index
int64 | repo_name
string | branch_name
string | path
string | content
string | import_graph
string |
|---|---|---|---|---|---|
33,459
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/convert/bigbird.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 numpy as np
import re
import tensorflow as tf
import torch
from os.path import abspath
from argparse import ArgumentParser
from transformers.utils import logging
from ..config.big_bird import PreTrained
from ...models.big_bird import ForPreTraining, ForQA
logging.set_verbosity_info()
log = logging.get_logger(__name__)
_SKIP = [
"adam_v",
"adam_m",
"AdamWeightDecayOptimizer",
"AdamWeightDecayOptimizer_1",
"global_step",
]
def load_src_weights(model, src_path, is_trivia=False):
src_path = abspath(src_path)
log.info(f"Loading from: {src_path}")
xs = tf.saved_model.load(src_path).variables if is_trivia else tf.train.list_variables(src_path)
assert len(xs) > 0
pt_names = list(model.state_dict().keys())
if is_trivia:
ns, ws = _load_trivia(xs)
else:
ns, ws = _load_weights(xs, src_path)
for n in ns:
xs = n.split("/")
if any(x in _SKIP for x in xs):
log.info(f"Skipping {'/'.join(xs)}")
continue
ts = []
p = model
for x in xs:
if re.fullmatch(r"[A-Za-z]+_\d+", x):
scopes = re.split(r"_(\d+)", x)
else:
scopes = [x]
if scopes[0] == "kernel" or scopes[0] == "gamma":
p = getattr(p, "weight")
ts.append("weight")
elif scopes[0] == "output_bias" or scopes[0] == "beta":
p = getattr(p, "bias")
ts.append("bias")
elif scopes[0] == "output_weights":
p = getattr(p, "weight")
ts.append("weight")
elif scopes[0] == "squad":
p = getattr(p, "classifier")
ts.append("classifier")
elif scopes[0] == "transform":
p = getattr(p, "transform")
ts.append("transform")
if ("bias" in xs) or ("kernel" in xs):
p = getattr(p, "dense")
ts.append("dense")
elif ("beta" in xs) or ("gamma" in xs):
p = getattr(p, "LayerNorm")
ts.append("LayerNorm")
else:
try:
p = getattr(p, scopes[0])
ts.append(f"{scopes[0]}")
except AttributeError:
log.info(f"Skipping {x}")
continue
if len(scopes) >= 2:
i = int(scopes[1])
p = p[i]
ts.append(f"{i}")
w = ws[n]
if x[-11:] == "_embeddings" or x == "embeddings":
p = getattr(p, "weight")
ts.append("weight")
elif x == "kernel":
w = np.transpose(w)
if len(w.shape) > len(p.shape) and math.prod(w.shape) == math.prod(p.shape):
if (
n.endswith("attention/self/key/kernel")
or n.endswith("attention/self/query/kernel")
or n.endswith("attention/self/value/kernel")
):
w = w.transpose(1, 0, 2).reshape(p.shape)
elif n.endswith("attention/output/dense/kernel"):
w = w.transpose(0, 2, 1).reshape(p.shape)
else:
w = w.reshape(p.shape)
assert p.shape == w.shape
t = ".".join(ts)
log.info(f"Initialize {t} from {n}")
p.data = torch.from_numpy(w)
ws.pop(n, None)
pt_names.remove(t)
log.info(f"Not copied: {', '.join(ws.keys())}.")
log.info(f"Not initialized: {', '.join(pt_names)}.")
return model
def _load_weights(xs, src_path):
ns = []
ws = {}
for n, shape in xs:
n = n.replace("bert/encoder/LayerNorm", "bert/embeddings/LayerNorm")
log.info(f"Loading TF weight {n} with shape {shape}")
ns.append(n)
ws[n] = tf.train.load_variable(src_path, n)
return ns, ws
_MAP = {
"big_bird_attention": "attention/self",
"output_layer_norm": "output/LayerNorm",
"attention_output": "attention/output/dense",
"output": "output/dense",
"self_attention_layer_norm": "attention/output/LayerNorm",
"intermediate": "intermediate/dense",
"tok_embed": "bert/embeddings/tok_embed",
"pos_embed": "bert/embeddings/pos_embed",
"type_embeddings": "bert/embeddings/token_type_embeddings",
"embeddings": "bert/embeddings",
"layer_normalization": "output/LayerNorm",
"layer_norm": "LayerNorm",
"trivia_qa_head": "qa_classifier",
"dense": "intermediate/dense",
"dense_1": "qa_outputs",
}
def _load_trivia(xs):
ns = []
ws = {}
for i, x in enumerate(xs):
ks = x.name.split("/")
if "transformer_scaffold" in ks[0]:
ls = ks[0].split("_")
if len(ls) < 3:
ls += [0]
ks[0] = f"bert/encoder/layer_{ls[2]}"
n = "/".join([_MAP[k] if k in _MAP else k for k in ks])[:-2]
if "self/attention/output" in n:
n = n.replace("self/attention/output", "output")
if i >= len(xs) - 2:
n = n.replace("intermediate", "output")
log.info(f"Loading TF weight {n} with shape {x.shape}")
ns.append(n)
ws[n] = x.value().numpy()
return ns, ws
def to_pytorch(src_path, cfg_path, save_path, is_trivia):
cfg = PreTrained.from_json_file(cfg_path)
print(f"Building from config: {cfg}")
if is_trivia:
m = ForQA(cfg)
else:
m = ForPreTraining(cfg)
load_src_weights(m, src_path, is_trivia=is_trivia)
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)
x.add_argument("--is_trivia", action="store_true")
y = x.parse_args()
to_pytorch(y.src_path, y.cfg_path, y.save_path, y.is_trivia)
|
{"/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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"/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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"/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"]}
|
33,460
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/models/gpt_neo.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
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.gpt_neo import PreTrained
log = logging.get_logger(__name__)
LIST = [
"EleutherAI/gpt-neo-1.3B",
]
class SelfAttention(qc.Module):
def __init__(self, config, attention_type):
super().__init__()
max_positions = config.n_pos
bias = torch.tril(torch.ones((max_positions, max_positions), dtype=torch.bool)).view(
1, 1, max_positions, max_positions
)
if attention_type == "local":
bias = torch.bitwise_xor(bias, torch.tril(bias, -config.s_win))
self.register_buffer("bias", bias)
self.register_buffer("masked_bias", torch.tensor(-1e9))
self.attn_dropout = qc.Dropout(config.drop_attn)
self.drop_resid = qc.Dropout(config.drop_resid)
self.embed_dim = config.d_model
self.n_heads = config.n_heads
self.head_dim = self.embed_dim // self.n_heads
if self.head_dim * self.n_heads != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by n_heads (got `embed_dim`: {self.embed_dim} and `n_heads`: {self.n_heads})."
