1024": "https://huggingface.co/xlm-clm-enfr-1024/resolve/main/merges.txt",
"xlm-clm-ende-1024": "https://huggingface.co/xlm-clm-ende-1024/resolve/main/merges.txt",
"xlm-mlm-17-1280": "https://huggingface.co/xlm-mlm-17-1280/resolve/main/merges.txt",
"xlm-mlm-100-1280": "https://huggingface.co/xlm-mlm-100-1280/resolve/main/merges.txt",
},
}
INPUT_CAPS = {
"xlm-mlm-en-2048": 512,
"xlm-mlm-ende-1024": 512,
"xlm-mlm-enfr-1024": 512,
"xlm-mlm-enro-1024": 512,
"xlm-mlm-tlm-xnli15-1024": 512,
"xlm-mlm-xnli15-1024": 512,
"xlm-clm-enfr-1024": 512,
"xlm-clm-ende-1024": 512,
"xlm-mlm-17-1280": 512,
"xlm-mlm-100-1280": 512,
}
PRETRAINED_INIT_CONFIGURATION = {
"xlm-mlm-en-2048": {"do_lowercase_and_remove_accent": True},
"xlm-mlm-ende-1024": {
"do_lowercase_and_remove_accent": True,
"id2lang": {0: "de", 1: "en"},
"lang2id": {"de": 0, "en": 1},
},
"xlm-mlm-enfr-1024": {
"do_lowercase_and_remove_accent": True,
"id2lang": {0: "en", 1: "fr"},
"lang2id": {"en": 0, "fr": 1},
},
"xlm-mlm-enro-1024": {
"do_lowercase_and_remove_accent": True,
"id2lang": {0: "en", 1: "ro"},
"lang2id": {"en": 0, "ro": 1},
},
"xlm-mlm-tlm-xnli15-1024": {
"do_lowercase_and_remove_accent": True,
"id2lang": {
0: "ar",
1: "bg",
2: "de",
3: "el",
4: "en",
5: "es",
6: "fr",
7: "hi",
8: "ru",
9: "sw",
10: "th",
11: "tr",
12: "ur",
13: "vi",
14: "zh",
},
"lang2id": {
"ar": 0,
"bg": 1,
"de": 2,
"el": 3,
"en": 4,
"es": 5,
"fr": 6,
"hi": 7,
"ru": 8,
"sw": 9,
"th": 10,
"tr": 11,
"ur": 12,
"vi": 13,
"zh": 14,
},
},
"xlm-mlm-xnli15-1024": {
"do_lowercase_and_remove_accent": True,
"id2lang": {
0: "ar",
1: "bg",
2: "de",
3: "el",
4: "en",
5: "es",
6: "fr",
7: "hi",
8: "ru",
9: "sw",
10: "th",
11: "tr",
12: "ur",
13: "vi",
14: "zh",
},
"lang2id": {
"ar": 0,
"bg": 1,
"de": 2,
"el": 3,
"en": 4,
"es": 5,
"fr": 6,
"hi": 7,
"ru": 8,
"sw": 9,
"th": 10,
"tr": 11,
"ur": 12,
"vi": 13,
"zh": 14,
},
},
"xlm-clm-enfr-1024": {
"do_lowercase_and_remove_accent": True,
"id2lang": {0: "en", 1: "fr"},
"lang2id": {"en": 0, "fr": 1},
},
"xlm-clm-ende-1024": {
"do_lowercase_and_remove_accent": True,
"id2lang": {0: "de", 1: "en"},
"lang2id": {"de": 0, "en": 1},
},
"xlm-mlm-17-1280": {
"do_lowercase_and_remove_accent": False,
"id2lang": {
0: "ar",
1: "de",
2: "en",
3: "es",
4: "fr",
5: "hi",
6: "it",
7: "ja",
8: "ko",
9: "nl",
10: "pl",
11: "pt",
12: "ru",
13: "sv",
14: "tr",
15: "vi",
16: "zh",
},
"lang2id": {
"ar": 0,
"de": 1,
"en": 2,
"es": 3,
"fr": 4,
"hi": 5,
"it": 6,
"ja": 7,
"ko": 8,
"nl": 9,
"pl": 10,
"pt": 11,
"ru": 12,
"sv": 13,
"tr": 14,
"vi": 15,
"zh": 16,
},
},
"xlm-mlm-100-1280": {
"do_lowercase_and_remove_accent": False,
"id2lang": {
0: "af",
1: "als",
2: "am",
3: "an",
4: "ang",
5: "ar",
6: "arz",
7: "ast",
8: "az",
9: "bar",
10: "be",
11: "bg",
12: "bn",
13: "br",
14: "bs",
15: "ca",
16: "ceb",
17: "ckb",
18: "cs",
19: "cy",
20: "da",
21: "de",
22: "el",
23: "en",
24: "eo",
25: "es",
26: "et",
27: "eu",
28: "fa",
29: "fi",
30: "fr",
31: "fy",
32: "ga",
33: "gan",
34: "gl",
35: "gu",
36: "he",
37: "hi",
38: "hr",
39: "hu",
40: "hy",
41: "ia",
42: "id",
43: "is",
44: "it",
45: "ja",
46: "jv",
47: "ka",
48: "kk",
49: "kn",
50: "ko",
51: "ku",
52: "la",
53: "lb",
54: "lt",
55: "lv",
56: "mk",
57: "ml",
58: "mn",
59: "mr",
60: "ms",
61: "my",
62: "nds",
63: "ne",
64: "nl",
65: "nn",
66: "no",
67: "oc",
68: "pl",
69: "pt",
70: "ro",
71: "ru",
72: "scn",
73: "sco",
74: "sh",
75: "si",
76: "simple",
77: "sk",
78: "sl",
79: "sq",
80: "sr",
81: "sv",
82: "sw",
83: "ta",
84: "te",
85: "th",
86: "tl",
87: "tr",
88: "tt",
89: "uk",
90: "ur",
91: "uz",
92: "vi",
93: "war",
94: "wuu",
95: "yi",
96: "zh",
97: "zh_classical",
98: "zh_min_nan",
99: "zh_yue",
},
"lang2id": {
"af": 0,
"als": 1,
"am": 2,
"an": 3,
"ang": 4,
"ar": 5,
"arz": 6,
"ast": 7,
"az": 8,
"bar": 9,
"be": 10,
"bg": 11,
"bn": 12,
"br": 13,
"bs": 14,
"ca": 15,
"ceb": 16,
"ckb": 17,
"cs": 18,
"cy": 19,
"da": 20,
"de": 21,
"el": 22,
"en": 23,
"eo": 24,
"es": 25,
"et": 26,
"eu": 27,
"fa": 28,
"fi": 29,
"fr": 30,
"fy": 31,
"ga": 32,
"gan": 33,
"gl": 34,
"gu": 35,
"he": 36,
"hi": 37,
"hr": 38,
"hu": 39,
"hy": 40,
"ia": 41,
"id": 42,
"is": 43,
"it": 44,
"ja": 45,
"jv": 46,
"ka": 47,
"kk": 48,
"kn": 49,
"ko": 50,
"ku": 51,
"la": 52,
"lb": 53,
"lt": 54,
"lv": 55,
"mk": 56,
"ml": 57,
"mn": 58,
"mr": 59,
"ms": 60,
"my": 61,
"nds": 62,
"ne": 63,
"nl": 64,
"nn": 65,
"no": 66,
"oc": 67,
"pl": 68,
"pt": 69,
"ro": 70,
"ru": 71,
"scn": 72,
"sco": 73,
"sh": 74,
"si": 75,
"simple": 76,
"sk": 77,
"sl": 78,
"sq": 79,
"sr": 80,
"sv": 81,
"sw": 82,
"ta": 83,
"te": 84,
"th": 85,
"tl": 86,
"tr": 87,
"tt": 88,
"uk": 89,
"ur": 90,
"uz": 91,
"vi": 92,
"war": 93,
"wuu": 94,
"yi": 95,
"zh": 96,
"zh_classical": 97,
"zh_min_nan": 98,
"zh_yue": 99,
},
},
}
def get_pairs(word):
pairs = set()
prev_char = word[0]
for char in word[1:]:
pairs.add((prev_char, char))
prev_char = char
return pairs
def lowercase_and_remove_accent(text):
text = " ".join(text)
text = text.lower()
text = unicodedata.normalize("NFD", text)
output = []
for char in text:
cat = unicodedata.category(char)
if cat == "Mn":
continue
output.append(char)
return "".join(output).lower().split(" ")
def replace_unicode_punct(text):
text = text.replace("，", ",")
text = re.sub(r"。\s*", ". ", text)
text = text.replace("、", ",")
text = text.replace("”", '"')
text = text.replace("“", '"')
text = text.replace("∶", ":")
text = text.replace("：", ":")
text = text.replace("？", "?")
text = text.replace("《", '"')
text = text.replace("》", '"')
text = text.replace("）", ")")
text = text.replace("！", "!")
text = text.replace("（", "(")
text = text.replace("；", ";")
text = text.replace("１", "1")
text = text.replace("」", '"')
text = text.replace("「", '"')
text = text.replace("０", "0")
text = text.replace("３", "3")
text = text.replace("２", "2")
text = text.replace("５", "5")
text = text.replace("６", "6")
text = text.replace("９", "9")
text = text.replace("７", "7")
text = text.replace("８", "8")
text = text.replace("４", "4")
text = re.sub(r"．\s*", ". ", text)
text = text.replace("～", "~")
text = text.replace("’", "'")
text = text.replace("…", "...")
text = text.replace("━", "-")
text = text.replace("〈", "<")
text = text.replace("〉", ">")
text = text.replace("【", "[")
text = text.replace("】", "]")
text = text.replace("％", "%")
return text
def remove_non_printing_char(text):
output = []
for char in text:
cat = unicodedata.category(char)
if cat.startswith("C"):
continue
output.append(char)
return "".join(output)
def romanian_preprocessing(text):
text = text.replace("\u015e", "\u0218").replace("\u015f", "\u0219")
text = text.replace("\u0162", "\u021a").replace("\u0163", "\u021b")
text = text.replace("\u0218", "S").replace("\u0219", "s") # s-comma
text = text.replace("\u021a", "T").replace("\u021b", "t") # t-comma
text = text.replace("\u0102", "A").replace("\u0103", "a")
text = text.replace("\u00C2", "A").replace("\u00E2", "a")
text = text.replace("\u00CE", "I").replace("\u00EE", "i")
return text
class Tokenizer(PreTrainedTokenizer):
vocab_fs = VOCAB_FS
vocab_map = VOCAB_MAP
pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
input_caps = INPUT_CAPS
def __init__(
self,
vocab_file,
merges_file,
unk="<unk>",
bos="<s>",
sep="</s>",
pad="<pad>",
cls="</s>",
msk="<special1>",
additional_special_tokens=[
"<special0>",
"<special1>",
"<special2>",
"<special3>",
"<special4>",
"<special5>",
"<special6>",
"<special7>",
"<special8>",
"<special9>",
],
lang2id=None,
id2lang=None,
do_lowercase_and_remove_accent=True,
**kw,
):
super().__init__(
unk=unk,
bos=bos,
sep=sep,
pad=pad,
cls=cls,
msk=msk,
additional_special_tokens=additional_special_tokens,
lang2id=lang2id,
id2lang=id2lang,
do_lowercase_and_remove_accent=do_lowercase_and_remove_accent,
**kw,
)
self.cache_moses_punct_normalizer = dict()
self.cache_moses_tokenizer = dict()
self.lang_with_custom_tokenizer = set(["zh", "th", "ja"])
self.do_lowercase_and_remove_accent = do_lowercase_and_remove_accent
self.lang2id = lang2id
self.id2lang = id2lang
if lang2id is not None and id2lang is not None:
assert len(lang2id) == len(id2lang)
self.ja_word_tokenizer = None
self.zh_word_tokenizer = None
with open(vocab_file, encoding="utf-8") as vocab_handle:
self.encoder = json.load(vocab_handle)
self.decoder = {v: k for k, v in self.encoder.items()}
with open(merges_file, encoding="utf-8") as merges_handle:
merges = merges_handle.read().split("\n")[:-1]
merges = [tuple(merge.split()[:2]) for merge in merges]
self.bpe_ranks = dict(zip(merges, range(len(merges))))
self.cache = {}
@property
def do_lower_case(self):
return self.do_lowercase_and_remove_accent
def moses_punct_norm(self, text, lang):
if lang not in self.cache_moses_punct_normalizer:
punct_normalizer = sm.MosesPunctNormalizer(lang=lang)
self.cache_moses_punct_normalizer[lang] = punct_normalizer
else:
punct_normalizer = self.cache_moses_punct_normalizer[lang]
return punct_normalizer.normalize(text)
def moses_tokenize(self, text, lang):
if lang not in self.cache_moses_tokenizer:
moses_tokenizer = sm.MosesTokenizer(lang=lang)
self.cache_moses_tokenizer[lang] = moses_tokenizer
else:
moses_tokenizer = self.cache_moses_tokenizer[lang]
return moses_tokenizer.tokenize(text, return_str=False, escape=False)
def moses_pipeline(self, text, lang):
text = replace_unicode_punct(text)
text = self.moses_punct_norm(text, lang)
text = remove_non_printing_char(text)
return text
def ja_tokenize(self, text):
if self.ja_word_tokenizer is None:
try:
import Mykytea
self.ja_word_tokenizer = Mykytea.Mykytea(
f"-model {os.path.expanduser('~')}/local/share/kytea/model.bin"
)
except (AttributeError, ImportError):
raise
return list(self.ja_word_tokenizer.getWS(text))
@property
def s_vocab(self):
return len(self.encoder)
def get_vocab(self):
return dict(self.encoder, **self.added_tokens_encoder)
def bpe(self, token):
word = tuple(token[:-1]) + (token[-1] + "</w>",)
if token in self.cache:
return self.cache[token]
pairs = get_pairs(word)
if not pairs:
return token + "</w>"
while True:
bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
if bigram not in self.bpe_ranks:
break
first, second = bigram
new_word = []
i = 0
while i < len(word):
try:
j = word.index(first, i)
except ValueError:
new_word.extend(word[i:])
break
else:
new_word.extend(word[i:j])
i = j
if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
new_word.append(first + second)
i += 2
else:
new_word.append(word[i])
i += 1
new_word = tuple(new_word)
word = new_word
if len(word) == 1:
break
else:
pairs = get_pairs(word)
word = " ".join(word)
if word == "\n </w>":
word = "\n</w>"
self.cache[token] = word
return word
def _tokenize(self, text, lang="en", bypass_tokenizer=False):
if lang and self.lang2id and lang not in self.lang2id:
logger.error(
"Supplied language code not found in lang2id mapping. Please check that your language is supported by the loaded pretrained model."
)
if bypass_tokenizer:
text = text.split()
elif lang not in self.lang_with_custom_tokenizer:
text = self.moses_pipeline(text, lang=lang)
# TODO: make sure we are using `xlm-mlm-enro-1024`, since XLM-100 doesn't have this step
if lang == "ro":
text = romanian_preprocessing(text)
text = self.moses_tokenize(text, lang=lang)
elif lang == "th":
text = self.moses_pipeline(text, lang=lang)
try:
if "pythainlp" not in sys.modules:
from pythainlp.tokenize import word_tokenize as th_word_tokenize
else:
th_word_tokenize = sys.modules["pythainlp"].word_tokenize
except (AttributeError, ImportError):
raise
text = th_word_tokenize(text)
elif lang == "zh":
try:
if "jieba" not in sys.modules:
import jieba
else:
jieba = sys.modules["jieba"]
except (AttributeError, ImportError):
raise
text = " ".join(jieba.cut(text))
text = self.moses_pipeline(text, lang=lang)
text = text.split()
elif lang == "ja":
text = self.moses_pipeline(text, lang=lang)
text = self.ja_tokenize(text)
else:
raise ValueError("It should not reach here")
if self.do_lowercase_and_remove_accent and not bypass_tokenizer:
text = lowercase_and_remove_accent(text)
split_tokens = []
for token in text:
if token:
split_tokens.extend([t for t in self.bpe(token).split(" ")])
return split_tokens
def _convert_token_to_id(self, token):
return self.encoder.get(token, self.encoder.get(self.unk))
def _convert_id_to_token(self, index):
return self.decoder.get(index, self.unk)
def convert_tokens_to_string(self, tokens):
out_string = "".join(tokens).replace("</w>", " ").strip()
return out_string
def build_inputs_with_special_tokens(self, toks_0, toks_1=None):
bos = [self.BOS]
sep = [self.SEP]
if toks_1 is None:
return bos + toks_0 + sep
return bos + toks_0 + sep + toks_1 + sep
def get_special_tokens_mask(
self,
toks_0,
toks_1=None,
has_specials=False,
):
if has_specials:
return super().get_special_tokens_mask(toks_0=toks_0, toks_1=toks_1, has_specials=True)
if toks_1 is not None:
return [1] + ([0] * len(toks_0)) + [1] + ([0] * len(toks_1)) + [1]
return [1] + ([0] * len(toks_0)) + [1]
def create_token_type_ids_from_sequences(self, toks_0, toks_1=None):
sep = [self.SEP]
cls = [self.cls_token_id]
if toks_1 is None:
return len(cls + toks_0 + sep) * [0]
return len(cls + toks_0 + sep) * [0] + len(toks_1 + sep) * [1]
def save_vocabulary(self, dir, pre=None):
vocab_file = os.path.join(
dir,
(pre + "-" if pre else "") + VOCAB_FS["vocab_file"],
)
merge_file = os.path.join(
dir,
(pre + "-" if pre else "") + VOCAB_FS["merges_file"],
)
with open(vocab_file, "w", encoding="utf-8") as f:
f.write(json.dumps(self.encoder, ensure_ascii=False))
index = 0
with open(merge_file, "w", encoding="utf-8") as writer:
for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
if index != token_index:
logger.warning(
f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
" Please check that the tokenizer is not corrupted!"
)
index = token_index
writer.write(" ".join(bpe_tokens) + "\n")
index += 1
return vocab_file, merge_file
|
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["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,566
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/convert/bert_tf2.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
import re
import tensorflow as tf
import torch
from argparse import ArgumentParser
from os.path import abspath
from transformers.utils import logging
from ..config.bert import PreTrained
from ...models.bert import Model
logging.set_verbosity_info()
log = logging.get_logger(__name__)
def load_src_weights(model, src_path, config):
src_path = abspath(src_path)
log.info(f"Loading from: {src_path}")
xs = tf.train.list_variables(src_path)
ns, ws = _load_weights(xs, src_path)
for n in ns:
ss = n.split("/")
p = model
trace = []
for s in ss:
if s == ".ATTRIBUTES":
break
if s.startswith("layer_with_weights"):
layer_num = int(s.split("-")[-1])
if layer_num <= 2:
continue
elif layer_num == 3:
trace.extend(["embeddings", "LayerNorm"])
p = getattr(p, "embeddings")
p = getattr(p, "LayerNorm")
elif layer_num > 3 and layer_num < config.n_lays + 4:
trace.extend(["encoder", "layer", str(layer_num - 4)])
p = getattr(p, "encoder")
p = getattr(p, "layer")
p = p[layer_num - 4]
elif layer_num == config.n_lays + 4:
trace.extend(["pooler", "dense"])
p = getattr(p, "pooler")
p = getattr(p, "dense")
elif s == "embeddings":
trace.append("embeddings")
p = getattr(p, "embeddings")
if layer_num == 0:
trace.append("tok_embed")
p = getattr(p, "tok_embed")
elif layer_num == 1:
trace.append("pos_embed")
p = getattr(p, "pos_embed")
elif layer_num == 2:
trace.append("token_type_embeddings")
p = getattr(p, "token_type_embeddings")
else:
raise ValueError("Unknown embedding layer with name {full_name}")
trace.append("weight")
p = getattr(p, "weight")
elif s == "_attention_layer":
trace.extend(["attention", "self"])
p = getattr(p, "attention")
p = getattr(p, "self")
elif s == "_attention_layer_norm":
trace.extend(["attention", "output", "LayerNorm"])
p = getattr(p, "attention")
p = getattr(p, "output")
p = getattr(p, "LayerNorm")
elif s == "_attention_output_dense":
trace.extend(["attention", "output", "dense"])
p = getattr(p, "attention")
p = getattr(p, "output")
p = getattr(p, "dense")
elif s == "_output_dense":
trace.extend(["output", "dense"])
p = getattr(p, "output")
p = getattr(p, "dense")
elif s == "_output_layer_norm":
trace.extend(["output", "LayerNorm"])
p = getattr(p, "output")
p = getattr(p, "LayerNorm")
elif s == "_key_dense":
trace.append("key")
p = getattr(p, "key")
elif s == "_query_dense":
trace.append("query")
p = getattr(p, "query")
elif s == "_value_dense":
trace.append("value")
p = getattr(p, "value")
elif s == "_intermediate_dense":
trace.extend(["intermediate", "dense"])
p = getattr(p, "intermediate")
p = getattr(p, "dense")
elif s == "_output_layer_norm":
trace.append("output")
p = getattr(p, "output")
elif s in ["bias", "beta"]:
trace.append("bias")
p = getattr(p, "bias")
elif s in ["kernel", "gamma"]:
trace.append("weight")
p = getattr(p, "weight")
else:
log.warning(f"Ignored {s}")
trace = ".".join(trace)
w = ws[n]
if re.match(r"(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)", trace) or re.match(
r"(\S+)\.attention\.output\.dense\.weight", trace
):
w = w.reshape(p.data.shape)
if "kernel" in n:
w = w.transpose()
assert p.shape == w.shape
p.data = torch.from_numpy(w)
return model
def _load_weights(xs, src_path):
ns = []
ws = []
ds = []
for n, _ in xs:
ss = n.split("/")
if n == "_CHECKPOINTABLE_OBJECT_GRAPH" or ss[0] in [
"global_step",
"save_counter",
]:
log.info(f"Skipping non-model layer {n}")
continue
if "optimizer" in n:
log.info(f"Skipping optimization layer {n}")
continue
if ss[0] == "model":
ss = ss[1:]
d = 0
for s in ss:
if s.startswith("layer_with_weights"):
d += 1
else:
break
ds.append(d)
ns.append("/".join(ss))
ws[n] = tf.train.load_variable(src_path, n)
log.info(f"Read {len(ws):,} layers")
if len(set(ds)) != 1:
raise ValueError(f"Found layers with different depths (layer depth {list(set(ds))})")
ds = list(set(ds))[0]
if ds != 1:
raise ValueError("Found more than just the embedding/encoder layers")
return ns, ws
def to_pytorch(src_path, cfg_path, save_path):
cfg = PreTrained.from_json_file(cfg_path)
print(f"Building from config: {cfg}")
m = Model(cfg)
load_src_weights(m, src_path, cfg)
print(f"Saving to: {save_path}")
torch.save(m.state_dict(), save_path)
if __name__ == "__main__":
x = ArgumentParser()
x.add_argument("--src_path", type=str, required=True)
x.add_argument("--cfg_path", type=str, required=True)
x.add_argument("--save_path", type=str, required=True)
y = x.parse_args()
to_pytorch(y.src_path, y.cfg_path, y.save_path)
|
{"/qnarre/prep/convert/xlnet.py": ["/qnarre/prep/config/xlnet.py", "/qnarre/models/xlnet.py"], "/qnarre/prep/convert/bert.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/base/doc/patcher.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/nominals.py"], "/qnarre/prep/convert/funnel.py": ["/qnarre/prep/config/funnel.py", "/qnarre/models/funnel.py"], "/qnarre/prep/tokens/fast/realm.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py", "/qnarre/tokens/utils.py"], "/qnarre/models/decision_transfo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/decision_transfo.py"], "/qnarre/models/fsmt.py": ["/qnarre/core/embed.py"], "/qnarre/models/roformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/xlnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc.py": ["/qnarre/base/author.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", 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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,567
|
quantapix/qnarre
|
refs/heads/main
|
/tools/triton/python/test/regression/test_performance.py
|
import subprocess
import sys
import pytest
import torch
import triton
import triton.language as tl
import triton.ops
from triton.testing import get_dram_gbps, get_max_tensorcore_tflops
DEVICE_NAME = {7: 'v100', 8: 'a100'}[torch.cuda.get_device_capability()[0]]
#######################
# Utilities
#######################
def print_perf(cur_ms, cur_util, ref_util):
# print on the same line cur_ms, cur_util and ref_util with 3 decimal places
print(f'{cur_ms:.3f} ms \t cur: {cur_util:.3f} \t ref: {ref_util:.3f} \t dif={cur_util - ref_util:.3f}', end='\t')
def nvsmi(attrs):
attrs = ','.join(attrs)
cmd = ['nvidia-smi', '-i', '0', '--query-gpu=' + attrs, '--format=csv,noheader,nounits']
out = subprocess.check_output(cmd)
ret = out.decode(sys.stdout.encoding).split(',')
ret = [int(x) for x in ret]
return ret
#######################
# Matrix Multiplication
#######################
sm_clocks = {'v100': 1350, 'a100': 1350}
mem_clocks = {'v100': 877, 'a100': 1215}
matmul_data = {
'v100': {
# square
(512, 512, 512): {'float16': 0.158},
(1024, 1024, 1024): {'float16': 0.466},
(2048, 2048, 2048): {'float16': 0.695},
(4096, 4096, 4096): {'float16': 0.831},
(8192, 8192, 8192): {'float16': 0.849},
# tall-skinny
(16, 1024, 1024): {'float16': 0.0128},
(16, 4096, 4096): {'float16': 0.0883},
(16, 8192, 8192): {'float16': 0.101},
(64, 1024, 1024): {'float16': 0.073},
(64, 4096, 4096): {'float16': 0.270},
(64, 8192, 8192): {'float16': 0.459},
(1024, 64, 1024): {'float16': 0.0692},
(4096, 64, 4096): {'float16': 0.264},
(8192, 64, 8192): {'float16': 0.452},
},
# NOTE:
# A100 in the CI server is slow-ish for some reason.
# On some other servers, we are getting about 90% peak for 8kx8x8k float16
'a100': {
(512, 512, 512): {'float16': 0.084, 'float32': 0.13, 'int8': 0.05},
(1024, 1024, 1024): {'float16': 0.332, 'float32': 0.35, 'int8': 0.169},
(2048, 2048, 2048): {'float16': 0.641, 'float32': 0.57, 'int8': 0.34},
(4096, 4096, 4096): {'float16': 0.785, 'float32': 0.75, 'int8': 0.46},
(8192, 8192, 8192): {'float16': 0.805, 'float32': 0.85, 'int8': 0.51},
# tall-skinny
(16, 1024, 1024): {'float16': 0.0077, 'float32': 0.0127, 'int8': 0.005},
(16, 4096, 4096): {'float16': 0.044, 'float32': 0.0457, 'int8': 0.0259},
(16, 8192, 8192): {'float16': 0.07, 'float32': 0.0648, 'int8': 0.0431},
(64, 1024, 1024): {'float16': 0.030, 'float32': 0.0509, 'int8': 0.0169},
(64, 4096, 4096): {'float16': 0.163, 'float32': 0.162, 'int8': 0.097},
(64, 8192, 8192): {'float16': 0.285, 'float32': 0.257, 'int8': 0.174},
(1024, 64, 1024): {'float16': 0.033, 'float32': 0.0458, 'int8': 0.017},
(4096, 64, 4096): {'float16': 0.16, 'float32': 0.177, 'int8': 0.102},
(8192, 64, 8192): {'float16': 0.254, 'float32': 0.230, 'int8': 0.177},
}
}
@pytest.mark.parametrize('M, N, K, dtype_str',
[(M, N, K, dtype_str)
for M, N, K in matmul_data[DEVICE_NAME].keys()
for dtype_str in ['float16']])
def test_matmul(M, N, K, dtype_str):
if dtype_str in ['float32', 'int8'] and DEVICE_NAME != 'a100':
pytest.skip('Only test float32 & int8 on a100')
dtype = {'float16': torch.float16, 'float32': torch.float32, 'int8': torch.int8}[dtype_str]
torch.manual_seed(0)
ref_gpu_util = matmul_data[DEVICE_NAME][(M, N, K)][dtype_str]
cur_sm_clock = nvsmi(['clocks.current.sm'])[0]
max_gpu_perf = get_max_tensorcore_tflops(dtype, clock_rate=cur_sm_clock * 1e3)
if dtype == torch.int8:
a = torch.randint(-128, 127, (M, K), dtype=dtype, device='cuda')
b = torch.randint(-128, 127, (N, K), dtype=dtype, device='cuda')
b = b.t() # only test row-col layout
else:
a = torch.randn((M, K), dtype=dtype, device='cuda')
b = torch.randn((K, N), dtype=dtype, device='cuda')
fn = lambda: triton.ops.matmul(a, b)
ms = triton.testing.do_bench(fn, return_mode="min", warmup=100, rep=300)
cur_gpu_perf = 2. * M * N * K / ms * 1e-9
cur_gpu_util = cur_gpu_perf / max_gpu_perf
print_perf(ms, cur_gpu_util, ref_gpu_util)
triton.testing.assert_close(cur_gpu_util, ref_gpu_util, atol=0.01, rtol=0.05)
#######################
# Element-Wise
#######################
@triton.jit
def _add(x_ptr, y_ptr, output_ptr, n_elements,
BLOCK_SIZE: tl.constexpr):
pid = tl.program_id(axis=0)
block_start = pid * BLOCK_SIZE
offsets = block_start + tl.arange(0, BLOCK_SIZE)
mask = offsets < n_elements
x = tl.load(x_ptr + offsets, mask=mask)
y = tl.load(y_ptr + offsets, mask=mask)
output = x + y
tl.store(output_ptr + offsets, output, mask=mask)
elementwise_data = {
'v100': {
1024 * 16: 0.0219,
1024 * 64: 0.0791,
1024 * 256: 0.243,
1024 * 1024: 0.530,
1024 * 4096: 0.796,
1024 * 16384: 0.905,
1024 * 65536: 0.939,
},
'a100': {
1024 * 16: 0.010,
1024 * 64: 0.040,
1024 * 256: 0.132,
1024 * 1024: 0.353,
1024 * 4096: 0.605,
1024 * 16384: 0.758,
1024 * 65536: 0.850,
}
}
@pytest.mark.parametrize('N', elementwise_data[DEVICE_NAME].keys())
def test_elementwise(N):
torch.manual_seed(0)
ref_gpu_util = elementwise_data[DEVICE_NAME][N]
max_gpu_perf = get_dram_gbps()
z = torch.empty((N, ), dtype=torch.float16, device='cuda')
x = torch.randn_like(z)
y = torch.randn_like(z)
grid = lambda args: (triton.cdiv(N, args['BLOCK_SIZE']), )
fn = lambda: _add[grid](x, y, z, N, BLOCK_SIZE=1024)
ms = triton.testing.do_bench(fn, return_mode="min", warmup=100, rep=500)
cur_gpu_perf = 3. * N * z.element_size() / ms * 1e-6
cur_gpu_util = cur_gpu_perf / max_gpu_perf
print_perf(ms, cur_gpu_util, ref_gpu_util)
triton.testing.assert_close(cur_gpu_util, ref_gpu_util, atol=0.01, rtol=0.05)
#######################
# Flash-Attention
#######################
flash_attention_data = {
"a100": {
(4, 48, 4096, 64, 'forward', 'float16'): 0.37,
(4, 48, 4096, 64, 'backward', 'float16'): 0.25,
}
}
@pytest.mark.parametrize("Z, H, N_CTX, D_HEAD", [[4, 48, 4096, 64]])
@pytest.mark.parametrize("mode", ['forward', 'backward'])
@pytest.mark.parametrize("dtype_str", ['float16'])
def test_flash_attention(Z, H, N_CTX, D_HEAD, mode, dtype_str):
is_backward = mode == 'backward'
capability = torch.cuda.get_device_capability()
if capability[0] < 8:
pytest.skip("Flash attention only supported for compute capability < 80")
torch.manual_seed(20)
dtype = {'float16': torch.float16, 'float32': torch.float32, 'int8': torch.int8}[dtype_str]
# init data
q = torch.empty((Z, H, N_CTX, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0.1, std=0.2).requires_grad_()
k = torch.empty((Z, H, N_CTX, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0.4, std=0.2).requires_grad_()
v = torch.empty((Z, H, N_CTX, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0.3, std=0.2).requires_grad_()
sm_scale = 0.2
# benchmark
fn = lambda: triton.ops.attention(q, k, v, sm_scale)
if is_backward:
o = fn()
do = torch.randn_like(o)
fn = lambda: o.backward(do, retain_graph=True)
ms = triton.testing.do_bench(fn, return_mode="min", warmup=100, rep=500)
# compute flops
flops_per_matmul = 2. * Z * H * N_CTX * N_CTX * D_HEAD * 0.5
total_flops = 2 * flops_per_matmul
if is_backward:
total_flops *= 2.5 # 2.0(bwd) + 0.5(recompute)
cur_gpu_perf = total_flops / ms * 1e-9
# maximum flops
cur_sm_clock = nvsmi(['clocks.current.sm'])[0]
max_gpu_perf = get_max_tensorcore_tflops(dtype, clock_rate=cur_sm_clock * 1e3)
cur_gpu_util = cur_gpu_perf / max_gpu_perf
ref_gpu_util = flash_attention_data[DEVICE_NAME][(Z, H, N_CTX, D_HEAD, mode, dtype_str)]
print_perf(ms, cur_gpu_util, ref_gpu_util)
triton.testing.assert_close(cur_gpu_util, ref_gpu_util, atol=0.01, rtol=0.05)
|
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"/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": ["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,568
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/base/doc/spread.py
|
# Copyright 2019 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
import pathlib as pth
from qnarre import load_from
def spread(root, **kw):
kw.update(root=root)
print('Loading {}...'.format(str(root)))
o = load_from(pth.Path('merged.org'), **kw)
print('...done')
print('Spreading ({} + 1)...'.format(len(o.docs)))
for d in o.docs:
d.save(**kw)
o.net.save(**kw)
print('...done')
if __name__ == '__main__':
from argparse import ArgumentParser
args = ArgumentParser()
args.add_argument('-r', '--root', help='Path to root', default=None)
args = args.parse_args()
spread(pth.Path.cwd() / (args.root or 'sample'))
|
{"/qnarre/prep/convert/xlnet.py": ["/qnarre/prep/config/xlnet.py", "/qnarre/models/xlnet.py"], "/qnarre/prep/convert/bert.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/base/doc/patcher.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/nominals.py"], "/qnarre/prep/convert/funnel.py": ["/qnarre/prep/config/funnel.py", "/qnarre/models/funnel.py"], "/qnarre/prep/tokens/fast/realm.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py", "/qnarre/tokens/utils.py"], "/qnarre/models/decision_transfo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/decision_transfo.py"], "/qnarre/models/fsmt.py": ["/qnarre/core/embed.py"], "/qnarre/models/roformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/xlnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc.py": ["/qnarre/base/author.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], "/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/dispatch.py": ["/qnarre/base/doc/blog.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/context.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/analyzer.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/images.py"], "/qnarre/models/big_bird.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/big_bird.py"], "/tools/triton/python/triton/runtime/jit.py": ["/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/fast/roberta.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/bigbird.py": ["/qnarre/prep/config/big_bird.py", "/qnarre/models/big_bird.py"], "/qnarre/models/gpt_neo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/gpt_neo.py"], "/qnarre/prep/convert/t5.py": ["/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/prep/tokens/fsmt.py": 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|
33,569
|
quantapix/qnarre
|
refs/heads/main
|
/notebooks/old/src/layers.py
|
# Copyright 2019 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
# !pip install -U tf-nightly-2.0-preview
import numpy as np
import tensorflow as tf
import dataset as qd
import ragged as qr
ks = tf.keras
kl = ks.layers
def pos_timing(width, depth):
assert depth % 2 == 0
d = np.arange(depth)[np.newaxis, :]
d = 1 / np.power(10000, (2 * (d // 2)) / np.float32(depth))
t = np.arange(width)[:, np.newaxis] * d
t = [np.sin(t[:, 0::2]), np.cos(t[:, 1::2])]
t = np.concatenate(t, axis=-1)[np.newaxis, ...]
return t
"""
pos = pos_timing(50, 512)
plt.pcolormesh(pos[0], cmap='RdBu')
plt.xlabel('Depth')
plt.xlim((0, 512))
plt.ylabel('Position')
plt.colorbar()
"""
class Layer(kl.Layer):
def __init__(self, ps, **kw):
kw.setdefault('dtype', tf.float32)
super().__init__(**kw)
self.ps = ps
class Embed(Layer):
def __init__(self, ps):
super().__init__(ps)
s = (ps.dim_vocab, ps.dim_hidden)
self.emb = self.add_weight(name='emb', shape=s)
p = pos_timing(ps.len_max_input, ps.dim_hidden)
p = tf.constant(p, dtype=tf.float32)
self.pos = tf.broadcast_to(p, [ps.dim_batch] + p.shape[1:])
def call(self, x):
fv, rs = x
x = tf.RaggedTensor.from_row_splits(fv, rs)
y = tf.ragged.map_flat_values(tf.nn.embedding_lookup, self.emb, x)
y += tf.RaggedTensor.from_tensor(self.pos, lengths=y.row_lengths())
return y
class Encode(Layer):
def __init__(self, ps):
super().__init__(ps)
self.encs = [Encoder(self, f'enc_{i}') for i in range(ps.dim_stacks)]
def call(self, x):
y = x
for e in self.encs:
y, ctx = e(y)
return [y, ctx]
class Decode(Layer):
def __init__(self, ps):
super().__init__(ps)
self.decs = [Decoder(self, f'dec_{i}') for i in range(ps.dim_stacks)]
def call(self, x):
y, ctx = x
for d in self.decs:
y = d([y, ctx])
return y
class Debed(Layer):
def __init__(self, ps):
super().__init__(ps)
self.max_len = u = ps.len_max_input
s = [u * ps.dim_hidden, ps.dim_vocab]
self.dbd = Dense(self, 'dbd', s)
def call(self, x):
y = x.to_tensor()
s = tf.shape(y)
y = tf.pad(y, [[0, 0], [0, self.max_len - s[-2]], [0, 0]])
y = tf.reshape(y, [-1, self.max_len * s[-1]])
y = self.dbd(y)
return y
class Encoder(tf.Module):
def __init__(self, layer, name=None):
super().__init__(name=name)
with self.name_scope:
self.reflect = Attention(layer, 'refl')
self.conclude = Conclusion(layer, 'conc')
@tf.Module.with_name_scope
def __call__(self, x):
y, ctx = self.reflect([x, None])
y = self.conclude(y)
return [y, ctx]
class Decoder(tf.Module):
def __init__(self, layer, name=None):
super().__init__(name=name)
with self.name_scope:
self.reflect = Attention(layer, 'refl')
self.consider = Attention(layer, 'cnsd')
self.conclude = Conclusion(layer, 'conc')
@tf.Module.with_name_scope
def __call__(self, x):
x, ctx = x
y, _ = self.reflect([x, None])
y, _ = self.consider([y, ctx])
y = self.conclude(y)
return y
class Attention(tf.Module):
def __init__(self, layer, name):
super().__init__(name=name)
h = layer.ps.dim_hidden
self.scale = 1 / (h**0.5)
with self.name_scope:
self.q = layer.add_weight('q', shape=(h, h))
self.k = layer.add_weight('k', shape=(h, h))
self.v = layer.add_weight('v', shape=(h, h))
@tf.Module.with_name_scope
def __call__(self, x):
x, ctx = x
q = x.with_values(tf.einsum('ni,ij->nj', x.flat_values, self.q))
k = x.with_values(tf.einsum('ni,ij->nj', x.flat_values, self.k))
v = x.with_values(tf.einsum('ni,ij->nj', x.flat_values, self.v))
y = tf.einsum('bsi,bzi->bsz', q.to_tensor(), k.to_tensor())
y = tf.nn.softmax(y * self.scale)
y = tf.einsum('bsz,bzi->bsi', y, v.to_tensor())
y = tf.RaggedTensor.from_tensor(y, lengths=x.row_lengths())
return [y, tf.constant(1)]
class Conclusion(tf.Module):
def __init__(self, layer, name):
super().__init__(name=name)
ps = layer.ps
self.max_len = w = ps.len_max_input
w *= ps.dim_hidden
with self.name_scope:
s = [w, ps.dim_dense]
self.inflate = Dense(layer, 'infl', s, activation='relu')
s = [ps.dim_dense, w]
self.deflate = Dense(layer, 'defl', s, bias=False)
@tf.Module.with_name_scope
def __call__(self, x):
y = x.to_tensor()
s = tf.shape(y)
y = tf.pad(y, [[0, 0], [0, self.max_len - s[-2]], [0, 0]])
y = tf.reshape(y, [-1, self.max_len * s[-1]])
y = self.inflate(y)
y = self.deflate(y)
y = tf.reshape(y, [-1, self.max_len, s[-1]])
y = tf.RaggedTensor.from_tensor(y, lengths=x.row_lengths())
return y
class Dense(tf.Module):
bias = None
activation = None
def __init__(self, layer, name, shape, activation=None, bias=True):
super().__init__(name=name)
with self.name_scope:
kw = dict(shape=shape, initializer='glorot_uniform')
self.kern = layer.add_weight('kern', **kw)
if bias:
kw.update(shape=shape[1:], initializer='zeros')
self.bias = layer.add_weight('bias', **kw)
self.activation = ks.activations.get(activation)
@tf.Module.with_name_scope
def __call__(self, x):
y = tf.einsum('bi,ij->bj', x, self.kern)
if self.bias is not None:
y = tf.nn.bias_add(y, self.bias)
if self.activation:
y = self.activation(y)
return y
def model_for(ps):
x = [ks.Input(shape=(), dtype='int32'), ks.Input(shape=(), dtype='int64')]
y = Embed(ps)(x)
y = Encode(ps)(y)
y = Decode(ps)(y)
y = Debed(ps)(y)
m = ks.Model(inputs=x, outputs=y)
m.compile(optimizer=ps.optimizer, loss=ps.loss, metrics=[ps.metric])
print(m.summary())
return m
params = dict(
dim_batch=2,
dim_dense=150,
dim_hidden=6,
dim_stacks=2,
dim_vocab=len(qd.vocab),
len_max_input=20,
loss=ks.losses.SparseCategoricalCrossentropy(from_logits=True),
metric=ks.metrics.SparseCategoricalAccuracy(),
num_epochs=5,
num_shards=2,
optimizer=ks.optimizers.Adam(),
)
if __name__ == '__main__':
ps = qd.Params(**params)
# import masking as qm
# qm.main_graph(ps, qr.dset_for(ps), model_for(ps))
qr.main_eager(ps, qr.dset_for(ps), model_for(ps))
|
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"/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], 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"/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], 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|
33,570
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/run/run_xnli.py
|
import os
import random
from dataclasses import dataclass, field
import numpy as np
from datasets import load_dataset, load_metric
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
Trainer,
default_data_collator,
)
@dataclass
class DataTrainingArguments:
max_seq_length = field(
default=128,
metadata={
"help": "The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
},
)
server_ip = field(default=None, metadata={"help": "For distant debugging."})
server_port = field(default=None, metadata={"help": "For distant debugging."})
def main():
if training_args.do_train:
if model_args.train_language is None:
train_dataset = load_dataset(
"xnli", model_args.language, split="train", cache_dir=model_args.cache_dir
)
else:
train_dataset = load_dataset(
"xnli", model_args.train_language, split="train", cache_dir=model_args.cache_dir
)
label_list = train_dataset.features["label"].names
if training_args.do_eval:
eval_dataset = load_dataset(
"xnli", model_args.language, split="validation", cache_dir=model_args.cache_dir
)
label_list = eval_dataset.features["label"].names
if training_args.do_test:
predict_dataset = load_dataset(
"xnli", model_args.language, split="test", cache_dir=model_args.cache_dir
)
label_list = predict_dataset.features["label"].names
# Labels
n_labels = len(label_list)
# Load pretrained model and tokenizer
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
config = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name,
n_labels=n_labels,
finetune="xnli",
cache_dir=model_args.cache_dir,
revision=model_args.model_version,
use_auth_token=True if model_args.use_auth_token else None,
)
tokenizer = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name,
lower_case=model_args.lower_case,
cache_dir=model_args.cache_dir,
use_fast=model_args.use_fast_tokenizer,
revision=model_args.model_version,
use_auth_token=True if model_args.use_auth_token else None,
)
model = AutoModelForSequenceClassification.from_pretrained(
model_args.model_name,
from_tf=bool(".ckpt" in model_args.model_name),
config=config,
cache_dir=model_args.cache_dir,
revision=model_args.model_version,
use_auth_token=True if model_args.use_auth_token else None,
)
# Preprocessing the datasets
# Padding strategy
if data_args.pad_to_max_length:
padding = "max_len"
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
padding = False
def preprocess_function(examples):
# Tokenize the texts
return tokenizer(
examples["premise"],
examples["hypothesis"],
padding=padding,
max_len=data_args.max_seq_length,
truncation=True,
)
if training_args.do_train:
if data_args.max_train_samples is not None:
train_dataset = train_dataset.select(range(data_args.max_train_samples))
with training_args.main_process_first(desc="train dataset map pre-processing"):
train_dataset = train_dataset.map(
preprocess_function,
batched=True,
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on train dataset",
)
# Log a few random samples from the training set:
for index in random.sample(range(len(train_dataset)), 3):
logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
if training_args.do_eval:
if data_args.max_eval_samples is not None:
eval_dataset = eval_dataset.select(range(data_args.max_eval_samples))
with training_args.main_process_first(desc="validation dataset map pre-processing"):
eval_dataset = eval_dataset.map(
preprocess_function,
batched=True,
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on validation dataset",
)
if training_args.do_test:
if data_args.max_test_samples is not None:
predict_dataset = predict_dataset.select(range(data_args.max_test_samples))
with training_args.main_process_first(desc="prediction dataset map pre-processing"):
predict_dataset = predict_dataset.map(
preprocess_function,
batched=True,
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on prediction dataset",
)
# Get the metric function
metric = load_metric("xnli")
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(p: EvalPrediction):
preds = p.predictions[0] if isinstance(p.predictions, tuple) else p.predictions
preds = np.argmax(preds, axis=1)
return metric.compute(predictions=preds, references=p.label_ids)
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
data_collator = default_data_collator
elif training_args.fp16:
data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8)
else:
data_collator = None
# Initialize our Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset if training_args.do_train else None,
eval_dataset=eval_dataset if training_args.do_eval else None,
compute_metrics=compute_metrics,
tokenizer=tokenizer,
data_collator=data_collator,
)
# Training
if training_args.do_train:
checkpoint = None
if training_args.resume_from_checkpoint is not None:
checkpoint = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
checkpoint = last_checkpoint
train_result = trainer.train(resume_from_checkpoint=checkpoint)
metrics = train_result.metrics
max_train_samples = (
data_args.max_train_samples
if data_args.max_train_samples is not None
else len(train_dataset)
)
metrics["train_samples"] = min(max_train_samples, len(train_dataset))
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics("train", metrics)
trainer.save_metrics("train", metrics)
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("*** Evaluate ***")
metrics = trainer.evaluate(eval_dataset=eval_dataset)
max_eval_samples = (
data_args.max_eval_samples
if data_args.max_eval_samples is not None
else len(eval_dataset)
)
metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset))
trainer.log_metrics("eval", metrics)
trainer.save_metrics("eval", metrics)
# Prediction
if training_args.do_test:
logger.info("*** Predict ***")
predictions, labels, metrics = trainer.predict(predict_dataset, metric_key_prefix="predict")
max_test_samples = (
data_args.max_test_samples
if data_args.max_test_samples is not None
else len(predict_dataset)
)
metrics["predict_samples"] = min(max_test_samples, len(predict_dataset))
trainer.log_metrics("predict", metrics)
trainer.save_metrics("predict", metrics)
predictions = np.argmax(predictions, axis=1)
output_predict_file = os.path.join(training_args.out_dir, "predictions.txt")
if trainer.is_world_process_zero():
with open(output_predict_file, "w") as writer:
writer.write("index\tprediction\n")
for index, item in enumerate(predictions):
item = label_list[item]
writer.write(f"{index}\t{item}\n")
if __name__ == "__main__":
main()
|
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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,571
|
quantapix/qnarre
|
refs/heads/main
|
/tools/triton/python/triton/ops/__init__.py
|
# from .conv import _conv, conv
from . import blocksparse
from .cross_entropy import _cross_entropy, cross_entropy
from .flash_attention import attention
from .matmul import _matmul, matmul
__all__ = [
"blocksparse",
"_cross_entropy",
"cross_entropy",
"_matmul",
"matmul",
"attention",
]
|
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"/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,572
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/core/norm.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
import torch
from qnarre.core.base import Module
from .. import core as qc
from . import utils as qu
class RMS(qc.Module):
def __init__(self, hidden_size, eps=1e-6):
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, x):
variance = x.to(torch.float32).pow(2).mean(-1, keepdim=True)
y = x * torch.rsqrt(variance + self.variance_epsilon)
if self.weight.dtype in [torch.float16, torch.bfloat16]:
y = y.to(self.weight.dtype)
return self.weight * y
def _layer_norm(self, inputs):
x = inputs
m = torch.reduce_mean(x, axis=-1, keepdims=True)
v = torch.reduce_mean(torch.square(x - m), axis=-1, keepdims=True)
y = (x - m) / torch.sqrt(v + self.cfg.eps)
y = y * self.norm_w + self.norm_b
return y
class Norm(qc.Module):
@staticmethod
def cfg_items(ps):
return dict(
ps.cfg_items(
"eps",
)
)
def build(self, input_shape):
s = input_shape[-1]
self.norm_w = self.add_weight("norm_w", s, initializer="ones")
self.norm_b = self.add_weight("norm_b", s, initializer="zeros")
return super().build(input_shape)
def forward(self, inputs):
return _layer_norm(self, inputs)
class LayerProc(Module):
cmd = ""
batch = None
@staticmethod
def cfg_items(ps):
return dict(
ps.cfg_items(
"bdims_prepost",
"cmd_post",
"cmd_pre",
"drop",
"drop_prepost",
"eps",
"norm_type",
)
)
def __init__(self, ps, **kw):
super().__init__(ps, **kw)
cfg = self.cfg
if cfg.norm_type == "batch":
self.batch = torch.BatchNormalization(epsilon=cfg.eps)
def build(self, input_shape):
s = input_shape[1][-1]
self.norm_w = self.add_weight("norm_w", s, initializer="ones")
self.norm_b = self.add_weight("norm_b", s, initializer="zeros")
# self.gamma = self.add_weight(shape=(), initializer='zeros')
return super().build(input_shape)
def forward(self, inputs):
prev, x = inputs
y = x
if self.cmd:
cfg = self.cfg
for c in self.cmd:
if c == "a":
y = prev + x
elif c == "z":
y = prev + x * self.gamma
elif c == "n":
if cfg.norm_type == "layer":
y = _layer_norm(self, x)
elif cfg.norm_type == "batch":
y = self.batch(x)
elif cfg.norm_type == "l2":
m = torch.reduce_mean(x, axis=-1, keepdims=True)
n = torch.square(x - m)
n = torch.reduce_sum(n, axis=-1, keepdims=True)
y = (x - m) / torch.sqrt(n + cfg.eps)
y = y * self.gain + self.bias
elif cfg.norm_type == "group":
sh = torch.int_shape(x)
assert len(sh) == 4 and sh[-1] % cfg.n_groups == 0
gs = (cfg.n_groups, sh[-1] // cfg.n_groups)
x = torch.reshape(x, sh[:-1] + gs)
m, v = torch.moments(x, [1, 2, 4], keep_dims=True)
y = (x - m) / torch.sqrt(v + cfg.group_eps)
y = torch.reshape(y, sh) * self.gain + self.bias
elif cfg.norm_type == "noam":
y = torch.cast_to_floatx(torch.int_shape(x)[-1])
y = torch.l2_normalize(x, axis=-1) * torch.sqrt(y)
else:
assert cfg.norm_type == "none"
else:
assert c == "d"
y = self.drop(y)
x = y
return y
def drop(self, x):
cfg = self.cfg
r = cfg.drop_prepost or cfg.drop
ns, ds = None, [int(i) for i in cfg.bdims_prepost.split(",") if i]
if ds:
sh = ()
n = len(sh)
ds = [d + n if d < 0 else d for d in ds]
ns = [1 if i in ds else sh[i] for i in range(n)]
return super().drop(x, r, noise_shape=ns)
class PreProc(LayerProc):
def __init__(self, ps, **kw):
super().__init__(ps, **kw)
self.cmd = self.cfg.cmd_pre
assert "a" not in self.cmd
assert "z" not in self.cmd
class PostProc(LayerProc):
def __init__(self, ps, **kw):
super().__init__(ps, **kw)
self.cmd = self.cfg.cmd_post
import torch
from torch.nn import LayerNorm as LayerNorm
def get_norm(neox_args):
if neox_args.norm == "rmsnorm":
norm = RMSNorm
eps = neox_args.rms_norm_epsilon
elif neox_args.norm == "layernorm":
eps = neox_args.layernorm_epsilon
norm = LayerNorm
elif neox_args.norm == "scalenorm":
eps = neox_args.scalenorm_epsilon
norm = ScaleNorm
else:
raise ValueError(f"norm {neox_args.norm} not recognized")
return norm, eps
class RMSNorm(torch.nn.Module):
def __init__(self, dim, p=-1.0, eps=1e-8, bias=False):
super(RMSNorm, self).__init__()
self.eps = eps
self.d = dim
self.p = p
self.bias = bias
self.scale = torch.nn.Parameter(torch.ones(dim))
self.register_parameter("scale", self.scale)
if self.bias:
self.offset = torch.nn.Parameter(torch.zeros(dim))
self.register_parameter("offset", self.offset)
def forward(self, x):
if self.p < 0.0 or self.p > 1.0:
norm_x = x.norm(2, dim=-1, keepdim=True)
d_x = self.d
else:
partial_size = int(self.d * self.p)
partial_x, _ = torch.split(x, [partial_size, self.d - partial_size], dim=-1)
norm_x = partial_x.norm(2, dim=-1, keepdim=True)
d_x = partial_size
rms_x = norm_x * d_x ** (-1.0 / 2)
x_normed = x / (rms_x + self.eps)
if self.bias:
return self.scale * x_normed + self.offset
return self.scale * x_normed
class ScaleNorm(torch.nn.Module):
def __init__(self, dim, eps=1e-5):
super().__init__()
self.g = torch.nn.Parameter(torch.ones(1))
self.eps = eps
def forward(self, x):
n = torch.norm(x, dim=-1, keepdim=True).clamp(min=self.eps)
return x / n * self.g
|
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"/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", 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"/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/dispatch.py": ["/qnarre/base/doc/blog.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/context.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/analyzer.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/images.py"], "/qnarre/models/big_bird.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/big_bird.py"], "/tools/triton/python/triton/runtime/jit.py": ["/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/fast/roberta.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/bigbird.py": ["/qnarre/prep/config/big_bird.py", "/qnarre/models/big_bird.py"], "/qnarre/models/gpt_neo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/gpt_neo.py"], "/qnarre/prep/convert/t5.py": ["/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/prep/tokens/fsmt.py": 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"/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/models/plbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/reformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/images.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/base.py"], "/qnarre/models/yoso.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/canine.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/blog.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/counter.py"], "/qnarre/models/longformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/fast/pegasus.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/pegasus.py"], "/qnarre/base/judgment.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py", "/qnarre/base/conflict.py", "/qnarre/base/conjecture.py"], "/qnarre/models/splinter.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/rag.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/convert/albert.py": ["/qnarre/prep/config/albert.py", "/qnarre/models/albert.py"], "/qnarre/prep/tokens/fast/gpt2.py": ["/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/fast/mbart.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/roformer.py": ["/qnarre/models/roformer.py"], "/qnarre/base/doc/util/tree.py": ["/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py", "/qnarre/base/doc/util/utils.py"], "/qnarre/models/rembert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/t5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/xlm.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/bert_tf2.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/tools/triton/python/triton/ops/__init__.py": ["/tools/triton/python/triton/ops/cross_entropy.py", "/tools/triton/python/triton/ops/matmul.py"], "/qnarre/core/norm.py": ["/qnarre/core/base.py"], "/qnarre/models/mpnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/author.py": ["/qnarre/base/named.py"], "/qnarre/base/doc/args.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/log.py"], "/qnarre/prep/tokens/gpt.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/distilbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/proof.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py"], "/qnarre/prep/tokens/byt5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/electra.py": ["/qnarre/prep/config/electra.py", "/qnarre/models/electra.py"], "/qnarre/models/luke.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": ["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,573
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/models/mpnet.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
import math
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import functional as F
from transformers.utils import logging
from .. import core as qc
from ..core import utils as qu
from ..core import forward as qf
from ..core import output as qo
from ..core import attention as qa
from ..core.embed import Embed
from ..core.mlp import Classifier, MLP, Predictor, Pool
from ..prep.config.mpnet import PreTrained
from torch.nn import CrossEntropyLoss
log = logging.get_logger(__name__)
LIST = [
"microsoft/mpnet-base",
]
def create_position_ids_from_input_ids(input_ids, padding_idx):
mask = input_ids.ne(padding_idx).int()
incremental_indices = torch.cumsum(mask, dim=1).type_as(mask) * mask
return incremental_indices.long() + padding_idx
class MPNetEmbeddings(qc.Module):
def __init__(self, config):
super().__init__()
self.padding_idx = 1
self.word_embeddings = qc.Embed(
config.s_vocab, config.d_model, padding_idx=self.padding_idx
)
self.position_embeddings = qc.Embed(
config.n_pos, config.d_model, padding_idx=self.padding_idx
)
self.norm = qc.LayerNorm(config.d_model, eps=config.eps)
self.drop = qc.Dropout(config.drop)
self.register_buffer("position_ids", torch.arange(config.n_pos).expand((1, -1)))
def forward(self, input_ids=None, position_ids=None, inputs_embeds=None, **kw):
if position_ids is None:
if input_ids is not None:
position_ids = create_position_ids_from_input_ids(input_ids, self.padding_idx)
else:
position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds)
if input_ids is not None:
input_shape = input_ids.size()
else:
input_shape = inputs_embeds.size()[:-1]
seq_length = input_shape[1]
if position_ids is None:
position_ids = self.position_ids[:, :seq_length]
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
position_embeddings = self.position_embeddings(position_ids)
embeddings = inputs_embeds + position_embeddings
embeddings = self.norm(embeddings)
embeddings = self.drop(embeddings)
return embeddings
def create_position_ids_from_inputs_embeds(self, inputs_embeds):
input_shape = inputs_embeds.size()[:-1]
sequence_length = input_shape[1]
position_ids = torch.arange(
self.padding_idx + 1,
sequence_length + self.padding_idx + 1,
dtype=torch.long,
device=inputs_embeds.device,
)
return position_ids.unsqueeze(0).expand(input_shape)
class MPNetSelfAttention(qc.Module):
def __init__(self, config):
super().__init__()
if config.d_model % config.n_heads != 0 and not hasattr(config, "d_embed"):
raise ValueError(
f"The hidden size ({config.d_model}) is not a multiple of the number of attention "
f"heads ({config.n_heads})"
)
self.n_heads = config.n_heads
self.attention_head_size = int(config.d_model / config.n_heads)
self.all_head_size = self.n_heads * self.attention_head_size
self.q = qc.Linear(config.d_model, self.all_head_size)
self.k = qc.Linear(config.d_model, self.all_head_size)
self.v = qc.Linear(config.d_model, self.all_head_size)
self.o = qc.Linear(config.d_model, config.d_model)
self.drop = qc.Dropout(config.drop_attn)
def transpose_for_scores(self, x):
new_x_shape = x.size()[:-1] + (self.n_heads, self.attention_head_size)
x = x.view(*new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(
self,
hiddens,
attention_mask=None,
head_mask=None,
position_bias=None,
output_attentions=False,
**kw,
):
q = self.q(hiddens)
k = self.k(hiddens)
v = self.v(hiddens)
q = self.transpose_for_scores(q)
k = self.transpose_for_scores(k)
v = self.transpose_for_scores(v)
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(q, k.transpose(-1, -2))
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
# Apply relative position embedding (precomputed in MPNetEncoder) if provided.
if position_bias is not None:
attention_scores += position_bias
if attention_mask is not None:
attention_scores = attention_scores + attention_mask
# Normalize the attention scores to probabilities.
attention_probs = F.softmax(attention_scores, dim=-1)
attention_probs = self.drop(attention_probs)
if head_mask is not None:
attention_probs = attention_probs * head_mask
c = torch.matmul(attention_probs, v)
c = c.permute(0, 2, 1, 3).contiguous()
new_c_shape = c.size()[:-2] + (self.all_head_size,)
c = c.view(*new_c_shape)
o = self.o(c)
outputs = (o, attention_probs) if output_attentions else (o,)
return outputs
class Attention(qc.Module):
def __init__(self, config):
super().__init__()
self.attn = MPNetSelfAttention(config)
self.norm = qc.LayerNorm(config.d_model, eps=config.eps)
self.drop = qc.Dropout(config.drop)
def forward(
self,
hiddens,
attention_mask=None,
head_mask=None,
position_bias=None,
output_attentions=False,
**kw,
):
self_outputs = self.attn(
hiddens,
attention_mask,
head_mask,
position_bias,
output_attentions=output_attentions,
)
attention_output = self.norm(self.drop(self_outputs[0]) + hiddens)
outputs = (attention_output,) + self_outputs[1:] # add attns if we output them
return outputs
# Copied from transformers.models.bert.modeling_bert.BertIntermediate
class MPNetIntermediate(qc.Module):
def __init__(self, cfg):
super().__init__()
self.dense = qc.Linear(cfg.d_model, cfg.d_ff)
self.act = qu.activation(cfg.act)
def forward(self, x):
y = self.dense(x)
y = self.act(y)
return y
# Copied from transformers.models.bert.modeling_bert.BertOutput
class MPNetOutput(qc.Module):
def __init__(self, config):
super().__init__()
self.dense = qc.Linear(config.d_ff, config.d_model)
self.norm = qc.LayerNorm(config.d_model, eps=config.eps)
self.drop = qc.Dropout(config.drop)
def forward(self, hiddens, input_tensor):
hiddens = self.dense(hiddens)
hiddens = self.drop(hiddens)
hiddens = self.norm(hiddens + input_tensor)
return hiddens
class Layer(qc.Module):
def __init__(self, config):
super().__init__()
self.attention = Attention(config)
self.intermediate = MPNetIntermediate(config)
self.output = MPNetOutput(config)
def forward(
self,
hiddens,
attention_mask=None,
head_mask=None,
position_bias=None,
output_attentions=False,
**kw,
):
self_attention_outputs = self.attention(
hiddens,
attention_mask,
head_mask,
position_bias=position_bias,
output_attentions=output_attentions,
)
attention_output = self_attention_outputs[0]
outputs = self_attention_outputs[1:] # add self attns if we output attention weights
intermediate_output = self.intermediate(attention_output)
layer_output = self.output(intermediate_output, attention_output)
outputs = (layer_output,) + outputs
return outputs
class Encoder(qc.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.n_heads = config.n_heads
self.layer = nn.ModuleList([Layer(config) for _ in range(config.n_lays)])
self.relative_attention_bias = qc.Embed(config.relative_attention_num_buckets, self.n_heads)
def forward(
self,
hiddens,
attention_mask=None,
head_mask=None,
output_attentions=False,
output_hidden_states=False,
return_dict=False,
**kw,
):
position_bias = self.compute_position_bias(hiddens)
all_hidden_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
for i, layer_module in enumerate(self.layer):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hiddens,)
layer_outputs = layer_module(
hiddens,
attention_mask,
head_mask[i],
position_bias,
output_attentions=output_attentions,
**kw,
)
hiddens = layer_outputs[0]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
# Add last layer
if output_hidden_states:
all_hidden_states = all_hidden_states + (hiddens,)
if not return_dict:
return tuple(v for v in [hiddens, all_hidden_states, all_attentions] if v is not None)
return qo.Base(
y=hiddens,
hiddens=all_hidden_states,
attns=all_attentions,
)
def compute_position_bias(self, x, position_ids=None, num_buckets=32):
bsz, qlen, klen = x.size(0), x.size(1), x.size(1)
if position_ids is not None:
context_position = position_ids[:, :, None]
memory_position = position_ids[:, None, :]
else:
context_position = torch.arange(qlen, dtype=torch.long)[:, None]
memory_position = torch.arange(klen, dtype=torch.long)[None, :]
relative_position = memory_position - context_position
rp_bucket = self.relative_position_bucket(relative_position, num_buckets=num_buckets)
rp_bucket = rp_bucket.to(x.device)
values = self.relative_attention_bias(rp_bucket)
values = values.permute([2, 0, 1]).unsqueeze(0)
values = values.expand((bsz, -1, qlen, klen)).contiguous()
return values
@staticmethod
def relative_position_bucket(relative_position, num_buckets=32, max_distance=128):
ret = 0
n = -relative_position
num_buckets //= 2
ret += (n < 0).to(torch.long) * num_buckets
n = torch.abs(n)
max_exact = num_buckets // 2
is_small = n < max_exact
val_if_large = max_exact + (
torch.log(n.float() / max_exact)
/ math.log(max_distance / max_exact)
* (num_buckets - max_exact)
).to(torch.long)
val_if_large = torch.min(val_if_large, torch.full_like(val_if_large, num_buckets - 1))
ret += torch.where(is_small, n, val_if_large)
return ret
class Model(PreTrained):
def __init__(self, config, add_pooling_layer=True):
super().__init__(config)
self.config = config
self.embeddings = MPNetEmbeddings(config)
self.encoder = Encoder(config)
self.pool = Pool(config) if add_pooling_layer else None
def forward(
self,
input_ids=None,
attention_mask=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
**kw,
):
output_attentions = (
output_attentions if output_attentions is not None else self.config.output_attentions
)
output_hidden_states = (
output_hidden_states
if output_hidden_states is not None
else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
input_shape = input_ids.size()
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
device = input_ids.device if input_ids is not None else inputs_embeds.device
if attention_mask is None:
attention_mask = torch.ones(input_shape, device=device)
extended_attention_mask = self.get_extended_attention_mask(
attention_mask, input_shape, device
)
head_mask = self.get_head_mask(head_mask, self.config.n_lays)
embedding_output = self.embeddings(
input_ids=input_ids, position_ids=position_ids, inputs_embeds=inputs_embeds
)
encoder_outputs = self.encoder(
embedding_output,
attention_mask=extended_attention_mask,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = encoder_outputs[0]
pooled_output = self.pool(sequence_output) if self.pool is not None else None
if not return_dict:
return (sequence_output, pooled_output) + encoder_outputs[1:]
return qo.BaseWithPooling(
y=sequence_output,
pools=pooled_output,
hiddens=encoder_outputs.hiddens,
attns=encoder_outputs.attns,
)
class ForMasked(PreTrained):
def __init__(self, **kw):
super().__init__(**kw)
self.get_cfg(kw)
self.model = Model(add_pool=False, **kw)
self.proj = Predictor(**kw)
forward = qf.forward_masked
class ForChoice(PreTrained):
def __init__(self, config):
super().__init__(config)
self.mpnet = Model(config)
self.drop = qc.Dropout(config.drop)
self.classifier = qc.Linear(config.d_model, 1)
def forward(
self,
input_ids=None,
attention_mask=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
flat_input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
flat_position_ids = (
position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
)
flat_attention_mask = (
attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
)
flat_inputs_embeds = (
inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
if inputs_embeds is not None
else None
)
outputs = self.mpnet(
flat_input_ids,
position_ids=flat_position_ids,
attention_mask=flat_attention_mask,
head_mask=head_mask,
inputs_embeds=flat_inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = outputs[1]
pooled_output = self.drop(pooled_output)
logits = self.classifier(pooled_output)
reshaped_logits = logits.view(-1, num_choices)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(reshaped_logits, labels)
if not return_dict:
output = (reshaped_logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return qo.WithLoss(
loss=loss,
logits=reshaped_logits,
hiddens=outputs.hiddens,
attns=outputs.attns,
)
class ForSeqClass(PreTrained):
def __init__(self, **kw):
super().__init__(**kw)
cfg = self.get_cfg(kw)
self.model = Model(add_pool=False, **kw)
self.proj = Classifier(cfg.d_model, "tanh", **kw)
forward = qf.forward_seq
class ForTokClass(PreTrained):
def __init__(self, **kw):
super().__init__(**kw)
self.get_cfg(kw)
self.model = Model(add_pool=False, **kw)
self.proj = Classifier(**kw)
forward = qf.forward_tok
class ForQA(PreTrained):
def __init__(self, **kw):
super().__init__(**kw)
cfg = self.get_cfg(kw)
self.model = Model(add_pool=False, **kw)
self.proj = qc.Linear(cfg.d_model, cfg.n_labels, **kw)
forward = qf.forward_qa
|
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"/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": 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"/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], 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["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], 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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,574
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/models/flash/opt.py
|
# Copyright (c) 2023, Tri Dao.
import math
import re
from collections import OrderedDict
import torch
import torch.nn.functional as F
from transformers import GPT2Config, OPTConfig
def remap_state_dict_hf_opt(state_dict, config):
def key_mapping_model(key):
key = re.sub(r'^model.decoder.', 'transformer.', key)
# The OPT-350m model uses '^decoder' instead of '^model.decoder'
key = re.sub(r'^decoder.', 'transformer.', key)
return key
state_dict = OrderedDict((key_mapping_model(k), v) for k, v in state_dict.items())
# Word embedding and position embedding
def key_mapping_emb(key):
key = re.sub(r'^transformer.embed_tokens.', 'transformer.embeddings.word_embeddings.', key)
# The OPT-350m model uses has project_in and project_out
key = re.sub(r'^transformer.project_in.', 'transformer.embeddings.project_in.', key)
key = re.sub(r'^transformer.project_out.', 'project_out.', key)
key = re.sub(r'^transformer.embed_positions.',
'transformer.embeddings.position_embeddings.', key)
return key
state_dict = OrderedDict((key_mapping_emb(k), v) for k, v in state_dict.items())
# OPT uses the first 2 indices of pos_emb for padding tokens
pos_embeddings = state_dict.pop('transformer.embeddings.position_embeddings.weight')
state_dict['transformer.embeddings.position_embeddings.weight'] = pos_embeddings[2:]
word_embeddings = state_dict.pop('transformer.embeddings.word_embeddings.weight')