)
self.k_proj = qc.Linear(self.embed_dim, self.embed_dim, bias=False)
self.v_proj = qc.Linear(self.embed_dim, self.embed_dim, bias=False)
self.q_proj = qc.Linear(self.embed_dim, self.embed_dim, bias=False)
self.out_proj = qc.Linear(self.embed_dim, self.embed_dim, bias=True)
def _split_heads(self, tensor, n_heads, attn_head_size):
new_shape = tensor.size()[:-1] + (n_heads, attn_head_size)
tensor = tensor.view(new_shape)
return tensor.permute(0, 2, 1, 3)
def _merge_heads(self, tensor, n_heads, attn_head_size):
tensor = tensor.permute(0, 2, 1, 3).contiguous()
new_shape = tensor.size()[:-2] + (n_heads * attn_head_size,)
return tensor.view(new_shape)
def _attn(self, query, key, value, attention_mask=None, head_mask=None):
query = query.to(torch.float32)
key = key.to(torch.float32)
attn_weights = torch.matmul(query, key.transpose(-1, -2))
query_length, key_length = query.size(-2), key.size(-2)
causal_mask = self.bias[:, :, key_length - query_length : key_length, :key_length]
mask_value = torch.finfo(attn_weights.dtype).min
mask_value = torch.tensor(mask_value, dtype=attn_weights.dtype).to(attn_weights.device)
attn_weights = torch.where(causal_mask, attn_weights, mask_value)
if attention_mask is not None:
attn_weights = attn_weights + attention_mask
attn_weights = F.softmax(attn_weights, dim=-1)
attn_weights = attn_weights.to(value.dtype)
attn_weights = self.attn_dropout(attn_weights)
if head_mask is not None:
attn_weights = attn_weights * head_mask
attn_output = torch.matmul(attn_weights, value)
return attn_output, attn_weights
def forward(
self,
hiddens,
attention_mask=None,
layer_past=None,
head_mask=None,
y_cache=False,
output_attentions=False,
):
query = self.q_proj(hiddens)
key = self.k_proj(hiddens)
value = self.v_proj(hiddens)
query = self._split_heads(query, self.n_heads, self.head_dim)
key = self._split_heads(key, self.n_heads, self.head_dim)
value = self._split_heads(value, self.n_heads, self.head_dim)
if layer_past is not None:
past_key = layer_past[0]
past_value = layer_past[1]
key = torch.cat((past_key, key), dim=-2)
value = torch.cat((past_value, value), dim=-2)
if y_cache is True:
present = (key, value)
else:
present = None
attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask)
attn_output = self._merge_heads(attn_output, self.n_heads, self.head_dim)
attn_output = self.out_proj(attn_output)
attn_output = self.drop_resid(attn_output)
outputs = (attn_output, present)
if output_attentions:
outputs += (attn_weights,)
return outputs
class Attention(qc.Module):
def __init__(self, config, layer_id=0):
super().__init__()
self.layer_id = layer_id
self.attention_layers = config.attention_layers
self.attention_type = self.attention_layers[layer_id]
if self.attention_type in ["global", "local"]:
self.attention = SelfAttention(config, self.attention_type)
else:
raise NotImplementedError(
"Only attn layer types 'global' and 'local' exist, but got `config.attention_layers`: "
f"{config.attention_layers}. Select attn layer types from ['global', 'local'] only."
)
def forward(
self,
hiddens,
layer_past=None,
attention_mask=None,
head_mask=None,
y_cache=False,
output_attentions=False,
):
return self.attention(
hiddens,
attention_mask=attention_mask,
layer_past=layer_past,
head_mask=head_mask,
y_cache=y_cache,
output_attentions=output_attentions,
)
class MLP(qc.Module):
def __init__(self, d_ff, config):
super().__init__()
embed_dim = config.d_model
self.c_fc = qc.Linear(embed_dim, d_ff)
self.c_proj = qc.Linear(d_ff, embed_dim)
self.act = qu.activation(config.act)
self.drop = qc.Dropout(config.drop_resid)
def forward(self, x):
y = self.c_fc(x)
y = self.act(y)
y = self.c_proj(y)
y = self.drop(y)
return y
class Block(qc.Module):
def __init__(self, config, layer_id):
super().__init__()
d_model = config.d_model
inner_dim = config.d_ff if config.d_ff is not None else 4 * d_model
self.ln_1 = qc.LayerNorm(d_model, eps=config.eps)
self.attn = Attention(config, layer_id)
self.ln_2 = qc.LayerNorm(d_model, eps=config.eps)
self.mlp = MLP(inner_dim, config)
def forward(
self,
hiddens,
layer_past=None,
attention_mask=None,
head_mask=None,
y_cache=False,
output_attentions=False,
):
residual = hiddens
hiddens = self.ln_1(hiddens)
attn_outputs = self.attn(
hiddens,
layer_past=layer_past,
attention_mask=attention_mask,
head_mask=head_mask,
y_cache=y_cache,
output_attentions=output_attentions,
)
attn_output = attn_outputs[0]
outputs = attn_outputs[1:]
hiddens = attn_output + residual
residual = hiddens
hiddens = self.ln_2(hiddens)
feed_forward_model_states = self.mlp(hiddens)
hiddens = residual + feed_forward_model_states
if y_cache:
outputs = (hiddens,) + outputs
else:
outputs = (hiddens,) + outputs[1:]
return outputs
class Model(PreTrained):
def __init__(self, config):
super().__init__(config)
self.embed_dim = config.d_model
self.wte = qc.Embed(config.s_vocab, self.embed_dim)
self.wpe = qc.Embed(config.n_pos, self.embed_dim)
self.drop = qc.Dropout(config.drop_embed)
self.h = nn.ModuleList([Block(config, layer_id=i) for i in range(config.n_lays)])
self.ln_f = qc.LayerNorm(self.embed_dim, eps=config.eps)
self.gradient_checkpointing = False
def forward(
self,
input_ids=None,
caches=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=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
)
y_cache = y_cache if y_cache is not None else self.config.y_cache
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()
input_ids = input_ids.view(-1, input_shape[-1])
batch_size = input_ids.shape[0]
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
batch_size = inputs_embeds.shape[0]
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 token_type_ids is not None:
token_type_ids = token_type_ids.view(-1, input_shape[-1])
if position_ids is not None:
position_ids = position_ids.view(-1, input_shape[-1])
if caches is None:
past_length = 0
caches = tuple([None] * len(self.h))
else:
past_length = caches[0][0].size(-2)
device = input_ids.device if input_ids is not None else inputs_embeds.device
if position_ids is None:
position_ids = torch.arange(
past_length, input_shape[-1] + past_length, dtype=torch.long, device=device
)
position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
if attention_mask is not None:
if batch_size <= 0:
raise ValueError("batch_size has to be defined and > 0")
attention_mask = attention_mask.view(batch_size, -1)
attention_mask = attention_mask[:, None, None, :]
attention_mask = attention_mask.to(dtype=self.dtype) # fp16 compatibility
attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min
head_mask = self.get_head_mask(head_mask, self.config.n_lays)
if inputs_embeds is None:
inputs_embeds = self.wte(input_ids)
position_embeds = self.wpe(position_ids)
hiddens = inputs_embeds + position_embeds
if token_type_ids is not None:
token_type_embeds = self.wte(token_type_ids)
hiddens = hiddens + token_type_embeds
hiddens = self.drop(hiddens)
output_shape = input_shape + (hiddens.size(-1),)
presents = () if y_cache else None
all_self_attentions = () if output_attentions else None
all_hidden_states = () if output_hidden_states else None
for i, (block, layer_past) in enumerate(zip(self.h, caches)):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hiddens,)
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, y_cache, output_attentions)
return custom_forward
outputs = torch.utils.checkpoint.checkpoint(
create_custom_forward(block),
hiddens,
None,
attention_mask,
head_mask[i],
)
else:
outputs = block(
hiddens,
layer_past=layer_past,
attention_mask=attention_mask,
head_mask=head_mask[i],
y_cache=y_cache,
output_attentions=output_attentions,
)
hiddens = outputs[0]
if y_cache is True:
presents = presents + (outputs[1],)
if output_attentions:
all_self_attentions = all_self_attentions + (outputs[2 if y_cache else 1],)
hiddens = self.ln_f(hiddens)
hiddens = hiddens.view(output_shape)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hiddens,)
if not return_dict:
return tuple(
v
for v in [hiddens, presents, all_hidden_states, all_self_attentions]
if v is not None
)
return qo.BaseWithPast(
y=hiddens,
caches=presents,
hiddens=all_hidden_states,
attns=all_self_attentions,
)
class ForCausal(PreTrained):