# It's possible that vocab_size is padded to be a multiple of 8, for example.
pad_vocab_size_multiple = getattr(config, 'pad_vocab_size_multiple', 1)
vocab_size = (math.ceil(config.vocab_size / pad_vocab_size_multiple) * pad_vocab_size_multiple)
state_dict['transformer.embeddings.word_embeddings.weight'] = F.pad(
word_embeddings, (0, 0, 0, vocab_size - word_embeddings.shape[0])
)
state_dict['lm_head.weight'] = state_dict['transformer.embeddings.word_embeddings.weight']
# LayerNorm
def key_mapping_ln(key):
key = re.sub(r'^transformer.final_layer_norm.', r'transformer.ln_f.', key)
# The OPT-175B checkpoint calls this 'decoder.layer_norm' instead of 'decoder.final_layer_norm'
key = re.sub(r'^transformer.layer_norm.', r'transformer.ln_f.', key)
key = re.sub(r'^transformer.layers.(\d+).self_attn_layer_norm.',
r'transformer.layers.\1.norm1.', key)
key = re.sub(r'^transformer.layers.(\d+).final_layer_norm.',
r'transformer.layers.\1.norm2.', key)
return key
state_dict = OrderedDict((key_mapping_ln(k), v) for k, v in state_dict.items())
# MLP
def key_mapping_mlp(key):
return re.sub(r'^transformer.layers.(\d+).fc(1|2).',
r'transformer.layers.\1.mlp.fc\2.', key)
state_dict = OrderedDict((key_mapping_mlp(k), v) for k, v in state_dict.items())
# Attention
for l in range(config.n_layer):
Wq = state_dict.pop(f'transformer.layers.{l}.self_attn.q_proj.weight')
Wk = state_dict.pop(f'transformer.layers.{l}.self_attn.k_proj.weight')
Wv = state_dict.pop(f'transformer.layers.{l}.self_attn.v_proj.weight')
bq = state_dict.pop(f'transformer.layers.{l}.self_attn.q_proj.bias')
bk = state_dict.pop(f'transformer.layers.{l}.self_attn.k_proj.bias')
bv = state_dict.pop(f'transformer.layers.{l}.self_attn.v_proj.bias')
state_dict[f'transformer.layers.{l}.mixer.Wqkv.weight'] = torch.cat([Wq, Wk, Wv], dim=0)
state_dict[f'transformer.layers.{l}.mixer.Wqkv.bias'] = torch.cat([bq, bk, bv], dim=0)
def key_mapping_attn(key):
return re.sub(r'^transformer.layers.(\d+).self_attn.out_proj.',
r'transformer.layers.\1.mixer.out_proj.', key)
state_dict = OrderedDict((key_mapping_attn(k), v) for k, v in state_dict.items())
return state_dict
def opt_config_to_gpt2_config(opt_config: OPTConfig) -> GPT2Config:
assert opt_config.layerdrop == 0.0
assert opt_config.layer_norm_elementwise_affine
word_embed_proj_dim = (None if opt_config.word_embed_proj_dim == opt_config.hidden_size
else opt_config.word_embed_proj_dim)
return GPT2Config(
vocab_size=opt_config.vocab_size,
n_positions=opt_config.max_position_embeddings,
n_embd=opt_config.hidden_size,
n_layer=opt_config.num_hidden_layers,
n_head=opt_config.num_attention_heads,
n_inner=opt_config.ffn_dim,
activation_function=opt_config.activation_function,
resid_pdrop=opt_config.dropout,
# HF's implementation of OPT doesn't seem to have embedding dropout
embd_pdrop=opt_config.dropout,
attn_pdrop=opt_config.attention_dropout,
initializer_range=opt_config.init_std,
bos_token_id=opt_config.bos_token_id,
eos_token_id=opt_config.eos_token_id,
# These are new arguments not in the original GPT2Config
prenorm=opt_config.do_layer_norm_before,
word_embed_proj_dim=word_embed_proj_dim
)
|
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"/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", 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|
33,575
|
quantapix/qnarre
|
refs/heads/main
|
/tools/triton/python/tutorials/06-fused-attention.py
|
"""
Fused Attention
===============
This is a Triton implementation of the Flash Attention algorithm
(see: Dao et al., https://arxiv.org/pdf/2205.14135v2.pdf; Rabe and Staats https://arxiv.org/pdf/2112.05682v2.pdf)
"""
import pytest
import torch
import triton
import triton.language as tl
@triton.jit
def _fwd_kernel(
Q, K, V, sm_scale,
L, M,
Out,
stride_qz, stride_qh, stride_qm, stride_qk,
stride_kz, stride_kh, stride_kn, stride_kk,
stride_vz, stride_vh, stride_vk, stride_vn,
stride_oz, stride_oh, stride_om, stride_on,
Z, H, N_CTX,
BLOCK_M: tl.constexpr, BLOCK_DMODEL: tl.constexpr,
BLOCK_N: tl.constexpr,
):
start_m = tl.program_id(0)
off_hz = tl.program_id(1)
# initialize offsets
offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M)
offs_n = tl.arange(0, BLOCK_N)
offs_d = tl.arange(0, BLOCK_DMODEL)
off_q = off_hz * stride_qh + offs_m[:, None] * stride_qm + offs_d[None, :] * stride_qk
off_k = off_hz * stride_qh + offs_n[None, :] * stride_kn + offs_d[:, None] * stride_kk
off_v = off_hz * stride_qh + offs_n[:, None] * stride_qm + offs_d[None, :] * stride_qk
# Initialize pointers to Q, K, V
q_ptrs = Q + off_q
k_ptrs = K + off_k
v_ptrs = V + off_v
# initialize pointer to m and l
m_prev = tl.zeros([BLOCK_M], dtype=tl.float32) - float("inf")
l_prev = tl.zeros([BLOCK_M], dtype=tl.float32)
acc = tl.zeros([BLOCK_M, BLOCK_DMODEL], dtype=tl.float32)
# load q: it will stay in SRAM throughout
q = tl.load(q_ptrs)
# loop over k, v and update accumulator
for start_n in range(0, (start_m + 1) * BLOCK_M, BLOCK_N):
# -- compute qk ----
k = tl.load(k_ptrs)
qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)
qk += tl.dot(q, k)
qk *= sm_scale
qk = tl.where(offs_m[:, None] >= (start_n + offs_n[None, :]), qk, float("-inf"))
# compute new m
m_curr = tl.maximum(tl.max(qk, 1), m_prev)
# correct old l
l_prev *= tl.exp(m_prev - m_curr)
# attention weights
p = tl.exp(qk - m_curr[:, None])
l_curr = tl.sum(p, 1) + l_prev
# rescale operands of matmuls
l_rcp = 1. / l_curr
p *= l_rcp[:, None]
acc *= (l_prev * l_rcp)[:, None]
# update acc
p = p.to(Q.dtype.element_ty)
v = tl.load(v_ptrs)
acc += tl.dot(p, v)
# update m_i and l_i
l_prev = l_curr
m_prev = m_curr
# update pointers
k_ptrs += BLOCK_N * stride_kn
v_ptrs += BLOCK_N * stride_vk
# rematerialize offsets to save registers
start_m = tl.program_id(0)
offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M)
# write back l and m
l_ptrs = L + off_hz * N_CTX + offs_m
m_ptrs = M + off_hz * N_CTX + offs_m
tl.store(l_ptrs, l_prev)
tl.store(m_ptrs, m_prev)
# initialize pointers to output
offs_n = tl.arange(0, BLOCK_DMODEL)
off_o = off_hz * stride_oh + offs_m[:, None] * stride_om + offs_n[None, :] * stride_on
out_ptrs = Out + off_o
tl.store(out_ptrs, acc)
@triton.jit
def _bwd_preprocess(
Out, DO, L,
NewDO, Delta,
BLOCK_M: tl.constexpr, D_HEAD: tl.constexpr,
):
off_m = tl.program_id(0) * BLOCK_M + tl.arange(0, BLOCK_M)
off_n = tl.arange(0, D_HEAD)
# load
o = tl.load(Out + off_m[:, None] * D_HEAD + off_n[None, :]).to(tl.float32)
do = tl.load(DO + off_m[:, None] * D_HEAD + off_n[None, :]).to(tl.float32)
denom = tl.load(L + off_m).to(tl.float32)
# compute
do = do / denom[:, None]
delta = tl.sum(o * do, axis=1)
# write-back
tl.store(NewDO + off_m[:, None] * D_HEAD + off_n[None, :], do)
tl.store(Delta + off_m, delta)
@triton.jit
def _bwd_kernel(
Q, K, V, sm_scale, Out, DO,
DQ, DK, DV,
L, M,
D,
stride_qz, stride_qh, stride_qm, stride_qk,
stride_kz, stride_kh, stride_kn, stride_kk,
stride_vz, stride_vh, stride_vk, stride_vn,
Z, H, N_CTX,
num_block,
BLOCK_M: tl.constexpr, BLOCK_DMODEL: tl.constexpr,
BLOCK_N: tl.constexpr,
):
off_hz = tl.program_id(0)
off_z = off_hz // H
off_h = off_hz % H
# offset pointers for batch/head
Q += off_z * stride_qz + off_h * stride_qh
K += off_z * stride_qz + off_h * stride_qh
V += off_z * stride_qz + off_h * stride_qh
DO += off_z * stride_qz + off_h * stride_qh
DQ += off_z * stride_qz + off_h * stride_qh
DK += off_z * stride_qz + off_h * stride_qh
DV += off_z * stride_qz + off_h * stride_qh
for start_n in range(0, num_block):
lo = start_n * BLOCK_M
# initialize row/col offsets
offs_qm = lo + tl.arange(0, BLOCK_M)
offs_n = start_n * BLOCK_M + tl.arange(0, BLOCK_M)
offs_m = tl.arange(0, BLOCK_N)
offs_k = tl.arange(0, BLOCK_DMODEL)
# initialize pointers to value-like data
q_ptrs = Q + (offs_qm[:, None] * stride_qm + offs_k[None, :] * stride_qk)
k_ptrs = K + (offs_n[:, None] * stride_kn + offs_k[None, :] * stride_kk)
v_ptrs = V + (offs_n[:, None] * stride_qm + offs_k[None, :] * stride_qk)
do_ptrs = DO + (offs_qm[:, None] * stride_qm + offs_k[None, :] * stride_qk)
dq_ptrs = DQ + (offs_qm[:, None] * stride_qm + offs_k[None, :] * stride_qk)
# pointer to row-wise quantities in value-like data
D_ptrs = D + off_hz * N_CTX
m_ptrs = M + off_hz * N_CTX
# initialize dv amd dk
dv = tl.zeros([BLOCK_M, BLOCK_DMODEL], dtype=tl.float32)
dk = tl.zeros([BLOCK_M, BLOCK_DMODEL], dtype=tl.float32)
# k and v stay in SRAM throughout
k = tl.load(k_ptrs)
v = tl.load(v_ptrs)
# loop over rows
for start_m in range(lo, num_block * BLOCK_M, BLOCK_M):
offs_m_curr = start_m + offs_m
# load q, k, v, do on-chip
q = tl.load(q_ptrs)
# recompute p = softmax(qk, dim=-1).T
# NOTE: `do` is pre-divided by `l`; no normalization here
qk = tl.dot(q, tl.trans(k))
qk = tl.where(offs_m_curr[:, None] >= (offs_n[None, :]), qk, float("-inf"))
m = tl.load(m_ptrs + offs_m_curr)
p = tl.exp(qk * sm_scale - m[:, None])
# compute dv
do = tl.load(do_ptrs)
dv += tl.dot(tl.trans(p.to(Q.dtype.element_ty)), do)
# compute dp = dot(v, do)
Di = tl.load(D_ptrs + offs_m_curr)
dp = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32) - Di[:, None]
dp += tl.dot(do, tl.trans(v))
# compute ds = p * (dp - delta[:, None])
ds = p * dp * sm_scale
# compute dk = dot(ds.T, q)
dk += tl.dot(tl.trans(ds.to(Q.dtype.element_ty)), q)
# compute dq
dq = tl.load(dq_ptrs)
dq += tl.dot(ds.to(Q.dtype.element_ty), k)
tl.store(dq_ptrs, dq)
# increment pointers
dq_ptrs += BLOCK_M * stride_qm
q_ptrs += BLOCK_M * stride_qm
do_ptrs += BLOCK_M * stride_qm
# write-back
dv_ptrs = DV + (offs_n[:, None] * stride_qm + offs_k[None, :] * stride_qk)
dk_ptrs = DK + (offs_n[:, None] * stride_kn + offs_k[None, :] * stride_kk)
tl.store(dv_ptrs, dv)
tl.store(dk_ptrs, dk)
empty = torch.empty(128, device="cuda")
class _attention(torch.autograd.Function):
@staticmethod
def forward(ctx, q, k, v, sm_scale):
BLOCK = 128
# shape constraints
Lq, Lk, Lv = q.shape[-1], k.shape[-1], v.shape[-1]
assert Lq == Lk and Lk == Lv
assert Lk in {16, 32, 64, 128}
o = torch.empty_like(q)
grid = (triton.cdiv(q.shape[2], BLOCK), q.shape[0] * q.shape[1], 1)
L = torch.empty((q.shape[0] * q.shape[1], q.shape[2]), device=q.device, dtype=torch.float32)
m = torch.empty((q.shape[0] * q.shape[1], q.shape[2]), device=q.device, dtype=torch.float32)
num_warps = 4 if Lk <= 64 else 8
_fwd_kernel[grid](
q, k, v, sm_scale,
L, m,
o,
q.stride(0), q.stride(1), q.stride(2), q.stride(3),
k.stride(0), k.stride(1), k.stride(2), k.stride(3),
v.stride(0), v.stride(1), v.stride(2), v.stride(3),
o.stride(0), o.stride(1), o.stride(2), o.stride(3),
q.shape[0], q.shape[1], q.shape[2],
BLOCK_M=BLOCK, BLOCK_N=BLOCK,
BLOCK_DMODEL=Lk, num_warps=num_warps,
num_stages=2,
)
# print(h.asm["ttgir"])
ctx.save_for_backward(q, k, v, o, L, m)
ctx.grid = grid
ctx.sm_scale = sm_scale
ctx.BLOCK_DMODEL = Lk
return o
@staticmethod
def backward(ctx, do):
BLOCK = 128
q, k, v, o, l, m = ctx.saved_tensors
do = do.contiguous()
dq = torch.zeros_like(q, dtype=torch.float32)
dk = torch.empty_like(k)
dv = torch.empty_like(v)
do_scaled = torch.empty_like(do)
delta = torch.empty_like(l)
_bwd_preprocess[(ctx.grid[0] * ctx.grid[1], )](
o, do, l,
do_scaled, delta,
BLOCK_M=BLOCK, D_HEAD=ctx.BLOCK_DMODEL,
)
_bwd_kernel[(ctx.grid[1],)](
q, k, v, ctx.sm_scale,
o, do_scaled,
dq, dk, dv,
l, m,
delta,
q.stride(0), q.stride(1), q.stride(2), q.stride(3),
k.stride(0), k.stride(1), k.stride(2), k.stride(3),
v.stride(0), v.stride(1), v.stride(2), v.stride(3),
q.shape[0], q.shape[1], q.shape[2],
ctx.grid[0],
BLOCK_M=BLOCK, BLOCK_N=BLOCK,
BLOCK_DMODEL=ctx.BLOCK_DMODEL, num_warps=8,
num_stages=1,
)
# print(h.asm["ttgir"])
return dq, dk, dv, None
attention = _attention.apply
@pytest.mark.parametrize('Z, H, N_CTX, D_HEAD', [(4, 48, 1024, 64)])
def test_op(Z, H, N_CTX, D_HEAD, dtype=torch.float16):
torch.manual_seed(20)
q = torch.empty((Z, H, N_CTX, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0.1, std=0.2).requires_grad_()
k = torch.empty((Z, H, N_CTX, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0.4, std=0.2).requires_grad_()
v = torch.empty((Z, H, N_CTX, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0.3, std=0.2).requires_grad_()
sm_scale = 0.2
dout = torch.randn_like(q)
# reference implementation
M = torch.tril(torch.ones((N_CTX, N_CTX), device="cuda"))
p = torch.matmul(q, k.transpose(2, 3)) * sm_scale
for z in range(Z):
for h in range(H):
p[:, :, M == 0] = float("-inf")
p = torch.softmax(p.float(), dim=-1).half()
# p = torch.exp(p)
ref_out = torch.matmul(p, v)
ref_out.backward(dout)
ref_dv, v.grad = v.grad.clone(), None
ref_dk, k.grad = k.grad.clone(), None
ref_dq, q.grad = q.grad.clone(), None
# # triton implementation
tri_out = attention(q, k, v, sm_scale)
# print(ref_out)
# print(tri_out)
tri_out.backward(dout)
tri_dv, v.grad = v.grad.clone(), None
tri_dk, k.grad = k.grad.clone(), None
tri_dq, q.grad = q.grad.clone(), None
# compare
assert torch.allclose(ref_out, tri_out, atol=1e-2, rtol=0)
assert torch.allclose(ref_dv, tri_dv, atol=1e-2, rtol=0)
assert torch.allclose(ref_dk, tri_dk, atol=1e-2, rtol=0)
assert torch.allclose(ref_dq, tri_dq, atol=1e-2, rtol=0)
try:
from flash_attn.flash_attn_interface import flash_attn_func
HAS_FLASH = True
except BaseException:
HAS_FLASH = False
BATCH, N_HEADS, N_CTX, D_HEAD = 4, 48, 4096, 64
# vary seq length for fixed head and batch=4
configs = [triton.testing.Benchmark(
x_names=['N_CTX'],
x_vals=[2**i for i in range(10, 14)],
line_arg='provider',
line_vals=['triton'] + (['flash'] if HAS_FLASH else []),
line_names=['Triton'] + (['Flash'] if HAS_FLASH else []),
styles=[('red', '-'), ('blue', '-')],
ylabel='ms',
plot_name=f'fused-attention-batch{BATCH}-head{N_HEADS}-d{D_HEAD}-{mode}',
args={'H': N_HEADS, 'BATCH': BATCH, 'D_HEAD': D_HEAD, 'dtype': torch.float16, 'mode': mode}
) for mode in ['fwd', 'bwd']]
@triton.testing.perf_report(configs)
def bench_flash_attention(BATCH, H, N_CTX, D_HEAD, mode, provider, dtype=torch.float16, device="cuda"):
assert mode in ['fwd', 'bwd']
warmup = 25
rep = 100
if provider == "triton":
q = torch.randn((BATCH, H, N_CTX, D_HEAD), dtype=dtype, device="cuda", requires_grad=True)
k = torch.randn((BATCH, H, N_CTX, D_HEAD), dtype=dtype, device="cuda", requires_grad=True)
v = torch.randn((BATCH, H, N_CTX, D_HEAD), dtype=dtype, device="cuda", requires_grad=True)
sm_scale = 1.3
fn = lambda: attention(q, k, v, sm_scale)
if mode == 'bwd':
o = fn()
do = torch.randn_like(o)
fn = lambda: o.backward(do, retain_graph=True)
ms = triton.testing.do_bench(fn, warmup=warmup, rep=rep)
return ms
if provider == "flash":
lengths = torch.full((BATCH,), fill_value=N_CTX, device=device)
cu_seqlens = torch.zeros((BATCH + 1,), device=device, dtype=torch.int32)
cu_seqlens[1:] = lengths.cumsum(0)
qkv = torch.randn((BATCH * N_CTX, 3, H, D_HEAD), dtype=dtype, device=device, requires_grad=True)
fn = lambda: flash_attn_func(qkv, cu_seqlens, 0., N_CTX, causal=True)
if mode == 'bwd':
o = fn()
do = torch.randn_like(o)
fn = lambda: o.backward(do, retain_graph=True)
ms = triton.testing.do_bench(fn, warmup=warmup, rep=rep)
return ms
# only works on post-Ampere GPUs right now
bench_flash_attention.run(save_path='.', print_data=True)
|
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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,576
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/check/distributed-gpu.py
|
#!/usr/bin/env python
# python -m torch.distributed.run --nproc_per_node 4 --nnodes 1 distributed-gpu.py
#
# If you get a hanging in `barrier` calls you have some network issues, you may try to debug this with:
#
# NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 distributed-gpu.py
import fcntl
import os
import socket
import torch
import torch.distributed as dist
def printflock(*msgs):
with open(__file__, "r") as fh:
fcntl.flock(fh, fcntl.LOCK_EX)
try:
print(*msgs)
finally:
fcntl.flock(fh, fcntl.LOCK_UN)
local_rank = int(os.environ["LOCAL_RANK"])
torch.cuda.set_device(local_rank)
device = torch.device("cuda", local_rank)
hostname = socket.gethostname()
gpu = f"[{hostname}-{local_rank}]"
try:
dist.init_process_group("nccl")
dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
dist.barrier()
torch.cuda.is_available()
torch.ones(1).cuda(local_rank)
rank = dist.get_rank()
world_size = dist.get_world_size()
printflock(f"{gpu} is OK (global rank: {rank}/{world_size})")
dist.barrier()
if rank == 0:
printflock(
f"pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}"
)
except Exception:
printflock(f"{gpu} is broken")
raise
|
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"/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,577
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/run/ner.py
|
# Copyright 2021 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
# fine-tune for token classification (NER, POS, CHUNKS)
import logging
import random
import torch
from datasets import ClassLabel, load_metric
from torch.utils.data import DataLoader
from transformers import (
CONFIG_MAPPING,
AutoConfig,
AutoModelForTokenClassification,
AutoTokenizer,
DataCollatorForTokenClassification,
PretrainedConfig,
default_data_collator,
)
from .params import TRAIN, EVAL, TEST, ALL, EACH
from .runner import Runner as Base
from .utils import get_list
log = logging.getLogger(__name__)
class Runner(Base):
AutoModel = AutoModelForTokenClassification
@property
def cols(self):
if self._cols is None:
ps, ds = self.params, self.dataset
x = ds[TRAIN] or ds[EVAL] or ds[TEST]
cs = x.column_names
fs = x.features
if ps.text_column is not None:
t = ps.text_column
else:
t = "tokens" if "tokens" in cs else cs[0]
if ps.label_column is not None:
l = ps.label_column
else:
l = f"{ps.task_name}_tags" if f"{ps.task_name}_tags" in cs else cs[1]
if isinstance(fs[l].feature, ClassLabel):
self.labels = ls = fs[l].feature.names
self.ids = {i: i for i in range(len(ls))}
else:
self.labels = ls = get_list(ds[TRAIN][l])
self.ids = {l: i for i, l in enumerate(ls)}
self.b_to_i = []
for i, x in enumerate(ls):
if x.startswith("B-") and x.replace("B-", "I-") in ls:
self.b_to_i.append(ls.index(x.replace("B-", "I-")))
else:
self.b_to_i.append(i)
self._cols = {ALL: cs, EACH: [t, l]}
return self._cols
@property
def config(self):
if self._config is None:
ps = self.params
x = ps.config_name if ps.config_name else ps.model_name
if x:
y = AutoConfig.from_pretrained(x, n_labels=len(self.labels))
else:
y = CONFIG_MAPPING[ps.model_type]()
log.warning("Creating new config")
self._config = y
return self._config
@property
def tokenizer(self):
if self._tokenizer is None:
ps = self.params
x = ps.tokenizer_name if ps.tokenizer_name else ps.model_name
if not x:
raise ValueError("Tokenizer from scratch is not supported")
if self.config.model_type in {"gpt2", "roberta"}:
y = AutoTokenizer.from_pretrained(x, use_fast=True, add_prefix_space=True)
else:
y = AutoTokenizer.from_pretrained(x, use_fast=True)
self._tokenizer = y
return self._tokenizer
@property
def model(self):
if self._model is None:
ls = self.labels
m = super().model
if m.config.label2id != PretrainedConfig(n_labels=len(self.labels)).label2id:
ids = {l: i for l, i in m.config.label2id.items()}
if list(sorted(ids.keys())) == list(sorted(ls)):
log.info(f"Using config label map: {ids}")
self.ids = {l(ids[l]) for l in ls}
else:
log.warning(
f"Ignoring mismatched {list(sorted(ids.keys()))} vs {list(sorted(ls))}"
)
else:
self.ids = {l: i for i, l in enumerate(ls)}
m.config.label2id = self.ids
m.config.id2label = {i: l for l, i in self.ids.items()}
return self._model
@property
def train_ds(self):
if self._train_ds is None:
ps, mgr, ds = self.params, self.mgr, self.dataset
with mgr.main_process_first():
self._dataset = y = ds.map(
self.prep_for_train,
batched=True,
remove_columns=self.cols[ALL],
desc="Running tokenizer on dataset",
)
y = y[TRAIN]
if ps.max_train_samples is not None:
y = y.select(range(ps.max_train_samples))
for i in random.sample(range(len(y)), 3):
log.info(f"Sample {i} of the training set: {y[i]}")
self._train_ds = y
return self._train_ds
def prep_for_train(self, xs):
ps = self.params
t, l = self.cols[EACH]
ys = self.tokenizer(
xs[t],
max_len=ps.max_len,
padding=self.padding,
truncation=True,
is_split_into_words=True,
)
labels = []
for i, x in enumerate(xs[l]):
ids = []
prev = None
ws = ys.word_ids(batch_index=i)
for w in ws:
if w is None:
ids.append(-100)
elif w != prev:
ids.append(self.ids[x[w]])
else:
if ps.label_all_tokens:
ids.append(self.b_to_i[self.ids[x[w]]])
else:
ids.append(-100)
prev = w
labels.append(ids)
ys["labels"] = labels
return ys
@property
def loaders(self):
if self._loaders is None:
ps, mgr = self.params, self.mgr
if ps.pad_to_max_length:
c = default_data_collator
else:
c = DataCollatorForTokenClassification(
self.tokenizer, pad_to_multiple_of=(8 if mgr.use_fp16 else None)
)
t = DataLoader(
self.train_ds, shuffle=True, collate_fn=c, batch_size=ps.train_batch_size
)
e = DataLoader(self.eval_ds, collate_fn=c, batch_size=ps.eval_batch_size)
self._loaders = {TRAIN: t, EVAL: e}
return self._loaders
@property
def metric(self):
if self._metric is None:
self._metric = load_metric("seqeval")
return self._metric
def eval_epoch(self, e):
m, mgr = self.model, self.mgr
m.eval()
for xs in self.loaders[EVAL]:
with torch.no_grad():
ys = m(**xs)
ys = ys.logits.argmax(dim=-1)
ls = xs["labels"]
if not self.params.pad_to_max_length:
ys = mgr.pad_across_processes(ys, dim=1, PAD=-100)
ls = mgr.pad_across_processes(ls, dim=1, PAD=-100)
ys, ls = self.get_labels(mgr.gather(ys), mgr.gather(ls))
self.metric.add_batch(predictions=ys, references=ls)
y = self.calc_metrics()
mgr.print(f"epoch {e}: {y}")
def get_labels(self, xs, ls):
mgr = self.mgr
if mgr.device.type == "cpu":
xs = xs.detach().clone().numpy()
ls = ls.detach().clone().numpy()
else:
xs = xs.detach().cpu().clone().numpy()
ls = ls.detach().cpu().clone().numpy()
ys = [[self.labels[x2] for (x2, l2) in zip(x, l) if l2 != -100] for x, l in zip(xs, ls)]
yl = [[self.labels[l2] for (_, l2) in zip(x, l) if l2 != -100] for x, l in zip(xs, ls)]
return ys, yl
def calc_metrics(self):
ps, xs = self.params, self.metric.compute()
if ps.return_entity_metrics:
ys = {}
for k, v in xs.items():
if isinstance(v, dict):
for n, v in v.items():
ys[f"{k}_{n}"] = v
else:
ys[k] = v
return ys
else:
return {
"precision": xs["overall_precision"],
"recall": xs["overall_recall"],
"f1": xs["overall_f1"],
"accuracy": xs["overall_accuracy"],
}
def main():
ps = [("--task_name", {"type", "default": "ner", "choices": ["ner", "pos", "chunk"]})]
x = Runner(ps)
x.dataset
x.cols
x.config
x.tokenizer
x.model
x.model.resize_token_embeddings(len(x.tokenizer))
x.loaders
x.prepare()
x.train()
x.save()
if __name__ == "__main__":
main()
"""
python ner.py \
--model_name bert-base-cased \
--dataset_name conll2003 \
--task_name ner \
--train_batch_size 32 \
--lr 2e-5 \
--out_dir /tmp/ner
accelerate config
accelerate test
accelerate launch ner.py \
--model_name bert-base-uncased \
--dataset_name conll2003 \
--task_name ner \
--train_batch_size 32 \
--lr 2e-5 \
--out_dir /tmp/ner
--pad_to_max_length \
--return_entity_metrics
"""
|
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"/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], "/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/dispatch.py": ["/qnarre/base/doc/blog.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/context.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/analyzer.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/images.py"], "/qnarre/models/big_bird.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/big_bird.py"], "/tools/triton/python/triton/runtime/jit.py": ["/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/fast/roberta.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/bigbird.py": ["/qnarre/prep/config/big_bird.py", "/qnarre/models/big_bird.py"], "/qnarre/models/gpt_neo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/gpt_neo.py"], "/qnarre/prep/convert/t5.py": ["/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/prep/tokens/fsmt.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/deberta.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/fast/splinter.py": ["/qnarre/tokens/fast.py"], "/qnarre/models/old/trafo.py": ["/qnarre/core/attention.py", "/qnarre/core/base.py", "/qnarre/core/mlp.py", "/qnarre/core/deduce.py", "/qnarre/core/norm.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/led.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/realm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/convbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/data2vec.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/tools/triton/python/triton/language/math.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/run/beam.py": ["/qnarre/run/qa.py"], "/tools/triton/python/triton/compiler/compiler.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/tools/disasm.py", "/tools/triton/python/triton/compiler/code_generator.py", "/tools/triton/python/triton/compiler/make_launcher.py"], "/qnarre/base/doc/graph.py": ["/qnarre/base/doc/base.py"], "/qnarre/base/activism.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py", "/qnarre/base/judgment.py"], "/qnarre/base/doc/exporter.py": ["/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/fnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/roformer.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/megatron.py": ["/qnarre/prep/config/megatron.py", "/qnarre/models/megatron.py"], "/qnarre/prep/tokens/fast/roformer.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/roformer.py"], "/qnarre/core/runner.py": ["/qnarre/core/params.py"], "/qnarre/run/seq2seq.py": ["/qnarre/run/qa.py"], "/qnarre/prep/tokens/luke.py": ["/qnarre/tokens/utils.py"], 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"/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/models/plbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/reformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/images.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/base.py"], "/qnarre/models/yoso.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/canine.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/blog.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/counter.py"], "/qnarre/models/longformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/fast/pegasus.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/pegasus.py"], "/qnarre/base/judgment.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py", "/qnarre/base/conflict.py", "/qnarre/base/conjecture.py"], "/qnarre/models/splinter.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/rag.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/convert/albert.py": ["/qnarre/prep/config/albert.py", "/qnarre/models/albert.py"], "/qnarre/prep/tokens/fast/gpt2.py": ["/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/fast/mbart.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/roformer.py": ["/qnarre/models/roformer.py"], "/qnarre/base/doc/util/tree.py": ["/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py", "/qnarre/base/doc/util/utils.py"], "/qnarre/models/rembert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/t5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/xlm.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/bert_tf2.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/tools/triton/python/triton/ops/__init__.py": ["/tools/triton/python/triton/ops/cross_entropy.py", "/tools/triton/python/triton/ops/matmul.py"], "/qnarre/core/norm.py": ["/qnarre/core/base.py"], "/qnarre/models/mpnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/author.py": ["/qnarre/base/named.py"], "/qnarre/base/doc/args.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/log.py"], "/qnarre/prep/tokens/gpt.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/distilbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/proof.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py"], "/qnarre/prep/tokens/byt5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/electra.py": ["/qnarre/prep/config/electra.py", "/qnarre/models/electra.py"], "/qnarre/models/luke.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": ["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,578
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/config/xlnet.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
from ... import core as qc
class PreTrained(qc.PreTrained):
hs = qc.Hypers(
["reuse_len"],
dict(
act_ffnet="gelu",
act_sum="tanh",
attn_type="bi",
bi_data=False,
BOS=1,
clamp_len=-1,
d_ff=4096,
d_head=64,
drop_sum_last=0.1,
drop=0.1,
end_n_top=5,
EOS=2,
eps=1e-12,
init_range=0.02,
mem_len=512,
model_type="xlnet",
n_lays=24,
PAD=5,
reuse_len=None,
s_vocab=32000,
same_length=False,
start_n_top=5,
sum_type="last",
sum_use_proj=True,
task_params={"text-generation": {"do_sample": True, "max_len": 250}},
untie_r=True,
use_mems_eval=True,
use_mems_train=False,
),
)
def __init__(self, d_model=1024, n_heads=16, **kw):
if d_model % n_heads != 0:
raise ValueError(f"'d_model % n_heads' ({d_model % n_heads}) should be equal to 0")
if "d_head" in kw:
if kw["d_head"] != d_model // n_heads:
raise ValueError(
f"`d_head` ({kw['d_head']}) should be equal to `d_model // n_heads` ({d_model // n_heads})"
)
self.d_head = d_model // n_heads
if "y_cache" in kw:
use_mems_eval = kw["y_cache"]
super().__init__(PAD=PAD, BOS=BOS, EOS=EOS, **kw)
def _init_weights(self, module):
if isinstance(module, qc.Linear):
module.weight.data.normal_(mean=0.0, std=self.cfg.init_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, qc.Embedding):
module.weight.data.normal_(mean=0.0, std=self.cfg.init_range)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, qc.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
elif isinstance(module, RelativeAttention):
for param in [
module.q,
module.k,
module.v,
module.o,
module.r,
module.r_r_bias,
module.r_s_bias,
module.r_w_bias,
module.seg_embed,
]:
param.data.normal_(mean=0.0, std=self.cfg.init_range)
elif isinstance(module, Model):
module.mask_emb.data.normal_(mean=0.0, std=self.cfg.init_range)
MAP = {
"xlnet-base-cased": dict(
archs=["LMHead"],
d_ff=3072,
d_model=768,
mem_len=None,
n_heads=12,
n_lays=12,
),
"xlnet-large-cased": dict(
archs=["LMHead"],
d_model=1024,
mem_len=None,
n_heads=16,
),
}
|
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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,579
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/base/author.py
|
# Copyright 2019 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
from .named import Named
class Author(Named):
title = ''
upper = None
def __init__(self, *, title=None, upper=None, **kw):
super().__init__(**kw)
if title:
self.title = title.title()
if upper:
self.upper = upper
@property
def value(self):
n = self.name.replace('-', ' ')
n = n.upper() if self.upper else n.title()
n = n.replace(' ', '-')
return self.title + ' ' + n
@property
def fields(self):
fs = {'Author': self.value}
fs.update(super().fields)
return fs
class Agent(Author):
def __init__(self, *, kind=None, **kw):
super().__init__(**kw)
if kind:
self.kind = kind
self.also_as(Author.to_tag())
@property
def fields(self):
fs = {'Kind': self.kind}
fs.update(super().fields)
return fs
class Authority(Agent):
agency = 'Sworn'
def __init__(self, *, agency=None, **kw):
super().__init__(**kw)
if agency:
self.agency = agency.title()
@property
def fields(self):
fs = {'Agency': self.agency}
fs.update(super().fields)
return fs
|
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"/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,580
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/base/doc/args.py
|
# Copyright 2019 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
import pathlib as pth
import argparse as ap
from .base import config
from .log import Logger, start_stop_log
log = Logger(__name__)
def default_base():
b = pth.Path.cwd()
s = b / config.SRC
assert s.exists() and s.is_dir()
d = b / config.DST
assert d.exists() and d.is_dir()
# q = b / config.QPY
# assert q.exists() and q.is_dir()
return b
class Namespace(ap.Namespace):
@property
def base(self):
b = self.basepath
b = pth.Path(b) if b else default_base()
assert b.exists() and b.is_dir()
return b
@property
def kw(self):
return self.__dict__
class BArgs(ap.ArgumentParser):
st = 'store_true'
def __init__(self, *args, **kw):
super().__init__(*args, **kw)
self.add_argument('-b', '--basepath', help='Path to base')
def parse_args(self, args=None):
return super().parse_args(args, Namespace())
class CArgs(BArgs):
def __init__(self, *args, **kw):
super().__init__(*args, **kw)
cs = (config.PRIV, config.PROT, config.PUBL, config.OPEN)
self.add_argument('-r', '--realm', choices=cs, help='Realm of action')
self.add_argument('-c', '--clear', action=self.st, help='Clear before')
def parse_args(self, args=None):
a = super().parse_args(args)
if a.clear:
with start_stop_log(log, 'Deleting *.qnr and *.lock'):
q = config.qnar_dst
for p in (a.base / q).glob('*.qnr'):
p.unlink()
for p in (a.base / q / 'filts').glob('*.qnr'):
p.unlink()
for p in (a.base / q).glob('*.lock'):
p.unlink()
for p in (a.base / q / 'filts').glob('*.lock'):
p.unlink()
return a
class MArgs(CArgs):
def __init__(self, *args, **kw):
super().__init__(*args, **kw)
self.add_argument('files', nargs='*', help='Files to read from')
self.add_argument('-w', '--wdir', help='Path to work dir')
self.add_argument('-p', '--pool', action=self.st, help='Pooled action')
self.add_argument(
'-d', '--dejunk_only', action=self.st, help='Only dejunk messages')
|
{"/qnarre/prep/convert/xlnet.py": ["/qnarre/prep/config/xlnet.py", "/qnarre/models/xlnet.py"], "/qnarre/prep/convert/bert.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/base/doc/patcher.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/nominals.py"], "/qnarre/prep/convert/funnel.py": ["/qnarre/prep/config/funnel.py", "/qnarre/models/funnel.py"], "/qnarre/prep/tokens/fast/realm.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py", "/qnarre/tokens/utils.py"], "/qnarre/models/decision_transfo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/decision_transfo.py"], "/qnarre/models/fsmt.py": ["/qnarre/core/embed.py"], "/qnarre/models/roformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/xlnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc.py": ["/qnarre/base/author.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], "/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/dispatch.py": ["/qnarre/base/doc/blog.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/context.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/analyzer.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/images.py"], "/qnarre/models/big_bird.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/big_bird.py"], "/tools/triton/python/triton/runtime/jit.py": ["/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/fast/roberta.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/bigbird.py": ["/qnarre/prep/config/big_bird.py", "/qnarre/models/big_bird.py"], "/qnarre/models/gpt_neo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/gpt_neo.py"], "/qnarre/prep/convert/t5.py": ["/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/prep/tokens/fsmt.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/deberta.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/fast/splinter.py": ["/qnarre/tokens/fast.py"], "/qnarre/models/old/trafo.py": ["/qnarre/core/attention.py", "/qnarre/core/base.py", "/qnarre/core/mlp.py", "/qnarre/core/deduce.py", "/qnarre/core/norm.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/led.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/realm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/convbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/data2vec.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/tools/triton/python/triton/language/math.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/run/beam.py": ["/qnarre/run/qa.py"], "/tools/triton/python/triton/compiler/compiler.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/tools/disasm.py", "/tools/triton/python/triton/compiler/code_generator.py", "/tools/triton/python/triton/compiler/make_launcher.py"], "/qnarre/base/doc/graph.py": ["/qnarre/base/doc/base.py"], "/qnarre/base/activism.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py", "/qnarre/base/judgment.py"], "/qnarre/base/doc/exporter.py": ["/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/fnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/roformer.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/megatron.py": ["/qnarre/prep/config/megatron.py", "/qnarre/models/megatron.py"], "/qnarre/prep/tokens/fast/roformer.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/roformer.py"], "/qnarre/core/runner.py": ["/qnarre/core/params.py"], "/qnarre/run/seq2seq.py": ["/qnarre/run/qa.py"], "/qnarre/prep/tokens/luke.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/util/table.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/tree.py", "/qnarre/base/doc/util/utils.py"], "/tools/triton/python/triton/language/standard.py": ["/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/language/__init__.py"], "/qnarre/base/doc/filters.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/args.py"], "/qnarre/prep/convert/gpt_neo.py": ["/qnarre/prep/config/gpt_neo.py", "/qnarre/models/gpt_neo.py"], "/qnarre/base/doc/section.py": ["/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/message.py"], "/qnarre/models/funnel.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/funnel.py"], "/tools/triton/python/triton/common/__init__.py": ["/tools/triton/python/triton/common/build.py"], "/qnarre/base/doc/qnn.py": ["/qnarre/base/doc/base.py", 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"/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": 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["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], 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["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], 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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,581
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/tokens/gpt.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
import json
import os
import re
from ...tokens.utils import PreTrainedTokenizer
from .bert import Tokenizer as Bert
VOCAB_FS = {
"vocab_file": "vocab.json",
"merges_file": "merges.txt",
}
VOCAB_MAP = {
"vocab_file": {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/vocab.json"},
"merges_file": {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/merges.txt"},
}
INPUT_CAPS = {
"openai-gpt": 512,
}
def get_pairs(word):
pairs = set()
prev_char = word[0]
for char in word[1:]:
pairs.add((prev_char, char))
prev_char = char
return pairs
def text_standardize(text):
text = text.replace("—", "-")
text = text.replace("–", "-")
text = text.replace("―", "-")
text = text.replace("…", "...")
text = text.replace("´", "'")
text = re.sub(
r"""(-+|~+|!+|"+|;+|\?+|\++|,+|\)+|\(+|\\+|\/+|\*+|\[+|\]+|}+|{+|\|+|_+)""", r" \1 ", text
)
text = re.sub(r"\s*\n\s*", " \n ", text)
text = re.sub(r"[^\S\n]+", " ", text)
return text.strip()
class Tokenizer(PreTrainedTokenizer):
vocab_fs = VOCAB_FS
vocab_map = VOCAB_MAP
input_caps = INPUT_CAPS
model_input_names = ["input_ids", "mask"]
def __init__(self, vocab_file, merges_file, unk="<unk>", **kw):
super().__init__(unk=unk, **kw)
try:
import ftfy
from spacy.lang.en import English
_nlp = English()
self.nlp = _nlp.tokenizer
self.fix_text = ftfy.fix_text
except ImportError:
logger.warning(
"ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy."
)
self.nlp = Bert(do_lower_case=True)
self.fix_text = None
with open(vocab_file, encoding="utf-8") as vocab_handle:
self.encoder = json.load(vocab_handle)
self.decoder = {v: k for k, v in self.encoder.items()}
with open(merges_file, encoding="utf-8") as merges_handle:
merges = merges_handle.read().split("\n")[1:-1]
merges = [tuple(merge.split()) for merge in merges]
self.bpe_ranks = dict(zip(merges, range(len(merges))))
self.cache = {}
@property
def do_lower_case(self):
return True
@property
def s_vocab(self):
return len(self.encoder)
def get_vocab(self):
return dict(self.encoder, **self.added_tokens_encoder)
def bpe(self, token):
word = tuple(token[:-1]) + (token[-1] + "</w>",)
if token in self.cache:
return self.cache[token]
pairs = get_pairs(word)
if not pairs:
return token + "</w>"
while True:
bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
if bigram not in self.bpe_ranks:
break
first, second = bigram
new_word = []
i = 0
while i < len(word):
try:
j = word.index(first, i)
except ValueError:
new_word.extend(word[i:])
break
else:
new_word.extend(word[i:j])
i = j
if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
new_word.append(first + second)
i += 2
else:
new_word.append(word[i])
i += 1
new_word = tuple(new_word)
word = new_word
if len(word) == 1:
break
else:
pairs = get_pairs(word)
word = " ".join(word)
if word == "\n </w>":
word = "\n</w>"
self.cache[token] = word
return word
def _tokenize(self, text):
split_tokens = []
if self.fix_text is None:
text = self.nlp.tokenize(text)
for token in text:
split_tokens.extend([t for t in self.bpe(token).split(" ")])
else:
text = self.nlp(text_standardize(self.fix_text(text)))
for token in text:
split_tokens.extend([t for t in self.bpe(token.text.lower()).split(" ")])
return split_tokens
def _convert_token_to_id(self, token):
return self.encoder.get(token, self.encoder.get(self.unk))
def _convert_id_to_token(self, index):
return self.decoder.get(index, self.unk)
def convert_tokens_to_string(self, tokens):
out_string = "".join(tokens).replace("</w>", " ").strip()
return out_string
def save_vocabulary(self, dir, pre=None):
vocab_file = os.path.join(
dir,
(pre + "-" if pre else "") + VOCAB_FS["vocab_file"],
)
merge_file = os.path.join(
dir,
(pre + "-" if pre else "") + VOCAB_FS["merges_file"],
)
with open(vocab_file, "w", encoding="utf-8") as f:
f.write(json.dumps(self.encoder, ensure_ascii=False))
index = 0
with open(merge_file, "w", encoding="utf-8") as writer:
writer.write("#version: 0.2\n")
for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
if index != token_index:
logger.warning(
f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
" Please check that the tokenizer is not corrupted!"
)
index = token_index
writer.write(" ".join(bpe_tokens) + "\n")
index += 1
return vocab_file, merge_file
|
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"/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,582
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/metric/seqeval.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
import importlib
import datasets as ds
from seqeval.metrics import accuracy_score, classification_report
class Seqeval(ds.Metric):
def _info(self):
return ds.MetricInfo(
description="",
citation="",
homepage="",
inputs_description="",
features=ds.Features(
{
"predictions": ds.Sequence(ds.Value("string", id="label"), id="sequence"),
"references": ds.Sequence(ds.Value("string", id="label"), id="sequence"),
}
),
)
def _compute(
self,
preds,
refs,
suffix=False,
scheme=None,
mode=None,
sample_weight=None,
zero_division="warn",
):
if scheme is not None:
try:
scheme_module = importlib.import_module("seqeval.scheme")
scheme = getattr(scheme_module, scheme)
except AttributeError:
raise ValueError()
report = classification_report(
y_true=refs,
y_pred=preds,
suffix=suffix,
output_dict=True,
scheme=scheme,
mode=mode,
sample_weight=sample_weight,
zero_division=zero_division,
)
report.pop("macro avg")
report.pop("weighted avg")
overall_score = report.pop("micro avg")
y = {
type_name: {
"precision": score["precision"],
"recall": score["recall"],
"f1": score["f1-score"],
"number": score["support"],
}
for type_name, score in report.items()
}
y["overall_precision"] = overall_score["precision"]
y["overall_recall"] = overall_score["recall"]
y["overall_f1"] = overall_score["f1-score"]
y["overall_accuracy"] = accuracy_score(y_true=refs, y_pred=preds)
return y
|
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"/qnarre/prep/convert/bert_tf2.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/tools/triton/python/triton/ops/__init__.py": ["/tools/triton/python/triton/ops/cross_entropy.py", "/tools/triton/python/triton/ops/matmul.py"], "/qnarre/core/norm.py": ["/qnarre/core/base.py"], "/qnarre/models/mpnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/author.py": ["/qnarre/base/named.py"], "/qnarre/base/doc/args.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/log.py"], "/qnarre/prep/tokens/gpt.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/distilbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/proof.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py"], "/qnarre/prep/tokens/byt5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/electra.py": ["/qnarre/prep/config/electra.py", "/qnarre/models/electra.py"], "/qnarre/models/luke.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": ["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,583
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/tokens/auto.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
import importlib
import json
import os
from collections import OrderedDict
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module
from ...file_utils import get_file_from_repo
from ...core.tokens.utils import PreTrainedTokenizer
from ...core.tokens.base import TOKENIZER_CONFIG_FILE
from ...core.tokens.fast import PreTrainedTokenizerFast
from ..encoder_decoder import EncoderDecoderConfig
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
CONFIG_MAPPING_NAMES,
AutoConfig,
config_class_to_model_type,
model_type_to_module_name,
)
TOKENIZER_MAPPING_NAMES = OrderedDict(
[
("albert", ("AlbertTokenizer", "AlbertTokenizerFast")),
]
)
TOKENIZER_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, TOKENIZER_MAPPING_NAMES)
CONFIG_TO_TYPE = {v: k for k, v in CONFIG_MAPPING_NAMES.items()}
def tokenizer_class_from_name(class_name):
if class_name == "PreTrainedTokenizerFast":
return PreTrainedTokenizerFast
for module_name, tokenizers in TOKENIZER_MAPPING_NAMES.items():
if class_name in tokenizers:
module_name = model_type_to_module_name(module_name)
module = importlib.import_module(f".{module_name}", "transformers.models")
return getattr(module, class_name)
for config, tokenizers in TOKENIZER_MAPPING._extra_content.items():
for tokenizer in tokenizers:
if getattr(tokenizer, "__name__", None) == class_name:
return tokenizer
return None
def get_tokenizer_config(
pretrained_model_name_or_path,
cache_dir=None,
force_download=False,
resume_download=False,
proxies=None,
use_auth_token=None,
revision=None,
local_files_only=False,
**kw,
):
resolved_config_file = get_file_from_repo(
pretrained_model_name_or_path,
TOKENIZER_CONFIG_FILE,
cache_dir=cache_dir,
force_download=force_download,
resume_download=resume_download,
proxies=proxies,
use_auth_token=use_auth_token,
revision=revision,
local_files_only=local_files_only,
)
if resolved_config_file is None:
logger.info(
"Could not locate the tokenizer configuration file, will try to use the model config instead."
)
return {}
with open(resolved_config_file, encoding="utf-8") as reader:
return json.load(reader)
class AutoTokenizer:
def __init__(self):
raise EnvironmentError(
"AutoTokenizer is designed to be instantiated "
"using the `AutoTokenizer.from_pretrained(pretrained_model_name_or_path)` method."
)
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, *inputs, **kw):
config = kw.pop("config", None)
kw["_from_auto"] = True
use_fast = kw.pop("use_fast", True)
tokenizer_type = kw.pop("tokenizer_type", None)
trust_remote_code = kw.pop("trust_remote_code", False)
# First, let's see whether the tokenizer_type is passed so that we can leverage it
if tokenizer_type is not None:
tokenizer_class = None
tokenizer_class_tuple = TOKENIZER_MAPPING_NAMES.get(tokenizer_type, None)
if tokenizer_class_tuple is None:
raise ValueError(
f"Passed `tokenizer_type` {tokenizer_type} does not exist. `tokenizer_type` should be one of "
f"{', '.join(c for c in TOKENIZER_MAPPING_NAMES.keys())}."
)
tokenizer_class_name, tokenizer_fast_class_name = tokenizer_class_tuple
if use_fast and tokenizer_fast_class_name is not None:
tokenizer_class = tokenizer_class_from_name(tokenizer_fast_class_name)
if tokenizer_class is None:
tokenizer_class = tokenizer_class_from_name(tokenizer_class_name)
if tokenizer_class is None:
raise ValueError(
f"Tokenizer class {tokenizer_class_name} is not currently imported."
)
return tokenizer_class.from_pretrained(pretrained_model_name_or_path, *inputs, **kw)
# Next, let's try to use the tokenizer_config file to get the tokenizer class.
tokenizer_config = get_tokenizer_config(pretrained_model_name_or_path, **kw)
config_tokenizer_class = tokenizer_config.get("tokenizer_class")
tokenizer_auto_map = None
if "auto_map" in tokenizer_config:
if isinstance(tokenizer_config["auto_map"], (tuple, list)):
# Legacy format for dynamic tokenizers
tokenizer_auto_map = tokenizer_config["auto_map"]
else:
tokenizer_auto_map = tokenizer_config["auto_map"].get("AutoTokenizer", None)
# If that did not work, let's try to use the config.
if config_tokenizer_class is None:
if not isinstance(config, PretrainedConfig):
config = AutoConfig.from_pretrained(
pretrained_model_name_or_path, trust_remote_code=trust_remote_code, **kw
)
config_tokenizer_class = config.tokenizer_class
if hasattr(config, "auto_map") and "AutoTokenizer" in config.auto_map:
tokenizer_auto_map = config.auto_map["AutoTokenizer"]
# If we have the tokenizer class from the tokenizer config or the model config we're good!
if config_tokenizer_class is not None:
tokenizer_class = None
if tokenizer_auto_map is not None:
if not trust_remote_code:
raise ValueError(
f"Loading {pretrained_model_name_or_path} requires you to execute the tokenizer file in that repo "
"on your local machine. Make sure you have read the code there to avoid malicious use, then set "
"the option `trust_remote_code=True` to remove this error."
)
if kw.get("revision", None) is None:
logger.warning(
"Explicitly passing a `revision` is encouraged when loading a model with custom code to ensure "
"no malicious code has been contributed in a newer revision."
)
if use_fast and tokenizer_auto_map[1] is not None:
class_ref = tokenizer_auto_map[1]
else:
class_ref = tokenizer_auto_map[0]
module_file, class_name = class_ref.split(".")
tokenizer_class = get_class_from_dynamic_module(
pretrained_model_name_or_path, module_file + ".py", class_name, **kw
)
elif use_fast and not config_tokenizer_class.endswith("Fast"):
tokenizer_class_candidate = f"{config_tokenizer_class}Fast"
tokenizer_class = tokenizer_class_from_name(tokenizer_class_candidate)
if tokenizer_class is None:
tokenizer_class_candidate = config_tokenizer_class
tokenizer_class = tokenizer_class_from_name(tokenizer_class_candidate)
if tokenizer_class is None:
raise ValueError(
f"Tokenizer class {tokenizer_class_candidate} does not exist or is not currently imported."
)
return tokenizer_class.from_pretrained(pretrained_model_name_or_path, *inputs, **kw)
# Otherwise we have to be creative.
# if model is an encoder decoder, the encoder tokenizer class is used by default
if isinstance(config, EncoderDecoderConfig):
if type(config.decoder) is not type(config.encoder): # noqa: E721
logger.warning(
f"The encoder model config class: {config.encoder.__class__} is different from the decoder model "
f"config class: {config.decoder.__class__}. It is not recommended to use the "
"`AutoTokenizer.from_pretrained()` method in this case. Please use the encoder and decoder "
"specific tokenizer classes."
)
config = config.encoder
model_type = config_class_to_model_type(type(config).__name__)
if model_type is not None:
tokenizer_class_py, tokenizer_class_fast = TOKENIZER_MAPPING[type(config)]
if tokenizer_class_fast and (use_fast or tokenizer_class_py is None):
return tokenizer_class_fast.from_pretrained(
pretrained_model_name_or_path, *inputs, **kw
)
else:
if tokenizer_class_py is not None:
return tokenizer_class_py.from_pretrained(
pretrained_model_name_or_path, *inputs, **kw
)
else:
raise ValueError(
"This tokenizer cannot be instantiated. Please make sure you have `sentencepiece` installed "
"in order to use this tokenizer."
)
raise ValueError(
f"Unrecognized configuration class {config.__class__} to build an AutoTokenizer.\n"
f"Model type should be one of {', '.join(c.__name__ for c in TOKENIZER_MAPPING.keys())}."
)
def register(config_class, slow_tokenizer_class=None, fast_tokenizer_class=None):
if slow_tokenizer_class is None and fast_tokenizer_class is None:
raise ValueError(
"You need to pass either a `slow_tokenizer_class` or a `fast_tokenizer_class"
)
if slow_tokenizer_class is not None and issubclass(
slow_tokenizer_class, PreTrainedTokenizerFast
):
raise ValueError("You passed a fast tokenizer in the `slow_tokenizer_class`.")
if fast_tokenizer_class is not None and issubclass(
fast_tokenizer_class, PreTrainedTokenizer
):
raise ValueError("You passed a slow tokenizer in the `fast_tokenizer_class`.")
if (
slow_tokenizer_class is not None
and fast_tokenizer_class is not None
and issubclass(fast_tokenizer_class, PreTrainedTokenizerFast)
and fast_tokenizer_class.slow_tokenizer_class != slow_tokenizer_class
):
raise ValueError(
"The fast tokenizer class you are passing has a `slow_tokenizer_class` attribute that is not "
"consistent with the slow tokenizer class you passed (fast tokenizer has "
f"{fast_tokenizer_class.slow_tokenizer_class} and you passed {slow_tokenizer_class}. Fix one of those "
"so they match!"
)
# Avoid resetting a set slow/fast tokenizer if we are passing just the other ones.
if config_class in TOKENIZER_MAPPING._extra_content:
existing_slow, existing_fast = TOKENIZER_MAPPING[config_class]
if slow_tokenizer_class is None:
slow_tokenizer_class = existing_slow
if fast_tokenizer_class is None:
fast_tokenizer_class = existing_fast
TOKENIZER_MAPPING.register(config_class, (slow_tokenizer_class, fast_tokenizer_class))
|
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"/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,584
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/models/distilbert.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
# https://arxiv.org/abs/1910.01108
import torch
import deepspeed
from torch import nn
from torch.nn import functional as F
from transformers.utils import logging
from .. import core as qc
from ..core import utils as qu
from ..core import forward as qf
from ..core import output as qo
from ..core import attention as qa
from ..core.embed import Embed
from ..core.mlp import MLP, Classifier, Predictor
from ..prep.config.distilbert import PreTrained
log = logging.get_logger(__name__)
class Model(PreTrained):
def __init__(self, **kw):
super().__init__(**kw)
cfg = self.get_cfg(kw)
self.emb = Embed(cfg.d_model, **kw)
self.enc = Encoder(**kw)
def forward(self, x, x_emb=None, mask=None, head_m=None, **kw):
cfg = self.cfg
if x is not None:
assert x_emb is None
s, d = x.size(), x.device
else:
s, d = x_emb.size()[:-1], x_emb.device
if mask is None:
mask = torch.ones(s, device=d)
head_m = self.get_head_m(head_m, cfg.n_lays)
y = self.emb(x, x_emb, **kw)
y = self.enc(y, mask=mask, head_m=head_m)
return y
class ForMasked(PreTrained):
def __init__(self, **kw):
super().__init__(**kw)
cfg = self.get_cfg(kw)
self.model = Model(add_pool=False, **kw)
self.proj = Predictor(cfg.d_model, eps=1e-12, **kw)
forward = qf.forward_masked
class ForChoice(PreTrained):
def __init__(self, **kw):
super().__init__(**kw)
cfg = self.get_cfg(kw)
self.model = Model(**kw)
self.proj = Classifier(cfg.d_model, "relu", n_labels=1, drop_proj=cfg.drop_seq, **kw)
def forward(self, x, x_emb=None, mask=None, labels=None, **kw):
n = x.shape[1] if x is not None else x_emb.shape[1]
x, mask = qu.view_2D(x, mask)
x_emb = qu.view_3D(x_emb)
ys = self.model(x, x_emb, mask=mask, **kw)
y = self.proj(ys[0][:, 0]).view(-1, n)
loss = None
if labels is not None:
loss = nn.CrossEntropyLoss()(y, labels)
ys = (y,) + ys[1:] + (loss,)
return qo.WithLoss(*ys)
class ForSeqClass(PreTrained):
def __init__(self, **kw):
super().__init__(**kw)
cfg = self.get_cfg(kw)
self.model = Model(**kw)
self.proj = Classifier(cfg.d_model, "relu", drop_proj=cfg.drop_seq, **kw)
forward = qf.forward_seq
class ForTokClass(PreTrained):
def __init__(self, **kw):
super().__init__(**kw)
self.get_cfg(kw)
self.model = Model(**kw)
self.proj = Classifier(**kw)
forward = qf.forward_tok
class ForQA(PreTrained):
def __init__(self, **kw):
super().__init__(**kw)
cfg = self.get_cfg(kw)
self.model = Model(**kw)
self.proj = Classifier(n_labels=2, drop_proj=cfg.drop_qa, **kw)
forward = qf.forward_qa
class Encoder(qc.Module):
hs = qc.Hypers({"n_lays"})
def __init__(self, n_lays=None, ps={}, hs=[], **kw):
if n_lays is not None:
kw.update(n_lays=n_lays)
super().__init__(ps, [self.hs] + hs, **kw)
cfg = self.get_cfg(kw)
self.lays = qc.Stack([Layer(**kw) for _ in range(cfg.n_lays)])
def forward(self, x, head_m=None, **kw):
y = x
attns = hiddens = ()
for i, lay in enumerate(self.lays):
hiddens += (y,)
ys = lay(y, head_m=head_m[i], **kw)
y = ys[0]
attns += (ys[1],)
hiddens += (y,)
return qo.Base(y, attns, hiddens)
class Layer(qc.Module):
def __init__(self, **kw):
super().__init__(**kw)
cfg = self.get_cfg(kw)
assert cfg.d_model % cfg.n_heads == 0
self.refl = Attention(**kw)
assert cfg.activation in ["relu", "gelu"]
self.ffnet = MLP(act=cfg.activation, drop=cfg.drop, **kw)
self.norm = qc.LayerNorm(cfg.d_model, 1e-12)
def forward(self, x, **kw):
y, a = self.refl(x, **kw)
y = self.norm(self.ffnet(y) + y)
return y, a
class Attention(qc.Module):
hs = qc.Hypers({"d_model", "n_heads"}, {"drop_attn": 0.0, "eps": 1e-12})
def __init__(self, ps={}, hs=[], **kw):
super().__init__(ps, [self.hs] + hs, **kw)
cfg = self.get_cfg(kw)
m, n = cfg.d_model, cfg.n_heads
assert m % n == 0
cfg.d_head = h = m // n
cfg.scale = 1 / (h**0.5)
self.query = qc.Linear(m, m, **kw)
self.key = qc.Linear(m, m, **kw)
self.value = qc.Linear(m, m, **kw)
self.proj = qc.Linear(m, m, **kw)
self.drop = qc.Dropout(cfg.drop_attn, **kw)
self.norm = qc.LayerNorm(cfg.d_model, **kw)
split_heads = qa.split_heads
def forward(self, x, head_m=None, mask=None, **kw):
cfg = self.cfg
q = self.split_heads(self.query(x))
k = self.split_heads(self.key(x))
v = self.split_heads(self.value(x))
q.mul_(cfg.scale)
a = torch.matmul(q, k.transpose(2, 3))
b = x.size()[0]
mask = (mask == 0).view((b, 1, 1, x.size(1))).expand_as(a)
a = a.masked_fill(mask, -float("inf"))
a = self.drop(F.softmax(a, dim=-1))
if head_m is not None:
a *= head_m
y = torch.matmul(a, v).transpose(1, 2).contiguous()
y = y.view(b, -1, cfg.n_heads * cfg.d_head)
y = self.norm(x + self.proj(y))
return y, a
|
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"/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/dispatch.py": ["/qnarre/base/doc/blog.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/context.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/analyzer.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/images.py"], "/qnarre/models/big_bird.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/big_bird.py"], "/tools/triton/python/triton/runtime/jit.py": ["/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/fast/roberta.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/bigbird.py": ["/qnarre/prep/config/big_bird.py", "/qnarre/models/big_bird.py"], "/qnarre/models/gpt_neo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/gpt_neo.py"], "/qnarre/prep/convert/t5.py": ["/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/prep/tokens/fsmt.py": 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"/qnarre/base/author.py", "/qnarre/base/conflict.py", "/qnarre/base/conjecture.py"], "/qnarre/models/splinter.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/rag.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/convert/albert.py": ["/qnarre/prep/config/albert.py", "/qnarre/models/albert.py"], "/qnarre/prep/tokens/fast/gpt2.py": ["/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/fast/mbart.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/roformer.py": ["/qnarre/models/roformer.py"], "/qnarre/base/doc/util/tree.py": ["/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py", "/qnarre/base/doc/util/utils.py"], "/qnarre/models/rembert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/t5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/xlm.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/bert_tf2.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/tools/triton/python/triton/ops/__init__.py": ["/tools/triton/python/triton/ops/cross_entropy.py", "/tools/triton/python/triton/ops/matmul.py"], "/qnarre/core/norm.py": ["/qnarre/core/base.py"], "/qnarre/models/mpnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/author.py": ["/qnarre/base/named.py"], "/qnarre/base/doc/args.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/log.py"], "/qnarre/prep/tokens/gpt.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/distilbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/proof.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py"], "/qnarre/prep/tokens/byt5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/electra.py": ["/qnarre/prep/config/electra.py", "/qnarre/models/electra.py"], "/qnarre/models/luke.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": ["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,585
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/config/rembert.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
from ... import core as qc
class PreTrained(qc.PreTrained):
hs = qc.Hypers(
[],
dict(
act="gelu",
BOS=312,
classifier_dropout_prob=0.1,
d_ff=4608,
d_hidden=1152,
drop_attn=0.0,
drop=0.0,
EOS=313,
eps=1e-12,
init_range=0.02,
input_embedding_size=256,
is_enc_dec=False,
model_type="rembert",
n_heads=18,
n_lays=32,
n_pos=512,
n_typ=2,
output_embedding_size=1664,
PAD=0,
s_vocab=250300,
y_cache=True,
grad_checkpoint=True,
),
)
def _init_weights(self, module):
if isinstance(module, qc.Linear):
module.weight.data.normal_(mean=0.0, std=self.config.init_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, qc.Embed):
module.weight.data.normal_(mean=0.0, std=self.config.init_range)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, qc.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
def _set_gradient_checkpointing(self, module, value=False):
if isinstance(module, RemBertEncoder):
module.gradient_checkpointing = value
MAP = {
"rembert": "https://huggingface.co/google/rembert/resolve/main/config.json",
}
|
{"/qnarre/prep/convert/xlnet.py": ["/qnarre/prep/config/xlnet.py", "/qnarre/models/xlnet.py"], "/qnarre/prep/convert/bert.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/base/doc/patcher.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/nominals.py"], "/qnarre/prep/convert/funnel.py": ["/qnarre/prep/config/funnel.py", "/qnarre/models/funnel.py"], "/qnarre/prep/tokens/fast/realm.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py", "/qnarre/tokens/utils.py"], "/qnarre/models/decision_transfo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/decision_transfo.py"], "/qnarre/models/fsmt.py": ["/qnarre/core/embed.py"], "/qnarre/models/roformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/xlnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc.py": ["/qnarre/base/author.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", 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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,586
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/base/proof.py
|
# Copyright 2019 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
from .claim import Claim
from .narrative import Node
from .author import Authority
class Proof(Node):
sign = '!p'
authority = None
def __init__(self,
text=None,
author=None,
agent=None,
authority=None,
factor=2,
**kw):
super().__init__(factor=factor, **kw)
if text:
for k in ('factor', 'bias', 'weight'):
kw.pop(k, None)
self.claim = Claim(text=text, **kw)
if not authority:
if agent:
authority = 'agent'
elif author:
authority = 'self'
if authority:
self.authority = Authority.create(name=authority)
@property
def weight(self):
p = self.partial(self.authority.weight, self.claim.weight)
return p + self.bias
@property
def credibility(self):
return self.weight
@property
def value(self):
a = self.authority.agency
return '{} {}: {}'.format(super().value, a, self.claim.value)
@property
def fields(self):
fs = self.authority.fields
fs.update(self.claim.fields)
fs.update(super().fields)
fs['Credibility'] = self.credibility
return fs
|
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["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,587
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/tokens/byt5.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
import warnings
from ...tokens.utils import AddedToken, PreTrainedTokenizer
class Tokenizer(PreTrainedTokenizer):
model_input_names = ["input_ids", "attention_mask"]
def __init__(
self,
eos="</s>",
unk="<unk>",
pad="<pad>",
extra_ids=125,
additional_special_tokens=None,
**kw,
):
if extra_ids > 0 and additional_special_tokens is None:
additional_special_tokens = [f"<extra_id_{i}>" for i in range(extra_ids)]
elif extra_ids > 0 and additional_special_tokens is not None:
extra_tokens = len(
set(filter(lambda x: ("extra_id" in str(x)), additional_special_tokens))
)
assert extra_tokens == extra_ids
pad = AddedToken(pad, lstrip=False, rstrip=False) if isinstance(pad, str) else pad
eos = AddedToken(eos, lstrip=False, rstrip=False) if isinstance(eos, str) else eos
unk = AddedToken(unk, lstrip=False, rstrip=False) if isinstance(unk, str) else unk
super().__init__(
eos=eos,
unk=unk,
pad=pad,
extra_ids=extra_ids,
additional_special_tokens=additional_special_tokens,
**kw,
)
self._extra_ids = extra_ids
self._utf_vocab_size = 2**8
self.special_tokens_encoder = {
self.pad: 0,
self.eos: 1,
self.unk: 2,
}
self._num_special_tokens = len(self.special_tokens_encoder)
n = len(additional_special_tokens)
for i, token in enumerate(additional_special_tokens):
self.special_tokens_encoder[token] = self.s_vocab + i - n
self.special_tokens_decoder = {v: k for k, v in self.special_tokens_encoder.items()}
@property
def s_vocab(self):
return self._utf_vocab_size + self._num_special_tokens + self._extra_ids
def get_special_tokens_mask(
self,
toks_0,
toks_1=None,
has_specials=False,
):
if has_specials:
return super().get_special_tokens_mask(toks_0=toks_0, toks_1=toks_1, has_specials=True)
if toks_1 is None:
return ([0] * len(toks_0)) + [1]
return ([0] * len(toks_0)) + [1] + ([0] * len(toks_1)) + [1]
def _add_eos_if_not_present(self, token_ids):
if len(token_ids) > 0 and token_ids[-1] == self.EOS:
warnings.warn(
f"This sequence already has {self.eos}. In future versions this behavior may lead to duplicated eos tokens being added."
)
return token_ids
else:
return token_ids + [self.EOS]
def create_token_type_ids_from_sequences(self, toks_0, toks_1=None):
eos = [self.EOS]
if toks_1 is None:
return len(toks_0 + eos) * [0]
return len(toks_0 + eos + toks_1 + eos) * [0]
def build_inputs_with_special_tokens(self, toks_0, toks_1=None):
toks_0 = self._add_eos_if_not_present(toks_0)
if toks_1 is None:
return toks_0
else:
toks_1 = self._add_eos_if_not_present(toks_1)
return toks_0 + toks_1
def _tokenize(self, text):
tokens = [chr(i) for i in text.encode("utf-8")]
return tokens
def _convert_token_to_id(self, token):
if token in self.special_tokens_encoder:
token_id = self.special_tokens_encoder[token]
elif token in self.added_tokens_encoder:
token_id = self.added_tokens_encoder[token]
elif len(token) != 1:
token_id = self.unk_token_id
else:
token_id = ord(token) + self._num_special_tokens
return token_id
def _convert_id_to_token(self, index):
if index in self.special_tokens_decoder:
token = self.special_tokens_decoder[index]
else:
token = chr(index - self._num_special_tokens)
return token
def convert_tokens_to_string(self, tokens):
bstring = b""
for token in tokens:
if token in self.special_tokens_decoder:
tok_string = self.special_tokens_decoder[token].encode("utf-8")
elif token in self.added_tokens_decoder:
tok_string = self.special_tokens_decoder[token].encode("utf-8")
elif token in self.special_tokens_encoder:
tok_string = token.encode("utf-8")
elif token in self.added_tokens_encoder:
tok_string = token.encode("utf-8")
else:
tok_string = bytes([ord(token)])
bstring += tok_string
string = bstring.decode("utf-8", errors="ignore")
return string
def save_vocabulary(self, dir, pre=None):
return ()
|
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"/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], "/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/dispatch.py": ["/qnarre/base/doc/blog.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/context.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/analyzer.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/images.py"], "/qnarre/models/big_bird.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/big_bird.py"], "/tools/triton/python/triton/runtime/jit.py": ["/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/fast/roberta.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/bigbird.py": ["/qnarre/prep/config/big_bird.py", "/qnarre/models/big_bird.py"], "/qnarre/models/gpt_neo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/gpt_neo.py"], "/qnarre/prep/convert/t5.py": ["/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/prep/tokens/fsmt.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/deberta.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/fast/splinter.py": ["/qnarre/tokens/fast.py"], "/qnarre/models/old/trafo.py": ["/qnarre/core/attention.py", "/qnarre/core/base.py", "/qnarre/core/mlp.py", "/qnarre/core/deduce.py", "/qnarre/core/norm.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/led.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/realm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/convbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/data2vec.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/tools/triton/python/triton/language/math.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/run/beam.py": ["/qnarre/run/qa.py"], "/tools/triton/python/triton/compiler/compiler.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/autotuner.py", 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"/qnarre/base/author.py", "/qnarre/base/conflict.py", "/qnarre/base/conjecture.py"], "/qnarre/models/splinter.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/rag.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/convert/albert.py": ["/qnarre/prep/config/albert.py", "/qnarre/models/albert.py"], "/qnarre/prep/tokens/fast/gpt2.py": ["/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/fast/mbart.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/roformer.py": ["/qnarre/models/roformer.py"], "/qnarre/base/doc/util/tree.py": ["/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py", "/qnarre/base/doc/util/utils.py"], "/qnarre/models/rembert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/t5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/xlm.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/bert_tf2.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/tools/triton/python/triton/ops/__init__.py": ["/tools/triton/python/triton/ops/cross_entropy.py", "/tools/triton/python/triton/ops/matmul.py"], "/qnarre/core/norm.py": ["/qnarre/core/base.py"], "/qnarre/models/mpnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/author.py": ["/qnarre/base/named.py"], "/qnarre/base/doc/args.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/log.py"], "/qnarre/prep/tokens/gpt.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/distilbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/proof.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py"], "/qnarre/prep/tokens/byt5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/electra.py": ["/qnarre/prep/config/electra.py", "/qnarre/models/electra.py"], "/qnarre/models/luke.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": ["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,588
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/convert/electra.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
import os
import re
import numpy as np
import tensorflow as tf
import torch
from argparse import ArgumentParser
from transformers.utils import logging
from ..config.electra import PreTrained
from ...models.electra import ForPreTraining, ForMasked
logging.set_verbosity_info()
log = logging.get_logger(__name__)
def load_src_weights(model, src_path, discriminator_or_generator="discriminator"):
src_path = os.path.abspath(src_path)
log.info(f"Loading from: {src_path}")
init_vars = tf.train.list_variables(src_path)
names = []
arrays = []
for name, shape in init_vars:
log.info(f"Loading TF weight {name} with shape {shape}")
array = tf.train.load_variable(src_path, name)
names.append(name)
arrays.append(array)
for name, array in zip(names, arrays):
original_name = name
try:
if isinstance(model, ForMasked):
name = name.replace("electra/embeddings/", "generator/embeddings/")
if discriminator_or_generator == "generator":
name = name.replace("electra/", "discriminator/")
name = name.replace("generator/", "electra/")
name = name.replace("dense_1", "dense_prediction")
name = name.replace("generator_predictions/output_bias", "generator_lm_head/bias")
name = name.split("/")
if any(n in ["global_step", "temperature"] for n in name):
log.info(f"Skipping {original_name}")
continue
p = model
for s in name:
if re.fullmatch(r"[A-Za-z]+_\d+", s):
scopes = re.split(r"_(\d+)", s)
else:
scopes = [s]
if scopes[0] == "kernel" or scopes[0] == "gamma":
p = getattr(p, "weight")
elif scopes[0] == "output_bias" or scopes[0] == "beta":
p = getattr(p, "bias")
elif scopes[0] == "output_weights":
p = getattr(p, "weight")
elif scopes[0] == "squad":
p = getattr(p, "classifier")
else:
p = getattr(p, scopes[0])
if len(scopes) >= 2:
num = int(scopes[1])
p = p[num]
if s.endswith("_embeddings"):
p = getattr(p, "weight")
elif s == "kernel":
array = np.transpose(array)
assert p.shape == array.shape
p.data = torch.from_numpy(array)
except AttributeError as e:
print(f"Skipping {original_name}", name, e)
continue
return model
def to_pytorch(src_path, cfg_path, save_path, discriminator_or_generator):
cfg = PreTrained.from_json_file(cfg_path)
print(f"Building from config: {cfg}")
if discriminator_or_generator == "discriminator":
m = ForPreTraining(cfg)
else:
assert discriminator_or_generator == "generator"
m = ForMasked(cfg)
load_src_weights(m, src_path, discriminator_or_generator)
print(f"Saving to: {save_path}")
torch.save(m.state_dict(), save_path)
if __name__ == "__main__":
x = ArgumentParser()
x.add_argument("--src_path", default=None, type=str, required=True)
x.add_argument("--cfg_path", default=None, type=str, required=True)
x.add_argument("--save_path", default=None, type=str, required=True)
x.add_argument("--discriminator_or_generator", default=None, type=str, required=True)
y = x.parse_args()
to_pytorch(y.src_path, y.cfg_path, y.save_path, y.discriminator_or_generator)
|
{"/qnarre/prep/convert/xlnet.py": ["/qnarre/prep/config/xlnet.py", "/qnarre/models/xlnet.py"], "/qnarre/prep/convert/bert.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/base/doc/patcher.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/nominals.py"], "/qnarre/prep/convert/funnel.py": ["/qnarre/prep/config/funnel.py", "/qnarre/models/funnel.py"], "/qnarre/prep/tokens/fast/realm.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py", "/qnarre/tokens/utils.py"], "/qnarre/models/decision_transfo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/decision_transfo.py"], "/qnarre/models/fsmt.py": ["/qnarre/core/embed.py"], "/qnarre/models/roformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/xlnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc.py": ["/qnarre/base/author.py", "/qnarre/base/named.py"], 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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,589
|
quantapix/qnarre
|
refs/heads/main
|
/tools/triton/python/test/unit/runtime/test_subproc.py
|
import multiprocessing
import os
import shutil
from collections import namedtuple
import torch
import triton
import triton.language as tl
tmpdir = ".tmp"
def reset_tmp_dir():
os.environ["TRITON_CACHE_DIR"] = tmpdir
if os.path.exists(tmpdir):
shutil.rmtree(tmpdir)
instance_descriptor = namedtuple("instance_descriptor", ["divisible_by_16", "equal_to_1"])
def compile_fn(config, cc):
@triton.jit
def kernel_sub(a, b, o, N: tl.constexpr):
idx = tl.arange(0, N)
tl.store(o + idx, tl.load(a + idx) - tl.load(b + idx) * 777)
triton.compile(
fn=kernel_sub,
signature={0: "*fp32", 1: "*fp32", 2: "*fp32"},
device=0,
constants={3: 32},
configs=[config],
warm_cache_only=True,
cc=cc,
)
def test_compile_in_subproc() -> None:
major, minor = torch.cuda.get_device_capability(0)
cc = major * 10 + minor
config = instance_descriptor(tuple(range(4)), ())
multiprocessing.set_start_method('fork')
proc = multiprocessing.Process(
target=compile_fn,
args=(config, cc))
proc.start()
proc.join()
assert proc.exitcode == 0
def compile_fn_dot(config, cc):
@triton.jit
def kernel_dot(Z):
offs = tl.arange(0, 16)[:, None] * 16 + tl.arange(0, 16)[None, :]
z = tl.load(Z + offs)
z = tl.dot(z, z)
tl.store(Z + offs, z)
triton.compile(
fn=kernel_dot,
signature={0: "*fp32"},
device=0,
configs=[config],
warm_cache_only=True,
cc=cc,
)
def test_compile_in_forked_subproc() -> None:
reset_tmp_dir()
major, minor = torch.cuda.get_device_capability(0)
cc = major * 10 + minor
config = instance_descriptor(tuple(range(1)), ())
assert multiprocessing.get_start_method() == 'fork'
proc = multiprocessing.Process(
target=compile_fn_dot,
args=(config, cc))
proc.start()
proc.join()
assert proc.exitcode == 0
|
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"/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], 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"/qnarre/base/doc/dispatch.py": ["/qnarre/base/doc/blog.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/context.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/analyzer.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/images.py"], "/qnarre/models/big_bird.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/big_bird.py"], "/tools/triton/python/triton/runtime/jit.py": ["/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/fast/roberta.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/bigbird.py": ["/qnarre/prep/config/big_bird.py", "/qnarre/models/big_bird.py"], "/qnarre/models/gpt_neo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/gpt_neo.py"], "/qnarre/prep/convert/t5.py": ["/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/prep/tokens/fsmt.py": 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["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,590
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/models/luke.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
import torch
import torch.utils.checkpoint
import math
from dataclasses import dataclass
from torch import nn
from torch.nn import functional as F
from transformers.utils import logging
from .. import core as qc
from ..core import utils as qu
from ..core import output as qo
from ..core import attention as qa
from ..core.embed import Embed
from ..core.mlp import Classifier, MLP, Predictor, Pool
from ..prep.config.bert import PreTrained
log = logging.get_logger(__name__)
from ...pytorch_utils import apply_chunking_to_forward
from ...utils import ModelOutput
LIST = [
"studio-ousia/luke-base",
"studio-ousia/luke-large",
]
@dataclass
class BaseLukeModelOutputWithPooling(BaseModelOutputWithPooling):
entity_last_hidden_state = None
entity_hidden_states = None
@dataclass
class BaseLukeModelOutput(BaseModelOutput):
entity_last_hidden_state = None
entity_hidden_states = None
@dataclass
class EntityClassificationOutput(ModelOutput):
loss = None
logits = None
hiddens = None
entity_hidden_states = None
attns = None
@dataclass
class EntityPairClassificationOutput(ModelOutput):
loss = None
logits = None
hiddens = None
entity_hidden_states = None
attns = None
@dataclass
class EntitySpanClassificationOutput(ModelOutput):
loss = None
logits = None
hiddens = None
entity_hidden_states = None
attns = None
class LukeEmbeddings(qc.Module):
def __init__(self, config):
super().__init__()
self.word_embeddings = qc.Embed(config.s_vocab, config.d_model, padding_idx=config.PAD)
self.position_embeddings = qc.Embed(config.n_pos, config.d_model)
self.token_type_embeddings = qc.Embed(config.n_typ, config.d_model)
self.norm = qc.LayerNorm(config.d_model, eps=config.eps)
self.drop = qc.Dropout(config.drop)
self.padding_idx = config.PAD
self.position_embeddings = qc.Embed(
config.n_pos, config.d_model, padding_idx=self.padding_idx
)
def forward(
self,
input_ids=None,
token_type_ids=None,
position_ids=None,
inputs_embeds=None,
):
if position_ids is None:
if input_ids is not None:
# Create the position ids from the input token ids. Any padded tokens remain padded.
position_ids = create_position_ids_from_input_ids(input_ids, self.padding_idx).to(
input_ids.device
)
else:
position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds)
if input_ids is not None:
input_shape = input_ids.size()
else:
input_shape = inputs_embeds.size()[:-1]
if token_type_ids is None:
token_type_ids = torch.zeros(
input_shape, dtype=torch.long, device=self.position_ids.device
)
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
position_embeddings = self.position_embeddings(position_ids)
token_type_embeddings = self.token_type_embeddings(token_type_ids)
embeddings = inputs_embeds + position_embeddings + token_type_embeddings
embeddings = self.norm(embeddings)
embeddings = self.drop(embeddings)
return embeddings
def create_position_ids_from_inputs_embeds(self, inputs_embeds):
input_shape = inputs_embeds.size()[:-1]
sequence_length = input_shape[1]
position_ids = torch.arange(
self.padding_idx + 1,
sequence_length + self.padding_idx + 1,
dtype=torch.long,
device=inputs_embeds.device,
)
return position_ids.unsqueeze(0).expand(input_shape)
class LukeEntityEmbeddings(qc.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.entity_embeddings = qc.Embed(
config.entity_vocab_size, config.entity_emb_size, padding_idx=0
)
if config.entity_emb_size != config.d_model:
self.entity_embedding_dense = qc.Linear(
config.entity_emb_size, config.d_model, bias=False
)
self.position_embeddings = qc.Embed(config.n_pos, config.d_model)
self.token_type_embeddings = qc.Embed(config.n_typ, config.d_model)
self.norm = qc.LayerNorm(config.d_model, eps=config.eps)
self.drop = qc.Dropout(config.drop)
def forward(
self,
entity_ids,
position_ids,
token_type_ids=None,
):
if token_type_ids is None:
token_type_ids = torch.zeros_like(entity_ids)
entity_embeddings = self.entity_embeddings(entity_ids)
if self.config.entity_emb_size != self.config.d_model:
entity_embeddings = self.entity_embedding_dense(entity_embeddings)
position_embeddings = self.position_embeddings(position_ids.clamp(min=0))
position_embedding_mask = (position_ids != -1).type_as(position_embeddings).unsqueeze(-1)
position_embeddings = position_embeddings * position_embedding_mask
position_embeddings = torch.sum(position_embeddings, dim=-2)
position_embeddings = position_embeddings / position_embedding_mask.sum(dim=-2).clamp(
min=1e-7
)
token_type_embeddings = self.token_type_embeddings(token_type_ids)
embeddings = entity_embeddings + position_embeddings + token_type_embeddings
embeddings = self.norm(embeddings)
embeddings = self.drop(embeddings)
return embeddings
class LukeSelfAttention(qc.Module):
def __init__(self, config):
super().__init__()
if config.d_model % config.n_heads != 0 and not hasattr(config, "d_embed"):
raise ValueError(
f"The hidden size {config.d_model,} is not a multiple of the number of attention "
f"heads {config.n_heads}."
)
self.n_heads = config.n_heads
self.attention_head_size = int(config.d_model / config.n_heads)
self.all_head_size = self.n_heads * self.attention_head_size
self.use_entity_aware_attention = config.use_entity_aware_attention
self.query = qc.Linear(config.d_model, self.all_head_size)
self.key = qc.Linear(config.d_model, self.all_head_size)
self.value = qc.Linear(config.d_model, self.all_head_size)
if self.use_entity_aware_attention:
self.w2e_query = qc.Linear(config.d_model, self.all_head_size)
self.e2w_query = qc.Linear(config.d_model, self.all_head_size)
self.e2e_query = qc.Linear(config.d_model, self.all_head_size)
self.drop = qc.Dropout(config.drop_attn)
def transpose_for_scores(self, x):
new_x_shape = x.size()[:-1] + (self.n_heads, self.attention_head_size)
x = x.view(*new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(
self,
word_model_states,
entity_hidden_states,
attention_mask=None,
head_mask=None,
output_attentions=False,
):
word_size = word_model_states.size(1)
if entity_hidden_states is None:
concat_hidden_states = word_model_states
else:
concat_hidden_states = torch.cat([word_model_states, entity_hidden_states], dim=1)
key_layer = self.transpose_for_scores(self.key(concat_hidden_states))
value_layer = self.transpose_for_scores(self.value(concat_hidden_states))
if self.use_entity_aware_attention and entity_hidden_states is not None:
# compute query vectors using word-word (w2w), word-entity (w2e), entity-word (e2w), entity-entity (e2e)
# query layers
w2w_query_layer = self.transpose_for_scores(self.query(word_model_states))
w2e_query_layer = self.transpose_for_scores(self.w2e_query(word_model_states))
e2w_query_layer = self.transpose_for_scores(self.e2w_query(entity_hidden_states))
e2e_query_layer = self.transpose_for_scores(self.e2e_query(entity_hidden_states))
# compute w2w, w2e, e2w, and e2e key vectors used with the query vectors computed above
w2w_key_layer = key_layer[:, :, :word_size, :]
e2w_key_layer = key_layer[:, :, :word_size, :]
w2e_key_layer = key_layer[:, :, word_size:, :]
e2e_key_layer = key_layer[:, :, word_size:, :]
# compute attention scores based on the dot product between the query and key vectors
w2w_attention_scores = torch.matmul(w2w_query_layer, w2w_key_layer.transpose(-1, -2))
w2e_attention_scores = torch.matmul(w2e_query_layer, w2e_key_layer.transpose(-1, -2))
e2w_attention_scores = torch.matmul(e2w_query_layer, e2w_key_layer.transpose(-1, -2))
e2e_attention_scores = torch.matmul(e2e_query_layer, e2e_key_layer.transpose(-1, -2))
# combine attention scores to create the final attention score matrix
word_attention_scores = torch.cat([w2w_attention_scores, w2e_attention_scores], dim=3)
entity_attention_scores = torch.cat([e2w_attention_scores, e2e_attention_scores], dim=3)
attention_scores = torch.cat([word_attention_scores, entity_attention_scores], dim=2)
else:
query_layer = self.transpose_for_scores(self.query(concat_hidden_states))
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in LukeModel forward() function)
attention_scores = attention_scores + attention_mask
# Normalize the attention scores to probabilities.
attention_probs = F.softmax(attention_scores, dim=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.drop(attention_probs)
# Mask heads if we want to
if head_mask is not None:
attention_probs = attention_probs * head_mask
context_layer = torch.matmul(attention_probs, value_layer)
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(*new_context_layer_shape)
output_word_model_states = context_layer[:, :word_size, :]
if entity_hidden_states is None:
output_entity_hidden_states = None
else:
output_entity_hidden_states = context_layer[:, word_size:, :]
if output_attentions:
outputs = (output_word_model_states, output_entity_hidden_states, attention_probs)
else:
outputs = (output_word_model_states, output_entity_hidden_states)
return outputs
# Copied from transformers.models.bert.modeling_bert.BertSelfOutput
class LukeSelfOutput(qc.Module):
def __init__(self, config):
super().__init__()
self.dense = qc.Linear(config.d_model, config.d_model)
self.norm = qc.LayerNorm(config.d_model, eps=config.eps)
self.drop = qc.Dropout(config.drop)
def forward(self, hiddens, input_tensor):
hiddens = self.dense(hiddens)
hiddens = self.drop(hiddens)
hiddens = self.norm(hiddens + input_tensor)
return hiddens
class Attention(qc.Module):
def __init__(self, config):
super().__init__()
self.self = LukeSelfAttention(config)
self.output = LukeSelfOutput(config)
def forward(
self,
word_model_states,
entity_hidden_states,
attention_mask=None,
head_mask=None,
output_attentions=False,
):
word_size = word_model_states.size(1)
self_outputs = self.self(
word_model_states,
entity_hidden_states,
attention_mask,
head_mask,
output_attentions,
)
if entity_hidden_states is None:
concat_self_outputs = self_outputs[0]
concat_hidden_states = word_model_states
else:
concat_self_outputs = torch.cat(self_outputs[:2], dim=1)
concat_hidden_states = torch.cat([word_model_states, entity_hidden_states], dim=1)
attention_output = self.output(concat_self_outputs, concat_hidden_states)
word_attention_output = attention_output[:, :word_size, :]
if entity_hidden_states is None:
entity_attention_output = None
else:
entity_attention_output = attention_output[:, word_size:, :]
# add attns if we output them
outputs = (word_attention_output, entity_attention_output) + self_outputs[2:]
return outputs
# Copied from transformers.models.bert.modeling_bert.BertIntermediate
class LukeIntermediate(qc.Module):
def __init__(self, cfg):
super().__init__()
self.dense = qc.Linear(cfg.d_model, cfg.d_ff)
self.act = qu.activation(cfg.act)
def forward(self, x):
y = self.dense(x)
y = self.act(y)
return y
# Copied from transformers.models.bert.modeling_bert.BertOutput
class LukeOutput(qc.Module):
def __init__(self, config):
super().__init__()
self.dense = qc.Linear(config.d_ff, config.d_model)
self.norm = qc.LayerNorm(config.d_model, eps=config.eps)
self.drop = qc.Dropout(config.drop)
def forward(self, hiddens, input_tensor):
hiddens = self.dense(hiddens)
hiddens = self.drop(hiddens)
hiddens = self.norm(hiddens + input_tensor)
return hiddens
class Layer(qc.Module):
def __init__(self, config):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = Attention(config)
self.intermediate = LukeIntermediate(config)
self.output = LukeOutput(config)
def forward(
self,
word_model_states,
entity_hidden_states,
attention_mask=None,
head_mask=None,
output_attentions=False,
):
word_size = word_model_states.size(1)
self_attention_outputs = self.attention(
word_model_states,
entity_hidden_states,
attention_mask,
head_mask,
output_attentions=output_attentions,
)
if entity_hidden_states is None:
concat_attention_output = self_attention_outputs[0]
else:
concat_attention_output = torch.cat(self_attention_outputs[:2], dim=1)
outputs = self_attention_outputs[2:] # add self attns if we output attention weights
layer_output = apply_chunking_to_forward(
self.feed_forward_chunk,
self.chunk_size_feed_forward,
self.seq_len_dim,
concat_attention_output,
)
word_layer_output = layer_output[:, :word_size, :]
if entity_hidden_states is None:
entity_layer_output = None
else:
entity_layer_output = layer_output[:, word_size:, :]
outputs = (word_layer_output, entity_layer_output) + outputs
return outputs
def feed_forward_chunk(self, attention_output):
intermediate_output = self.intermediate(attention_output)
layer_output = self.output(intermediate_output, attention_output)
return layer_output
class Encoder(qc.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([Layer(config) for _ in range(config.n_lays)])
self.gradient_checkpointing = False
def forward(
self,
word_model_states,
entity_hidden_states,
attention_mask=None,
head_mask=None,
output_attentions=False,
output_hidden_states=False,
return_dict=True,
):
all_word_model_states = () if output_hidden_states else None
all_entity_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
for i, layer_module in enumerate(self.layer):
if output_hidden_states:
all_word_model_states = all_word_model_states + (word_model_states,)
all_entity_hidden_states = all_entity_hidden_states + (entity_hidden_states,)
layer_head_mask = head_mask[i] if head_mask is not None else None
if self.gradient_checkpointing and self.training:
def create_custom_forward(module):
def custom_forward(*inputs):
return module(*inputs, output_attentions)
return custom_forward
layer_outputs = torch.utils.checkpoint.checkpoint(
create_custom_forward(layer_module),
word_model_states,
entity_hidden_states,
attention_mask,
layer_head_mask,
)
else:
layer_outputs = layer_module(
word_model_states,
entity_hidden_states,
attention_mask,
layer_head_mask,
output_attentions,
)
word_model_states = layer_outputs[0]
if entity_hidden_states is not None:
entity_hidden_states = layer_outputs[1]
if output_attentions:
all_self_attentions = all_self_attentions + (layer_outputs[2],)
if output_hidden_states:
all_word_model_states = all_word_model_states + (word_model_states,)
all_entity_hidden_states = all_entity_hidden_states + (entity_hidden_states,)
if not return_dict:
return tuple(
v
for v in [
word_model_states,
all_word_model_states,
all_self_attentions,
entity_hidden_states,
all_entity_hidden_states,
]
if v is not None
)
return BaseLukeModelOutput(
y=word_model_states,
hiddens=all_word_model_states,
attns=all_self_attentions,
entity_last_hidden_state=entity_hidden_states,
entity_hidden_states=all_entity_hidden_states,
)
class EntityPredictionHeadTransform(qc.Module):
def __init__(self, cfg):
super().__init__()
self.dense = qc.Linear(cfg.d_model, cfg.entity_emb_size)
self.act = qu.activation(cfg.act)
self.norm = qc.LayerNorm(cfg.entity_emb_size, eps=cfg.eps)
def forward(self, x):
y = self.dense(x)
y = self.act(y)
y = self.norm(y)
return y
class EntityPredictionHead(qc.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.transform = EntityPredictionHeadTransform(config)
self.decoder = qc.Linear(config.entity_emb_size, config.entity_vocab_size, bias=False)
self.bias = nn.Parameter(torch.zeros(config.entity_vocab_size))
def forward(self, hiddens):
hiddens = self.transform(hiddens)
hiddens = self.decoder(hiddens) + self.bias
return hiddens
class Model(PreTrained):
_keys_to_ignore_on_load_missing = [r"position_ids"]
def __init__(self, config, add_pooling_layer=True):
super().__init__(config)
self.config = config
self.embeddings = LukeEmbeddings(config)
self.entity_embeddings = LukeEntityEmbeddings(config)
self.encoder = Encoder(config)
self.pool = Pool(config) if add_pooling_layer else None
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
entity_ids=None,
entity_attention_mask=None,
entity_token_type_ids=None,
entity_position_ids=None,
head_mask=None,
inputs_embeds=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
output_attentions = (
output_attentions if output_attentions is not None else self.config.output_attentions
)
output_hidden_states = (
output_hidden_states
if output_hidden_states is not None
else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
input_shape = input_ids.size()
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
batch_size, seq_length = input_shape
device = input_ids.device if input_ids is not None else inputs_embeds.device
if attention_mask is None:
attention_mask = torch.ones((batch_size, seq_length), device=device)
if token_type_ids is None:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
if entity_ids is not None:
entity_seq_length = entity_ids.size(1)
if entity_attention_mask is None:
entity_attention_mask = torch.ones((batch_size, entity_seq_length), device=device)
if entity_token_type_ids is None:
entity_token_type_ids = torch.zeros(
(batch_size, entity_seq_length), dtype=torch.long, device=device
)
head_mask = self.get_head_mask(head_mask, self.config.n_lays)
# First, compute word embeddings
word_embedding_output = self.embeddings(
input_ids=input_ids,
position_ids=position_ids,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
)
# Second, compute extended attention mask
extended_attention_mask = self.get_extended_attention_mask(
attention_mask, entity_attention_mask
)
# Third, compute entity embeddings and concatenate with word embeddings
if entity_ids is None:
entity_embedding_output = None
else:
entity_embedding_output = self.entity_embeddings(
entity_ids, entity_position_ids, entity_token_type_ids
)
# Fourth, send embeddings through the model
encoder_outputs = self.encoder(
word_embedding_output,
entity_embedding_output,
attention_mask=extended_attention_mask,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
# Fifth, get the output. LukeModel outputs the same as BertModel, namely sequence_output of shape (batch_size, seq_len, d_model)
sequence_output = encoder_outputs[0]
# Sixth, we compute the pooled_output, word_sequence_output and entity_sequence_output based on the sequence_output
pooled_output = self.pool(sequence_output) if self.pool is not None else None
if not return_dict:
return (sequence_output, pooled_output) + encoder_outputs[1:]
return BaseLukeModelOutputWithPooling(
y=sequence_output,
pools=pooled_output,
hiddens=encoder_outputs.hiddens,
attns=encoder_outputs.attns,
entity_last_hidden_state=encoder_outputs.entity_last_hidden_state,
entity_hidden_states=encoder_outputs.entity_hidden_states,
)
def get_extended_attention_mask(
self,
word_attention_mask,
entity_attention_mask,
):
attention_mask = word_attention_mask
if entity_attention_mask is not None:
attention_mask = torch.cat([attention_mask, entity_attention_mask], dim=-1)
if attention_mask.dim() == 3:
extended_attention_mask = attention_mask[:, None, :, :]
elif attention_mask.dim() == 2:
extended_attention_mask = attention_mask[:, None, None, :]
else:
raise ValueError(f"Wrong shape for attention_mask (shape {attention_mask.shape})")
extended_attention_mask = extended_attention_mask.to(dtype=self.dtype) # fp16 compatibility
extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
return extended_attention_mask
def create_position_ids_from_input_ids(input_ids, padding_idx):
mask = input_ids.ne(padding_idx).int()
incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask)) * mask
return incremental_indices.long() + padding_idx
@dataclass
class WithLoss(ModelOutput):
loss = None
mlm_loss = None
mep_loss = None
logits = None
entity_logits = None
hiddens = None
entity_hidden_states = None
attns = None
class ForMasked(PreTrained):
def __init__(self, **kw):
super().__init__(**kw)
cfg = self.get_cfg(kw)
self.model = Model(add_pool=False, **kw)
self.proj = Predictor(cfg.d_model, eps=1e-12, **kw)
self.ent_proj = EntityPredictionHead(**kw)
def forward_masked(self, x, labels=None, ent_labels=None, **kw):
ys = self.model(x, **kw)
y = self.proj(ys[0])
loss, mlm = None, None
if labels is not None:
loss = mlm = nn.CrossEntropyLoss()(y.view(-1, self.cfg.s_vocab), labels.view(-1))
mep = None
y2 = self.ent_proj(ys.entity_last_hidden_state)
if ent_labels is not None:
mep = nn.CrossEntropyLoss()(y2.view(-1, self.cfg.ent_s_vocab), ent_labels.view(-1))
loss = mep if loss is None else loss + mep
ys = (y, y2) + ys[1:] + (loss, mlm, mep)
return WithLoss(*ys)
class LukeForEntityClassification(PreTrained):
def __init__(self, config):
super().__init__(config)
self.luke = Model(config)
self.n_labels = config.n_labels
self.drop = qc.Dropout(config.drop)
self.classifier = qc.Linear(config.d_model, config.n_labels)
# Initialize weights and apply final processing
self.post_init()
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
entity_ids=None,
entity_attention_mask=None,
entity_token_type_ids=None,
entity_position_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.luke(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
entity_ids=entity_ids,
entity_attention_mask=entity_attention_mask,
entity_token_type_ids=entity_token_type_ids,
entity_position_ids=entity_position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=True,
)
feature_vector = outputs.entity_last_hidden_state[:, 0, :]
feature_vector = self.drop(feature_vector)
logits = self.classifier(feature_vector)
loss = None
if labels is not None:
# When the number of dimension of `labels` is 1, cross entropy is used as the loss function. The binary
# cross entropy is used otherwise.
if labels.ndim == 1:
loss = F.cross_entropy(logits, labels)
else:
loss = F.binary_cross_entropy_with_logits(
logits.view(-1), labels.view(-1).type_as(logits)
)
if not return_dict:
output = (
logits,
outputs.hiddens,
outputs.entity_hidden_states,
outputs.attns,
)
return ((loss,) + output) if loss is not None else output
return EntityClassificationOutput(
loss=loss,
logits=logits,
hiddens=outputs.hiddens,
entity_hidden_states=outputs.entity_hidden_states,
attns=outputs.attns,
)
class LukeForEntityPairClassification(PreTrained):
def __init__(self, config):
super().__init__(config)
self.luke = Model(config)
self.n_labels = config.n_labels
self.drop = qc.Dropout(config.drop)
self.classifier = qc.Linear(config.d_model * 2, config.n_labels, False)
# Initialize weights and apply final processing
self.post_init()
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
entity_ids=None,
entity_attention_mask=None,
entity_token_type_ids=None,
entity_position_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.luke(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
entity_ids=entity_ids,
entity_attention_mask=entity_attention_mask,
entity_token_type_ids=entity_token_type_ids,
entity_position_ids=entity_position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=True,
)
feature_vector = torch.cat(
[outputs.entity_last_hidden_state[:, 0, :], outputs.entity_last_hidden_state[:, 1, :]],
dim=1,
)
feature_vector = self.drop(feature_vector)
logits = self.classifier(feature_vector)
loss = None
if labels is not None:
# When the number of dimension of `labels` is 1, cross entropy is used as the loss function. The binary
# cross entropy is used otherwise.
if labels.ndim == 1:
loss = F.cross_entropy(logits, labels)
else:
loss = F.binary_cross_entropy_with_logits(
logits.view(-1), labels.view(-1).type_as(logits)
)
if not return_dict:
output = (
logits,
outputs.hiddens,
outputs.entity_hidden_states,
outputs.attns,
)
return ((loss,) + output) if loss is not None else output
return EntityPairClassificationOutput(
loss=loss,
logits=logits,
hiddens=outputs.hiddens,
entity_hidden_states=outputs.entity_hidden_states,
attns=outputs.attns,
)
class LukeForEntitySpanClassification(PreTrained):
def __init__(self, config):
super().__init__(config)
self.luke = Model(config)
self.n_labels = config.n_labels
self.drop = qc.Dropout(config.drop)
self.classifier = qc.Linear(config.d_model * 3, config.n_labels)
# Initialize weights and apply final processing
self.post_init()
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
entity_ids=None,
entity_attention_mask=None,
entity_token_type_ids=None,
entity_position_ids=None,
entity_start_positions=None,
entity_end_positions=None,
head_mask=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.luke(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
entity_ids=entity_ids,
entity_attention_mask=entity_attention_mask,
entity_token_type_ids=entity_token_type_ids,
entity_position_ids=entity_position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=True,
)
d_model = outputs.y.size(-1)
entity_start_positions = entity_start_positions.unsqueeze(-1).expand(-1, -1, d_model)
start_states = torch.gather(outputs.y, -2, entity_start_positions)
entity_end_positions = entity_end_positions.unsqueeze(-1).expand(-1, -1, d_model)
end_states = torch.gather(outputs.y, -2, entity_end_positions)
feature_vector = torch.cat(
[start_states, end_states, outputs.entity_last_hidden_state], dim=2
)
feature_vector = self.drop(feature_vector)
logits = self.classifier(feature_vector)
loss = None
if labels is not None:
# When the number of dimension of `labels` is 2, cross entropy is used as the loss function. The binary
# cross entropy is used otherwise.
if labels.ndim == 2:
loss = F.cross_entropy(logits.view(-1, self.n_labels), labels.view(-1))
else:
loss = F.binary_cross_entropy_with_logits(
logits.view(-1), labels.view(-1).type_as(logits)
)
if not return_dict:
output = (
logits,
outputs.hiddens,
outputs.entity_hidden_states,
outputs.attns,
)
return ((loss,) + output) if loss is not None else output
return EntitySpanClassificationOutput(
loss=loss,
logits=logits,
hiddens=outputs.hiddens,
entity_hidden_states=outputs.entity_hidden_states,
attns=outputs.attns,
)
|
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"/qnarre/base/author.py", "/qnarre/base/conflict.py", "/qnarre/base/conjecture.py"], "/qnarre/models/splinter.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/rag.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/convert/albert.py": ["/qnarre/prep/config/albert.py", "/qnarre/models/albert.py"], "/qnarre/prep/tokens/fast/gpt2.py": ["/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/fast/mbart.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/roformer.py": ["/qnarre/models/roformer.py"], "/qnarre/base/doc/util/tree.py": ["/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py", "/qnarre/base/doc/util/utils.py"], "/qnarre/models/rembert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/t5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/xlm.py": ["/qnarre/tokens/utils.py"], 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"/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": ["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], 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["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,591
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/base/doc/recs.py
|
# Copyright 2019 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
# from difflib import unified_diff
from .log import Logger
from .base import config
from .edit import redact
from .chain import Chains
from .counter import counters
from .resource import Resource
from .record import EmlRec, TxtRec, MixRec, ScrRec, DocRec, PicRec
from .record import StoryRec, BlogRec
from .date import Date # needed dynamically
from .record import InlRec, FwdRec
from .header import Header
log = Logger(__name__)
src_to_cls = {
config.MBOX: EmlRec,
config.TBOX: TxtRec,
config.BBOX: MixRec,
config.SBOX: ScrRec,
config.REPO[1:-1]: DocRec,
config.BLOG[1:-1]: BlogRec,
config.MAIN[1:-1]: StoryRec,
config.PICS: PicRec,
}
class Recs(Resource):
_res_path = config.qnar_dst + 'recs.qnr'
_chains = None
@classmethod
def globals(cls):
return globals()
@property
def recs(self):
return self.elems
@property
def chains(self):
if self._chains is None:
self._chains = Chains.create(self.base, self.realm)
return self._chains
def rename_msg(self, old, new):
super().rename(old, new)
for m in self.values():
m.rename(old, new)
def save(self, pref=None):
super().save(pref)
if self._chains:
self._chains.save(pref)
def no_secs(self):
ns = {}
for r in self.values():
for d in r.zero_secs:
if d in ns:
ns[d].append(r)
else:
ns[d] = [r]
return ns
def importer(self, rec, *, no_secs, only_one=None, **kw):
if only_one is None or rec.hdr.name.startswith(only_one):
qs = []
for lv, c in rec.reducer(**kw):
if c:
for m in self.importer(c, **kw, no_secs=no_secs):
yield m
if lv == 1 and c is m:
qs.append(c.name)
if qs:
rec.hdr.quoting = tuple(qs)
rss = []
for k in rec.zero_secs:
try:
rs = no_secs[k]
except KeyError:
no_secs[k] = rs = []
else:
rec = rec.consolidate(rs, **kw)
if not rec:
return
rss.append(rs)
rec.register(**kw)
for rs in rss:
rs.append(rec)
yield rec
import_args = ((('scanned', '.'), ('excluded', '-'), ('imported', '+'),
('equal', '='), ('less', '<'), ('greater',
'>'), ('failed', 'F')), '')
def import_from(self, src, **kw):
kw.update(no_secs=self.no_secs()) # , only_one='10-10-25|22:42:44')
with counters(self.import_args, kw) as cs:
src = self.base / src
for c in src_to_cls[src.stem].importer(src, **kw):
for r in self.importer(c, **kw):
self[r.name] = r
cs.incr('+')
for r in self.values():
r.rectify(**kw)
return cs
def copy_from(self, src, editor, **kw):
with counters(self.import_args, kw) as cs:
for r in src.recs.values():
n = r.name
if n not in self:
h = Header(vars(r.hdr), **kw)
h.subject = redact(h.subject)
r = type(r)(h, r.source, editor(r.text(src)))
r.register(**kw)
self[n] = r
cs.incr('+')
else:
cs.incr('.')
return cs
def grapher(self, ctxt, types=(), **kw):
for r in self.values():
c = type(r)
if types is not None and (not types or c in types):
yield r.name, r.text(ctxt), c
yield from r.edger(**kw)
for a in (getattr(ctxt, n) for n in ('topics', 'subjects', 'sources')):
yield from a.grapher(**kw)
def chainer(self, cntr, **kw):
ts = {}
for r in self.chains.chainer(self.grapher(**kw), **kw):
ts.setdefault(r.topic(**kw), []).append(r)
cntr.incr('.')
yield from ts.items()
|
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"/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], "/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], 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"/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,592
|
quantapix/qnarre
|
refs/heads/main
|
/tools/triton/python/triton/runtime/autotuner.py
|
from __future__ import annotations
import builtins
import time
from typing import Dict
from ..testing import do_bench
from .jit import KernelInterface
class OutOfResources(Exception):
def __init__(self, required, limit, name):
self.message = f'out of resource: {name}, '\
f'Required: {required}, '\
f'Hardware limit: {limit}'
self.message += '. Reducing block sizes or `num_stages` may help.'
self.required = required
self.limit = limit
self.name = name
super().__init__(self.message)
def __reduce__(self):
# this is necessary to make CompilationError picklable
return (type(self), (self.required, self.limit, self.name))
class Autotuner(KernelInterface):
def __init__(self, fn, arg_names, configs, key, reset_to_zero, prune_configs_by: Dict = None):
'''
:param prune_configs_by: a dict of functions that are used to prune configs, fields:
'perf_model': performance model used to predicate running time with different configs, returns running time
'top_k': number of configs to bench
'prune_num_stages_by'(optional): a function used to prune num_stages. It takes configs:List[Config] as its input, and returns pruned configs.