def __init__(self, config):
super().__init__(config)
self.transformer = Model(config)
self.lm_head = qc.Linear(config.d_model, config.s_vocab, bias=False)
def forward(
self,
input_ids=None,
caches=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=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
transformer_outputs = self.transformer(
input_ids,
caches=caches,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
y_cache=y_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hiddens = transformer_outputs[0]
lm_logits = self.lm_head(hiddens)
loss = None
if labels is not None:
lm_logits = lm_logits.to(torch.float32)
# Shift so that tokens < n predict n
shift_logits = lm_logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = CrossEntropyLoss()
loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
lm_logits = lm_logits.to(hiddens.dtype)
loss = loss.to(hiddens.dtype)
if not return_dict:
output = (lm_logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return CausalLMOutputWithPast(
loss=loss,
logits=lm_logits,
caches=transformer_outputs.caches,
hiddens=transformer_outputs.hiddens,
attns=transformer_outputs.attns,
)
@staticmethod
def _reorder_cache(past, beam_idx):
return tuple(
tuple(
past_state.index_select(0, beam_idx.to(past_state.device))
for past_state in layer_past
)
for layer_past in past
)
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
def post_proj(self, x):
cfg = self.cfg
b, _ = x.shape[:2]
if cfg.PAD is None:
n = -1
else:
assert b == 1
n = -1 if x is None else torch.ne(x, cfg.PAD).sum(-1) - 1
return x[torch.arange(b, device=self.device), n]
|
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|
33,461
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/feature/perceiver.py
|
import numpy as np
from PIL import Image
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...image_utils import (
IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
ImageFeatureExtractionMixin,
ImageInput,
is_torch_tensor,
)
from ...utils import TensorType, logging
logger = logging.get_logger(__name__)
class PerceiverFeatureExtractor(FeatureExtractionMixin, ImageFeatureExtractionMixin):
model_input_names = ["pixel_values"]
def __init__(
self,
do_center_crop=True,
crop_size=256,
do_resize=True,
size=224,
resample=Image.BICUBIC,
do_normalize=True,
image_mean=None,
image_std=None,
**kw,
):
super().__init__(**kw)
self.do_center_crop = do_center_crop
self.crop_size = crop_size
self.do_resize = do_resize
self.size = size
self.resample = resample
self.do_normalize = do_normalize
self.image_mean = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
self.image_std = image_std if image_std is not None else IMAGENET_DEFAULT_STD
def center_crop(self, image):
"""
Crops `image` to *self.crop_size* using a center crop. Note that if the image is too small to be cropped to the
size given, it will be padded (so the returned result has the size asked).
Args:
image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`):
The image to resize.
"""
if isinstance(image, Image.Image):
image = self.to_numpy_array(image)
image_height, image_width = image.shape[-2:]
padded_center_crop_size = (
(self.size / (self.crop_size))
* np.minimum(image_height, image_width).astype(np.float32)
).astype(np.int32)
offset_height = ((image_height - padded_center_crop_size) + 1) // 2
offset_width = ((image_width - padded_center_crop_size) + 1) // 2
crop_window = [
offset_height,
offset_width,
padded_center_crop_size,
padded_center_crop_size,
]
image = image[
:,
crop_window[0] : crop_window[0] + crop_window[2],
crop_window[1] : crop_window[1] + crop_window[3],
]
return image
def __call__(self, images: ImageInput, return_tensors=None, **kw):
"""
Main method to prepare for the model one or several image(s).
<Tip warning={true}>
NumPy arrays and PyTorch tensors are converted to PIL images when resizing, so the most efficient is to pass
PIL images.
</Tip>
Args:
images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a
number of channels, H and W are image height and width.
return_tensors (`str` or [`~utils.TensorType`], *optional*, defaults to `'np'`):
If set, will return tensors of a particular framework. Acceptable values are:
- `'tf'`: Return TensorFlow `tf.constant` objects.
- `'pt'`: Return PyTorch `torch.Tensor` objects.
- `'np'`: Return NumPy `np.ndarray` objects.
- `'jax'`: Return JAX `jnp.ndarray` objects.
Returns:
[`BatchFeature`]: A [`BatchFeature`] with the following fields:
- **pixel_values** -- Pixel values to be fed to a model, of shape (batch_size, num_channels, height,
width).
"""
# Input type checking for clearer error
valid_images = False
# Check that images has a valid type
if isinstance(images, (Image.Image, np.ndarray)) or is_torch_tensor(images):
valid_images = True
elif isinstance(images, (list, tuple)):
if (
len(images) == 0
or isinstance(images[0], (Image.Image, np.ndarray))
or is_torch_tensor(images[0])
):
valid_images = True
if not valid_images:
raise ValueError(
"Images must of type `PIL.Image.Image`, `np.ndarray` or `torch.Tensor` (single example),"
"`List[PIL.Image.Image]`, `List[np.ndarray]` or `List[torch.Tensor]` (batch of examples)."
)
is_batched = bool(
isinstance(images, (list, tuple))
and (isinstance(images[0], (Image.Image, np.ndarray)) or is_torch_tensor(images[0]))
)
if not is_batched:
images = [images]
# transformations (center cropping + resizing + normalization)
if self.do_center_crop and self.crop_size is not None:
images = [self.center_crop(image) for image in images]
if self.do_resize and self.size is not None and self.resample is not None:
images = [
self.resize(image=image, size=self.size, resample=self.resample) for image in images
]
if self.do_normalize:
images = [
self.normalize(image=image, mean=self.image_mean, std=self.image_std)
for image in images
]
# return as BatchFeature
data = {"pixel_values": images}
encoded_inputs = BatchFeature(data=data, tensor_type=return_tensors)
return encoded_inputs
|
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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"]}
|
33,462
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/convert/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 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.t5 import PreTrained
from ...models.t5 import ForCondGen
logging.set_verbosity_info()
log = logging.get_logger(__name__)
_SKIP = [
"adam_v",
"adam_m",
"AdamWeightDecayOptimizer",
"AdamWeightDecayOptimizer_1",
"global_step",
]
def load_src_weights(model, config, src_path):
src_path = abspath(src_path)
log.info(f"Loading from: {src_path}")
xs = tf.train.list_variables(src_path)
names = []
tf_weights = {}
for name, shape in xs:
log.info(f"Loading TF weight {name} with shape {shape}")
array = tf.train.load_variable(src_path, name)
names.append(name)
tf_weights[name] = array
for txt_name in names:
name = txt_name.split("/")
if any(n in _SKIP for n in name):
log.info(f"Skipping {'/'.join(name)}")
tf_weights.pop(txt_name, None)
continue
if "_slot_" in name[-1]:
log.info(f"Skipping {'/'.join(name)}")
tf_weights.pop(txt_name, None)
continue
p = model
array = tf_weights[txt_name]
for m_name in name:
if re.fullmatch(r"[A-Za-z]+_\d+", m_name):
scopes = re.split(r"_(\d+)", m_name)
else:
scopes = [m_name]
if scopes[0] in ["kernel", "scale", "embedding"]:
p = getattr(p, "weight")
elif scopes[0] == "self_attention":
p = getattr(p, "layer")
p = p[0]
elif scopes[0] == "enc_dec_attention":
p = getattr(p, "layer")
p = p[1]
elif scopes[0] == "dense_relu_dense":
p = getattr(p, "layer")
p = p[2]
elif scopes[0] == "rms_norm":
if hasattr(p, "layer_norm"):
p = getattr(p, "layer_norm")
elif hasattr(p, "final_layer_norm"):
p = getattr(p, "final_layer_norm")
elif scopes[0] == "scale":
p = getattr(p, "weight")
elif scopes[0] == "output_bias" or scopes[0] == "beta":
p = getattr(p, "bias")
elif scopes[0] == "squad":
p = getattr(p, "classifier")
elif scopes[0] == "decoder" and name[1] == "logits":
continue
elif scopes[0] == "logits":
p = getattr(p, "lm_head")
elif scopes[0] == "wi" and len(scopes) > 1 and scopes[1].isdigit():
p = getattr(p, f"wi_{scopes[1]}")
continue
else:
try:
p = getattr(p, scopes[0])
except AttributeError:
log.info(f"Skipping {'/'.join(name)}")
continue
if len(scopes) >= 2:
p = p[int(scopes[1])]
if scopes[0] not in ["kernel", "scale", "embedding"]:
p = getattr(p, "weight")
if scopes[0] != "embedding":
log.info(f"Transposing numpy weight of shape {array.shape} for {name}")
array = np.transpose(array)
assert p.shape == array.shape
log.info(f"Initialize PyTorch weight {name}")
p.data = torch.from_numpy(array.astype(np.float32))
tf_weights.pop(txt_name, None)
log.info(f"Weights not copied to PyTorch model: {', '.join(tf_weights.keys())}.")
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 = 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/__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"]}
|
33,463
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/config/deberta2.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",
d_ff=6144,
d_hidden=1536,
drop_attn=0.1,
drop=0.1,
eps=1e-7,
init_range=0.02,
max_relative_positions=-1,
model_type="deberta-v2",
n_heads=24,
n_lays=24,
n_pos=512,
n_typ=0,
PAD=0,
pooler_dropout=0,
pooler_hidden_act="gelu",
pos_att_type=None,
position_biased_input=True,
relative_attention=False,
s_vocab=128100,
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_()
def _set_gradient_checkpointing(self, module, value=False):
if isinstance(module, DebertaV2Encoder):
module.gradient_checkpointing = value
MAP = {
"microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json",
"microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json",
"microsoft/deberta-v2-xlarge-mnli": "https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json",
"microsoft/deberta-v2-xxlarge-mnli": "https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json",
}
|
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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/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"]}
|
33,464
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/models/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
import inspect
from dataclasses import dataclass
import torch
import torch.nn as nn
from torch.nn import functional as F
from .. import core as qc
from ..core import utils as qu
class MLP(nn.Module):
def __init__(self, cfg):
super().__init__()
self.c_fc = nn.Linear(cfg.d_model, 4 * cfg.d_model, bias=cfg.bias)
self.proj = nn.Linear(4 * cfg.d_model, cfg.d_model, bias=cfg.bias)
self.drop = nn.Dropout(cfg.drop)
self.act = qu.activation("gelu_new")
def forward(self, x):
x = self.c_fc(x)
x = self.act(x)