'''
if not configs:
self.configs = [Config({}, num_warps=4, num_stages=2)]
else:
self.configs = configs
self.key_idx = [arg_names.index(k) for k in key]
self.cache = {}
# hook to reset all required tensor to zeros before relaunching a kernel
self.hook = lambda args: 0
if reset_to_zero is not None:
self.reset_idx = [arg_names.index(k) for k in reset_to_zero]
def _hook(args):
for i in self.reset_idx:
args[i].zero_()
self.hook = _hook
self.arg_names = arg_names
# prune configs
if prune_configs_by:
perf_model, top_k = prune_configs_by['perf_model'], prune_configs_by['top_k']
if 'early_config_prune' in prune_configs_by:
early_config_prune = prune_configs_by['early_config_prune']
else:
perf_model, top_k, early_config_prune = None, None, None
self.perf_model, self.configs_top_k = perf_model, top_k
self.early_config_prune = early_config_prune
self.fn = fn
def _bench(self, *args, config, **meta):
# check for conflicts, i.e. meta-parameters both provided
# as kwargs and by the autotuner
conflicts = meta.keys() & config.kwargs.keys()
if conflicts:
raise ValueError(
f"Conflicting meta-parameters: {', '.join(conflicts)}."
" Make sure that you don't re-define auto-tuned symbols."
)
# augment meta-parameters with tunable ones
current = dict(meta, **config.kwargs)
def kernel_call():
if config.pre_hook:
config.pre_hook(self.nargs)
self.hook(args)
self.fn.run(*args, num_warps=config.num_warps, num_stages=config.num_stages, **current)
try:
return do_bench(kernel_call, quantiles=(0.5, 0.2, 0.8))
except OutOfResources:
return [float('inf'), float('inf'), float('inf')]
def run(self, *args, **kwargs):
self.nargs = dict(zip(self.arg_names, args))
if len(self.configs) > 1:
all_args = {**self.nargs, **kwargs}
_args = []
for name in self.arg_names:
if name in all_args:
_args.append(all_args[name])
key = tuple(_args[i] for i in self.key_idx)
if key not in self.cache:
# prune configs
pruned_configs = self.prune_configs(kwargs)
bench_start = time.time()
timings = {config: self._bench(*args, config=config, **kwargs)
for config in pruned_configs}
bench_end = time.time()
self.bench_time = bench_end - bench_start
self.cache[key] = builtins.min(timings, key=timings.get)
self.hook(args)
self.configs_timings = timings
config = self.cache[key]
else:
config = self.configs[0]
self.best_config = config
if config.pre_hook is not None:
config.pre_hook(self.nargs)
return self.fn.run(*args, num_warps=config.num_warps, num_stages=config.num_stages, **kwargs, **config.kwargs)
def prune_configs(self, kwargs):
pruned_configs = self.configs
if self.early_config_prune:
pruned_configs = self.early_config_prune(self.configs, self.nargs)
if self.perf_model:
top_k = self.configs_top_k
if isinstance(top_k, float) and top_k <= 1.0:
top_k = int(len(self.configs) * top_k)
if len(pruned_configs) > top_k:
est_timing = {
config: self.perf_model(**self.nargs, **kwargs, **config.kwargs, num_stages=config.num_stages,
num_warps=config.num_warps)
for config in pruned_configs
}
pruned_configs = sorted(est_timing.keys(), key=lambda x: est_timing[x])[:top_k]
return pruned_configs
def warmup(self, *args, **kwargs):
self.nargs = dict(zip(self.arg_names, args))
for config in self.prune_configs(kwargs):
self.fn.warmup(
*args,
num_warps=config.num_warps,
num_stages=config.num_stages,
**kwargs,
**config.kwargs,
)
self.nargs = None
class Config:
"""
An object that represents a possible kernel configuration for the auto-tuner to try.
:ivar meta: a dictionary of meta-parameters to pass to the kernel as keyword arguments.
:type meta: dict[Str, Any]
:ivar num_warps: the number of warps to use for the kernel when compiled for GPUs. For example, if
`num_warps=8`, then each kernel instance will be automatically parallelized to
cooperatively execute using `8 * 32 = 256` threads.
:type num_warps: int
:ivar num_stages: the number of stages that the compiler should use when software-pipelining loops.
Mostly useful for matrix multiplication workloads on SM80+ GPUs.
:type num_stages: int
:ivar pre_hook: a function that will be called before the kernel is called. Parameters of this
function are args.
"""
def __init__(self, kwargs, num_warps=4, num_stages=2, pre_hook=None):
self.kwargs = kwargs
self.num_warps = num_warps
self.num_stages = num_stages
self.pre_hook = pre_hook
def __str__(self):
res = []
for k, v in self.kwargs.items():
res.append(f'{k}: {v}')
res.append(f'num_warps: {self.num_warps}')
res.append(f'num_stages: {self.num_stages}')
return ', '.join(res)
def autotune(configs, key, prune_configs_by=None, reset_to_zero=None):
"""
Decorator for auto-tuning a :code:`triton.jit`'d function.
.. highlight:: python
.. code-block:: python
@triton.autotune(configs=[
triton.Config(meta={'BLOCK_SIZE': 128}, num_warps=4),
triton.Config(meta={'BLOCK_SIZE': 1024}, num_warps=8),
],
key=['x_size'] # the two above configs will be evaluated anytime
# the value of x_size changes
)
@triton.jit
def kernel(x_ptr, x_size, **META):
BLOCK_SIZE = META['BLOCK_SIZE']
:note: When all the configurations are evaluated, the kernel will run multiple times.
This means that whatever value the kernel updates will be updated multiple times.
To avoid this undesired behavior, you can use the `reset_to_zero` argument, which
resets the value of the provided tensor to `zero` before running any configuration.
:param configs: a list of :code:`triton.Config` objects
:type configs: list[triton.Config]
:param key: a list of argument names whose change in value will trigger the evaluation of all provided configs.
:type key: list[str]
:param prune_configs_by: a dict of functions that are used to prune configs, fields:
'perf_model': performance model used to predicate running time with different configs, returns running time
'top_k': number of configs to bench
'early_config_prune'(optional): a function used to do early prune (eg, num_stages). It takes configs:List[Config] as its input, and returns pruned configs.
:param reset_to_zero: a list of argument names whose value will be reset to zero before evaluating any configs.
:type reset_to_zero: list[str]
"""
def decorator(fn):
return Autotuner(fn, fn.arg_names, configs, key, reset_to_zero, prune_configs_by)
return decorator
class Heuristics(KernelInterface):
def __init__(self, fn, arg_names, values) -> None:
self.fn = fn
self.values = values
self.arg_names = arg_names
def run(self, *args, **kwargs):
for v, heur in self.values.items():
kwargs[v] = heur({**dict(zip(self.arg_names, args)), **kwargs})
return self.fn.run(*args, **kwargs)
def heuristics(values):
"""
Decorator for specifying how the values of certain meta-parameters may be computed.
This is useful for cases where auto-tuning is prohibitevely expensive, or just not applicable.
.. highlight:: python
.. code-block:: python
@triton.heuristics(values={'BLOCK_SIZE': lambda args: 2 ** int(math.ceil(math.log2(args[1])))})
@triton.jit
def kernel(x_ptr, x_size, **META):
BLOCK_SIZE = META['BLOCK_SIZE'] # smallest power-of-two >= x_size
:param values: a dictionary of meta-parameter names and functions that compute the value of the meta-parameter.
each such function takes a list of positional arguments as input.
:type values: dict[str, Callable[[list[Any]], Any]]
"""
def decorator(fn):
return Heuristics(fn, fn.arg_names, values)
return decorator
|
{"/qnarre/prep/convert/xlnet.py": ["/qnarre/prep/config/xlnet.py", "/qnarre/models/xlnet.py"], "/qnarre/prep/convert/bert.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/base/doc/patcher.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/nominals.py"], "/qnarre/prep/convert/funnel.py": ["/qnarre/prep/config/funnel.py", "/qnarre/models/funnel.py"], "/qnarre/prep/tokens/fast/realm.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py", "/qnarre/tokens/utils.py"], "/qnarre/models/decision_transfo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/decision_transfo.py"], "/qnarre/models/fsmt.py": ["/qnarre/core/embed.py"], "/qnarre/models/roformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/xlnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc.py": ["/qnarre/base/author.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], "/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], 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["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], 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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,593
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/convert/roberta.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
import pathlib
import torch
from argparse import ArgumentParser
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncLayer
from transformers.utils import logging
from ..config.bert import PreTrained
from ...models.bert import ForMasked, ForSeqClass
logging.set_verbosity_info()
log = logging.get_logger(__name__)
SAMPLE_TEXT = "Hello world! cécé herlolip"
def to_pytorch(src_path, save_path, classification_head):
roberta = FairseqRobertaModel.from_pretrained(src_path)
roberta.eval() # disable drop
roberta_sent_encoder = roberta.model.encoder.sentence_encoder
cfg = PreTrained(
s_vocab=roberta_sent_encoder.embed_tokens.num_embeddings,
d_hidden=roberta.args.encoder_embed_dim,
n_lays=roberta.args.n_enc_lays,
n_heads=roberta.args.n_enc_heads,
d_ffnet=roberta.args.encoder_ffn_embed_dim,
n_pos=514,
n_typ=1,
norm_eps=1e-5,
)
if classification_head:
cfg.n_labels = roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]
print("Our BERT config:", cfg)
m = ForSeqClass(cfg) if classification_head else ForMasked(cfg)
m.eval()
m.roberta.embeddings.tok_embed.weight = roberta_sent_encoder.embed_tokens.weight
m.roberta.embeddings.pos_embed.weight = roberta_sent_encoder.embed_positions.weight
m.roberta.embeddings.token_type_embeddings.weight.data = torch.zeros_like(
m.roberta.embeddings.token_type_embeddings.weight
)
m.roberta.embeddings.LayerNorm.weight = roberta_sent_encoder.emb_layer_norm.weight
m.roberta.embeddings.LayerNorm.bias = roberta_sent_encoder.emb_layer_norm.bias
for i in range(cfg.n_lays):
layer = m.roberta.encoder.layer[i]
roberta_layer: TransformerSentenceEncLayer = roberta_sent_encoder.layers[i]
self_attn = layer.attention.self
assert (
roberta_layer.self_attn.k_proj.weight.data.shape
== roberta_layer.self_attn.q_proj.weight.data.shape
== roberta_layer.self_attn.v_proj.weight.data.shape
== torch.Size((cfg.d_hidden, cfg.d_hidden))
)
self_attn.query.weight.data = roberta_layer.self_attn.q_proj.weight
self_attn.query.bias.data = roberta_layer.self_attn.q_proj.bias
self_attn.key.weight.data = roberta_layer.self_attn.k_proj.weight
self_attn.key.bias.data = roberta_layer.self_attn.k_proj.bias
self_attn.value.weight.data = roberta_layer.self_attn.v_proj.weight
self_attn.value.bias.data = roberta_layer.self_attn.v_proj.bias
self_output = layer.attention.output
assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
self_output.dense.weight = roberta_layer.self_attn.out_proj.weight
self_output.dense.bias = roberta_layer.self_attn.out_proj.bias
self_output.LayerNorm.weight = roberta_layer.self_attn_layer_norm.weight
self_output.LayerNorm.bias = roberta_layer.self_attn_layer_norm.bias
intermediate = layer.intermediate
assert intermediate.dense.weight.shape == roberta_layer.fc1.weight.shape
intermediate.dense.weight = roberta_layer.fc1.weight
intermediate.dense.bias = roberta_layer.fc1.bias
bert_output = layer.output
assert bert_output.dense.weight.shape == roberta_layer.fc2.weight.shape
bert_output.dense.weight = roberta_layer.fc2.weight
bert_output.dense.bias = roberta_layer.fc2.bias
bert_output.LayerNorm.weight = roberta_layer.final_layer_norm.weight
bert_output.LayerNorm.bias = roberta_layer.final_layer_norm.bias
if classification_head:
m.classifier.dense.weight = roberta.model.classification_heads["mnli"].dense.weight
m.classifier.dense.bias = roberta.model.classification_heads["mnli"].dense.bias
m.classifier.out_proj.weight = roberta.model.classification_heads["mnli"].out_proj.weight
m.classifier.out_proj.bias = roberta.model.classification_heads["mnli"].out_proj.bias
else:
m.lm_head.dense.weight = roberta.model.encoder.lm_head.dense.weight
m.lm_head.dense.bias = roberta.model.encoder.lm_head.dense.bias
m.lm_head.layer_norm.weight = roberta.model.encoder.lm_head.layer_norm.weight
m.lm_head.layer_norm.bias = roberta.model.encoder.lm_head.layer_norm.bias
m.lm_head.decoder.weight = roberta.model.encoder.lm_head.weight
m.lm_head.decoder.bias = roberta.model.encoder.lm_head.bias
input_ids = roberta.encode(SAMPLE_TEXT).unsqueeze(0) # batch of size 1
our_output = m(input_ids)[0]
if classification_head:
their_output = roberta.model.classification_heads["mnli"](
roberta.extract_features(input_ids)
)
else:
their_output = roberta.model(input_ids)[0]
print(our_output.shape, their_output.shape)
max_absolute_diff = torch.max(torch.abs(our_output - their_output)).item()
print(f"max_absolute_diff = {max_absolute_diff}") # ~ 1e-7
success = torch.allclose(our_output, their_output, atol=1e-3)
print("Do both models output the same tensors?", "🔥" if success else "💩")
if not success:
raise Exception("Something went wRoNg")
pathlib.Path(save_path).mkdir(parents=True, exist_ok=True)
print(f"Saving model to {save_path}")
m.save_pretrained(save_path)
if __name__ == "__main__":
x = ArgumentParser()
x.add_argument("--roberta_checkpoint_path", default=None, type=str, required=True)
x.add_argument("--save_path", default=None, type=str, required=True)
x.add_argument("--classification_head", action="store_true")
y = x.parse_args()
to_pytorch(y.roberta_checkpoint_path, y.save_path, y.classification_head)
|
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"/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], "/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], 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"/qnarre/prep/convert/bert_tf2.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/tools/triton/python/triton/ops/__init__.py": ["/tools/triton/python/triton/ops/cross_entropy.py", "/tools/triton/python/triton/ops/matmul.py"], "/qnarre/core/norm.py": ["/qnarre/core/base.py"], "/qnarre/models/mpnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/author.py": ["/qnarre/base/named.py"], "/qnarre/base/doc/args.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/log.py"], "/qnarre/prep/tokens/gpt.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/distilbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/proof.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py"], "/qnarre/prep/tokens/byt5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/electra.py": ["/qnarre/prep/config/electra.py", "/qnarre/models/electra.py"], "/qnarre/models/luke.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": ["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], 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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,594
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/core/test/deduce.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
# pytest -s qnarre/neura/layers/embed_test.py
import numpy as np
import torch
from tensorflow.python.framework import test_util as tf_test_util
from tensorflow.python.keras import keras_parameterized
from tensorflow.python.keras import testing_utils
from tensorflow.python.platform import test
from tensorflow.python.training import adagrad
import qnarre.core.utils as U
from qnarre.core.embed import TokEmbed
params = dict(
PAD=0,
brackets=None,
dim_embed=4,
dim_hidden=8,
one_hot=None,
num_toks=16,
n_typ=4,
len_src=3,
len_tgt=3,
pos_max_len=None,
pos_max=1.0e4,
pos_min=1.0,
pos_start=0,
)
ps = U.Params(params).init_comps()
def test_tokembed():
e = TokEmbed(ps)
e.build((1, 5))
src = torch.constant([1, 2, 0, 3, 0], shape=(1, 5))
e.call(src)
ps.one_hot = True
e = TokEmbed(ps)
e.build((1, 5))
e.call(src)
def test_w_grad():
e = TokEmbed(ps)
e.build((None, 3))
ins = torch.constant([[0, 1, 0]], dtype="int32")
with torch.GradientTape() as tape:
out = e(ins)
print("===", out, e.weights)
gs = tape.gradient(out, e.weights)
opt = adagrad.AdagradOptimizer(0.1)
opt.apply_gradients(zip(gs, e.weights))
print("###", len(gs), 1)
"""
class EmbedTest(keras_parameterized.TestCase):
@keras_parameterized.run_all_keras_modes
def test_embedding(self):
if tf_test_util.is_gpu_available():
self.skipTest('Only test embedding on CPU.')
testing_utils.layer_test(TokEmbed,
kw={'ps': ps},
input_shape=(1, 3),
input_dtype='int32',
expected_output_dtype='float32')
@keras_parameterized.run_all_keras_modes
def test_correctness(self):
lay = TokEmbed(ps)
mod = torch.Sequential([lay])
lay.set_weights([np.array([[1, 1], [2, 2]])])
mod.run_eagerly = testing_utils.should_run_eagerly()
outputs = mod.predict(np.array([[0, 1, 0]], dtype='int32'))
self.assertAllClose(outputs, [[[1, 1], [2, 2], [1, 1]]])
@tf_test_util.run_in_graph_and_eager_modes
def test_eager_gpu_cpu(self):
lay = TokEmbed(ps)
lay.build((None, 2))
ins = torch.constant([[0, 1, 0]], dtype='int32')
with torch.GradientTape() as tape:
out = lay(ins)
gs = tape.gradient(out, lay.weights)
opt = adagrad.AdagradOptimizer(0.1)
opt.apply_gradients(zip(gs, lay.weights))
self.assertAllEqual(len(gs), 1)
if __name__ == '__main__':
test.main()
"""
|
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"/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], 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"/qnarre/base/author.py", "/qnarre/base/conflict.py", "/qnarre/base/conjecture.py"], "/qnarre/models/splinter.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/rag.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/convert/albert.py": ["/qnarre/prep/config/albert.py", "/qnarre/models/albert.py"], "/qnarre/prep/tokens/fast/gpt2.py": ["/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/fast/mbart.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/roformer.py": ["/qnarre/models/roformer.py"], "/qnarre/base/doc/util/tree.py": ["/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py", "/qnarre/base/doc/util/utils.py"], "/qnarre/models/rembert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/t5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/xlm.py": ["/qnarre/tokens/utils.py"], 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"/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": ["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,595
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/tokens/fast/gpt_neox.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
import json
from tokenizers import pre_tokenizers
from ....tokens.fast import PreTrainedTokenizerFast
VOCAB_FS = {
"vocab_file": "vocab.json",
"merges_file": "merges.txt",
"tokenizer_file": "tokenizer.json",
}
VOCAB_MAP = {
"tokenizer_file": {
"EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json",
},
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"gpt-neox-20b": 2048,
}
class Tokenizer(PreTrainedTokenizerFast):
vocab_files_names = VOCAB_FS
pretrained_vocab_files_map = VOCAB_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
model_input_names = ["input_ids", "mask"]
def __init__(
self,
vocab_file=None,
merges_file=None,
tokenizer_file=None,
unk_token="<|endoftext|>",
bos_token="<|endoftext|>",
eos_token="<|endoftext|>",
add_prefix_space=False,
**kw,
):
super().__init__(
vocab_file,
merges_file,
tokenizer_file=tokenizer_file,
unk_token=unk_token,
bos_token=bos_token,
eos_token=eos_token,
add_prefix_space=add_prefix_space,
**kw,
)
pre_tok_state = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
if pre_tok_state.get("add_prefix_space", add_prefix_space) != add_prefix_space:
pre_tok_class = getattr(pre_tokenizers, pre_tok_state.pop("type"))
pre_tok_state["add_prefix_space"] = add_prefix_space
self.backend_tokenizer.pre_tokenizer = pre_tok_class(**pre_tok_state)
self.add_prefix_space = add_prefix_space
def save_vocabulary(self, save_directory: str, filename_prefix=None):
files = self._tokenizer.model.save(save_directory, name=filename_prefix)
return tuple(files)
def _build_conversation_input_ids(self, conversation):
input_ids = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(text, add_special_tokens=False) + [self.EOS])
if len(input_ids) > self.model_max_length:
input_ids = input_ids[-self.model_max_length :]
return input_ids
|
{"/qnarre/prep/convert/xlnet.py": ["/qnarre/prep/config/xlnet.py", "/qnarre/models/xlnet.py"], "/qnarre/prep/convert/bert.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/base/doc/patcher.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/nominals.py"], "/qnarre/prep/convert/funnel.py": ["/qnarre/prep/config/funnel.py", "/qnarre/models/funnel.py"], "/qnarre/prep/tokens/fast/realm.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py", "/qnarre/tokens/utils.py"], "/qnarre/models/decision_transfo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/decision_transfo.py"], "/qnarre/models/fsmt.py": ["/qnarre/core/embed.py"], "/qnarre/models/roformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/xlnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc.py": ["/qnarre/base/author.py", "/qnarre/base/named.py"], 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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,596
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/tokens/rag.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
import os
import warnings
from contextlib import contextmanager
from .configuration_rag import RagConfig
class Tokenizer:
def __init__(self, question_encoder, generator):
self.question_encoder = question_encoder
self.generator = generator
self.current_tokenizer = self.question_encoder
def save_pretrained(self, dir):
if os.path.isfile(dir):
raise ValueError(f"Provided path ({dir}) should be a directory, not a file")
os.makedirs(dir, exist_ok=True)
question_encoder_path = os.path.join(dir, "question_encoder_tokenizer")
generator_path = os.path.join(dir, "generator_tokenizer")
self.question_encoder.save_pretrained(question_encoder_path)
self.generator.save_pretrained(generator_path)
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, **kw):
# dynamically import AutoTokenizer
from ..auto.tokenization_auto import AutoTokenizer
config = kw.pop("config", None)
if config is None:
config = RagConfig.from_pretrained(pretrained_model_name_or_path)
question_encoder = AutoTokenizer.from_pretrained(
pretrained_model_name_or_path,
config=config.question_encoder,
subfolder="question_encoder_tokenizer",
)
generator = AutoTokenizer.from_pretrained(
pretrained_model_name_or_path, config=config.generator, subfolder="generator_tokenizer"
)
return cls(question_encoder=question_encoder, generator=generator)
def __call__(self, *args, **kw):
return self.current_tokenizer(*args, **kw)
def batch_decode(self, *args, **kw):
return self.generator.batch_decode(*args, **kw)
def decode(self, *args, **kw):
return self.generator.decode(*args, **kw)
@contextmanager
def as_target_tokenizer(self):
self.current_tokenizer = self.generator
yield
self.current_tokenizer = self.question_encoder
def prepare_seq2seq_batch(
self,
src_texts,
tgt_texts=None,
max_length=None,
max_target_length=None,
padding="longest",
return_tensors=None,
truncation=True,
**kw,
):
warnings.warn(
"`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the "
"regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` "
"context manager to prepare your targets. See the documentation of your specific tokenizer for more "
"details",
FutureWarning,
)
if max_length is None:
max_length = self.current_tokenizer.model_max_length
model_inputs = self(
src_texts,
add_special_tokens=True,
return_tensors=return_tensors,
max_length=max_length,
padding=padding,
truncation=truncation,
**kw,
)
if tgt_texts is None:
return model_inputs
with self.as_target_tokenizer():
if max_target_length is None:
max_target_length = self.current_tokenizer.model_max_length
labels = self(
tgt_texts,
add_special_tokens=True,
return_tensors=return_tensors,
padding=padding,
max_length=max_target_length,
truncation=truncation,
**kw,
)
model_inputs["labels"] = labels["input_ids"]
return model_inputs
|
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"/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], "/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], 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["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,597
|
quantapix/qnarre
|
refs/heads/main
|
/tools/standalone/test/python/smoketest.py
|
# RUN: %python %s | FileCheck %s
from mlir_standalone.ir import *
from mlir_standalone.dialects import (
builtin as builtin_d,
standalone as standalone_d
)
with Context():
standalone_d.register_dialect()
module = Module.parse("""
%0 = arith.constant 2 : i32
%1 = standalone.foo %0 : i32
""")
# CHECK: %[[C:.*]] = arith.constant 2 : i32
# CHECK: standalone.foo %[[C]] : i32
print(str(module))
|
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"/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], "/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/dispatch.py": ["/qnarre/base/doc/blog.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/context.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/analyzer.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/images.py"], "/qnarre/models/big_bird.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/big_bird.py"], "/tools/triton/python/triton/runtime/jit.py": ["/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/fast/roberta.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/bigbird.py": ["/qnarre/prep/config/big_bird.py", "/qnarre/models/big_bird.py"], "/qnarre/models/gpt_neo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/gpt_neo.py"], "/qnarre/prep/convert/t5.py": ["/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/prep/tokens/fsmt.py": 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"/qnarre/base/author.py", "/qnarre/base/conflict.py", "/qnarre/base/conjecture.py"], "/qnarre/models/splinter.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/rag.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/convert/albert.py": ["/qnarre/prep/config/albert.py", "/qnarre/models/albert.py"], "/qnarre/prep/tokens/fast/gpt2.py": ["/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/fast/mbart.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/roformer.py": ["/qnarre/models/roformer.py"], "/qnarre/base/doc/util/tree.py": ["/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py", "/qnarre/base/doc/util/utils.py"], "/qnarre/models/rembert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/t5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/xlm.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/bert_tf2.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/tools/triton/python/triton/ops/__init__.py": ["/tools/triton/python/triton/ops/cross_entropy.py", "/tools/triton/python/triton/ops/matmul.py"], "/qnarre/core/norm.py": ["/qnarre/core/base.py"], "/qnarre/models/mpnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/author.py": ["/qnarre/base/named.py"], "/qnarre/base/doc/args.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/log.py"], "/qnarre/prep/tokens/gpt.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/distilbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/proof.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py"], "/qnarre/prep/tokens/byt5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/electra.py": ["/qnarre/prep/config/electra.py", "/qnarre/models/electra.py"], "/qnarre/models/luke.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": ["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", 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["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,598
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/run/qa.py
|
# Copyright 2021 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
# fine-tune for question answering
import collections
import json
import logging
import numpy as np
import os
import random
import torch
from datasets import load_metric
from torch.utils.data import DataLoader
from tqdm.auto import tqdm
from transformers import (
default_data_collator,
DataCollatorWithPadding,
AutoModelForQuestionAnswering,
EvalPrediction,
)
from ..params import TRAIN, EVAL, TEST, ALL, EACH
from ..runner import Runner as Base
from ..utils import init_array
log = logging.getLogger(__name__)
class Runner(Base):
AutoModel = AutoModelForQuestionAnswering
@property
def cols(self):
if self._cols is None:
cs = self.dataset[TRAIN].column_names
q = "question" if "question" in cs else cs[0]
c = "context" if "context" in cs else cs[1]
a = "answers" if "answers" in cs else cs[2]
self._cols = {ALL: cs, EACH: [q, c, a]}
return self._cols
@property
def tokenizer(self):
if self._tokenizer is None:
ps, t = self.params, super().tokenizer
self.pad_on_right = t.padding_side == "right"
if ps.max_seq_length > t.model_max_length:
log.warning(f"Using max_seq_length={t.model_max_length}")
self.max_seq_length = min(ps.max_seq_length, t.model_max_length)
return self._tokenizer
@property
def train_ds(self):
if self._train_ds is None:
ps, mgr, ds = self.params, self.mgr, self.dataset
y = ds[TRAIN]
if ps.max_train_samples is not None:
y = y.select(range(ps.max_train_samples))
with mgr.main_process_first():
y = y.map(
self.prep_for_train,
batched=True,
num_proc=ps.num_workers,
remove_columns=self.cols[ALL],
load_from_cache_file=not ps.overwrite_cache,
desc="Running tokenizer on train dataset",
)
for i in random.sample(range(len(y)), 3):
log.info(f"Sample {i} of the training set: {y[i]}")
self._train_ds = y
return self._train_ds
def prep_for_train(self, xs):
ps, pad_on_right = self.params, self.pad_on_right
q, c, a = self.cols[EACH]
xs[q] = [x.lstrip() for x in xs[q]]
ys = self.tokenizer(
xs[q if pad_on_right else c],
xs[c if pad_on_right else q],
truncation="only_second" if pad_on_right else "only_first",
max_len=self.max_seq_length,
stride=ps.doc_stride,
return_overflowing_tokens=True,
return_offsets_mapping=True,
padding=self.padding,
)
map = ys.pop("overflow_to_sample_mapping")
ys["start_positions"] = []
ys["end_positions"] = []
for i, offs in enumerate(ys.pop("offset_mapping")):
ins = ys["input_ids"][i]
ids = ys.sequence_ids(i)
ans = xs[a][map[i]]
cls = ins.index(self.tokenizer.cls_token_id)
por = 1 if pad_on_right else 0
if len(ans["answer_start"]) == 0:
ys["start_positions"].append(cls)
ys["end_positions"].append(cls)
else:
s = ans["answer_start"][0]
e = s + len(ans["text"][0])
j = 0
while ids[j] != por:
j += 1
k = len(ins) - 1
while ids[k] != por:
k -= 1
if not (offs[j][0] <= s and offs[k][1] >= e):
ys["start_positions"].append(cls)
ys["end_positions"].append(cls)
else:
while j < len(offs) and offs[j][0] <= s:
j += 1
ys["start_positions"].append(j - 1)
while offs[k][1] >= e:
k -= 1
ys["end_positions"].append(k + 1)
return ys
@property
def eval_ds(self):
if self._eval_ds is None:
ps, mgr = self.params, self.mgr
self.evals = y = super().eval_ds
with mgr.main_process_first():
y = y.map(
self.prep_for_eval,
batched=True,
num_proc=ps.num_workers,
remove_columns=self.cols[ALL],
load_from_cache_file=not ps.overwrite_cache,
desc="Running tokenizer on eval dataset",
)
self._eval_ds = y
return self._eval_ds
def prep_for_eval(self, xs):
ps, pad_on_right = self.params, self.pad_on_right
q, c, _ = self.cols[EACH]
xs[q] = [q.lstrip() for q in xs[q]]
ys = self.tokenizer(
xs[q if pad_on_right else c],
xs[c if pad_on_right else q],
truncation="only_second" if pad_on_right else "only_first",
max_len=self.max_seq_length,
stride=ps.doc_stride,
return_overflowing_tokens=True,
return_offsets_mapping=True,
padding=self.padding,
)
map = ys.pop("overflow_to_sample_mapping")
ys["example_id"] = []
for i in range(len(ys["input_ids"])):
ids = ys.sequence_ids(i)
pi = 1 if pad_on_right else 0
ys["example_id"].append(xs["id"][map[i]])
ys["offset_mapping"][i] = [
(v if ids[k] == pi else None) for k, v in enumerate(ys["offset_mapping"][i])
]
return ys
@property
def test_ds(self):
if self._test_ds is None:
ps, mgr = self.params, self.mgr
self.tests = y = super().test_ds
with mgr.main_process_first():
y = y.map(
self.prep_for_eval,
batched=True,
num_proc=ps.num_workers,
remove_columns=self.cols[ALL],
load_from_cache_file=not ps.overwrite_cache,
desc="Running tokenizer on test dataset",
)
self._test_ds = y
return self._test_ds
@property
def loaders(self):
if self._loaders is None:
ps, mgr = self.params, self.mgr
if ps.pad_to_max_length:
c = default_data_collator
else:
c = DataCollatorWithPadding(
self.tokenizer, pad_to_multiple_of=(8 if mgr.use_fp16 else None)
)
t = DataLoader(
self.train_ds, shuffle=True, collate_fn=c, batch_size=ps.train_batch_size
)
x = self.eval_ds.remove_columns(["example_id", "offset_mapping"])
e = DataLoader(x, collate_fn=c, batch_size=ps.eval_batch_size)
self._loaders = {TRAIN: t, EVAL: e}
if ps.do_test:
x = self.test_ds.remove_columns(["example_id", "offset_mapping"])
p = DataLoader(x, collate_fn=c, batch_size=ps.eval_batch_size)
self._loaders[TEST] = p
return self._loaders
@property
def metric(self):
if self._metric is None:
self._metric = load_metric("squad_v2" if self.ps.version_2_with_negative else "squad")
return self._metric
def prepare(self):
mgr, ls = self.mgr, self.loaders
t, e = ls[TRAIN], ls[EVAL]
self._model, self._optimizer, t, e = mgr.prepare(self.model, self.optimizer, t, e)
self._loaders = {TRAIN: t, EVAL: e, TEST: ls[TEST]}
def eval(self):
ps, mgr, ds = self.params, self.mgr
ds, src = self.eval_ds, self.loaders[EVAL]
log.info("*** Evaluating ***")
log.info(f" Num samples = {len(ds)}")
log.info(f" Batch size per device = {ps.eval_batch_size}")
sss = []
ess = []
for xs in src:
with torch.no_grad():
ys = self.model(**xs)
ss, es = ys.start_logits, ys.end_logits
if not ps.pad_to_max_length:
ss = mgr.pad_across_processes(ss, dim=1, PAD=-100)
es = mgr.pad_across_processes(es, dim=1, PAD=-100)
sss.append(mgr.gather(ss).cpu().numpy())
ess.append(mgr.gather(es).cpu().numpy())
l = max([x.shape[1] for x in sss])
ss = init_array(sss, ds, l)
es = init_array(ess, ds, l)
del sss
del ess
y = self.post_proc(self.evals, ds, (ss, es))
y = self.metric.compute(predictions=y.predictions, references=y.label_ids)
log.info(f"Evaluation metrics: {y}")
def post_proc(self, xs, features, preds, stage="eval"):
ps = self.params
ys = proc_tests(
examples=xs,
features=features,
predictions=preds,
version_2_with_negative=ps.version_2_with_negative,
n_best_size=ps.n_best_size,
max_answer_length=ps.max_answer_length,
null_score_diff_threshold=ps.null_score_diff_threshold,
out_dir=ps.out_dir,
prefix=stage,
)
if ps.version_2_with_negative:
ys = [
{"id": k, "prediction_text": v, "no_answer_probability": 0.0} for k, v in ys.items()
]
else:
ys = [{"id": k, "prediction_text": v} for k, v in ys.items()]
ids = [{"id": x["id"], "answers": x[self.cols[EACH][2]]} for x in xs]
return EvalPrediction(predictions=ys, label_ids=ids)
def test(self):
ps, mgr = self.params, self.mgr
if ps.do_test:
ds, src = self.test_ds, self.loaders[TEST]
log.info("*** Prediction ***")
log.info(f" Num samples = {len(ds)}")
log.info(f" Batch size per device = {ps.eval_batch_size}")
sss = []
ess = []
for xs in src:
with torch.no_grad():
ys = self.model(**xs)
ss, es = ys.start_logits, ys.end_logits
if not ps.pad_to_max_length:
ss = mgr.pad_across_processes(ss, dim=1, PAD=-100)
es = mgr.pad_across_processes(ss, dim=1, PAD=-100)
sss.append(mgr.gather(ss).cpu().numpy())
ess.append(mgr.gather(es).cpu().numpy())
l = max([x.shape[1] for x in sss])
ss = init_array(sss, ds, l)
es = init_array(ess, ds, l)
del sss
del ess
y = self.post_proc(self.tests, ds, (ss, es))
x = self.metric.compute(predictions=y.predictions, references=y.label_ids)
log.info(f"Prediction metrics: {x}")
def proc_tests(
examples,
features,
predictions,
version_2_with_negative=False,
n_best_size=20,
max_answer_length=30,
null_score_diff_threshold=0.0,
out_dir=None,
prefix=None,
log_level=logging.WARNING,
):
if len(predictions) != 2:
raise ValueError(
"`predictions` should be a tuple with two elements (start_logits, end_logits)."
)
all_start_logits, all_end_logits = predictions
if len(predictions[0]) != len(features):
raise ValueError(f"Got {len(predictions[0])} predictions and {len(features)} features.")
example_id_to_index = {k: i for i, k in enumerate(examples["id"])}
features_per_example = collections.defaultdict(list)
for i, feature in enumerate(features):
features_per_example[example_id_to_index[feature["example_id"]]].append(i)
all_predictions = collections.OrderedDict()
all_nbest_json = collections.OrderedDict()
if version_2_with_negative:
scores_diff_json = collections.OrderedDict()
log.setLevel(log_level)
log.info(
f"Post-processing {len(examples)} example predictions split into {len(features)} features."
)
for example_index, example in enumerate(tqdm(examples)):
feature_indices = features_per_example[example_index]
min_null_prediction = None
prelim_predictions = []
for f in feature_indices:
start_logits = all_start_logits[f]
end_logits = all_end_logits[f]
offset_mapping = features[f]["offset_mapping"]
token_is_max_context = features[f].get("token_is_max_context", None)
feature_null_score = start_logits[0] + end_logits[0]
if min_null_prediction is None or min_null_prediction["score"] > feature_null_score:
min_null_prediction = {
"offsets": (0, 0),
"score": feature_null_score,
"start_logit": start_logits[0],
"end_logit": end_logits[0],
}
start_indexes = np.argsort(start_logits)[-1 : -n_best_size - 1 : -1].tolist()
end_indexes = np.argsort(end_logits)[-1 : -n_best_size - 1 : -1].tolist()
for i in start_indexes:
for j in end_indexes:
if (
i >= len(offset_mapping)
or j >= len(offset_mapping)
or offset_mapping[i] is None
or offset_mapping[j] is None
):
continue
if j < i or j - i + 1 > max_answer_length:
continue
if token_is_max_context is not None and not token_is_max_context.get(
str(i), False
):
continue
prelim_predictions.append(
{
"offsets": (offset_mapping[i][0], offset_mapping[j][1]),
"score": start_logits[i] + end_logits[j],
"start_logit": start_logits[i],
"end_logit": end_logits[j],
}
)
if version_2_with_negative:
prelim_predictions.append(min_null_prediction)
null_score = min_null_prediction["score"]
predictions = sorted(prelim_predictions, key=lambda x: x["score"], reverse=True)[
:n_best_size
]
if version_2_with_negative and not any(p["offsets"] == (0, 0) for p in predictions):
predictions.append(min_null_prediction)
context = example["context"]
for pred in predictions:
offsets = pred.pop("offsets")
pred["text"] = context[offsets[0] : offsets[1]]
if len(predictions) == 0 or (len(predictions) == 1 and predictions[0]["text"] == ""):
predictions.insert(
0, {"text": "empty", "start_logit": 0.0, "end_logit": 0.0, "score": 0.0}
)
scores = np.array([pred.pop("score") for pred in predictions])
exp_scores = np.exp(scores - np.max(scores))
probs = exp_scores / exp_scores.sum()
for prob, pred in zip(probs, predictions):
pred["probability"] = prob
if not version_2_with_negative:
all_predictions[example["id"]] = predictions[0]["text"]
else:
i = 0
while predictions[i]["text"] == "":
i += 1
best_non_null_pred = predictions[i]
score_diff = (
null_score - best_non_null_pred["start_logit"] - best_non_null_pred["end_logit"]
)
scores_diff_json[example["id"]] = float(score_diff) # To be JSON-serializable.
if score_diff > null_score_diff_threshold:
all_predictions[example["id"]] = ""
else:
all_predictions[example["id"]] = best_non_null_pred["text"]
all_nbest_json[example["id"]] = [
{
k: (float(v) if isinstance(v, (np.float16, np.float32, np.float64)) else v)
for k, v in pred.items()
}
for pred in predictions
]
if out_dir is not None:
if not os.path.isdir(out_dir):
raise EnvironmentError(f"{out_dir} is not a directory.")
prediction_file = os.path.join(
out_dir, "predictions.json" if prefix is None else f"{prefix}_predictions.json"
)
nbest_file = os.path.join(
out_dir,
"nbest_predictions.json" if prefix is None else f"{prefix}_nbest_predictions.json",
)
if version_2_with_negative:
null_odds_file = os.path.join(
out_dir, "null_odds.json" if prefix is None else f"{prefix}_null_odds.json"
)
log.info(f"Saving predictions to {prediction_file}.")
with open(prediction_file, "w") as writer:
writer.write(json.dumps(all_predictions, indent=4) + "\n")
log.info(f"Saving nbest_preds to {nbest_file}.")
with open(nbest_file, "w") as writer:
writer.write(json.dumps(all_nbest_json, indent=4) + "\n")
if version_2_with_negative:
log.info(f"Saving null_odds to {null_odds_file}.")
with open(null_odds_file, "w") as writer:
writer.write(json.dumps(scores_diff_json, indent=4) + "\n")
return all_predictions
def main():
x = Runner()
x.dataset
x.cols
x.config
x.tokenizer
x.model
x.loaders
x.prepare()
x.train()
x.eval()
x.test()
x.save()
if __name__ == "__main__":
main()
"""
python qa.py \
--model_name bert-base-uncased \
--dataset_name squad \
--max_seq_length 384 \
--doc_stride 128 \
--out_dir ~/tmp/debug_squad
accelerate launch qa.py \
--model_name bert-base-uncased \
--dataset_name squad \
--max_seq_length 384 \
--doc_stride 128 \
--out_dir ~/tmp/debug_squad
"""
|
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"/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": ["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], 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"/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,599
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/base/doc/nominals.py
|
# Copyright 2019 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
import re
from .base import config
def para_make(txt):
ps = (p.strip() for p in txt.strip().split('\n\n') if p)
ps = (' '.join(p.split()).strip() for p in ps if p)
return '\n\n'.join(p for p in ps if p)
def para_split(txt):
return txt.split('\n\n') if txt else ()
def para_join(paras):
return '\n\n'.join(paras)
flags = r'(?aim)'
chars = r'[^a-z0-9]'
chars = re.compile(flags + chars)
nbs = r'[^<>\n]'
def nominal(txt):
t = txt.lower()
for p in tuple(re.compile(flags + p) for p in config.nominal_offs):
t = p.sub('', t)
return ''.join(chars.split(t))
def compare(left, right):
if left == right:
return config.EQ
left, right = nominal(left), nominal(right)
if left == right:
return config.EQ
if right.startswith(left):
return config.LT
if left.startswith(right):
return config.GT
fields = r'^ ?(date|subject|from|to|cc|bcc|in-reply-to|message-id): '
fields = re.compile(flags + fields)
om = r'^ ?-----Original_Message-----$'
om = re.compile(flags + om)
eom = r'^ ?-----End_Original_Message-----$'
eom = re.compile(flags + eom)
begin = r'^ ?(on .+?wrote:$)|from: '
begin = re.compile(flags + begin)
def quoter(lines, level=0):
qs = []
nqs = []
quoting = None if level else False
fwd = False
for ln in lines:
if ln.startswith('>'):
if ln.startswith('>>'):
qs.append(ln[1:])
else:
ln = ln[2:] if ln.startswith('> ') else ln[1:]
if not quoting and not fwd:
if fields.match(ln) or om.match(ln) or begin.match(ln):
fwd = True
else:
nqs.append('| ' + ln)
continue
qs.append(ln)
elif quoting:
if eom.match(ln):
quoting = False
elif not om.match(ln):
qs.append(ln)
elif om.match(ln):
quoting = True
elif begin.match(ln):
if quoting is None:
quoting = False
nqs.append(ln)
else:
quoting = True
qs.append(ln)
else:
nqs.append(ln)
nqs = '\n'.join(nqs).strip().splitlines()
if nqs:
yield level, nqs
qs = '\n'.join(qs).strip().splitlines()
if qs:
yield from quoter(qs, level=level + 1)
ow = r'(On .+?wrote:\n)|(Original_Message)'
ow = re.compile(flags + ow)
class Nominals:
def __init__(self, txts):
self.seq = ''.join(nominal(t) for t in txts)
def __contains__(self, txt):
return txt and (nominal(txt) in self.seq
or nominal(ow.split(txt, maxsplit=1)[0]) in self.seq)
def append(self, txt):
n = nominal(txt)
if n and n not in self.seq:
self.seq += n
return n
|
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"/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", 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"/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], 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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,600
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/config/ctrl.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
from ... import core as qc
class PreTrained(qc.PreTrained):
hs = qc.Hypers(
{"act_sum", "finetune"},
dict(
d_ff=8192,
d_model=1280,
drop_attn=0.1,
drop_resid=0.1,
drop_sum_first=0.1,
drop=0.1,
eps=1e-6,
init_range=0.02,
model_type="ctrl",
n_ctx=512,
n_heads=16,
n_labels=1,
n_lays=48,
n_pos=256,
s_vocab=246534,
sum_proj=True,
sum_type="cls_index",
sum_use_proj=True,
y_cache=True,
),
)
def _init_weights(self, m):
if isinstance(m, (qc.Linear, qc.Conv1D)):
m.weight.data.normal_(mean=0.0, std=self.cfg.init_range)
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, qc.Embedding):
m.weight.data.normal_(mean=0.0, std=self.cfg.init_range)
if m.padding_idx is not None:
m.weight.data[m.padding_idx].zero_()
elif isinstance(m, qc.LayerNorm):
m.bias.data.zero_()
m.weight.data.fill_(1.0)
MAP = {
"ctrl": dict(
from_tf=False,
n_pos=50000,
eps=1e-06,
torchscript=False,
use_bfloat16=False,
)
}
|
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["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,601
|
quantapix/qnarre
|
refs/heads/main
|
/notebooks/old/src/gpus.py
|
# Copyright 2019 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
# !pip install -U tf-nightly-2.0-preview
import numpy as np
import tensorflow as tf
from datetime import datetime
ks = tf.keras
kl = ks.layers
cfg = tf.config.experimental
# tf.debugging.set_log_device_placement(True)
devs = ((None, None, None, None, None),)
devs = ((None,), (1000, 1000, 1000, 1000, 1000, 1000), (1000, 1000, 1000, 1000, 1000, 1000))
cfg.set_visible_devices(cfg.get_visible_devices("CPU")[:1], "CPU")
cfg.set_visible_devices(cfg.get_visible_devices("GPU")[: len(devs) - 1], "GPU")
for d, ms in zip(cfg.get_visible_devices(), devs):
vs = [cfg.VirtualDeviceConfiguration(m) for m in ms]
cfg.set_virtual_device_configuration(d, vs)
devs = cfg.list_logical_devices("CPU")
devs += cfg.list_logical_devices("GPU")
print("devices:", [d.name for d in devs])
tf.config.set_soft_device_placement(False)
# cfg.set_device_policy('warn')
class Layer(kl.Layer):
def __init__(self, i, ps, **kw):
super().__init__(**kw)
self.idx = min(i + 1, len(devs) - 1)
self.ps = ps
def build(self, input_shape):
s = input_shape[-1]
with tf.device(devs[self.idx].name):
self.w = self.add_weight(name="l_w", shape=(s, s))
self.b = self.add_weight(name="l_b", shape=(s,))
return super().build(input_shape)
def call(self, x):
with tf.device(devs[self.idx].name):
y = tf.einsum("bi,ij->bj", x, self.w) + self.b
return y
def model_for(ps):
m = ks.Sequential()
m.add(kl.Dense(ps.dim_hidden, input_dim=ps.dim_input, name="in"))
for i in range(ps.n_lays):
m.add(Layer(i, ps, name=f"lay_{i}"))
m.add(kl.Dense(ps.dim_input, name="out"))
m.compile(optimizer=ps.optimizer(), loss=ps.loss(), metrics=[ps.metrics()])
print(m.summary())
return m
params = dict(
dim_hidden=1000,
dim_input=100,
loss=ks.losses.MeanAbsoluteError,
metrics=ks.metrics.MeanAbsoluteError,
n_lays=10,
optimizer=ks.optimizers.SGD,
)
class Params:
def __init__(self, **kw):
for k, v in kw.items():
setattr(self, k, v)
def main(_):
ps = Params(**params)
d = np.ones((100, ps.dim_input))
# with tf.distribute.MirroredStrategy().scope():
m = model_for(ps)
ld = datetime.now().strftime("%Y%m%d-%H%M%S")
ld = f"/tmp/q/logs/{ld}"
cs = [ks.callbacks.TensorBoard(log_dir=ld, histogram_freq=1)]
m.fit(d, d, callbacks=cs, epochs=10, batch_size=10)
if __name__ == "__main__":
from absl import app
app.run(main)
|
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"/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], "/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/dispatch.py": ["/qnarre/base/doc/blog.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/context.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/analyzer.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/images.py"], "/qnarre/models/big_bird.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/big_bird.py"], "/tools/triton/python/triton/runtime/jit.py": ["/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/fast/roberta.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/bigbird.py": ["/qnarre/prep/config/big_bird.py", "/qnarre/models/big_bird.py"], "/qnarre/models/gpt_neo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/gpt_neo.py"], "/qnarre/prep/convert/t5.py": ["/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/prep/tokens/fsmt.py": 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"/qnarre/base/author.py", "/qnarre/base/conflict.py", "/qnarre/base/conjecture.py"], "/qnarre/models/splinter.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/rag.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/convert/albert.py": ["/qnarre/prep/config/albert.py", "/qnarre/models/albert.py"], "/qnarre/prep/tokens/fast/gpt2.py": ["/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/fast/mbart.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/roformer.py": ["/qnarre/models/roformer.py"], "/qnarre/base/doc/util/tree.py": ["/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py", "/qnarre/base/doc/util/utils.py"], "/qnarre/models/rembert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/t5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/xlm.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/bert_tf2.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/tools/triton/python/triton/ops/__init__.py": ["/tools/triton/python/triton/ops/cross_entropy.py", "/tools/triton/python/triton/ops/matmul.py"], "/qnarre/core/norm.py": ["/qnarre/core/base.py"], "/qnarre/models/mpnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/author.py": ["/qnarre/base/named.py"], "/qnarre/base/doc/args.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/log.py"], "/qnarre/prep/tokens/gpt.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/distilbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/proof.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py"], "/qnarre/prep/tokens/byt5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/electra.py": ["/qnarre/prep/config/electra.py", "/qnarre/models/electra.py"], "/qnarre/models/luke.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": ["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,602
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/models/ibert.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
import math
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import functional as F
from transformers.utils import logging
from .. import core as qc
from ..core import utils as qu
from ..core import forward as qf
from ..core import output as qo
from ..core import attention as qa
from ..core.embed import Embed
from ..core.mlp import Classifier, MLP, Predictor, Pool
from ..prep.config.bert import PreTrained
log = logging.get_logger(__name__)
from .quant_modules import IntGELU, IntLayerNorm, IntSoftmax, QuantAct, QuantEmbedding, QuantLinear
LIST = [
"kssteven/ibert-roberta-base",
"kssteven/ibert-roberta-large",
"kssteven/ibert-roberta-large-mnli",
]
def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0):
mask = input_ids.ne(padding_idx).int()
incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask
return incremental_indices.long() + padding_idx
class IBertEmbeddings(qc.Module):
def __init__(self, config):
super().__init__()
self.quant_mode = config.quant_mode
self.embedding_bit = 8
self.embedding_act_bit = 16
self.act_bit = 8
self.ln_input_bit = 22
self.ln_output_bit = 32
self.word_embeddings = QuantEmbedding(
config.s_vocab,
config.d_model,
padding_idx=config.PAD,
weight_bit=self.embedding_bit,
quant_mode=self.quant_mode,
)
self.token_type_embeddings = QuantEmbedding(
config.n_typ,
config.d_model,
weight_bit=self.embedding_bit,
quant_mode=self.quant_mode,
)
self.register_buffer("position_ids", torch.arange(config.n_pos).expand((1, -1)))
self.pos_type = getattr(config, "pos_type", "absolute")
self.padding_idx = config.PAD
self.position_embeddings = QuantEmbedding(
config.n_pos,
config.d_model,
padding_idx=self.padding_idx,
weight_bit=self.embedding_bit,
quant_mode=self.quant_mode,
)
self.embeddings_act1 = QuantAct(self.embedding_act_bit, quant_mode=self.quant_mode)
self.embeddings_act2 = QuantAct(self.embedding_act_bit, quant_mode=self.quant_mode)
self.norm = IntLayerNorm(
config.d_model,
eps=config.eps,
output_bit=self.ln_output_bit,
quant_mode=self.quant_mode,
force_dequant=config.force_dequant,
)
self.output_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode)
self.drop = qc.Dropout(config.drop)
def forward(
self,
input_ids=None,
token_type_ids=None,
position_ids=None,
inputs_embeds=None,
past_key_values_length=0,
):
if position_ids is None:
if input_ids is not None:
position_ids = create_position_ids_from_input_ids(
input_ids, self.padding_idx, past_key_values_length
).to(input_ids.device)
else:
position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds)
if input_ids is not None:
input_shape = input_ids.size()
else:
input_shape = inputs_embeds.size()[:-1]
if token_type_ids is None:
token_type_ids = torch.zeros(
input_shape, dtype=torch.long, device=self.position_ids.device
)
if inputs_embeds is None:
inputs_embeds, inputs_embeds_scaling_factor = self.word_embeddings(input_ids)
else:
inputs_embeds_scaling_factor = None
token_type_embeddings, token_type_embeddings_scaling_factor = self.token_type_embeddings(
token_type_ids
)
embeddings, embeddings_scaling_factor = self.embeddings_act1(
inputs_embeds,
inputs_embeds_scaling_factor,
identity=token_type_embeddings,
identity_scaling_factor=token_type_embeddings_scaling_factor,
)
if self.pos_type == "absolute":
position_embeddings, position_embeddings_scaling_factor = self.position_embeddings(
position_ids
)
embeddings, embeddings_scaling_factor = self.embeddings_act1(
embeddings,
embeddings_scaling_factor,
identity=position_embeddings,
identity_scaling_factor=position_embeddings_scaling_factor,
)
embeddings, embeddings_scaling_factor = self.norm(embeddings, embeddings_scaling_factor)
embeddings = self.drop(embeddings)
embeddings, embeddings_scaling_factor = self.output_activation(
embeddings, embeddings_scaling_factor
)
return embeddings, embeddings_scaling_factor
def create_position_ids_from_inputs_embeds(self, inputs_embeds):
input_shape = inputs_embeds.size()[:-1]
sequence_length = input_shape[1]
position_ids = torch.arange(
self.padding_idx + 1,
sequence_length + self.padding_idx + 1,
dtype=torch.long,
device=inputs_embeds.device,
)
return position_ids.unsqueeze(0).expand(input_shape)
class IBertSelfAttention(qc.Module):
def __init__(self, config):
super().__init__()
if config.d_model % config.n_heads != 0 and not hasattr(config, "d_embed"):
raise ValueError(
f"The hidden size ({config.d_model}) is not a multiple of the number of attention "
f"heads ({config.n_heads})"
)
self.quant_mode = config.quant_mode
self.weight_bit = 8
self.bias_bit = 32
self.act_bit = 8
self.n_heads = config.n_heads
self.attention_head_size = int(config.d_model / config.n_heads)
self.all_head_size = self.n_heads * self.attention_head_size
# Q, K, V Linear layers
self.query = QuantLinear(
config.d_model,
self.all_head_size,
bias=True,
weight_bit=self.weight_bit,
bias_bit=self.bias_bit,
quant_mode=self.quant_mode,
per_channel=True,
)
self.key = QuantLinear(
config.d_model,
self.all_head_size,
bias=True,
weight_bit=self.weight_bit,
bias_bit=self.bias_bit,
quant_mode=self.quant_mode,
per_channel=True,
)
self.value = QuantLinear(
config.d_model,
self.all_head_size,
bias=True,
weight_bit=self.weight_bit,
bias_bit=self.bias_bit,
quant_mode=self.quant_mode,
per_channel=True,
)
self.query_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode)
self.key_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode)
self.value_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode)
self.output_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode)
self.drop = qc.Dropout(config.drop_attn)
self.pos_type = getattr(config, "pos_type", "absolute")
if self.pos_type != "absolute":
raise ValueError("I-BERT only supports 'absolute' for `config.pos_type`")
self.softmax = IntSoftmax(
self.act_bit, quant_mode=self.quant_mode, force_dequant=config.force_dequant
)
def transpose_for_scores(self, x):
new_x_shape = x.size()[:-1] + (self.n_heads, self.attention_head_size)
x = x.view(*new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(
self,
hiddens,
hidden_states_scaling_factor,
attention_mask=None,
head_mask=None,
output_attentions=False,
):
# Projection
mixed_query_layer, mixed_query_layer_scaling_factor = self.query(
hiddens, hidden_states_scaling_factor
)
mixed_key_layer, mixed_key_layer_scaling_factor = self.key(
hiddens, hidden_states_scaling_factor
)
mixed_value_layer, mixed_value_layer_scaling_factor = self.value(
hiddens, hidden_states_scaling_factor
)
# Requantization
query_layer, query_layer_scaling_factor = self.query_activation(
mixed_query_layer, mixed_query_layer_scaling_factor
)
key_layer, key_layer_scaling_factor = self.key_activation(
mixed_key_layer, mixed_key_layer_scaling_factor
)
value_layer, value_layer_scaling_factor = self.value_activation(
mixed_value_layer, mixed_value_layer_scaling_factor
)
# Transpose
query_layer = self.transpose_for_scores(query_layer)
key_layer = self.transpose_for_scores(key_layer)
value_layer = self.transpose_for_scores(value_layer)
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
scale = math.sqrt(self.attention_head_size)
attention_scores = attention_scores / scale
if self.quant_mode:
attention_scores_scaling_factor = (
query_layer_scaling_factor * key_layer_scaling_factor / scale
)
else:
attention_scores_scaling_factor = None
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in IBertModel forward() function)
attention_scores = attention_scores + attention_mask
# Normalize the attention scores to probabilities.
attention_probs, attention_probs_scaling_factor = self.softmax(
attention_scores, attention_scores_scaling_factor
)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.drop(attention_probs)
# Mask heads if we want to
if head_mask is not None:
attention_probs = attention_probs * head_mask
context_layer = torch.matmul(attention_probs, value_layer)
if attention_probs_scaling_factor is not None:
context_layer_scaling_factor = (
attention_probs_scaling_factor * value_layer_scaling_factor
)
else:
context_layer_scaling_factor = None
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(*new_context_layer_shape)
context_layer, context_layer_scaling_factor = self.output_activation(
context_layer, context_layer_scaling_factor
)
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
output_scaling_factor = (
(context_layer_scaling_factor, attention_probs_scaling_factor)
if output_attentions
else (context_layer_scaling_factor,)
)
return outputs, output_scaling_factor
class IBertSelfOutput(qc.Module):
def __init__(self, config):
super().__init__()
self.quant_mode = config.quant_mode
self.act_bit = 8
self.weight_bit = 8
self.bias_bit = 32
self.ln_input_bit = 22
self.ln_output_bit = 32
self.dense = QuantLinear(
config.d_model,
config.d_model,
bias=True,
weight_bit=self.weight_bit,
bias_bit=self.bias_bit,
quant_mode=self.quant_mode,
per_channel=True,
)
self.ln_input_act = QuantAct(self.ln_input_bit, quant_mode=self.quant_mode)
self.norm = IntLayerNorm(
config.d_model,
eps=config.eps,
output_bit=self.ln_output_bit,
quant_mode=self.quant_mode,
force_dequant=config.force_dequant,
)
self.output_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode)
self.drop = qc.Dropout(config.drop)
def forward(
self, hiddens, hidden_states_scaling_factor, input_tensor, input_tensor_scaling_factor
):
hiddens, hidden_states_scaling_factor = self.dense(hiddens, hidden_states_scaling_factor)
hiddens = self.drop(hiddens)
hiddens, hidden_states_scaling_factor = self.ln_input_act(
hiddens,
hidden_states_scaling_factor,
identity=input_tensor,
identity_scaling_factor=input_tensor_scaling_factor,
)
hiddens, hidden_states_scaling_factor = self.norm(hiddens, hidden_states_scaling_factor)
hiddens, hidden_states_scaling_factor = self.output_activation(
hiddens, hidden_states_scaling_factor
)
return hiddens, hidden_states_scaling_factor
class Attention(qc.Module):
def __init__(self, config):
super().__init__()
self.quant_mode = config.quant_mode
self.self = IBertSelfAttention(config)
self.output = IBertSelfOutput(config)
def forward(
self,
hiddens,
hidden_states_scaling_factor,
attention_mask=None,
head_mask=None,
output_attentions=False,
):
self_outputs, self_outputs_scaling_factor = self.self(
hiddens,
hidden_states_scaling_factor,
attention_mask,
head_mask,
output_attentions,
)
attention_output, attention_output_scaling_factor = self.output(
self_outputs[0],
self_outputs_scaling_factor[0],
hiddens,
hidden_states_scaling_factor,
)
outputs = (attention_output,) + self_outputs[1:] # add attns if we output them
outputs_scaling_factor = (attention_output_scaling_factor,) + self_outputs_scaling_factor[
1:
]
return outputs, outputs_scaling_factor
class IBertIntermediate(qc.Module):
def __init__(self, config):
super().__init__()
self.quant_mode = config.quant_mode
self.act_bit = 8
self.weight_bit = 8
self.bias_bit = 32
self.dense = QuantLinear(
config.d_model,
config.d_ff,
bias=True,
weight_bit=self.weight_bit,
bias_bit=self.bias_bit,
quant_mode=self.quant_mode,
per_channel=True,
)
assert config.act == "gelu"
self.intermediate_act_fn = IntGELU(
quant_mode=self.quant_mode, force_dequant=config.force_dequant
)
self.output_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode)
def forward(self, hiddens, hidden_states_scaling_factor):
hiddens, hidden_states_scaling_factor = self.dense(hiddens, hidden_states_scaling_factor)
hiddens, hidden_states_scaling_factor = self.intermediate_act_fn(
hiddens, hidden_states_scaling_factor
)
hiddens, hidden_states_scaling_factor = self.output_activation(
hiddens, hidden_states_scaling_factor
)
return hiddens, hidden_states_scaling_factor
class IBertOutput(qc.Module):
def __init__(self, config):
super().__init__()
self.quant_mode = config.quant_mode
self.act_bit = 8
self.weight_bit = 8
self.bias_bit = 32
self.ln_input_bit = 22
self.ln_output_bit = 32
self.dense = QuantLinear(
config.d_ff,
config.d_model,
bias=True,
weight_bit=self.weight_bit,
bias_bit=self.bias_bit,
quant_mode=self.quant_mode,
per_channel=True,
)
self.ln_input_act = QuantAct(self.ln_input_bit, quant_mode=self.quant_mode)
self.norm = IntLayerNorm(
config.d_model,
eps=config.eps,
output_bit=self.ln_output_bit,
quant_mode=self.quant_mode,
force_dequant=config.force_dequant,
)
self.output_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode)
self.drop = qc.Dropout(config.drop)
def forward(
self, hiddens, hidden_states_scaling_factor, input_tensor, input_tensor_scaling_factor
):
hiddens, hidden_states_scaling_factor = self.dense(hiddens, hidden_states_scaling_factor)
hiddens = self.drop(hiddens)
hiddens, hidden_states_scaling_factor = self.ln_input_act(
hiddens,
hidden_states_scaling_factor,
identity=input_tensor,
identity_scaling_factor=input_tensor_scaling_factor,
)
hiddens, hidden_states_scaling_factor = self.norm(hiddens, hidden_states_scaling_factor)
hiddens, hidden_states_scaling_factor = self.output_activation(
hiddens, hidden_states_scaling_factor
)
return hiddens, hidden_states_scaling_factor
class Layer(qc.Module):
def __init__(self, config):
super().__init__()
self.quant_mode = config.quant_mode
self.act_bit = 8
self.seq_len_dim = 1
self.attention = Attention(config)
self.intermediate = IBertIntermediate(config)
self.output = IBertOutput(config)
self.pre_intermediate_act = QuantAct(self.act_bit, quant_mode=self.quant_mode)
self.pre_output_act = QuantAct(self.act_bit, quant_mode=self.quant_mode)
def forward(
self,
hiddens,
hidden_states_scaling_factor,
attention_mask=None,
head_mask=None,
output_attentions=False,
):
self_attention_outputs, self_attention_outputs_scaling_factor = self.attention(
hiddens,
hidden_states_scaling_factor,
attention_mask,
head_mask,
output_attentions=output_attentions,
)
attention_output = self_attention_outputs[0]
attention_output_scaling_factor = self_attention_outputs_scaling_factor[0]
outputs = self_attention_outputs[1:] # add self attns if we output attention weights
layer_output, layer_output_scaling_factor = self.feed_forward_chunk(
attention_output, attention_output_scaling_factor
)
outputs = (layer_output,) + outputs
return outputs
def feed_forward_chunk(self, attention_output, attention_output_scaling_factor):
attention_output, attention_output_scaling_factor = self.pre_intermediate_act(
attention_output, attention_output_scaling_factor
)
intermediate_output, intermediate_output_scaling_factor = self.intermediate(
attention_output, attention_output_scaling_factor
)
intermediate_output, intermediate_output_scaling_factor = self.pre_output_act(
intermediate_output, intermediate_output_scaling_factor
)
layer_output, layer_output_scaling_factor = self.output(
intermediate_output,
intermediate_output_scaling_factor,
attention_output,
attention_output_scaling_factor,
)
return layer_output, layer_output_scaling_factor
class Encoder(qc.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.quant_mode = config.quant_mode
self.layer = nn.ModuleList([Layer(config) for _ in range(config.n_lays)])
def forward(
self,
hiddens,
hidden_states_scaling_factor,
attention_mask=None,
head_mask=None,
output_attentions=False,
output_hidden_states=False,
return_dict=True,
):
all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
all_cross_attentions = None # `config.add_cross_attention` is not supported
next_decoder_cache = None # `config.y_cache` is not supported
for i, layer_module in enumerate(self.layer):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hiddens,)
layer_head_mask = head_mask[i] if head_mask is not None else None
layer_outputs = layer_module(
hiddens,
hidden_states_scaling_factor,
attention_mask,
layer_head_mask,
output_attentions,
)
hiddens = layer_outputs[0]
if output_attentions:
all_self_attentions = all_self_attentions + (layer_outputs[1],)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hiddens,)
if not return_dict:
return tuple(
v
for v in [
hiddens,
next_decoder_cache,
all_hidden_states,
all_self_attentions,
all_cross_attentions,
]
if v is not None
)
return qo.CachesCrosses(
y=hiddens,
caches=next_decoder_cache,
hiddens=all_hidden_states,
attns=all_self_attentions,
crosses=all_cross_attentions,
)
class Model(PreTrained):
def __init__(self, config, add_pooling_layer=True):
super().__init__(config)
self.config = config
self.quant_mode = config.quant_mode
self.embeddings = IBertEmbeddings(config)
self.encoder = Encoder(config)
self.pool = Pool(config) if add_pooling_layer else None
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
output_attentions = (
output_attentions if output_attentions is not None else self.config.output_attentions
)
output_hidden_states = (
output_hidden_states
if output_hidden_states is not None
else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
input_shape = input_ids.size()
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
batch_size, seq_length = input_shape
device = input_ids.device if input_ids is not None else inputs_embeds.device
if attention_mask is None:
attention_mask = torch.ones(((batch_size, seq_length)), device=device)
if token_type_ids is None:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
extended_attention_mask = self.get_extended_attention_mask(
attention_mask, input_shape, device
)
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [n_heads] or [n_lays x n_heads]
# and head_mask is converted to shape [n_lays x batch x n_heads x seq_length x seq_length]
head_mask = self.get_head_mask(head_mask, self.config.n_lays)
embedding_output, embedding_output_scaling_factor = self.embeddings(
input_ids=input_ids,
position_ids=position_ids,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
)
encoder_outputs = self.encoder(
embedding_output,
embedding_output_scaling_factor,
attention_mask=extended_attention_mask,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = encoder_outputs[0]
pooled_output = self.pool(sequence_output) if self.pool is not None else None
if not return_dict:
return (sequence_output, pooled_output) + encoder_outputs[1:]
return qo.BaseWithPoolingAndCrossAttentions(
y=sequence_output,
pools=pooled_output,
caches=encoder_outputs.caches,
hiddens=encoder_outputs.hiddens,
attns=encoder_outputs.attns,
crosses=encoder_outputs.crosses,
)
class ForMasked(PreTrained):
def __init__(self, **kw):
super().__init__(**kw)
self.get_cfg(kw)
self.model = Model(add_pool=False, **kw)
self.proj = Predictor(**kw)
forward = qf.forward_masked
class ForChoice(PreTrained):
def __init__(self, config):
super().__init__(config)
self.ibert = Model(config)
self.drop = qc.Dropout(config.drop)
self.classifier = qc.Linear(config.d_model, 1)
def forward(
self,
input_ids=None,
token_type_ids=None,
attention_mask=None,
labels=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
flat_input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
flat_position_ids = (
position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
)
flat_token_type_ids = (
token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
)
flat_attention_mask = (
attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
)
flat_inputs_embeds = (
inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
if inputs_embeds is not None
else None
)
outputs = self.ibert(
flat_input_ids,
position_ids=flat_position_ids,
token_type_ids=flat_token_type_ids,
attention_mask=flat_attention_mask,
head_mask=head_mask,
inputs_embeds=flat_inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = outputs[1]
pooled_output = self.drop(pooled_output)
logits = self.classifier(pooled_output)
reshaped_logits = logits.view(-1, num_choices)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(reshaped_logits, labels)
if not return_dict:
output = (reshaped_logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return qo.WithLoss(
loss=loss,
logits=reshaped_logits,
hiddens=outputs.hiddens,
attns=outputs.attns,
)
class ForSeqClass(PreTrained):
def __init__(self, **kw):
super().__init__(**kw)
cfg = self.get_cfg(kw)
self.model = Model(add_pool=False, **kw)
self.proj = Classifier(cfg.d_model, "tanh", **kw)
forward = qf.forward_seq
class ForTokClass(PreTrained):
def __init__(self, **kw):
super().__init__(**kw)
self.get_cfg(kw)
self.model = Model(add_pool=False, **kw)
self.proj = Classifier(**kw)
forward = qf.forward_tok
class ForQA(PreTrained):
def __init__(self, **kw):
super().__init__(**kw)
cfg = self.get_cfg(kw)
self.model = Model(add_pool=False, **kw)
self.proj = qc.Linear(cfg.d_model, cfg.n_labels, **kw)
forward = qf.forward_qa
|
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"/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], "/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/dispatch.py": ["/qnarre/base/doc/blog.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/context.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/analyzer.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/images.py"], "/qnarre/models/big_bird.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/big_bird.py"], "/tools/triton/python/triton/runtime/jit.py": ["/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/fast/roberta.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/bigbird.py": ["/qnarre/prep/config/big_bird.py", "/qnarre/models/big_bird.py"], "/qnarre/models/gpt_neo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/gpt_neo.py"], "/qnarre/prep/convert/t5.py": ["/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/prep/tokens/fsmt.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/deberta.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/fast/splinter.py": ["/qnarre/tokens/fast.py"], "/qnarre/models/old/trafo.py": ["/qnarre/core/attention.py", "/qnarre/core/base.py", "/qnarre/core/mlp.py", "/qnarre/core/deduce.py", "/qnarre/core/norm.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/led.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/realm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/convbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/data2vec.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/tools/triton/python/triton/language/math.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/run/beam.py": ["/qnarre/run/qa.py"], "/tools/triton/python/triton/compiler/compiler.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/autotuner.py", 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"/qnarre/base/author.py", "/qnarre/base/conflict.py", "/qnarre/base/conjecture.py"], "/qnarre/models/splinter.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/rag.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/convert/albert.py": ["/qnarre/prep/config/albert.py", "/qnarre/models/albert.py"], "/qnarre/prep/tokens/fast/gpt2.py": ["/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/fast/mbart.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/roformer.py": ["/qnarre/models/roformer.py"], "/qnarre/base/doc/util/tree.py": ["/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py", "/qnarre/base/doc/util/utils.py"], "/qnarre/models/rembert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/t5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/xlm.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/bert_tf2.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/tools/triton/python/triton/ops/__init__.py": ["/tools/triton/python/triton/ops/cross_entropy.py", "/tools/triton/python/triton/ops/matmul.py"], "/qnarre/core/norm.py": ["/qnarre/core/base.py"], "/qnarre/models/mpnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/author.py": ["/qnarre/base/named.py"], "/qnarre/base/doc/args.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/log.py"], "/qnarre/prep/tokens/gpt.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/distilbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/proof.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py"], "/qnarre/prep/tokens/byt5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/electra.py": ["/qnarre/prep/config/electra.py", "/qnarre/models/electra.py"], "/qnarre/models/luke.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": ["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], 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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,603
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/dataset/xsum.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
import json
import os
import datasets as ds
_URL_DATA = "http://bollin.inf.ed.ac.uk/public/direct/XSUM-EMNLP18-Summary-Data-Original.tar.gz"
_URL_SPLITS = "https://raw.githubusercontent.com/EdinburghNLP/XSum/master/XSum-Dataset/XSum-TRAINING-DEV-TEST-SPLIT-90-5-5.json"
_DOC = "document"
_SUM = "summary"
_ID = "id"
_REMOVE_LINES = set(
[
"Share this with\n",
"Email\n",
"Facebook\n",
"Messenger\n",
"Twitter\n",
"Pinterest\n",
"WhatsApp\n",
"Linkedin\n",
"LinkedIn\n",
"Copy this link\n",
"These are external links and will open in a new window\n",
]
)
class Xsum(ds.GeneratorBasedBuilder):
VERSION = ds.Version("1.2.0")
def _info(self):
return ds.DatasetInfo(
description="",
citation="",
homepage="",
license="",
features=ds.Features(
{
_DOC: ds.Value("string"),
_SUM: ds.Value("string"),
_ID: ds.Value("string"),
}
),
supervised_keys=(_DOC, _SUM),
)
def _split_generators(self, mgr):
fs = mgr.download({"data": _URL_DATA, "splits": _URL_SPLITS})
return [
ds.SplitGenerator(
name=ds.Split.TRAIN,
gen_kw={
"split_path": fs["splits"],
"split_name": "train",
"data_dir": "bbc-summary-data",
"files": mgr.iter_archive(fs["data"]),
},
),
ds.SplitGenerator(
name=ds.Split.VALIDATION,
gen_kw={
"split_path": fs["splits"],
"split_name": "validation",
"data_dir": "bbc-summary-data",
"files": mgr.iter_archive(fs["data"]),
},
),
ds.SplitGenerator(
name=ds.Split.TEST,
gen_kw={
"split_path": fs["splits"],
"split_name": "test",
"data_dir": "bbc-summary-data",
"files": mgr.iter_archive(fs["data"]),
},
),
]
def _generate_examples(self, split_path, split_name, data_dir, files):
with open(split_path, "r", encoding="utf-8") as f:
split_ids = json.load(f)
split_ids = {k: set(v) for k, v in split_ids.items()}
for path, f in files:
if not split_ids[split_name]:
break
elif path.startswith(data_dir) and path.endswith(".summary"):
i = os.path.basename(path).split(".")[0]
if i in split_ids[split_name]:
split_ids[split_name].remove(i)
text = "".join(
[
line.decode("utf-8")
for line in f.readlines()
if line.decode("utf-8") not in _REMOVE_LINES and line.strip()
]
)
segs = text.split("[SN]")
yield i, {_DOC: segs[8].strip(), _SUM: segs[6].strip(), _ID: i}
|
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["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,604
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/base/doc/resource.py
|
# Copyright 2019 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
import time
import pprint as pp
import pathlib as pth
import contextlib as cl
from .log import Logger
from .base import config
from .part import Registry
log = Logger(__name__)
@cl.contextmanager
def filelock(path, timeout=None):
f = FileLock(path, timeout)
f.acquire()
yield
f.release()
class FileLock:
locked = False
def __init__(self, path, timeout=None):
self.lock = path.with_suffix('.lock')
self.timeout = timeout or 10
def acquire(self):
assert not self.locked
for _ in range(self.timeout):
try:
self.lock.touch(exist_ok=False)
except FileExistsError:
time.sleep(1)
else:
self.locked = True
return
raise TimeoutError
def release(self):
assert self.locked
self.lock.unlink()
del self.locked
def res_path(obj, pref=None):
p = pth.Path(obj._res_path)
pref = (obj._realm or pref or config.PROT) + '_'
return p.with_name(pref + p.name).with_suffix(p.suffix)
class Resource(Registry):
_realm = config.PROT
_res_path = None
@classmethod
def globals(cls):
return globals()
@classmethod
def create(cls, base, realm=None, **kw):
b = pth.Path(base)
p = b / res_path(cls, realm)
p.parent.mkdir(parents=True, exist_ok=True)
if p.exists():
i = eval(p.read_text(), cls.globals())
i._base = b
if realm:
i._realm = realm
log.info('Restored resource from {}', p)
else:
i = cls(**kw, base=b, realm=realm)
return i
def __init__(self, elems=None, base=None, realm=None, **kw):
super().__init__(elems, **kw)
self._base = base
if realm:
self._realm = realm
__hash__ = None
def __eq__(self, other):
if isinstance(other, type(self)):
return self._elems == other._elems
return NotImplemented
def __repr__(self):
es = pp.pformat(self._elems, indent=4)
return '{}({})'.format(type(self).__name__, es)
@property
def base(self):
return self._base
@property
def realm(self):
return self._realm
@property
def elems(self):
es = self._elems
return (es[k] for k in sorted(es.keys()))
res_path = res_path
def rename(self, old, new):
es = self._elems
try:
es[new] = es.pop(old)
except KeyError:
pass
# log.warning('Renaming missing in {} from {} to {}',
# type(self), old, new)
def merge(self, other):
for k, v in other.items():
if k in self:
if self[k] != v:
log.info('Values different for {}', k)
else:
self[k] = v
def save(self, pref=None):
p = self.base / self.res_path(pref)
if p.exists():
with filelock(p):
self.merge(eval(p.read_text(), type(self).globals()))
p.write_text(repr(self))
else:
p.parent.mkdir(parents=True, exist_ok=True)
p.write_text(repr(self))
@cl.contextmanager
def resource(r):
yield r
r.save()
class Mids(Resource):
_res_path = config.qnar_dst + 'mids.qnr'
def __init__(self, elems=None, **kw):
super().__init__(elems, **kw)
if not elems:
for m in config.exclude_mids:
self._elems[m] = config.EXCLUDED
def __setitem__(self, mid, name):
if '|' in mid:
assert mid == name
else:
es = self._elems
try:
n = es[mid]
assert n == name
except KeyError:
es[mid] = name
def rename_msg(self, old, new):
es = self._elems
self._elems = {m: n if n != old else new for (m, n) in es.items()}
def save(self, pref=None):
super().save(pref)
ns = {}
for m, n in self.items():
assert n
if n in ns:
log.warning('MIDs {} and {} with same name {}', m, ns[n], n)
elif n is not config.EXCLUDED:
ns[n] = m
class Names(Resource):
_res_path = 'names.qnr'
def __init__(self, elems, **kw):
super().__init__({k: v for k, v in elems.items() if k != v}, **kw)
def __bool__(self):
return bool(len(self))
|
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"/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], "/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/dispatch.py": ["/qnarre/base/doc/blog.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/context.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/analyzer.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/images.py"], "/qnarre/models/big_bird.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/big_bird.py"], "/tools/triton/python/triton/runtime/jit.py": ["/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/fast/roberta.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/bigbird.py": ["/qnarre/prep/config/big_bird.py", "/qnarre/models/big_bird.py"], "/qnarre/models/gpt_neo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/gpt_neo.py"], "/qnarre/prep/convert/t5.py": ["/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/prep/tokens/fsmt.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/deberta.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/fast/splinter.py": ["/qnarre/tokens/fast.py"], "/qnarre/models/old/trafo.py": ["/qnarre/core/attention.py", "/qnarre/core/base.py", "/qnarre/core/mlp.py", "/qnarre/core/deduce.py", "/qnarre/core/norm.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/led.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/realm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/convbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/data2vec.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/tools/triton/python/triton/language/math.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/run/beam.py": ["/qnarre/run/qa.py"], "/tools/triton/python/triton/compiler/compiler.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/tools/disasm.py", "/tools/triton/python/triton/compiler/code_generator.py", "/tools/triton/python/triton/compiler/make_launcher.py"], "/qnarre/base/doc/graph.py": ["/qnarre/base/doc/base.py"], "/qnarre/base/activism.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py", "/qnarre/base/judgment.py"], "/qnarre/base/doc/exporter.py": ["/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/fnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/roformer.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/megatron.py": ["/qnarre/prep/config/megatron.py", "/qnarre/models/megatron.py"], "/qnarre/prep/tokens/fast/roformer.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/roformer.py"], "/qnarre/core/runner.py": ["/qnarre/core/params.py"], "/qnarre/run/seq2seq.py": ["/qnarre/run/qa.py"], "/qnarre/prep/tokens/luke.py": ["/qnarre/tokens/utils.py"], 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"/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/models/plbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/reformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/images.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/base.py"], "/qnarre/models/yoso.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/canine.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/blog.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/counter.py"], "/qnarre/models/longformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/fast/pegasus.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/pegasus.py"], "/qnarre/base/judgment.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py", "/qnarre/base/conflict.py", "/qnarre/base/conjecture.py"], "/qnarre/models/splinter.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/rag.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/convert/albert.py": ["/qnarre/prep/config/albert.py", "/qnarre/models/albert.py"], "/qnarre/prep/tokens/fast/gpt2.py": ["/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/fast/mbart.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/roformer.py": ["/qnarre/models/roformer.py"], "/qnarre/base/doc/util/tree.py": ["/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py", "/qnarre/base/doc/util/utils.py"], "/qnarre/models/rembert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/t5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/xlm.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/bert_tf2.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/tools/triton/python/triton/ops/__init__.py": ["/tools/triton/python/triton/ops/cross_entropy.py", "/tools/triton/python/triton/ops/matmul.py"], "/qnarre/core/norm.py": ["/qnarre/core/base.py"], "/qnarre/models/mpnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/author.py": ["/qnarre/base/named.py"], "/qnarre/base/doc/args.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/log.py"], "/qnarre/prep/tokens/gpt.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/distilbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/proof.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py"], "/qnarre/prep/tokens/byt5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/electra.py": ["/qnarre/prep/config/electra.py", "/qnarre/models/electra.py"], "/qnarre/models/luke.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": ["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,605
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/base/doc/util/item.py
|
# Copyright 2019 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
import shutil as sh
import pathlib as pth
import collections.abc as abc
from hashlib import blake2b
from .log import Logger
log = Logger(__name__)
class Item(abc.MutableMapping):
_digest = ''
def __init__(self, suffs=(), digest='', path='', **_):
super().__init__()
self._suffs = dict.fromkeys(suffs)
if path:
path = pth.Path(path)
self._suffs[path.suffix] = path
if digest:
self._digest = digest
def __bool__(self):
return True
__hash__ = None
def __eq__(self, other):
if isinstance(other, type(self)):
return (self.suffs == other.suffs
and self._digest == other._digest)
return NotImplemented
def __len__(self):
return len(self._suffs)
def __iter__(self):
return iter(self._suffs)
def __getitem__(self, s):
return self._suffs[s]
def __setitem__(self, s, value):
self._suffs[s] = value
def __delitem__(self, s):
del self._suffs[s]
def __repr__(self):
s = type(self).__name__
s += '({}'.format(repr(tuple(self.suffs)))
if self._digest:
s += ', {}'.format(repr(self._digest))
s += ')'
return s
def stringer(self, digest=None, **_):
s = '('
for n, suff in enumerate(self.suffs):
s += '{}{}'.format(', ' if n else '', suff)
s += ')'
if self._digest:
s += ' {}'.format(self._digest if digest else '#..')