x = self.proj(x)
x = self.drop(x)
return x
class Block(nn.Module):
def __init__(self, cfg):
super().__init__()
self.ln_1 = qc.LayerNorm(cfg.d_model, bias=cfg.bias)
self.attn = Attention(cfg)
self.ln_2 = qc.LayerNorm(cfg.d_model, bias=cfg.bias)
self.mlp = MLP(cfg)
def forward(self, x):
x = x + self.attn(self.ln_1(x))
x = x + self.mlp(self.ln_2(x))
return x
class Attention(qc.Module):
hs = qc.Hypers({"d_model", "drop", "n_heads", "n_pos"})
def __init__(self, is_cross=False, lay_i=None, ps={}, hs=[], **kw):
super().__init__(ps, [self.hs] + hs, **kw)
cfg = self.get_cfg(kw)
d, h = cfg.d_model, cfg.n_heads
assert d % h == 0
self.attn = nn.Linear(d, 3 * d, bias=cfg.bias)
self.proj = nn.Linear(d, d, bias=cfg.bias)
self.drop_attn = nn.Dropout(cfg.drop)
self.drop = nn.Dropout(cfg.drop)
self.flash = hasattr(torch.nn.functional, "scaled_dot_product_attention")
if not self.flash:
p, t = cfg.n_pos, torch.bool
self.register_buffer("bias", torch.tril(torch.ones((p, p), dtype=t)).view(1, 1, p, p))
def forward(self, x):
cfg = self.cfg
B, T, C = x.size() # batch size, sequence length, embedding dimensionality (n_hidden)
q, k, v = self.attn(x).split(cfg.d_model, dim=2)
k = k.view(B, T, cfg.n_heads, C // cfg.n_heads).transpose(1, 2)
q = q.view(B, T, cfg.n_heads, C // cfg.n_heads).transpose(1, 2)
v = v.view(B, T, cfg.n_heads, C // cfg.n_heads).transpose(1, 2)
if self.flash:
y = torch.nn.functional.scaled_dot_product_attention(
q,
k,
v,
attn_mask=None,
dropout_p=cfg.drop if self.training else 0,
is_causal=True,
)
else:
att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
att = att.masked_fill(self.bias[:, :, :T, :T] == 0, float("-inf"))
att = F.softmax(att, dim=-1)
att = self.drop_attn(att)
y = att @ v # (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs)
y = y.transpose(1, 2).contiguous().view(B, T, C)
y = self.drop(self.proj(y))
return y
class GPT(nn.Module):
def __init__(self, cfg):
super().__init__()
assert cfg.s_vocab is not None
assert cfg.n_pos is not None
self.cfg = cfg
self.transformer = nn.ModuleDict(
dict(
wte=nn.Embedding(cfg.s_vocab, cfg.d_model),
wpe=nn.Embedding(cfg.n_pos, cfg.d_model),
drop=nn.Dropout(cfg.drop),
h=nn.ModuleList([Block(cfg) for _ in range(cfg.n_layer)]),
ln_f=LayerNorm(cfg.d_model, bias=cfg.bias),
)
)
self.lm_head = nn.Linear(cfg.d_model, cfg.s_vocab, bias=False)
# with weight tying when using torch.compile() some warnings get generated:
# "UserWarning: functional_call was passed multiple values for tied weights.
# This behavior is deprecated and will be an error in future versions"
# not 100% sure what this is, so far seems to be harmless. TODO investigate
self.transformer.wte.weight = (
self.lm_head.weight
) # https://paperswithcode.com/method/weight-tying
# init all weights
self.apply(self._init_weights)
# apply special scaled init to the residual projections, per GPT-2 paper
for pn, p in self.named_parameters():
if pn.endswith("proj.weight"):
torch.nn.init.normal_(p, mean=0.0, std=0.02 / math.sqrt(2 * cfg.n_layer))
# report number of parameters
print("number of parameters: %.2fM" % (self.get_num_params() / 1e6,))
def get_num_params(self, non_embedding=True):
"""
Return the number of parameters in the model.
For non-embedding count (default), the position embeddings get subtracted.
The token embeddings would too, except due to the parameter sharing these
params are actually used as weights in the final layer, so we include them.
"""
n_params = sum(p.numel() for p in self.parameters())
if non_embedding:
n_params -= self.transformer.wpe.weight.numel()
return n_params
def _init_weights(self, module):
if isinstance(module, nn.Linear):
torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
if module.bias is not None:
torch.nn.init.zeros_(module.bias)
elif isinstance(module, nn.Embedding):
torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
def forward(self, idx, targets=None):
device = idx.device
b, t = idx.size()
assert (
t <= self.cfg.n_pos
), f"Cannot forward sequence of length {t}, block size is only {self.cfg.n_pos}"
pos = torch.arange(0, t, dtype=torch.long, device=device).unsqueeze(0) # shape (1, t)
# forward the GPT model itself
tok_emb = self.transformer.wte(idx) # token embeddings of shape (b, t, n_hidden)
pos_emb = self.transformer.wpe(pos) # position embeddings of shape (1, t, n_hidden)
x = self.transformer.drop(tok_emb + pos_emb)
for block in self.transformer.h:
x = block(x)
x = self.transformer.ln_f(x)
if targets is not None:
# if we are given some desired targets also calculate the loss
logits = self.lm_head(x)
loss = F.cross_entropy(
logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1
)
else:
# inference-time mini-optimization: only forward the lm_head on the very last position
logits = self.lm_head(x[:, [-1], :]) # note: using list [-1] to preserve the time dim
loss = None
return logits, loss
def crop_n_pos(self, n_pos):
# model surgery to decrease the block size if necessary
# e.g. we may load the GPT2 pretrained model checkpoint (block size 1024)
# but want to use a smaller block size for some smaller, simpler model
assert n_pos <= self.cfg.n_pos
self.cfg.n_pos = n_pos
self.transformer.wpe.weight = nn.Parameter(self.transformer.wpe.weight[:n_pos])
for block in self.transformer.h:
if hasattr(block.attn, "bias"):
block.attn.bias = block.attn.bias[:, :, :n_pos, :n_pos]
@classmethod
def from_pretrained(cls, model_type, override_args=None):
assert model_type in {"gpt2", "gpt2-medium", "gpt2-large", "gpt2-xl"}
override_args = override_args or {} # default to empty dict
# only dropout can be overridden see more notes below
assert all(k == "dropout" for k in override_args)
from transformers import GPT2LMHeadModel
print("loading weights from pretrained gpt: %s" % model_type)
# n_layer, n_head and n_hidden are determined from model_type
cfg_args = {
"gpt2": dict(n_layer=12, n_head=12, n_hidden=768), # 124M params
"gpt2-medium": dict(n_layer=24, n_head=16, n_hidden=1024), # 350M params
"gpt2-large": dict(n_layer=36, n_head=20, n_hidden=1280), # 774M params
"gpt2-xl": dict(n_layer=48, n_head=25, n_hidden=1600), # 1558M params
}[model_type]
print("forcing s_vocab=50257, n_pos=1024, bias=True")
cfg_args["s_vocab"] = 50257 # always 50257 for GPT model checkpoints
cfg_args["n_pos"] = 1024 # always 1024 for GPT model checkpoints
cfg_args["bias"] = True # always True for GPT model checkpoints
# we can override the dropout rate, if desired
if "dropout" in override_args:
print(f"overriding dropout rate to {override_args['dropout']}")
cfg_args["dropout"] = override_args["dropout"]
# create a from-scratch initialized minGPT model
cfg = GPTcfg(**cfg_args)
model = GPT(cfg)
sd = model.state_dict()
sd_keys = sd.keys()
sd_keys = [
k for k in sd_keys if not k.endswith(".attn.bias")
] # discard this mask / buffer, not a param
# init a huggingface/transformers model
model_hf = GPT2LMHeadModel.from_pretrained(model_type)
sd_hf = model_hf.state_dict()
# copy while ensuring all of the parameters are aligned and match in names and shapes
sd_keys_hf = sd_hf.keys()
sd_keys_hf = [
k for k in sd_keys_hf if not k.endswith(".attn.bias_m")
] # ignore these, just a buffer
sd_keys_hf = [
k for k in sd_keys_hf if not k.endswith(".attn.bias")
] # same, just the mask (buffer)
transposed = [
"attn.attn.weight",
"attn.proj.weight",
"mlp.c_fc.weight",
"mlp.proj.weight",
]
# basically the openai checkpoints use a "Conv1D" module, but we only want to use a vanilla Linear
# this means that we have to transpose these weights when we import them
assert len(sd_keys_hf) == len(
sd_keys
), f"mismatched keys: {len(sd_keys_hf)} != {len(sd_keys)}"
for k in sd_keys_hf:
if any(k.endswith(w) for w in transposed):
# special treatment for the Conv1D weights we need to transpose
assert sd_hf[k].shape[::-1] == sd[k].shape
with torch.no_grad():
sd[k].copy_(sd_hf[k].t())
else:
# vanilla copy over the other parameters
assert sd_hf[k].shape == sd[k].shape
with torch.no_grad():
sd[k].copy_(sd_hf[k])
return model
def cfgure_optimizers(self, weight_decay, learning_rate, betas, device_type):
"""
This long function is unfortunately doing something very simple and is being very defensive:
We are separating out all parameters of the model into two buckets: those that will experience
weight decay for regularization and those that won't (biases, and layernorm/embedding weights).
We are then returning the PyTorch optimizer object.
"""
# separate out all parameters to those that will and won't experience regularizing weight decay
decay = set()
no_decay = set()
whitelist_weight_modules = (torch.nn.Linear,)
blacklist_weight_modules = (torch.nn.LayerNorm, LayerNorm, torch.nn.Embedding)
for mn, m in self.named_modules():
for pn, p in m.named_parameters():
fpn = "%s.%s" % (mn, pn) if mn else pn # full param name
# random note: because named_modules and named_parameters are recursive
# we will see the same tensors p many many times. but doing it this way
# allows us to know which parent module any tensor p belongs to...
if pn.endswith("bias"):
# all biases will not be decayed
no_decay.add(fpn)
elif pn.endswith("weight") and isinstance(m, whitelist_weight_modules):
# weights of whitelist modules will be weight decayed
decay.add(fpn)
elif pn.endswith("weight") and isinstance(m, blacklist_weight_modules):
# weights of blacklist modules will NOT be weight decayed
no_decay.add(fpn)
# subtle: 'transformer.wte.weight' and 'lm_head.weight' are tied, so they
# will appear in the no_decay and decay sets respectively after the above.
# In addition, because named_parameters() doesn't return duplicates, it
# will only return the first occurence, key'd by 'transformer.wte.weight', below.
# so let's manually remove 'lm_head.weight' from decay set. This will include
# this tensor into optimization via transformer.wte.weight only, and not decayed.
decay.remove("lm_head.weight")
# validate that we considered every parameter
param_dict = {pn: p for pn, p in self.named_parameters()}
inter_params = decay & no_decay
union_params = decay | no_decay
assert len(inter_params) == 0, "parameters %s made it into both decay/no_decay sets!" % (
str(inter_params),
)
assert (
len(param_dict.keys() - union_params) == 0
), "parameters %s were not separated into either decay/no_decay set!" % (
str(param_dict.keys() - union_params),
)
# create the pytorch optimizer object
optim_groups = [
{
"params": [param_dict[pn] for pn in sorted(list(decay))],
"weight_decay": weight_decay,
},
{"params": [param_dict[pn] for pn in sorted(list(no_decay))], "weight_decay": 0.0},
]
# new PyTorch nightly has a new 'fused' option for AdamW that is much faster
use_fused = (device_type == "cuda") and (
"fused" in inspect.signature(torch.optim.AdamW).parameters
)
print(f"using fused AdamW: {use_fused}")
extra_args = dict(fused=True) if use_fused else dict()
optimizer = torch.optim.AdamW(optim_groups, lr=learning_rate, betas=betas, **extra_args)
return optimizer
def estimate_mfu(self, fwdbwd_per_iter, dt):
"""estimate model flops utilization (MFU) in units of A100 bfloat16 peak FLOPS"""