return s
@property
def name(self):
for p in self.paths():
return p.stem
return ''
@property
def suffs(self):
return sorted([s for s in self._suffs.keys() if s])
def paths(self, path=None):
ps = None if path is None else path.suffix
path = self._suffs.get('') if path is None else path
for s, p in sorted(self._suffs.items(), key=lambda x: x[0]):
if s:
if p:
assert s == p.suffix
yield p
elif path:
if ps:
assert s == ps
yield path
else:
yield path.with_suffix(s)
@property
def extern_path(self):
for p in self.paths():
return True
return False
@property
def digest(self):
d = self._digest
if not d:
raise ValueError
return d
def calc_digest(self, path=None, check=False, result=None, **_):
if result is None:
digest = blake2b(digest_size=20)
for p in self.paths(path):
if p.exists():
size = 0
with open(p, 'rb') as f:
for d in iter(lambda: f.read(65536), b''):
size += len(d)
digest.update(d)
assert size == p.stat().st_size
digest = digest.hexdigest()
if check:
return digest == self._digest
self._digest = digest
return digest
elif isinstance(result, str):
self._digest = result
return True
else:
return result
def touch(self, path, suff):
path = path.with_suffix(suff)
if suff not in self._suffs or not path.exists():
path.parent.mkdir(parents=True, exist_ok=True)
path.touch()
self._suffs[suff] = None
def rename(self, path, frm, to):
frm = path.with_name(frm)
to = path.with_name(to)
for s in self.suffs:
f = frm.with_suffix(s)
if f.exists():
f.rename(to.with_suffix(s))
def merge(self, other, name):
d = self._digest
od = other._digest
assert od
if d != od:
if d:
log.warning('Overwriting digest {}', name)
self._digest = od
for s, op in other._suffs.items():
assert op
p = self._suffs.get(s)
if p and d != od:
assert p == op
else:
self._suffs[s] = op
def copy(self, frm=None, to=None, touch_f=None):
def _copy(frm, to):
if frm.exists():
if not to.exists() or not frm.samefile(to):
to.parent.mkdir(parents=True, exist_ok=True)
sh.copyfile(str(frm), str(to))
return True
return False
if frm is None:
for f in self.paths():
s = f.suffix
if _copy(f, to.with_suffix(s)) and touch_f:
touch_f(s)
for s in self._suffs.keys():
if s:
self._suffs[s] = None
else:
del self._suffs[s]
else:
for s in self.suffs:
_copy(frm.with_suffix(s), to.with_suffix(s))
def probe(self, path, suff):
if suff in self._suffs:
path = path.with_suffix(suff)
if path.exists():
return True if path.stat().st_size == 0 else path
return False
def expand(self, other, path, ref):
def _try_add(s, p=True):
o = other.probe(path, s)
if o and (ref or (p and isinstance(o, pth.Path))):
self._suffs[s] = o
for s, p in self._suffs.items():
if s and not isinstance(p, pth.Path):
if not p or not ref:
_try_add(s, p)
if ref:
for s in other._suffs.keys():
if s and s not in self._suffs:
_try_add(s)
return all(isinstance(p, pth.Path) for p in self._suffs.values())
|
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"/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,606
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/config/electra.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
from collections import OrderedDict
from ... import core as qc
class PreTrained(qc.PreTrained):
hs = qc.Hypers(
{"drop_proj"},
dict(
act="gelu",
act_sum="gelu",
d_embed=128,
d_ff=1024,
d_model=256,
drop_attn=0.1,
drop_sum_last=0.1,
drop=0.1,
grad_checkpoint=True,
init_range=0.02,
model_type="electra",
n_heads=4,
n_lays=12,
n_pos=512,
n_typ=2,
eps=1e-12,
PAD=0,
pos_type="absolute",
s_vocab=30522,
sum_type="first",
sum_use_proj=True,
y_cache=True,
),
)
def _init_weights(self, module):
if isinstance(module, qc.Linear):
module.weight.data.normal_(mean=0.0, std=self.cfg.init_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, qc.Embedding):
module.weight.data.normal_(mean=0.0, std=self.cfg.init_range)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, qc.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
def _set_grad_checkpoint(self, module, value=False):
if isinstance(module, ElectraEncoder):
module.grad_checkpoint = value
def __init__(self, **kw):
super().__init__(PAD="PAD", **kw)
MAP = {
"google/electra-small-generator": dict(
archs=["ForMasked"],
),
"google/electra-base-generator": dict(
archs=["ForMasked"],
d_embed=768,
),
"google/electra-large-generator": dict(
archs=["ForMasked"],
d_embed=1024,
n_lays=24,
),
"google/electra-small-discriminator": dict(
archs=["ForPreTraining"],
),
"google/electra-base-discriminator": dict(
archs=["ForPreTraining"],
d_embed=768,
d_ff=3072,
d_model=768,
n_heads=12,
),
"google/electra-large-discriminator": dict(
archs=["ForPreTraining"],
d_embed=1024,
d_ff=4096,
d_model=1024,
n_heads=16,
n_lays=24,
),
}
class Onnx:
@property
def inputs(self):
return OrderedDict(
[
("input_ids", {0: "batch", 1: "sequence"}),
("mask", {0: "batch", 1: "sequence"}),
("typ_ids", {0: "batch", 1: "sequence"}),
]
)
|
{"/qnarre/prep/convert/xlnet.py": ["/qnarre/prep/config/xlnet.py", "/qnarre/models/xlnet.py"], "/qnarre/prep/convert/bert.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/base/doc/patcher.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/nominals.py"], "/qnarre/prep/convert/funnel.py": ["/qnarre/prep/config/funnel.py", "/qnarre/models/funnel.py"], "/qnarre/prep/tokens/fast/realm.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py", "/qnarre/tokens/utils.py"], "/qnarre/models/decision_transfo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/decision_transfo.py"], "/qnarre/models/fsmt.py": ["/qnarre/core/embed.py"], "/qnarre/models/roformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/xlnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc.py": ["/qnarre/base/author.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], "/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/dispatch.py": ["/qnarre/base/doc/blog.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/context.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/analyzer.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/images.py"], "/qnarre/models/big_bird.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/big_bird.py"], "/tools/triton/python/triton/runtime/jit.py": ["/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/fast/roberta.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/bigbird.py": ["/qnarre/prep/config/big_bird.py", "/qnarre/models/big_bird.py"], "/qnarre/models/gpt_neo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/gpt_neo.py"], "/qnarre/prep/convert/t5.py": ["/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/prep/tokens/fsmt.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/deberta.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/fast/splinter.py": ["/qnarre/tokens/fast.py"], "/qnarre/models/old/trafo.py": ["/qnarre/core/attention.py", "/qnarre/core/base.py", "/qnarre/core/mlp.py", "/qnarre/core/deduce.py", "/qnarre/core/norm.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/led.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/realm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/convbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/data2vec.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/tools/triton/python/triton/language/math.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/run/beam.py": ["/qnarre/run/qa.py"], "/tools/triton/python/triton/compiler/compiler.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/tools/disasm.py", "/tools/triton/python/triton/compiler/code_generator.py", "/tools/triton/python/triton/compiler/make_launcher.py"], "/qnarre/base/doc/graph.py": ["/qnarre/base/doc/base.py"], "/qnarre/base/activism.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py", "/qnarre/base/judgment.py"], "/qnarre/base/doc/exporter.py": ["/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/fnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/roformer.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/megatron.py": ["/qnarre/prep/config/megatron.py", "/qnarre/models/megatron.py"], "/qnarre/prep/tokens/fast/roformer.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/roformer.py"], "/qnarre/core/runner.py": ["/qnarre/core/params.py"], "/qnarre/run/seq2seq.py": ["/qnarre/run/qa.py"], "/qnarre/prep/tokens/luke.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/util/table.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/tree.py", "/qnarre/base/doc/util/utils.py"], "/tools/triton/python/triton/language/standard.py": ["/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/language/__init__.py"], "/qnarre/base/doc/filters.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/args.py"], "/qnarre/prep/convert/gpt_neo.py": ["/qnarre/prep/config/gpt_neo.py", "/qnarre/models/gpt_neo.py"], "/qnarre/base/doc/section.py": ["/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/message.py"], "/qnarre/models/funnel.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/funnel.py"], "/tools/triton/python/triton/common/__init__.py": ["/tools/triton/python/triton/common/build.py"], "/qnarre/base/doc/qnn.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/models/plbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/reformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/images.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/base.py"], "/qnarre/models/yoso.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/canine.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/blog.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/counter.py"], "/qnarre/models/longformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/fast/pegasus.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/pegasus.py"], "/qnarre/base/judgment.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py", "/qnarre/base/conflict.py", "/qnarre/base/conjecture.py"], "/qnarre/models/splinter.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/rag.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/convert/albert.py": ["/qnarre/prep/config/albert.py", "/qnarre/models/albert.py"], "/qnarre/prep/tokens/fast/gpt2.py": ["/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/fast/mbart.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/roformer.py": ["/qnarre/models/roformer.py"], "/qnarre/base/doc/util/tree.py": ["/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py", "/qnarre/base/doc/util/utils.py"], "/qnarre/models/rembert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/t5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/xlm.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/bert_tf2.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/tools/triton/python/triton/ops/__init__.py": ["/tools/triton/python/triton/ops/cross_entropy.py", "/tools/triton/python/triton/ops/matmul.py"], "/qnarre/core/norm.py": ["/qnarre/core/base.py"], "/qnarre/models/mpnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/author.py": ["/qnarre/base/named.py"], "/qnarre/base/doc/args.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/log.py"], "/qnarre/prep/tokens/gpt.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/distilbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/proof.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py"], "/qnarre/prep/tokens/byt5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/electra.py": ["/qnarre/prep/config/electra.py", "/qnarre/models/electra.py"], "/qnarre/models/luke.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": ["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,607
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/core/search.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
# https://arxiv.org/pdf/1904.09751.pdf
import torch
from qnarre.core import utils
from tensorflow.python.util import nest
from qnarre.core.base import Module
class Beam(Module):
@staticmethod
def cfg_items(ps):
return dict(
ps.cfg_items(
"END",
"beam_alpha",
"beam_size",
"num_toks",
)
)
def __init__(self, ps, owner, **kw):
super().__init__(ps, **kw)
self.to_logp = lambda *a, **kw: owner.to_logp(*a, **kw)
def build(self, input_shape):
cfg = self.cfg
tgt = input_shape[0]
assert tgt[0] == cfg.batch_size
y = torch.constant([[0.0] + [-float("inf")] * (cfg.beam_size - 1)])
self._logp = torch.tile(y, [cfg.batch_size, 1])
sh = (cfg.batch_size, cfg.beam_size)
self._score = torch.ones(shape=sh) * utils.big_neg
self._flag = torch.zeros(dtype="bool", shape=sh)
return super().build(input_shape)
def forward(self, inputs):
cfg = self.cfg
x, ctx = inputs
x = x[
:,
None,
]
self.tgt = self.out = torch.tile(x, [1, cfg.beam_size, 1])
self.logp = self._logp
self.score = self._score
self.flag = self._flag
i = 1
while self.not_done(i):
logp, idx = self.top_logp(ctx, i)
tgt = self.append_tgt(idx, i)
self.tgt, self.logp = self.top_tgt(tgt, logp)
self.out, self.score, self.flag = self.top_out(tgt, logp, i)
i += 1
out = torch.where(torch.reduce_any(self.flag, axis=1), self.out, self.tgt)
score = torch.where(torch.reduce_any(self.flag, axis=1), self.score, self.logp)
return out, score
def not_done(self, i):
y = self.score * torch.cast(self.flag, torch.floatx())
y = torch.reduce_min(y, axis=1)
fs = torch.reduce_any(self.flags, axis=1)
old = y + (1.0 - torch.cast(fs, torch.floatx())) * utils.big_neg
n = torch.int_shape(self.tgt)[-1]
new = self.logp[:, 0] / self.penalty(n)
done = torch.reduce_all(torch.greater(old, new))
return torch.logical_and(torch.less(i, n), torch.logical_not(done))
def top_logp(self, ctx, bias, i):
cfg = self.cfg
y = torch.zeros(
(
cfg.batch_size,
cfg.beam_size,
cfg.num_toks,
)
)
y += torch.expand_dims(self.logp, axis=2)
b = torch.range(cfg.batch_size)
ii = torch.constant([i] * cfg.batch_size)
for j in range(cfg.beam_size):
jj = torch.constant([j] * cfg.batch_size)
sel = torch.stack([b, jj, ii])
yj = self.to_logp(self.tgt[:, j, :], ctx, bias, i)[1]
y = torch.tensor_scatter_nd_add(y, sel, yj)
y = torch.reshape(y, (-1, cfg.beam_size * cfg.num_toks))
logp, idx = torch.top_k(y, k=2 * cfg.beam_size)
return logp, idx
def append_tok(self, idx, i, **kw):
cfg = self.cfg
k = 2 * cfg.beam_size
b = torch.range(cfg.batch_size * k) // k
b = torch.reshape(b, (cfg.batch_size, k))
beam = idx // cfg.num_toks
sel = torch.stack([b, beam], axis=2)
y = torch.gather_nd(self.tgt, sel)
ii = torch.constant([i] * cfg.batch_size * k)
ii = torch.reshape(ii, (cfg.batch_size, k))
sel = torch.stack([b, beam, ii], axis=2)
u = torch.expand_dims(idx % cfg.num_toks, axis=2)
tgt = torch.tensor_scatter_nd_update(y, sel, u)
return tgt
def top_tgt(self, x, lp):
cfg = self.cfg
fs = torch.equal(x[:, :, -1], cfg.END)
lp += torch.cast(fs, torch.floatx()) * utils.big_neg
return self.top_beams([x, lp], lp)
def top_out(self, x, lp, i):
cfg = self.cfg
score = lp / self.penalty(i + 1)
flag = torch.equal(x[:, :, -1], cfg.END)
score += (1.0 - torch.cast(flag, torch.floatx())) * utils.big_neg
return self.top_beams([x, score, flag], score)
def gather_beams(self, xs, beams, k):
cfg = self.cfg
b = torch.range(cfg.batch_size * k) // k
b = torch.reshape(b, (cfg.batch_size, k))
sel = torch.stack([b, beams], axis=2)
return nest.map_structure(lambda x: torch.gather_nd(x, sel), xs)
def top_beams(self, xs, vs):
k = self.cfg.beam_size
_, beams = torch.top_k(vs, k=k)
return self.gather_beams(xs, beams, k)
def penalty(self, n):
n = torch.cast(n, torch.floatx())
y = torch.pow(((5.0 + n) / 6.0), self.cfg.beam_alpha)
return y
|
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"/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], 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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,608
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/tokens/fast/gpt.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
from ....tokens.fast import PreTrainedTokenizerFast
from ..gpt import Tokenizer as GPT
VOCAB_FS = {
"vocab_file": "vocab.json",
"merges_file": "merges.txt",
"tokenizer_file": "tokenizer.json",
}
VOCAB_MAP = {
"vocab_file": {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/vocab.json"},
"merges_file": {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/merges.txt"},
"tokenizer_file": {
"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/tokenizer.json"
},
}
INPUT_CAPS = {
"openai-gpt": 512,
}
class Tokenizer(PreTrainedTokenizerFast):
vocab_fs = VOCAB_FS
vocab_map = VOCAB_MAP
input_caps = INPUT_CAPS
model_input_names = ["input_ids", "mask"]
slow_tokenizer_class = GPT
def __init__(self, vocab_file=None, merges_file=None, tokenizer_file=None, unk="<unk>", **kw):
super().__init__(vocab_file, merges_file, tokenizer_file=tokenizer_file, unk=unk, **kw)
@property
def do_lower_case(self):
return True
def save_vocabulary(self, dir, pre=None):
return tuple(self._tokenizer.model.save(dir, name=pre))
|
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"/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,609
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/core/embed.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
import math
import torch
import numpy as np
from torch import nn
from torch.nn import functional as F
from torch.nn.parameter import Parameter
from .. import core as qc
from . import utils as qu
class Embed(qc.Module):
hs = qc.Hypers(
{"d_embed", "drop", "eps", "n_pos", "n_typ", "s_vocab"},
{"pos_type": "absolute", "rescale": False},
)
def __init__(self, d_embed=None, ps={}, hs=[], **kw):
if d_embed is not None:
kw.update(d_embed=d_embed)
super().__init__([self.hs] + hs, ps, **kw)
cfg = self.get_cfg(kw)
self.tok = qc.Embed(cfg.s_vocab, cfg.d_embed, **kw)
if cfg.n_pos is not None:
self.pos = qc.Embed(cfg.n_pos, cfg.d_embed, **kw)
self.register_buffer("pos", torch.arange(cfg.n_pos).expand((1, -1)))
if cfg.pos_sin:
sin_embed(n_pos=cfg.n_pos, dim=cfg.d_embed, out=self.pos.weight)
if cfg.n_typ is not None:
s = self.pos.size()
self.typ = qc.Embed(cfg.n_typ, cfg.d_embed, **kw)
self.register_buffer("typ", torch.zeros(s, dtype=torch.long), persistent=False)
self.norm = qc.LayerNorm(cfg.d_embed, cfg.eps, **kw)
self.drop = qc.Dropout(cfg.drop, **kw)
def forward(self, x, n_kv=0, pos=None, typ=None, x_emb=None, **_):
cfg = self.cfg
if x_emb is None:
x_emb = self.tok(x)
s = x.size()
else:
s = x_emb.size()[:-1]
if cfg.rescale:
x_emb = x_emb * (cfg.d_embed**0.5)
b, n = s[:2]
if typ is None:
if hasattr(self, "typ"):
typ = self.typ[:, :n].expand(b, n)
else:
typ = torch.zeros(s, dtype=torch.long, device=self.pos.device)
y = x_emb + self.typ(typ)
if cfg.pos_type == "absolute":
if pos is None:
p = self.cfg.PAD
if hasattr(self, "pos"):
pos = self.pos[:, n_kv : n + n_kv]
elif x is None:
pos = torch.arange(p + 1, n + p + 1, dtype=torch.long, device=x.device)
pos = pos.unsqueeze(0).expand_as(x)
else:
mask = x.ne(p).int()
pos = (torch.cumsum(mask, dim=1).type_as(mask) + n_kv) * mask
pos = pos.long() + p
y = y + self.pos(pos)
y = self.norm(self.drop(y))
return y
def sin_embed(n_pos, dim, y):
y.requires_grad = False
pos = np.array(
[[i / np.power(10000, 2 * (j // 2) / dim) for j in range(dim)] for i in range(n_pos)]
)
y[:, 0::2] = torch.FloatTensor(np.sin(pos[:, 0::2]))
y[:, 1::2] = torch.FloatTensor(np.cos(pos[:, 1::2]))
y.detach_()
def pos_enc(cfg, qlen, klen, bsz=None):
freq_seq = torch.arange(0, cfg.d_model, 2.0, dtype=torch.float)
inv_freq = 1 / torch.pow(10000, (freq_seq / cfg.d_model))
if cfg.attn_type == "bi":
beg, end = klen, -qlen
elif cfg.attn_type == "uni":
beg, end = klen, -1
else:
raise ValueError(f"Unknown `attn_type` {cfg.attn_type}.")
def pos_emb(pos_seq, inv_freq, batch=None):
seq = torch.einsum("i,d->id", pos_seq, inv_freq)
y = torch.cat([torch.sin(seq), torch.cos(seq)], dim=-1)
y = y[:, None, :]
if batch is not None:
y = y.expand(-1, batch, -1)
return y
if cfg.bi_data:
fwd = torch.arange(beg, end, -1.0, dtype=torch.float)
bwd = torch.arange(-beg, -end, 1.0, dtype=torch.float)
if cfg.clamp_len > 0:
fwd = fwd.clamp(-cfg.clamp_len, cfg.clamp_len)
bwd = bwd.clamp(-cfg.clamp_len, cfg.clamp_len)
if bsz is not None:
fwd_pos = pos_emb(fwd, inv_freq, bsz // 2)
bwd_pos = pos_emb(bwd, inv_freq, bsz // 2)
else:
fwd_pos = pos_emb(fwd, inv_freq)
bwd_pos = pos_emb(bwd, inv_freq)
y = torch.cat([fwd_pos, bwd_pos], dim=1)
else:
fwd = torch.arange(beg, end, -1.0)
if cfg.clamp_len > 0:
fwd = fwd.clamp(-cfg.clamp_len, cfg.clamp_len)
y = pos_emb(fwd, inv_freq, bsz)
y = y.to(cfg.device)
return y
class TokEmbed(qc.Module):
hs = qc.Hypers(
{"brackets", "d_embed", "d_model", "one_hot", "max_norm", "s_vocab", "PAD"},
{"norm_type": 2.0, "scale_grad": False, "sparse": False},
)
def __init__(self, s_vocab=None, d_embed=None, hs=[], **kw):
if s_vocab is not None:
kw.update(s_vocab=s_vocab)
if d_embed is not None:
kw.update(d_embed=d_embed)
kw.update(hs=[self.hs] + hs)
super().__init__(**kw)
cfg = self.cfg
h = cfg.d_model
d = cfg.d_embed or h
bs = (cfg.brackets or []) + [cfg.s_vocab]
b = 0
self.weights = []
self.adjusts = []
assert b == cfg.PAD
kw = {"device": cfg.device, "dtype": cfg.dtype}
for i, e in enumerate(bs):
p = d // (len(bs) ** i)
t = Parameter(torch.empty((e - b, p), **kw))
self.weights.append(t)
a = None if p == h else Parameter(torch.empty((p, h), **kw))
self.adjusts.append(a)
b = e
self.one_hot = cfg.one_hot
def build(self):
self.reset_params()
def reset_params(self):
for w in self.weights:
nn.init.normal_(w)
for a in self.adjusts:
if a is not None:
nn.init.normal_(a)
cfg = self.cfg
if cfg.PAD is not None:
with torch.no_grad():
for w in self.weights:
w[cfg.PAD].fill_(0)
def forward(self, x):
cfg = self.cfg
y = torch.zeros(torch.int_shape(x) + (cfg.d_model,))
bs = (cfg.brackets or []) + [cfg.s_vocab]
b = 0
for i, e in enumerate(bs):
m = (x >= (b or 1)) & (x < e)
u = torch.boolean_mask(x, m)
u = self.lookup(u - b, i)
y = torch.tensor_scatter_nd_add(y, torch.where(m), u)
b = e
y *= y.shape[-1] ** 0.5
y.mask = torch.not_equal(x, cfg.PAD)
return y
def lookup(self, x, i):
t = self.weights[i]
if self.one_hot:
y = torch.one_hot(x, torch.shape(t)[0], axis=-1)
y = torch.einsum("np,in->ip", t, y)
else:
cfg = self.cfg
y = F.embedding(x, t, cfg.PAD, cfg.max_norm, cfg.norm_type, cfg.scale_grad, cfg.sparse)
a = self.adjusts[i]
if a is not None:
y = torch.einsum("ip,ph->ih", y, a)
return y
class PosEmbed(qc.Embed):
def __init__(self, n_embed, d_embed):
self.offset = 2
super().__init__(n_embed + self.offset, d_embed)
def forward(self, x, n_kv=0):
b, n = x.shape[:2]
y = torch.arange(n_kv, n_kv + n, dtype=torch.long, device=self.weight.device).expand(b, -1)
return super().forward(y + self.offset)
class PosEmbed2(qc.Module):
hs = qc.Hypers({"d_model", "d_src", "d_tgt", "pos_max_len"})
def __init__(self, n_typ=None, d_embed=None, hs=[], **kw):
if n_typ is not None:
kw.update(n_typ=n_typ)
if d_embed is not None:
kw.update(d_embed=d_embed)
kw.update(hs=[self.hs] + hs)
super().__init__(**kw)
cfg = self.cfg
kw = {"device": cfg.device, "dtype": cfg.dtype}
self.pos_b = Parameter(torch.empty((cfg.n_typ, cfg.d_model), **kw))
def build(self):
cfg = self.cfg
p = max(cfg.pos_max_len or 0, cfg.d_src, cfg.d_tgt)
self.pos_b = self.add_weight("pos_b", (p, cfg.d_model))
def forward(self, x, mask=None):
y = self.pos_b[: x.shape[1], :]
if mask is not None:
y *= torch.cast(mask, self.pos_b.dtype)
return x + y
class PosTiming(qc.Module):
hs = qc.Hypers(
{"d_model", "pos_max", "pos_min", "pos_start", "d_src", "d_tgt", "pos_max_len"},
)
def build(self):
cfg = self.cfg
m = cfg.d_model
p = max(cfg.pos_max_len or 0, cfg.d_src, cfg.d_tgt)
a = (cfg.pos_max, cfg.pos_min, cfg.pos_start)
self.pos_b = qu.pos_timing(m, p, *a)
def forward(self, x, mask=None):
y = self.pos_b
if mask is not None:
y *= torch.cast(mask, self.pos_b.dtype)
return x + y
class RelEmbed(qc.Module):
hs = qc.Hypers({"d_model", "d_src", "d_tgt", "pos_max_len"})
def __init__(self, n_typ=None, d_embed=None, hs=[], **kw):
if n_typ is not None:
kw.update(n_typ=n_typ)
if d_embed is not None:
kw.update(d_embed=d_embed)
kw.update(hs=[self.hs] + hs)
super().__init__(**kw)
cfg = self.cfg
kw = {"device": cfg.device, "dtype": cfg.dtype}
self.pos_b = Parameter(torch.empty((cfg.n_typ, cfg.d_model), **kw))
def build(self):
cfg = self.cfg
p = max(cfg.pos_max_len or 0, cfg.d_src, cfg.d_tgt)
self.pos_b = qu.PosTiming(cfg.d_model, p)
def forward(self, x, mask=None):
y = self.pos_b
if mask is not None:
y *= torch.cast(mask, self.pos_b.dtype)
return x + y
class Positional(qc.Module):
hs = qc.Hypers({"d_embed"})
def __init__(self, **kw):
super().__init__(**kw)
cfg = self.get_cfg(kw)
d = cfg.d_embed
self.register_buffer("inv_freq", 1 / (10000 ** (torch.arange(0.0, d, 2.0) / d)))
def forward(self, x, b=None):
y = torch.ger(x, self.inv_freq)
y = torch.cat([y.sin(), y.cos()], dim=-1)[:, None, :]
return y if b is None else y.expand(-1, b, -1)
class Adaptive(qc.Module):
def __init__(self, s_vocab, d_embed, d_proj, cutoffs, div_val=1, sample_softmax=False):
super().__init__()
cfg = self.cfg
cfg.scale = d_proj**0.5
cfg.cutoffs = cutoffs + [s_vocab]
cfg.ends = [0] + cfg.cutoffs
self.lays = qc.Stack()
self.projs = nn.ParameterList()
if div_val == 1:
self.lays.append(qc.Embed(s_vocab, d_embed, sparse=sample_softmax > 0))
if d_proj != d_embed:
self.projs.append(nn.Parameter(torch.FloatTensor(d_proj, d_embed)))
else:
for i in range(len(cfg.cutoffs)):
left, right = cfg.ends[i], cfg.ends[i + 1]
d = d_embed // (div_val**i)
self.lays.append(qc.Embed(right - left, d))
self.projs.append(nn.Parameter(torch.FloatTensor(d_proj, d)))
def forward(self, x):
cfg = self.cfg
if cfg.div_val == 1:
y = self.lays[0](x)
if cfg.d_proj != cfg.d_embed:
y = F.linear(y, self.projs[0])
else:
p = next(self.parameters())
y = x.view(-1)
ys = torch.zeros([y.size(0), cfg.d_proj], dtype=p.dtype, device=p.device)
for i in range(len(cfg.cutoffs)):
left, right = cfg.ends[i], cfg.ends[i + 1]
mask = (y >= left) & (y < right)
j = mask.nonzero().squeeze()
if j.numel() == 0:
continue
k = self.lays[i](y.index_select(0, j) - left)
k = F.linear(k, self.projs[i])
ys.index_copy_(0, j, k)
y = ys.view(x.size() + (cfg.d_proj,))
y.mul_(self.scale)
return y
class SinEmbed(qc.Embed):
def __init__(self, n_pos, d_embed, PAD):
self.make_weight(n_pos, d_embed, PAD)
def make_weight(self, n_pos, d_embed, PAD):
w = self.get_embedding(n_pos, d_embed, PAD)
if not hasattr(self, "weight"):
super().__init__(n_pos, d_embed, PAD, _weight=w)
else:
w = w.to(dtype=self.weight.dtype, device=self.weight.device)
self.weight = nn.Parameter(w)
self.weight.detach_()
self.weight.requires_grad = False
@staticmethod
def get_embedding(n_embed, d_embed, PAD):
half = d_embed // 2
y = math.log(10000) / (half - 1)
y = torch.exp(torch.arange(half, dtype=torch.float) * -y)
y = torch.arange(n_embed, dtype=torch.float).unsqueeze(1) * y.unsqueeze(0)
y = torch.cat([torch.sin(y), torch.cos(y)], dim=1).view(n_embed, -1)
if d_embed % 2 == 1:
y = torch.cat([y, torch.zeros(n_embed, 1)], dim=1)
if PAD is not None:
y[PAD, :] = 0
return y
@staticmethod
def make_positions(x, PAD):
mask = x.ne(PAD).int()
return (torch.cumsum(mask, dim=1).type_as(mask) * mask).long() + PAD
def forward(self, x, incremental_state=None, timestep=None):
b, n = x.shape[:2]
max_pos = self.PAD + 1 + n
if max_pos > self.weight.size(0):
self.make_weight(max_pos, cfg.d_embed, self.PAD)
pos = self.make_positions(x, self.PAD)
return super().forward(pos)
class SinEmbed2(qc.Embed):
def __init__(self, n_pos, d_embed, PAD=None):
super().__init__(n_pos, d_embed)
self.weight = self._init_weight(self.weight)
@staticmethod
def _init_weight(y):
n, d = y.shape
pos = np.array(
[[i / np.power(10000, 2 * (j // 2) / d) for j in range(d)] for i in range(n)]
)
y.requires_grad = False
sentinel = d // 2 if d % 2 == 0 else (d // 2) + 1
y[:, 0:sentinel] = torch.FloatTensor(np.sin(pos[:, 0::2]))
y[:, sentinel:] = torch.FloatTensor(np.cos(pos[:, 1::2]))
y.detach_()
return y
@torch.no_grad()
def forward(self, shape, n_kv=0):
b, n = shape[:2]
pos = torch.arange(n_kv, n_kv + n, dtype=torch.long, device=self.weight.device)
return super().forward(pos)
class RotaryEmbed(qc.Module):
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
super().__init__()
inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(device) / dim))
self.register_buffer("inv_freq", inv_freq)
self.max_seq_len_cached = max_position_embeddings
t = torch.arange(
self.max_seq_len_cached, device=self.inv_freq.device, dtype=self.inv_freq.dtype
)
freqs = torch.einsum("i,j->ij", t, self.inv_freq)
emb = torch.cat((freqs, freqs), dim=-1)
self.register_buffer("cos_cached", emb.cos()[None, None, :, :], persistent=False)
self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False)
def forward(self, x, seq_len=None):
if seq_len > self.max_seq_len_cached:
self.max_seq_len_cached = seq_len
t = torch.arange(self.max_seq_len_cached, device=x.device, dtype=self.inv_freq.dtype)
freqs = torch.einsum("i,j->ij", t, self.inv_freq)
emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
self.register_buffer("cos_cached", emb.cos()[None, None, :, :], persistent=False)
self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False)
return (
self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
)
def rotate_half(x):
y1 = x[..., : x.shape[-1] // 2]
y2 = x[..., x.shape[-1] // 2 :]
return torch.cat((-y2, y1), dim=-1)
def apply_rotary_pos_emb(q, k, cos, sin, xs):
ys = xs[:, None, :, None] # [bs, 1, seq_len, 1]
ys = ys.repeat(1, cos.shape[1], 1, cos.shape[3])
cos = torch.gather(cos.repeat(ys.shape[0], 1, 1, 1), 2, ys)
sin = torch.gather(sin.repeat(ys.shape[0], 1, 1, 1), 2, ys)
return (q * cos) + (rotate_half(q) * sin), (k * cos) + (rotate_half(k) * sin)
# Copyright (c) 2021, EleutherAI
#
# 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.
import torch
import math
class SinusoidalPositionalEmbedding(torch.nn.Module):
def __init__(self, dim, base=10000, precision=torch.half):
super().__init__()
inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim))
self.register_buffer("inv_freq", inv_freq)
self.precision = precision
def forward(self, x, seq_dim=1):
t = torch.arange(x.shape[seq_dim], device=x.device).type_as(self.inv_freq)
sinusoid_inp = torch.einsum("i,j->ij", t, self.inv_freq)
if self.precision == torch.bfloat16:
sinusoid_inp = sinusoid_inp.float()
sin, cos = sinusoid_inp.sin(), sinusoid_inp.cos()
if self.precision == torch.bfloat16:
sin, cos = sin.bfloat16(), cos.bfloat16()
emb = torch.cat((sin, cos), dim=-1)
return emb[None, :, :]
class RotaryEmbedding(torch.nn.Module):
def __init__(self, dim, base=10000, precision=torch.half):
super().__init__()
inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim))
self.register_buffer("inv_freq", inv_freq)
self.seq_len_cached = None
self.cos_cached = None
self.sin_cached = None
self.precision = precision
def forward(self, x, seq_dim=1, seq_len=None):
if seq_len is None:
seq_len = x.shape[seq_dim]
if seq_len != self.seq_len_cached:
self.seq_len_cached = seq_len
t = torch.arange(seq_len, device=x.device).type_as(self.inv_freq)
freqs = torch.einsum("i,j->ij", t, self.inv_freq)
emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
if self.precision == torch.bfloat16:
emb = emb.float()
self.cos_cached = emb.cos()[:, None, None, :]
self.sin_cached = emb.sin()[:, None, None, :]
if self.precision == torch.bfloat16:
self.cos_cached = self.cos_cached.bfloat16()
self.sin_cached = self.sin_cached.bfloat16()
return self.cos_cached, self.sin_cached
# rotary pos emb helpers:
def rotate_half(x):
x1, x2 = x[..., : x.shape[-1] // 2], x[..., x.shape[-1] // 2 :]
return torch.cat((-x2, x1), dim=x1.ndim - 1) # dim=-1 triggers a bug in earlier torch versions
@torch.jit.script
def apply_rotary_pos_emb(q, k, cos, sin, offset: int = 0):
cos, sin = (
cos[offset : q.shape[0] + offset, ...],
sin[offset : q.shape[0] + offset, ...],
)
return (q * cos) + (rotate_half(q) * sin), (k * cos) + (rotate_half(k) * sin)
def apply_rotary_pos_emb_torch(q, k, cos, sin, offset: int = 0): # jitting fails with bf16
cos, sin = (
cos[offset : q.shape[0] + offset, ...],
sin[offset : q.shape[0] + offset, ...],
)
return (q * cos) + (rotate_half(q) * sin), (k * cos) + (rotate_half(k) * sin)
class AliBi(torch.nn.Module):
def __init__(self, num_heads, mp_size=1, mp_rank=1):
super().__init__()
# megatron splits across heads, so we need to make sure each
# head receives the correct matrix
assert mp_size <= num_heads and mp_rank <= mp_size
self.mp_size = mp_size
self.mp_rank = mp_rank
self.num_heads = num_heads
self.slice_size = num_heads // mp_size
self.cached_matrix = None
self.cached_seq_len = None
slopes = torch.Tensor(self._get_slopes(num_heads))[
mp_rank * self.slice_size : (mp_rank + 1) * self.slice_size
]
self.register_buffer("slopes", slopes)
def _get_slopes(self, n):
"""
Get slopes for Alibi positional embedding
n : int = number of heads.
For best performance, restrict n to a power of 2.
"""
def get_slopes_power_of_2(n):
start = 2 ** (-(2 ** -(math.log2(n) - 3)))
ratio = start
return [start * ratio**i for i in range(n)]
if math.log2(n).is_integer():
return get_slopes_power_of_2(n)
else:
closest_power_of_2 = 2 ** math.floor(math.log2(n))
return (
get_slopes_power_of_2(closest_power_of_2)
+ self._get_slopes(2 * closest_power_of_2)[0::2][: n - closest_power_of_2]
)
def bias(self, seq_len_q, seq_len_k, device, dtype):
# [b, np, sq, sk]
# seq_len_q = x.shape[-2]
# seq_len_k = x.shape[-1]
# Initialize the AliBi matrix to match the first provided key length; grow it exponentially
# afterwards if longer inputs are provided. This is important for inference, where we will
# encounter progressively longer samples; it should have no effect at training time.
if self.cached_seq_len is not None and self.cached_seq_len >= seq_len_k:
a = self.cached_matrix
else:
target_seq_len = seq_len_k if self.cached_seq_len is None else self.cached_seq_len * 4
a = -torch.tril(
torch.arange(target_seq_len).view(target_seq_len, 1).repeat(1, target_seq_len)
+ torch.arange(0, -target_seq_len, -1)
)
a = a.to(device).to(dtype)
slopes = self.slopes.to(a.device).to(a.dtype)
a = a * slopes.view(self.slopes.shape[0], 1, 1)
self.cached_seq_len = target_seq_len
self.cached_matrix = a
# If the AliBi matrix is larger than the key length, clip it.
if self.cached_seq_len > seq_len_k:
a = self.cached_matrix[:, :seq_len_k, :seq_len_k]
if seq_len_q != seq_len_k:
# In the train case x has dimensionality [b, np, sq, sk] with sq == sk
# The number of query tokens is equal to the number of key tokens
# At inference time with cache in layer_past sq is not equal to sk. sq only contains one token (the last one in the full sequence)
# In this case we use the appropriate token index of the cache matrix.
# As the cache matrix could already be bigger from a past inference, not the last token index in the sq sequence is used
assert (
seq_len_q == 1
), "assumption sq == sk unless at inference time with cache in layer_past with sq == 1"
a = a[:, seq_len_k - 1, :].view(
a.shape[0], 1, a.shape[2]
) # seq_len_k - 1 points to the last token index in the current inference batch.
return a
def forward(self, x):
# [b, np, sq, sk]
seq_len_q = x.shape[-2]
seq_len_k = x.shape[-1]
# Initialize the AliBi matrix to match the first provided key length; grow it exponentially
# afterwards if longer inputs are provided. This is important for inference, where we will
# encounter progressively longer samples; it should have no effect at training time.
if self.cached_seq_len is not None and self.cached_seq_len >= seq_len_k:
a = self.cached_matrix
else:
target_seq_len = seq_len_k if self.cached_seq_len is None else self.cached_seq_len * 4
a = -torch.tril(
torch.arange(target_seq_len).view(target_seq_len, 1).repeat(1, target_seq_len)
+ torch.arange(0, -target_seq_len, -1)
)
a = a.to(x.device).to(x.dtype)
slopes = self.slopes.to(a.device).to(a.dtype)
a = a * slopes.view(self.slopes.shape[0], 1, 1)
self.cached_seq_len = target_seq_len
self.cached_matrix = a
# If the AliBi matrix is larger than the key length, clip it.
if self.cached_seq_len > seq_len_k:
a = self.cached_matrix[:, :seq_len_k, :seq_len_k]
if seq_len_q != seq_len_k:
# In the train case x has dimensionality [b, np, sq, sk] with sq == sk
# The number of query tokens is equal to the number of key tokens
# At inference time with cache in layer_past sq is not equal to sk. sq only contains one token (the last one in the full sequence)
# In this case we use the appropriate token index of the cache matrix.
# As the cache matrix could already be bigger from a past inference, not the last token index in the sq sequence is used
assert (
seq_len_q == 1
), "assumption sq == sk unless at inference time with cache in layer_past with sq == 1"
a = a[:, seq_len_k - 1, :].view(
a.shape[0], 1, a.shape[2]
) # seq_len_k - 1 points to the last token index in the current inference batch.
return x + a
# Copyright (c) 2021, EleutherAI
#
# 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.
import torch
import math
from torch.nn.parameter import Parameter
from megatron import mpu
from megatron.model.positional_embeddings import SinusoidalPositionalEmbedding
from megatron.model.init_functions import get_init_methods
class Embedding(torch.nn.Module):
"""Language model embeddings.
Arguments:
hidden_size: hidden size
vocab_size: vocabulary size
max_sequence_length: maximum size of sequence. This
is used for positional embedding
embedding_dropout_prob: dropout probability for embeddings
init_method: weight initialization method
num_tokentypes: size of the token-type embeddings. 0 value
will ignore this embedding
"""
def __init__(
self,
neox_args,
hidden_size,
vocab_size,
max_sequence_length,
embedding_dropout_prob,
init_method,
num_tokentypes=0,
use_pos_emb=True,
):
super(Embedding, self).__init__()
self.hidden_size = hidden_size
self.init_method = init_method
self.num_tokentypes = num_tokentypes
self.use_mup = neox_args.use_mup
self.mup_embedding_mult = neox_args.mup_embedding_mult
self.mup_rp_embedding_mult = neox_args.mup_rp_embedding_mult
# Word embeddings (parallel).
self.word_embeddings = mpu.VocabParallelEmbedding(
neox_args=neox_args,
num_embeddings=vocab_size,
embedding_dim=self.hidden_size,
init_method=self.init_method,
)
self._word_embeddings_key = "word_embeddings"
if neox_args.use_bnb_optimizer:
try:
import bitsandbytes as bnb
self.embedding_module = bnb.nn.StableEmbedding
except ModuleNotFoundError:
print(
"Please install bitsandbytes following https://github.com/facebookresearch/bitsandbytes."
)
raise Exception
else:
self.embedding_module = torch.nn.Embedding
# Position embedding (serial).
self.use_pos_emb = use_pos_emb
if self.use_pos_emb:
self.embedding_type = neox_args.pos_emb
if self.embedding_type == "learned":
self.position_embeddings = self.embedding_module(
max_sequence_length, self.hidden_size
)
self._position_embeddings_key = "position_embeddings"
# Initialize the position embeddings.
self.init_method(self.position_embeddings.weight)
elif self.embedding_type == "sinusoidal":
self.position_embeddings = SinusoidalPositionalEmbedding(self.hidden_size)
# Token type embedding.
# Add this as an optional field that can be added through
# method call so we can load a pretrain model without
# token types and add them as needed.
self._tokentype_embeddings_key = "tokentype_embeddings"
if self.num_tokentypes > 0:
self.tokentype_embeddings = self.embedding_module(self.num_tokentypes, self.hidden_size)
# Initialize the token-type embeddings.
self.init_method(self.tokentype_embeddings.weight)
else:
self.tokentype_embeddings = None
# Embeddings dropout
self.embedding_dropout = torch.nn.Dropout(embedding_dropout_prob)
self.opt_pos_emb_offset = neox_args.opt_pos_emb_offset
# For ticking position ids forward
self.layer_past = None
def add_tokentype_embeddings(self, num_tokentypes):
"""Add token-type embedding. This function is provided so we can add
token-type embeddings in case the pretrained model does not have it.
This allows us to load the model normally and then add this embedding.
"""
if self.tokentype_embeddings is not None:
raise Exception("tokentype embeddings is already initialized")
if torch.distributed.get_rank() == 0:
print("adding embedding for {} tokentypes".format(num_tokentypes), flush=True)
self.num_tokentypes = num_tokentypes
self.tokentype_embeddings = self.embedding_module(num_tokentypes, self.hidden_size)
# Initialize the token-type embeddings.
self.init_method(self.tokentype_embeddings.weight)
def forward(self, input_ids, position_ids, tokentype_ids=None):
# Embeddings.
words_embeddings = self.word_embeddings(input_ids)
if self.use_pos_emb and self.embedding_type in ["learned", "sinusoidal"]:
if self.opt_pos_emb_offset:
if self.layer_past is not None:
position_ids = position_ids + self.layer_past + 1
self.layer_past = position_ids[:, -1]
# OPT always adds 2 for some reason, according to the HF implementation
position_ids = position_ids + self.opt_pos_emb_offset
position_embeddings = self.position_embeddings(position_ids)
position_embeddings.mul_(self.mup_rp_embedding_mult)
embeddings = words_embeddings + position_embeddings
else:
embeddings = words_embeddings
if tokentype_ids is not None:
assert self.tokentype_embeddings is not None
embeddings = embeddings + self.tokentype_embeddings(tokentype_ids)
else:
assert self.tokentype_embeddings is None
# Dropout.
embeddings = self.embedding_dropout(embeddings)
if self.use_mup:
with torch.no_grad():
embeddings.mul_(self.mup_embedding_mult)
return embeddings
class EmbeddingPipe(Embedding):
"""Extends Embedding to forward attention_mask through the pipeline."""
@property
def word_embeddings_weight(self):
"""Easy accessory for the pipeline engine to tie embeddings across stages."""
return self.word_embeddings.weight
def forward(self, args):
assert (
len(args) == 3
), f"Expected 3 arguments (input_ids, position_ids, attention_mask), but got {len(args)}."
input_ids = args[0]
position_ids = args[1]
attention_mask = args[2]
embeddings = super().forward(input_ids, position_ids)
return embeddings, attention_mask
class SoftEmbedding(torch.nn.Module):
def __init__(
self,
neox_args,
wte,
n_tokens: int = 10,
init_range: float = 0.5,
init_string: str = "",
):
super(SoftEmbedding, self).__init__()
self.n_tokens = n_tokens
self.neox_args = neox_args
self.init_range = init_range
self.init_string = init_string
self.soft_embedding_weight = torch.nn.parameter.Parameter(self.initialize_embedding(wte))
def initialize_embedding(self):
if self.init_string:
embeds = torch.LongTensor(self.neox_args.tokenizer.tokenize(self.init_string)).to(
self.embedding_module.weight.device
)
embeds = self.embedding_module(embeds)
if embeds.shape[0] >= self.n_tokens:
embeds = embeds[: self.n_tokens, :] # slice
else:
embeds = embeds.repeat(math.ceil(self.n_tokens / embeds.shape[0]), 1)[
: self.n_tokens, :
] # pad up to n_tokens
return embeds
return torch.Tensor(n_tokens, neox_args.hidden_size).uniform_(
-self.random_range, self.random_range
)
def forward(self, args: tuple):
in_inference = len(args) == 3 # embeddings, layer_past, attention_mask
in_train = len(args) == 2 # embeddings, attention_mask
if in_train:
embedding, attention_mask = args
else:
embedding, layer_past, attention_mask = args
soft_embedding = self.soft_embedding_weight.repeat(
embedding.shape[0], 1, 1
) # repeat batch_size times
if in_train:
# append soft embedding at the beginning in training
embedding = torch.cat((soft_embedding, embedding), dim=1)
embedding = embedding[:, : self.neox_args.seq_length, ...]
return embedding, attention_mask
else:
if not (exists(layer_past) and layer_past.numel() > 0):
# if in inference, on the first forward pass, we want to do the same as in training (append soft embedding)
embedding = torch.cat((soft_embedding, embedding), dim=1)
embedding = embedding[:, : self.neox_args.seq_length, ...]
# otherwise, we're in incremental mode, and just want to forward the single embedding (since the soft prompt has already been cached)
return embedding, layer_past, attention_mask
|
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"/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/models/plbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/reformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/images.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/base.py"], "/qnarre/models/yoso.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/canine.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/blog.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/counter.py"], "/qnarre/models/longformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/fast/pegasus.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/pegasus.py"], "/qnarre/base/judgment.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py", "/qnarre/base/conflict.py", "/qnarre/base/conjecture.py"], "/qnarre/models/splinter.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/rag.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/convert/albert.py": ["/qnarre/prep/config/albert.py", "/qnarre/models/albert.py"], "/qnarre/prep/tokens/fast/gpt2.py": ["/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/fast/mbart.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/roformer.py": ["/qnarre/models/roformer.py"], "/qnarre/base/doc/util/tree.py": ["/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py", "/qnarre/base/doc/util/utils.py"], "/qnarre/models/rembert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/t5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/xlm.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/bert_tf2.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/tools/triton/python/triton/ops/__init__.py": ["/tools/triton/python/triton/ops/cross_entropy.py", "/tools/triton/python/triton/ops/matmul.py"], "/qnarre/core/norm.py": ["/qnarre/core/base.py"], "/qnarre/models/mpnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/author.py": ["/qnarre/base/named.py"], "/qnarre/base/doc/args.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/log.py"], "/qnarre/prep/tokens/gpt.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/distilbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/proof.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py"], "/qnarre/prep/tokens/byt5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/electra.py": ["/qnarre/prep/config/electra.py", "/qnarre/models/electra.py"], "/qnarre/models/luke.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": ["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", 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["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,610
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/base/doc/header.py
|
# Copyright 2019 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
import re
from .date import Date
from .log import Logger
from .nominals import flags
from .exporter import Exporter
from .category import Subjects
from .sanitizer import sanitize
from .error import ExcludeException
from .base import config, Adrs, Hdr, traits_for
from .base import LnkDate, LnkSubject, LnkFrom, LnkTo, LnkCc, LnkBcc
log = Logger(__name__)
on_wrote = r'^\W*on .+?wrote:$'
on_wrote = re.compile(flags + on_wrote)
class Line:
keys = {
'date: ': LnkDate.label,
'sent: ': LnkDate.label,
'subject: ': LnkSubject.label,
'from: ': LnkFrom.label,
'to: ': LnkTo.label,
'cc: ': LnkCc.label,
'bcc: ': LnkBcc.label,
'in-reply-to: ': None,
'reply-to: ': None
}
has_adrs = 'has_adrs'
has_date = 'has_date'
ignore = 'ignore'
key = None
def __init__(self, txt, **_):
self.txt = t = txt
if t:
colon = ': '
ps = t.split(colon, 1)
if len(ps) == 2:
try:
v = self.keys[ps[0].lower() + colon]
self.key = self.ignore if v is None else v
except KeyError:
return
if ' ' not in ps[0]:
log.warning('Key {} not recognized', ps[0])
elif on_wrote.match(t):
if t.startswith('On '):
self.key = LnkFrom.label
elif Adrs.has_adr(t):
self.key = self.has_adrs
elif Date.has_date(t):
self.key = self.has_date
def extract(dst, src):
for f in Hdr._fields:
try:
v = src[f]
if v:
setattr(dst, f, v)
else:
delattr(dst, f)
except:
pass
return dst
def merge(dst, src):
for f in Hdr._fields:
o = getattr(dst, f, ())
if isinstance(o, tuple):
n = getattr(src, f, ())
if n and isinstance(n, tuple):
n = tuple(sorted(set((*o, *n))))
if n != o:
setattr(dst, f, n)
return dst
class TxtFields:
@classmethod
def extract(cls, raw, **_):
date, from_, to, host, txt = raw
to = (to, from_, None if from_ == host else host)
from_ = (from_, None, None)
return extract(cls(), locals()), txt
class ScrFields:
@classmethod
def extract(cls, raw, **_):
date, topic, from_, txt = raw
from_ = (from_, None, None)
return extract(cls(), locals()), txt
class InlFields:
on, wrote = 'On ', ' wrote:'
@classmethod
def extract(cls, quote, *, ctxt, **_):
h = quote[0]
if not h.startswith(cls.on) or not h.endswith(cls.wrote):
raise ValueError('Not an inline message')
txt = '\n'.join(quote[1:]).strip()
try:
h = h[len(cls.on):-len(cls.wrote)]
i = h.index(':') + 6
try:
date = Date.from_inl(h[:i])
except:
i -= 3
date = Date.from_inl(h[:i])
from_ = Adrs.from_txt(h[i + 1:])
return extract(cls(), locals()), txt
except Exception as e:
if txt and txt not in ctxt.nominals:
raise e
class FwdFields:
@classmethod
def extract(cls, quote, *, ctxt, **_):
date = from_ = prev = exc = None
txt = ''
for i, ln in enumerate(quote):
if not ln:
txt = '\n'.join(quote[i + 1:]).strip()
break
ps = ln.split(':', 1)
try:
if len(ps) == 2:
k, v = ps
k = k.lower()
v = v.strip()
prev = None
elif prev:
k, p = prev
v = p + ' ' + ln
else:
# print(quote)
raise ValueError('Forward fields need label: ' + ln)
if k == 'date' or k == 'sent':
date = Date.from_fwd(v)
elif k == 'subject':
subject = v
prev = k, v
elif k == 'from':
from_ = Adrs.from_txt(v)
elif k == 'to':
to = Adrs.from_txt(v)
prev = k, v
elif k == 'cc':
cc = Adrs.from_txt(v)
prev = k, v
elif k == 'bcc':
bcc = Adrs.from_txt(v)
prev = k, v
elif k == 'in-reply-to':
replying = v
elif k == 'message-id':
record_id = v
else:
raise ValueError('Unrecognized forward label: ' + k)
except Exception as e:
exc = exc or e
if not exc and date and from_:
return extract(cls(), locals()), txt
if txt and txt not in ctxt.nominals:
if exc:
raise exc
raise ValueError('No date and/or sender for forward message')
class MixFields:
@classmethod
def extract(cls, form, **kw):
txt = form.pop('txt', ())
# for v in form.values():
# print(v)
q = [l for l in (*form.values(), *txt)]
ln = form.setdefault(LnkFrom.label, config.def_from)
if ln.startswith('On '):
return InlFields.extract(q, **kw)
elif ln.startswith('From: '):
return FwdFields.extract(q, **kw)
log.warning('Form not recognized {!r}', form.items())
raise ValueError('Form not recognized')
class DocFields:
@classmethod
def extract(cls, raw, *, ctxt, **kw):
date, topic, ls = raw
subject = date.short
exc = None
txt = ''
if topic in ('orders', 'transcripts'):
from_ = Adrs.from_txt('Court')
if topic in ('affidavits', 'exhibits', 'hearings', 'trials'):
to = Adrs.from_txt('Court')
for i, l in enumerate(ls):
if not l:
txt = '\n'.join(ls[i + 1:]).strip()
break
ps = l.split(':', 1)
try:
if len(ps) == 2:
k, v = ps
k = k.lower()
v = v.strip()
else:
# print(ls)
raise ValueError('Doc fields need label: ' + l)
if k == 'title':
title = v
elif k == 'subject':
subject = v
elif k == 'summary':
summary = v
elif k == 'from':
from_ = Adrs.from_txt(v)
elif k == 'to':
to = Adrs.from_txt(v)
elif k == 'cc':
cc = Adrs.from_txt(v)
elif k == 'replying':
replying = v
elif k == 'source':
source = v
elif k == 'tags':
tags = (w.strip() for w in v.split(','))
tags = tuple(sorted(set(w for w in tags if w)))
else:
raise ValueError('Unrecognized doc label: ' + k)
except Exception as e:
exc = exc or e
if not exc and date and from_:
if not txt:
txt = summary
return extract(cls(), locals()), txt
if txt and txt not in ctxt.nominals:
if exc:
raise exc
raise ValueError('No date and/or sender for doc')
class EmlFields:
@classmethod
def extract(cls, raw, **_):
idx = None
date = None
def _value(f):
nonlocal idx
if f.endswith('_'):
f = f[:-1]
f = f.replace('_', '-')
f = 'in-reply-to' if f == 'replying' else f
v = raw.get_all(f)
if isinstance(v, list):
c = len(v)
if c == 0:
v = None
elif c == 1:
v = v[0]
else:
for i in v[1:]:
if i and i != v[0]:
if f == 'message-id':
for i, s in enumerate(v):
if s.endswith(r'@mx.google.com>'):
idx = i
return s
if idx is None:
log.info('Field {} with multi values {}', f, v)
v = v[0]
else:
v = v[idx]
break
else:
v = v[0]
if v and f == 'date':
v = v.datetime.replace(microsecond=0).astimezone()
if not v.second:
v = v.replace(second=1)
nonlocal date
date = v = Date(v)
return v
fs = {f: _value(f) for f in Hdr._fields}
return extract(cls(), fs), None
class Header(Exporter):
def __init__(self, fields, ctxt=None, raise_exclude=True, **_):
super().__init__()
for k, v in fields.items():
setattr(self, k, v)
if ctxt:
f = self.from_
if hasattr(f, 'addresses'):
pf, from_ = ctxt.slugs_for(f)
else:
pf, from_ = ctxt.slugs_for(*f)
t = self.to
if not t or hasattr(t, 'addresses'):
pt, to = ctxt.slugs_for(t)
else:
pt, to = ctxt.slugs_for(*t)
c = self.cc
if not c or hasattr(c, 'addresses'):
pc, cc = ctxt.slugs_for(c)
else:
pc, cc = ctxt.slugs_for(*c)
b = self.bcc
if not b or hasattr(b, 'addresses'):
pb, bcc = ctxt.slugs_for(b)
else:
pb, bcc = ctxt.slugs_for(*b)
# if not to and not cc and not bcc:
# raise ExcludeException()
ps = (pf, pt, pc, pb)
if not any(ps) and any([True for p in ps if p is False]):
if raise_exclude:
raise ExcludeException()
s = self.subject
try:
s = sanitize(s) if s else ''
except UnicodeError:
if raise_exclude:
raise ExcludeException()
s = 'UnicodeError'
subject = Subjects.dejunk(s, ctxt)
self.extract(locals())
def __repr__(self):
return '{}({!r})'.format(type(self).__name__, vars(self))
@property
def name(self):
return self.date.name
@property
def slug(self):
return self.date.slug
extract = extract
merge = merge
cmp_ks = set((LnkFrom.label, LnkTo.label, LnkCc.label, LnkBcc.label,
LnkSubject.label))
def compare(self, other):
if not isinstance(other, type(self)):
return NotImplemented
dc = self.date.compare(other.date)
if dc is config.EQ:
ks = set((*vars(self).keys(), *vars(other).keys())) & self.cmp_ks
for k in ks:
try:
if getattr(self, k) != getattr(other, k):
break
except AttributeError:
break
else:
return config.EQ
elif dc in (config.LT, config.GT):
k = LnkFrom.label
try:
if getattr(self, k) == getattr(other, k):
return dc
except AttributeError:
pass
def mboxer(self, ctxt, **_):
yield 'message-id', self.record_id or self.name
if self.replying:
yield 'in-reply-to', self.replying
yield 'date', self.date.raw
yield 'from', ctxt.name(self.from_[0])
if self.to:
yield 'to', ', '.join(ctxt.name(s) for s in self.to)
if self.cc:
yield 'cc', ', '.join(ctxt.name(s) for s in self.cc)
if self.bcc:
yield 'bcc', ', '.join(ctxt.name(s) for s in self.bcc)
def plainer(self, ctxt, **_):
f = ctxt.name(self.from_[0])
t = self.to or ()
cc = self.cc or ()
if len(t) + len(cc) > 1:
t = ' to audience'
elif t and ctxt[config.DEFAULT].name not in (*f, *t):
t = ' to ' + t[0]
else:
t = ''
yield '**On {}, {} wrote{}:**'.format(self.date.to_inl, f, t)
yield '\n'
def htmer(self, just, frame, ctxt, **_):
ts = [traits_for(f) for f in self.from_]
if just:
j = just.calc_just(t.justify for t in ts)
else:
j = 'justify-content-start'
bs = (t.background for t in ts if t.background is not None)
yield frame[2].format(j, next(bs, 'e8e8e8'))
if just:
yield frame[3].format(self.date.to_inl, ctxt.name(self.from_[0]))
yield '\n'
def blogger(self, **_):
v = self.title
yield v
yield '=' * len(v)
yield ':Date: ' + self.date.to_rst
yield ':From: ' + ', '.join(self.from_)
v = self.to
if v:
yield ':To: ' + ', '.join(v)
v = self.cc
if v:
yield ':Cc: ' + ', '.join(v)
v = self.bcc
if v:
yield ':Bcc: ' + ', '.join(v)
yield '\n'
def footer(self, **_):
pass
for f in Hdr._fields:
setattr(Header, f, None)
|
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["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": 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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,611
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/dataset/orange_sum.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
import datasets as ds
_URL = {
"abstract": "https://raw.githubusercontent.com/Tixierae/OrangeSum/main/data/docs/splits/abstract.tgz",
"title": "https://raw.githubusercontent.com/Tixierae/OrangeSum/main/data/docs/splits/title.tgz",
}
_DOC = "text"
_SUM = "summary"
class OrangeSum(ds.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
ds.BuilderConfig(name="abstract", version=ds.Version("1.1.0")),
ds.BuilderConfig(name="title", version=ds.Version("1.1.0")),
]
def _info(self):
return ds.DatasetInfo(
description="",
citation="",
homepage="",
license="",
features=ds.Features(
{
_DOC: ds.Value("string"),
_SUM: ds.Value("string"),
}
),
supervised_keys=(_DOC, _SUM),
)
def _split_generators(self, mgr):
x = mgr.download(_URL[self.config.name])
return [
ds.SplitGenerator(
name=ds.Split.TRAIN,
gen_kw={
"source_files": mgr.iter_archive(x),
"target_files": mgr.iter_archive(x),
"split": "train",
},
),
ds.SplitGenerator(
name=ds.Split.TEST,
gen_kw={
"source_files": mgr.iter_archive(x),
"target_files": mgr.iter_archive(x),
"split": "test",
},
),
ds.SplitGenerator(
name=ds.Split.VALIDATION,
gen_kw={
"source_files": mgr.iter_archive(x),
"target_files": mgr.iter_archive(x),
"split": "valid",
},
),
]
def _generate_examples(self, src, tgt, split):
sp = f"{self.config.name}/{split}.source"
tp = f"{self.config.name}/{split}.target"
for x, fx in src:
if x == sp:
for y, fy in tgt:
if y == tp:
for i, (d, s) in enumerate(zip(fx, fy)):
yield i, {_DOC: d.decode("utf-8"), _SUM: s.decode("utf-8")}
break
break
|
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"/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], 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"/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/tools/disasm.py", "/tools/triton/python/triton/compiler/code_generator.py", "/tools/triton/python/triton/compiler/make_launcher.py"], "/qnarre/base/doc/graph.py": ["/qnarre/base/doc/base.py"], "/qnarre/base/activism.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py", "/qnarre/base/judgment.py"], "/qnarre/base/doc/exporter.py": ["/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/fnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/roformer.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/megatron.py": ["/qnarre/prep/config/megatron.py", "/qnarre/models/megatron.py"], "/qnarre/prep/tokens/fast/roformer.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/roformer.py"], "/qnarre/core/runner.py": ["/qnarre/core/params.py"], "/qnarre/run/seq2seq.py": ["/qnarre/run/qa.py"], "/qnarre/prep/tokens/luke.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/util/table.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/tree.py", "/qnarre/base/doc/util/utils.py"], "/tools/triton/python/triton/language/standard.py": ["/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/language/__init__.py"], "/qnarre/base/doc/filters.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/args.py"], "/qnarre/prep/convert/gpt_neo.py": ["/qnarre/prep/config/gpt_neo.py", "/qnarre/models/gpt_neo.py"], "/qnarre/base/doc/section.py": ["/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/message.py"], "/qnarre/models/funnel.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/funnel.py"], "/tools/triton/python/triton/common/__init__.py": ["/tools/triton/python/triton/common/build.py"], "/qnarre/base/doc/qnn.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/models/plbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/reformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/images.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/base.py"], "/qnarre/models/yoso.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/canine.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/blog.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/counter.py"], "/qnarre/models/longformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/fast/pegasus.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/pegasus.py"], "/qnarre/base/judgment.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py", "/qnarre/base/conflict.py", "/qnarre/base/conjecture.py"], "/qnarre/models/splinter.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/rag.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/convert/albert.py": ["/qnarre/prep/config/albert.py", "/qnarre/models/albert.py"], "/qnarre/prep/tokens/fast/gpt2.py": ["/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/fast/mbart.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/roformer.py": ["/qnarre/models/roformer.py"], "/qnarre/base/doc/util/tree.py": ["/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py", "/qnarre/base/doc/util/utils.py"], "/qnarre/models/rembert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/t5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/xlm.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/bert_tf2.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/tools/triton/python/triton/ops/__init__.py": ["/tools/triton/python/triton/ops/cross_entropy.py", "/tools/triton/python/triton/ops/matmul.py"], "/qnarre/core/norm.py": ["/qnarre/core/base.py"], "/qnarre/models/mpnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/author.py": ["/qnarre/base/named.py"], "/qnarre/base/doc/args.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/log.py"], "/qnarre/prep/tokens/gpt.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/distilbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/proof.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py"], "/qnarre/prep/tokens/byt5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/electra.py": ["/qnarre/prep/config/electra.py", "/qnarre/models/electra.py"], "/qnarre/models/luke.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": ["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,612
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/base/doc/meta.py
|
# Copyright 2019 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
class converter:
by_name = {}
@classmethod
def convert(cls, src, **kw):
return cls.by_name[type(src).__name__].convert(src, **kw)
def __init__(self, name):
self.name = name
def __call__(self, cls):
self.by_name[self.name] = cls
return cls
class with_current:
def __init__(self):
pass
def __call__(self, cls):
setattr(cls, 'current', cls())
return cls
class with_class_init:
def __init__(self):
pass
def __call__(self, cls):
cls.init()
return cls
class with_property:
default = None
def __init__(self, name, creator, default=None):
self.name = name
self.multi = name.endswith('s')
self.creator = creator
if default is not None:
self.default = default
elif self.multi:
self.default = ()
def __call__(self, cls):
n = '_' + self.name
setattr(cls, n, self.default)
def getter(self):
return getattr(self, n)
c = self.creator
if self.multi:
def setter(self, vs):
if vs:
setattr(self, n, tuple(c(vs)))
else:
self.__dict__.pop(n, None)
else:
def setter(self, v):
if v:
setattr(self, n, c(v))
else:
self.__dict__.pop(n, None)
setattr(cls, self.name, property(getter, setter))
return cls
if __name__ == '__main__':
@with_current()
class A:
def __init__(self):
self.a = 'a'
assert A.current.a == 'a'
print('0 passed')
class Name:
@classmethod
def create(cls, v):
return v
@with_property('name', Name.create, default='')
class A:
pass
a = A()
assert a.name is ''
assert '_name' not in vars(a)
a.name = 'b'
assert '_name' in vars(a)
assert a.name == 'b'
a.name = ''
assert a.name is ''
assert '_name' not in vars(a)
print('A passed')
class Link:
@classmethod
def create(cls, v):
return v
@with_property('link', Link.create)
class B:
pass
a = B()
assert a.link is None
assert '_link' not in vars(a)
a.link = 'b'
assert '_link' in vars(a)
assert a.link == 'b'
a.link = None
assert a.link is None
assert '_link' not in vars(a)
print('B passed')
class Value:
@classmethod
def creator(cls, vs):
for v in vs:
yield v
@with_property('values', Value.creator)
class C:
pass
a = C()
assert a.values is ()
assert '_values' not in vars(a)
a.values = ('b', 'c')
assert '_values' in vars(a)
assert a.values == ('b', 'c')
a.values = ()
assert a.values is ()
assert '_values' not in vars(a)
print('C passed')
class Other:
def meth(self, v):
return v
Other.obj = Other()
@with_property('extra', creator=Other.obj.meth)
class D:
pass
a = D()
assert a.extra is None
assert '_extra' not in vars(a)
a.extra = 'b'
assert '_extra' in vars(a)
assert a.extra == 'b'
a.extra = None
assert a.extra is None
assert '_extra' not in vars(a)
print('D passed')
|
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"/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", 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["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,613
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/convert/rag.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
import os
import pickle
import time
import numpy as np
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ...utils import (
cached_path,
is_datasets_available,
is_faiss_available,
is_remote_url,
logging,
requires_backends,
)
from .configuration_rag import RagConfig
from .tokenization_rag import RagTokenizer
if is_datasets_available():
from datasets import Dataset, load_dataset, load_from_disk
if is_faiss_available():
import faiss
logger = logging.get_logger(__name__)
LEGACY_INDEX_PATH = "https://storage.googleapis.com/huggingface-nlp/datasets/wiki_dpr/"
class Index:
def get_doc_dicts(self, doc_ids: np.ndarray):
raise NotImplementedError
def get_top_docs(self, question_hidden_states: np.ndarray, n_docs=5):
raise NotImplementedError
def is_initialized(self):
raise NotImplementedError
def init_index(self):
raise NotImplementedError
class LegacyIndex(Index):
INDEX_FILENAME = "hf_bert_base.hnswSQ8_correct_phi_128.c_index"
PASSAGE_FILENAME = "psgs_w100.tsv.pkl"
def __init__(self, vector_size, index_path):
self.index_id_to_db_id = []
self.index_path = index_path
self.passages = self._load_passages()
self.vector_size = vector_size
self.index = None
self._index_initialized = False
def _resolve_path(self, index_path, filename):
assert os.path.isdir(index_path) or is_remote_url(
index_path
), "Please specify a valid `index_path`."
archive_file = os.path.join(index_path, filename)
try:
# Load from URL or cache if already cached
resolved_archive_file = cached_path(archive_file)
except EnvironmentError:
msg = (
f"Can't load '{archive_file}'. Make sure that:\n\n"
f"- '{index_path}' is a correct remote path to a directory containing a file named {filename}\n\n"
f"- or '{index_path}' is the correct path to a directory containing a file named {filename}.\n\n"
)
raise EnvironmentError(msg)
if resolved_archive_file == archive_file:
logger.info(f"loading file {archive_file}")
else:
logger.info(f"loading file {archive_file} from cache at {resolved_archive_file}")
return resolved_archive_file
def _load_passages(self):
logger.info(f"Loading passages from {self.index_path}")
passages_path = self._resolve_path(self.index_path, self.PASSAGE_FILENAME)
with open(passages_path, "rb") as passages_file:
passages = pickle.load(passages_file)
return passages
def _deserialize_index(self):
logger.info(f"Loading index from {self.index_path}")
resolved_index_path = self._resolve_path(
self.index_path, self.INDEX_FILENAME + ".index.dpr"
)
self.index = faiss.read_index(resolved_index_path)
resolved_meta_path = self._resolve_path(
self.index_path, self.INDEX_FILENAME + ".index_meta.dpr"
)
with open(resolved_meta_path, "rb") as metadata_file:
self.index_id_to_db_id = pickle.load(metadata_file)
assert (
len(self.index_id_to_db_id) == self.index.ntotal
), "Deserialized index_id_to_db_id should match faiss index size"
def is_initialized(self):
return self._index_initialized
def init_index(self):
index = faiss.IndexHNSWFlat(self.vector_size + 1, 512)
index.hnsw.efSearch = 128
index.hnsw.efConstruction = 200
self.index = index
self._deserialize_index()
self._index_initialized = True
def get_doc_dicts(self, doc_ids: np.array):
doc_list = []
for doc_ids_i in doc_ids:
ids = [str(int(doc_id)) for doc_id in doc_ids_i]
docs = [self.passages[doc_id] for doc_id in ids]
doc_list.append(docs)
doc_dicts = []
for docs in doc_list:
doc_dict = {}
doc_dict["title"] = [doc[1] for doc in docs]
doc_dict["text"] = [doc[0] for doc in docs]
doc_dicts.append(doc_dict)
return doc_dicts
def get_top_docs(self, question_hidden_states: np.ndarray, n_docs=5):
aux_dim = np.zeros(len(question_hidden_states), dtype="float32").reshape(-1, 1)
query_nhsw_vectors = np.hstack((question_hidden_states, aux_dim))
_, docs_ids = self.index.search(query_nhsw_vectors, n_docs)
vectors = [
[self.index.reconstruct(int(doc_id))[:-1] for doc_id in doc_ids] for doc_ids in docs_ids
]
ids = [[int(self.index_id_to_db_id[doc_id]) for doc_id in doc_ids] for doc_ids in docs_ids]
return np.array(ids), np.array(vectors)
class HFIndexBase(Index):
def __init__(self, vector_size, dataset, index_initialized=False):
self.vector_size = vector_size
self.dataset = dataset
self._index_initialized = index_initialized
self._check_dataset_format(with_index=index_initialized)
dataset.set_format(
"numpy", columns=["embeddings"], output_all_columns=True, dtype="float32"
)
def _check_dataset_format(self, with_index):
if not isinstance(self.dataset, Dataset):
raise ValueError(
f"Dataset should be a datasets.Dataset object, but got {type(self.dataset)}"
)
if len({"title", "text", "embeddings"} - set(self.dataset.column_names)) > 0:
raise ValueError(
"Dataset should be a dataset with the following columns: "
"title (str), text (str) and embeddings (arrays of dimension vector_size), "
f"but got columns {self.dataset.column_names}"
)
if with_index and "embeddings" not in self.dataset.list_indexes():
raise ValueError(
"Missing faiss index in the dataset. Make sure you called `dataset.add_faiss_index` to compute it "
"or `dataset.load_faiss_index` to load one from the disk."
)
def init_index(self):
raise NotImplementedError()
def is_initialized(self):
return self._index_initialized
def get_doc_dicts(self, doc_ids: np.ndarray):
return [self.dataset[doc_ids[i].tolist()] for i in range(doc_ids.shape[0])]
def get_top_docs(self, question_hidden_states: np.ndarray, n_docs=5):
_, ids = self.dataset.search_batch("embeddings", question_hidden_states, n_docs)
docs = [self.dataset[[i for i in indices if i >= 0]] for indices in ids]
vectors = [doc["embeddings"] for doc in docs]
for i in range(len(vectors)):
if len(vectors[i]) < n_docs:
vectors[i] = np.vstack(
[vectors[i], np.zeros((n_docs - len(vectors[i]), self.vector_size))]
)
return np.array(ids), np.array(
vectors
) # shapes (batch_size, n_docs) and (batch_size, n_docs, d)
class CanonicalHFIndex(HFIndexBase):
def __init__(
self,
vector_size,
dataset_name="wiki_dpr",
dataset_split="train",
index_name=None,
index_path=None,
use_dummy_dataset=False,
):
if int(index_path is None) + int(index_name is None) != 1:
raise ValueError("Please provide `index_name` or `index_path`.")
self.dataset_name = dataset_name
self.dataset_split = dataset_split
self.index_name = index_name
self.index_path = index_path
self.use_dummy_dataset = use_dummy_dataset
logger.info(f"Loading passages from {self.dataset_name}")
dataset = load_dataset(
self.dataset_name,
with_index=False,
split=self.dataset_split,
dummy=self.use_dummy_dataset,
)
super().__init__(vector_size, dataset, index_initialized=False)
def init_index(self):
if self.index_path is not None:
logger.info(f"Loading index from {self.index_path}")
self.dataset.load_faiss_index("embeddings", file=self.index_path)
else:
logger.info(f"Loading index from {self.dataset_name} with index name {self.index_name}")
self.dataset = load_dataset(
self.dataset_name,
with_embeddings=True,
with_index=True,
split=self.dataset_split,
index_name=self.index_name,
dummy=self.use_dummy_dataset,
)
self.dataset.set_format("numpy", columns=["embeddings"], output_all_columns=True)
self._index_initialized = True
class CustomHFIndex(HFIndexBase):
def __init__(self, vector_size, dataset, index_path=None):
super().__init__(vector_size, dataset, index_initialized=index_path is None)
self.index_path = index_path
@classmethod
def load_from_disk(cls, vector_size, dataset_path, index_path):
logger.info(f"Loading passages from {dataset_path}")
if dataset_path is None or index_path is None:
raise ValueError(
"Please provide `dataset_path` and `index_path` after calling `dataset.save_to_disk(dataset_path)` "
"and `dataset.get_index('embeddings').save(index_path)`."
)
dataset = load_from_disk(dataset_path)
return cls(vector_size=vector_size, dataset=dataset, index_path=index_path)
def init_index(self):
if not self.is_initialized():
logger.info(f"Loading index from {self.index_path}")
self.dataset.load_faiss_index("embeddings", file=self.index_path)
self._index_initialized = True
class RagRetriever:
def __init__(
self,
config,
question_encoder_tokenizer,
generator_tokenizer,
index=None,
init_retrieval=True,
):
self._init_retrieval = init_retrieval
requires_backends(self, ["datasets", "faiss"])
super().__init__()
self.index = index or self._build_index(config)
self.generator_tokenizer = generator_tokenizer
self.question_encoder_tokenizer = question_encoder_tokenizer
self.n_docs = config.n_docs
self.batch_size = config.retrieval_batch_size
self.config = config
if self._init_retrieval:
self.init_retrieval()
self.ctx_encoder_tokenizer = None
self.return_tokenized_docs = False
@staticmethod
def _build_index(config):
if config.index_name == "legacy":
return LegacyIndex(
config.retrieval_vector_size,
config.index_path or LEGACY_INDEX_PATH,
)
elif config.index_name == "custom":
return CustomHFIndex.load_from_disk(
vector_size=config.retrieval_vector_size,
dataset_path=config.passages_path,
index_path=config.index_path,
)
else:
return CanonicalHFIndex(
vector_size=config.retrieval_vector_size,
dataset_name=config.dataset,
dataset_split=config.dataset_split,
index_name=config.index_name,
index_path=config.index_path,
use_dummy_dataset=config.use_dummy_dataset,
)
@classmethod
def from_pretrained(cls, retriever_name_or_path, indexed_dataset=None, **kw):
requires_backends(cls, ["datasets", "faiss"])
config = kw.pop("config", None) or RagConfig.from_pretrained(retriever_name_or_path, **kw)
rag_tokenizer = RagTokenizer.from_pretrained(retriever_name_or_path, config=config)
question_encoder_tokenizer = rag_tokenizer.question_encoder
generator_tokenizer = rag_tokenizer.generator
if indexed_dataset is not None:
config.index_name = "custom"
index = CustomHFIndex(config.retrieval_vector_size, indexed_dataset)
else:
index = cls._build_index(config)
return cls(
config,
question_encoder_tokenizer=question_encoder_tokenizer,
generator_tokenizer=generator_tokenizer,
index=index,
)
def save_pretrained(self, save_directory):
if isinstance(self.index, CustomHFIndex):
if self.config.index_path is None:
index_path = os.path.join(save_directory, "hf_dataset_index.faiss")
self.index.dataset.get_index("embeddings").save(index_path)
self.config.index_path = index_path
if self.config.passages_path is None:
passages_path = os.path.join(save_directory, "hf_dataset")
# datasets don't support save_to_disk with indexes right now
faiss_index = self.index.dataset._indexes.pop("embeddings")
self.index.dataset.save_to_disk(passages_path)
self.index.dataset._indexes["embeddings"] = faiss_index
self.config.passages_path = passages_path
self.config.save_pretrained(save_directory)
rag_tokenizer = RagTokenizer(
question_encoder=self.question_encoder_tokenizer,
generator=self.generator_tokenizer,
)
rag_tokenizer.save_pretrained(save_directory)
def init_retrieval(self):
logger.info("initializing retrieval")
self.index.init_index()
def postprocess_docs(self, docs, input_strings, prefix, n_docs, return_tensors=None):
def cat_input_and_doc(doc_title, doc_text, input_string, prefix):
if doc_title.startswith('"'):
doc_title = doc_title[1:]
if doc_title.endswith('"'):
doc_title = doc_title[:-1]
if prefix is None:
prefix = ""
out = (
prefix
+ doc_title
+ self.config.title_sep
+ doc_text
+ self.config.doc_sep
+ input_string
).replace(" ", " ")
return out
rag_input_strings = [
cat_input_and_doc(
docs[i]["title"][j],
docs[i]["text"][j],
input_strings[i],
prefix,
)
for i in range(len(docs))
for j in range(n_docs)
]
contextualized_inputs = self.generator_tokenizer.batch_encode_plus(
rag_input_strings,
max_length=self.config.max_combined_length,
return_tensors=return_tensors,
padding="max_length",
truncation=True,
)
return contextualized_inputs["input_ids"], contextualized_inputs["attention_mask"]
def _chunk_tensor(self, t, chunk_size):
return [t[i : i + chunk_size] for i in range(0, len(t), chunk_size)]
def _main_retrieve(self, question_hidden_states: np.ndarray, n_docs):
question_hidden_states_batched = self._chunk_tensor(question_hidden_states, self.batch_size)
ids_batched = []
vectors_batched = []
for question_hidden_states in question_hidden_states_batched:
start_time = time.time()
ids, vectors = self.index.get_top_docs(question_hidden_states, n_docs)
logger.debug(
f"index search time: {time.time() - start_time} sec, batch size {question_hidden_states.shape}"
)
ids_batched.extend(ids)
vectors_batched.extend(vectors)
return (
np.array(ids_batched),
np.array(vectors_batched),
) # shapes (batch_size, n_docs) and (batch_size, n_docs, d)
def retrieve(self, question_hidden_states: np.ndarray, n_docs):
doc_ids, retrieved_doc_embeds = self._main_retrieve(question_hidden_states, n_docs)
return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(doc_ids)
def set_ctx_encoder_tokenizer(self, ctx_encoder_tokenizer: PreTrainedTokenizer):
# used in end2end retriever training
self.ctx_encoder_tokenizer = ctx_encoder_tokenizer
self.return_tokenized_docs = True
def __call__(
self,
question_input_ids,
question_hidden_states,
prefix=None,
n_docs=None,
return_tensors=None,
):
n_docs = n_docs if n_docs is not None else self.n_docs
prefix = prefix if prefix is not None else self.config.generator.prefix
retrieved_doc_embeds, doc_ids, docs = self.retrieve(question_hidden_states, n_docs)
input_strings = self.question_encoder_tokenizer.batch_decode(
question_input_ids, skip_special_tokens=True
)
context_input_ids, context_attention_mask = self.postprocess_docs(
docs, input_strings, prefix, n_docs, return_tensors=return_tensors
)
if self.return_tokenized_docs:
retrived_doc_text = []
retrived_doc_title = []
for b_idx in range(len(docs)):
for doc_idx in range(n_docs):
retrived_doc_text.append(docs[b_idx]["text"][doc_idx])
retrived_doc_title.append(docs[b_idx]["title"][doc_idx])
tokenized_docs = self.ctx_encoder_tokenizer(
retrived_doc_title,
retrived_doc_text,
truncation=True,
padding="longest",
return_tensors=return_tensors,
)
return BatchEncoding(
{
"context_input_ids": context_input_ids,
"context_attention_mask": context_attention_mask,
"retrieved_doc_embeds": retrieved_doc_embeds,
"doc_ids": doc_ids,
"tokenized_doc_ids": tokenized_docs["input_ids"],
"tokenized_doc_attention_mask": tokenized_docs["attention_mask"],
},
tensor_type=return_tensors,
)
else:
return BatchEncoding(
{
"context_input_ids": context_input_ids,
"context_attention_mask": context_attention_mask,
"retrieved_doc_embeds": retrieved_doc_embeds,
"doc_ids": doc_ids,
},
tensor_type=return_tensors,
)
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"/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,614
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/models/flash/gpt.py
|
# Copyright (c) 2023, Tri Dao.
import logging
import math
import re
from functools import partial
from collections import namedtuple, OrderedDict
from collections.abc import Sequence
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import GPT2Config
from einops import rearrange
from flash_attn.ops.activations import sqrelu_fwd
from flash_attn.modules.mha import MHA, ParallelMHA
from flash_attn.modules.mlp import Mlp, GatedMlp, FusedMLP, ParallelFusedMLP
from flash_attn.modules.block import Block, ParallelBlock
from flash_attn.modules.embedding import GPT2Embeddings, ParallelGPT2Embeddings
from flash_attn.utils.distributed import sync_shared_params, all_gather_raw
from flash_attn.utils.pretrained import state_dict_from_pretrained
from flash_attn.utils.generation import GenerationMixin
from flash_attn.models.opt import remap_state_dict_hf_opt
from flash_attn.models.gptj import remap_state_dict_hf_gptj
from flash_attn.models.gpt_neox import remap_state_dict_hf_gpt_neox
try:
from flash_attn.ops.fused_dense import ColumnParallelLinear
except ImportError:
ColumnParallelLinear = None
try:
from flash_attn.ops.layer_norm import dropout_add_layer_norm
except ImportError:
dropout_add_layer_norm = None
try:
from flash_attn.ops.layer_norm import dropout_add_layer_norm_parallel_residual
except ImportError:
dropout_add_layer_norm_parallel_residual = None
try:
from flash_attn.ops.rms_norm import RMSNorm, dropout_add_rms_norm
except ImportError:
RMSNorm, dropout_add_rms_norm = None
try:
from flash_attn.ops.rms_norm import dropout_add_rms_norm_parallel_residual
except ImportError:
dropout_add_rms_norm_parallel_residual = None
try:
from flash_attn.ops.triton.mlp import FusedDenseSqreluDense
except ImportError:
FusedDenseSqreluDense = None
logger = logging.getLogger(__name__)
def create_mixer_cls(config, layer_idx=None, process_group=None, device=None, dtype=None):
factory_kwargs = {'device': device, 'dtype': dtype}
head_dim = getattr(config, 'head_dim', config.hidden_size // config.num_attention_heads)
softmax_scale = 1.0 if not config.scale_attn_weights else head_dim ** (-0.5)
if config.scale_attn_by_inverse_layer_idx:
assert layer_idx is not None
softmax_scale /= float(layer_idx + 1)
dwconv = getattr(config, 'attn_dwconv', False)
if dwconv:
assert process_group is None, 'TensorParallel MHA does not support dwconv yet'
qkv_proj_bias = getattr(config, 'qkv_proj_bias', True)
out_proj_bias = getattr(config, 'out_proj_bias', True)
rotary_emb_dim = int(getattr(config, 'rotary_emb_fraction', 0.0) * head_dim)
rotary_emb_scale_base = getattr(config, 'rotary_emb_scale_base', None)
rotary_emb_interleaved = getattr(config, 'rotary_emb_interleaved', False)
use_flash_attn = getattr(config, 'use_flash_attn', False)
fused_bias_fc = getattr(config, 'fused_bias_fc', False)
if not fused_bias_fc:
assert process_group is None, 'TensorParallel MHA requires fused_bias_fc'
mha_cls = MHA if process_group is None else ParallelMHA
serial_kwargs = ({'fused_bias_fc': fused_bias_fc, 'dwconv': dwconv}
if process_group is None else {})
parallel_kwargs = ({'process_group': process_group,
'sequence_parallel': getattr(config, 'sequence_parallel', True)}
if process_group is not None else {})
mixer_cls = partial(mha_cls, num_heads=config.num_attention_heads,
qkv_proj_bias=qkv_proj_bias, out_proj_bias=out_proj_bias,
dropout=config.attn_pdrop,
softmax_scale=softmax_scale, causal=True, layer_idx=layer_idx,
rotary_emb_dim=rotary_emb_dim, rotary_emb_scale_base=rotary_emb_scale_base,
rotary_emb_interleaved=rotary_emb_interleaved,
use_flash_attn=use_flash_attn,
**serial_kwargs, **parallel_kwargs, **factory_kwargs)
return mixer_cls
def create_mlp_cls(config, layer_idx=None, process_group=None, device=None, dtype=None):
factory_kwargs = {'device': device, 'dtype': dtype}
mlp_fc1_bias = getattr(config, 'mlp_fc1_bias', True)
mlp_fc2_bias = getattr(config, 'mlp_fc2_bias', True)
fused_mlp = getattr(config, 'fused_mlp', False)
if fused_mlp:
assert config.activation_function in ['gelu_new', 'gelu_fast', 'gelu_approx', 'relu', 'sqrelu']
fused_dense_sqrelu_dense = getattr(config, 'fused_dense_sqrelu_dense', False)
if fused_dense_sqrelu_dense:
assert config.activation_function == 'sqrelu', ('fused_dense_sqrelu_dense only '
'supports approximate activation_function sqrelu')
assert not (fused_dense_sqrelu_dense and fused_mlp)
if process_group is not None:
assert fused_mlp, 'Tensor Parallel is only implemented for FusedMLP'
if not fused_mlp and not fused_dense_sqrelu_dense:
assert config.activation_function in ['gelu_new', 'gelu_fast', 'gelu_approx', 'relu',
'sqrelu', 'glu', 'swiglu', 'geglu']
if config.activation_function in ['glu', 'swiglu', 'geglu']:
activation = (F.sigmoid if config.activation_function == 'glu'
else (F.silu if config.activation_function == 'swiglu'
else F.gelu))
mlp_cls = partial(GatedMlp, hidden_features=config.n_inner, activation=activation,
bias1=mlp_fc1_bias, bias2=mlp_fc2_bias, **factory_kwargs)
else:
if config.activation_function == 'relu':
activation = partial(F.relu, inplace=True)
elif config.activation_function == 'sqrelu':
activation = sqrelu_fwd
else:
approximate = ('tanh' if config.activation_function
in ['gelu_new', 'gelu_fast', 'gelu_approx'] else 'none')
activation=partial(F.gelu, approximate=approximate)
mlp_cls = partial(Mlp, hidden_features=config.n_inner, activation=activation,
bias1=mlp_fc1_bias, bias2=mlp_fc2_bias, **factory_kwargs)
else:
mlp_checkpoint_lvl = getattr(config, 'mlp_checkpoint_lvl', 0)
# mlp_checkpoint_lvl could be a list, which contains the checkpoint_lvl for each layer
if isinstance(mlp_checkpoint_lvl, Sequence):
assert layer_idx is not None
mlp_checkpoint_lvl = mlp_checkpoint_lvl[layer_idx]
if fused_mlp:
if FusedMLP is None:
raise ImportError('fused_dense is not installed')
activation = ('gelu_approx' if config.activation_function
in ['gelu_new', 'gelu_fast', 'gelu_approx'] else config.activation_function)
mlp_cls = FusedMLP if process_group is None else ParallelFusedMLP
parallel_kwargs = ({'process_group': process_group,
'sequence_parallel': getattr(config, 'sequence_parallel', True)}
if process_group is not None else {})
mlp_cls = partial(mlp_cls, hidden_features=config.n_inner, activation=activation,
checkpoint_lvl=mlp_checkpoint_lvl,
bias1=mlp_fc1_bias, bias2=mlp_fc2_bias,
**parallel_kwargs, **factory_kwargs)
elif fused_dense_sqrelu_dense:
assert FusedDenseSqreluDense is not None
mlp_cls = partial(FusedDenseSqreluDense, hidden_features=config.n_inner,
checkpoint_lvl=mlp_checkpoint_lvl, **factory_kwargs)
else:
raise RuntimeError('MLP type not supported')
return mlp_cls
def create_block(config, layer_idx=None, process_group=None, device=None, dtype=None):
factory_kwargs = {'device': device, 'dtype': dtype}
sequence_parallel = getattr(config, 'sequence_parallel', True)
mixer_cls = create_mixer_cls(config, layer_idx, process_group=process_group, **factory_kwargs)
mlp_cls = create_mlp_cls(config, layer_idx, process_group=process_group, **factory_kwargs)
use_rms_norm = getattr(config, 'rms_norm', False)
norm_cls = partial(nn.LayerNorm if not use_rms_norm else RMSNorm,
eps=config.layer_norm_epsilon, **factory_kwargs)
# TD [2022-07-30]: Force residual in fp32, seems to make fp16 training more stable
residual_in_fp32 = getattr(config, 'residual_in_fp32', False)
resid_dropout1 = config.resid_pdrop if layer_idx is None or layer_idx > 0 else config.embd_pdrop
prenorm = getattr(config, 'prenorm', True)
parallel_block = getattr(config, 'parallel_block', False)
if not parallel_block:
block = Block(
config.hidden_size, mixer_cls, mlp_cls, norm_cls=norm_cls,
prenorm=prenorm, resid_dropout1=resid_dropout1, resid_dropout2=config.resid_pdrop,
fused_dropout_add_ln=getattr(config, 'fused_dropout_add_ln', False),
residual_in_fp32=residual_in_fp32,
sequence_parallel=sequence_parallel and process_group is not None,
mark_shared_params=process_group is not None
)
else:
assert prenorm
block = ParallelBlock(
config.hidden_size, mixer_cls, mlp_cls, norm_cls=norm_cls,
resid_dropout1=resid_dropout1, resid_dropout2=config.resid_pdrop,
tied_norm=getattr(config, 'parallel_block_tied_norm', False),
fused_dropout_add_ln=getattr(config, 'fused_dropout_add_ln', False),
residual_in_fp32=residual_in_fp32,
sequence_parallel=sequence_parallel and process_group is not None,
mark_shared_params=process_group is not None
)
block.layer_idx = layer_idx
return block
class GPTPreTrainedModel(nn.Module):
""" An abstract class to handle weights initialization and
a simple interface for dowloading and loading pretrained models.
"""
def __init__(self, config, *inputs, **kwargs):
super().__init__()
if not isinstance(config, GPT2Config):
raise ValueError(
"Parameter config in `{}(config)` should be an instance of class `GPT2Config`. "
"To create a model from a Google pretrained model use "
"`model = {}.from_pretrained(PRETRAINED_MODEL_NAME)`".format(
self.__class__.__name__, self.__class__.__name__
))
self.config = config
@classmethod
def from_pretrained(cls, model_name, config, *args, strict=True, device=None, dtype=None,
world_size=1, rank=0, **kwargs):
"""
Instantiate a GPTPreTrainedModel from a pre-trained model file or a pytorch state dict.
Download and cache the pre-trained model file if needed.
"""
# Instantiate model.
model = cls(config, *args, device=device, dtype=dtype, **kwargs)
# Load state_dict in cpu because we already initialized the model in GPU, and we don't
# want extra stuff taking up more GPU memory
state_dict = state_dict_from_pretrained(
model_name, device='cpu', dtype=dtype
)
if model_name.startswith('gpt2'):
state_dict = remap_state_dict_hf_gpt2(state_dict, config)
elif model_name.startswith('facebook/opt'):
state_dict = remap_state_dict_hf_opt(state_dict, config)
elif model_name.startswith('EleutherAI/gpt-j-'):
state_dict = remap_state_dict_hf_gptj(state_dict, config)
strict = False # We have rotary_emb.inf_freq buffers not in the GPT-J checkpoint
elif model_name.startswith('EleutherAI/gpt-neox-'):
state_dict = remap_state_dict_hf_gpt_neox(state_dict, config)
else:
raise NotImplementedError(f'Model {model_name} not supported')
if world_size > 1:
state_dict = shard_state_dict_tp(state_dict, config, world_size, rank)
load_return = model.load_state_dict(state_dict, strict=strict)
logger.info(load_return)
return model
# https://github.com/huggingface/transformers/blob/c28d04e9e252a1a099944e325685f14d242ecdcd/src/transformers/models/gpt2/modeling_gpt2.py#L454
def _init_weights(module, n_layer, initializer_range=0.02, rescale_prenorm_residual=True):
if isinstance(module, nn.Linear):
nn.init.normal_(module.weight, std=initializer_range)
if module.bias is not None:
nn.init.zeros_(module.bias)
elif isinstance(module, nn.Embedding):
nn.init.normal_(module.weight, std=initializer_range)
if rescale_prenorm_residual:
# Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme:
# > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale
# > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers.
# > -- GPT-2 :: https://openai.com/blog/better-language-models/
#
# Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py
for name, p in module.named_parameters():
if name in ["out_proj.weight", "fc2.weight"]:
# Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block
nn.init.normal_(p, mean=0.0, std=initializer_range / math.sqrt(2 * n_layer))
class GPTModel(GPTPreTrainedModel):
def __init__(self, config: GPT2Config, process_group=None, device=None, dtype=None):
super().__init__(config)
factory_kwargs = {'device': device, 'dtype': dtype}
self.process_group = process_group
self.sequence_parallel = getattr(config, 'sequence_parallel', True)
assert config.activation_function in ['gelu', 'gelu_new', 'gelu_fast', 'gelu_approx',
'relu', 'sqrelu', 'glu', 'swiglu', 'geglu']
pad_vocab_size_multiple = getattr(config, 'pad_vocab_size_multiple', 1)
vocab_size = (math.ceil(config.vocab_size / pad_vocab_size_multiple)
* pad_vocab_size_multiple)
# TD [2022-07-30]: Force residual in fp32, seems to make fp16 training more stable
self.residual_in_fp32 = getattr(config, 'residual_in_fp32', False)
# These 2 options are for OPT-350m
self.prenorm = getattr(config, 'prenorm', True)
use_rms_norm = getattr(config, 'rms_norm', False)
word_embed_proj_dim = getattr(config, 'word_embed_proj_dim', None)
# For GPT-J, GPT-NeoX
self.parallel_block = getattr(config, 'parallel_block', False)
if process_group is None:
self.embeddings = GPT2Embeddings(
config.hidden_size, vocab_size, config.max_position_embeddings,
word_embed_proj_dim=word_embed_proj_dim, **factory_kwargs
)
else:
self.embeddings = ParallelGPT2Embeddings(
config.hidden_size, vocab_size, config.max_position_embeddings,
process_group=process_group, sequence_parallel=self.sequence_parallel,
**factory_kwargs
)
# We change the order of dropout, residual and layer norm:
# Instead of LN -> Attn / MLP -> Dropout -> Add, we do:
# Dropout -> Add -> LN -> Attn / MLP, returning both the residual branch (output of Add) and
# the main branch (output of MLP). The model definition is unchanged, but the mapping of the
# nn.Dropout probabilities are changed.
# This is for performance reason: we can fuse dropout + add + layer_norm.
self.layers = nn.ModuleList([create_block(config, layer_idx=i, process_group=process_group,
**factory_kwargs)
for i in range(config.num_hidden_layers)])
self.fused_dropout_add_ln = getattr(config, 'fused_dropout_add_ln', False)
if self.fused_dropout_add_ln:
if ((not self.parallel_block and dropout_add_layer_norm is None)
or (self.parallel_block and dropout_add_layer_norm_parallel_residual is None)):
raise ImportError('dropout_layer_norm is not installed')
if self.prenorm:
self.drop_f = nn.Dropout(config.resid_pdrop)
norm_cls = nn.LayerNorm if not use_rms_norm else RMSNorm
self.ln_f = norm_cls(config.hidden_size, eps=config.layer_norm_epsilon,
**factory_kwargs)
if process_group is not None:
for p in self.ln_f.parameters():
# Mark the norm parameters as "shared_params" so that we sync their values at init.
p._shared_params = True
# Mark the norm params as "sequence_parallel" so we run all-reduce on their grads.
if self.sequence_parallel:
p._sequence_parallel = True
self.apply(partial(_init_weights, n_layer=config.num_hidden_layers,
initializer_range=config.initializer_range))
self.tie_weights()
def tie_weights(self):
if self.process_group is not None:
sync_shared_params(self, self.process_group)
def allocate_inference_cache(self, batch_size, max_seqlen, dtype=None, **kwargs):
return {i: layer.allocate_inference_cache(batch_size, max_seqlen, dtype=dtype, **kwargs)
for i, layer in enumerate(self.layers)}
def forward(self, input_ids, position_ids=None, inference_params=None):
# If using Tensor Parallel with sequence parallel, we combine the batch and the seqlen
# dimensions so that we can split on it easily, in case of small batch size.
# Only the attention layers need to know the seqlen.
embedding_kwargs = ({'combine_batch_seqlen_dim': True}
if self.process_group is not None and self.sequence_parallel else {})
hidden_states = self.embeddings(input_ids, position_ids=position_ids, **embedding_kwargs)
if self.parallel_block:
hidden_states2 = None
residual = None
mixer_kwargs = ({'seqlen': input_ids.shape[1]}
if self.process_group is not None and self.sequence_parallel else {})
if inference_params is not None:
mixer_kwargs['inference_params'] = inference_params
for layer in self.layers:
if self.prenorm:
if not self.parallel_block:
hidden_states, residual = layer(hidden_states, residual,
mixer_kwargs=mixer_kwargs)
else:
hidden_states, hidden_states2, residual = layer(
hidden_states, hidden_states2, residual, mixer_kwargs=mixer_kwargs
)
else:
hidden_states = layer(hidden_states, mixer_kwargs=mixer_kwargs)
if self.prenorm:
if not self.fused_dropout_add_ln:
dropped = self.drop_f(hidden_states)
if not self.parallel_block:
residual = (dropped + residual) if residual is not None else dropped
else:
dropped2 = self.drop_f(hidden_states2)
residual = ((residual + dropped + dropped2)
if residual is not None else dropped + dropped2)
hidden_states = self.ln_f(residual.to(dtype=self.ln_f.weight.dtype))
else:
# Set prenorm=False here since we don't need the residual
if not self.parallel_block:
hidden_states = dropout_add_layer_norm(
hidden_states, residual, self.ln_f.weight, self.ln_f.bias,
self.drop_f.p if self.training else 0.0, self.ln_f.eps, prenorm=False,
residual_in_fp32=self.residual_in_fp32
)
else:
hidden_states, _ = dropout_add_layer_norm_parallel_residual(
hidden_states, hidden_states2, residual, self.ln_f.weight, self.ln_f.bias,
None, None, self.drop_f.p if self.training else 0.0, self.ln_f.eps,
prenorm=False, residual_in_fp32=self.residual_in_fp32
)
return hidden_states
class GPTLMHeadModel(GPTPreTrainedModel, GenerationMixin):
def __init__(self, config: GPT2Config, process_group=None, device=None, dtype=None):
factory_kwargs = {'device': device, 'dtype': dtype}
super().__init__(config)
self.process_group = process_group
self.transformer = GPTModel(config, process_group=process_group, **factory_kwargs)
self.tie_word_embeddings = getattr(config, 'tie_word_embeddings', True)
lm_head_bias = getattr(config, 'lm_head_bias', False)
pad_vocab_size_multiple = getattr(config, 'pad_vocab_size_multiple', 1)
vocab_size = (math.ceil(config.vocab_size / pad_vocab_size_multiple)
* pad_vocab_size_multiple)
# This option is for OPT-350m
word_embed_proj_dim = getattr(config, 'word_embed_proj_dim', None)
embed_dim = config.n_embd if word_embed_proj_dim is None else word_embed_proj_dim
if word_embed_proj_dim is not None:
self.project_out = nn.Linear(config.n_embd, embed_dim, bias=False, **factory_kwargs)
else:
self.project_out = None
if process_group is None:
self.lm_head = nn.Linear(embed_dim, vocab_size, bias=lm_head_bias, **factory_kwargs)
else:
if ColumnParallelLinear is None:
raise ImportError('fused_dense_lib is not installed')
self.lm_head = ColumnParallelLinear(
embed_dim, vocab_size, process_group, bias=lm_head_bias,
sequence_parallel=getattr(config, 'sequence_parallel', True), **factory_kwargs
)
# Initialize weights and apply final processing
self.apply(partial(_init_weights, n_layer=config.num_hidden_layers,
initializer_range=config.initializer_range))
self.tie_weights()
def tie_weights(self):
if self.tie_word_embeddings:
self.lm_head.weight = self.transformer.embeddings.word_embeddings.weight
if self.process_group is not None:
sync_shared_params(self, self.process_group)
def allocate_inference_cache(self, batch_size, max_seqlen, dtype=None, **kwargs):
return self.transformer.allocate_inference_cache(batch_size, max_seqlen, dtype=dtype,
**kwargs)
def forward(self, input_ids, position_ids=None, inference_params=None, last_token_only=False):
"""
inference_params: for generation. Adapted from Megatron-LM (and Apex)
https://github.com/NVIDIA/apex/blob/3ff1a10f72ec07067c4e44759442329804ac5162/apex/transformer/testing/standalone_transformer_lm.py#L470
last_token_only: whether to return the logit for the last token only,
of shape (batch_size, vocab_size)
"""
hidden_states = self.transformer(input_ids, position_ids=position_ids,
inference_params=inference_params)
if last_token_only:
hidden_states = hidden_states[:, -1]
if self.project_out is not None:
hidden_states = self.project_out(hidden_states)
lm_logits = self.lm_head(hidden_states)
# During inference, we want the full logit for sampling
if isinstance(self.lm_head, ColumnParallelLinear) and inference_params is not None:
lm_logits, _ = all_gather_raw(lm_logits, self.lm_head.process_group)
lm_logits = rearrange(lm_logits, '(n b) ... d -> b ... (n d)', b=hidden_states.shape[0])
CausalLMOutput = namedtuple('CausalLMOutput', ['logits'])
return CausalLMOutput(logits=lm_logits)
def load_state_dict(self, state_dict, strict=True):
# Remapping from our checkpoints that used a different ordering of layers in the block
# Previous: Attn / MLP -> Dropout -> Add -> LN
# Current: Dropout -> Add -> LN -> Attn / MLP
if 'transformer.ln_0.weight' in state_dict:
n_layers = len(self.transformer.layers)
ln_weight = state_dict.pop(f'transformer.layers.{n_layers - 1}.norm2.weight')
ln_bias = state_dict.pop(f'transformer.layers.{n_layers - 1}.norm2.bias')
state_dict['transformer.ln_f.weight'] = ln_weight
state_dict['transformer.ln_f.bias'] = ln_bias
for l in reversed(range(n_layers)):
ln_weight = state_dict.pop(f'transformer.layers.{l}.norm1.weight')
ln_bias = state_dict.pop(f'transformer.layers.{l}.norm1.bias')
state_dict[f'transformer.layers.{l}.norm2.weight'] = ln_weight
state_dict[f'transformer.layers.{l}.norm2.bias'] = ln_bias
if l > 0:
ln_weight = state_dict.pop(f'transformer.layers.{l - 1}.norm2.weight')
ln_bias = state_dict.pop(f'transformer.layers.{l - 1}.norm2.bias')
state_dict[f'transformer.layers.{l}.norm1.weight'] = ln_weight
state_dict[f'transformer.layers.{l}.norm1.bias'] = ln_bias
ln_weight = state_dict.pop('transformer.ln_0.weight')
ln_bias = state_dict.pop('transformer.ln_0.bias')
state_dict[f'transformer.layers.0.norm1.weight'] = ln_weight
state_dict[f'transformer.layers.0.norm1.bias'] = ln_bias
return super().load_state_dict(state_dict, strict=strict)
def shard_state_dict_tp(state_dict, config, world_size, rank):
"""Convert the state_dict of a standard GPT model to the state_dict of a GPT model
with tensor parallel.
"""
pad_vocab_size_multiple = getattr(config, 'pad_vocab_size_multiple', 1)
vocab_size = (math.ceil(config.vocab_size / pad_vocab_size_multiple) * pad_vocab_size_multiple)
assert vocab_size % world_size == 0
assert config.hidden_size % world_size == 0
inner_dim = config.n_inner if config.n_inner is not None else 4 * config.hidden_size
assert inner_dim % world_size == 0
def shard_first_dim(state_dict, key):
x = state_dict[key]
dim = x.shape[0] // world_size
state_dict[key] = x[rank * dim:(rank + 1) * dim]
def shard_last_dim(state_dict, key):
x = state_dict[key]
dim = x.shape[-1] // world_size
state_dict[key] = x[..., rank * dim:(rank + 1) * dim]
def shard_qkv_headdim(state_dict, key):
x = rearrange(state_dict[key], '(three d) ... -> three d ...', three=3)
dim = x.shape[1] // world_size
state_dict[key] = rearrange(x[:, rank * dim:(rank + 1) * dim],
'three d ... -> (three d) ...')
shard_first_dim(state_dict, 'transformer.embeddings.word_embeddings.weight')
if 'lm_head.weight' in state_dict:
shard_first_dim(state_dict, 'lm_head.weight')
if 'transformer.embeddings.position_embeddings.weight' in state_dict:
shard_last_dim(state_dict, 'transformer.embeddings.position_embeddings.weight')
for i in range(config.num_hidden_layers):
shard_qkv_headdim(state_dict, f'transformer.layers.{i}.mixer.Wqkv.weight')
shard_qkv_headdim(state_dict, f'transformer.layers.{i}.mixer.Wqkv.bias')
shard_last_dim(state_dict, f'transformer.layers.{i}.mixer.out_proj.weight')
if rank != 0:
state_dict.pop(f'transformer.layers.{i}.mixer.out_proj.bias')
shard_first_dim(state_dict, f'transformer.layers.{i}.mlp.fc1.weight')
shard_first_dim(state_dict, f'transformer.layers.{i}.mlp.fc1.bias')
shard_last_dim(state_dict, f'transformer.layers.{i}.mlp.fc2.weight')
if rank != 0:
state_dict.pop(f'transformer.layers.{i}.mlp.fc2.bias')
return state_dict
def combine_state_dicts_tp(state_dicts, config):
"""Convert the state_dict of a standard GPT model to the state_dict of a GPT model
with tensor parallel.
"""
world_size = len(state_dicts)
keys = state_dicts[0].keys()
pad_vocab_size_multiple = getattr(config, 'pad_vocab_size_multiple', 1)
vocab_size = (math.ceil(config.vocab_size / pad_vocab_size_multiple) * pad_vocab_size_multiple)
assert vocab_size % world_size == 0
assert config.hidden_size % world_size == 0
inner_dim = config.n_inner if config.n_inner is not None else 4 * config.hidden_size
assert inner_dim % world_size == 0
# Sometimes the word embeddings are sharded on the 0th dim, sometimes on the 1st dim.
# vocab_size // world_size coordinates are nonzero.
def combine_word_embeddings(state_dicts, state_dict, key):
dim = 0 if state_dicts[0][key].shape[0] == vocab_size // world_size else 1
state_dict[key] = torch.cat([s[key] for s in state_dicts], dim=dim)
def combine_dim(state_dicts, state_dict, key, dim=-1):
if key in state_dict:
state_dict[key] = torch.cat([s[key] for s in state_dicts], dim=dim)
def combine_qkv_headdim(state_dicts, state_dict, key):
if key in state_dict:
xs = [rearrange(s[key], '(three d) ... -> three d ...', three=3) for s in state_dicts]
state_dict[key] = rearrange(torch.cat(xs, dim=1), 'three d ... -> (three d) ...')
def combine_gated_mlp(state_dicts, state_dict, key):
if key in state_dict:
xs = [rearrange(s[key], '(two d) ... -> two d ...', two=2) for s in state_dicts]
state_dict[key] = rearrange(torch.cat(xs, dim=1), 'two d ... -> (two d) ...')
state_dict = state_dicts[0].copy() # don't modify state_dict[0] inplace
combine_word_embeddings(state_dicts, state_dict, 'transformer.embeddings.word_embeddings.weight')
if 'lm_head.weight' in state_dict:
combine_word_embeddings(state_dicts, state_dict, 'lm_head.weight')
if 'transformer.embeddings.position_embeddings.weight' in state_dict:
combine_dim(state_dicts, state_dict, 'transformer.embeddings.position_embeddings.weight', -1)
mlp_combine_fn = (combine_gated_mlp if config.activation_function in ['glu', 'swiglu', 'geglu']
else partial(combine_dim, dim=0))
for i in range(config.num_hidden_layers):
combine_qkv_headdim(state_dicts, state_dict, f'transformer.layers.{i}.mixer.Wqkv.weight')
combine_qkv_headdim(state_dicts, state_dict, f'transformer.layers.{i}.mixer.Wqkv.bias')
combine_dim(state_dicts, state_dict, f'transformer.layers.{i}.mixer.out_proj.weight', -1)
mlp_combine_fn(state_dicts, state_dict, f'transformer.layers.{i}.mlp.fc1.weight')
combine_dim(state_dicts, state_dict, f'transformer.layers.{i}.mlp.fc1.bias', 0)
combine_dim(state_dicts, state_dict, f'transformer.layers.{i}.mlp.fc2.weight', -1)
return state_dict
def remap_state_dict_hf_gpt2(state_dict, config):
# Word embedding and position embedding
def key_mapping_pos_emb(key):
return re.sub(r'^wpe.', 'transformer.embeddings.position_embeddings.', key)
state_dict = OrderedDict((key_mapping_pos_emb(k), v) for k, v in state_dict.items())
word_embeddings = state_dict.pop('wte.weight')
# It's possible that vocab_size is padded to be a multiple of 8, for example.
pad_vocab_size_multiple = getattr(config, 'pad_vocab_size_multiple', 1)
vocab_size = (math.ceil(config.vocab_size / pad_vocab_size_multiple) * pad_vocab_size_multiple)
state_dict['transformer.embeddings.word_embeddings.weight'] = F.pad(
word_embeddings, (0, 0, 0, vocab_size - word_embeddings.shape[0])
)
state_dict['lm_head.weight'] = state_dict['transformer.embeddings.word_embeddings.weight']
# LayerNorm
def key_mapping_ln(key):
key = re.sub(r'^ln_f.(weight|bias)', r'transformer.ln_f.\1', key)
key = re.sub(r'^h.(\d+).ln_(1|2).(weight|bias)', r'transformer.layers.\1.norm\2.\3', key)
return key
state_dict = OrderedDict((key_mapping_ln(k), v) for k, v in state_dict.items())
# MLP
for d in range(config.num_hidden_layers):
W1 = state_dict.pop(f'h.{d}.mlp.c_fc.weight')
state_dict[f'transformer.layers.{d}.mlp.fc1.weight'] = W1.t()
W2 = state_dict.pop(f'h.{d}.mlp.c_proj.weight')
state_dict[f'transformer.layers.{d}.mlp.fc2.weight'] = W2.t()
def key_mapping_mlp(key):
key = re.sub(r'^h.(\d+).mlp.c_fc.bias', r'transformer.layers.\1.mlp.fc1.bias', key)
key = re.sub(r'^h.(\d+).mlp.c_proj.bias', r'transformer.layers.\1.mlp.fc2.bias', key)
return key
state_dict = OrderedDict((key_mapping_mlp(k), v) for k, v in state_dict.items())
# Attention
for d in range(config.num_hidden_layers):
state_dict.pop(f'h.{d}.attn.bias') # We don't store this bias
Wqkv = state_dict.pop(f'h.{d}.attn.c_attn.weight')
state_dict[f'transformer.layers.{d}.mixer.Wqkv.weight'] = Wqkv.t()
Wout = state_dict.pop(f'h.{d}.attn.c_proj.weight')
state_dict[f'transformer.layers.{d}.mixer.out_proj.weight'] = Wout.t()
def key_mapping_attn(key):
key = re.sub(r'^h.(\d+).attn.c_attn.bias', r'transformer.layers.\1.mixer.Wqkv.bias', key)
key = re.sub(r'^h.(\d+).attn.c_proj.bias', r'transformer.layers.\1.mixer.out_proj.bias', key)
return key
state_dict = OrderedDict((key_mapping_attn(k), v) for k, v in state_dict.items())
return state_dict
def remap_state_dict_megatron(state_dict, config):
def key_mapping_transformer(key):
key = re.sub(r'^language_model.encoder.', 'transformer.', key)
key = re.sub(r'^language_model.', 'transformer.', key)
return key
state_dict = OrderedDict((key_mapping_transformer(k), v) for k, v in state_dict.items())
# Word embedding and position embedding
def key_mapping_pos_emb(key):
return re.sub(r'^wpe.', 'transformer.embeddings.position_embeddings.', key)
state_dict = OrderedDict((key_mapping_pos_emb(k), v) for k, v in state_dict.items())
word_embeddings = state_dict.pop('transformer.embedding.word_embeddings.weight')
# It's possible that vocab_size is padded to be a multiple of 8, for example.
pad_vocab_size_multiple = getattr(config, 'pad_vocab_size_multiple', 1)
vocab_size = (math.ceil(word_embeddings.shape[0] / pad_vocab_size_multiple)
* pad_vocab_size_multiple)
state_dict['transformer.embeddings.word_embeddings.weight'] = F.pad(
word_embeddings, (0, 0, 0, vocab_size - word_embeddings.shape[0])
)
state_dict['lm_head.weight'] = state_dict['transformer.embeddings.word_embeddings.weight']
# LayerNorm
def key_mapping_ln(key):
key = re.sub(r'^transformer.final_layernorm.(weight|bias)', r'transformer.ln_f.\1', key)
key = re.sub(r'^transformer.layers.(\d+).input_layernorm.(weight|bias)',
r'transformer.layers.\1.norm1.\2', key)
key = re.sub(r'^transformer.layers.(\d+).post_attention_layernorm.(weight|bias)',
r'transformer.layers.\1.norm2.\2', key)
return key
state_dict = OrderedDict((key_mapping_ln(k), v) for k, v in state_dict.items())
# MLP
def key_mapping_mlp(key):
key = re.sub(r'^transformer.layers.(\d+).mlp.dense_h_to_4h.(weight|bias)',
r'transformer.layers.\1.mlp.fc1.\2', key)
key = re.sub(r'^transformer.layers.(\d+).mlp.dense_4h_to_h.(weight|bias)',
r'transformer.layers.\1.mlp.fc2.\2', key)
return key
state_dict = OrderedDict((key_mapping_mlp(k), v) for k, v in state_dict.items())
# Attention
def key_mapping_attn(key):
key = re.sub(r'^transformer.layers.(\d+).self_attention.rotary_emb.inv_freq',
r'transformer.layers.\1.mixer.rotary_emb.inv_freq', key)
key = re.sub(r'^transformer.layers.(\d+).self_attention.query_key_value.(weight|bias)',
r'transformer.layers.\1.mixer.Wqkv.\2', key)
key = re.sub(r'^transformer.layers.(\d+).self_attention.dense.(weight|bias)',
r'transformer.layers.\1.mixer.out_proj.\2', key)
return key
state_dict = OrderedDict((key_mapping_attn(k), v) for k, v in state_dict.items())
# Megatron stores Wqkv as ((nheads 3 headdim), hidden_dim)
# while we store Wqkv as ((3 nheads headdim), hidden_dim)
headdim = config.hidden_size // config.num_attention_heads
for d in range(config.num_hidden_layers):
Wqkv = state_dict.pop(f'transformer.layers.{d}.mixer.Wqkv.weight')
state_dict[f'transformer.layers.{d}.mixer.Wqkv.weight'] = rearrange(
Wqkv, '(nheads three headdim) ... -> (three nheads headdim) ...',
three=3, headdim=headdim
)
bqkv = state_dict.pop(f'transformer.layers.{d}.mixer.Wqkv.bias')
state_dict[f'transformer.layers.{d}.mixer.Wqkv.bias'] = rearrange(
bqkv, '(nheads three headdim) -> (three nheads headdim)',
three=3, headdim=headdim
)
return state_dict
|
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["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", 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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,615
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/config/nanogpt.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
import math
from ... import core as qc
@dataclass
class GPTConfig:
n_pos = 1024
s_vocab = 50304 # GPT-2 s_vocab of 50257, padded up to nearest multiple of 64 for efficiency
n_layer = 12
n_head = 12
n_hidden = 768
dropout = 0.0
bias = True # True: bias in Linears and LayerNorms, like GPT-2. False: a bit better and faster
class PreTrained(qc.PreTrained):
hs = qc.Hypers(
{"n_inner", "act_sum"},
dict(
act="gelu_new",
archs=["LMHead"],
BOS=50256,
drop_attn=0.1,
drop_embed=0.1,
drop_sum_first=0.1,
drop=0.1,
EOS=50256,
eps=1e-5,
init_range=0.02,
model_type="gpt2",
n_ctx=1024,
n_heads=12,
n_hidden=768,
n_lays=12,
n_pos=1024,
reorder_and_upcast_attn=False,
s_vocab=50257,
scale_by_inv=False,
scale=True,
sum_proj=True,
sum_type="cls_index",
sum_use_proj=True,
task_params={"text-generation": {"do_sample": True, "max_len": 50}},
y_cache=True,
),
)
def __init__(self, *xs, **kw):
super().__init__(*xs, **kw)
def _init_weights(self, module):
if isinstance(module, (qc.Linear, qc.Conv1D)):
module.weight.data.normal_(mean=0.0, std=self.cfg.init_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, qc.Embedding):
module.weight.data.normal_(mean=0.0, std=self.cfg.init_range)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, qc.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
for name, p in module.named_parameters():
if name == "c_proj.weight":
p.data.normal_(
mean=0.0,
std=(self.cfg.init_range / math.sqrt(2 * self.cfg.n_lays)),
)
def _set_grad_checkpoint(self, module, value=False):
if isinstance(module, GPT2Model):
module.grad_checkpoint = value
MAP = {
"gpt2": dict(),
"gpt2-medium": dict(
n_heads=16,
n_hidden=1024,
n_lays=24,
n_special=0,
predict_special_tokens=True,
),
"gpt2-large": dict(
n_heads=20,
n_hidden=1280,
n_lays=36,
),
"gpt2-xl": dict(
n_heads=25,
n_hidden=1600,
n_lays=48,
y_prev=True,
),
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id2label={"0": "LABEL_0"},
label2id={"LABEL_0": 0},
n_labels=1,
n_lays=6,
),
}
|
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["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,616
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/models/ctrl.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
import numpy as np
import torch
from torch import nn
from transformers.utils import logging
from .. import core as qc
from ..core import utils as qu
from ..core import output as qo
from ..core.embed import sin_embed
from ..prep.config.ctrl import PreTrained
log = logging.get_logger(__name__)
class ForSeqClass(PreTrained):
def __init__(self, **kw):
super().__init__(**kw)
cfg = self.get_cfg(kw)
self.model = Model(**kw)
self.proj = qc.Linear(cfg.d_model, cfg.n_labels, bias=False, **kw)
def forward(self, x, x_emb=None, labels=None, **kw):
cfg = self.cfg
ys = self.model(x, x_emb=x_emb, **kw)
b = (x.shape[:2] if x is not None else x_emb.shape[:2])[0]
if cfg.PAD is None:
n = -1
else:
assert b == 1
if x is not None:
n = torch.ne(x, cfg.PAD).sum(-1) - 1
else:
n = -1
y = self.proj(ys[0])[range(b), n]
loss = None
if labels is not None:
if cfg.problem is None:
dt = labels.dtype
if cfg.n_labels == 1:
cfg.problem = "regression"
elif cfg.n_labels > 1 and (dt == torch.long or dt == torch.int):
cfg.problem = "single_label"
else:
cfg.problem = "multi_label"
if cfg.problem == "regression":
if cfg.n_labels == 1:
loss = nn.MSELoss()(y.squeeze(), labels.squeeze())
else:
loss = nn.MSELoss()(y, labels)
elif cfg.problem == "single_label":
loss = nn.CrossEntropyLoss()(y.view(-1, cfg.n_labels), labels.view(-1))
elif cfg.problem == "multi_label":
loss = nn.BCEWithLogitsLoss()(y, labels)
ys = (y,) + ys[2:] + (loss,)
return qo.WithLoss(*ys)
class LMHead(PreTrained):
def __init__(self, **kw):
super().__init__(**kw)
cfg = self.get_cfg(kw)
self.model = Model(**kw)
self.proj = qc.Linear(cfg.d_model, cfg.s_vocab, bias=True, **kw)
def forward(self, x, labels=None, **kw):
ys = self.model(x, **kw)
y = self.proj(ys[0])
loss = None
if labels is not None:
sl = y[..., :-1, :].contiguous()
ls = labels[..., 1:].contiguous()
loss = nn.CrossEntropyLoss()(sl.view(-1, sl.size(-1)), ls.view(-1))
ys = (y,) + ys[1:] + (loss,)
return qo.LossCaches(*ys)
class Model(PreTrained):
def __init__(self, **kw):
super().__init__(**kw)
cfg = self.get_cfg(kw)
m = cfg.d_model
cfg.scale = m**0.5 if cfg.scale else 1.0
self.tok_emb = qc.Embed(cfg.s_vocab, m, **kw)
self.pos_emb = qc.Embed(cfg.n_pos, m, **kw)
sin_embed(cfg.n_pos, m, out=self.pos_emb.weight)
self.drop = qc.Dropout(cfg.drop, **kw)
self.lays = qc.Stack(
[Encoder(m, cfg.n_heads, cfg.d_ff, cfg.drop_resid) for _ in range(cfg.n_lays)]
)
self.norm = qc.LayerNorm(m, **kw)
def forward(
self, x, x_emb=None, prev_kv=None, mask=None, typ=None, pos=None, head_m=None, **kw
):
cfg = self.cfg
if x is not None:
s = x_emb.size()[:-1]
else:
assert x_emb is None
s = x.size()
x = x.view(-1, s[-1])
b = s[0]
d = x.device if x is not None else x_emb.device
if prev_kv is None:
past_length = 0
prev_kv = tuple([None] * len(self.lays))
else:
past_length = prev_kv[0][0].size(-2)
if pos is None:
pos = torch.arange(past_length, s[-1] + past_length, dtype=torch.long, device=d)
pos = pos.unsqueeze(0).view(-1, s[-1])
if mask is not None:
assert b > 0
mask = mask.view(b, -1)
mask = mask.unsqueeze(1).unsqueeze(2)
mask = mask.to(dtype=self.dtype)
mask = (1.0 - mask) * -10000.0
head_m = self.get_head_m(head_m, cfg.n_lays)
if typ is None:
typ = 0
else:
typ = self.tok_emb(typ.view(-1, s[-1])) * cfg.scale
pos = pos.view(-1, s[-1])
if x_emb is None:
x_emb = self.tok_emb(x)
# x_emb = embedded.unsqueeze(0) if len(x.shape)<2 else embedded
n = s[-1]
mask = torch.triu(torch.ones(n + past_length, n + past_length), 1).to(d)
x_emb *= cfg.scale
pos = self.pos_emb[pos, :].to(d)
y = x_emb + pos + typ
y = self.drop(y)
attns = caches = hiddens = ()
for i, (lay, layer_past) in enumerate(zip(self.lays, prev_kv)):
hiddens += (y,)
ys = lay(
y,
mask,
layer_past=layer_past,
mask=mask,
head_m=head_m[i],
**kw,
)
y, present = ys[:2]
attns += (ys[2],)
caches += (present,)
y = self.norm(y)
hiddens += (y,)
return qo.WithCaches(y, attns, caches, hiddens)
class Encoder(qc.Module):
def __init__(self, d_model, n_heads, d_ff, rate=0.1):
super().__init__()
self.attn = Attention(d_model, n_heads)
self.ffn = point_wise_feed_forward_network(d_model, d_ff)
self.norm1 = qc.LayerNorm(d_model, eps=1e-6)
self.norm2 = qc.LayerNorm(d_model, eps=1e-6)
self.drop1 = qc.Dropout(rate)
self.drop2 = qc.Dropout(rate)
def forward(self, x, mask, layer_past=None, head_m=None, **kw):
normed = self.norm1(x)
ys = self.attn(
normed, normed, normed, mask, layer_past=layer_past, mask=mask, head_m=head_m
)
out1 = x + self.drop1(ys[0])
out2 = self.norm2(out1)
ffn_output = self.ffn(out2)
ffn_output = self.drop2(ffn_output)
out2 = out1 + ffn_output
y = (out2,) + ys[1:]
return y
class Attention(qc.Module):
def __init__(self, **kw):
super().__init__(**kw)
cfg = self.get_cfg(kw)
m, n = cfg.d_model, cfg.n_heads
assert m % n == 0 # or cfg.d_embed is not None
cfg.d_head = h = m // n
cfg.scale = 1 / (h**0.5)
self.query = qc.Linear(m, m, **kw)
self.key = qc.Linear(m, m, **kw)
self.value = qc.Linear(m, m, **kw)
self.proj = qc.Linear(m, m, **kw)
def split_into_heads(self, x, batch_size):
cfg = self.cfg
x = x.reshape(batch_size, -1, cfg.n_heads, cfg.d_head)
return x.permute([0, 2, 1, 3])
def forward(self, v, k, q, mask, layer_past=None, head_m=None, **kw):
cfg = self.cfg
b = q.shape[0]
q = self.query(q)
k = self.key(k)
v = self.value(v)
q = self.split_into_heads(q, b)
k = self.split_into_heads(k, b)
v = self.split_into_heads(v, b)
if layer_past is not None:
past_key, past_value = layer_past[0], layer_past[1]
k = torch.cat((past_key, k), dim=-2)
v = torch.cat((past_value, v), dim=-2)
present = torch.stack((k, v))
ys = scaled_dot_product_attention(q, k, v, mask, mask, head_m)
scaled_attention = ys[0].permute([0, 2, 1, 3])
attn = ys[1]
original_size_attention = scaled_attention.reshape(b, -1, cfg.d_model)
ys = self.proj(original_size_attention)
return ys, present, attn
def scaled_dot_product_attention(q, k, v, mask, head_m=None):
matmul_qk = torch.matmul(q, k.permute(0, 1, 3, 2))
dk = k.shape[-1]
ys = matmul_qk / np.sqrt(dk)
if mask is not None:
nd, ns = ys.size(-2), ys.size(-1)
ys += mask[ns - nd : ns, :ns] * -1e4
if mask is not None:
ys = ys + mask
ys = torch.softmax(ys, dim=-1)
if head_m is not None:
ys = ys * head_m
y = torch.matmul(ys, v)
return y, ys
def point_wise_feed_forward_network(d_model, d_ff):
return nn.Sequential(qc.Linear(d_model, d_ff), nn.ReLU(), qc.Linear(d_ff, d_model))
|
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"/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], "/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/dispatch.py": ["/qnarre/base/doc/blog.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/context.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/analyzer.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/images.py"], "/qnarre/models/big_bird.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/big_bird.py"], "/tools/triton/python/triton/runtime/jit.py": ["/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/fast/roberta.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/bigbird.py": ["/qnarre/prep/config/big_bird.py", "/qnarre/models/big_bird.py"], "/qnarre/models/gpt_neo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/gpt_neo.py"], "/qnarre/prep/convert/t5.py": ["/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/prep/tokens/fsmt.py": 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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,617
|
quantapix/qnarre
|
refs/heads/main
|
/tools/triton/python/triton/runtime/errors.py
|
class OutOfResources(Exception):
def __init__(self, required, limit, name):
self.message = f'out of resource: {name}, '\
f'Required: {required}, '\
f'Hardware limit: {limit}'
self.message += '. Reducing block sizes or `num_stages` may help.'
self.required = required
self.limit = limit
self.name = name
super().__init__(self.message)
def __reduce__(self):
# this is necessary to make CompilationError picklable
return (type(self), (self.required, self.limit, self.name))
|
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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,618
|
quantapix/qnarre
|
refs/heads/main
|
/setup.py
|
# Copyright 2019 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
import re
from os import path
from codecs import open
from setuptools import setup, find_packages
here = path.abspath(path.dirname(__file__))
with open(path.join(here, 'README.md')) as f:
long_description = f.read()
with open(path.join(here, 'qnarre', '__init__.py')) as f:
m = re.search(r"^__version__ = ['\"]([^'\"]*)['\"]", f.read(), re.M)
if m:
version = m.group(1)
else:
raise RuntimeError("Unable to find version string.")
setup(
name='qnarre',
version=version,
description="Qnarre project",
long_description=long_description,
url='https://github.com/quantapix/qnarre.git',
author='Quantapix, Inc.',
author_email='quantapix@gmail.com',
license='Apache-2.0',
# See https://pypi.python.org/pypi?%3Aaction=list_classifiers
classifiers=[
# How mature is this project? Common values are
# 3 - Alpha
# 4 - Beta
# 5 - Production/Stable
'Development Status :: 3 - Alpha',
# Indicate who your project is intended for
'Intended Audience :: Developers',
'Topic :: Software Development :: Build Tools',
# Pick your license as you wish (should match "license" above)
'License :: OSI Approved :: Apache-2.0 License',
# Specify the Python versions you support here. In particular, ensure
# that you indicate whether you support Python 2, Python 3 or both.
'Programming Language :: Python :: 3.7',
],
# What does your project relate to?
keywords='quantapix development',
# You can just specify the packages manually here if your project is
# simple. Or you can use find_packages().
packages=find_packages(exclude=['tests']),
# Alternatively, if you want to distribute just a my_module.py, uncomment
# this:
# py_modules=["my_module"],
# List run-time dependencies here. These will be installed by pip when
# your project is installed. For an analysis of "install_requires" vs pip's
# requirements files see:
# https://packaging.python.org/en/latest/requirements.html
install_requires=['networkx', 'markdown'],
# List additional groups of dependencies here (e.g. development
# dependencies). You can install these using the following syntax,
# for example:
# $ pip install -e .[dev,test]
extras_require={
'web': ['django'],
# 'dev': ['check-manifest'],
# 'test': ['coverage'],
},
# If there are data files included in your packages that need to be
# installed, specify them here. If using Python 2.6 or less, then these
# have to be included in MANIFEST.in as well.
package_data={
# 'qnarre': ['def_junks.txt'],
},
# Although 'package_data' is the preferred approach, in some case you may
# need to place data files outside of your packages. See:
# http://docs.python.org/3.4/distutils/setupscript.html#installing-additional-files # noqa
# In this case, 'data_file' will be installed into '<sys.prefix>/my_data'
data_files=[], # ('my_data', ['data/data_file'])],
# To provide executable scripts, use entry points in preference to the
# "scripts" keyword. Entry points provide cross-platform support and allow
# pip to create the appropriate form of executable for the target platform.
entry_points={
'console_scripts': [
'qfilt-mbox=qnarre.command:filt_mbox',
'qmerge-mbox=qnarre.command:merge_mbox',
'qstrip-mbox=qnarre.command:strip_mbox',
'qimp-main=qnarre.command:import_main',
'qimp-blog=qnarre.command:import_blog',
'qimp-priv=qnarre.command:import_priv',
'qimp-docs=qnarre.command:import_docs',
'qimp-sbox=qnarre.command:import_sbox',
'qimp-mbox=qnarre.command:import_mbox',
'qimp-tbox=qnarre.command:import_tbox',
'qimp-bbox=qnarre.command:import_bbox',
'qimp-pics=qnarre.command:import_pics',
'qprotect=qnarre.command:protect',
'qredact=qnarre.command:redact',
'qobfuscate=qnarre.command:obfuscate',
'qcheck-recs=qnarre.command:check_recs',
'qgraph-recs=qnarre.command:graph_recs',
'qnn-setup=qnarre.command:qnn_setup',
'qnn-learn=qnarre.command:qnn_learn',
'qnn-guess=qnarre.command:qnn_guess',
'qexp-mbox=qnarre.command:export_mbox',
'qexp-blog=qnarre.command:export_blog',
'qexp-pngs=qnarre.command:export_pngs',
'qexp-jpgs=qnarre.command:export_jpgs',
'qexp-orgs=qnarre.command:export_orgs',
],
},
# test_suite='nose.collector',
# tests_require=['nose'],
)
|
{"/qnarre/prep/convert/xlnet.py": ["/qnarre/prep/config/xlnet.py", "/qnarre/models/xlnet.py"], "/qnarre/prep/convert/bert.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/base/doc/patcher.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/nominals.py"], "/qnarre/prep/convert/funnel.py": ["/qnarre/prep/config/funnel.py", "/qnarre/models/funnel.py"], "/qnarre/prep/tokens/fast/realm.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py", "/qnarre/tokens/utils.py"], "/qnarre/models/decision_transfo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/decision_transfo.py"], "/qnarre/models/fsmt.py": ["/qnarre/core/embed.py"], "/qnarre/models/roformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/xlnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc.py": ["/qnarre/base/author.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], "/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/dispatch.py": ["/qnarre/base/doc/blog.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/context.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/analyzer.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/images.py"], "/qnarre/models/big_bird.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/big_bird.py"], "/tools/triton/python/triton/runtime/jit.py": ["/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/fast/roberta.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/bigbird.py": ["/qnarre/prep/config/big_bird.py", "/qnarre/models/big_bird.py"], "/qnarre/models/gpt_neo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/gpt_neo.py"], "/qnarre/prep/convert/t5.py": ["/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/prep/tokens/fsmt.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/deberta.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/fast/splinter.py": ["/qnarre/tokens/fast.py"], "/qnarre/models/old/trafo.py": ["/qnarre/core/attention.py", "/qnarre/core/base.py", "/qnarre/core/mlp.py", "/qnarre/core/deduce.py", "/qnarre/core/norm.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/led.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/realm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/convbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/data2vec.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/tools/triton/python/triton/language/math.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/run/beam.py": ["/qnarre/run/qa.py"], "/tools/triton/python/triton/compiler/compiler.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/tools/disasm.py", "/tools/triton/python/triton/compiler/code_generator.py", "/tools/triton/python/triton/compiler/make_launcher.py"], "/qnarre/base/doc/graph.py": ["/qnarre/base/doc/base.py"], "/qnarre/base/activism.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py", "/qnarre/base/judgment.py"], "/qnarre/base/doc/exporter.py": ["/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/fnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/roformer.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/megatron.py": ["/qnarre/prep/config/megatron.py", "/qnarre/models/megatron.py"], "/qnarre/prep/tokens/fast/roformer.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/roformer.py"], "/qnarre/core/runner.py": ["/qnarre/core/params.py"], "/qnarre/run/seq2seq.py": ["/qnarre/run/qa.py"], "/qnarre/prep/tokens/luke.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/util/table.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/tree.py", "/qnarre/base/doc/util/utils.py"], "/tools/triton/python/triton/language/standard.py": ["/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/language/__init__.py"], "/qnarre/base/doc/filters.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/args.py"], "/qnarre/prep/convert/gpt_neo.py": ["/qnarre/prep/config/gpt_neo.py", "/qnarre/models/gpt_neo.py"], "/qnarre/base/doc/section.py": ["/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/message.py"], "/qnarre/models/funnel.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/funnel.py"], "/tools/triton/python/triton/common/__init__.py": ["/tools/triton/python/triton/common/build.py"], "/qnarre/base/doc/qnn.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/models/plbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/reformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/images.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/base.py"], "/qnarre/models/yoso.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/canine.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/blog.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/counter.py"], "/qnarre/models/longformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/fast/pegasus.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/pegasus.py"], "/qnarre/base/judgment.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py", "/qnarre/base/conflict.py", "/qnarre/base/conjecture.py"], "/qnarre/models/splinter.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/rag.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/convert/albert.py": ["/qnarre/prep/config/albert.py", "/qnarre/models/albert.py"], "/qnarre/prep/tokens/fast/gpt2.py": ["/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/fast/mbart.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/roformer.py": ["/qnarre/models/roformer.py"], "/qnarre/base/doc/util/tree.py": ["/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py", "/qnarre/base/doc/util/utils.py"], "/qnarre/models/rembert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/t5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/xlm.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/bert_tf2.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/tools/triton/python/triton/ops/__init__.py": ["/tools/triton/python/triton/ops/cross_entropy.py", "/tools/triton/python/triton/ops/matmul.py"], "/qnarre/core/norm.py": ["/qnarre/core/base.py"], "/qnarre/models/mpnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/author.py": ["/qnarre/base/named.py"], "/qnarre/base/doc/args.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/log.py"], "/qnarre/prep/tokens/gpt.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/distilbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/proof.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py"], "/qnarre/prep/tokens/byt5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/electra.py": ["/qnarre/prep/config/electra.py", "/qnarre/models/electra.py"], "/qnarre/models/luke.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": ["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,619
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/config/bert.py
|
# Copyright 2023 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
from collections import OrderedDict
from ... import core as qc
class PreTrained(qc.PreTrained):
hs = qc.Hypers(
{"drop_proj"},
dict(
act="gelu",
archs=["ForMasked"],
d_ff=3072,
d_model=768,
drop_attn=0.1,
drop=0.1,
grad_checkpoint=True,
init_range=0.02,
model_type="bert",
n_heads=12,
n_lays=12,
n_pos=512,
n_typ=2,
eps=1e-12,
PAD=0,
pos_type="absolute",
s_vocab=30522,
y_cache=True,
),
)
def _init_weights(self, x):
cfg = self.cfg
if isinstance(x, qc.Linear):
x.weight.data.normal_(mean=0.0, std=cfg.init_range)
if x.bias is not None:
x.bias.data.zero_()
elif isinstance(x, qc.Embed):
x.weight.data.normal_(mean=0.0, std=cfg.init_range)
if x.padding_idx is not None:
x.weight.data[cfg.PAD].zero_()
elif isinstance(x, qc.LayerNorm):
x.bias.data.zero_()
x.weight.data.fill_(1.0)
def _set_grad_checkpoint(self, x, value=False):
if isinstance(x, Encoder):
x.grad_checkpoint = value
MAP = {
"bert-base-uncased": dict(
grad_checkpoint=False,
n_lays=12,
),
"bert-large-uncased": dict(
d_ff=4096,
d_model=1024,
grad_checkpoint=False,
n_heads=16,
n_lays=24,
),
"bert-base-cased": dict(
grad_checkpoint=False,
s_vocab=28996,
),
"bert-large-cased": dict(
d_ff=4096,
d_model=1024,
direction="bidi",
grad_checkpoint=False,
n_heads=16,
n_lays=24,
pooler_fc_size=768,
pooler_num_attention_heads=12,
pooler_num_fc_layers=3,
pooler_size_per_head=128,
pooler_type="first_token_transform",
s_vocab=28996,
),
"bert-base-multilingual-uncased": dict(
direction="bidi",
pooler_fc_size=768,
pooler_num_attention_heads=12,
pooler_num_fc_layers=3,
pooler_size_per_head=128,
pooler_type="first_token_transform",
s_vocab=105879,
),
"bert-base-multilingual-cased": dict(
direction="bidi",
pooler_fc_size=768,
pooler_num_attention_heads=12,
pooler_num_fc_layers=3,
pooler_size_per_head=128,
pooler_type="first_token_transform",
s_vocab=119547,
),
"bert-base-chinese": dict(
direction="bidi",
pooler_fc_size=768,
pooler_num_attention_heads=12,
pooler_num_fc_layers=3,
pooler_size_per_head=128,
pooler_type="first_token_transform",
s_vocab=21128,
),
"bert-base-german-cased": dict(
s_vocab=30000,
),
"bert-large-uncased-whole-word-masking": dict(
d_ff=4096,
d_model=1024,
n_heads=16,
n_lays=24,
),
"bert-large-cased-whole-word-masking": dict(
d_ff=4096,
d_model=1024,
direction="bidi",
n_heads=16,
n_lays=24,
pooler_fc_size=768,
pooler_num_attention_heads=12,
pooler_num_fc_layers=3,
pooler_size_per_head=128,
pooler_type="first_token_transform",
s_vocab=28996,
),
"bert-large-uncased-whole-word-masking-finetuned-squad": dict(
archs=["ForQA"],
d_ff=4096,
d_model=1024,
n_heads=16,
n_lays=24,
),
"bert-large-cased-whole-word-masking-finetuned-squad": dict(
archs=["ForQA"],
d_ff=4096,
d_model=1024,
direction="bidi",
n_heads=16,
n_lays=24,
pooler_fc_size=768,
pooler_num_attention_heads=12,
pooler_num_fc_layers=3,
pooler_size_per_head=128,
pooler_type="first_token_transform",
s_vocab=28996,
),
"bert-base-cased-finetuned-mrpc": dict(
s_vocab=28996,
),
}
class Onnx:
@property
def inputs(self):
return OrderedDict(
[
("input_ids", {0: "batch", 1: "sequence"}),
("mask", {0: "batch", 1: "sequence"}),
("typ_ids", {0: "batch", 1: "sequence"}),
]
)
|
{"/qnarre/prep/convert/xlnet.py": ["/qnarre/prep/config/xlnet.py", "/qnarre/models/xlnet.py"], "/qnarre/prep/convert/bert.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/base/doc/patcher.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/nominals.py"], "/qnarre/prep/convert/funnel.py": ["/qnarre/prep/config/funnel.py", "/qnarre/models/funnel.py"], "/qnarre/prep/tokens/fast/realm.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py", "/qnarre/tokens/utils.py"], "/qnarre/models/decision_transfo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/decision_transfo.py"], "/qnarre/models/fsmt.py": ["/qnarre/core/embed.py"], "/qnarre/models/roformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/xlnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc.py": ["/qnarre/base/author.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], "/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], 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|
33,620
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quantapix/qnarre
|
refs/heads/main
|
/notebooks/old/src/dataset.py
|
# Copyright 2019 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
# !pip install -U tf-nightly-2.0-preview
import numpy as np
import pathlib as pth
import tensorflow as tf
td = tf.data
tt = tf.train
vocab = (' ', ':', '|')
vocab += ('x', 'y', '=', ',', '+', '-', '*')
vocab += ('0', '1', '2', '3', '4', '5', '6', '7', '8', '9')
tokens = {c: i for i, c in enumerate(vocab)}
SPC, SEP, STP = [tokens[c] for c in vocab[:3]]
assert SPC == 0
params = dict(
max_val=10,
num_samples=4,
num_shards=3,
)
class Params:
def __init__(self, **kw):
for k, v in kw.items():
setattr(self, k, v)
def py_gen(ps):
m, n = ps.max_val, ps.num_samples
# x, y vals in defs
vals = np.random.randint(low=1 - m, high=m, size=(2, n))
# (x, y) order if 1 in defs [0] and op [1], respectively
ords = np.random.randint(2, size=(2, n))
# index of ['+', '-', '*']
ops = np.array(['+', '-', '*'])
ops.reshape((1, 3))
ops = ops[np.random.randint(3, size=n)]
for i in range(n):
x, y = vals[:, i]
res = f'x={x},y={y}:' if ords[0, i] else f'y={y},x={x}:'
o = ops[i]
res += (f'x{o}y:' if ords[1, i] else f'y{o}x:')
if o == '+':
res += f'{x + y}'
elif o == '*':
res += f'{x * y}'
else:
assert o == '-'
res += (f'{x - y}' if ords[1, i] else f'{y - x}')
yield res
def gen_src(ps):
ds = td.Dataset.from_generator(
lambda: py_gen(ps),
tf.string,
tf.TensorShape([]),
)
return ds
def src_dset(ps):
ds = np.array(list(py_gen(ps)))
ds = td.Dataset.from_tensor_slices(ds)
return ds
@tf.function
def filterer(x):
r = tf.strings.length(x) < 15
tf.print(tf.strings.format('filtering {}... ', x) + ('in' if r else 'out'))
return r
@tf.function
def splitter(x):
fs = tf.strings.split(x, ':')
return {'defs': fs[0], 'op': fs[1], 'res': fs[2]}
@tf.function
def tokenizer(d):
return {
k: tf.numpy_function(
lambda x: tf.constant([tokens[chr(c)] for c in x]),
[v],
Tout=tf.int32,
)
for k, v in d.items()
}
def shards(ps):
for _ in range(ps.num_shards):
yield src_dset(ps).map(splitter).map(tokenizer)
def records(dset):
for s in dset:
fs = tt.Features(
feature={
'defs': tt.Feature(int64_list=tt.Int64List(value=s['defs'])),
'op': tt.Feature(int64_list=tt.Int64List(value=s['op'])),
'res': tt.Feature(int64_list=tt.Int64List(value=s['res'])),
})
yield tt.Example(features=fs).SerializeToString()
def dump(ps):
d = pth.Path('/tmp/q/dataset')
d.mkdir(parents=True, exist_ok=True)
for i, ds in enumerate(shards(ps)):
i = '{:0>4d}'.format(i)
f = str(d / f'shard_{i}.tfrecords')
print(f'dumping {f}...')
with tf.io.TFRecordWriter(f) as w:
for r in records(ds):
w.write(r)
yield f
features = {
'defs': tf.io.VarLenFeature(tf.int64),
'op': tf.io.VarLenFeature(tf.int64),
'res': tf.io.VarLenFeature(tf.int64),
}
def files(ps):
d = pth.Path('/tmp/q/dataset')
for i in range(ps.num_shards):
i = '{:0>4d}'.format(i)
yield str(d / f'shard_{i}.tfrecords')
def load(ps, files):
ds = td.TFRecordDataset(files)
if ps.dim_batch:
ds = ds.batch(ps.dim_batch)
return ds.map(lambda x: tf.io.parse_example(x, features))
return ds.map(lambda x: tf.io.parse_single_example(x, features))
@tf.function
def caster(d):
return {k: tf.cast(v, tf.int32) for k, v in d.items()}
@tf.function
def adapter(d):
return [
tf.sparse.to_dense(d['defs']),
tf.sparse.to_dense(d['op']),
tf.sparse.to_dense(d['res']),
]
def main(ps):
for s in py_gen(ps):
print(s)
print('Ops on datasets')
dg = gen_src(ps)
for s in dg.take(2):
print(s)
ds = src_dset(ps)
for i, s in ds.take(2).concatenate(dg).enumerate():
print(i, s)
print('Filter elements')
for i, s in enumerate(ds.filter(filterer)):
print(i, s)
print('Split elements')
for s in ds.map(splitter).take(1):
print(s)
for s in ds.map(splitter).map(tokenizer).take(1):
print(s)
fs = [f for f in dump(ps)]
ps.dim_batch = None
for i, s in enumerate(load(ps, fs).map(adapter)):
print(i, s)
ps.dim_batch = 2
for i, s in enumerate(load(ps, fs).map(adapter)):
print(i, s)
ps.max_val = 100
ps.num_samples = 1000
ps.num_shards = 10
fs = [f for f in dump(ps)]
ps.dim_batch = 100
for i, _ in enumerate(load(ps, fs).map(adapter)):
pass
print(i)
if __name__ == '__main__':
ps = Params(**params)
main(ps)
|
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"/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], 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["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], 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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,621
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/convert/byt5.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
from argparse import ArgumentParser
from transformers.utils import logging
from .t5 import load_src_weights
from ..config.t5 import PreTrained
from ...models.t5 import ForCondGen
logging.set_verbosity_info()
def to_pytorch(src_path, cfg_path, save_path):
cfg = PreTrained.from_json_file(cfg_path)
print(f"Building from config: {cfg}")
m = ForCondGen(cfg)
load_src_weights(m, cfg, src_path)
print(f"Saving to: {save_path}")
m.save_pretrained(save_path)
if __name__ == "__main__":
x = ArgumentParser()
x.add_argument("--src_path", default=None, type=str, required=True)
x.add_argument("--cfg_path", default=None, type=str, required=True)
x.add_argument("--save_path", default=None, type=str, required=True)
y = x.parse_args()
to_pytorch(y.src_path, y.cfg_path, y.save_path)
|
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"/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], "/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/dispatch.py": ["/qnarre/base/doc/blog.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/context.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/analyzer.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/images.py"], "/qnarre/models/big_bird.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/big_bird.py"], "/tools/triton/python/triton/runtime/jit.py": ["/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/fast/roberta.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/bigbird.py": ["/qnarre/prep/config/big_bird.py", "/qnarre/models/big_bird.py"], "/qnarre/models/gpt_neo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/gpt_neo.py"], "/qnarre/prep/convert/t5.py": ["/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/prep/tokens/fsmt.py": 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"/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/models/plbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/reformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/images.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/base.py"], "/qnarre/models/yoso.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/canine.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/blog.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/counter.py"], "/qnarre/models/longformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/fast/pegasus.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/pegasus.py"], "/qnarre/base/judgment.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py", "/qnarre/base/conflict.py", "/qnarre/base/conjecture.py"], "/qnarre/models/splinter.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/rag.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/convert/albert.py": ["/qnarre/prep/config/albert.py", "/qnarre/models/albert.py"], "/qnarre/prep/tokens/fast/gpt2.py": ["/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/fast/mbart.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/roformer.py": ["/qnarre/models/roformer.py"], "/qnarre/base/doc/util/tree.py": ["/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py", "/qnarre/base/doc/util/utils.py"], "/qnarre/models/rembert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/t5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/xlm.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/bert_tf2.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/tools/triton/python/triton/ops/__init__.py": ["/tools/triton/python/triton/ops/cross_entropy.py", "/tools/triton/python/triton/ops/matmul.py"], "/qnarre/core/norm.py": ["/qnarre/core/base.py"], "/qnarre/models/mpnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/author.py": ["/qnarre/base/named.py"], "/qnarre/base/doc/args.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/log.py"], "/qnarre/prep/tokens/gpt.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/distilbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/proof.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py"], "/qnarre/prep/tokens/byt5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/electra.py": ["/qnarre/prep/config/electra.py", "/qnarre/models/electra.py"], "/qnarre/models/luke.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": ["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,622
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/run/sum.py
|
# Copyright 2021 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
# fine-tune on summarization
import logging
import nltk
import numpy as np
import random
import torch
from datasets import load_metric
from filelock import FileLock
from torch.utils.data import DataLoader
from transformers import AutoModelForSeq2SeqLM, DataCollatorForSeq2Seq, AutoModelForSeq2SeqLM
from transformers.file_utils import is_offline_mode
from .params import TRAIN, EVAL, ALL, EACH
from .runner import Runner as Base
try:
nltk.data.find("tokenizers/punkt")
except (LookupError, OSError):
if is_offline_mode():
raise LookupError(
"Offline mode: run this script without TRANSFORMERS_OFFLINE first to download nltk data files"
)
with FileLock(".lock") as lock:
nltk.download("punkt", quiet=True)
COLS = {
"amazon_reviews_multi": ("review_body", "review_title"),
"big_patent": ("description", "abstract"),
"cnn_dailymail": ("article", "highlights"),
"orange_sum": ("text", "summary"),
"pn_summary": ("article", "summary"),
"psc": ("extract_text", "summary_text"),
"samsum": ("dialogue", "summary"),
"thaisum": ("body", "summary"),
"xglue": ("news_body", "news_title"),
"xsum": ("document", "summary"),
"wiki_summary": ("article", "highlights"),
}
log = logging.getLogger(__name__)
def postproc(xs, ls):
xs = [x.strip() for x in xs]
ls = [x.strip() for x in ls]
xs = ["\n".join(nltk.sent_tokenize(x)) for x in xs]
ls = ["\n".join(nltk.sent_tokenize(x)) for x in ls]
return xs, ls
class Runner(Base):
AutoModel = AutoModelForSeq2SeqLM
def __init__(self:
super().__init__()
ps = self.params
if ps.source_prefix is None and ps.model_name in [
"t5-small",
"t5-base",
"t5-large",
"t5-3b",
"t5-11b",
]:
log.warning("Running a t5 model without source prefix")
self.prefix = ps.source_prefix if ps.source_prefix is not None else ""
@property
def cols(self):
if self._cols is None:
ps, cs = self.params, self.dataset[TRAIN].column_names
xs = COLS.get(ps.dataset_name, None)
if ps.text_column is None:
t = xs[0] if xs is not None else cs[0]
else:
t = ps.text_column
if t not in cs:
raise ValueError(f"--text_column={ps.text_column}' should be: {', '.join(cs)}")
if ps.summary_column is None:
s = xs[1] if xs is not None else cs[1]
else:
s = ps.summary_column
if s not in cs:
raise ValueError(
f"--summary_column={ps.summary_column}' should be: {', '.join(cs)}"
)
self._cols = {ALL: cs, EACH: [t, s]}
return self._cols
@property
def train_ds(self):
if self._train_ds is None:
ps, mgr, ds = self.params, self.mgr, self.dataset
with mgr.main_process_first():
self._dataset = y = ds.map(
self.prep_for_train,
batched=True,
remove_columns=self.cols[ALL],
load_from_cache_file=not ps.overwrite_cache,
desc="Running tokenizer on dataset",
)
y = y[TRAIN]
if ps.max_train_samples is not None:
y = y.select(range(ps.max_train_samples))
for i in random.sample(range(len(y)), 3):
log.info(f"Sample {i} of the training set: {y[i]}")
self._train_ds = y
return self._train_ds
def prep_for_train(self, xs):
ps = self.params
t_col, s_col = self.cols[EACH]
ins, outs = xs[t_col], xs[s_col]
ins = [self.prefix + x for x in ins]
tok = self.tokenizer
ys = tok(ins, max_len=ps.max_source_length, padding=self.padding, truncation=True)
with tok.as_target_tokenizer():
ls = tok(outs, max_len=ps.max_target_length, padding=self.padding, truncation=True)
if self.padding == "max_len" and ps.ignore_pad_token_for_loss:
ls["input_ids"] = [
[(x if x != tok.PAD else -100) for x in l] for l in ls["input_ids"]
]
ys["labels"] = ls["input_ids"]
return ys
@property
def loaders(self):
if self._loaders is None:
ps, mgr = self.params, self.mgr
c = DataCollatorForSeq2Seq(
self.tokenizer,
model=self.model,
label_pad_token_id=-100
if ps.ignore_pad_token_for_loss
else self.tokenizer.PAD,
pad_to_multiple_of=8 if mgr.use_fp16 else None,
)
t = DataLoader(
self.train_ds, shuffle=True, collate_fn=c, batch_size=ps.train_batch_size
)
e = DataLoader(self.eval_ds, collate_fn=c, batch_size=ps.eval_batch_size)
self._loaders = {TRAIN: t, EVAL: e}
return self._loaders
@property
def metric(self):
if self._metric is None:
self._metric = load_metric("rouge")
return self._metric
def eval_epoch(self, e):
ps, t, m, mgr = self.params, self.tokenizer, self.model, self.mgr
m.eval()
if ps.val_max_target_length is None:
ps.val_max_target_length = ps.max_target_length
kw = {
"max_len": ps.val_max_target_length if ps is not None else self.config.max_len,
"n_beams": ps.n_beams,
}
for xs in self.loaders[EVAL]:
with torch.no_grad():
ys = mgr.unwrap_model(m).generate(
xs["input_ids"],
mask=xs["mask"],
**kw,
)
p = t.PAD
ys = mgr.pad_across_processes(ys, dim=1, PAD=p)
ls = xs["labels"]
if not ps.pad_to_max_length:
ls = mgr.pad_across_processes(ls, dim=1, PAD=p)
ys = mgr.gather(ys).cpu().numpy()
ls = mgr.gather(ls).cpu().numpy()
if ps.ignore_pad_token_for_loss:
ls = np.where(ls != -100, ls, p)
if isinstance(ys, tuple):
ys = ys[0]
ys = t.batch_decode(ys, skip_special_tokens=True)
ls = t.batch_decode(ls, skip_special_tokens=True)
ys, ls = postproc(ys, ls)
self.metric.add_batch(predictions=ys, references=ls)
y = self.metric.compute(use_stemmer=True)
y = {k: v.mid.fmeasure * 100 for k, v in y.items()}
y = {k: round(v, 4) for k, v in y.items()}
mgr.print(f"epoch {e}: {y}")
def main():
x = Runner()
x.dataset
x.config
x.tokenizer
x.model
x.model.resize_token_embeddings(len(x.tokenizer))
if x.model.config.dec_START is None:
raise ValueError("Needs `config.dec_START`")
x.loaders
x.prepare()
x.train()
x.save()
if __name__ == "__main__":
main()
"""
python sum.py \
--model_name t5-small \
--dataset_name cnn_dailymail \
--dataset_config "3.0.0" \
--source_prefix "summarize: " \
--out_dir ~/tmp/tst-summarization
accelerate launch sum.py \
--model_name t5-small \
--dataset_name cnn_dailymail \
--dataset_config "3.0.0" \
--source_prefix "summarize: " \
--out_dir ~/tmp/tst-summarization
"""
|
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"/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", 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"/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], 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"/qnarre/base/author.py", "/qnarre/base/conflict.py", "/qnarre/base/conjecture.py"], "/qnarre/models/splinter.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/rag.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/convert/albert.py": ["/qnarre/prep/config/albert.py", "/qnarre/models/albert.py"], "/qnarre/prep/tokens/fast/gpt2.py": ["/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/fast/mbart.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/roformer.py": ["/qnarre/models/roformer.py"], "/qnarre/base/doc/util/tree.py": ["/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py", "/qnarre/base/doc/util/utils.py"], "/qnarre/models/rembert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/t5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/xlm.py": ["/qnarre/tokens/utils.py"], 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"/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": 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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,623
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/core/squad.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
import torch
from qnarre.core import base
from qnarre.core.bert import Bert
def adapter(ps, feats, x):
d = torch.parse_example(x, feats)
img = torch.to_dense(d["flt_img"])
# img = torch.cast(d['int_img'], torch.float32) / 255.
lbl = d["int_lbl"]
return img, lbl
def model(ps):
seq = torch.Input(shape=(), dtype=torch.float32)
typ = torch.Input(shape=(), dtype=torch.float32)
opt = torch.Input(shape=(), dtype=torch.float32)
beg = torch.Input(shape=(), dtype=torch.float32)
end = torch.Input(shape=(), dtype=torch.float32)
uid = torch.Input(shape=(), dtype=torch.float32)
ins = [seq, typ, opt, beg, end, uid]
y = Squad(ps)([seq, typ])
outs = [SquadLoss(ps)([beg, end], y)]
m = torch.Model(name="SquadModel", inputs=ins, outputs=outs)
return m
class Squad(base.Module):
def __init__(self, ps, **kw):
super().__init__(ps, **kw) # dtype='float32', **kw)
self.bert = Bert(ps)
def build(self, input_shape):
_, slen = input_shape[0]
cfg = self.cfg
assert slen == cfg.max_seq_len
sh = (2, cfg.d_model)
self.gain = self.add_weight(shape=sh, initializer=cfg.initializer)
self.bias = self.add_weight(shape=2, initializer="zeros")
return super().build(input_shape)
def forward(self, inputs, **kw):
y = self.bert.transformer([inputs, None], **kw)
y = torch.bias_add(torch.matmul(y, self.gain, transpose_b=True), self.bias)
return list(torch.unstack(torch.transpose(y, [2, 0, 1]), axis=0))
class SquadLoss(base.Module):
def __init__(self, ps, **kw):
super().__init__(ps, **kw) # dtype='float32', **kw)
self.slen = self.cfg.max_seq_len
def build(self, input_shape):
cfg = self.cfg
sh = (2, cfg.d_model)
self.gain = self.add_weight(shape=sh, initializer=cfg.initializer)
self.bias = self.add_weight(shape=2, initializer="zeros")
return super().build(input_shape)
def forward(self, inputs, **_):
span, pred = inputs
def _loss(i):
y = torch.log_softmax(pred[i], axis=-1)
y = torch.one_hot(span[:, i], self.slen) * y
return -torch.reduce_mean(torch.reduce_sum(y, axis=-1))
self.add_loss((_loss(0) + _loss(1)) / 2.0)
return pred
|
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"/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": ["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,624
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/base/doc/mboxes.py
|
# Copyright 2019 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
import os
import email
import pathlib as pth
import contextlib as cl
from mailbox import mbox
from email.message import EmailMessage as MboxEntry
from .log import Logger
from .base import config
from .reader import Reader
from .record import EmlRec
from .nominals import nominal
from .sanitizer import sanitize
from .counter import counters, Counters, Pool
log = Logger(__name__)
class Mbox(mbox):
@staticmethod
def qfactory(fp):
from email import policy
return email.message_from_binary_file(fp, policy=policy.default)
@classmethod
def qsrc(cls, path, sort_mbox=False, **_):
b = cls(path)
if sort_mbox:
return sorted(b, key=lambda m: m['date'].datetime.astimezone())
return b
def __init__(self, path, factory=None, create=False):
t = None
if path.suffix in ('.xz', ):
import tempfile
t = tempfile.NamedTemporaryFile()
super().__init__(
t.name if t else path, factory=self.qfactory, create=create)
if t:
if create:
self.qpath = path
else:
import lzma
self._file = lzma.open(path)
def close(self):
if hasattr(self, 'qpath'):
self._file.seek(0)
import lzma
with lzma.open(self.qpath, 'wb') as dst:
import shutil
shutil.copyfileobj(self._file, dst)
super().close()
def qextract_text(self, dejunk=True):
vs = []
for p in self.walk():
if p.get_content_type() == 'text/' + config.PLAIN:
v = sanitize(p.get_content())
if dejunk:
v = EmlRec.junk.dejunk_text(v)
if v:
vs.append(v)
return vs
assert not hasattr(MboxEntry, 'qextract_text')
MboxEntry.qextract_text = qextract_text
class MboxEntry(MboxEntry):
@classmethod
def qcreate(cls, src):
i = cls()
for k, v in src:
if k in ('text/' + config.PLAIN, 'text/' + config.HTML):
try:
v = sanitize(v)
except UnicodeError:
v = 'UnicodeError'
i.set_content(v, subtype=k.split('/')[1], cte='7bit')
else:
i[k] = v
return i
class Writer:
def __init__(self, path):
self.path = path
@cl.contextmanager
def to_mbox(self):
p = self.path
if p.exists():
p.unlink()
mb = Mbox(p, create=True)
yield mb
mb.flush()
mb.close()
SUFF = '.mbox'
PROT = config.PROT
ORIG = config.recs_src + '/' + config.MBOX
ARCH = config.recs_arch + config.MBOX
REPO = config.recs_repo + config.MBOX
class Mboxes:
def __init__(self, base):
self.base = base
def filt_one(self, stem, cntr, **kw):
n = stem + SUFF
with Writer(self.base / ARCH / n).to_mbox() as mb:
kw.update(files=(stem, ), cntr=cntr)
for m, _, _, _ in EmlRec.filterer(self.base / ORIG, **kw):
mb.add(m)
cntr.incr('+')
filt_args = ((('excluded', '-'), ('allowed', '+'), ('failed', 'F')),
'Filtering:')
def filt_mbox(self, files, **kw):
with counters(self.filt_args, kw):
for f in files:
self.filt_one(f, **kw)
def pool_filt(self, **kw):
pool = Pool(Counters(*self.filt_args))
with os.scandir(self.base / ORIG) as es:
for e in es:
p = pth.Path(e.path)
if p.is_file() and p.suffix == SUFF:
pool.call_worker(Mboxes.filt_one, self, (p.stem, ), **kw)
pool.run()
def merge_two(self, dst, src, wdir, cntr, **kw):
assert dst != src
kw.update(cntr=cntr)
wdir = self.base / wdir
ms = {}
for _, m in EmlRec.reader(Reader(wdir), **kw, files=(dst, )):
ms.setdefault(m['message-id'], []).append(m)
def body(m):
b = m.get_body(preferencelist=(config.PLAIN, ))
return nominal(b.get_content()) if b else None
dirty = False
for _, m in EmlRec.reader(Reader(wdir), **kw, files=(src, )):
mid = m['message-id']
if mid in ms:
b1 = body(m)
ls = ms[mid]
for m2 in ls:
if b1 == body(m2):
cntr.incr('-')
break
else:
ls.append(m)
cntr.incr('+')
dirty = True
else:
ms[mid] = [m]
cntr.incr('+')
dirty = True
(wdir / src).with_suffix(SUFF).unlink()
if dirty:
ms = (m for l in ms.values() for m in l)
ms = sorted(ms, key=lambda m: m['date'].datetime.astimezone())
with Writer((wdir / dst).with_suffix(SUFF)).to_mbox() as mb:
mb.clear()
for m in ms:
mb.add(m)
merge_args = ((('added', '+'), ('skipped', '-'), ('failed', 'F')),
'Merging:')
def merge_mbox(self, files, **kw):
with counters(self.merge_args, kw):
dirty = True
while dirty:
dirty = False
prev = None
fs = []
for f in files:
if prev:
self.merge_two(prev, f, **kw)
fs.append(prev)
dirty = True
prev = None
else:
prev = f
if dirty:
files = fs
def pool_merge(self, files, wdir, **kw):
kw.update(wdir=wdir)
dirty = True
while dirty:
dirty = False
prev = None
pool = Pool(Counters(*self.merge_args))
with os.scandir(self.base / wdir) as es:
for e in es:
p = pth.Path(e.path)
if p.is_file() and p.stem != PROT and p.suffix == SUFF:
if prev:
pool.call_worker(Mboxes.merge_two, self,
(prev.stem, p.stem), **kw)
dirty = True
prev = None
else:
prev = p
if dirty:
pool.run()
def strip_one(self, stem, wdir, cntr, dejunk_only=None, **kw):
n = stem + SUFF
with Writer(self.base / REPO / n).to_mbox() as mb:
kw.update(files=(stem, ), cntr=cntr)
if dejunk_only:
for _, m in EmlRec.reader(Reader(self.base / wdir), **kw):
try:
t = '\n'.join(m.qextract_text()) # dejunk=False
except UnicodeError:
cntr.incr('-')
else:
m.clear_content()
m.set_content(t, subtype=config.PLAIN, cte='7bit')
mb.add(m)
cntr.incr('+')
else:
for m in EmlRec.importer(self.base / wdir, **kw):
m.junk = False
mb.add(MboxEntry.qcreate(m.mboxer(**kw)))
cntr.incr('+')
strip_args = ((('excluded', '-'), ('stripped', '+'), ('failed', 'F')),
'Stripping:')
def strip_mbox(self, files, **kw):
with counters(self.strip_args, kw):
for f in files:
self.strip_one(f, **kw)
def pool_strip(self, files, wdir, **kw):
kw.update(wdir=wdir)
pool = Pool(Counters(*self.strip_args))
with os.scandir(self.base / wdir) as es:
for e in es:
p = pth.Path(e.path)
if p.is_file() and p.stem != PROT and p.suffix == SUFF:
pool.call_worker(Mboxes.strip_one, self, (p.stem, ), **kw)
pool.run()
export_args = ((('chained', '.'), ('exported', '+'), ('excluded', '-'),
('failed', 'F')), 'Exporting:')
def export_to(self, dst, ctxt, **kw):
kw.update(ctxt=ctxt)
dst = (self.base / dst).with_suffix(SUFF)
with counters(self.export_args, kw) as cs:
for t, ms in ctxt.recs.chainer(**kw):
p = dst
if t:
p = p.with_name(p.stem + '_' + t).with_suffix(p.suffix)
with Writer(p).to_mbox() as mb:
for m in ms:
mb.add(MboxEntry.qcreate(m.mboxer(**kw)))
cs.incr('+')
return cs
|
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"/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,625
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/dataset/amazon_reviews_multi.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
import json
import datasets as ds
_URL = (
"https://amazon-reviews-ml.s3-us-west-2.amazonaws.com/json/{split}/dataset_{lang}_{split}.json"
)
_LANGS = {
"de": "German",
"en": "English",
}
_TRAIN = [_URL.format(split="train", lang=x) for x in _LANGS]
_VALID = [_URL.format(split="dev", lang=x) for x in _LANGS]
_TEST = [_URL.format(split="test", lang=x) for x in _LANGS]
class AmazonReviewsMulti(ds.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
ds.BuilderConfig(
name=_LANGS,
version=ds.Version("1.0.0"),
languages=_LANGS,
)
] + [
ds.BuilderConfig(
name=x,
version=ds.Version("1.0.0"),
languages=[x],
)
for x in _LANGS
]
def _info(self):
return ds.DatasetInfo(
description="",
citation="",
homepage="",
license="",
features=ds.Features(
{
"review_id": ds.Value("string"),
"product_id": ds.Value("string"),
"reviewer_id": ds.Value("string"),
"stars": ds.Value("int32"),
"review_body": ds.Value("string"),
"review_title": ds.Value("string"),
"language": ds.Value("string"),
"product_category": ds.Value("string"),
}
),
)
def _split_generators(self, mgr):
train = mgr.download_and_extract(_TRAIN)
valid = mgr.download_and_extract(_VALID)
test = mgr.download_and_extract(_TEST)
return [
ds.SplitGenerator(name=ds.Split.TRAIN, gen_kw={"file_paths": train}),
ds.SplitGenerator(name=ds.Split.VALIDATION, gen_kw={"file_paths": valid}),
ds.SplitGenerator(name=ds.Split.TEST, gen_kw={"file_paths": test}),
]
def _generate_examples(self, path):
i = 0
for p in path:
with open(p, "r", encoding="utf-8") as f:
for x in f:
yield i, json.loads(x)
i += 1
|
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"/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], "/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/dispatch.py": ["/qnarre/base/doc/blog.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/context.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/analyzer.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/images.py"], "/qnarre/models/big_bird.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/big_bird.py"], "/tools/triton/python/triton/runtime/jit.py": ["/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/fast/roberta.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/bigbird.py": ["/qnarre/prep/config/big_bird.py", "/qnarre/models/big_bird.py"], "/qnarre/models/gpt_neo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/gpt_neo.py"], "/qnarre/prep/convert/t5.py": ["/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/prep/tokens/fsmt.py": 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"/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/tools/disasm.py", "/tools/triton/python/triton/compiler/code_generator.py", "/tools/triton/python/triton/compiler/make_launcher.py"], "/qnarre/base/doc/graph.py": ["/qnarre/base/doc/base.py"], "/qnarre/base/activism.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py", "/qnarre/base/judgment.py"], "/qnarre/base/doc/exporter.py": ["/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/fnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/roformer.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/megatron.py": ["/qnarre/prep/config/megatron.py", "/qnarre/models/megatron.py"], "/qnarre/prep/tokens/fast/roformer.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/roformer.py"], "/qnarre/core/runner.py": ["/qnarre/core/params.py"], "/qnarre/run/seq2seq.py": ["/qnarre/run/qa.py"], "/qnarre/prep/tokens/luke.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/util/table.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/tree.py", "/qnarre/base/doc/util/utils.py"], "/tools/triton/python/triton/language/standard.py": ["/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/language/__init__.py"], "/qnarre/base/doc/filters.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/args.py"], "/qnarre/prep/convert/gpt_neo.py": ["/qnarre/prep/config/gpt_neo.py", "/qnarre/models/gpt_neo.py"], "/qnarre/base/doc/section.py": ["/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/message.py"], "/qnarre/models/funnel.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/funnel.py"], "/tools/triton/python/triton/common/__init__.py": ["/tools/triton/python/triton/common/build.py"], "/qnarre/base/doc/qnn.py": ["/qnarre/base/doc/base.py", 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"/qnarre/base/author.py", "/qnarre/base/conflict.py", "/qnarre/base/conjecture.py"], "/qnarre/models/splinter.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/rag.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/convert/albert.py": ["/qnarre/prep/config/albert.py", "/qnarre/models/albert.py"], "/qnarre/prep/tokens/fast/gpt2.py": ["/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/fast/mbart.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/roformer.py": ["/qnarre/models/roformer.py"], "/qnarre/base/doc/util/tree.py": ["/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py", "/qnarre/base/doc/util/utils.py"], "/qnarre/models/rembert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/t5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/xlm.py": ["/qnarre/tokens/utils.py"], 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"/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": 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["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,626
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/tokens/fast/deberta.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
from ....tokens.base import AddedToken
from .gpt2 import Tokenizer as GPT2Fast
from ..deberta import Tokenizer as Deberta
VOCAB_FS = {
"vocab_file": "vocab.json",
"merges_file": "merges.txt",
"tokenizer_file": "tokenizer.json",
}
VOCAB_MAP = {
"vocab_file": {
"microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json",
"microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json",
"microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json",
"microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json",
"microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json",
"microsoft/deberta-xlarge-mnli": "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json",
},
"merges_file": {
"microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt",
"microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt",
"microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt",
"microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt",
"microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt",
"microsoft/deberta-xlarge-mnli": "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt",
},
}
INPUT_CAPS = {
"microsoft/deberta-base": 512,
"microsoft/deberta-large": 512,
"microsoft/deberta-xlarge": 512,
"microsoft/deberta-base-mnli": 512,
"microsoft/deberta-large-mnli": 512,
"microsoft/deberta-xlarge-mnli": 512,
}
PRETRAINED_INIT_CONFIGURATION = {
"microsoft/deberta-base": {"do_lower_case": False},
"microsoft/deberta-large": {"do_lower_case": False},
}
class DebertaTokenizerFast(GPT2Fast):
vocab_fs = VOCAB_FS
vocab_map = VOCAB_MAP
input_caps = INPUT_CAPS
model_input_names = ["input_ids", "attention_mask", "token_type_ids"]
slow_tokenizer_class = Deberta
def __init__(
self,
vocab_file=None,
merges_file=None,
tokenizer_file=None,
errors="replace",
bos="[CLS]",
eos="[SEP]",
sep="[SEP]",
cls="[CLS]",
unk="[UNK]",
pad="[PAD]",
msk="[MASK]",
add_prefix_space=False,
**kw,
):
super().__init__(
vocab_file,
merges_file,
tokenizer_file=tokenizer_file,
errors=errors,
bos=bos,
eos=eos,
unk=unk,
sep=sep,
cls=cls,
pad=pad,
msk=msk,
add_prefix_space=add_prefix_space,
**kw,
)
@property
def msk(self):
if self._mask_token is None and self.verbose:
logger.error("Using msk, but it is not set yet.")
return None
return str(self._mask_token)
@msk.setter
def msk(self, value):
value = AddedToken(value, lstrip=True, rstrip=False) if isinstance(value, str) else value
self._mask_token = value
def build_inputs_with_special_tokens(self, toks_0, toks_1=None):
if toks_1 is None:
return [self.cls_token_id] + toks_0 + [self.sep_token_id]
cls = [self.cls_token_id]
sep = [self.sep_token_id]
return cls + toks_0 + sep + toks_1 + sep
def create_token_type_ids_from_sequences(self, toks_0, toks_1=None):
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if toks_1 is None:
return len(cls + toks_0 + sep) * [0]
return len(cls + toks_0 + sep + toks_1 + sep) * [0]
|
{"/qnarre/prep/convert/xlnet.py": ["/qnarre/prep/config/xlnet.py", "/qnarre/models/xlnet.py"], "/qnarre/prep/convert/bert.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/base/doc/patcher.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/nominals.py"], "/qnarre/prep/convert/funnel.py": ["/qnarre/prep/config/funnel.py", "/qnarre/models/funnel.py"], "/qnarre/prep/tokens/fast/realm.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py", "/qnarre/tokens/utils.py"], "/qnarre/models/decision_transfo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/decision_transfo.py"], "/qnarre/models/fsmt.py": ["/qnarre/core/embed.py"], "/qnarre/models/roformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/xlnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc.py": ["/qnarre/base/author.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], 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"/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], 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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,627
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/core/test/attend.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
# pytest -s qnarre/neura/layers/attn_test.py
import torch
import qnarre.core.utils as U
import qnarre.core as L
params = dict(
dim_attn=8,
dim_attn_k=None,
dim_attn_v=None,
dim_hidden=16,
drop_attn=None,
drop=0.1,
n_heads=4,
)
class Owner:
pre = post = None
def __init__(self):
self.ps = U.Params(params).init_comps()
self.pre = None
self.post = None
i = torch.constant([0.0] * (4 * 10), shape=(4, 10))
self.src_b = torch.Variable(initial_value=i)
i = torch.constant([0.0] * (4 * 10), shape=(4, 10))
self.mem_b = torch.Variable(initial_value=i)
def test_owner_none():
a = L.Attend(Owner())
a.build([(4, 10, 16)])
src = torch.constant([0.0] * (4 * 10 * 16), shape=(4, 10, 16))
a.call([src])
bias = torch.constant([0.0] * (4 * 10), shape=(4, 10))
bias = torch.expand_dims(torch.expand_dims(bias, axis=1), axis=3)
a.call([src, bias])
ctx = torch.constant([0.0] * (4 * 15 * 16), shape=(4, 15, 16))
a.call([src, bias, None, ctx])
def test_with_owner():
a = L.Attend(Owner())
a.build([(4, 10, 16), (), (4, 18, 16), ()])
src = torch.constant([0.0] * (4 * 10 * 16), shape=(4, 10, 16))
bias = torch.constant([0.0] * (4 * 10), shape=(4, 10))
bias = torch.expand_dims(torch.expand_dims(bias, axis=1), axis=3)
mem = torch.constant([0.0] * (4 * 15 * 16), shape=(4, 15, 16))
ctx = torch.constant([0.0] * (4 * 15 * 16), shape=(4, 15, 16))
a.call([src, bias, mem, ctx])
def test_shift():
a = L.Attend(Owner())
x = torch.constant([1, 2, 3, 4, 5, 6], shape=(1, 1, 2, 3))
torch.print(x)
x = a.shift(x)
torch.print(x)
|
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"/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,628
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/metric/squad2.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
import collections
import datasets as ds
import re
import string
from collections import Counter
class Squad2(ds.Metric):
def _info(self):
return ds.MetricInfo(
description="",
citation="",
inputs_description="",
features=ds.Features(
{
"predictions": {
"id": ds.Value("string"),
"prediction_text": ds.Value("string"),
"no_answer_probability": ds.Value("float32"),
},
"references": {
"id": ds.Value("string"),
"answers": ds.features.Sequence(
{"text": ds.Value("string"), "answer_start": ds.Value("int32")}
),
},
}
),
codebase_urls=[],
reference_urls=[],
)
def _compute(self, preds, refs, no_answer_threshold=1.0):
probs = {p["id"]: p["no_answer_probability"] for p in preds}
preds = {p["id"]: p["prediction_text"] for p in preds}
ds = [{"paragraphs": [{"qas": refs}]}]
has_ans = _make_map(ds)
ans_ids = [k for k, v in has_ans.items() if v]
no_ids = [k for k, v in has_ans.items() if not v]
ms_raw, f1_raw = _raw_scores(ds, preds)
ms = _apply(ms_raw, probs, has_ans, no_answer_threshold)
f1 = _apply(f1_raw, probs, has_ans, no_answer_threshold)
ys = _eval(ms, f1)
if ans_ids:
_merge(ys, _eval(ms, f1, ans_ids), "HasAns")
if no_ids:
_merge(ys, _eval(ms, f1, no_ids), "NoAns")
_best_thresh(ys, preds, ms_raw, f1_raw, probs, has_ans)
return dict(ys)
OPTS = None
def _make_map(ds):
ys = {}
for e in ds:
for p in e["paragraphs"]:
for x in p["qas"]:
ys[x["id"]] = bool(x["answers"]["text"])
return ys
def _raw_scores(ds, preds):
ms = {}
f1 = {}
for e in ds:
for p in e["paragraphs"]:
for q in p["qas"]:
i = q["id"]
if i not in preds:
print(f"Missing prediction for {i}")
continue
x = preds[i]
ts = [t for t in q["answers"]["text"] if _normalize(t)]
ts = ts if ts else [""]
ms[i] = max(_match(x, t) for t in ts)
f1[i] = max(_f1(x, t) for t in ts)
return ms, f1
def _match(x, t):
return int(_normalize(x) == _normalize(t))
def _f1(x, t):
xs = _normalize(x).split() if x else []
ts = _normalize(t).split() if t else []
if len(xs) == 0 or len(ts) == 0:
return int(xs == ts)
common = Counter(ts) & Counter(xs)
s = sum(common.values())
if s == 0:
return 0
precision = 1.0 * s / len(xs)
recall = 1.0 * s / len(ts)
f1 = (2 * precision * recall) / (precision + recall)
return f1
def _apply(scores, probs, has_ans, thresh):
ys = {}
for i, s in scores.items():
if probs[i] > thresh:
ys[i] = float(not has_ans[i])
else:
ys[i] = s
return ys
def _eval(ms, f1, ids):
if not ids:
n = len(ms)
return collections.OrderedDict(
[
("exact", 100.0 * sum(ms.values()) / n),
("f1", 100.0 * sum(f1.values()) / n),
("total", n),
]
)
else:
n = len(ids)
return collections.OrderedDict(
[
("exact", 100.0 * sum(ms[i] for i in ids) / n),
("f1", 100.0 * sum(f1[i] for i in ids) / n),
("total", n),
]
)
def _merge(ys, xs, pre):
for x in xs:
ys[f"{pre}_{x}"] = xs[x]
def _best_thresh(ys, preds, ms_raw, f1_raw, probs, has_ans):
ms, m_thresh = _find_best(preds, ms_raw, probs, has_ans)
f1, f1_thresh = _find_best(preds, f1_raw, probs, has_ans)
ys["best_exact"] = ms
ys["best_exact_thresh"] = m_thresh
ys["best_f1"] = f1
ys["best_f1_thresh"] = f1_thresh
def _find_best(preds, scores, probs, has_ans):
y = x = sum(1 for k in has_ans if not has_ans[k])
t = 0.0
ids = sorted(probs, key=lambda k: probs[k])
for i in ids:
if i not in scores:
continue
if has_ans[i]:
d = scores[i]
else:
d = -1 if preds[i] else 0
x += d
if x > y:
y = x
t = probs[i]
return 100.0 * y / len(scores), t
def _normalize(t):
def no_punc(x):
exclude = set(string.punctuation)
return "".join(c for c in x if c not in exclude)
def no_articles(x):
return re.sub(re.compile(r"\b(a|an|the)\b", re.UNICODE), " ", x)
def ws_fix(x):
return " ".join(x.split())
return ws_fix(no_articles(no_punc(t.lower())))
|
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"/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], 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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,629
|
quantapix/qnarre
|
refs/heads/main
|
/tools/triton/python/test/unit/runtime/test_driver.py
|
import sys
import triton
def test_is_lazy():
from importlib import reload
reload(sys.modules["triton.runtime.driver"])
reload(sys.modules["triton.runtime"])
mod = sys.modules[triton.runtime.driver.__module__]
assert isinstance(triton.runtime.driver, getattr(mod, "LazyProxy"))
assert triton.runtime.driver._obj is None
utils = triton.runtime.driver.utils # noqa: F841
assert issubclass(triton.runtime.driver._obj.__class__, getattr(mod, "DriverBase"))
|
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"/qnarre/base/author.py", "/qnarre/base/conflict.py", "/qnarre/base/conjecture.py"], "/qnarre/models/splinter.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/rag.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/convert/albert.py": ["/qnarre/prep/config/albert.py", "/qnarre/models/albert.py"], "/qnarre/prep/tokens/fast/gpt2.py": ["/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/fast/mbart.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/roformer.py": ["/qnarre/models/roformer.py"], "/qnarre/base/doc/util/tree.py": ["/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py", "/qnarre/base/doc/util/utils.py"], "/qnarre/models/rembert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/t5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/xlm.py": ["/qnarre/tokens/utils.py"], 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"/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], 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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,630
|
quantapix/qnarre
|
refs/heads/main
|
/notebooks/old/src/callbacks.py
|
# Copyright 2019 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
# !pip install -U tf-nightly-2.0-preview
import numpy as np
import pathlib as pth
import tensorflow as tf
from datetime import datetime
ks = tf.keras
kl = ks.layers
vocab = (' ', ':', '|')
vocab += ('x', 'y', '=', ',', '+', '-', '*')
vocab += ('0', '1', '2', '3', '4', '5', '6', '7', '8', '9')
tokens = {c: i for i, c in enumerate(vocab)}
tokens.update({v: k for k, v in tokens.items()})
SPC, SEP, STP = [tokens[c] for c in vocab[:3]]
assert SPC == 0
def paths(ps):
d = pth.Path('/tmp/q/dataset')
for i in range(ps.num_shards):
i = '{:0>4d}'.format(i)
yield str(d / f'shard_{i}.tfrecords')
@tf.function
def caster(d):
return {k: tf.cast(v, tf.int32) for k, v in d.items()}
@tf.function
def formatter(d):
ds = tf.RaggedTensor.from_sparse(d['defs'])
n = ds.nrows()
os = tf.RaggedTensor.from_sparse(d['op'])
tf.debugging.assert_equal(n, os.nrows())
ss = tf.fill([n, 1], SEP)
enc = tf.concat([ds, ss, os, ss], axis=1)
rs = tf.RaggedTensor.from_sparse(d['res'])
tf.debugging.assert_equal(n, rs.nrows())
tgt = tf.concat([rs, tf.fill([n, 1], STP)], axis=1)
def mask(x):
y = x.flat_values
mv = tf.shape(y)[0]
s = mv // 2
i = tf.random.uniform([s], maxval=mv, dtype=tf.int32)[:, None]
y = tf.tensor_scatter_nd_update(y, i, tf.zeros([s], dtype=tf.int32))
return x.with_flat_values(y)
return {'enc': enc, 'dec': mask(tgt), 'tgt': tgt}
@tf.function
def adapter(d):
enc, dec, tgt = d['enc'], d['dec'], d['tgt']
return (
(
enc.flat_values,
enc.row_splits,
dec.flat_values,
dec.row_splits,
tgt.flat_values,
tgt.row_splits,
),
tgt.to_tensor(),
)
def dset_for(ps):
ds = tf.data.TFRecordDataset(list(paths(ps)))
ds = ds.take(1000)
ds = ds.batch(ps.dim_batch)
fs = {
'defs': tf.io.VarLenFeature(tf.int64),
'op': tf.io.VarLenFeature(tf.int64),
'res': tf.io.VarLenFeature(tf.int64),
}
ds = ds.map(lambda x: tf.io.parse_example(x, fs))
return ds.map(caster).map(formatter).map(adapter)
class Layer(kl.Layer):
def __init__(self, ps, **kw):
kw.setdefault('dtype', tf.float32)
super().__init__(**kw)
self.ps = ps
class ToRagged(kl.Layer):
@tf.function
def call(self, x):
ys = []
for i in range(3):
i *= 2
fv, rs = x[i:i + 2]
ys.append(tf.RaggedTensor.from_row_splits(fv, rs))
return ys
class Frames(Layer):
def __init__(self, ps):
super().__init__(ps, dtype=tf.int32) # , dynamic=True)
s = (ps.dim_batch, ps.width_enc)
kw = dict(initializer='zeros', trainable=False, use_resource=True)
self.prev = self.add_weight('prev', shape=s, **kw)
@tf.function
def call(self, x):
xe, xd, xt = x
# tf.debugging.assert_greater_equal(self.ps.width_enc,
# xe.bounding_shape(axis=1, out_type=tf.int32))
ye = tf.concat([self.prev, xe], axis=1)
el = tf.cast(xe.row_lengths(), dtype=tf.int32)
ye = tf.gather_nd(ye, self.calc_idxs(el))
c = self.ps.width_dec - xd.bounding_shape(axis=1, out_type=tf.int32)
# tf.debugging.assert_greater_equal(c, 0)
yd = tf.pad(xd.to_tensor(), [[0, 0], [0, c]])
dl = tf.cast(xd.row_lengths(), dtype=tf.int32)
# tf.debugging.assert_greater_equal(self.ps.width_enc,
# xt.bounding_shape(axis=1, out_type=tf.int32))
p = tf.concat([ye, xt], axis=1)
tl = tf.cast(xt.row_lengths(), dtype=tf.int32)
p = tf.gather_nd(p, self.calc_idxs(tl))
self.prev.assign(p)
# tf.map_fn(print_prev, self.prev)
return [ye, el, yd, dl]
def calc_idxs(self, lens):
y, w = self.ps.dim_batch, self.ps.width_enc
y = tf.broadcast_to(tf.range(y)[:, None], [y, w])
i = tf.range(w)[None, ] + lens[:, None]
y = tf.stack([y, i], axis=2)
return y
@tf.function
def print_prev(x):
tf.print(''.join([tokens[t] for t in x]))
class Embed(Layer):
@staticmethod
def pos_timing(width, depth):
assert depth % 2 == 0
d = np.arange(depth)[np.newaxis, :]
d = 1 / np.power(10000, (2 * (d // 2)) / np.float32(depth))
t = np.arange(width)[:, np.newaxis] * d
t = [np.sin(t[:, 0::2]), np.cos(t[:, 1::2])]
t = np.concatenate(t, axis=-1)[np.newaxis, ...]
t = tf.constant(t, dtype=tf.float32)
return t
def __init__(self, ps):
super().__init__(ps)
s = (ps.dim_vocab, ps.dim_hidden)
self.emb = self.add_weight('emb', shape=s)
p = self.pos_timing(ps.width_enc, ps.dim_hidden)
self.enc_p = tf.broadcast_to(p, [ps.dim_batch] + p.shape[1:])
p = self.pos_timing(ps.width_dec, ps.dim_hidden)
self.dec_p = tf.broadcast_to(p, [ps.dim_batch] + p.shape[1:])
@tf.function
def call(self, x):
x, lens = x
y = tf.nn.embedding_lookup(self.emb, x)
y = (y * y.shape[-1]**0.5) # + self.pos[:, :y.shape[1], :]
return [y, lens]
class Encode(Layer):
def __init__(self, ps):
super().__init__(ps)
self.width = ps.width_enc
self.encs = [Encoder(self, f'enc_{i}') for i in range(ps.dim_stacks)]
@tf.function
def call(self, x):
y = x
for e in self.encs:
y = e(y)
return y
class Decode(Layer):
def __init__(self, ps):
super().__init__(ps)
self.width = ps.width_dec
self.decs = [Decoder(self, f'dec_{i}') for i in range(ps.dim_stacks)]
@tf.function
def call(self, x):
y, ye = x[:-1], x[-1]
for d in self.decs:
y = d(y + [ye])
return y
class Debed(Layer):
def __init__(self, ps):
super().__init__(ps)
self.dbd = Dense(self, 'dbd', [ps.dim_hidden, ps.dim_vocab])
@tf.function
def call(self, x):
x, lens = x
s = tf.shape(x)
y = tf.reshape(x, [s[0] * s[1], -1])
y = self.dbd(y)
y = tf.reshape(y, [s[0], s[1], -1])
y = y[:, :tf.math.reduce_max(lens), :]
return y
class Encoder(tf.Module):
def __init__(self, layer, name):
super().__init__(name=name)
with self.name_scope:
self.reflect = Attention(layer, 'refl')
self.conclude = Conclusion(layer, 'conc')
# @tf.Module.with_name_scope
@tf.function
def __call__(self, x):
y = self.reflect(x + [x[0]])
y = self.conclude(y)
return y
class Decoder(tf.Module):
def __init__(self, layer, name):
super().__init__(name=name)
with self.name_scope:
self.reflect = Attention(layer, 'refl')
self.consider = Attention(layer, 'cnsd')
self.conclude = Conclusion(layer, 'conc')
# @tf.Module.with_name_scope
@tf.function
def __call__(self, x):
x, ye = x[:-1], x[-1]
y = self.reflect(x + [x[0]])
y = self.consider(y + [ye])
y = self.conclude(y)
return y
class Attention(tf.Module):
def __init__(self, layer, name):
super().__init__(name=name)
h = layer.ps.dim_hidden
self.scale = 1 / (h**0.5)
with self.name_scope:
self.q = layer.add_weight('q', shape=(h, h))
self.k = layer.add_weight('k', shape=(h, h))
self.v = layer.add_weight('v', shape=(h, h))
# @tf.Module.with_name_scope
@tf.function
def __call__(self, x):
x, lens, ctx = x
off = tf.math.reduce_max(lens)
q = tf.einsum('bni,ij->bnj', x[:, -off:, :], self.q)
k = tf.einsum('bni,ij->bnj', ctx, self.k)
y = tf.einsum('bni,bmi->bnm', q, k)
# use lens
y = tf.nn.softmax(y * self.scale)
v = tf.einsum('bni,ij->bnj', ctx, self.v)
y = tf.einsum('bnm,bmi->bni', y, v)
y = tf.concat([x[:, :-off, :], y], axis=1)
return [y, lens]
class Conclusion(tf.Module):
def __init__(self, layer, name):
super().__init__(name=name)
self.layer = layer
ps = layer.ps
w = layer.width * ps.dim_hidden
with self.name_scope:
s = [w, ps.dim_dense]
self.inflate = Dense(layer, 'infl', s, activation='relu')
s = [ps.dim_dense, w]
self.deflate = Dense(layer, 'defl', s, bias=False)
# @tf.Module.with_name_scope
@tf.function
def __call__(self, x):
x, lens = x
w = self.layer.width
d = self.layer.ps.dim_hidden
y = tf.reshape(x, [-1, w * d])
y = self.inflate(y)
y = self.deflate(y)
y = tf.reshape(y, [-1, w, d])
return [y, lens]
class Dense(tf.Module):
bias = None
activation = None
def __init__(self, layer, name, shape, activation=None, bias=True):
super().__init__(name=name)
with self.name_scope:
kw = dict(shape=shape, initializer='glorot_uniform')
self.kern = layer.add_weight('kern', **kw)
if bias:
kw.update(shape=[shape[1]], initializer='zeros')
self.bias = layer.add_weight('bias', **kw)
self.activation = ks.activations.get(activation)
# @tf.Module.with_name_scope
@tf.function
def __call__(self, x):
y = tf.einsum('bi,ij->bj', x, self.kern)
if self.bias is not None:
y = tf.nn.bias_add(y, self.bias)
if self.activation:
y = self.activation(y)
return y
def model_for(ps):
x = [ks.Input(shape=(), dtype='int32'), ks.Input(shape=(), dtype='int64')]
x += [ks.Input(shape=(), dtype='int32'), ks.Input(shape=(), dtype='int64')]
x += [ks.Input(shape=(), dtype='int32'), ks.Input(shape=(), dtype='int64')]
y = ToRagged()(x)
y = Frames(ps)(y)
embed = Embed(ps)
ye = Encode(ps)(embed(y[:2]))
yd = Decode(ps)(embed(y[2:]) + [ye[0]])
y = Debed(ps)(yd)
m = ks.Model(inputs=x, outputs=y)
m.compile(optimizer=ps.optimizer, loss=ps.loss, metrics=[ps.metric])
print(m.summary())
return m
class Loss(ks.losses.Loss):
@staticmethod
def xent(y_true, y_pred):
kw = dict(labels=y_true, logits=y_pred)
return tf.nn.sparse_softmax_cross_entropy_with_logits(**kw)
def __init__(self):
super().__init__(name='loss')
def call(self, y_true, y_pred):
return self.xent(y_true, y_pred)
class Metric(ks.metrics.Metric):
def __init__(self):
super().__init__(name='metric', dtype=tf.float32)
self.total = self.add_weight('total', initializer='zeros')
self.count = self.add_weight('count', initializer='zeros')
def update_state(self, y_true, y_pred, sample_weight=None):
vs = Loss.xent(y_true, y_pred)
self.total.assign_add(tf.math.reduce_sum(vs))
return self.count.assign_add(tf.cast(tf.size(vs), dtype=tf.float32))
def result(self):
return tf.math.divide_no_nan(self.total, self.count)
params = dict(
dim_batch=10,
dim_dense=150,
dim_hidden=6,
dim_stacks=2,
dim_vocab=len(vocab) + 5,
# loss=Loss(),
loss=ks.losses.SparseCategoricalCrossentropy(from_logits=True),
# metric=Metric(),
metric=ks.metrics.SparseCategoricalCrossentropy(from_logits=True),
num_epochs=5,
num_shards=2,
optimizer=ks.optimizers.Adam(),
width_dec=15,
width_enc=25,
)
class Params:
def __init__(self, **kw):
for k, v in kw.items():
setattr(self, k, v)
def main_eager(_):
ps = Params(**params)
ds = dset_for(ps)
m = model_for(ps)
def step(x, y):
with tf.GradientTape() as tape:
yy = m(x)
loss = ps.loss(y, yy)
loss += sum(m.losses)
xent = ps.metric(y, yy)
for v in m.trainable_variables:
print('---', v)
grads = tape.gradient(loss, m.trainable_variables)
ps.optimizer.apply_gradients(zip(grads, m.trainable_variables))
return loss, xent
@tf.function
def epoch():
s, loss, xent = 0, 0.0, 0.0
for x, y in ds:
s += 1
loss, xent = step(x, y)
if tf.equal(s % 10, 0):
e = ps.metric.result()
tf.print('Step:', s, ', loss:', loss, ', xent:', e)
return loss, xent
for e in range(ps.num_epochs):
loss, xent = epoch()
print(f'Epoch {e} loss:', loss, ', xent:', xent)
def main_graph(_):
ps = Params(**params)
ds = dset_for(ps)
m = model_for(ps)
ld = datetime.now().strftime('%Y%m%d-%H%M%S')
ld = f'/tmp/q/logs/{ld}'
cs = [ks.callbacks.TensorBoard(log_dir=ld, histogram_freq=1)]
m.fit(ds, callbacks=cs, epochs=ps.num_epochs)
if __name__ == '__main__':
# tf.autograph.set_verbosity(10)
from absl import app # , logging
# logging.set_verbosity(logging.INFO)
# app.run(main_graph)
app.run(main_eager)
|
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"/qnarre/base/author.py", "/qnarre/base/conflict.py", "/qnarre/base/conjecture.py"], "/qnarre/models/splinter.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/rag.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/convert/albert.py": ["/qnarre/prep/config/albert.py", "/qnarre/models/albert.py"], "/qnarre/prep/tokens/fast/gpt2.py": ["/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/fast/mbart.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/roformer.py": ["/qnarre/models/roformer.py"], "/qnarre/base/doc/util/tree.py": ["/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py", "/qnarre/base/doc/util/utils.py"], "/qnarre/models/rembert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/t5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/xlm.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/bert_tf2.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/tools/triton/python/triton/ops/__init__.py": ["/tools/triton/python/triton/ops/cross_entropy.py", "/tools/triton/python/triton/ops/matmul.py"], "/qnarre/core/norm.py": ["/qnarre/core/base.py"], "/qnarre/models/mpnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/author.py": ["/qnarre/base/named.py"], "/qnarre/base/doc/args.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/log.py"], "/qnarre/prep/tokens/gpt.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/distilbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/proof.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py"], "/qnarre/prep/tokens/byt5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/electra.py": ["/qnarre/prep/config/electra.py", "/qnarre/models/electra.py"], "/qnarre/models/luke.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": ["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,631
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/config/transfo_xl.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
from torch import nn
from ... import core as qc
class PreTrained(qc.PreTrained):
hs = qc.Hypers(
kw=dict(
adaptive=True,
clamp_len=1000,
cutoffs=[20000, 40000, 200000],
d_embed=1024,
d_ff=4096,
d_head=64,
d_model=1024,
div_val=4,
drop_attn=0.0,
drop=0.1,
EOS=0,
eps=1e-5,
init_range=0.01,
init_std=0.02,
init="normal",
mem_len=1600,
model_type="transfo-xl",
n_heads=16,
n_lays=18,
pre_lnorm=False,
proj_init_std=0.01,
proj_share_all_but_first=True,
s_vocab=267735,
same_length=True,
sample_softmax=-1,
untie_r=True,
),
)
def __init__(self, **kw):
self.cutoffs = []
self.cutoffs.extend(cutoffs)
if proj_share_all_but_first:
self.tie_projs = [False] + [True] * len(self.cutoffs)
else:
self.tie_projs = [False] + [False] * len(self.cutoffs)
super().__init__(EOS=EOS, **kw)
def _init_weight(self, weight):
if self.cfg.init == "uniform":
nn.init.uniform_(weight, -self.cfg.init_range, self.cfg.init_range)
elif self.cfg.init == "normal":
nn.init.normal_(weight, 0.0, self.cfg.init_std)
def _init_bias(self, bias):
nn.init.constant_(bias, 0.0)
def _init_weights(self, m):
classname = m.__class__.__name__
if classname.find("Linear") != -1:
if hasattr(m, "weight") and m.weight is not None:
self._init_weight(m.weight)
if hasattr(m, "bias") and m.bias is not None:
self._init_bias(m.bias)
elif classname.find("AdaptiveEmbedding") != -1:
if hasattr(m, "emb_projs"):
for i in range(len(m.emb_projs)):
if m.emb_projs[i] is not None:
nn.init.normal_(m.emb_projs[i], 0.0, self.cfg.proj_init_std)
elif classname.find("Embedding") != -1:
if hasattr(m, "weight"):
self._init_weight(m.weight)
elif classname.find("ProjectedAdaptiveLogSoftmax") != -1:
if hasattr(m, "cluster_weight") and m.cluster_weight is not None:
self._init_weight(m.cluster_weight)
if hasattr(m, "cluster_bias") and m.cluster_bias is not None:
self._init_bias(m.cluster_bias)
if hasattr(m, "out_projs"):
for i in range(len(m.out_projs)):
if m.out_projs[i] is not None:
nn.init.normal_(m.out_projs[i], 0.0, self.cfg.proj_init_std)
elif classname.find("LayerNorm") != -1:
if hasattr(m, "weight"):
nn.init.normal_(m.weight, 1.0, self.cfg.init_std)
if hasattr(m, "bias") and m.bias is not None:
self._init_bias(m.bias)
else:
if hasattr(m, "r_emb"):
self._init_weight(m.r_emb)
if hasattr(m, "r_w_bias"):
self._init_weight(m.r_w_bias)
if hasattr(m, "r_r_bias"):
self._init_weight(m.r_r_bias)
if hasattr(m, "r_bias"):
self._init_bias(m.r_bias)
MAP = {
"transfo-xl-wt103": dict(
archs=["LMHead"],
ext_len=0,
task_params={"text-generation": {"do_sample": True, "max_len": 250}},
tgt_len=128,
tie_projs=[False, True, True, True],
tie_weight=True,
),
}
|
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"/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,632
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/core/forward.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
import torch
from torch import nn
from . import output as qo
def forward_masked(self, x, labels=None, **kw):
ys = self.model(x, **kw)
y = self.proj(ys[0])
loss = None
if labels is not None:
loss = nn.CrossEntropyLoss()(y.view(-1, self.cfg.s_vocab), labels.view(-1))
ys = (y,) + ys[1:] + (loss,)
return qo.WithLoss(*ys)
def forward_qa(self, x, beg=None, end=None, **kw):
ys = self.model(x, **kw)
b, e = self.proj(ys[0]).split(1, dim=-1)
b = b.squeeze(-1).contiguous()
e = e.squeeze(-1).contiguous()
loss = None
if beg is not None and end is not None:
if len(beg.size()) > 1:
beg = beg.squeeze(-1)
if len(end.size()) > 1:
end = end.squeeze(-1)
i = b.size(1)
beg.clamp_(0, i)
end.clamp_(0, i)
f = nn.CrossEntropyLoss(ignore_index=i)
loss = (f(b, beg) + f(e, end)) / 2
ys = (b, e) + ys[1:] + (loss,)
return qo.LossQA(*ys)
# return qo.LossSeq2SeqQA(*ys)
def forward_seq(self, x, labels=None, **kw):
cfg = self.cfg
ys = self.model(x, **kw)
y = self.proj(ys[1]) # ys[0][:, 0]; ys[0][:, 0, :];
loss = None
if labels is not None:
if cfg.problem is None:
dt = labels.dtype
if cfg.n_labels == 1:
cfg.problem = "regression"
elif cfg.n_labels > 1 and (dt == torch.long or dt == torch.int):
cfg.problem = "single_label"
else:
cfg.problem = "multi_label"
if cfg.problem == "regression":
if cfg.n_labels == 1:
loss = nn.MSELoss()(y.squeeze(), labels.squeeze())
else:
loss = nn.MSELoss()(y, labels)
elif cfg.problem == "single_label":
loss = nn.CrossEntropyLoss()(y.view(-1, cfg.n_labels), labels.view(-1))
elif cfg.problem == "multi_label":
loss = nn.BCEWithLogitsLoss()(y, labels)
ys = (y,) + ys[2:] + (loss,) # ys[1:]
return qo.WithLoss(*ys)
# return qo.LossSeq2Seq(*ys)
def forward_tok(self, x, mask=None, labels=None, **kw):
cfg = self.cfg
ys = self.model(x, mask=mask, **kw)
y = self.proj(ys[0])
loss = None
if labels is not None:
labels = labels.to(y.device)
f = nn.CrossEntropyLoss()
l = labels.view(-1)
if mask is not None:
l = torch.where(mask.view(-1) == 1, l, torch.tensor(f.ignore_index).type_as(l))
loss = f(y.view(-1, cfg.n_labels), l)
ys = (y,) + ys[2:] + (loss,)
return qo.WithLoss(*ys)
|
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"/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], "/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], 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"/qnarre/prep/convert/bert_tf2.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/tools/triton/python/triton/ops/__init__.py": ["/tools/triton/python/triton/ops/cross_entropy.py", "/tools/triton/python/triton/ops/matmul.py"], "/qnarre/core/norm.py": ["/qnarre/core/base.py"], "/qnarre/models/mpnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/author.py": ["/qnarre/base/named.py"], "/qnarre/base/doc/args.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/log.py"], "/qnarre/prep/tokens/gpt.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/distilbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/proof.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py"], "/qnarre/prep/tokens/byt5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/electra.py": ["/qnarre/prep/config/electra.py", "/qnarre/models/electra.py"], "/qnarre/models/luke.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": ["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,633
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/dataset/wikitext.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
import datasets as ds
from os.path import join
_URL = "https://s3.amazonaws.com/research.metamind.io/wikitext/"
_URLS = {
"103-v1": _URL + "wikitext-103-v1.zip",
"2-v1": _URL + "wikitext-2-v1.zip",
"103-raw-v1": _URL + "wikitext-103-raw-v1.zip",
"2-raw-v1": _URL + "wikitext-2-raw-v1.zip",
}
class Wikitext(ds.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
ds.BuilderConfig(name="103-v1", version=ds.Version("0.1.0")),
ds.BuilderConfig(name="2-v1", version=ds.Version("0.1.0")),
ds.BuilderConfig(name="103-raw-v1", version=ds.Version("0.1.0")),
ds.BuilderConfig(name="2-raw-v1", version=ds.Version("0.1.0")),
]
def _info(self):
return ds.DatasetInfo(
description="",
citation="",
homepage="",
license="",
features=ds.Features({"text": ds.Value("string")}),
)
def _split_generators(self, mgr):
n = self.config.name
f = mgr.download_and_extract(_URLS[n])
if n == "103-v1":
d = join(f, "wikitext-103")
return [
ds.SplitGenerator(
name=ds.Split.TEST,
gen_kw={"data_file": join(d, "wiki.test.tokens"), "split": "test"},
),
ds.SplitGenerator(
name=ds.Split.TRAIN,
gen_kw={"data_file": join(d, "wiki.train.tokens"), "split": "train"},
),
ds.SplitGenerator(
name=ds.Split.VALIDATION,
gen_kw={"data_file": join(d, "wiki.valid.tokens"), "split": "valid"},
),
]
if n == "wikitext-103-raw-v1":
d = join(f, "wikitext-103-raw")
return [
ds.SplitGenerator(
name=ds.Split.TEST,
gen_kw={"data_file": join(d, "wiki.test.raw"), "split": "test"},
),
ds.SplitGenerator(
name=ds.Split.TRAIN,
gen_kw={"data_file": join(d, "wiki.train.raw"), "split": "train"},
),
ds.SplitGenerator(
name=ds.Split.VALIDATION,
gen_kw={"data_file": join(d, "wiki.valid.raw"), "split": "valid"},
),
]
if n == "wikitext-2-raw-v1":
d = join(f, "wikitext-2-raw")
return [
ds.SplitGenerator(
name=ds.Split.TEST,
gen_kw={"data_file": join(d, "wiki.test.raw"), "split": "test"},
),
ds.SplitGenerator(
name=ds.Split.TRAIN,
gen_kw={"data_file": join(d, "wiki.train.raw"), "split": "train"},
),
ds.SplitGenerator(
name=ds.Split.VALIDATION,
gen_kw={"data_file": join(d, "wiki.valid.raw"), "split": "valid"},
),
]
if n == "wikitext-2-v1":
d = join(f, "wikitext-2")
return [
ds.SplitGenerator(
name=ds.Split.TEST,
gen_kw={"data_file": join(d, "wiki.test.tokens"), "split": "test"},
),
ds.SplitGenerator(
name=ds.Split.TRAIN,
gen_kw={"data_file": join(d, "wiki.train.tokens"), "split": "train"},
),
ds.SplitGenerator(
name=ds.Split.VALIDATION,
gen_kw={"data_file": join(d, "wiki.valid.tokens"), "split": "valid"},
),
]
def _generate_examples(self, x, _):
with open(x, encoding="utf-8") as f:
for i, t in enumerate(f):
if t.strip():
yield i, {"text": t}
else:
yield i, {"text": ""}
|
{"/qnarre/prep/convert/xlnet.py": ["/qnarre/prep/config/xlnet.py", "/qnarre/models/xlnet.py"], "/qnarre/prep/convert/bert.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/base/doc/patcher.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/nominals.py"], "/qnarre/prep/convert/funnel.py": ["/qnarre/prep/config/funnel.py", "/qnarre/models/funnel.py"], "/qnarre/prep/tokens/fast/realm.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py", "/qnarre/tokens/utils.py"], "/qnarre/models/decision_transfo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/decision_transfo.py"], "/qnarre/models/fsmt.py": ["/qnarre/core/embed.py"], "/qnarre/models/roformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/xlnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc.py": ["/qnarre/base/author.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], "/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/dispatch.py": ["/qnarre/base/doc/blog.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/context.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/analyzer.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/images.py"], "/qnarre/models/big_bird.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/big_bird.py"], "/tools/triton/python/triton/runtime/jit.py": ["/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/fast/roberta.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/bigbird.py": ["/qnarre/prep/config/big_bird.py", "/qnarre/models/big_bird.py"], "/qnarre/models/gpt_neo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/gpt_neo.py"], "/qnarre/prep/convert/t5.py": ["/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/prep/tokens/fsmt.py": 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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,634
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/run/ddp2.py
|
import torch
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
class MyTrainDataset(Dataset):
def __init__(self, size):
self.size = size
self.data = [(torch.rand(20), torch.rand(1)) for _ in range(size)]
def __len__(self):
return self.size
def __getitem__(self, index):
return self.data[index]
class Trainer:
def __init__(
self,
model: torch.nn.Module,
train_data: DataLoader,
optimizer: torch.optim.Optimizer,
gpu_id: int,
save_every: int,
) -> None:
self.gpu_id = gpu_id
self.model = model.to(gpu_id)
self.train_data = train_data
self.optimizer = optimizer
self.save_every = save_every
def _run_batch(self, source, targets):
self.optimizer.zero_grad()
output = self.model(source)
loss = F.cross_entropy(output, targets)
loss.backward()
self.optimizer.step()
def _run_epoch(self, epoch):
b_sz = len(next(iter(self.train_data))[0])
print(
f"[GPU{self.gpu_id}] Epoch {epoch} | Batchsize: {b_sz} | Steps: {len(self.train_data)}"
)
for source, targets in self.train_data:
source = source.to(self.gpu_id)
targets = targets.to(self.gpu_id)
self._run_batch(source, targets)
def _save_checkpoint(self, epoch):
ckp = self.model.state_dict()
PATH = "checkpoint.pt"
torch.save(ckp, PATH)
print(f"Epoch {epoch} | Training checkpoint saved at {PATH}")
def train(self, max_epochs: int):
for epoch in range(max_epochs):
self._run_epoch(epoch)
if epoch % self.save_every == 0:
self._save_checkpoint(epoch)
def load_train_objs():
train_set = MyTrainDataset(2048) # load your dataset
model = torch.nn.Linear(20, 1) # load your model
optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)
return train_set, model, optimizer
def prepare_dataloader(dataset: Dataset, batch_size: int):
return DataLoader(dataset, batch_size=batch_size, pin_memory=True, shuffle=True)
def main(device, total_epochs, save_every, batch_size):
dataset, model, optimizer = load_train_objs()
train_data = prepare_dataloader(dataset, batch_size)
trainer = Trainer(model, train_data, optimizer, device, save_every)
trainer.train(total_epochs)
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description="simple distributed training job")
parser.add_argument("total_epochs", type=int, help="Total epochs to train the model")
parser.add_argument("save_every", type=int, help="How often to save a snapshot")
parser.add_argument(
"--batch_size", default=32, type=int, help="Input batch size on each device (default: 32)"
)
args = parser.parse_args()
device = 0 # shorthand for cuda:0
main(device, args.total_epochs, args.save_every, args.batch_size)
import torch
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
from datautils import MyTrainDataset
import torch.multiprocessing as mp
from torch.utils.data.distributed import DistributedSampler
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.distributed import init_process_group, destroy_process_group
import os
def ddp_setup(rank, world_size):
"""
Args:
rank: Unique identifier of each process
world_size: Total number of processes
"""
os.environ["MASTER_ADDR"] = "localhost"
os.environ["MASTER_PORT"] = "12355"
init_process_group(backend="nccl", rank=rank, world_size=world_size)
class Trainer:
def __init__(
self,
model: torch.nn.Module,
train_data: DataLoader,
optimizer: torch.optim.Optimizer,
gpu_id: int,
save_every: int,
) -> None:
self.gpu_id = gpu_id
self.model = model.to(gpu_id)
self.train_data = train_data
self.optimizer = optimizer
self.save_every = save_every
self.model = DDP(model, device_ids=[gpu_id])
def _run_batch(self, source, targets):
self.optimizer.zero_grad()
output = self.model(source)
loss = F.cross_entropy(output, targets)
loss.backward()
self.optimizer.step()
def _run_epoch(self, epoch):
b_sz = len(next(iter(self.train_data))[0])
print(
f"[GPU{self.gpu_id}] Epoch {epoch} | Batchsize: {b_sz} | Steps: {len(self.train_data)}"
)
self.train_data.sampler.set_epoch(epoch)
for source, targets in self.train_data:
source = source.to(self.gpu_id)
targets = targets.to(self.gpu_id)
self._run_batch(source, targets)
def _save_checkpoint(self, epoch):
ckp = self.model.module.state_dict()
PATH = "checkpoint.pt"
torch.save(ckp, PATH)
print(f"Epoch {epoch} | Training checkpoint saved at {PATH}")
def train(self, max_epochs: int):
for epoch in range(max_epochs):
self._run_epoch(epoch)
if self.gpu_id == 0 and epoch % self.save_every == 0:
self._save_checkpoint(epoch)
def load_train_objs():
train_set = MyTrainDataset(2048) # load your dataset
model = torch.nn.Linear(20, 1) # load your model
optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)
return train_set, model, optimizer
def prepare_dataloader(dataset: Dataset, batch_size: int):
return DataLoader(
dataset,
batch_size=batch_size,
pin_memory=True,
shuffle=False,
sampler=DistributedSampler(dataset),
)
def main(rank: int, world_size: int, save_every: int, total_epochs: int, batch_size: int):
ddp_setup(rank, world_size)
dataset, model, optimizer = load_train_objs()
train_data = prepare_dataloader(dataset, batch_size)
trainer = Trainer(model, train_data, optimizer, rank, save_every)
trainer.train(total_epochs)
destroy_process_group()
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description="simple distributed training job")
parser.add_argument("total_epochs", type=int, help="Total epochs to train the model")
parser.add_argument("save_every", type=int, help="How often to save a snapshot")
parser.add_argument(
"--batch_size", default=32, type=int, help="Input batch size on each device (default: 32)"
)
args = parser.parse_args()
world_size = torch.cuda.device_count()
mp.spawn(
main,
args=(world_size, args.save_every, args.total_epochs, args.batch_size),
nprocs=world_size,
)
import torch
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
from datautils import MyTrainDataset
import torch.multiprocessing as mp
from torch.utils.data.distributed import DistributedSampler
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.distributed import init_process_group, destroy_process_group
import os
def ddp_setup():
init_process_group(backend="nccl")
class Trainer:
def __init__(
self,
model: torch.nn.Module,
train_data: DataLoader,
optimizer: torch.optim.Optimizer,
save_every: int,
snapshot_path: str,
) -> None:
self.gpu_id = int(os.environ["LOCAL_RANK"])
self.model = model.to(self.gpu_id)
self.train_data = train_data
self.optimizer = optimizer
self.save_every = save_every
self.epochs_run = 0
self.snapshot_path = snapshot_path
if os.path.exists(snapshot_path):
print("Loading snapshot")
self._load_snapshot(snapshot_path)
self.model = DDP(self.model, device_ids=[self.gpu_id])
def _load_snapshot(self, snapshot_path):
loc = f"cuda:{self.gpu_id}"
snapshot = torch.load(snapshot_path, map_location=loc)
self.model.load_state_dict(snapshot["MODEL_STATE"])
self.epochs_run = snapshot["EPOCHS_RUN"]
print(f"Resuming training from snapshot at Epoch {self.epochs_run}")
def _run_batch(self, source, targets):
self.optimizer.zero_grad()
output = self.model(source)
loss = F.cross_entropy(output, targets)
loss.backward()
self.optimizer.step()
def _run_epoch(self, epoch):
b_sz = len(next(iter(self.train_data))[0])
print(
f"[GPU{self.gpu_id}] Epoch {epoch} | Batchsize: {b_sz} | Steps: {len(self.train_data)}"
)
self.train_data.sampler.set_epoch(epoch)
for source, targets in self.train_data:
source = source.to(self.gpu_id)
targets = targets.to(self.gpu_id)
self._run_batch(source, targets)
def _save_snapshot(self, epoch):
snapshot = {
"MODEL_STATE": self.model.module.state_dict(),
"EPOCHS_RUN": epoch,
}
torch.save(snapshot, self.snapshot_path)
print(f"Epoch {epoch} | Training snapshot saved at {self.snapshot_path}")
def train(self, max_epochs: int):
for epoch in range(self.epochs_run, max_epochs):
self._run_epoch(epoch)
if self.gpu_id == 0 and epoch % self.save_every == 0:
self._save_snapshot(epoch)
def load_train_objs():
train_set = MyTrainDataset(2048) # load your dataset
model = torch.nn.Linear(20, 1) # load your model
optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)
return train_set, model, optimizer
def prepare_dataloader(dataset: Dataset, batch_size: int):
return DataLoader(
dataset,
batch_size=batch_size,
pin_memory=True,
shuffle=False,
sampler=DistributedSampler(dataset),
)
def main(save_every: int, total_epochs: int, batch_size: int, snapshot_path: str = "snapshot.pt"):
ddp_setup()
dataset, model, optimizer = load_train_objs()
train_data = prepare_dataloader(dataset, batch_size)
trainer = Trainer(model, train_data, optimizer, save_every, snapshot_path)
trainer.train(total_epochs)
destroy_process_group()
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description="simple distributed training job")
parser.add_argument("total_epochs", type=int, help="Total epochs to train the model")
parser.add_argument("save_every", type=int, help="How often to save a snapshot")
parser.add_argument(
"--batch_size", default=32, type=int, help="Input batch size on each device (default: 32)"
)
args = parser.parse_args()
main(args.save_every, args.total_epochs, args.batch_size)
import torch
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
from datautils import MyTrainDataset
import torch.multiprocessing as mp
from torch.utils.data.distributed import DistributedSampler
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.distributed import init_process_group, destroy_process_group
import os
def ddp_setup():
init_process_group(backend="nccl")
class Trainer:
def __init__(
self,
model: torch.nn.Module,
train_data: DataLoader,
optimizer: torch.optim.Optimizer,
save_every: int,
snapshot_path: str,
) -> None:
self.local_rank = int(os.environ["LOCAL_RANK"])
self.global_rank = int(os.environ["RANK"])
self.model = model.to(self.local_rank)
self.train_data = train_data
self.optimizer = optimizer
self.save_every = save_every
self.epochs_run = 0
self.snapshot_path = snapshot_path
if os.path.exists(snapshot_path):
print("Loading snapshot")
self._load_snapshot(snapshot_path)
self.model = DDP(self.model, device_ids=[self.local_rank])
def _load_snapshot(self, snapshot_path):
loc = f"cuda:{self.local_rank}"
snapshot = torch.load(snapshot_path, map_location=loc)
self.model.load_state_dict(snapshot["MODEL_STATE"])
self.epochs_run = snapshot["EPOCHS_RUN"]
print(f"Resuming training from snapshot at Epoch {self.epochs_run}")
def _run_batch(self, source, targets):
self.optimizer.zero_grad()
output = self.model(source)
loss = F.cross_entropy(output, targets)
loss.backward()
self.optimizer.step()
def _run_epoch(self, epoch):
b_sz = len(next(iter(self.train_data))[0])
print(
f"[GPU{self.global_rank}] Epoch {epoch} | Batchsize: {b_sz} | Steps: {len(self.train_data)}"
)
self.train_data.sampler.set_epoch(epoch)
for source, targets in self.train_data:
source = source.to(self.local_rank)
targets = targets.to(self.local_rank)
self._run_batch(source, targets)
def _save_snapshot(self, epoch):
snapshot = {
"MODEL_STATE": self.model.module.state_dict(),
"EPOCHS_RUN": epoch,
}
torch.save(snapshot, self.snapshot_path)
print(f"Epoch {epoch} | Training snapshot saved at {self.snapshot_path}")
def train(self, max_epochs: int):
for epoch in range(self.epochs_run, max_epochs):
self._run_epoch(epoch)
if self.local_rank == 0 and epoch % self.save_every == 0:
self._save_snapshot(epoch)
def load_train_objs():
train_set = MyTrainDataset(2048) # load your dataset
model = torch.nn.Linear(20, 1) # load your model
optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)
return train_set, model, optimizer
def prepare_dataloader(dataset: Dataset, batch_size: int):
return DataLoader(
dataset,
batch_size=batch_size,
pin_memory=True,
shuffle=False,
sampler=DistributedSampler(dataset),
)
def main(save_every: int, total_epochs: int, batch_size: int, snapshot_path: str = "snapshot.pt"):
ddp_setup()
dataset, model, optimizer = load_train_objs()
train_data = prepare_dataloader(dataset, batch_size)
trainer = Trainer(model, train_data, optimizer, save_every, snapshot_path)
trainer.train(total_epochs)
destroy_process_group()
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description="simple distributed training job")
parser.add_argument("total_epochs", type=int, help="Total epochs to train the model")
parser.add_argument("save_every", type=int, help="How often to save a snapshot")
parser.add_argument(
"--batch_size", default=32, type=int, help="Input batch size on each device (default: 32)"
)
args = parser.parse_args()
main(args.save_every, args.total_epochs, args.batch_size)
#!/bin/bash
#SBATCH --job-name=multinode-example
#SBATCH --nodes=4
#SBATCH --ntasks=4
#SBATCH --gpus-per-task=1
#SBATCH --cpus-per-task=4
nodes=( $( scontrol show hostnames $SLURM_JOB_NODELIST ) )
nodes_array=($nodes)
head_node=${nodes_array[0]}
head_node_ip=$(srun --nodes=1 --ntasks=1 -w "$head_node" hostname --ip-address)
echo Node IP: $head_node_ip
export LOGLEVEL=INFO
srun torchrun \
--nnodes 4 \
--nproc_per_node 1 \
--rdzv_id $RANDOM \
--rdzv_backend c10d \
--rdzv_endpoint $head_node_ip:29500 \
/shared/examples/multinode_torchrun.py 50 10
|
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"/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/models/plbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/reformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/images.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/base.py"], "/qnarre/models/yoso.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/canine.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/blog.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/counter.py"], "/qnarre/models/longformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/fast/pegasus.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/pegasus.py"], "/qnarre/base/judgment.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py", "/qnarre/base/conflict.py", "/qnarre/base/conjecture.py"], "/qnarre/models/splinter.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/rag.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/convert/albert.py": ["/qnarre/prep/config/albert.py", "/qnarre/models/albert.py"], "/qnarre/prep/tokens/fast/gpt2.py": ["/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/fast/mbart.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/roformer.py": ["/qnarre/models/roformer.py"], "/qnarre/base/doc/util/tree.py": ["/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py", "/qnarre/base/doc/util/utils.py"], "/qnarre/models/rembert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/t5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/xlm.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/bert_tf2.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/tools/triton/python/triton/ops/__init__.py": ["/tools/triton/python/triton/ops/cross_entropy.py", "/tools/triton/python/triton/ops/matmul.py"], "/qnarre/core/norm.py": ["/qnarre/core/base.py"], "/qnarre/models/mpnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/author.py": ["/qnarre/base/named.py"], "/qnarre/base/doc/args.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/log.py"], "/qnarre/prep/tokens/gpt.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/distilbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/proof.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py"], "/qnarre/prep/tokens/byt5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/electra.py": ["/qnarre/prep/config/electra.py", "/qnarre/models/electra.py"], "/qnarre/models/luke.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": ["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", 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["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,635
|
quantapix/qnarre
|
refs/heads/main
|
/tools/triton/python/triton/compiler/code_generator.py
|
import ast
import inspect
import re
import sys
import warnings
from typing import Any, Callable, Dict, Optional, Tuple, Type, Union
from .. import language
from ..language import constexpr, tensor
# ideally we wouldn't need any runtime component
from ..runtime import JITFunction
from .errors import (CompilationError, CompileTimeAssertionFailure,
UnsupportedLanguageConstruct)
from triton._C.libtriton.triton import ir
def mangle_ty(ty):
if ty.is_ptr():
return 'P' + mangle_ty(ty.element_ty)
if ty.is_int():
SIGNED = language.dtype.SIGNEDNESS.SIGNED
prefix = 'i' if ty.int_signedness == SIGNED else 'u'
return prefix + str(ty.int_bitwidth)
if ty.is_fp8():
return 'fp8'
if ty.is_fp16():
return 'fp16'
if ty.is_bf16():
return 'bf16'
if ty.is_fp32():
return 'fp32'
if ty.is_fp64():
return 'fp64'
if ty.is_block():
elt = mangle_ty(ty.scalar)
shape = '_'.join(map(str, ty.shape))
return f'{elt}S{shape}S'
if ty.is_void():
return 'V'
assert False, "Unsupported type"
def mangle_fn(name, arg_tys, constants):
# doesn't mangle ret type, which must be a function of arg tys
mangled_arg_names = '_'.join([mangle_ty(ty) for ty in arg_tys])
mangled_constants = '_'.join([f'{i}c{repr(constants[i])}' for i in sorted(constants)])
mangled_constants = mangled_constants.replace('.', '_d_')
mangled_constants = mangled_constants.replace("'", '_sq_')
# [ and ] are not allowed in LLVM identifiers
mangled_constants = mangled_constants.replace('[', '_').replace(']', '_')
ret = f'{name}__{mangled_arg_names}__{mangled_constants}'
return ret
def _is_triton_tensor(o: Any) -> bool:
return isinstance(o, tensor)
def _is_constexpr(o: Any) -> bool:
return isinstance(o, constexpr)
def _is_triton_scalar(o: Any) -> bool:
return _is_triton_tensor(o) and (not o.type.is_block() or o.type.numel == 1)
def _unwrap_if_constexpr(o: Any):
return o.value if isinstance(o, constexpr) else o
def _check_fn_args(node, fn, args):
if fn.noinline:
for idx, arg in enumerate(args):
if not _is_constexpr(arg) and not _is_triton_scalar(arg):
raise UnsupportedLanguageConstruct(fn.src, node, f'Function {fn.__name__} is marked noinline, but was called with non-scalar argument {fn.arg_names[idx]}:{arg}')
_condition_types = {bool, int, type(None)} # Python types accepted for conditionals inside kernels
class enter_sub_region:
def __init__(self, generator):
self.generator = generator
def __enter__(self):
# record lscope & local_defs in the parent scope
self.liveins = self.generator.lscope.copy()
self.prev_defs = self.generator.local_defs.copy()
self.generator.local_defs = {}
self.insert_block = self.generator.builder.get_insertion_block()
self.insert_point = self.generator.builder.get_insertion_point()
return self.liveins, self.insert_block
def __exit__(self, *args, **kwargs):
self.generator.builder.restore_insertion_point(self.insert_point)
self.generator.lscope = self.liveins
self.generator.local_defs = self.prev_defs
# Check if the given syntax node has an "early" return
class ContainsReturnChecker(ast.NodeVisitor):
def __init__(self, gscope):
self.gscope = gscope
def _visit_stmts(self, body) -> bool:
for s in body:
if self.visit(s):
return True
return False
def _visit_function(self, fn) -> bool:
# Currently we only support JITFunctions defined in the global scope
if isinstance(fn, JITFunction) and not fn.noinline:
fn_node = fn.parse()
return ContainsReturnChecker(self.gscope).visit(fn_node)
return False
def generic_visit(self, node) -> bool:
ret = False
for _, value in ast.iter_fields(node):
if isinstance(value, list):
for item in value:
if isinstance(item, ast.AST):
ret = ret or self.visit(item)
elif isinstance(value, ast.AST):
ret = ret or self.visit(value)
return ret
def visit_Attribute(self, node: ast.Attribute) -> bool:
# If the left part is a name, it's possible that
# we call triton native function or a jit function from another module.
# If the left part is not a name, it must return a tensor or a constexpr
# whose methods do not contain return statements
# e.g., (tl.load(x)).to(y)
# So we only check if the expressions within value have return or not
if isinstance(node.value, ast.Name):
if node.value.id in self.gscope:
value = self.gscope[node.value.id]
fn = getattr(value, node.attr)
return self._visit_function(fn)
return False
return self.visit(node.value)
def visit_Name(self, node: ast.Name) -> bool:
if type(node.ctx) == ast.Store:
return False
if node.id in self.gscope:
fn = self.gscope[node.id]
return self._visit_function(fn)
return False
def visit_Return(self, node: ast.Return) -> bool:
return True
def visit_Assign(self, node: ast.Assign) -> bool:
# There couldn't be an early return
# x = ...
return False
def visit_AugAssign(self, node: ast.AugAssign) -> bool:
# There couldn't be an early return
# x += ...
return False
def visit_Module(self, node: ast.Module) -> bool:
return self._visit_stmts(node.body)
def visit_FunctionDef(self, node: ast.FunctionDef) -> bool:
return self._visit_stmts(node.body)
def visit_If(self, node: ast.If) -> bool:
# TODO: optimize the following case in which we actually don't have
# a return when static_cond is false:
# if dynamic_cond
# if static_cond
# func_with_return
# else
# func_without_return
ret = self._visit_stmts(node.body)
if node.orelse:
ret = ret or self._visit_stmts(node.orelse)
return ret
def visit_IfExp(self, node: ast.IfExp) -> bool:
return self.visit(node.body) or self.visit(node.orelse)
def visit_Call(self, node: ast.Call) -> bool:
return self.visit(node.func)
class CodeGenerator(ast.NodeVisitor):
def __init__(self, context, prototype, gscope, attributes, constants, function_name,
module=None, is_kernel=False, function_types: Optional[Dict] = None,
debug=False, noinline=False):
self.builder = ir.builder(context)
self.module = self.builder.create_module() if module is None else module
self.function_ret_types = {} if function_types is None else function_types
self.prototype = prototype
self.gscope = gscope
self.lscope = dict()
self.attributes = attributes
self.constants = constants
self.function_name = function_name
self.is_kernel = is_kernel
self.last_node = None
self.debug = debug
self.noinline = noinline
self.scf_stack = []
self.last_ret_type = None
# SSA-construction
# name => language.tensor
self.local_defs: Dict[str, tensor] = {}
self.global_uses: Dict[str, tensor] = {}
self.dereference_name: Callable[[str], Any] = self._define_name_lookup()
builtin_namespace: Dict[str, Any] = {_.__name__: _ for _ in (range, float, int, isinstance, getattr)}
builtin_namespace.update((
('print', language.core.device_print),
('min', language.minimum),
))
def _define_name_lookup(self):
def local_lookup(name: str, absent):
value = self.lscope.get(name, absent) # this needs to be re-fetched from `self` every time, because it gets switched occasionally
if value is not absent and name not in self.local_defs:
self.global_uses[name] = value
return value
absent_marker = object()
def name_lookup(name: str) -> Any:
absent = absent_marker
for lookup_function in local_lookup, self.gscope.get, self.builtin_namespace.get:
value = lookup_function(name, absent)
if value is not absent:
return value
raise NameError(f'{name} is not defined')
return name_lookup
def set_value(self, name: str,
value: Union[tensor, constexpr]) -> None:
''' This function:
called by visit_Assign() & visit_FunctionDef() to store left value (lvalue)
1. record local defined name (FIXME: should consider control flow)
2. store tensor in self.lvalue
'''
self.lscope[name] = value
self.local_defs[name] = value
#
# AST visitor
#
def visit_compound_statement(self, stmts):
for stmt in stmts:
ret_type = self.visit(stmt)
if ret_type is not None and isinstance(stmt, ast.Return):
self.last_ret_type = ret_type
def visit_Module(self, node):
ast.NodeVisitor.generic_visit(self, node)
def visit_List(self, node):
ctx = self.visit(node.ctx)
assert ctx is None
elts = [self.visit(elt) for elt in node.elts]
return elts
# By design, only non-kernel functions can return
def visit_Return(self, node):
ret_value = self.visit(node.value)
# ret_block = self.builder.create_block()
# post_ret_block = self.builder.create_block()
# self.builder.create_branch(ret_block)
# self.builder.set_insertion_point_to_end(ret_block)
if ret_value is None:
self.builder.ret([])
ret_ty = None
elif isinstance(ret_value, tuple):
ret_values = [language.core._to_tensor(v, self.builder) for v in ret_value]
ret_types = [v.type for v in ret_values]
self.builder.ret([v.handle for v in ret_values])
ret_ty = tuple(ret_types)
else:
ret = language.core._to_tensor(ret_value, self.builder)
self.builder.ret([ret.handle])
ret_ty = ret.type
# self.builder.create_branch(post_ret_block)
# self.builder.set_insertion_point_to_end(post_ret_block)
return ret_ty
def visit_FunctionDef(self, node):
arg_names, kwarg_names = self.visit(node.args)
# initialize defaults
for i, default_value in enumerate(node.args.defaults):
arg_node = node.args.args[-i - 1]
annotation = arg_node.annotation
name = arg_node.arg
st_target = ast.Name(id=name, ctx=ast.Store())
if annotation is None:
init_node = ast.Assign(targets=[st_target], value=default_value)
else:
init_node = ast.AnnAssign(target=st_target, value=default_value, annotation=annotation)
self.visit(init_node)
# initialize function
visibility = "public" if self.is_kernel else "private"
fn = self.builder.get_or_insert_function(self.module, self.function_name, self.prototype.to_ir(self.builder), visibility, self.noinline)
self.module.push_back(fn)
entry = fn.add_entry_block()
arg_values = []
idx = 0
for i, arg_name in enumerate(arg_names):
if i in self.constants:
cst = self.constants[i]
if not _is_constexpr(cst):
cst = constexpr(self.constants[i])
arg_values.append(cst)
continue
else:
if i in self.attributes:
fn.set_arg_attr(idx, "tt.divisibility", self.attributes[i][1])
arg_values.append(tensor(fn.args(idx), self.prototype.param_types[idx]))
idx += 1
insert_pt = self.builder.get_insertion_block()
for arg_name, arg_value in zip(arg_names, arg_values):
self.set_value(arg_name, arg_value)
self.builder.set_insertion_point_to_start(entry)
# visit function body
self.visit_compound_statement(node.body)
# finalize function
if self.last_ret_type is None:
self.builder.ret([])
else:
# update return type
if isinstance(self.last_ret_type, tuple):
self.prototype.ret_types = list(self.last_ret_type)
fn.reset_type(self.prototype.to_ir(self.builder))
else:
self.prototype.ret_types = [self.last_ret_type]
fn.reset_type(self.prototype.to_ir(self.builder))
if insert_pt:
self.builder.set_insertion_point_to_end(insert_pt)
# Remove dead code
fn.finalize()
def visit_arguments(self, node):
arg_names = []
for arg in node.args:
arg_names += [self.visit(arg)]
kwarg_names = self.visit(node.kwarg)
return arg_names, kwarg_names
def visit_arg(self, node):
ast.NodeVisitor.generic_visit(self, node)
return node.arg
def visit_AnnAssign(self, node):
# extract attributes
annotation = self.visit(node.annotation)
target = self.visit(node.target)
value = self.visit(node.value)
# constexpr
if annotation == constexpr:
if target in self.lscope:
raise ValueError(f'{target} is already defined.'
f' constexpr cannot be reassigned.')
if not _is_constexpr(value):
value = constexpr(value)
self.lscope[target] = value
return self.lscope[target]
# default: call visit_Assign
return self.visit_Assign(node)
def visit_Assign(self, node):
_names = []
for target in node.targets:
_names += [self.visit(target)]
if len(_names) > 1:
raise UnsupportedLanguageConstruct(None, node, "simultaneous multiple assignment is not supported.")
names = _names[0]
values = self.visit(node.value)
if not isinstance(names, tuple):
names = [names]
if not isinstance(values, tuple):
values = [values]
native_nontensor_types = (language.dtype, )
for name, value in zip(names, values):
# by default, constexpr are assigned into python variable
value = _unwrap_if_constexpr(value)
if not _is_triton_tensor(value) and \
not isinstance(value, native_nontensor_types):
value = language.core._to_tensor(value, self.builder)
self.set_value(name, value)
def visit_AugAssign(self, node):
name = node.target.id
lhs = ast.Name(id=name, ctx=ast.Load())
rhs = ast.BinOp(lhs, node.op, node.value)
assign = ast.Assign(targets=[node.target], value=rhs)
self.visit(assign)
return self.dereference_name(name)
def visit_Name(self, node):
if type(node.ctx) == ast.Store:
return node.id
return self.dereference_name(node.id)
def visit_Store(self, node):
ast.NodeVisitor.generic_visit(self, node)
def visit_Load(self, node):
ast.NodeVisitor.generic_visit(self, node)
def visit_Tuple(self, node):
args = [self.visit(x) for x in node.elts]
return tuple(args)
def _apply_binary_method(self, method_name, lhs, rhs):
# TODO: raise something meaningful if getattr fails below, esp for reverse method
if _is_triton_tensor(lhs):
return getattr(lhs, method_name)(rhs, _builder=self.builder)
if _is_triton_tensor(rhs):
reverse_method_name = re.sub(r"__(.*)__", r"__r\1__", method_name)
return getattr(rhs, reverse_method_name)(lhs, _builder=self.builder)
return getattr(lhs, method_name)(rhs)
def visit_BinOp(self, node):
lhs = self.visit(node.left)
rhs = self.visit(node.right)
method_name = self._method_name_for_bin_op.get(type(node.op))
if method_name is None:
raise UnsupportedLanguageConstruct(None, node, "AST binary operator '{}' is not (currently) implemented.".format(node.op.__name__))
return self._apply_binary_method(method_name, lhs, rhs)
_method_name_for_bin_op: Dict[Type[ast.operator], str] = {
ast.Add: '__add__', ast.Sub: '__sub__', ast.Mult: '__mul__', ast.Div: '__truediv__',
ast.FloorDiv: '__floordiv__', ast.Mod: '__mod__', ast.Pow: '__pow__',
ast.LShift: '__lshift__', ast.RShift: '__rshift__', ast.BitAnd: '__and__', ast.BitOr: '__or__', ast.BitXor: '__xor__',
}
def visit_then_else_blocks(self, node, liveins, then_block, else_block):
# then block
self.builder.set_insertion_point_to_start(then_block)
self.visit_compound_statement(node.body)
then_block = self.builder.get_insertion_block()
then_defs = self.local_defs.copy()
# else block
else_defs = {}
if node.orelse:
self.builder.set_insertion_point_to_start(else_block)
self.lscope = liveins.copy()
self.local_defs = {}
self.visit_compound_statement(node.orelse)
else_defs = self.local_defs.copy()
else_block = self.builder.get_insertion_block()
# update block arguments
names = []
ret_types = []
ir_ret_types = []
# variables in livein whose value is updated in `if`
for name in liveins:
# check type
for defs, block_name in [(then_defs, 'then'), (else_defs, 'else')]:
if name in defs:
assert defs[name].type == liveins[name].type,\
f'initial value for `{name}` is of type {liveins[name].type}, '\
f'but the {block_name} block redefines it as {defs[name].type}'
if name in then_defs or name in else_defs:
names.append(name)
ret_types.append(then_defs[name].type if name in then_defs else else_defs[name].type)
ir_ret_types.append(then_defs[name].handle.get_type() if name in then_defs else else_defs[name].handle.get_type())
# variable defined in then but not in else
if name in then_defs and name not in else_defs:
else_defs[name] = liveins[name]
# variable defined in else but not in then
if name in else_defs and name not in then_defs:
then_defs[name] = liveins[name]
# variables that are both in then and else but not in liveins
# TODO: could probably be cleaned up
for name in then_defs.keys() & else_defs.keys():
if name in names:
continue
then_ty = then_defs[name].type
else_ty = else_defs[name].type
assert then_ty == else_ty,\
f'mismatched type for {name} between then block ({then_ty}) '\
f'and else block ({else_ty})'
names.append(name)
ret_types.append(then_ty)
ir_ret_types.append(then_defs[name].handle.get_type())
return then_defs, else_defs, then_block, else_block, names, ret_types, ir_ret_types
def visit_if_top_level(self, cond, node):
has_endif_block = True
with enter_sub_region(self) as sr:
liveins, ip_block = sr
then_block = self.builder.create_block()
else_block = self.builder.create_block()
# create basic-block after conditional
endif_block = self.builder.create_block()
# create branch
self.builder.set_insertion_point_to_end(ip_block)
self.builder.create_cond_branch(cond.handle, then_block, else_block)
# visit then and else blocks
then_defs, else_defs, then_block, else_block, names, ret_types, ir_ret_types = \
self.visit_then_else_blocks(node, liveins, then_block, else_block)
# then terminator
self.builder.set_insertion_point_to_end(then_block)
if then_block.has_return() and else_block.has_return():
has_endif_block = False
endif_block.erase()
if not then_block.has_terminator() and has_endif_block:
self.builder.create_branch(endif_block, [then_defs[n].handle for n in names])
# else terminator
self.builder.set_insertion_point_to_end(else_block)
if not else_block.has_terminator() and has_endif_block:
self.builder.create_branch(endif_block, [else_defs[n].handle for n in names])
if has_endif_block:
for ty in ir_ret_types:
endif_block.add_argument(ty)
if has_endif_block:
# change block
self.builder.set_insertion_point_to_start(endif_block)
# update value
for i, name in enumerate(names):
new_tensor = language.core.tensor(endif_block.arg(i), ret_types[i])
self.set_value(name, new_tensor)
# TODO: refactor
def visit_if_scf(self, cond, node):
with enter_sub_region(self) as sr:
liveins, _ = sr
ip = self.builder.get_insertion_point()
then_block = self.builder.create_block()
else_block = self.builder.create_block() if node.orelse else None
then_defs, else_defs, then_block, else_block, names, ret_types, _ = \
self.visit_then_else_blocks(node, liveins, then_block, else_block)
# create if op
self.builder.restore_insertion_point(ip)
if_op = self.builder.create_if_op([ty.to_ir(self.builder) for ty in ret_types], cond.handle, True)
then_block.merge_block_before(if_op.get_then_block())
self.builder.set_insertion_point_to_end(if_op.get_then_block())
if len(names) > 0:
self.builder.create_yield_op([then_defs[n].handle for n in names])
if not node.orelse:
else_block = if_op.get_else_block()
else:
else_block.merge_block_before(if_op.get_else_block())
self.builder.set_insertion_point_to_end(if_op.get_else_block())
if len(names) > 0:
self.builder.create_yield_op([else_defs[n].handle for n in names])
# update values
for i, name in enumerate(names):
new_tensor = language.core.tensor(if_op.get_result(i), ret_types[i])
self.set_value(name, new_tensor)
def visit_If(self, node):
cond = self.visit(node.test)
if _is_triton_tensor(cond):
cond = cond.to(language.int1, _builder=self.builder)
if self.scf_stack or not ContainsReturnChecker(self.gscope).visit(node):
self.visit_if_scf(cond, node)
else:
self.visit_if_top_level(cond, node)
else:
cond = _unwrap_if_constexpr(cond)
if type(cond) not in _condition_types: # not isinstance - we insist the real thing, no subclasses and no ducks
raise UnsupportedLanguageConstruct(
None, node, "`if` conditionals can only accept values of type {{{}}}, not objects of type {}".format(
', '.join(_.__name__ for _ in _condition_types), type(cond).__name__))
if cond:
self.visit_compound_statement(node.body)
else:
self.visit_compound_statement(node.orelse)
def visit_IfExp(self, node):
cond = self.visit(node.test)
if _is_triton_tensor(cond):
cond = cond.to(language.int1, _builder=self.builder)
if _unwrap_if_constexpr(cond):
return self.visit(node.body)
else:
return self.visit(node.orelse)
def visit_Pass(self, node):
pass
def visit_Compare(self, node):
if not (len(node.comparators) == 1 and len(node.ops) == 1):
raise UnsupportedLanguageConstruct(None, node, "simultaneous multiple comparison is not supported")
lhs = _unwrap_if_constexpr(self.visit(node.left))
rhs = _unwrap_if_constexpr(self.visit(node.comparators[0]))
if type(node.ops[0]) == ast.Is:
return constexpr(lhs is rhs)
if type(node.ops[0]) == ast.IsNot:
return constexpr(lhs is not rhs)
method_name = self._method_name_for_comp_op.get(type(node.ops[0]))
if method_name is None:
raise UnsupportedLanguageConstruct(None, node, "AST comparison operator '{}' is not (currently) implemented.".format(node.ops[0].__name__))
return self._apply_binary_method(method_name, lhs, rhs)
_method_name_for_comp_op: Dict[Type[ast.cmpop], str] = {
ast.Eq: '__eq__', ast.NotEq: '__ne__', ast.Lt: '__lt__', ast.LtE: '__le__', ast.Gt: '__gt__', ast.GtE: '__ge__'
}
def visit_UnaryOp(self, node):
op = self.visit(node.operand)
fn = self._method_name_for_unary_op.get(type(node.op))
if fn is None:
raise UnsupportedLanguageConstruct(None, node, "AST unary operator '{}' is not (currently) implemented.".format(node.op.__name__))
if _is_triton_tensor(op):
return getattr(op, fn)(_builder=self.builder)
return getattr(op, fn)()
_method_name_for_unary_op: Dict[Type[ast.unaryop], str] = {ast.USub: '__neg__', ast.UAdd: '__pos__', ast.Not: '__not__', ast.Invert: '__invert__'}
def visit_While(self, node):
with enter_sub_region(self) as sr:
liveins, insert_block = sr
# loop body (the after region)
# loop_block = self.builder.create_block()
dummy = self.builder.create_block()
self.builder.set_insertion_point_to_start(dummy)
self.scf_stack.append(node)
self.visit_compound_statement(node.body)
self.scf_stack.pop()
loop_defs = self.local_defs
# collect loop-carried values
names = []
ret_types = []
init_args = []
for name in loop_defs:
if name in liveins:
# We should not def new constexpr
assert _is_triton_tensor(loop_defs[name])
assert _is_triton_tensor(liveins[name])
assert loop_defs[name].type == liveins[name].type
# these are loop-carried values
names.append(name)
ret_types.append(loop_defs[name].type)
init_args.append(liveins[name])
self.builder.set_insertion_point_to_end(insert_block)
while_op = self.builder.create_while_op([ty.to_ir(self.builder) for ty in ret_types],
[arg.handle for arg in init_args])
# merge the condition region
before_block = self.builder.create_block_with_parent(while_op.get_before(),
[ty.to_ir(self.builder) for ty in ret_types])
self.builder.set_insertion_point_to_start(before_block)
for i, name in enumerate(names):
self.lscope[name] = language.core.tensor(before_block.arg(i), ret_types[i])
self.local_defs[name] = self.lscope[name]
cond = self.visit(node.test)
self.builder.set_insertion_point_to_end(before_block)
# create ConditionOp: e.g., scf.condition(%cond) %arg0, %arg1, ...
self.builder.create_condition_op(cond.handle, [before_block.arg(i) for i in range(len(init_args))])
# merge the loop body
after_block = self.builder.create_block_with_parent(while_op.get_after(),
[ty.to_ir(self.builder) for ty in ret_types])
# generate loop body
self.builder.set_insertion_point_to_start(after_block)
for i, name in enumerate(names):
self.lscope[name] = language.core.tensor(after_block.arg(i), ret_types[i])
self.local_defs[name] = self.lscope[name]
self.scf_stack.append(node)
self.visit_compound_statement(node.body)
self.scf_stack.pop()
loop_defs = self.local_defs
yields = []
for name in loop_defs:
if name in liveins:
yields.append(loop_defs[name])
self.builder.create_yield_op([y.handle for y in yields])
# update global uses in while_op
for i, name in enumerate(names):
after_block.replace_use_in_block_with(init_args[i].handle, after_block.arg(i))
# WhileOp defines new values, update the symbol table (lscope, local_defs)
for i, name in enumerate(names):
new_def = language.core.tensor(while_op.get_result(i), ret_types[i])
self.lscope[name] = new_def
self.local_defs[name] = new_def
for stmt in node.orelse:
assert False, "Not implemented"
ast.NodeVisitor.generic_visit(self, stmt)
def visit_Subscript(self, node):
assert node.ctx.__class__.__name__ == "Load"
lhs = self.visit(node.value)
slices = self.visit(node.slice)
if _is_triton_tensor(lhs):
return lhs.__getitem__(slices, _builder=self.builder)
return lhs[slices]
def visit_ExtSlice(self, node):
return [self.visit(dim) for dim in node.dims]
def visit_For(self, node):
IteratorClass = self.visit(node.iter.func)
iter_args = [self.visit(arg) for arg in node.iter.args]
if IteratorClass == language.static_range:
iterator = IteratorClass(*iter_args)
static_range = range(iterator.start.value,
iterator.end.value,
iterator.step.value)
for i in static_range:
self.lscope[node.target.id] = constexpr(i)
self.visit_compound_statement(node.body)
for stmt in node.orelse:
ast.NodeVisitor.generic_visit(self, stmt)
return
if IteratorClass is not range:
raise RuntimeError('Only `range` and `static_range` iterators are currently supported')
# visit iterator arguments
# note: only `range` iterator is supported now
# collect lower bound (lb), upper bound (ub), and step
lb = iter_args[0] if len(iter_args) > 1 else self.visit(ast.Num(0))
ub = iter_args[1] if len(iter_args) > 1 else self.visit(node.iter.args[0])
step = iter_args[2] if len(iter_args) > 2 else self.visit(ast.Num(1))
# handle negative constant step (not supported by scf.for in MLIR)
negative_step = False
if _is_constexpr(step) and step.value < 0:
step = constexpr(-step.value)
negative_step = True
lb, ub = ub, lb
lb = language.core._to_tensor(lb, self.builder)
ub = language.core._to_tensor(ub, self.builder)
step = language.core._to_tensor(step, self.builder)
# induction variable type
if not lb.dtype.is_int() or not ub.dtype.is_int() or not step.dtype.is_int():
raise TypeError(f"For loop bounds and step must all be ints, are ({lb.dtype}, {ub.dtype}, {step.dtype})")
iv_type = language.semantic.integer_promote_impl(lb.dtype, ub.dtype)
iv_type = language.semantic.integer_promote_impl(iv_type, step.dtype)
iv_ir_type = iv_type.to_ir(self.builder)
iv_is_signed = iv_type.int_signedness == language.core.dtype.SIGNEDNESS.SIGNED
# lb/ub/step might be constexpr, we need to cast them to tensor
lb = lb.handle
ub = ub.handle
step = step.handle
# ForOp can only accept IndexType as lb/ub/step. Cast integer to Index
lb = self.builder.create_int_cast(lb, iv_ir_type, iv_is_signed)
ub = self.builder.create_int_cast(ub, iv_ir_type, iv_is_signed)
step = self.builder.create_int_cast(step, iv_ir_type, iv_is_signed)
# Create placeholder for the loop induction variable
iv = self.builder.create_undef(iv_ir_type)
self.set_value(node.target.id, language.core.tensor(iv, iv_type))
with enter_sub_region(self) as sr:
liveins, insert_block = sr
ip = self.builder.get_insertion_point()
# create loop body block
block = self.builder.create_block()
self.builder.set_insertion_point_to_start(block)
# dry visit loop body
self.scf_stack.append(node)
self.visit_compound_statement(node.body)
self.scf_stack.pop()
block.erase()
# If a variable (name) is defined in both its parent & itself, then it's
# a loop-carried variable. (They must be of the same type)
init_args = []
yields = []
names = []
for name in self.local_defs:
if name in liveins:
assert _is_triton_tensor(self.local_defs[name]), f'{name} is not tensor'
assert _is_triton_tensor(liveins[name])
assert self.local_defs[name].type == liveins[name].type,\
f'Loop-carried variable {name} has initial type {liveins[name].type} '\
f'but is re-assigned to {self.local_defs[name].type} in loop! '\
f'Please make sure that the type stays consistent.'
names.append(name)
init_args.append(language.core._to_tensor(liveins[name], self.builder))
yields.append(language.core._to_tensor(self.local_defs[name], self.builder))
# create ForOp
self.builder.restore_insertion_point(ip)
for_op = self.builder.create_for_op(lb, ub, step, [arg.handle for arg in init_args])
self.scf_stack.append(node)
self.builder.set_insertion_point_to_start(for_op.get_body(0))
for i, name in enumerate(names):
self.set_value(name, language.core.tensor(for_op.get_body(0).arg(i + 1), yields[i].type))
self.visit_compound_statement(node.body)
self.scf_stack.pop()
yields = []
for name in self.local_defs:
if name in liveins:
yields.append(language.core._to_tensor(self.local_defs[name], self.builder))
# create YieldOp
if len(yields) > 0:
self.builder.create_yield_op([y.handle for y in yields])
for_op_region = for_op.get_body(0).get_parent()
assert for_op_region.size() == 1, "We use SCF, so the loop body should only have one block"
# update induction variable with actual value, and replace all uses
self.builder.set_insertion_point_to_start(for_op.get_body(0))
iv = for_op.get_induction_var()
if negative_step:
iv = self.builder.create_sub(ub, iv)
iv = self.builder.create_add(iv, lb)
self.lscope[node.target.id].handle.replace_all_uses_with(iv)
self.set_value(node.target.id, language.core.tensor(iv, iv_type))
# update lscope & local_defs (ForOp defines new values)
for i, name in enumerate(names):
self.set_value(name, language.core.tensor(for_op.get_result(i), yields[i].type))
for stmt in node.orelse:
assert False, "Don't know what to do with else after for"
ast.NodeVisitor.generic_visit(self, stmt)
def visit_Slice(self, node):
lower = self.visit(node.lower)
upper = self.visit(node.upper)
step = self.visit(node.step)
return slice(lower, upper, step)
def visit_Index(self, node):
return self.visit(node.value)
def visit_keyword(self, node) -> Tuple[str, Any]:
return node.arg, self.visit(node.value)
def visit_Assert(self, node) -> Any:
if not self.debug:
return
test = self.visit(node.test)
msg = self.visit(node.msg)
# Convert assert to triton's device_assert which happens on the device
return language.core.device_assert(test, msg, _builder=self.builder)
def call_JitFunction(self, fn: JITFunction, args, kwargs):
args = inspect.getcallargs(fn.fn, *args, **kwargs)
args = [args[name] for name in fn.arg_names]
args = [arg if _is_triton_tensor(arg)
else constexpr(arg) for arg in args]
# generate function def
attributes = dict()
constexprs = [i for i, arg in enumerate(args) if _is_constexpr(arg)]
constants = {i: args[i] for i in constexprs}
# generate call
args = [None if i in constexprs else arg for i, arg in enumerate(args)]
arg_vals = [arg.handle for arg in args if arg is not None]
arg_types = [arg.type for arg in args if arg is not None]
fn_name = mangle_fn(fn.__name__, arg_types, constants)
# generate function def if necessary
if not self.module.has_function(fn_name):
prototype = language.function_type([], arg_types)
gscope = sys.modules[fn.fn.__module__].__dict__
generator = CodeGenerator(self.builder.context, prototype, gscope, attributes, constants, module=self.module, function_name=fn_name, function_types=self.function_ret_types, debug=fn.debug, noinline=fn.noinline)
generator.visit(fn.parse())
callee_ret_type = generator.last_ret_type
self.function_ret_types[fn_name] = callee_ret_type
else:
callee_ret_type = self.function_ret_types[fn_name]
symbol = self.module.get_function(fn_name)
call_op = self.builder.call(symbol, arg_vals)
if call_op.get_num_results() == 0 or callee_ret_type is None:
return None
elif call_op.get_num_results() == 1:
return tensor(call_op.get_result(0), callee_ret_type)
else:
# should return a tuple of tl.tensor
results = []
for i in range(call_op.get_num_results()):
results.append(tensor(call_op.get_result(i), callee_ret_type[i]))
return tuple(results)
def visit_Call(self, node):
fn = _unwrap_if_constexpr(self.visit(node.func))
static_implementation = self.statically_implemented_functions.get(fn)
if static_implementation is not None:
return static_implementation(self, node)
kws = dict(self.visit(keyword) for keyword in node.keywords)
args = [self.visit(arg) for arg in node.args]
if fn is language.core.device_assert: # TODO: this should not be so hardcoded
if not self.debug:
return
if isinstance(fn, JITFunction):
_check_fn_args(node, fn, args)
return self.call_JitFunction(fn, args, kws)
if (hasattr(fn, '__self__') and _is_triton_tensor(fn.__self__)) or language.core.is_builtin(fn):
extra_kwargs = dict(_builder=self.builder)
sig = inspect.signature(fn)
if '_generator' in sig.parameters:
extra_kwargs['_generator'] = self
return fn(*args, **extra_kwargs, **kws)
if fn in self.builtin_namespace.values():
args = map(_unwrap_if_constexpr, args)
return fn(*args, **kws)
def visit_Constant(self, node):
return constexpr(node.value)
def visit_BoolOp(self, node: ast.BoolOp):
if len(node.values) != 2:
raise UnsupportedLanguageConstruct(None, node, "chained boolean operators (A or B or C) are not supported; use parentheses to split the chain.")
lhs = self.visit(node.values[0])
rhs = self.visit(node.values[1])
method_name = self._method_name_for_bool_op.get(type(node.op))
if method_name is None:
raise UnsupportedLanguageConstruct(None, node, "AST boolean operator '{}' is not (currently) implemented.".format(node.op.__name__))
return self._apply_binary_method(method_name, lhs, rhs)
_method_name_for_bool_op: Dict[Type[ast.boolop], str] = {ast.And: 'logical_and', ast.Or: 'logical_or'}
if sys.version_info < (3, 8):
def visit_NameConstant(self, node):
return constexpr(node.value)
def visit_Num(self, node):
return constexpr(node.n)
def visit_Str(self, node):
return constexpr(ast.literal_eval(node))
def visit_Attribute(self, node):
lhs = self.visit(node.value)
if _is_triton_tensor(lhs):
if node.attr == "T":
return language.semantic.trans(lhs, builder=self.builder)
return getattr(lhs, node.attr)
def visit_Expr(self, node):
ast.NodeVisitor.generic_visit(self, node)
def visit_NoneType(self, node):
return None
def visit_JoinedStr(self, node):
values = list(node.values)
for i, value in enumerate(values):
if isinstance(value, ast.Constant):
values[i] = str(value.value)
elif isinstance(value, ast.FormattedValue):
conversion_code = value.conversion
evaluated = self.visit(value.value)
if not _is_constexpr(evaluated):
raise UnsupportedLanguageConstruct(
None, node, "Cannot evaluate f-string containing non-constexpr conversion values, found conversion of type " + str(type(evaluated)))
values[i] = ("{}" if conversion_code < 0 else "{!" + chr(conversion_code) + "}").format(evaluated.value)
else:
raise AssertionError("encountered unexpected node of type {} in a JoinedStr node".format(type(value)))
return ''.join(values)
def visit(self, node):
if node is not None:
self.last_node = node
with warnings.catch_warnings():
# The ast library added visit_Constant and deprecated some other
# methods but we can't move to that without breaking Python 3.6 and 3.7.
warnings.simplefilter("ignore", DeprecationWarning) # python 3.9
warnings.simplefilter("ignore", PendingDeprecationWarning) # python 3.8
return super().visit(node)
def generic_visit(self, node):
raise UnsupportedLanguageConstruct(None, node, "unsupported AST node type: {}".format(type(node).__name__))
def execute_static_print(self, node: ast.Call) -> None:
# TODO: too simplistic? Perhaps do something else with non-constexpr
kws = {name: _unwrap_if_constexpr(value) for name, value in (self.visit(keyword) for keyword in node.keywords)}
args = [_unwrap_if_constexpr(self.visit(arg)) for arg in node.args]
print(*args, **kws)
def execute_static_assert(self, node: ast.Call) -> None:
arg_count = len(node.args)
if not (0 < arg_count <= 2) or len(node.keywords):
raise TypeError("`static_assert` requires one or two positional arguments only")
passed = self.visit(node.args[0])
if not isinstance(passed, bool):
raise NotImplementedError("Assertion condition could not be determined at compile-time. Make sure that it depends only on `constexpr` values")
if not passed:
if arg_count == 1:
message = ""
else:
try:
message = self.visit(node.args[1])
except Exception as e:
message = "<failed to evaluate assertion message: " + repr(e) + ">"
raise CompileTimeAssertionFailure(None, node, _unwrap_if_constexpr(message))
return None
statically_implemented_functions: Dict[object, Callable[[ast.Call], Any]] = {
language.core.static_assert: execute_static_assert,
language.core.static_print: execute_static_print,
}
def str_to_ty(name):
if name[0] == "*":
ty = str_to_ty(name[1:])
return language.pointer_type(ty)
tys = {
"fp8e5": language.float8e5,
"fp8e4": language.float8e4,
"fp16": language.float16,
"bf16": language.bfloat16,
"fp32": language.float32,
"fp64": language.float64,
"i1": language.int1,
"i8": language.int8,
"i16": language.int16,
"i32": language.int32,
"i64": language.int64,
"u8": language.uint8,
"u16": language.uint16,
"u32": language.uint32,
"u64": language.uint64,
"B": language.int1,
}
return tys[name]
def kernel_suffix(signature, specialization):
# suffix format:
# <argid><'c' if equal to 1><'d' if divisible by 16>
suffix = ''
for i, _ in enumerate(signature):
suffix += str(i)
if i in specialization.equal_to_1:
suffix += 'c'
if i in specialization.divisible_by_16:
suffix += 'd'
return suffix
def ast_to_ttir(fn, signature, specialization, constants, debug):
# canonicalize signature
if isinstance(signature, str):
signature = {k: v.strip() for k, v in enumerate(signature.split(","))}
context = ir.context()
context.load_triton()
# create kernel prototype
cst_key = lambda i: fn.arg_names.index(i) if isinstance(i, str) else i
constants = {cst_key(key): value for key, value in constants.items()}
# visit kernel AST
gscope = fn.__globals__.copy()
function_name = '_'.join([fn.__name__, kernel_suffix(signature.values(), specialization)])
tys = list(signature.values())
new_constants = {k: True if k in tys and tys[k] == "i1" else 1 for k in specialization.equal_to_1}
new_attrs = {k: ("multiple_of", 16) for k in specialization.divisible_by_16}
all_constants = constants.copy()
all_constants.update(new_constants)
arg_types = [str_to_ty(v) for k, v in signature.items() if k not in constants]
prototype = language.function_type([], arg_types)
generator = CodeGenerator(context, prototype, gscope=gscope, constants=all_constants,
function_name=function_name, attributes=new_attrs,
is_kernel=True, debug=debug)
try:
generator.visit(fn.parse())
except CompilationError as e:
if e.src is None:
e.set_source_code(fn.src)
raise
except Exception as e:
node = generator.last_node
if node is None:
raise
raise CompilationError(fn.src, node, repr(e)) from e
ret = generator.module
# module takes ownership of the context
ret.context = context
return ret
|
{"/qnarre/prep/convert/xlnet.py": ["/qnarre/prep/config/xlnet.py", "/qnarre/models/xlnet.py"], "/qnarre/prep/convert/bert.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/base/doc/patcher.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/nominals.py"], "/qnarre/prep/convert/funnel.py": ["/qnarre/prep/config/funnel.py", "/qnarre/models/funnel.py"], "/qnarre/prep/tokens/fast/realm.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py", "/qnarre/tokens/utils.py"], "/qnarre/models/decision_transfo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/decision_transfo.py"], "/qnarre/models/fsmt.py": ["/qnarre/core/embed.py"], "/qnarre/models/roformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/xlnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc.py": ["/qnarre/base/author.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], "/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/dispatch.py": ["/qnarre/base/doc/blog.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/context.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/analyzer.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/images.py"], "/qnarre/models/big_bird.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/big_bird.py"], "/tools/triton/python/triton/runtime/jit.py": ["/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/fast/roberta.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/bigbird.py": ["/qnarre/prep/config/big_bird.py", "/qnarre/models/big_bird.py"], "/qnarre/models/gpt_neo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/gpt_neo.py"], "/qnarre/prep/convert/t5.py": ["/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/prep/tokens/fsmt.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/deberta.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/fast/splinter.py": ["/qnarre/tokens/fast.py"], "/qnarre/models/old/trafo.py": ["/qnarre/core/attention.py", "/qnarre/core/base.py", "/qnarre/core/mlp.py", "/qnarre/core/deduce.py", "/qnarre/core/norm.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/led.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/realm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/convbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/data2vec.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/tools/triton/python/triton/language/math.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/run/beam.py": ["/qnarre/run/qa.py"], "/tools/triton/python/triton/compiler/compiler.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/tools/disasm.py", "/tools/triton/python/triton/compiler/code_generator.py", "/tools/triton/python/triton/compiler/make_launcher.py"], "/qnarre/base/doc/graph.py": ["/qnarre/base/doc/base.py"], "/qnarre/base/activism.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py", "/qnarre/base/judgment.py"], "/qnarre/base/doc/exporter.py": ["/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/fnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/roformer.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/megatron.py": ["/qnarre/prep/config/megatron.py", "/qnarre/models/megatron.py"], "/qnarre/prep/tokens/fast/roformer.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/roformer.py"], "/qnarre/core/runner.py": ["/qnarre/core/params.py"], "/qnarre/run/seq2seq.py": ["/qnarre/run/qa.py"], "/qnarre/prep/tokens/luke.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/util/table.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/tree.py", "/qnarre/base/doc/util/utils.py"], "/tools/triton/python/triton/language/standard.py": ["/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/language/__init__.py"], "/qnarre/base/doc/filters.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/args.py"], "/qnarre/prep/convert/gpt_neo.py": ["/qnarre/prep/config/gpt_neo.py", "/qnarre/models/gpt_neo.py"], "/qnarre/base/doc/section.py": ["/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/message.py"], "/qnarre/models/funnel.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/funnel.py"], "/tools/triton/python/triton/common/__init__.py": ["/tools/triton/python/triton/common/build.py"], "/qnarre/base/doc/qnn.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/models/plbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/reformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/images.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/base.py"], "/qnarre/models/yoso.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/canine.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/blog.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/counter.py"], "/qnarre/models/longformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/fast/pegasus.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/pegasus.py"], "/qnarre/base/judgment.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py", "/qnarre/base/conflict.py", "/qnarre/base/conjecture.py"], "/qnarre/models/splinter.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/rag.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/convert/albert.py": ["/qnarre/prep/config/albert.py", "/qnarre/models/albert.py"], "/qnarre/prep/tokens/fast/gpt2.py": ["/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/fast/mbart.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/roformer.py": ["/qnarre/models/roformer.py"], "/qnarre/base/doc/util/tree.py": ["/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py", "/qnarre/base/doc/util/utils.py"], "/qnarre/models/rembert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/t5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/xlm.py": ["/qnarre/tokens/utils.py"], 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"/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,636
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/run/run_benchmark.py
|
from transformers import HfArgumentParser, PyTorchBenchmark, PyTorchBenchmarkArguments
def main():
parser = HfArgumentParser(PyTorchBenchmarkArguments)
try:
benchmark_args = parser.parse_args_into_dataclasses()[0]
except ValueError as e:
arg_error_msg = "Arg --no_{0} is no longer used, please use --no-{0} instead."
begin_error_msg = " ".join(str(e).split(" ")[:-1])
full_error_msg = ""
depreciated_args = eval(str(e).split(" ")[-1])
wrong_args = []
for arg in depreciated_args:
# arg[2:] removes '--'
if arg[2:] in PyTorchBenchmarkArguments.deprecated_args:
# arg[5:] removes '--no_'
full_error_msg += arg_error_msg.format(arg[5:])
else:
wrong_args.append(arg)
if len(wrong_args) > 0:
full_error_msg = full_error_msg + begin_error_msg + str(wrong_args)
raise ValueError(full_error_msg)
benchmark = PyTorchBenchmark(args=benchmark_args)
benchmark.run()
if __name__ == "__main__":
main()
|
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"/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], 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|
33,637
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/models/old/bert2.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
import torch
from qnarre.core import base
from qnarre.core.norm import Norm
from qnarre.core.trafo import Trafo
def adapter(ps, feats, x):
d = torch.parse_example(x, feats)
img = torch.to_dense(d["flt_img"])
# img = torch.cast(d['int_img'], torch.float32) / 255.
lbl = d["int_lbl"]
return img, lbl
def model(ps):
sh = (ps.len_src,)
src = torch.Input(shape=sh, dtype="int32", name="src")
typ = torch.Input(shape=sh, dtype="int32", name="typ")
sh = (ps.len_tgt,)
idx = torch.Input(shape=sh, dtype="int32", name="mlm_idx")
val = torch.Input(shape=sh, dtype="int32", name="mlm_val")
fit = torch.Input(shape=sh, dtype="bool", name="fit")
mlm = torch.Input(shape=sh, dtype="float32", name="mlm")
ins = [src, typ, fit, idx, val, mlm]
outs = [Bert(ps)(ins)]
m = torch.Model(name="BertModel", inputs=ins, outputs=outs)
return m
class Bert(base.Module):
def __init__(self, ps, **kw):
super().__init__(ps, **kw) # dtype='float32', **kw)
cfg = self.get_cfg(kw)
self.trafo = Trafo(cfg)
self.pool = torch.Dense(cfg.d_hidden, torch.Tanh, kernel_initializer=cfg.initializer)
self.mlm_dense = torch.Dense(cfg.d_hidden, cfg.act_hidden, **kw)
self.norm = Norm()
def build(self, input_shape):
cfg = self.cfg
sh = (2, cfg.d_hidden)
self.gain = self.add_weight(shape=sh, initializer=cfg.initializer)
self.mlm_bias = self.add_weight(shape=cfg.s_vocab, initializer="zeros")
self.bias = self.add_weight(shape=2, initializer="zeros")
return super().build(input_shape)
def compute_output_shape(self, _):
return self.mlm_dense.output_shape
def forward(self, inputs, **kw):
cfg = self.cfg
seq, typ, idx, val, fit, mlm = inputs
seq = y = self.trafo([[seq, typ], None], **kw)
fit_y = self.pool(torch.squeeze(y[:, 0:1, :], axis=1), **kw)
y = torch.gather(y, idx, axis=1)
y = self.norm(self.mlm_dense(y, **kw), **kw)
e = self.trafo.tok_embed.embeddings
y = torch.matmul(y, e, transpose_b=True)
y = torch.log_softmax(torch.bias_add(y, self.mlm_bias), axis=-1)
mlm_loss = -torch.reduce_sum(y * torch.one_hot(val, cfg.s_vocab), axis=-1)
y = torch.matmul(fit_y, self.gain, transpose_b=True)
y = torch.log_softmax(torch.bias_add(y, self.bias), axis=-1)
fit_loss = -torch.reduce_sum(y * torch.one_hot(fit, 2), axis=-1)
loss = torch.reduce_sum(mlm * mlm_loss)
loss /= (torch.reduce_sum(mlm) + 1e-5) + torch.reduce_mean(fit_loss)
return seq, loss
|
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["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], 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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,638
|
quantapix/qnarre
|
refs/heads/main
|
/notebooks/gpus/run.py
|
#!/usr/bin/env python
import os
import sys
import tempfile
import torch
import torch.distributed as dist
import torch.nn as nn
import torch.optim as optim
import torch.multiprocessing as mp
import evaluate
from torch.nn.parallel import DistributedDataParallel as DDP
from transformers import AutoTokenizer
from transformers import AutoModelForSequenceClassification
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import get_scheduler
from tqdm.auto import tqdm
def setup(rank, world_size, fn=None, backend="gloo"): # 'tcp'
os.environ["MASTER_ADDR"] = "localhost"
os.environ["MASTER_PORT"] = "12355"
dist.init_process_group(backend, rank=rank, world_size=world_size)
if fn is not None:
fn(rank, world_size)
def cleanup():
dist.destroy_process_group()
def run_old(rank, size):
print(dist.get_world_size())
tensor = torch.ones(1)
list = [torch.zeros(1) for _ in range(size)]
# dist.gather(tensor, dst=0, gather_list=list, group=0)
# print('Rank ', rank, ' has data ', sum(list)[0])
def run(rank, size):
group = dist.new_group([0, 1, 2, 3])
tensor = torch.ones(1)
dist.all_reduce(tensor, op=dist.ReduceOp.SUM, group=group)
print("Rank ", rank, " has data ", tensor[0])
def run_blocking(rank, size):
tensor = torch.zeros(1)
if rank == 0:
tensor += 1
dist.send(tensor=tensor, dst=1)
else:
dist.recv(tensor=tensor, src=0)
print("Rank ", rank, " has data ", tensor[0])
def run_nonblocking(rank, size):
tensor = torch.zeros(1)
req = None
if rank == 0:
tensor += 1
req = dist.isend(tensor=tensor, dst=1)
print("Rank 0 started sending")
else:
req = dist.irecv(tensor=tensor, src=0)
print("Rank 1 started receiving")
req.wait()
print("Rank ", rank, " has data ", tensor[0])
def run_model():
dataset = load_dataset("imdb")
tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
def tokenize_function(examples):
return tokenizer(examples["text"], padding="max_length", truncation=True)
tokenized = dataset.map(tokenize_function, batched=True)
tokenized = tokenized.remove_columns(["text"])
tokenized = tokenized.rename_column("label", "labels")
tokenized.set_format("torch")
train_ds = tokenized["train"].shuffle(seed=42).select(range(1000))
eval_ds = tokenized["test"].shuffle(seed=42).select(range(1000))
train_dataloader = DataLoader(train_ds, shuffle=True, batch_size=8)
eval_dataloader = DataLoader(eval_ds, batch_size=8)
model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=5)
optimizer = optim.AdamW(model.parameters(), lr=5e-5)
num_epochs = 3
num_training_steps = num_epochs * len(train_dataloader)
lr_scheduler = get_scheduler(
name="linear",
optimizer=optimizer,
num_warmup_steps=0,
num_training_steps=num_training_steps,
)
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model.to(device)
progress_bar = tqdm(range(num_training_steps))
model.train()
for epoch in range(num_epochs):
for batch in train_dataloader:
batch = {k: v.to(device) for k, v in batch.items()}
outputs = model(**batch)
loss = outputs.loss
loss.backward()
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
progress_bar.update(1)
metric = evaluate.load("accuracy")
model.eval()
for batch in eval_dataloader:
batch = {k: v.to(device) for k, v in batch.items()}
with torch.no_grad():
outputs = model(**batch)
logits = outputs.logits
predictions = torch.argmax(logits, dim=-1)
metric.add_batch(predictions=predictions, references=batch["labels"])
metric.compute()
def gather(tensor, rank, list=None, root=0, group=None):
if group is None:
group = dist.group.WORLD
if rank == root:
assert list is not None
dist.gather_recv(list, tensor, group)
else:
dist.gather_send(tensor, root, group)
if __name__ == "__main__":
size = 4
processes = []
mp.set_start_method("spawn")
for rank in range(size):
p = mp.Process(target=setup, args=(rank, size, run))
p.start()
processes.append(p)
for p in processes:
p.join()
|
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"/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], 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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,639
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/base/__init__.py
|
# Copyright 2019 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
from .org import Org
from .proof import Proof
from .net import Boot, Net
from .doc import Genre, Doc
from .claim import Claim, Place
from .author import Author, Agent, Authority
from .narrative import Topic, Node, Narrative
from .named import Tagged, Saved, Named, Preset
from .conflict import Inherent, Conceal, Deceive, Fraud, Extort, Repeat
from .judgment import Bias, Confusion, Disregard, Fabrication, Validation
from .activism import (Activism, Exclude, Insinuate, Polarize, Recast, Elevate,
Victimize, Exploit, Perpetuate)
from .conjecture import (Reality, Dissent, Misreading, Fairness, Negligence,
Proportionality, Contradiction, Isolation, Omission,
Distortion)
__all__ = (
Activism,
Agent,
Author,
Authority,
Bias,
Boot,
Claim,
Conceal,
Confusion,
Contradiction,
Deceive,
Disregard,
Dissent,
Distortion,
Doc,
Elevate,
Exclude,
Exploit,
Extort,
Fabrication,
Fairness,
Fraud,
Genre,
Inherent,
Insinuate,
Isolation,
Misreading,
Named,
Narrative,
Negligence,
Net,
Node,
Omission,
Org,
Perpetuate,
Place,
Polarize,
Preset,
Proof,
Proportionality,
Reality,
Recast,
Repeat,
Saved,
Tagged,
Topic,
Validation,
Victimize,
)
all_nodes = (
Bias,
Confusion,
(Contradiction, 'x'),
Deceive,
(Disregard, 'g'),
(Distortion, 's'),
Elevate,
Extort,
Exclude,
Exploit,
Fabrication,
(Fairness, 'a'),
(Fraud, 'u'),
Insinuate,
Isolation,
Misreading,
Negligence,
Omission,
Perpetuate,
Polarize,
Proof,
(Proportionality, 'y'),
Reality,
Recast,
(Repeat, 't'),
Validation,
Victimize,
)
all_genres = (
'affidavit',
'deposition',
'letter',
'message',
'motion',
'order',
'report',
'service',
'trial',
)
|
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["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], 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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,640
|
quantapix/qnarre
|
refs/heads/main
|
/tools/standalone/python/mlir_standalone/dialects/standalone.py
|
# Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
# See https://llvm.org/LICENSE.txt for license information.
# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
from ._standalone_ops_gen import *
from .._mlir_libs._standaloneDialects.standalone import *
|
{"/qnarre/prep/convert/xlnet.py": ["/qnarre/prep/config/xlnet.py", "/qnarre/models/xlnet.py"], "/qnarre/prep/convert/bert.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/base/doc/patcher.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/nominals.py"], "/qnarre/prep/convert/funnel.py": ["/qnarre/prep/config/funnel.py", "/qnarre/models/funnel.py"], "/qnarre/prep/tokens/fast/realm.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py", "/qnarre/tokens/utils.py"], "/qnarre/models/decision_transfo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/decision_transfo.py"], "/qnarre/models/fsmt.py": ["/qnarre/core/embed.py"], "/qnarre/models/roformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/xlnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc.py": ["/qnarre/base/author.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], "/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], 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"/qnarre/base/doc/util/table.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/tree.py", "/qnarre/base/doc/util/utils.py"], "/tools/triton/python/triton/language/standard.py": ["/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/language/__init__.py"], "/qnarre/base/doc/filters.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/args.py"], "/qnarre/prep/convert/gpt_neo.py": ["/qnarre/prep/config/gpt_neo.py", "/qnarre/models/gpt_neo.py"], "/qnarre/base/doc/section.py": ["/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/message.py"], "/qnarre/models/funnel.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/funnel.py"], "/tools/triton/python/triton/common/__init__.py": ["/tools/triton/python/triton/common/build.py"], "/qnarre/base/doc/qnn.py": ["/qnarre/base/doc/base.py", 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|
33,641
|
quantapix/qnarre
|
refs/heads/main
|
/tools/triton/python/triton/ops/cross_entropy.py
|
import torch
import triton
import triton.language as tl
def num_warps(N):
if N < 2048:
return 4
elif N < 8192:
return 8
return 16
@triton.heuristics({'num_warps': lambda nargs: num_warps(nargs['N'])})
@triton.heuristics({'BLOCK': lambda nargs: triton.next_power_of_2(nargs['N'])})
@triton.jit
def _forward(LOGITS, PROBS, IDX, LOSS, N, BLOCK: tl.constexpr):
row = tl.program_id(0)
cols = tl.arange(0, BLOCK)
idx = tl.load(IDX + row)
# pointers to logit and probs
LOGITS = LOGITS + row * N + cols
WRIT_PROBS = PROBS + row * N + cols
READ_PROBS = PROBS + row * N + idx
# write-back negative log-probs
logits = tl.load(LOGITS, mask=cols < N, other=-float('inf'))
logits = logits.to(tl.float32)
logits = logits - tl.max(logits, 0)
probs = tl.log(tl.sum(tl.exp(logits), 0)) - logits
tl.store(WRIT_PROBS, probs, mask=cols < N)
# There is a bug in the compiler, which fails to insert a barrier here.
# We add it explicitly for now. Will be fixed soon.
tl.debug_barrier()
# write-back loss
probs = tl.load(READ_PROBS)
tl.store(LOSS + row, probs)
@triton.heuristics({'num_warps': lambda nargs: num_warps(nargs['N'])})
@triton.heuristics({'BLOCK': lambda nargs: triton.next_power_of_2(nargs['N'])})
@triton.jit
def _backward(PROBS, IDX, DPROBS, N, BLOCK: tl.constexpr):
row = tl.program_id(0)
cols = tl.arange(0, BLOCK)
idx = tl.load(IDX + row)
# pointers to probs
PROBS = PROBS + row * N + cols
# We know d(-log(p[i])/dlogit[k] = -id_mat[i,k] + p[k]
# and we have -log(p[k]) stored in PROBS, so this is easy
probs = -tl.load(PROBS, mask=cols < N, other=float('inf'))
probs = tl.exp(probs.to(tl.float32))
delta = cols == idx
# write result in-place in PROBS
dout = tl.load(DPROBS + row)
din = (probs - delta) * dout
tl.store(PROBS, din.to(PROBS.dtype.element_ty), mask=cols < N)
class _cross_entropy(torch.autograd.Function):
@classmethod
def forward(cls, ctx, logits, indices):
# make sure we can use triton
assert (indices.dtype == torch.int64), "Indices are expected to be of type long."
# make kernel
device, dtype = logits.device, logits.dtype
n_cols = logits.shape[-1]
# run the kernel
result = torch.empty_like(indices, dtype=dtype, device=device)
neg_logprobs = torch.empty_like(logits, dtype=dtype, device=device)
grid = lambda opt: (logits.numel() // n_cols, )
_forward[grid](logits, neg_logprobs, indices, result, n_cols)
# save for backward
ctx.save_for_backward(neg_logprobs, indices)
return result
@classmethod
def backward(cls, ctx, dneg_logprobs):
"""We know d(-log(p[i])/dlogit[k] = -id_mat[i,k] + p[k]
so we initialize the gradient as neg_logprobs, so we can just exponentiate
to get p[k], which is most of what we need... neg_logprobs will be
modified in place to become the gradient we want
"""
# load saved tensors
neg_logprobs, indices = ctx.saved_tensors
# run the kernel
# neg_logprobs will be modified in place to become our gradient:
n_cols = neg_logprobs.shape[-1]
grid = lambda opt: (neg_logprobs.numel() // n_cols, )
_backward[grid](neg_logprobs, indices, dneg_logprobs, n_cols)
return neg_logprobs, None
cross_entropy = _cross_entropy.apply
|
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["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,642
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/models/t5.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
# https://arxiv.org/abs/1910.10683
# https://github.com/google-research/text-to-text-transfer-transformer
import math
import torch
from torch import nn
from torch.nn import functional as F
from torch.utils.checkpoint import checkpoint
from transformers.utils import logging
# from apex.normalization import FusedRMSNorm
# LayerNorm = FusedRMSNorm
from .. import core as qc
from ..core import utils as qu
from ..core import output as qo
from ..prep.config.t5 import PreTrained
log = logging.get_logger(__name__)
class Model(PreTrained):
def __init__(self, **kw):
super().__init__(**kw)
cfg = self.get_cfg(kw)
self.embed = qc.Embed(cfg.s_vocab, cfg.d_model, **kw)
kw.update(is_dec=False, is_enc_dec=False)
self.enc = Encoder(self.embed, **kw)
kw.update(is_dec=True, is_enc_dec=False, n_lays=cfg.n_dec_lays)
self.dec = Encoder(self.embed, **kw)
def forward(
self,
x,
dec_head_m=None,
dec_m=None,
head_m=None,
mask=None,
x_dec_emb=None,
x_dec=None,
x_emb=None,
y_enc=None,
**kw,
):
cfg = self.cfg
if head_m is not None and dec_head_m is None:
if cfg.n_lays == cfg.n_dec_lays:
dec_head_m = head_m
if y_enc is None:
y_enc = self.enc(x, mask=mask, x_emb=x_emb, head_m=head_m, **kw)
y = self.dec(
x_dec,
**kw,
enc_m=mask,
enc=y_enc[0],
head_m=dec_head_m,
mask=dec_m,
x_emb=x_dec_emb,
)
ys = y + y_enc
return qo.Seq2Seq(*ys)
class ForCondGen(PreTrained):
def __init__(self, **kw):
super().__init__(**kw)
cfg = self.get_cfg(kw)
self.embed = qc.Embed(cfg.s_vocab, cfg.d_model, **kw)
kw.update(is_dec=False, is_enc_dec=False)
self.enc = Encoder(self.embed, **kw)
kw.update(is_dec=True, is_enc_dec=False, n_lays=cfg.n_dec_lays)
self.dec = Encoder(self.embed, **kw)
self.proj = qc.Linear(cfg.d_model, cfg.s_vocab, bias=False, **kw)
def forward(
self,
x,
dec_head_m=None,
dec_m=None,
head_m=None,
labels=None,
mask=None,
x_dec_emb=None,
x_dec=None,
y_enc=None,
**kw,
):
cfg = self.cfg
if head_m is not None and dec_head_m is None:
if cfg.n_lays == cfg.n_dec_lays:
dec_head_m = head_m
if y_enc is None:
y_enc = self.enc(x, mask=mask, head_m=head_m, **kw)
if labels is not None and x_dec is None and x_dec_emb is None:
x_dec = self._shift_right(labels)
ys = self.dec(
x_dec,
**kw,
enc_m=mask,
enc=y_enc[0],
head_m=dec_head_m,
mask=dec_m,
x_emb=x_dec_emb,
)
y = ys[0]
if cfg.tie_word_embeds:
y = y * (cfg.d_model**-0.5)
y = self.proj(y)
loss = None
if labels is not None:
f = nn.CrossEntropyLoss(ignore_index=-100)
loss = f(y.view(-1, y.size(-1)), labels.view(-1))
ys = (y,) + ys[1:] + (loss,)
return qo.LossSeq2Seq(*ys)
class LayerNorm(qc.Module):
def __init__(self, d_model, eps=1e-6):
super().__init__()
self.weight = nn.Parameter(torch.ones(d_model))
self.variance_eps = eps
def forward(self, x):
variance = x.to(torch.float32).pow(2).mean(-1, keepdim=True)
y = x * torch.rsqrt(variance + self.variance_eps)
if self.weight.dtype in [torch.float16, torch.bfloat16]:
y = y.to(self.weight.dtype)
return self.weight * y
class DenseReluDense(qc.Module):
def __init__(self, cfg):
super().__init__()
self.wi = qc.Linear(cfg.d_model, cfg.d_ff, bias=False)
self.wo = qc.Linear(cfg.d_ff, cfg.d_model, bias=False)
self.drop = qc.Dropout(cfg.drop_rate)
def forward(self, x):
y = self.wi(x)
y = F.relu(y)
y = self.drop(y)
y = self.wo(y)
return y
class DenseGatedGeluDense(qc.Module):
def __init__(self, cfg):
super().__init__()
self.wi_0 = qc.Linear(cfg.d_model, cfg.d_ff, bias=False)
self.wi_1 = qc.Linear(cfg.d_model, cfg.d_ff, bias=False)
self.wo = qc.Linear(cfg.d_ff, cfg.d_model, bias=False)
self.drop = qc.Dropout(cfg.drop_rate)
self.act = qu.activation("gelu_new")
def forward(self, x):
hidden_gelu = self.act(self.wi_0(x))
hidden_linear = self.wi_1(x)
y = hidden_gelu * hidden_linear
y = self.drop(y)
y = self.wo(y)
return y
class LayerFF(qc.Module):
def __init__(self, cfg):
super().__init__()
if cfg.feed_forward_proj == "relu":
self.DenseReluDense = DenseReluDense(cfg)
elif cfg.feed_forward_proj == "gated-gelu":
self.DenseReluDense = DenseGatedGeluDense(cfg)
else:
raise ValueError(
f"{cfg.feed_forward_proj} is not supported. Choose between `relu` and `gated-gelu`"
)
self.norm = LayerNorm(cfg.d_model, cfg.eps)
self.drop = qc.Dropout(cfg.drop_rate)
def forward(self, x):
y = self.norm(x)
y = self.DenseReluDense(y)
y = x + self.drop(y)
return y
class Attention(qc.Module):
def __init__(self, cfg, has_relative_attention_bias=False):
super().__init__()
self.is_dec = cfg.is_dec
self.has_relative_attention_bias = has_relative_attention_bias
self.relative_attention_num_buckets = cfg.relative_attention_num_buckets
cfg.d_model = cfg.d_model
self.key_value_proj_dim = cfg.d_kv
cfg.n_heads = cfg.n_heads
self.drop = cfg.drop_rate
self.inner_dim = cfg.n_heads * self.key_value_proj_dim
self.q = qc.Linear(cfg.d_model, self.inner_dim, bias=False)
self.k = qc.Linear(cfg.d_model, self.inner_dim, bias=False)
self.v = qc.Linear(cfg.d_model, self.inner_dim, bias=False)
self.o = qc.Linear(self.inner_dim, cfg.d_model, bias=False)
if self.has_relative_attention_bias:
self.relative_attention_bias = qc.Embed(
self.relative_attention_num_buckets, cfg.n_heads
)
self.grad_checkpoint = False
@staticmethod
def _relative_position_bucket(
relative_position, bidirectional=True, num_buckets=32, max_distance=128
):
relative_buckets = 0
if bidirectional:
num_buckets //= 2
relative_buckets += (relative_position > 0).to(torch.long) * num_buckets
relative_position = torch.abs(relative_position)
else:
relative_position = -torch.min(relative_position, torch.zeros_like(relative_position))
max_exact = num_buckets // 2
is_small = relative_position < max_exact
relative_postion_if_large = max_exact + (
torch.log(relative_position.float() / max_exact)
/ math.log(max_distance / max_exact)
* (num_buckets - max_exact)
).to(torch.long)
relative_postion_if_large = torch.min(
relative_postion_if_large, torch.full_like(relative_postion_if_large, num_buckets - 1)
)
relative_buckets += torch.where(is_small, relative_position, relative_postion_if_large)
return relative_buckets
def compute_bias(self, query_length, key_length):
context_position = torch.arange(
query_length, dtype=torch.long, device=self.relative_attention_bias.weight.device
)[:, None]
memory_position = torch.arange(
key_length, dtype=torch.long, device=self.relative_attention_bias.weight.device
)[None, :]
relative_position = memory_position - context_position # shape (query_length, key_length)
relative_position_bucket = self._relative_position_bucket(
relative_position,
bidirectional=(not self.is_dec),
num_buckets=self.relative_attention_num_buckets,
)
y = self.relative_attention_bias(relative_position_bucket)
y = y.permute([2, 0, 1]).unsqueeze(0)
return y
def forward(
self, x, mask=None, kv=None, pos=None, prev_kv=None, head_m=None, query_length=None, **kw
):
cfg = self.cfg
b, seq_length = x.shape[:2]
real_seq_length = seq_length
if prev_kv is not None:
assert len(prev_kv) == 2
real_seq_length += prev_kv[0].shape[2] if query_length is None else query_length
key_length = real_seq_length if kv is None else kv.shape[1]
def shape(x):
return x.view(b, -1, cfg.n_heads, self.key_value_proj_dim).transpose(1, 2)
def unshape(x):
return x.transpose(1, 2).contiguous().view(b, -1, self.inner_dim)
def project(x, proj_layer, prev_kv):
if kv is None:
x = shape(proj_layer(x))
elif prev_kv is None:
x = shape(proj_layer(kv))
if prev_kv is not None:
if kv is None:
x = torch.cat([prev_kv, x], dim=2)
else:
x = prev_kv
return x
q = shape(self.q(x))
k = project(x, self.k, prev_kv[0] if prev_kv is not None else None)
v = project(x, self.v, prev_kv[1] if prev_kv is not None else None)
y = torch.matmul(q, k.transpose(3, 2))
if pos is None:
if not self.has_relative_attention_bias:
pos = torch.zeros(
(1, cfg.n_heads, real_seq_length, key_length),
device=y.device,
dtype=y.dtype,
)
if self.grad_checkpoint and self.training:
pos.requires_grad = True
else:
pos = self.compute_bias(real_seq_length, key_length)
if prev_kv is not None:
pos = pos[:, :, -x.size(1) :, :]
if mask is not None:
pos = pos + mask
y += pos
a = F.softmax(y.float(), dim=-1).type_as(y)
a = self.drop(a)
if head_m is not None:
a = a * head_m
y = unshape(torch.matmul(a, v))
y = self.o(y)
kv = (k, v) if (self.is_dec) else None
return y, kv, pos, a
class Reflection(qc.Module):
def __init__(self, cfg, has_relative_attention_bias=False):
super().__init__()
self.refl = Attention(cfg, has_relative_attention_bias=has_relative_attention_bias)
self.norm = LayerNorm(cfg.d_model, cfg.eps)
self.drop = qc.Dropout(cfg.drop_rate)
def forward(self, x, **kw):
x = self.norm(x)
ys = self.refl(x, **kw)
x = x + self.drop(ys[0])
y = (x,) + ys[1:]
return y
class Cross(qc.Module):
def __init__(self, cfg):
super().__init__()
self.attn = Attention(cfg, has_relative_attention_bias=False)
self.norm = LayerNorm(cfg.d_model, cfg.eps)
self.drop = qc.Dropout(cfg.drop_rate)
def forward(self, x, key_value_states, **kw):
x = self.norm(x)
ys = self.attn(x, key_value_states=key_value_states, **kw)
y = x + self.drop(ys[0])
y = (y,) + ys[1:]
return y
class Block(qc.Module):
def __init__(self, cfg, has_relative_attention_bias=False):
super().__init__()
self.is_dec = cfg.is_dec
self.lays = qc.Stack()
self.lays.append(Reflection(cfg, has_relative_attention_bias=has_relative_attention_bias))
if self.is_dec:
self.lays.append(Cross(cfg))
self.lays.append(LayerFF(cfg))
def forward(
self,
y,
enc=None,
enc_m=None,
encoder_decoder_position_bias=None,
cross_m=None,
prev_kv=None,
**kw,
):
cfg = self.cfg
if prev_kv is not None:
assert self.is_dec
expected_num_past_key_values = 2 if enc is None else 4
assert len(prev_kv) == expected_num_past_key_values
pkv = prev_kv[:2]
pkv2 = prev_kv[2:]
else:
pkv, pkv2 = None, None
ys = self.lays[0](y, prev_kv=pkv, **kw)
y, kv = ys[:2]
ys = ys[2:]
if y.dtype == torch.float16 and torch.isinf(y).any():
clamp = torch.finfo(y.dtype).max - 1000
y = torch.clamp(y, min=-clamp, max=clamp)
if self.is_dec and enc is not None:
if kv is not None:
query_length = kv[0].shape[2]
else:
query_length = None
ys2 = self.lays[1](
y,
**kw,
enc=enc,
mask=enc_m,
position_bias=encoder_decoder_position_bias,
head_m=cross_m,
prev_kv=pkv2,
query_length=query_length,
)
y = ys2[0]
if y.dtype == torch.float16 and torch.isinf(y).any():
clamp = torch.finfo(y.dtype).max - 1000
y = torch.clamp(y, min=-clamp, max=clamp)
if kv is not None:
kv = kv + ys2[1]
ys = ys + ys2[2:]
y = self.lays[-1](y)
if y.dtype == torch.float16 and torch.isinf(y).any():
clamp = torch.finfo(y.dtype).max - 1000
y = torch.clamp(y, min=-clamp, max=clamp)
return y + (kv,) + ys
class Encoder(qc.Module):
hs = qc.Hypers({"add_cross", "n_lays"})
def __init__(self, tok_emb=None, ps={}, hs=[], **kw):
super().__init__(ps, [self.hs] + hs, **kw)
kw.update(y_cache=False, is_enc_dec=False)
cfg = self.get_cfg(kw)
m = cfg.d_model
self.tok_emb = qc.Embed(cfg.s_vocab, m, **kw) if tok_emb is None else tok_emb
self.lays = qc.Stack(
[Block(**kw, has_relative_attention_bias=bool(i == 0)) for i in range(cfg.n_lays)]
)
self.norm = LayerNorm(m, **kw)
self.drop = qc.Dropout(cfg.drop_rate, **kw)
def forward(
self,
x,
cache=None,
cross_m=None,
enc_m=None,
enc=None,
head_m=None,
mask=None,
x_emb=None,
**kw,
):
cfg = self.cfg
if x is None:
s = x_emb.size()[:-1]
else:
assert x_emb is None
s = x.size()
x = x.view(-1, s[-1])
if x_emb is None:
x_emb = self.tok_emb(x)
b, n = s
mask_seq_length = cache[0][0].shape[2] + n if cache is not None else n
if mask is None:
mask = torch.ones(b, mask_seq_length).to(x_emb.device)
mask = self.get_mask(mask, s, x_emb.device)
if cfg.is_dec and enc_m is None and enc is not None:
enc_m = torch.ones(b, enc.shape[1], device=x_emb.device, dtype=torch.long)
if cache is None:
cache = tuple([None] * len(self.lays))
if cfg.is_dec and enc is not None:
if enc_m is None:
enc_m = torch.ones(enc.size()[:2], device=x_emb.device)
enc_m = self.invert_mask(enc_m)
else:
enc_m = None
head_m = self.get_head_m(head_m, cfg.n_lays)
cross_m = self.get_head_m(cross_m, cfg.n_lays)
attns = caches = crosses = hiddens = ()
pos = None
enc_dec_pos = None
y = self.drop(x_emb)
for i, (lay, c) in enumerate(zip(self.lays, cache)):
hiddens += (y,)
kw.update(
cross_m=cross_m[i],
enc_dec_pos=enc_dec_pos,
enc_m=enc_m,
enc=enc,
head_m=head_m[i],
mask=mask,
pos=pos,
)
if self.grad_checkpoint and self.training:
def create_forward(x):
def forward(*xs):
return x(*xs, cache=c)
return forward
ys = checkpoint(create_forward(lay), y, **kw)
else:
ys = lay(y, cache=c, **kw)
y, kv = ys[:2]
pos = ys[2]
if self.is_dec and enc is not None:
enc_dec_pos = ys[4]
attns += (ys[3],)
if self.is_dec:
crosses += (ys[5],)
caches += (kv,)
y = self.drop(self.norm(y))
hiddens += (y,)
return qo.CachesCrosses(y, attns, caches, crosses, hiddens)
|
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["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,643
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/core/deduce.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
import torch
from qnarre.core import utils
from qnarre.core.base import Module
from qnarre.core.search import Beam
class Deduce(Module):
@staticmethod
def cfg_items(ps):
return dict(
ps.cfg_items(
"END",
"PAD",
"UNK",
"batch_size",
"beam_size",
"brackets",
"dim_embed",
"dim_hidden",
"one_hot",
"num_toks",
"share_adapt",
"share_table",
)
)
def __init__(self, ps, owner, **kw):
super().__init__(ps, **kw)
cfg = self.cfg
self.table_ws = owner.tok_embed.table_ws if cfg.share_table else []
self.table_bs = []
self.adapt_ws = owner.tok_embed.adapt_ws if cfg.share_adapt else []
def build(self, input_shape):
cfg = self.cfg
h = cfg.dim_hidden
d = cfg.dim_embed or h
bs = cfg.brackets or []
n = len(bs)
if n:
self.clust_w = self.add_weight(f"clust_w", (n, d))
self.clust_b = self.add_bias(f"clust_b", (n,))
bs += [cfg.num_toks]
b = 0
assert b == cfg.PAD
for i, e in enumerate(bs):
p = d // (len(bs) ** i)
if len(self.table_ws) == i:
t = self.add_weight(f"table_w{i}", (e - b, p))
self.table_ws.append(t)
t = self.add_bias(f"table_b{i}", (e - b,))
self.table_bs.append(t)
if len(self.adapt_ws) == i:
a = None if p == h else self.add_weight(f"adapt_w{i}", (p, h))
self.adapt_ws.append(a)
b = e
self.one_hot = cfg.one_hot
return super().build(input_shape)
def compute_output_shape(self, input_shape):
return input_shape[0]
def forward(self, inputs):
cfg = self.cfg
x, tgt = inputs
if cfg.brackets:
y = torch.zeros_like(tgt, dtype=torch.floatx())
bs = cfg.brackets + [cfg.num_toks]
b = 0
for i, e in enumerate(bs):
msk = (tgt >= (b or 1)) & (tgt < e)
mt = torch.boolean_mask(tgt, msk) - b
gi = torch.stack([torch.range(torch.shape(mt)[0]), mt])
if i == 0:
logp = torch.log_softmax(self.logits(x, i))
mp = torch.boolean_mask(logp, msk)
u = torch.gather_nd(mp, gi)
else:
mp = torch.boolean_mask(logp, msk)
u = mp[:, bs[i - 1]]
mc = torch.boolean_mask(x, msk)[None]
mp = torch.log_softmax(self.logits(mc, i))
mp = torch.squeeze(mp, 0)
u += torch.gather_nd(mp, gi)
y = torch.tensor_scatter_nd_add(y, torch.where(msk), -u)
b = e
else:
y = self.logits(x)
# f = torch.SparseCategoricalAccuracy
# self.add_metric(f(name='acc')(tgt, y))
f = torch.sparse_softmax_cross_entropy_with_logits
loss = f(labels=tgt, logits=y)
# self.add_loss(lambda: torch.reduce_mean(loss))
return y
def logits(self, x, i=None):
y = x
a = self.adapt_ws[i or 0]
if a is not None:
y = torch.einsum("bih,ph->bip", y, a)
t = self.table_ws[i or 0]
b = self.table_bs[i or 0]
if i == 0:
t = torch.concat([t, self.clust_w], 0)
b = torch.concat([b, self.clust_b], 0)
y = torch.einsum("bie,ne->bin", y, t) + b
return y
class Search(Deduce):
beam = None
def __init__(self, ps, owner, **kw):
super().__init__(ps, owner, **kw)
cfg = self.cfg
if cfg.beam_size:
self.beam = Beam(ps, self, name="beam")
def build(self, input_shape):
return super().build(input_shape)
def compute_output_shape(self, input_shape):
return input_shape
def forward(self, inputs):
x, ctx = inputs
cfg = self.cfg
return x
"""
if self.beam is not None:
tgt, score = self.beam([x, ctx])
else:
logp, logi, unk = self.search(tgt, ctx)
sh = tgt.shape
b = torch.range(cfg.batch_size)
for i in range(sh[-1]):
if torch.reduce_any(unk[:, i]) is True:
y = torch.argmax(logp[:, i, :], axis=1, output_type=torch.int32)
ii = torch.constant([i] * cfg.batch_size)
sel = torch.stack([b, ii])
tgt = torch.tensor_scatter_nd_update(tgt, sel, y)
e = torch.equal(tgt, cfg.END)
if torch.reduce_all(torch.reduce_any(e, axis=1)) is True:
break
logp, logi, unk = self.to_logp(tgt, ctx, i)
return torch.one_hot(tgt, cfg.num_toks, 0.0, utils.big_neg)
"""
def search(self, tgt, ctx, i=None):
cfg = self.cfg
unk = torch.equal(tgt, cfg.UNK)
prior = torch.one_hot(tgt, cfg.num_toks, 0.0, utils.big_neg)
if i is not None:
unk = unk[:, i]
prior = prior[:, i, :]
if torch.reduce_all(unk) is True:
logi = prior
else:
y = self.decode(tgt, ctx)
if i is not None:
y = y[:, i, :]
sh = y.shape # torch.int_shape(y)
y = torch.reshape(y, (-1, sh[-1]))
y = self.logits(y)
y = torch.reshape(y, sh[:-1] + y.shape[-1:])
u = torch.expand_dims(unk, axis=2)
u = torch.broadcast_to(u, y.shape)
logi = torch.where(u, y, prior)
logp = y - torch.reduce_logsumexp(y, axis=-1, keepdims=True)
return logp, logi, unk
|
{"/qnarre/prep/convert/xlnet.py": ["/qnarre/prep/config/xlnet.py", "/qnarre/models/xlnet.py"], "/qnarre/prep/convert/bert.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/base/doc/patcher.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/nominals.py"], "/qnarre/prep/convert/funnel.py": ["/qnarre/prep/config/funnel.py", "/qnarre/models/funnel.py"], "/qnarre/prep/tokens/fast/realm.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py", "/qnarre/tokens/utils.py"], "/qnarre/models/decision_transfo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/decision_transfo.py"], "/qnarre/models/fsmt.py": ["/qnarre/core/embed.py"], "/qnarre/models/roformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/xlnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc.py": ["/qnarre/base/author.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", 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|
33,644
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quantapix/qnarre
|
refs/heads/main
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/qnarre/prep/convert/fsmt.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
import json
import os
import re
import fairseq
import torch
from collections import OrderedDict
from argparse import ArgumentParser
from os.path import basename, dirname
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary
from transformers.file_utils import WEIGHTS_NAME
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FS
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import logging
from ..config.fsmt import PreTrained
from ...run.fsmt import ForConditionalGen
logging.set_verbosity_warning()
json_indent = 2
# based on the results of a search on a range of `n_beams`, `len_penalty` and `early_stop`
# values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
#
# * `n_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
# * `early_stop`: `False` consistently scored better
# * `len_penalty` varied, so will assign the best one depending on the model
best_score_hparams = {
# fairseq:
"wmt19-ru-en": {"len_penalty": 1.1},
"wmt19-en-ru": {"len_penalty": 1.15},
"wmt19-en-de": {"len_penalty": 1.0},
"wmt19-de-en": {"len_penalty": 1.1},
# allenai:
"wmt16-en-de-dist-12-1": {"len_penalty": 0.6},
"wmt16-en-de-dist-6-1": {"len_penalty": 0.6},
"wmt16-en-de-12-1": {"len_penalty": 0.8},
"wmt19-de-en-6-6-base": {"len_penalty": 0.6},
"wmt19-de-en-6-6-big": {"len_penalty": 0.6},
}
org_names = {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
org_names[m] = "facebook"
for m in [
"wmt16-en-de-dist-12-1",
"wmt16-en-de-dist-6-1",
"wmt16-en-de-12-1",
"wmt19-de-en-6-6-base",
"wmt19-de-en-6-6-big",
]:
org_names[m] = "allenai"
def rewrite_dict_keys(d):
d2 = dict(
(re.sub(r"@@$", "", k), v) if k.endswith("@@") else (re.sub(r"$", "</w>", k), v)
for k, v in d.items()
)
keep_keys = "<s> <pad> </s> <unk>".split()
for k in keep_keys:
del d2[f"{k}</w>"]
d2[k] = d[k] # restore
return d2
def to_pytorch(fsmt_checkpoint_path, save_path):
assert os.path.exists(fsmt_checkpoint_path)
os.makedirs(save_path, exist_ok=True)
print(f"Writing results to {save_path}")
checkpoint_file = basename(fsmt_checkpoint_path)
fsmt_folder_path = dirname(fsmt_checkpoint_path)
cls = fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
models = cls.hub_models()
kw = {"bpe": "fastbpe", "tokenizer": "moses"}
data_name_or_path = "."
print(f"using checkpoint {checkpoint_file}")
chkpt = hub_utils.from_pretrained(
fsmt_folder_path, checkpoint_file, data_name_or_path, archive_map=models, **kw
)
args = vars(chkpt["args"]["model"])
src_lang = args["source_lang"]
tgt_lang = args["target_lang"]
data_root = dirname(save_path)
model_dir = basename(save_path)
src_dict_file = os.path.join(fsmt_folder_path, f"dict.{src_lang}.txt")
tgt_dict_file = os.path.join(fsmt_folder_path, f"dict.{tgt_lang}.txt")
src_dict = Dictionary.load(src_dict_file)
src_vocab = rewrite_dict_keys(src_dict.indices)
s_src_vocab = len(src_vocab)
src_vocab_file = os.path.join(save_path, "vocab-src.json")
print(f"Generating {src_vocab_file} of {s_src_vocab} of {src_lang} records")
with open(src_vocab_file, "w", encoding="utf-8") as f:
f.write(json.dumps(src_vocab, ensure_ascii=False, indent=json_indent))
do_lower_case = True
for k in src_vocab.keys():
if not k.islower():
do_lower_case = False
break
tgt_dict = Dictionary.load(tgt_dict_file)
tgt_vocab = rewrite_dict_keys(tgt_dict.indices)
s_tgt_vocab = len(tgt_vocab)
tgt_vocab_file = os.path.join(save_path, "vocab-tgt.json")
print(f"Generating {tgt_vocab_file} of {s_tgt_vocab} of {tgt_lang} records")
with open(tgt_vocab_file, "w", encoding="utf-8") as f:
f.write(json.dumps(tgt_vocab, ensure_ascii=False, indent=json_indent))
merges_file = os.path.join(save_path, VOCAB_FS["merges_file"])
for fn in ["bpecodes", "code"]: # older fairseq called the merges file "code"
fsmt_merges_file = os.path.join(fsmt_folder_path, fn)
if os.path.exists(fsmt_merges_file):
break
with open(fsmt_merges_file, encoding="utf-8") as fin:
merges = fin.read()
merges = re.sub(r" \d+$", "", merges, 0, re.M) # remove frequency number
print(f"Generating {merges_file}")
with open(merges_file, "w", encoding="utf-8") as fout:
fout.write(merges)
fsmt_model_config_file = os.path.join(save_path, "config.json")
assert args["bpe"] == "fastbpe", f"need to extend tokenizer to support bpe={args['bpe']}"
assert (
args["tokenizer"] == "moses"
), f"need to extend tokenizer to support bpe={args['tokenizer']}"
model_conf = {
"archs": ["FSMTForConditionalGeneration"],
"model_type": "fsmt",
"drop_act": args["drop_act"],
"act_fun": "relu",
"drop_attn": args["drop_attn"],
"d_hidden": args["decoder_embed_dim"],
"drop": args["drop"],
"init_std": 0.02,
"n_pos": args["max_source_positions"],
"n_lays": args["n_enc_lays"],
"s_src_vocab": s_src_vocab,
"s_tgt_vocab": s_tgt_vocab,
"langs": [src_lang, tgt_lang],
"n_enc_heads": args["n_enc_heads"],
"d_enc_ffn": args["encoder_ffn_embed_dim"],
"drop_enc": args["drop_enc"],
"n_enc_lays": args["n_enc_lays"],
"n_dec_heads": args["n_dec_heads"],
"d_dec_ffn": args["decoder_ffn_embed_dim"],
"drop_dec": args["drop_dec"],
"n_dec_lays": args["n_dec_lays"],
"BOS": 0,
"PAD": 1,
"EOS": 2,
"is_enc_dec": True,
"scale": not args["no_scale_embedding"],
"tie_word_embeds": args["share_all_embeddings"],
}
model_conf["n_beams"] = 5
model_conf["early_stop"] = False
if model_dir in best_score_hparams and "len_penalty" in best_score_hparams[model_dir]:
model_conf["len_penalty"] = best_score_hparams[model_dir]["len_penalty"]
else:
model_conf["len_penalty"] = 1.0
print(f"Generating {fsmt_model_config_file}")
with open(fsmt_model_config_file, "w", encoding="utf-8") as f:
f.write(json.dumps(model_conf, ensure_ascii=False, indent=json_indent))
fsmt_tokenizer_config_file = os.path.join(save_path, TOKENIZER_CONFIG_FILE)
tokenizer_conf = {
"langs": [src_lang, tgt_lang],
"model_max_length": 1024,
"do_lower_case": do_lower_case,
}
print(f"Generating {fsmt_tokenizer_config_file}")
with open(fsmt_tokenizer_config_file, "w", encoding="utf-8") as f:
f.write(json.dumps(tokenizer_conf, ensure_ascii=False, indent=json_indent))
model = chkpt["models"][0]
model_state_dict = model.state_dict()
model_state_dict = OrderedDict(("model." + k, v) for k, v in model_state_dict.items())
ignore_keys = [
"model.model",
"model.encoder.version",
"model.decoder.version",
"model.encoder_embed_tokens.weight",
"model.decoder_embed_tokens.weight",
"model.encoder.embed_positions._float_tensor",
"model.decoder.embed_positions._float_tensor",
]
for k in ignore_keys:
model_state_dict.pop(k, None)
config = PreTrained.from_pretrained(save_path)
model_new = ForConditionalGen(config)
model_new.load_state_dict(model_state_dict, strict=False)
pytorch_weights_dump_path = os.path.join(save_path, WEIGHTS_NAME)
print(f"Generating {pytorch_weights_dump_path}")
torch.save(model_state_dict, pytorch_weights_dump_path)
print("Conversion is done!")
print("\nLast step is to upload the files to s3")
print(f"cd {data_root}")
print(f"transformers-cli upload {model_dir}")
if __name__ == "__main__":
x = ArgumentParser()
x.add_argument("--src_path", default=None, type=str, required=True)
x.add_argument("--save_path", default=None, type=str, required=True)
args = x.parse_args()
to_pytorch(args.src_path, args.save_path)
|
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"/qnarre/base/author.py", "/qnarre/base/conflict.py", "/qnarre/base/conjecture.py"], "/qnarre/models/splinter.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/rag.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/convert/albert.py": ["/qnarre/prep/config/albert.py", "/qnarre/models/albert.py"], "/qnarre/prep/tokens/fast/gpt2.py": ["/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/fast/mbart.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/roformer.py": ["/qnarre/models/roformer.py"], "/qnarre/base/doc/util/tree.py": ["/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py", "/qnarre/base/doc/util/utils.py"], "/qnarre/models/rembert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/t5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/xlm.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/bert_tf2.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/tools/triton/python/triton/ops/__init__.py": ["/tools/triton/python/triton/ops/cross_entropy.py", "/tools/triton/python/triton/ops/matmul.py"], "/qnarre/core/norm.py": ["/qnarre/core/base.py"], "/qnarre/models/mpnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/author.py": ["/qnarre/base/named.py"], "/qnarre/base/doc/args.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/log.py"], "/qnarre/prep/tokens/gpt.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/distilbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/proof.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py"], "/qnarre/prep/tokens/byt5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/electra.py": ["/qnarre/prep/config/electra.py", "/qnarre/models/electra.py"], "/qnarre/models/luke.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": ["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,645
|
quantapix/qnarre
|
refs/heads/main
|
/tools/triton/python/setup.py
|
import os
import platform
import re
import shutil
import subprocess
import sys
import sysconfig
import tarfile
import tempfile
import urllib.request
from pathlib import Path
from typing import NamedTuple
from setuptools import Extension, setup
from setuptools.command.build_ext import build_ext
# Taken from https://github.com/pytorch/pytorch/blob/master/tools/setup_helpers/env.py
def check_env_flag(name: str, default: str = "") -> bool:
return os.getenv(name, default).upper() in ["ON", "1", "YES", "TRUE", "Y"]
def get_build_type():
if check_env_flag("DEBUG"):
return "Debug"
elif check_env_flag("REL_WITH_DEB_INFO"):
return "RelWithDebInfo"
elif check_env_flag("TRITON_REL_BUILD_WITH_ASSERTS"):
return "TritonRelBuildWithAsserts"
else:
# TODO: change to release when stable enough
return "TritonRelBuildWithAsserts"
# --- third party packages -----
class Package(NamedTuple):
package: str
name: str
url: str
include_flag: str
lib_flag: str
syspath_var_name: str
# pybind11
def get_pybind11_package_info():
name = "pybind11-2.10.0"
url = "https://github.com/pybind/pybind11/archive/refs/tags/v2.10.0.tar.gz"
return Package("pybind11", name, url, "PYBIND11_INCLUDE_DIR", "", "PYBIND11_SYSPATH")
# llvm
def get_llvm_package_info():
# download if nothing is installed
system = platform.system()
if system == "Darwin":
system_suffix = "apple-darwin"
elif system == "Linux":
vglibc = tuple(map(int, platform.libc_ver()[1].split('.')))
vglibc = vglibc[0] * 100 + vglibc[1]
linux_suffix = 'ubuntu-18.04' if vglibc > 217 else 'centos-7'
system_suffix = f"linux-gnu-{linux_suffix}"
else:
return Package("llvm", "LLVM-C.lib", "", "LLVM_INCLUDE_DIRS", "LLVM_LIBRARY_DIR", "LLVM_SYSPATH")
use_assert_enabled_llvm = check_env_flag("TRITON_USE_ASSERT_ENABLED_LLVM", "False")
release_suffix = "assert" if use_assert_enabled_llvm else "release"
name = f'llvm+mlir-17.0.0-x86_64-{system_suffix}-{release_suffix}'
version = "llvm-17.0.0-c5dede880d17"
url = f"https://github.com/ptillet/triton-llvm-releases/releases/download/{version}/{name}.tar.xz"
return Package("llvm", name, url, "LLVM_INCLUDE_DIRS", "LLVM_LIBRARY_DIR", "LLVM_SYSPATH")
def get_thirdparty_packages(triton_cache_path):
packages = [get_pybind11_package_info(), get_llvm_package_info()]
thirdparty_cmake_args = []
for p in packages:
package_root_dir = os.path.join(triton_cache_path, p.package)
package_dir = os.path.join(package_root_dir, p.name)
if p.syspath_var_name in os.environ:
package_dir = os.environ[p.syspath_var_name]
version_file_path = os.path.join(package_dir, "version.txt")
if p.syspath_var_name not in os.environ and\
(not os.path.exists(version_file_path) or Path(version_file_path).read_text() != p.url):
try:
shutil.rmtree(package_root_dir)
except Exception:
pass
os.makedirs(package_root_dir, exist_ok=True)
print(f'downloading and extracting {p.url} ...')
ftpstream = urllib.request.urlopen(p.url)
file = tarfile.open(fileobj=ftpstream, mode="r|*")
file.extractall(path=package_root_dir)
# write version url to package_dir
with open(os.path.join(package_dir, "version.txt"), "w") as f:
f.write(p.url)
if p.include_flag:
thirdparty_cmake_args.append(f"-D{p.include_flag}={package_dir}/include")
if p.lib_flag:
thirdparty_cmake_args.append(f"-D{p.lib_flag}={package_dir}/lib")
return thirdparty_cmake_args
# ---- package data ---
def download_and_copy_ptxas():
base_dir = os.path.dirname(__file__)
src_path = "bin/ptxas"
version = "12.1.105"
url = f"https://conda.anaconda.org/nvidia/label/cuda-12.1.1/linux-64/cuda-nvcc-{version}-0.tar.bz2"
dst_prefix = os.path.join(base_dir, "triton")
dst_suffix = os.path.join("third_party", "cuda", src_path)
dst_path = os.path.join(dst_prefix, dst_suffix)
is_linux = platform.system() == "Linux"
download = False
if is_linux:
download = True
if os.path.exists(dst_path):
curr_version = subprocess.check_output([dst_path, "--version"]).decode("utf-8").strip()
curr_version = re.search(r"V([.|\d]+)", curr_version).group(1)
download = curr_version != version
if download:
print(f'downloading and extracting {url} ...')
ftpstream = urllib.request.urlopen(url)
file = tarfile.open(fileobj=ftpstream, mode="r|*")
with tempfile.TemporaryDirectory() as temp_dir:
file.extractall(path=temp_dir)
src_path = os.path.join(temp_dir, src_path)
os.makedirs(os.path.split(dst_path)[0], exist_ok=True)
shutil.copy(src_path, dst_path)
return dst_suffix
# ---- cmake extension ----
class CMakeExtension(Extension):
def __init__(self, name, path, sourcedir=""):
Extension.__init__(self, name, sources=[])
self.sourcedir = os.path.abspath(sourcedir)
self.path = path
class CMakeBuild(build_ext):
user_options = build_ext.user_options + [('base-dir=', None, 'base directory of Triton')]
def initialize_options(self):
build_ext.initialize_options(self)
self.base_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), os.pardir))
def finalize_options(self):
build_ext.finalize_options(self)
def run(self):
try:
out = subprocess.check_output(["cmake", "--version"])
except OSError:
raise RuntimeError(
"CMake must be installed to build the following extensions: " + ", ".join(e.name for e in self.extensions)
)
match = re.search(r"version\s*(?P<major>\d+)\.(?P<minor>\d+)([\d.]+)?", out.decode())
cmake_major, cmake_minor = int(match.group("major")), int(match.group("minor"))
if (cmake_major, cmake_minor) < (3, 18):
raise RuntimeError("CMake >= 3.18.0 is required")
for ext in self.extensions:
self.build_extension(ext)
def get_cmake_dir(self):
plat_name = sysconfig.get_platform()
python_version = sysconfig.get_python_version()
dir_name = f"cmake.{plat_name}-{sys.implementation.name}-{python_version}"
cmake_dir = Path(self.base_dir) / "python" / "build" / dir_name
cmake_dir.mkdir(parents=True, exist_ok=True)
return cmake_dir
def build_extension(self, ext):
lit_dir = shutil.which('lit')
user_home = os.getenv("HOME") or os.getenv("USERPROFILE") or \
os.getenv("HOMEPATH") or None
if not user_home:
raise RuntimeError("Could not find user home directory")
triton_cache_path = os.path.join(user_home, ".triton")
# lit is used by the test suite
thirdparty_cmake_args = get_thirdparty_packages(triton_cache_path)
extdir = os.path.abspath(os.path.dirname(self.get_ext_fullpath(ext.path)))
# create build directories
if not os.path.exists(self.build_temp):
os.makedirs(self.build_temp)
# python directories
python_include_dir = sysconfig.get_path("platinclude")
cmake_args = [
"-DLLVM_ENABLE_WERROR=ON",
"-DCMAKE_LIBRARY_OUTPUT_DIRECTORY=" + extdir,
"-DTRITON_BUILD_TUTORIALS=OFF",
"-DTRITON_BUILD_PYTHON_MODULE=ON",
"-DPython3_EXECUTABLE:FILEPATH=" + sys.executable,
"-DCMAKE_VERBOSE_MAKEFILE:BOOL=ON",
"-DPYTHON_INCLUDE_DIRS=" + python_include_dir,
]
if lit_dir is not None:
cmake_args.append("-DLLVM_EXTERNAL_LIT=" + lit_dir)
cmake_args.extend(thirdparty_cmake_args)
# configuration
cfg = get_build_type()
build_args = ["--config", cfg]
if platform.system() == "Windows":
cmake_args += [f"-DCMAKE_RUNTIME_OUTPUT_DIRECTORY_{cfg.upper()}={extdir}"]
if sys.maxsize > 2**32:
cmake_args += ["-A", "x64"]
build_args += ["--", "/m"]
else:
cmake_args += ["-DCMAKE_BUILD_TYPE=" + cfg]
max_jobs = os.getenv("MAX_JOBS", str(2 * os.cpu_count()))
build_args += ['-j' + max_jobs]
if check_env_flag("TRITON_BUILD_WITH_CLANG_LLD"):
cmake_args += ["-DCMAKE_C_COMPILER=clang",
"-DCMAKE_CXX_COMPILER=clang++",
"-DCMAKE_LINKER=lld",
"-DCMAKE_EXE_LINKER_FLAGS=-fuse-ld=lld",
"-DCMAKE_MODULE_LINKER_FLAGS=-fuse-ld=lld",
"-DCMAKE_SHARED_LINKER_FLAGS=-fuse-ld=lld"]
env = os.environ.copy()
cmake_dir = self.get_cmake_dir()
subprocess.check_call(["cmake", self.base_dir] + cmake_args, cwd=cmake_dir, env=env)
subprocess.check_call(["cmake", "--build", "."] + build_args, cwd=cmake_dir)
download_and_copy_ptxas()
setup(
name="triton",
version="2.1.0",
author="Philippe Tillet",
author_email="phil@openai.com",
description="A language and compiler for custom Deep Learning operations",
long_description="",
packages=[
"triton",
"triton/_C",
"triton/common",
"triton/compiler",
"triton/debugger",
"triton/language",
"triton/language/extra",
"triton/ops",
"triton/ops/blocksparse",
"triton/runtime",
"triton/runtime/backends",
"triton/third_party/cuda/bin",
"triton/third_party/cuda/include",
"triton/third_party/cuda/lib",
"triton/tools",
],
install_requires=[
"filelock",
],
include_package_data=True,
ext_modules=[CMakeExtension("triton", "triton/_C/")],
cmdclass={"build_ext": CMakeBuild},
zip_safe=False,
# for PyPI
keywords=["Compiler", "Deep Learning"],
url="https://github.com/openai/triton/",
classifiers=[
"Development Status :: 4 - Beta",
"Intended Audience :: Developers",
"Topic :: Software Development :: Build Tools",
"License :: OSI Approved :: MIT License",
"Programming Language :: Python :: 3.7",
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
],
test_suite="tests",
extras_require={
"build": [
"cmake>=3.18",
"lit",
],
"tests": [
"autopep8",
"flake8",
"isort",
"numpy",
"pytest",
"scipy>=1.7.1",
],
"tutorials": [
"matplotlib",
"pandas",
"tabulate",
],
},
)
|
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"/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,646
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/models/old/bert.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
# https://arxiv.org/pdf/1810.04805.pdf
# https://github.com/google-research/bert
from qnarre.core.models import trafo
from qnarre.core.session import session_for
from qnarre.feeds.dset.squad import dset as bert_dset
from qnarre.core.squad import model as bert_model
from qnarre.core.squad import adapter as bert_adapter
def dset_for(ps, kind):
ds, feats = bert_dset(ps, kind)
if kind == "train":
ds = ds.shuffle(10000)
ds = ds.batch(1 if ps.eager_mode else ps.batch_size)
ds = ds.map(lambda d: bert_adapter(ps, feats, d))
# ds = ds.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)
return ds
def model_for(ps, compiled=False):
m = bert_model(ps)
if compiled:
m.compile(
optimizer=ps.optimizer,
loss=ps.losses,
metrics=[ps.metrics],
# target_tensors=[ins[4]],
)
print(m.summary())
return m
params = dict(
attn_drop=0.1,
attn_heads=12, # bert 12
attn_k_size=0,
attn_v_size=0,
batch_size=32,
checkpoint_steps=1000,
decode_layers=0,
dupe_factor=10,
embed_drop=0.6,
encode_layers=0,
eval_batch_size=8,
eval_steps=100,
ffn_act="gelu",
ffn_drop=0.2,
ffn_units=3072,
act_hidden="gelu",
hidden_drop=0.1,
d_hidden=768, # 512
init_checkpoint=None,
init_stddev=0.02, # stdev truncated_normal for all weights
iters_per_loop=1000,
l2_penalty=None, # 1e-6, 1e-4
learn_rate=5e-5,
lower_case=None,
max_pos_len=512,
max_seq_preds=20,
max_seq_len=128,
token_types=16,
param_attn_k_size=0,
param_attn_v_size=0,
pos_embed="timing", # embed
post_drop=0.1,
prepost_drop=0.1,
random_seed=12345,
stack_layers=12,
symbol_drop=0.0,
train_steps=100000,
s_vocab=None,
warmup_steps=10000,
model="uncased_L-12_H-768_A-12",
)
def load_params():
return trafo.load_params().override(params)
def load_flags():
trafo.load_flags()
from absl import flags
flags.DEFINE_bool("lower_case", None, "")
flags.DEFINE_integer("max_preds_per_seq", None, "")
flags.DEFINE_string("bert_config", None, "")
flags.DEFINE_string("init_checkpoint", None, "")
def main(_):
ps = load_params()
# tf.autograph.set_verbosity(1)
# print(tf.autograph.to_code(Trafo.embed.python_function))
session_for(ps)(dset_for, model_for)
if __name__ == "__main__":
from absl import app, flags, logging
logging.set_verbosity(logging.INFO) # DEBUG
load_flags()
flags.DEFINE_integer("xxx", None, "")
app.run(main)
"""
def metric_fn(masked_lm_example_loss, masked_lm_log_probs,
val, masked_lm_weights,
next_sentence_example_loss, next_sentence_log_probs,
fit):
masked_lm_log_probs = T.reshape(
masked_lm_log_probs, [-1, masked_lm_log_probs.shape[-1]])
masked_lm_predictions = T.argmax(
masked_lm_log_probs, axis=-1, output_type=T.int32)
masked_lm_example_loss = T.reshape(masked_lm_example_loss,
[-1])
val = T.reshape(val, [-1])
masked_lm_weights = T.reshape(masked_lm_weights, [-1])
masked_lm_accuracy = T.metrics.accuracy(
labels=val,
predictions=masked_lm_predictions,
weights=masked_lm_weights)
masked_lm_mean_loss = T.metrics.mean(
values=masked_lm_example_loss, weights=masked_lm_weights)
next_sentence_log_probs = T.reshape(
next_sentence_log_probs,
[-1, next_sentence_log_probs.shape[-1]])
next_sentence_predictions = T.argmax(
next_sentence_log_probs, axis=-1, output_type=T.int32)
fit = T.reshape(fit, [-1])
next_sentence_accuracy = T.metrics.accuracy(
labels=fit,
predictions=next_sentence_predictions)
next_sentence_mean_loss = T.metrics.mean(
values=next_sentence_example_loss)
return {
"masked_lm_accuracy": masked_lm_accuracy,
"masked_lm_loss": masked_lm_mean_loss,
"next_sentence_accuracy": next_sentence_accuracy,
"next_sentence_loss": next_sentence_mean_loss,
}
"""
|
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"/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], 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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,647
|
quantapix/qnarre
|
refs/heads/main
|
/tools/triton/python/triton/compiler/__init__.py
|
from .compiler import CompiledKernel, compile
from .errors import CompilationError
__all__ = ["compile", "CompiledKernel", "CompilationError"]
|
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["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/deberta.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/fast/splinter.py": ["/qnarre/tokens/fast.py"], "/qnarre/models/old/trafo.py": ["/qnarre/core/attention.py", "/qnarre/core/base.py", "/qnarre/core/mlp.py", "/qnarre/core/deduce.py", "/qnarre/core/norm.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/led.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/realm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/convbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/data2vec.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/tools/triton/python/triton/language/math.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/run/beam.py": ["/qnarre/run/qa.py"], "/tools/triton/python/triton/compiler/compiler.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/tools/disasm.py", "/tools/triton/python/triton/compiler/code_generator.py", "/tools/triton/python/triton/compiler/make_launcher.py"], "/qnarre/base/doc/graph.py": ["/qnarre/base/doc/base.py"], "/qnarre/base/activism.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py", "/qnarre/base/judgment.py"], "/qnarre/base/doc/exporter.py": ["/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/fnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/roformer.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/megatron.py": ["/qnarre/prep/config/megatron.py", "/qnarre/models/megatron.py"], "/qnarre/prep/tokens/fast/roformer.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/roformer.py"], "/qnarre/core/runner.py": ["/qnarre/core/params.py"], "/qnarre/run/seq2seq.py": ["/qnarre/run/qa.py"], "/qnarre/prep/tokens/luke.py": ["/qnarre/tokens/utils.py"], 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"/qnarre/base/author.py", "/qnarre/base/conflict.py", "/qnarre/base/conjecture.py"], "/qnarre/models/splinter.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/rag.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/convert/albert.py": ["/qnarre/prep/config/albert.py", "/qnarre/models/albert.py"], "/qnarre/prep/tokens/fast/gpt2.py": ["/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/fast/mbart.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/roformer.py": ["/qnarre/models/roformer.py"], "/qnarre/base/doc/util/tree.py": ["/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py", "/qnarre/base/doc/util/utils.py"], "/qnarre/models/rembert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/t5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/xlm.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/bert_tf2.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/tools/triton/python/triton/ops/__init__.py": ["/tools/triton/python/triton/ops/cross_entropy.py", "/tools/triton/python/triton/ops/matmul.py"], "/qnarre/core/norm.py": ["/qnarre/core/base.py"], "/qnarre/models/mpnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/author.py": ["/qnarre/base/named.py"], "/qnarre/base/doc/args.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/log.py"], "/qnarre/prep/tokens/gpt.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/distilbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/proof.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py"], "/qnarre/prep/tokens/byt5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/electra.py": ["/qnarre/prep/config/electra.py", "/qnarre/models/electra.py"], "/qnarre/models/luke.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": ["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], 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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,648
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/base/doc/command.py
|
# Copyright 2019 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
from .qnn import Qnn
from .log import Logger
from .base import config
from .args import BArgs, CArgs, MArgs
from .resource import resource
from .dispatch import Dispatch
log = Logger(__name__)
def filt_mbox():
a = MArgs().parse_args()
with resource(Dispatch.create(a.base)) as d:
d.filt_mbox(**a.kw)
def merge_mbox():
a = MArgs().parse_args()
with resource(Dispatch.create(a.base)) as d:
d.merge_mbox(**a.kw)
def strip_mbox():
a = MArgs().parse_args()
with resource(Dispatch.create(a.base, a.realm)) as d:
d.strip_mbox(**a.kw)
def import_from(src):
a = MArgs().parse_args()
with resource(Dispatch.create(a.base, a.realm)) as d:
d.import_from(src, **a.kw)
def import_main():
import_from(config.main_src)
def import_blog():
import_from(config.blog_src)
def import_priv():
import_from(config.priv_src)
def import_docs():
import_from(config.docs_src)
def import_sbox():
import_from(config.sbox_src)
def import_mbox():
import_from(config.mbox_src)
def import_tbox():
import_from(config.tbox_src)
def import_bbox():
import_from(config.bbox_src)
def import_pics():
import_from(config.PICS)
def protect():
a = BArgs().parse_args()
Dispatch.create(a.base, config.PROT).protect(**a.kw)
def redact():
a = BArgs().parse_args()
Dispatch.create(a.base, config.PUBL).redact(**a.kw)
def obfuscate():
a = BArgs().parse_args()
Dispatch.create(a.base, config.OPEN).obfuscate(**a.kw)
def check_recs():
a = MArgs().parse_args()
with resource(Dispatch.create(a.base, a.realm)) as d:
d.check_recs(**a.kw)
def graph_recs():
a = CArgs().parse_args()
with resource(Dispatch.create(a.base, a.realm)) as d:
d.graph_recs()
def qnn_setup():
a = BArgs().parse_args()
Qnn.create(a.base, config.PROT).setup(**a.kw)
def qnn_learn():
a = BArgs().parse_args()
Qnn.create(a.base, config.PROT).learn(**a.kw)
def qnn_guess():
a = BArgs().parse_args()
Qnn.create(a.base, config.PROT).guess(**a.kw)
def export_all(kind):
a = CArgs().parse_args()
assert a.realm
with resource(Dispatch.create(a.base, a.realm)) as d:
d.export_all(kind, **a.kw)
def export_orgs():
export_all(config.ORGS)
def export_pngs():
export_all(config.IMGS)
def export_jpgs():
export_all(config.PICS)
def export_mbox():
export_all(config.MBOX)
def export_blog():
export_all(config.BLOG)
|
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"/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], "/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/dispatch.py": ["/qnarre/base/doc/blog.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/context.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/analyzer.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/images.py"], "/qnarre/models/big_bird.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/big_bird.py"], "/tools/triton/python/triton/runtime/jit.py": ["/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/fast/roberta.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/bigbird.py": ["/qnarre/prep/config/big_bird.py", "/qnarre/models/big_bird.py"], "/qnarre/models/gpt_neo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/gpt_neo.py"], "/qnarre/prep/convert/t5.py": ["/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/prep/tokens/fsmt.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/deberta.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/fast/splinter.py": ["/qnarre/tokens/fast.py"], "/qnarre/models/old/trafo.py": ["/qnarre/core/attention.py", "/qnarre/core/base.py", "/qnarre/core/mlp.py", "/qnarre/core/deduce.py", "/qnarre/core/norm.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/led.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/realm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/convbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/data2vec.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/tools/triton/python/triton/language/math.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/run/beam.py": ["/qnarre/run/qa.py"], "/tools/triton/python/triton/compiler/compiler.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/tools/disasm.py", "/tools/triton/python/triton/compiler/code_generator.py", "/tools/triton/python/triton/compiler/make_launcher.py"], "/qnarre/base/doc/graph.py": ["/qnarre/base/doc/base.py"], "/qnarre/base/activism.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py", "/qnarre/base/judgment.py"], "/qnarre/base/doc/exporter.py": ["/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/fnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/roformer.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/megatron.py": ["/qnarre/prep/config/megatron.py", "/qnarre/models/megatron.py"], "/qnarre/prep/tokens/fast/roformer.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/roformer.py"], "/qnarre/core/runner.py": ["/qnarre/core/params.py"], "/qnarre/run/seq2seq.py": ["/qnarre/run/qa.py"], "/qnarre/prep/tokens/luke.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/util/table.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/tree.py", "/qnarre/base/doc/util/utils.py"], "/tools/triton/python/triton/language/standard.py": ["/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/language/__init__.py"], "/qnarre/base/doc/filters.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/args.py"], "/qnarre/prep/convert/gpt_neo.py": ["/qnarre/prep/config/gpt_neo.py", "/qnarre/models/gpt_neo.py"], "/qnarre/base/doc/section.py": ["/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/message.py"], "/qnarre/models/funnel.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/funnel.py"], "/tools/triton/python/triton/common/__init__.py": ["/tools/triton/python/triton/common/build.py"], "/qnarre/base/doc/qnn.py": ["/qnarre/base/doc/base.py", 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"/qnarre/base/author.py", "/qnarre/base/conflict.py", "/qnarre/base/conjecture.py"], "/qnarre/models/splinter.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/rag.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/convert/albert.py": ["/qnarre/prep/config/albert.py", "/qnarre/models/albert.py"], "/qnarre/prep/tokens/fast/gpt2.py": ["/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/fast/mbart.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/roformer.py": ["/qnarre/models/roformer.py"], "/qnarre/base/doc/util/tree.py": ["/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py", "/qnarre/base/doc/util/utils.py"], "/qnarre/models/rembert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/t5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/xlm.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/bert_tf2.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/tools/triton/python/triton/ops/__init__.py": ["/tools/triton/python/triton/ops/cross_entropy.py", "/tools/triton/python/triton/ops/matmul.py"], "/qnarre/core/norm.py": ["/qnarre/core/base.py"], "/qnarre/models/mpnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/author.py": ["/qnarre/base/named.py"], "/qnarre/base/doc/args.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/log.py"], "/qnarre/prep/tokens/gpt.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/distilbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/proof.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py"], "/qnarre/prep/tokens/byt5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/electra.py": ["/qnarre/prep/config/electra.py", "/qnarre/models/electra.py"], "/qnarre/models/luke.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": ["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,649
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/config/plbart.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
from ... import core as qc
from collections import OrderedDict
class PreTrained(qc.PreTrained):
hs = qc.Hypers(
[],
dict(
act="gelu",
BOS=0,
d_model=768,
decoder_attention_heads=12,
decoder_ffn_dim=3072,
decoder_layerdrop=0.0,
decoder_layers=6,
drop_act=0.0,
drop_attn=0.1,
drop_proj=0.0,
drop=0.1,
encoder_attention_heads=12,
encoder_ffn_dim=3072,
encoder_layerdrop=0.0,
encoder_layers=6,
EOS=2,
forced_eos_token_id=2,
init_std=0.02,
is_enc_dec=True,
model_type="plbart",
n_pos=1024,
PAD=1,
s_vocab=50005,
scale_embedding=True,
y_cache=True,
grad_checkpoint=True,
),
)
def _init_weights(self, module):
std = self.config.init_std
if isinstance(module, qc.Linear):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, qc.Embed):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
def _set_gradient_checkpointing(self, module, value=False):
if isinstance(module, (PLBartDecoder, PLBartEncoder)):
module.gradient_checkpointing = value
MAP = {
"uclanlp/plbart-base": "https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json",
}
class PLBartOnnxConfig(OnnxConfigWithPast):
@property
def inputs(self):
return OrderedDict(
[
("input_ids", {0: "batch", 1: "sequence"}),
("attention_mask", {0: "batch", 1: "sequence"}),
]
)
@property
def outputs(self):
if self.use_past:
return OrderedDict(
[
("last_hidden_state", {0: "batch", 1: "sequence"}),
("past_keys", {0: "batch", 2: "sequence"}),
("encoder_last_hidden_state", {0: "batch", 1: "sequence"}),
]
)
else:
return OrderedDict(
[
("last_hidden_state", {0: "batch", 1: "sequence"}),
("encoder_last_hidden_state", {0: "batch", 1: "sequence"}),
]
)
|
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"/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], "/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/dispatch.py": ["/qnarre/base/doc/blog.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/context.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/analyzer.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/images.py"], "/qnarre/models/big_bird.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/big_bird.py"], "/tools/triton/python/triton/runtime/jit.py": ["/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/fast/roberta.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/bigbird.py": ["/qnarre/prep/config/big_bird.py", "/qnarre/models/big_bird.py"], "/qnarre/models/gpt_neo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/gpt_neo.py"], "/qnarre/prep/convert/t5.py": ["/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/prep/tokens/fsmt.py": 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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,650
|
quantapix/qnarre
|
refs/heads/main
|
/tools/triton/python/test/unit/language/assert_helper.py
|
import sys
import torch
from torch.testing import assert_close
import triton
import triton.language as tl
@triton.jit
def kernel_device_assert(X, Y, BLOCK: tl.constexpr):
x = tl.load(X + tl.arange(0, BLOCK))
tl.device_assert(x == 0, "x != 0")
tl.store(Y + tl.arange(0, BLOCK), x)
@triton.jit
def kernel_device_assert_scalar(X, Y, BLOCK: tl.constexpr):
x = tl.load(X + tl.arange(0, BLOCK))
# Trivial assert
tl.device_assert(0 == 0, "x != 0")
tl.store(Y + tl.arange(0, BLOCK), x)
@triton.jit
def kernel_assert(X, Y, BLOCK: tl.constexpr):
x = tl.load(X + tl.arange(0, BLOCK))
assert x == 0, "x != 0"
tl.store(Y + tl.arange(0, BLOCK), x)
@triton.jit
def kernel_static_assert(X, Y, BLOCK: tl.constexpr):
x = tl.load(X + tl.arange(0, BLOCK))
tl.static_assert(BLOCK == 128, "BLOCK != 128")
tl.store(Y + tl.arange(0, BLOCK), x)
def test_assert(func: str):
shape = (128, )
x = torch.arange(0, shape[0], dtype=torch.int32, device='cuda')
y = torch.zeros(shape, dtype=x.dtype, device="cuda")
if func == "device_assert":
kernel_device_assert[(1,)](x, y, BLOCK=shape[0])
kernel_device_assert_scalar[(1,)](x, y, BLOCK=shape[0])
elif func == "assert":
kernel_assert[(1,)](x, y, BLOCK=shape[0])
elif func == "static_assert":
kernel_static_assert[(1,)](x, y, BLOCK=shape[0])
assert_close(y, x)
if __name__ == "__main__":
test_assert(sys.argv[1])
|
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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,651
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/convert/pegasus.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
import os
import tensorflow as tf
import torch
from pathlib import Path
from argparse import ArgumentParser
from tqdm import tqdm
from transformers import PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_params
from ..config.pegasus import PreTrained
from ...run.pegasus import ForConditionalGen
PATTERNS = [
["memory_attention", "encoder_attn"],
["attention", "attn"],
["/", "."],
[".LayerNorm.gamma", "_layer_norm.weight"],
[".LayerNorm.beta", "_layer_norm.bias"],
["r.layer_", "r.layers."],
["output_proj", "out_proj"],
["ffn.dense_1.", "fc2."],
["ffn.dense.", "fc1."],
["ffn_layer_norm", "final_layer_norm"],
["kernel", "weight"],
["encoder_layer_norm.", "encoder.layer_norm."],
["decoder_layer_norm.", "decoder.layer_norm."],
["embeddings.weights", "shared.weight"],
]
def rename_state_dict_key(k):
for n, t in PATTERNS:
k = k.replace(n, t)
return k
def convert_pegasus(tf_weights, cfg_updates):
cfg_kw = DEFAULTS.copy()
cfg_kw.update(cfg_updates)
cfg = PreTrained(**cfg_kw)
m = ForConditionalGen(cfg)
sd = m.model.state_dict()
mapping = {}
for k, v in tf_weights.items():
new_k = rename_state_dict_key(k)
if new_k not in sd:
raise ValueError(f"could not find new key {new_k} in state dict. (converted from {k})")
if "dense" in k or "proj" in new_k:
v = v.T
mapping[new_k] = torch.tensor(v, dtype=sd[new_k].dtype)
assert v.shape == sd[new_k].shape, f"{new_k}, {k}, {v.shape}, {sd[new_k].shape}"
mapping["shared.weight"][cfg.PAD] = torch.zeros_like(mapping["shared.weight"][cfg.PAD + 1])
mapping["encoder.embed_tokens.weight"] = mapping["shared.weight"]
mapping["decoder.embed_tokens.weight"] = mapping["shared.weight"]
empty_biases = {
k: torch.zeros_like(v) for k, v in sd.items() if k.endswith("bias") and k not in mapping
}
mapping.update(**empty_biases)
missing, extra = m.model.load_state_dict(mapping, strict=False)
unexpected_missing = [
k
for k in missing
if k not in ["encoder.embed_positions.weight", "decoder.embed_positions.weight"]
]
assert unexpected_missing == []
assert extra == []
return m
def get_tf_weights_as_numpy(path="./ckpt/aeslc/model.ckpt-32000"):
xs = tf.train.list_variables(path)
ys = {}
ignore_name = ["Adafactor", "global_step"]
for n, shape in tqdm(xs, desc="converting tf checkpoint to dict"):
if any([x in n for x in ignore_name]):
continue
ys[n] = tf.train.load_variable(path, n)
return ys
def to_pytorch(ckpt_path, save_path):
dataset = Path(ckpt_path).parent.name
desired_max_model_length = task_params[f"sum_{dataset}"]["n_pos"]
tok = PegasusTokenizer.from_pretrained(
"sshleifer/pegasus", model_max_length=desired_max_model_length
)
assert tok.model_max_length == desired_max_model_length
tok.save_pretrained(save_path)
tf_weights = get_tf_weights_as_numpy(ckpt_path)
cfg_updates = task_params[f"sum_{dataset}"]
if dataset == "large":
cfg_updates["task_params"] = task_params
torch_model = convert_pegasus(tf_weights, cfg_updates)
torch_model.save_pretrained(save_path)
sd = torch_model.state_dict()
sd.pop("model.decoder.embed_positions.weight")
sd.pop("model.encoder.embed_positions.weight")
torch.save(sd, Path(save_path) / "pytorch_model.bin")
if __name__ == "__main__":
x = ArgumentParser()
x.add_argument("src_path", type=str)
x.add_argument("save_path", default=None, type=str)
y = x.parse_args()
if y.save_path is None:
y.save_path = os.path.join("pegasus", Path(y.src_path).parent.name)
to_pytorch(y.src_path, y.save_path)
|
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"/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], 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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,652
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/models/old/xlnet/xlnet.py
|
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import json
import os
import tensorflow as tf
import modeling
def _get_initializer(FLAGS):
"""Get variable intializer."""
if FLAGS.init == "uniform":
initializer = tf.initializers.random_uniform(
minval=-FLAGS.init_range, maxval=FLAGS.init_range, seed=None
)
elif FLAGS.init == "normal":
initializer = tf.initializers.random_normal(stddev=FLAGS.init_std, seed=None)
else:
raise ValueError("Initializer {} not supported".format(FLAGS.init))
return initializer
class XLNetConfig(object):
"""XLNetConfig contains hyperparameters that are specific to a model checkpoint;
i.e., these hyperparameters should be the same between
pretraining and finetuning.
The following hyperparameters are defined:
n_lays, the number of layers.
d_model, the hidden size.
n_heads, the number of attention heads.
d_head, the dimension size of each attention head.
d_inner, the hidden size in feed-forward layers.
ff_activation, "relu" or "gelu".
untie_r, whether to untie the biases in attention.
n_token, the vocab size.
"""
def __init__(self, FLAGS=None, json_path=None):
"""Constructing an XLNetConfig.
One of FLAGS or json_path should be provided."""
assert FLAGS is not None or json_path is not None
self.keys = [
"n_lays",
"d_model",
"n_heads",
"d_head",
"d_inner",
"ff_activation",
"untie_r",
"n_token",
]
if FLAGS is not None:
self.init_from_flags(FLAGS)
if json_path is not None:
self.init_from_json(json_path)
def init_from_flags(self, FLAGS):
for key in self.keys:
setattr(self, key, getattr(FLAGS, key))
def init_from_json(self, json_path):
with tf.gfile.Open(json_path) as f:
json_data = json.load(f)
for key in self.keys:
setattr(self, key, json_data[key])
def to_json(self, json_path):
"""Save XLNetConfig to a json file."""
json_data = {}
for key in self.keys:
json_data[key] = getattr(self, key)
json_dir = os.path.dirname(json_path)
if not tf.gfile.Exists(json_dir):
tf.gfile.MakeDirs(json_dir)
with tf.gfile.Open(json_path, "w") as f:
json.dump(json_data, f, indent=4, sort_keys=True)
def create_run_config(is_training, is_finetune, FLAGS):
kw = dict(
is_training=is_training,
use_tpu=FLAGS.use_tpu,
use_bfloat16=FLAGS.use_bfloat16,
drop=FLAGS.drop,
dropatt=FLAGS.dropatt,
init=FLAGS.init,
init_range=FLAGS.init_range,
init_std=FLAGS.init_std,
clamp_len=FLAGS.clamp_len,
)
if not is_finetune:
kw.update(
dict(
mem_len=FLAGS.mem_len,
reuse_len=FLAGS.reuse_len,
bi_data=FLAGS.bi_data,
clamp_len=FLAGS.clamp_len,
same_length=FLAGS.same_length,
)
)
return RunConfig(**kw)
class RunConfig(object):
"""RunConfig contains hyperparameters that could be different
between pretraining and finetuning.
These hyperparameters can also be changed from run to run.
We store them separately from XLNetConfig for flexibility.
"""
def __init__(
self,
is_training,
use_tpu,
use_bfloat16,
drop,
dropatt,
init="normal",
init_range=0.1,
init_std=0.02,
mem_len=None,
reuse_len=None,
bi_data=False,
clamp_len=-1,
same_length=False,
):
"""
Args:
is_training, whether in training mode.
use_tpu, whether TPUs are used.
use_bfloat16, use bfloat16 instead of float32.
drop: float, drop rate.
dropatt: float, drop rate on attention probabilities.
init, the initialization scheme, either "normal" or "uniform".
init_range: float, initialize the parameters with a uniform distribution
in [-init_range, init_range]. Only effective when init="uniform".
init_std: float, initialize the parameters with a normal distribution
with mean 0 and stddev init_std. Only effective when init="normal".
mem_len, the number of tokens to cache.
reuse_len, the number of tokens in the currect batch to be cached
and reused in the future.
bi_data, whether to use bidirectional input pipeline.
Usually set to True during pretraining and False during finetuning.
clamp_len, clamp all relative distances larger than clamp_len.
-1 means no clamping.
same_length, whether to use the same attention length for each token.
"""
self.init = init
self.init_range = init_range
self.init_std = init_std
self.is_training = is_training
self.drop = drop
self.dropatt = dropatt
self.use_tpu = use_tpu
self.use_bfloat16 = use_bfloat16
self.mem_len = mem_len
self.reuse_len = reuse_len
self.bi_data = bi_data
self.clamp_len = clamp_len
self.same_length = same_length
class XLNetModel(object):
"""A wrapper of the XLNet model used during both pretraining and finetuning."""
def __init__(
self,
xlnet_config,
run_config,
input_ids,
seg_ids,
input_mask,
mems=None,
perm_mask=None,
target_mapping=None,
inp_q=None,
**kw,
):
initializer = _get_initializer(run_config)
tfm_args = dict(
n_token=xlnet_config.n_token,
initializer=initializer,
attn_type="bi",
n_lays=xlnet_config.n_lays,
d_model=xlnet_config.d_model,
n_heads=xlnet_config.n_heads,
d_head=xlnet_config.d_head,
d_inner=xlnet_config.d_inner,
ff_activation=xlnet_config.ff_activation,
untie_r=xlnet_config.untie_r,
is_training=run_config.is_training,
use_bfloat16=run_config.use_bfloat16,
use_tpu=run_config.use_tpu,
drop=run_config.drop,
dropatt=run_config.dropatt,
mem_len=run_config.mem_len,
reuse_len=run_config.reuse_len,
bi_data=run_config.bi_data,
clamp_len=run_config.clamp_len,
same_length=run_config.same_length,
)
input_args = dict(
inp_k=input_ids,
seg_id=seg_ids,
input_mask=input_mask,
mems=mems,
perm_mask=perm_mask,
target_mapping=target_mapping,
inp_q=inp_q,
)
tfm_args.update(input_args)
with tf.variable_scope("model", reuse=tf.AUTO_REUSE):
(self.output, self.new_mems, self.lookup_table) = modeling.transformer_xl(**tfm_args)
self.input_mask = input_mask
self.initializer = initializer
self.xlnet_config = xlnet_config
self.run_config = run_config
def get_pooled_out(self, sum_type, use_summ_proj=True):
"""
Args:
sum_type, "last", "first", "mean", or "attn". The method
to pool the input to get a vector representation.
use_summ_proj, whether to use a linear projection during pooling.
Returns:
float32 Tensor in shape [bsz, d_model], the pooled representation.
"""
xlnet_config = self.xlnet_config
run_config = self.run_config
with tf.variable_scope("model", reuse=tf.AUTO_REUSE):
summary = modeling.summarize_sequence(
sum_type=sum_type,
hidden=self.output,
d_model=xlnet_config.d_model,
n_heads=xlnet_config.n_heads,
d_head=xlnet_config.d_head,
drop=run_config.drop,
dropatt=run_config.dropatt,
is_training=run_config.is_training,
input_mask=self.input_mask,
initializer=self.initializer,
use_proj=use_summ_proj,
)
return summary
def get_sequence_output(self):
"""
Returns:
float32 Tensor in shape [len, bsz, d_model]. The last layer hidden
representation of XLNet.
"""
return self.output
def get_new_memory(self):
"""
Returns:
list of float32 Tensors in shape [mem_len, bsz, d_model], the new
memory that concatenates the previous memory with the current input
representations.
The length of the list equals n_lays.
"""
return self.new_mems
def get_embedding_table(self):
"""
Returns:
float32 Tensor in shape [n_token, d_model]. The embedding lookup table.
Used for tying embeddings between input and output layers.
"""
return self.lookup_table
def get_initializer(self):
"""
Returns:
A tf initializer. Used to initialize variables in layers on top of XLNet.
"""
return self.initializer
|
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|
33,653
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/models/old/transfo-xl/model.py
|
import torch
from torch import nn
from torch.nn import functional as F
from ... import core as qc
CUDA_MAJOR = int(torch.version.cuda.split(".")[0])
CUDA_MINOR = int(torch.version.cuda.split(".")[1])
class Positional(qc.Module):
pass
class Positionwise(qc.Module):
pass
class Attention(qc.Module):
hs = qc.Hypers(
{"d_hidden", "drop", "n_heads", "d_head"},
{"drop_attn": 0.0, "norm_eps": 1e-5, "pre_norm": False},
)
def __init__(self, ps={}, hs=[], **kw):
super().__init__(ps, [self.hs] + hs, **kw)
cfg = self.get_cfg(kw)
d, n, h = cfg.d_hidden, cfg.n_heads, cfg.d_head
cfg.scale = 1 / (h**0.5)
self.query = qc.Linear(d, n * h, bias=False, **kw)
self.kv = qc.Linear(d, 2 * n * h, bias=False, **kw)
self.drop = qc.Dropout(cfg.drop, **kw)
self.drop_attn = qc.Dropout(cfg.drop_attn, **kw)
self.proj = qc.Linear(n * h, d, bias=False, **kw)
self.norm = qc.LayerNorm(d, **kw)
def forward(self, x, mask=None, mems=None):
cfg = self.cfg
y = x if mems is None else torch.cat([mems, x], 0)
y = self.norm(y) if cfg.pre_norm else y
q = self.query(y)
k, v = torch.chunk(self.kv(y), 2, -1)
qlen, klen = x.size(0), y.size(0)
b, n, h = x.size(1), cfg.n_heads, cfg.d_head
q = q.view(qlen, b, n, h)
k = k.view(klen, b, n, h)
v = v.view(klen, b, n, h)
a = torch.einsum("ibnd,jbnd->ijbn", (q, k))
a.mul_(cfg.scale)
if mask is not None and mask.any().item():
i = self.get_minus_inf()
if mask.dim() == 2:
a = a.float().masked_fill(mask[None, :, :, None], i).type_as(a)
elif mask.dim() == 3:
a = a.float().masked_fill(mask[:, :, :, None], i).type_as(a)
a = self.drop_attn(F.softmax(a, dim=1))
y = torch.einsum("ijbn,jbnd->ibnd", (a, v))
y = y.contiguous().view(y.size(0), y.size(1), n * h)
y = x + self.drop(self.proj(y))
return y if cfg.pre_norm else self.norm(y)
class BaseAttn(qc.Module):
def __init__(self, tgt_len=None, ext_len=None, mem_len=None, ps={}, hs=[], **kw):
super().__init__(ps, [self.hs] + hs, **kw)
cfg = self.get_cfg(kw)
m, n, h = cfg.d_hidden, cfg.n_heads, cfg.d_head
cfg.scale = 1 / (h**0.5)
self.qkv = qc.Linear(m, 3 * n * h, bias=False, **kw)
self.drop = qc.Dropout(cfg.drop, **kw)
self.drop_attn = qc.Dropout(cfg.drop_attn, **kw)
self.proj = qc.Linear(n * h, m, bias=False, **kw)
self.norm = qc.LayerNorm(m, **kw)
def _parallelogram_mask(self, h, w, left=False):
y = torch.ones((h, w)).byte()
m = min(h, w)
y[:m, :m] = torch.triu(y[:m, :m])
y[-m:, -m:] = torch.tril(y[-m:, -m:])
return y if left else y.flip(0)
def _shift(self, x, qlen, klen, mask, left=False):
kw = dict(device=x.device, dtype=x.dtype)
if qlen > 1:
y = torch.zeros((x.size(0), qlen - 1, x.size(2), x.size(3)), **kw)
else:
y = torch.zeros(0, **kw)
if left:
mask = mask.flip(1)
y = torch.cat([y, x], dim=1).expand(qlen, -1, -1, -1)
else:
y = torch.cat([x, y], dim=1).expand(qlen, -1, -1, -1)
return y.masked_select(mask[:, :, None, None]).view(qlen, klen, x.size(2), x.size(3))
def rel_shift(self, x, zero_triu=False):
kw = dict(device=x.device, dtype=x.dtype)
y = torch.zeros((x.size(0), 1, *x.size()[2:]), **kw)
y = torch.cat([y, x], dim=1)
y = y.view(x.size(1) + 1, x.size(0), *x.size()[2:])
x = y[1:].view_as(x)
if zero_triu:
ones = torch.ones((x.size(0), x.size(1)))
x = x * torch.tril(ones, x.size(1) - x.size(0))[:, :, None, None]
return x
class PartialAttn(BaseAttn):
def __init__(self, *xs, **kw):
super().__init__(*xs, **kw)
cfg = self.cfg
self.r_net = qc.Linear(cfg.d_hidden, cfg.n_heads * cfg.d_head, bias=False, **kw)
def forward(self, x, r, q_bias, r_bias, mask=None, mems=None):
cfg = self.cfg
y = x if mems is None else torch.cat([mems, x], 0)
y = self.qkv(self.norm(y) if cfg.pre_norm else y)
r = self.r_net(r)
q, k, v = torch.chunk(a, 3, dim=-1)
qlen, klen, rlen = x.size(0), k.size(0), r.size(0)
q = q if mems is None else q[-qlen:]
b, n, h = x.size(1), cfg.n_heads, cfg.d_head
q = q.view(qlen, b, n, h)
k = k.view(klen, b, n, h)
v = v.view(klen, b, n, h)
r = r.view(rlen, n, h)
AC = torch.einsum("ibnd,jbnd->ijbn", (q + q_bias, k))
BD = self.rel_shift(torch.einsum("ibnd,jnd->ijbn", (q + r_bias, r)))
a = AC + BD
a.mul_(cfg.scale)
if mask is not None and mask.any().item():
i = self.get_minus_inf()
if mask.dim() == 2:
a = a.float().masked_fill(mask[None, :, :, None], i).type_as(a)
elif mask.dim() == 3:
a = a.float().masked_fill(mask[:, :, :, None], i).type_as(a)
a = self.drop_attn(F.softmax(a, dim=1))
y = torch.einsum("ijbn,jbnd->ibnd", (a, v))
y = y.contiguous().view(y.size(0), y.size(1), n * h)
y = x + self.drop(self.proj(y))
return y if cfg.pre_norm else self.norm(y)
class LearnableAttn(BaseAttn):
def __init__(self, *xs, **kw):
super().__init__(*xs, **kw)
def forward(self, x, r, q_bias, r_bias, mask=None, mems=None):
cfg = self.cfg
y = x if mems is None else torch.cat([mems, x], 0)
y = self.qkv(self.norm(y) if cfg.pre_norm else y)
q, k, v = torch.chunk(a, 3, dim=-1)
qlen, klen, rlen = x.size(0), k.size(0), r.size(0)
q = q if mems is None else q[-qlen:]
b, n, h = x.size(1), cfg.n_heads, cfg.d_head
q = q.view(qlen, b, n, h)
k = k.view(klen, b, n, h)
v = v.view(klen, b, n, h)
if klen > rlen:
r = torch.cat([r[0:1].expand(klen - rlen, -1, -1), r], 0)
r_bias = torch.cat([r_bias[0:1].expand(klen - r_bias.size(0), -1), r_bias], 0)
else:
r = r[-klen:]
r_bias = r_bias[-klen:]
AC = torch.einsum("ibnd,jbnd->ijbn", (q + q_bias[None], k))
BD = self.rel_shift(torch.einsum("ibnd,jnd->ijbn", (q, r)) + r_bias[None, :, None])
a = AC + BD
a.mul_(cfg.scale)
if mask is not None and mask.any().item():
i = self.get_minus_inf()
if mask.dim() == 2:
a = a.float().masked_fill(mask[None, :, :, None], i).type_as(a)
elif mask.dim() == 3:
a = a.float().masked_fill(mask[:, :, :, None], i).type_as(a)
a = self.drop_attn(F.softmax(a, dim=1))
y = torch.einsum("ijbn,jbnd->ibnd", (a, v))
y = y.contiguous().view(y.size(0), y.size(1), n * h)
y = x + self.drop(self.proj(y))
return y if cfg.pre_norm else self.norm(y)
class Layer(qc.Module):
def __init__(self, **kw):
super().__init__(**kw)
self.attn = Attention(**kw)
self.proj = Positionwise(**kw)
def forward(self, x, dec_m=None, mems=None):
y = self.attn(x, mask=dec_m, mems=mems)
y = self.proj(y)
return y
class LearnableLay(qc.Module):
def __init__(self, **kw):
super().__init__(**kw)
self.attn = LearnableAttn(**kw)
self.proj = Positionwise(**kw)
def forward(self, x, r, x_bias, r_bias, dec_m=None, mems=None):
y = self.attn(x, r, x_bias, r_bias, mask=dec_m, mems=mems)
y = self.proj(y)
return y
class PartialLay(qc.Module):
def __init__(self, **kw):
super().__init__(**kw)
self.attn = PartialAttn(**kw)
self.proj = Positionwise(**kw)
def forward(self, x, r, x_bias, r_bias, dec_m=None, mems=None):
y = self.attn(x, r, x_bias, r_bias, mask=dec_m, mems=mems)
y = self.proj(y)
return y
class Adaptive(qc.Module):
pass
class MemTransformerLM(qc.Module):
def __init__(
self,
tie_weight=True,
d_embed=None,
div_val=1,
tie_projs=[False],
pre_norm=False,
tgt_len=None,
ext_len=None,
mem_len=None,
cutoffs=[],
adapt_inp=False,
same_length=False,
attn_type=0,
clamp_len=-1,
sample_softmax=-1,
):
super().__init__()
d_embed = d_hidden if d_embed is None else d_embed
self.tok_emb = Adaptive(s_vocab, d_embed, d_hidden, cutoffs, div_val=div_val)
self.drop = qc.Dropout(drop)
self.tgt_len = tgt_len
self.mem_len = mem_len
self.ext_len = ext_len
self.max_klen = tgt_len + ext_len + mem_len
self.attn_type = attn_type
self.lays = qc.ModuleList()
if attn_type == 0: # the default attention
for i in range(n_lays):
self.lays.append(
PartialLay(
d_inner,
tgt_len=tgt_len,
ext_len=ext_len,
mem_len=mem_len,
pre_norm=pre_norm,
)
)
elif attn_type == 1: # learnable embeddings
for i in range(cfg.n_lays):
self.lays.append(
LearnableLay(
d_inner,
tgt_len=tgt_len,
ext_len=ext_len,
mem_len=mem_len,
pre_norm=pre_norm,
)
)
elif attn_type in [2, 3]: # absolute embeddings
for i in range(cfg.n_lays):
self.lays.append(
Layer(
d_inner,
pre_norm=pre_norm,
)
)
self.sample_softmax = sample_softmax
if sample_softmax > 0:
self.out_layer = qc.Linear(d_hidden, s_vocab)
if tie_weight:
self.out_layer.weight = self.tok_emb.weight
self.tie_weight = tie_weight
self.sampler = LogUniformSampler(s_vocab, sample_softmax)
else:
self.crit = ProjecLogSoftmax(s_vocab, d_embed, d_hidden, cutoffs, div_val=div_val)
if tie_weight:
for i in range(len(self.crit.lays)):
self.crit.lays[i].weight = self.tok_emb.lays[i].weight
if tie_projs:
for i, tie_proj in enumerate(tie_projs):
if tie_proj and div_val == 1 and d_hidden != d_embed:
self.crit.projs[i] = self.tok_emb.projs[0]
elif tie_proj and div_val != 1:
self.crit.projs[i] = self.tok_emb.projs[i]
self.same_length = same_length
self.clamp_len = clamp_len
self._create_params()
def reset_length(self, tgt_len, ext_len, mem_len):
self.tgt_len = tgt_len
self.mem_len = mem_len
self.ext_len = ext_len
def _update_mems(self, hids, mems, qlen, mlen):
if mems is None:
return None
assert len(hids) == len(mems), "len(hids) != len(mems)"
with torch.no_grad():
ys = []
end_idx = mlen + max(0, qlen - 0 - self.ext_len)
beg_idx = max(0, end_idx - self.mem_len)
for i in range(len(hids)):
cat = torch.cat([mems[i], hids[i]], dim=0)
ys.append(cat[beg_idx:end_idx].detach())
return ys
def _forward(self, x, mems=None):
qlen, bsz = x.size()
word_emb = self.tok_emb(x)
mlen = mems[0].size(0) if mems is not None else 0
klen = mlen + qlen
if self.same_length:
all_ones = word_emb.new_ones(qlen, klen)
mask_len = klen - self.mem_len
if mask_len > 0:
mask_shift_len = qlen - mask_len
else:
mask_shift_len = qlen
dec_m = (torch.triu(all_ones, 1 + mlen) + torch.tril(all_ones, -mask_shift_len)).byte()[
:, :, None
]
else:
dec_m = torch.triu(word_emb.new_ones(qlen, klen), diagonal=1 + mlen).byte()[:, :, None]
hids = []
if self.attn_type == 0: # default
pos_seq = torch.arange(klen - 1, -1, -1.0, device=word_emb.device, dtype=word_emb.dtype)
if self.clamp_len > 0:
pos_seq.clamp_(max=self.clamp_len)
pos_emb = self.pos_emb(pos_seq)
y = self.drop(word_emb)
pos_emb = self.drop(pos_emb)
hids.append(y)
for i, lay in enumerate(self.lays):
mems_i = None if mems is None else mems[i]
y = lay(
y,
pos_emb,
self.r_w_bias,
self.r_r_bias,
dec_m=dec_m,
mems=mems_i,
)
hids.append(y)
elif self.attn_type == 1: # learnable
y = self.drop(word_emb)
hids.append(y)
for i, lay in enumerate(self.lays):
if self.clamp_len > 0:
r_emb = self.r_emb[i][-self.clamp_len :]
r_bias = self.r_bias[i][-self.clamp_len :]
else:
r_emb, r_bias = self.r_emb[i], self.r_bias[i]
mems_i = None if mems is None else mems[i]
y = lay(
y,
r_emb,
self.r_w_bias[i],
r_bias,
dec_m=dec_m,
mems=mems_i,
)
hids.append(y)
elif self.attn_type == 2: # absolute
pos_seq = torch.arange(klen - 1, -1, -1.0, device=word_emb.device, dtype=word_emb.dtype)
if self.clamp_len > 0:
pos_seq.clamp_(max=self.clamp_len)
pos_emb = self.pos_emb(pos_seq)
y = self.drop(word_emb + pos_emb[-qlen:])
hids.append(y)
for i, lay in enumerate(self.lays):
mems_i = None if mems is None else mems[i]
if mems_i is not None and i == 0:
mems_i += pos_emb[:mlen]
y = lay(y, dec_m=dec_m, mems=mems_i)
hids.append(y)
elif self.attn_type == 3:
y = self.drop(word_emb)
hids.append(y)
for i, lay in enumerate(self.lays):
mems_i = None if mems is None else mems[i]
if mems_i is not None and mlen > 0:
cur_emb = self.r_emb[i][:-qlen]
cur_size = cur_emb.size(0)
if cur_size < mlen:
cur_emb_pad = cur_emb[0:1].expand(mlen - cur_size, -1, -1)
cur_emb = torch.cat([cur_emb_pad, cur_emb], 0)
else:
cur_emb = cur_emb[-mlen:]
mems_i += cur_emb.view(mlen, 1, -1)
y += self.r_emb[i][-qlen:].view(qlen, 1, -1)
y = lay(y, dec_m=dec_m, mems=mems_i)
hids.append(y)
y = self.drop(y)
new_mems = self._update_mems(hids, mems, mlen, qlen)
return y, new_mems
def forward(self, data, target, *mems):
if not mems:
mems = self.init_mems()
tgt_len = target.size(0)
hidden, new_mems = self._forward(data, mems=mems)
pred_hid = hidden[-tgt_len:]
if self.sample_softmax > 0 and self.training:
assert self.tie_weight
logit = sample_logits(self.tok_emb, self.out_layer.bias, target, pred_hid, self.sampler)
loss = -F.log_softmax(logit, -1)[:, :, 0]
else:
loss = self.crit(pred_hid.view(-1, pred_hid.size(-1)), target.view(-1))
loss = loss.view(tgt_len, -1)
return [loss] if new_mems is None else [loss] + new_mems
class AdaptLogSoftmax(qc.Module):
def __init__(self, in_features, n_classes, cutoffs, keep_order=False):
super().__init__()
cutoffs = list(cutoffs)
if (
(cutoffs != sorted(cutoffs))
or (min(cutoffs) <= 0)
or (max(cutoffs) >= (n_classes - 1))
or (len(set(cutoffs)) != len(cutoffs))
or any([int(c) != c for c in cutoffs])
):
raise ValueError(
"cutoffs should be a sequence of unique, positive "
"integers sorted in an increasing order, where "
"each value is between 1 and n_classes-1"
)
self.in_features = in_features
self.n_classes = n_classes
self.cutoffs = cutoffs + [n_classes]
self.shortlist_size = self.cutoffs[0]
self.n_clusters = len(self.cutoffs) - 1
self.head_size = self.shortlist_size + self.n_clusters
self.cluster_weight = nn.Parameter(torch.zeros(self.n_clusters, self.in_features))
self.cluster_bias = nn.Parameter(torch.zeros(self.n_clusters))
self.keep_order = keep_order
def forward(self, hidden, target, weight, bias, keep_order=False):
if hidden.size(0) != target.size(0):
raise RuntimeError(
"Input and target should have the same size " "in the batch dimension."
)
head_weight = torch.cat([weight[: self.shortlist_size], self.cluster_weight], dim=0)
head_bias = torch.cat([bias[: self.shortlist_size], self.cluster_bias], dim=0)
head_logit = F.linear(hidden, head_weight, bias=head_bias)
head_logprob = F.log_softmax(head_logit, dim=1)
nll = torch.zeros_like(target, dtype=hidden.dtype, device=hidden.device)
offset = 0
cutoff_values = [0] + self.cutoffs
for i in range(len(cutoff_values) - 1):
l_idx, h_idx = cutoff_values[i], cutoff_values[i + 1]
mask_i = (target >= l_idx) & (target < h_idx)
indices_i = mask_i.nonzero().squeeze()
if indices_i.numel() == 0:
continue
target_i = target.index_select(0, indices_i) - l_idx
head_logprob_i = head_logprob.index_select(0, indices_i)
if i == 0:
logprob_i = head_logprob_i.gather(1, target_i[:, None]).squeeze(1)
else:
weight_i = weight[l_idx:h_idx]
bias_i = bias[l_idx:h_idx]
hidden_i = hidden.index_select(0, indices_i)
tail_logit_i = F.linear(hidden_i, weight_i, bias=bias_i)
tail_logprob_i = F.log_softmax(tail_logit_i, dim=1)
logprob_i = head_logprob_i[:, -i] + tail_logprob_i.gather(
1, target_i[:, None]
).squeeze(1)
if (hasattr(self, "keep_order") and self.keep_order) or keep_order:
nll.index_copy_(0, indices_i, -logprob_i)
else:
nll[offset : offset + logprob_i.size(0)].copy_(-logprob_i)
offset += logprob_i.size(0)
return nll
class ProjecLogSoftmax(qc.Module):
def __init__(self, s_vocab, d_embed, d_proj, cutoffs, div_val=1, keep_order=False):
super(ProjecLogSoftmax, self).__init__()
self.cutoffs = cutoffs + [s_vocab]
self.cutoff_ends = [0] + self.cutoffs
self.div_val = div_val
self.shortlist_size = self.cutoffs[0]
self.n_clusters = len(self.cutoffs) - 1
self.head_size = self.shortlist_size + self.n_clusters
if self.n_clusters > 0:
self.cluster_weight = nn.Parameter(torch.zeros(self.n_clusters, self.d_embed))
self.cluster_bias = nn.Parameter(torch.zeros(self.n_clusters))
self.lays = qc.ModuleList()
self.projs = nn.ParameterList()
if div_val == 1:
for i in range(len(self.cutoffs)):
if d_proj != d_embed:
self.projs.append(nn.Parameter(torch.Tensor(d_proj, d_embed)))
else:
self.projs.append(None)
self.lays.append(qc.Linear(d_embed, s_vocab))
else:
for i in range(len(self.cutoffs)):
l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i + 1]
d_emb_i = d_embed // (div_val**i)
self.projs.append(nn.Parameter(torch.Tensor(d_proj, d_emb_i)))
self.lays.append(qc.Linear(d_emb_i, r_idx - l_idx))
self.keep_order = keep_order
def _compute_logit(self, hidden, weight, bias, proj):
if proj is None:
logit = F.linear(hidden, weight, bias=bias)
else:
# if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
proj_hid = F.linear(hidden, proj.t().contiguous())
logit = F.linear(proj_hid, weight, bias=bias)
# else:
# logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t()))
# if bias is not None:
# logit = logit + bias
return logit
def forward(self, hidden, target, keep_order=False):
if hidden.size(0) != target.size(0):
raise RuntimeError(
"Input and target should have the same size " "in the batch dimension."
)
if self.n_clusters == 0:
logit = self._compute_logit(
hidden, self.lays[0].weight, self.lays[0].bias, self.projs[0]
)
nll = -F.log_softmax(logit, dim=-1).gather(1, target.unsqueeze(1)).squeeze(1)
else:
weights, biases = [], []
for i in range(len(self.cutoffs)):
if self.div_val == 1:
l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i + 1]
weight_i = self.lays[0].weight[l_idx:r_idx]
bias_i = self.lays[0].bias[l_idx:r_idx]
else:
weight_i = self.lays[i].weight
bias_i = self.lays[i].bias
if i == 0:
weight_i = torch.cat([weight_i, self.cluster_weight], dim=0)
bias_i = torch.cat([bias_i, self.cluster_bias], dim=0)
weights.append(weight_i)
biases.append(bias_i)
head_weight, head_bias, head_proj = weights[0], biases[0], self.projs[0]
head_logit = self._compute_logit(hidden, head_weight, head_bias, head_proj)
head_logprob = F.log_softmax(head_logit, dim=1)
nll = torch.zeros_like(target, dtype=hidden.dtype, device=hidden.device)
offset = 0
cutoff_values = [0] + self.cutoffs
for i in range(len(cutoff_values) - 1):
l_idx, r_idx = cutoff_values[i], cutoff_values[i + 1]
mask_i = (target >= l_idx) & (target < r_idx)
indices_i = mask_i.nonzero().squeeze()
if indices_i.numel() == 0:
continue
target_i = target.index_select(0, indices_i) - l_idx
head_logprob_i = head_logprob.index_select(0, indices_i)
if i == 0:
logprob_i = head_logprob_i.gather(1, target_i[:, None]).squeeze(1)
else:
weight_i, bias_i, proj_i = weights[i], biases[i], self.projs[i]
hidden_i = hidden.index_select(0, indices_i)
tail_logit_i = self._compute_logit(hidden_i, weight_i, bias_i, proj_i)
tail_logprob_i = F.log_softmax(tail_logit_i, dim=1)
logprob_i = head_logprob_i[:, -i] + tail_logprob_i.gather(
1, target_i[:, None]
).squeeze(1)
if (hasattr(self, "keep_order") and self.keep_order) or keep_order:
nll.index_copy_(0, indices_i, -logprob_i)
else:
nll[offset : offset + logprob_i.size(0)].copy_(-logprob_i)
offset += logprob_i.size(0)
return nll
class LogUniformSampler(object):
def __init__(self, range_max, n_sample):
with torch.no_grad():
self.range_max = range_max
log_indices = torch.arange(1.0, range_max + 2.0, 1.0).log_()
self.dist = (log_indices[1:] - log_indices[:-1]) / log_indices[-1]
# print('P', self.dist.numpy().tolist()[-30:])
self.log_q = (-(-self.dist.double().log1p_() * 2 * n_sample).expm1_()).log_().float()
self.n_sample = n_sample
def sample(self, labels):
# neg_samples = torch.empty(0).long()
n_sample = self.n_sample
n_tries = 2 * n_sample
with torch.no_grad():
neg_samples = torch.multinomial(self.dist, n_tries, replacement=True).unique()
device = labels.device
neg_samples = neg_samples.to(device)
true_log_probs = self.log_q[labels].to(device)
samp_log_probs = self.log_q[neg_samples].to(device)
return true_log_probs, samp_log_probs, neg_samples
def sample_logits(embedding, bias, labels, inputs, sampler):
true_log_probs, samp_log_probs, neg_samples = sampler.sample(labels)
n_sample = neg_samples.size(0)
b1, b2 = labels.size(0), labels.size(1)
all_ids = torch.cat([labels.view(-1), neg_samples])
all_w = embedding(all_ids)
true_w = all_w[:-n_sample].view(b1, b2, -1)
sample_w = all_w[-n_sample:].view(n_sample, -1)
all_b = bias[all_ids]
true_b = all_b[:-n_sample].view(b1, b2)
sample_b = all_b[-n_sample:]
hit = (labels[:, :, None] == neg_samples).detach()
true_logits = torch.einsum("ijk,ijk->ij", [true_w, inputs]) + true_b - true_log_probs
sample_logits = torch.einsum("lk,ijk->ijl", [sample_w, inputs]) + sample_b - samp_log_probs
sample_logits.masked_fill_(hit, -1e30)
logits = torch.cat([true_logits[:, :, None], sample_logits], -1)
return logits
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description="unit test")
parser.add_argument("--n_lays", type=int, default=4, help="")
parser.add_argument("--n_rel_layer", type=int, default=4, help="")
parser.add_argument("--n_heads", type=int, default=2, help="")
parser.add_argument("--d_head", type=int, default=2, help="")
parser.add_argument("--d_hidden", type=int, default=200, help="")
parser.add_argument("--d_embed", type=int, default=200, help="")
parser.add_argument("--d_inner", type=int, default=200, help="")
parser.add_argument("--drop", type=float, default=0.0, help="")
parser.add_argument("--cuda", action="store_true", help="")
parser.add_argument("--seed", type=int, default=1111, help="")
parser.add_argument("--multi_gpu", action="store_true", help="")
args = parser.parse_args()
device = torch.device("cuda" if args.cuda else "cpu")
B = 4
len, mem_len, ext_len = 36, 36, 0
data_len = len * 20
args.s_vocab = 10000
import data_utils
data = torch.LongTensor(data_len * B).random_(0, args.s_vocab).to(device)
diter = data_utils.LMOrderedIterator(data, B, len, device=device, ext_len=ext_len)
cutoffs = [args.s_vocab // 2]
tie_projs = [False] + [True] * len(cutoffs)
for div_val in [1, 2]:
for d_embed in [200, 100]:
model = MemTransformerLM(
tie_weight=True,
d_embed=d_embed,
div_val=div_val,
tie_projs=tie_projs,
pre_norm=True,
tgt_len=len,
ext_len=ext_len,
mem_len=mem_len,
cutoffs=cutoffs,
attn_type=0,
).to(device)
print(sum(p.numel() for p in model.parameters()))
mems = tuple()
for idx, (inp, tgt, seqlen) in enumerate(diter):
print("batch {}".format(idx))
out = model(inp, tgt, *mems)
mems = out[1:]
|
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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,654
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/base/doc/justifier.py
|
# Copyright 2019 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
class Justifier:
def __init__(self, **kw):
super().__init__(**kw)
self.justs = [0] * 9
self.offsets = [(0, 0, 0, 1, 1, 1, 1, 1, 1),
(0, -1, -2, 0, 0, 0, 1, 1, 1),
(0, -1, -2, 0, -1, -2, 0, 0, 0)]
def init_justs(self, justs):
for i in justs:
i = i // 3
os = self.offsets[i]
if os:
self.justs = [sum(x) for x in zip(self.justs, os)]
self.offsets[i] = None
def calc_just(self, justs):
for i in justs:
i = self.justs[i] + (i % 3)
if i == 1:
return 'justify-content-center'
elif i > 1:
return 'justify-content-end'
return 'justify-content-start'
|
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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,655
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/base/doc/util/sphinx.py
|
# Copyright 2019 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
from docutils import statemachine
from docutils.parsers.rst import Directive, directives
from .log import Logger
from .dispatch import Dispatch
log = Logger(__name__)
class Excerpt(Directive):
required_arguments = 1
optional_arguments = 0
final_argument_whitespace = True
option_spec = {
'inline': directives.flag,
'start-line',
'end-line',
'start-after': directives.unchanged_required,
'end-before': directives.unchanged_required
}
_dispatch = None
@property
def dispatch(self):
if self._dispatch is None:
type(self)._dispatch = Dispatch.create()
return self._dispatch
def run(self):
sm = self.state_machine
s = sm.input_lines.source(self.lineno - sm.input_offset - 1)
p = self.arguments[0]
self.state.document.settings.record_dependencies.add(p)
kw = dict(
start_line=self.options.get('start-line', None),
end_line=self.options.get('end-line', None),
start_after=self.options.get('start-after', None),
end_before=self.options.get('end-before', None))
t = self.dispatch.excerpt(s, p, **kw)
ls = statemachine.string2lines(t, convert_whitespace=True)
sm.insert_input(ls, p)
return []
def setup(app):
app.add_directive('excerpt', Excerpt)
return {'version': '0.1'}
|
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"/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], 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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,656
|
quantapix/qnarre
|
refs/heads/main
|
/tools/triton/python/triton/ops/matmul.py
|
import torch
import triton
import triton.language as tl
from .matmul_perf_model import early_config_prune, estimate_matmul_time
def init_to_zero(name):
return lambda nargs: nargs[name].zero_()
def get_configs_io_bound():
configs = []
for num_stages in [2, 3, 4, 5, 6]:
for block_m in [16, 32]:
for block_k in [32, 64]:
for block_n in [32, 64, 128, 256]:
num_warps = 2 if block_n <= 64 else 4
configs.append(
triton.Config({'BLOCK_M': block_m, 'BLOCK_N': block_n, 'BLOCK_K': block_k, 'SPLIT_K': 1},
num_stages=num_stages, num_warps=num_warps))
# split_k
for split_k in [2, 4, 8, 16]:
configs.append(triton.Config({'BLOCK_M': block_m, 'BLOCK_N': block_n, 'BLOCK_K': block_k, 'SPLIT_K': split_k},
num_stages=num_stages, num_warps=num_warps, pre_hook=init_to_zero('C')))
return configs
@triton.autotune(
configs=[
# basic configs for compute-bound matmuls
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 256, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=3, num_warps=8),
triton.Config({'BLOCK_M': 256, 'BLOCK_N': 128, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=3, num_warps=8),
triton.Config({'BLOCK_M': 256, 'BLOCK_N': 64, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 256, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 64, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 128, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 32, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 32, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=5, num_warps=2),
# good for int8
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 256, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=3, num_warps=8),
triton.Config({'BLOCK_M': 256, 'BLOCK_N': 128, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=3, num_warps=8),
triton.Config({'BLOCK_M': 256, 'BLOCK_N': 64, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 256, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 64, 'BLOCK_K': 64, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 128, 'BLOCK_K': 64, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 32, 'BLOCK_K': 64, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 32, 'BLOCK_K': 64, 'SPLIT_K': 1}, num_stages=5, num_warps=2),
] + get_configs_io_bound(),
key=['M', 'N', 'K'],
prune_configs_by={
'early_config_prune': early_config_prune,
'perf_model': estimate_matmul_time,
'top_k': 10
},
)
@triton.heuristics({
'EVEN_K': lambda args: args['K'] % (args['BLOCK_K'] * args['SPLIT_K']) == 0,
})
@triton.jit
def _kernel(A, B, C, M, N, K,
stride_am, stride_ak,
stride_bk, stride_bn,
stride_cm, stride_cn,
dot_out_dtype: tl.constexpr,
BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
GROUP_M: tl.constexpr, SPLIT_K: tl.constexpr, EVEN_K: tl.constexpr,
):
# matrix multiplication
pid = tl.program_id(0)
pid_z = tl.program_id(1)
grid_m = tl.cdiv(M, BLOCK_M)
grid_n = tl.cdiv(N, BLOCK_N)
# re-order program ID for better L2 performance
width = GROUP_M * grid_n
group_id = pid // width
group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
pid_m = group_id * GROUP_M + (pid % group_size)
pid_n = (pid % width) // (group_size)
# do matrix multiplication
rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
ram = tl.max_contiguous(tl.multiple_of(rm % M, BLOCK_M), BLOCK_M)
rbn = tl.max_contiguous(tl.multiple_of(rn % N, BLOCK_N), BLOCK_N)
rk = pid_z * BLOCK_K + tl.arange(0, BLOCK_K)
# pointers
A = A + (ram[:, None] * stride_am + rk[None, :] * stride_ak)
B = B + (rk[:, None] * stride_bk + rbn[None, :] * stride_bn)
acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=dot_out_dtype)
for k in range(0, tl.cdiv(K, BLOCK_K * SPLIT_K)):
if EVEN_K:
a = tl.load(A)
b = tl.load(B)
else:
k_remaining = K - k * (BLOCK_K * SPLIT_K)
a = tl.load(A, mask=rk[None, :] < k_remaining, other=0.)
b = tl.load(B, mask=rk[:, None] < k_remaining, other=0.)
acc += tl.dot(a, b, out_dtype=dot_out_dtype)
A += BLOCK_K * SPLIT_K * stride_ak
B += BLOCK_K * SPLIT_K * stride_bk
acc = acc.to(C.dtype.element_ty)
# rematerialize rm and rn to save registers
rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
C = C + (rm[:, None] * stride_cm + rn[None, :] * stride_cn)
mask = (rm < M)[:, None] & (rn < N)[None, :]
# handles write-back with reduction-splitting
if SPLIT_K == 1:
tl.store(C, acc, mask=mask)
else:
tl.atomic_add(C, acc, mask=mask)
class _matmul(torch.autograd.Function):
kernel = _kernel
_locks = {}
@staticmethod
def _call(a, b, dot_out_dtype):
device = a.device
# handle non-contiguous inputs if necessary
if a.stride(0) > 1 and a.stride(1) > 1:
a = a.contiguous()
if b.stride(0) > 1 and b.stride(1) > 1:
b = b.contiguous()
# checks constraints
assert a.shape[1] == b.shape[0], "incompatible dimensions"
M, K = a.shape
_, N = b.shape
# allocates output
c = torch.empty((M, N), device=device, dtype=a.dtype)
if dot_out_dtype is None:
if a.dtype in [torch.float16, torch.float32, torch.bfloat16]:
dot_out_dtype = tl.float32
else:
dot_out_dtype = tl.int32
else:
assert isinstance(dot_out_dtype, torch.dtype), "dot_out_dtype must be a torch.dtype"
if dot_out_dtype == torch.float16:
dot_out_dtype = tl.float16
elif dot_out_dtype in [torch.float32, torch.bfloat16]:
dot_out_dtype = tl.float32
else:
dot_out_dtype = tl.int32
# launch kernel
grid = lambda META: (triton.cdiv(M, META['BLOCK_M']) * triton.cdiv(N, META['BLOCK_N']), META['SPLIT_K'])
_kernel[grid](a, b, c, M, N, K,
a.stride(0), a.stride(1),
b.stride(0), b.stride(1),
c.stride(0), c.stride(1),
dot_out_dtype=dot_out_dtype,
GROUP_M=8)
return c
@staticmethod
def forward(ctx, a, b, dot_out_dtype=None):
return _matmul._call(a, b, dot_out_dtype=dot_out_dtype)
matmul = _matmul.apply
|
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"/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,657
|
quantapix/qnarre
|
refs/heads/main
|
/tools/triton/python/triton/debugger/tl_lang.py
|
import triton
from .core import ExecutionContext
from .memory_map import MemoryMap
from triton.debugger import torch_wrapper
torch = torch_wrapper.torch
def _primitive_to_tensor(x):
"""
Converts various Python primitive data types to PyTorch tensor.
"""
tensor_args = {"device": "cuda"}
if isinstance(x, bool):
return torch.tensor([x], dtype=torch.bool, **tensor_args)
elif isinstance(x, int):
if -(2**31) <= x < 2**31:
return torch.tensor([x], dtype=torch.int32, **tensor_args)
elif -(2**63) <= x < 2**63:
return torch.tensor([x], dtype=torch.int64, **tensor_args)
else:
raise RuntimeError(f"Nonrepresentable integer {x}.")
elif isinstance(x, float):
return torch.tensor([x], dtype=torch.float32, **tensor_args)
elif torch.is_tensor(x):
return x
elif isinstance(x, WrappedTensor):
return x
elif isinstance(x, debugger_constexpr):
if x.value is None:
return None
return _primitive_to_tensor(x.value)
elif x is None:
return None
assert False, f"cannot convert {x} of type {type(x)} to tensor"
def _infer_tensor(func):
"""
A decorator function to harmonize function args:
- converts primitives to PyTorch tensors
- wraps PyTorch tensors with WrappedTensors
"""
def wrapper(*args):
new_args = tuple(map(lambda v: _primitive_to_tensor(v), args))
new_args = tuple(map(lambda v: WrappedTensor(v) if torch.is_tensor(v) else v, new_args))
return func(*new_args)
return wrapper
def _tensor_operation(func):
"""
A decorator function to unwrap WrappedTensors and debugger_constexpr before calling the function.
Can be combined with _infer_tensor decorator to harmonize args (everything to torch tensor).
"""
def wrapper(*args, **kwargs):
for arg in args:
assert not torch.is_tensor(arg), "unexpected tensor argument"
def unwrap_tensor(v):
if isinstance(v, WrappedTensor):
return v.tensor
if isinstance(v, debugger_constexpr):
return v.value
return v
new_args = tuple(map(unwrap_tensor, args))
new_kwargs = {k: unwrap_tensor(v) for k, v in kwargs.items()}
result = func(args[0], *new_args[1:], **new_kwargs)
return WrappedTensor(result) if torch.is_tensor(result) else result
return wrapper
class debugger_constexpr:
def __init__(self, value):
if isinstance(value, debugger_constexpr):
self.value = value.value
else:
self.value = value
def __str__(self) -> str:
return "debugger_constexpr(" + str(self.value) + ")"
def __index__(self) -> int:
return self.value
def __bool__(self):
return bool(self.value)
def __ge__(self, other):
other = other.value if isinstance(other, debugger_constexpr) else other
return self.value >= other
def __gt__(self, other):
other = other.value if isinstance(other, debugger_constexpr) else other
return self.value > other
def __le__(self, other):
other = other.value if isinstance(other, debugger_constexpr) else other
return self.value <= other
def __lt__(self, other):
other = other.value if isinstance(other, debugger_constexpr) else other
return self.value < other
def __eq__(self, other):
other = other.value if isinstance(other, debugger_constexpr) else other
return self.value == other
def __or__(self, other):
other = other.value if isinstance(other, debugger_constexpr) else other
return self.value | other
def __ror__(self, other):
other = other.value if isinstance(other, debugger_constexpr) else other
return self.value | other
def __and__(self, other):
other = other.value if isinstance(other, debugger_constexpr) else other
return self.value & other
def __rand__(self, other):
other = other.value if isinstance(other, debugger_constexpr) else other
return self.value & other
def to(self, dtype, bitcast=False, _builder=None):
if dtype in [torch.int64]:
ret_ty = int
elif dtype == torch.bool:
ret_ty = bool
elif dtype in [torch.float64]:
ret_ty = float
else:
raise ValueError("dtype not supported in debugger")
return debugger_constexpr(ret_ty(self.value))
class WrappedTensor:
def __init__(self, tensor):
self.tensor = tensor
def __index__(self) -> int:
return self.tensor.item()
def __str__(self) -> str:
return "wrapped_" + str(self.tensor)
def __bool__(self) -> bool:
return torch.all(self.tensor == True).item() # noqa: E712
@property
def dtype(self):
return self.tensor.dtype
@_infer_tensor
@_tensor_operation
def __add__(self, other):
return torch.add(self.tensor, other)
@_infer_tensor
@_tensor_operation
def __radd__(self, other):
return self.__add__(other)
@_infer_tensor
@_tensor_operation
def __sub__(self, other):
return torch.sub(self.tensor, other)
@_infer_tensor
@_tensor_operation
def __rsub__(self, other):
return torch.sub(other, self.tensor)
@_infer_tensor
@_tensor_operation
def __mul__(self, other):
return torch.mul(self.tensor, other)
@_infer_tensor
@_tensor_operation
def __rmul__(self, other):
return self.__mul__(other)
@_infer_tensor
@_tensor_operation
def __truediv__(self, other):
return torch.div(self.tensor, other)
@_infer_tensor
@_tensor_operation
def __rtruediv__(self, other):
return torch.div(other, self.tensor)
@_infer_tensor
@_tensor_operation
def __floordiv__(self, other):
return torch.floor_divide(self.tensor, other)
@_infer_tensor
@_tensor_operation
def __rfloordiv__(self, other):
return torch.floor_divide(other, self.tensor)
@_infer_tensor
@_tensor_operation
def __mod__(self, other):
return torch.remainder(self.tensor, other)
@_infer_tensor
@_tensor_operation
def __rmod__(self, other):
return torch.remainder(other, self.tensor)
@_infer_tensor
@_tensor_operation
def __neg__(self):
return -self.tensor
@_infer_tensor
@_tensor_operation
def __invert__(self):
return ~self.tensor
@_infer_tensor
@_tensor_operation
def __and__(self, other):
return torch.bitwise_and(self.tensor, other)
@_infer_tensor
@_tensor_operation
def __or__(self, other):
return torch.bitwise_or(self.tensor, other)
@_infer_tensor
@_tensor_operation
def __xor__(self, other):
return torch.bitwise_xor(self.tensor, other)
@_infer_tensor
@_tensor_operation
def __lshift__(self, other):
return torch.bitwise_left_shift(self.tensor, other)
@_infer_tensor
@_tensor_operation
def __rshift__(self, other):
return torch.bitwise_right_shift(self.tensor, other)
@_infer_tensor
@_tensor_operation
def __gt__(self, other):
return self.tensor > other
@_infer_tensor
@_tensor_operation
def __rgt__(self, other):
return other > self.tensor
@_infer_tensor
@_tensor_operation
def __ge__(self, other):
return self.tensor >= other
@_infer_tensor
@_tensor_operation
def __rge__(self, other):
return other >= self.tensor
@_infer_tensor
@_tensor_operation
def __lt__(self, other):
return self.tensor < other
@_infer_tensor
@_tensor_operation
def __rlt__(self, other):
return other < self.tensor
@_infer_tensor
@_tensor_operation
def __le__(self, other):
return self.tensor <= other
@_infer_tensor
@_tensor_operation
def __rle__(self, other):
return other <= self.tensor
@_infer_tensor
@_tensor_operation
def __eq__(self, other):
return torch.equal(self.tensor, other)
@_infer_tensor
@_tensor_operation
def __ne__(self, other):
return not torch.equal(self.tensor, other)
@_tensor_operation
def __getitem__(self, slices):
return self.tensor.__getitem__(slices)
# if isinstance(slices, slice):
# slices = [slices]
# src_shape = self.shape
# dst_shape = []
# curr = 0
# for sl in slices:
# if isinstance(sl, constexpr) and sl.value is None:
# dst_shape.append(1)
# elif sl == slice(None, None, None):
# dst_shape.append(src_shape[curr].value)
# curr += 1
# ret = torch.reshape(self.tensor, dst_shape, )
# return ret
@_tensor_operation
def to(self, dtype, bitcast=False):
return self.tensor.to(dtype)
# if isinstance(bitcast, constexpr):
# bitcast = bitcast.value
# if bitcast:
# return semantic.bitcast(self, dtype, )
# return semantic.cast(self, dtype, )
def _constexpr_to_value(v):
if isinstance(v, debugger_constexpr):
return v.value
return v
class TritonLangProxy:
_memory_map: MemoryMap
_context: ExecutionContext
def __init__(self, memory_map: MemoryMap, context: ExecutionContext):
self._memory_map = memory_map
self._context = context
# Types
# Removed void, int1, float8, uint16, uint32, uint64, pi32_t
# constexpr = debugger_constexpr
# Program functions
@_tensor_operation
def load(
self,
pointer: torch.Tensor,
mask: torch.Tensor = None,
other=0.0,
cache_modifier="",
eviction_policy="",
volatile=False,
):
return self._memory_map.load(pointer, mask, other)
@_tensor_operation
def store(self, pointer: torch.Tensor, value: torch.Tensor, mask=None):
return self._memory_map.store(pointer, value, mask)
@_tensor_operation
def program_id(self, axis):
assert axis < len(self._context.program_id)
return torch.tensor([self._context.program_id[axis]], dtype=torch.int32, device="cuda")
@_tensor_operation
def num_programs(self, axis):
assert axis < len(self._context.program_size)
return torch.tensor([self._context.program_size[axis]], dtype=torch.int32, device="cuda")
@_tensor_operation
def arange(self, start, end):
return torch.arange(start=start, end=end, dtype=torch.int32, device="cuda")
@_tensor_operation
def zeros(self, shape, dtype):
for i, d in enumerate(shape):
if not isinstance(d, debugger_constexpr):
raise TypeError(f"Shape element {i} must have type `constexpr`")
if not isinstance(d.value, int):
raise TypeError(f"Shape element {i} must have type `constexpr[int]`, got `constexpr[{type(d.value)}]")
shape = [x.value for x in shape]
if isinstance(dtype, triton.language.core.dtype):
if dtype.is_fp32():
dtype = torch.float32
elif dtype.is_fp16():
dtype = torch.float16
elif dtype.is_bf16():
dtype = torch.bfloat16
elif dtype.is_int32():
dtype = torch.int32
elif dtype.is_int16():
dtype = torch.int16
elif dtype.is_int8():
dtype = torch.int8
else:
raise TypeError(f"Unsupported dtype {dtype}")
return torch.zeros(size=shape, dtype=dtype, device="cuda")
@_tensor_operation
def dequantize(self, input, scale, shift, nbit, dst_ty=torch.float16):
raise NotImplementedError()
@_tensor_operation
def broadcast(self, input, other):
raise NotImplementedError()
@_tensor_operation
def broadcast_to(self, input, shape):
raise NotImplementedError()
@_tensor_operation
def cat(self, input, shape):
raise NotImplementedError()
@_tensor_operation
def reshape(self, input, shape):
raise NotImplementedError()
@_tensor_operation
def dot(self, input, other, trans_a=False, trans_b=False, allow_tf32=True):
assert input.dtype == other.dtype
if trans_a:
input = input.T
if trans_b:
other = other.T
return torch.matmul(input=input, other=other)
@_tensor_operation
def atomic_cas(self, pointer, cmp, val):
stored = self._memory_map.load(pointer, None, 0.0)
if not isinstance(cmp, torch.Tensor):
cmp = torch.tensor([cmp], dtype=stored.dtype, device="cuda")
if not isinstance(val, torch.Tensor):
val = torch.tensor([val], dtype=stored.dtype, device="cuda")
if stored == cmp:
self._memory_map.store(pointer, val, None)
return stored
@_tensor_operation
def atomic_xchg(self, pointer, val, mask=None):
if isinstance(val, int):
val = torch.tensor([val], dtype=torch.int32, device="cuda")
stored = self._memory_map.load(pointer, mask, 0.0)
self._memory_map.store(pointer, val, mask)
return stored
@_tensor_operation
def atomic_add(self, pointer, val, mask=None):
# arbitrary other value as it will masked during storing
stored = self._memory_map.load(pointer, mask, 0.0)
result = stored + val
self._memory_map.store(pointer, result, mask)
return stored
@_tensor_operation
def atomic_max(self, pointer, val, mask=None):
stored = self._memory_map.load(pointer, mask, 0.0)
result = torch.maximum(stored, val)
self._memory_map.store(pointer, result, mask)
return stored
@_tensor_operation
def atomic_min(self, pointer, val, mask=None):
stored = self._memory_map.load(pointer, mask, 0.0)
result = torch.minimum(stored, val)
self._memory_map.store(pointer, result, mask)
return stored
@_tensor_operation
def atomic_and(self, pointer, val, mask=None):
stored = self._memory_map.load(pointer, mask, 0)
result = torch.bitwise_and(stored, val)
self._memory_map.store(pointer, result, mask)
return stored
@_tensor_operation
def atomic_or(self, pointer, val, mask=None):
stored = self._memory_map.load(pointer, mask, 0)
result = torch.bitwise_or(stored, val)
self._memory_map.store(pointer, result, mask)
return stored
@_tensor_operation
def atomic_xor(self, pointer, val, mask=None):
stored = self._memory_map.load(pointer, mask, 0)
result = torch.bitwise_xor(stored, val)
self._memory_map.store(pointer, result, mask)
return stored
@_tensor_operation
def where(self, condition, x, y):
condition = _primitive_to_tensor(condition)
x = _primitive_to_tensor(x)
y = _primitive_to_tensor(y)
return torch.where(condition, x, y)
@_tensor_operation
def umulhi(self, x, y):
raise NotImplementedError()
@_tensor_operation
def fdiv(self, x, y, ieee_rounding=False):
raise NotImplementedError()
@_tensor_operation
def exp(self, x):
return torch.exp(x)
@_tensor_operation
def log(self, x):
return torch.log(x)
@_tensor_operation
def cos(self, x):
return torch.cos(x)
@_tensor_operation
def sin(self, x):
return torch.sin(x)
@_tensor_operation
def sqrt(self, x):
return torch.sqrt(x)
@_tensor_operation
def globaltimer(self):
raise NotImplementedError()
@_tensor_operation
def clock(self):
raise NotImplementedError()
@_tensor_operation
def debug_barrier(self):
raise NotImplementedError()
@_tensor_operation
def multiple_of(self, input, values):
return input
@_tensor_operation
def max_contiguous(self, input, values):
return input
@_tensor_operation
def abs(self, x):
return torch.abs(x)
@_tensor_operation
def cdiv(self, x, div):
return (x + div - 1) // div
@_tensor_operation
def minimum(self, x, y):
if isinstance(x, int):
x = torch.tensor(x, device="cuda")
if isinstance(y, int):
y = torch.tensor(y, device="cuda")
return torch.minimum(x, y)
@_tensor_operation
def maximum(self, x, y):
return torch.maximum(x, y)
@_tensor_operation
def sigmoid(self, x):
raise NotImplementedError()
@_tensor_operation
def softmax(self, x, ieee_rounding=False):
raise NotImplementedError()
@_tensor_operation
def ravel(self, x):
raise NotImplementedError()
@_tensor_operation
def swizzle2d(self, i, j, size_i, size_j, size_g):
raise NotImplementedError()
@_tensor_operation
def zeros_like(self, input):
raise NotImplementedError()
@_tensor_operation
def max(self, input, axis=None):
if axis is None:
return torch.max(input)
return torch.max(input, dim=axis).values
@_tensor_operation
def argmax(self, input, axis):
raise NotImplementedError()
@_tensor_operation
def min(self, input, axis=None):
if axis is None:
return torch.min(input)
return torch.min(input, dim=axis).values
@_tensor_operation
def argmin(self, input, axis):
raise NotImplementedError()
@_tensor_operation
def sum(self, input, axis=None):
if axis is None:
return torch.sum(input)
return torch.sum(input, dim=axis)
@_tensor_operation
def xor_sum(self, input, axis):
raise NotImplementedError()
|
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["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,658
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/tokens/marian.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# 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.
# =============================================================================
import json
import os
import re
import warnings
import sentencepiece
from contextlib import contextmanager
from pathlib import Path
from shutil import copyfile
from ...tokens.utils import PreTrainedTokenizer
VOCAB_FS = {
"source_spm": "source.spm",
"target_spm": "target.spm",
"vocab": "vocab.json",
"tokenizer_config_file": "tokenizer_config.json",
}
VOCAB_MAP = {
"source_spm": {
"Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/source.spm"
},
"target_spm": {
"Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/target.spm"
},
"vocab": {
"Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json"
},
"tokenizer_config_file": {
"Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/tokenizer_config.json"
},
}
INPUT_CAPS = {"Helsinki-NLP/opus-mt-en-de": 512}
PRETRAINED_INIT_CONFIGURATION = {}
class Tokenizer(PreTrainedTokenizer):
vocab_fs = VOCAB_FS
vocab_map = VOCAB_MAP
pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
input_caps = INPUT_CAPS
model_input_names = ["input_ids", "mask"]
language_code_re = re.compile(">>.+<<") # type: re.Pattern
def __init__(
self,
vocab,
source_spm,
target_spm,
source_lang=None,
target_lang=None,
unk="<unk>",
eos="</s>",
pad="<pad>",
model_max_length=512,
sp_model_kw=None,
**kw,
):
self.sp_model_kw = {} if sp_model_kw is None else sp_model_kw
super().__init__(
source_lang=source_lang,
target_lang=target_lang,
unk=unk,
eos=eos,
pad=pad,
model_max_length=model_max_length,
sp_model_kw=self.sp_model_kw,
**kw,
)
assert Path(source_spm).exists(), f"cannot find spm source {source_spm}"
self.encoder = load_json(vocab)
if self.unk not in self.encoder:
raise KeyError("<unk> token must be in vocab")
assert self.pad in self.encoder
self.decoder = {v: k for k, v in self.encoder.items()}
self.source_lang = source_lang
self.target_lang = target_lang
self.supported_language_codes: list = [
k for k in self.encoder if k.startswith(">>") and k.endswith("<<")
]
self.spm_files = [source_spm, target_spm]
self.spm_source = load_spm(source_spm, self.sp_model_kw)
self.spm_target = load_spm(target_spm, self.sp_model_kw)
self.current_spm = self.spm_source
self._setup_normalizer()
def _setup_normalizer(self):
try:
from sacremoses import MosesPunctNormalizer
self.punc_normalizer = MosesPunctNormalizer(self.source_lang).normalize
except (ImportError, FileNotFoundError):
warnings.warn("Recommended: pip install sacremoses.")
self.punc_normalizer = lambda x: x
def normalize(self, x):
return self.punc_normalizer(x) if x else ""
def _convert_token_to_id(self, token):
return self.encoder.get(token, self.encoder[self.unk])
def remove_language_code(self, text):
match = self.language_code_re.match(text)
code: list = [match.group(0)] if match else []
return code, self.language_code_re.sub("", text)
def _tokenize(self, text):
code, text = self.remove_language_code(text)
pieces = self.current_spm.encode(text, out_type=str)
return code + pieces
def _convert_id_to_token(self, index):
return self.decoder.get(index, self.unk)
def batch_decode(self, sequences, **kw):
return super().batch_decode(sequences, **kw)
def decode(self, token_ids, **kw):
return super().decode(token_ids, **kw)
def convert_tokens_to_string(self, tokens):
if self._decode_use_source_tokenizer:
return self.spm_source.DecodePieces(tokens)
else:
return self.spm_target.DecodePieces(tokens)
def build_inputs_with_special_tokens(self, toks_0, toks_1=None):
if toks_1 is None:
return toks_0 + [self.EOS]
return toks_0 + toks_1 + [self.EOS]
@contextmanager
def as_target_tokenizer(self):
self.current_spm = self.spm_target
yield
self.current_spm = self.spm_source
@property
def s_vocab(self):
return len(self.encoder)
def save_vocabulary(self, dir, pre=None):
saved_files = []
path = os.path.join(
dir,
(pre + "-" if pre else "") + VOCAB_FS["vocab"],
)
save_json(self.encoder, path)
saved_files.append(path)
for spm_save_filename, spm_orig_path, spm_model in zip(
[VOCAB_FS["source_spm"], VOCAB_FS["target_spm"]],
self.spm_files,
[self.spm_source, self.spm_target],
):
spm_save_path = os.path.join(
dir,
(pre + "-" if pre else "") + spm_save_filename,
)
if os.path.abspath(spm_orig_path) != os.path.abspath(spm_save_path) and os.path.isfile(
spm_orig_path
):
copyfile(spm_orig_path, spm_save_path)
saved_files.append(spm_save_path)
elif not os.path.isfile(spm_orig_path):
with open(spm_save_path, "wb") as fi:
content_spiece_model = spm_model.serialized_model_proto()
fi.write(content_spiece_model)
saved_files.append(spm_save_path)
return tuple(saved_files)
def get_vocab(self):
vocab = self.encoder.copy()
vocab.update(self.added_tokens_encoder)
return vocab
def __getstate__(self):
state = self.__dict__.copy()
state.update(
{k: None for k in ["spm_source", "spm_target", "current_spm", "punc_normalizer"]}
)
return state
def __setstate__(self, d):
self.__dict__ = d
if not hasattr(self, "sp_model_kw"):
self.sp_model_kw = {}
self.spm_source, self.spm_target = (load_spm(f, self.sp_model_kw) for f in self.spm_files)
self.current_spm = self.spm_source
self._setup_normalizer()
def num_special_tokens_to_add(self, *args, **kw):
return 1
def _special_token_mask(self, seq):
all_special_ids = set(self.all_special_ids)
all_special_ids.remove(self.unk_token_id)
return [1 if x in all_special_ids else 0 for x in seq]
def get_special_tokens_mask(
self,
toks_0,
toks_1=None,
has_specials=False,
):
if has_specials:
return self._special_token_mask(toks_0)
elif toks_1 is None:
return self._special_token_mask(toks_0) + [1]
else:
return self._special_token_mask(toks_0 + toks_1) + [1]
def load_spm(path, sp_model_kw):
spm = sentencepiece.SentencePieceProcessor(**sp_model_kw)
spm.Load(path)
return spm
def save_json(data, path):
with open(path, "w") as f:
json.dump(data, f, indent=2)
def load_json(path):
with open(path, "r") as f:
return json.load(f)
|
{"/qnarre/prep/convert/xlnet.py": ["/qnarre/prep/config/xlnet.py", "/qnarre/models/xlnet.py"], "/qnarre/prep/convert/bert.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/base/doc/patcher.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/nominals.py"], "/qnarre/prep/convert/funnel.py": ["/qnarre/prep/config/funnel.py", "/qnarre/models/funnel.py"], "/qnarre/prep/tokens/fast/realm.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py", "/qnarre/tokens/utils.py"], "/qnarre/models/decision_transfo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/decision_transfo.py"], "/qnarre/models/fsmt.py": ["/qnarre/core/embed.py"], "/qnarre/models/roformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/xlnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc.py": ["/qnarre/base/author.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], "/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/dispatch.py": ["/qnarre/base/doc/blog.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/context.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/analyzer.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/images.py"], "/qnarre/models/big_bird.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/big_bird.py"], "/tools/triton/python/triton/runtime/jit.py": ["/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/fast/roberta.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/bigbird.py": ["/qnarre/prep/config/big_bird.py", "/qnarre/models/big_bird.py"], "/qnarre/models/gpt_neo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/gpt_neo.py"], "/qnarre/prep/convert/t5.py": ["/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/prep/tokens/fsmt.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/deberta.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/fast/splinter.py": ["/qnarre/tokens/fast.py"], "/qnarre/models/old/trafo.py": ["/qnarre/core/attention.py", "/qnarre/core/base.py", "/qnarre/core/mlp.py", "/qnarre/core/deduce.py", "/qnarre/core/norm.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/led.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/realm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/convbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/data2vec.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/tools/triton/python/triton/language/math.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/run/beam.py": ["/qnarre/run/qa.py"], "/tools/triton/python/triton/compiler/compiler.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/autotuner.py", 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