# first estimate the number of flops we do per iteration.
# see PaLM paper Appendix B as ref: https://arxiv.org/abs/2204.02311
N = self.get_num_params()
cfg = self.cfg
L, H, Q, T = cfg.n_layer, cfg.n_heads, cfg.d_model // cfg.n_heads, cfg.n_pos
flops_per_token = 6 * N + 12 * L * H * Q * T
flops_per_fwdbwd = flops_per_token * T
flops_per_iter = flops_per_fwdbwd * fwdbwd_per_iter
# express our flops throughput as ratio of A100 bfloat16 peak flops
flops_achieved = flops_per_iter * (1.0 / dt) # per second
flops_promised = 312e12 # A100 GPU bfloat16 peak flops is 312 TFLOPS
mfu = flops_achieved / flops_promised
return mfu
@torch.no_grad()
def generate(self, idx, max_new_tokens, temperature=1.0, top_k=None):
"""
Take a conditioning sequence of indices idx (LongTensor of shape (b,t)) and complete
the sequence max_new_tokens times, feeding the predictions back into the model each time.
Most likely you'll want to make sure to be in model.eval() mode of operation for this.
"""
for _ in range(max_new_tokens):
# if the sequence context is growing too long we must crop it at n_pos
idx_cond = idx if idx.size(1) <= self.cfg.n_pos else idx[:, -self.cfg.n_pos :]
# forward the model to get the logits for the index in the sequence
logits, _ = self(idx_cond)
# pluck the logits at the final step and scale by desired temperature
logits = logits[:, -1, :] / temperature
# optionally crop the logits to only the top k options
if top_k is not None:
v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
logits[logits < v[:, [-1]]] = -float("Inf")
# apply softmax to convert logits to (normalized) probabilities
probs = F.softmax(logits, dim=-1)
# sample from the distribution
idx_next = torch.multinomial(probs, num_samples=1)
# append sampled index to the running sequence and continue
idx = torch.cat((idx, idx_next), dim=1)
return idx
|
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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"]}
|
33,465
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/metric/sacrebleu.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 sacrebleu as scb
import datasets as ds
class Sacrebleu(ds.Metric):
def _info(self):
return ds.MetricInfo(
description="",
citation="",
homepage="",
inputs_description="",
features=ds.Features(
{
"predictions": ds.Value("string", id="sequence"),
"references": ds.Sequence(ds.Value("string", id="sequence"), id="references"),
}
),
)
def _compute(
self,
preds,
refs,
smooth_method="exp",
smooth_value=None,
force=False,
lowercase=False,
tokenize=None,
use_effective_order=False,
):
references_per_prediction = len(refs[0])
if any(len(refs) != references_per_prediction for refs in refs):
raise ValueError("Sacrebleu requires the same number of references for each prediction")
transformed_references = [
[refs[i] for refs in refs] for i in range(references_per_prediction)
]
y = scb.corpus_bleu(
preds,
transformed_references,
smooth_method=smooth_method,
smooth_value=smooth_value,
force=force,
lowercase=lowercase,
use_effective_order=use_effective_order,
**(dict(tokenize=tokenize) if tokenize else {}),
)
return {
"score": y.score,
"counts": y.counts,
"totals": y.totals,
"precisions": y.precisions,
"bp": y.bp,
"sys_len": y.sys_len,
"ref_len": y.ref_len,
}
|
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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"], "/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"]}
|
33,466
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/tokens/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 unicodedata
import sacremoses as sm
from ...tokens.utils import PreTrainedTokenizer
VOCAB_FS = {
"src_vocab_file": "vocab-src.json",
"tgt_vocab_file": "vocab-tgt.json",
"merges_file": "merges.txt",
}
VOCAB_MAP = {
"src_vocab_file": {
"stas/tiny-wmt19-en-de": "https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/vocab-src.json"
},
"tgt_vocab_file": {
"stas/tiny-wmt19-en-de": "https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/vocab-tgt.json"
},
"merges_file": {
"stas/tiny-wmt19-en-de": "https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/merges.txt"
},
}
INPUT_CAPS = {"stas/tiny-wmt19-en-de": 1024}
PRETRAINED_INIT_CONFIGURATION = {
"stas/tiny-wmt19-en-de": {
"langs": ["en", "de"],
"model_max_length": 1024,
"special_tokens_map_file": None,
"full_tokenizer_file": None,
}
}
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 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)
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"]
def __init__(
self,
langs=None,
src_vocab_file=None,
tgt_vocab_file=None,
merges_file=None,
do_lower_case=False,
unk="<unk>",
bos="<s>",
sep="</s>",
pad="<pad>",
**kw,
):
super().__init__(
langs=langs,
src_vocab_file=src_vocab_file,
tgt_vocab_file=tgt_vocab_file,
merges_file=merges_file,
do_lower_case=do_lower_case,
unk=unk,
bos=bos,
sep=sep,
pad=pad,
**kw,
)
self.src_vocab_file = src_vocab_file
self.tgt_vocab_file = tgt_vocab_file
self.merges_file = merges_file
self.do_lower_case = do_lower_case
self.cache_moses_punct_normalizer = dict()
self.cache_moses_tokenizer = dict()
self.cache_moses_detokenizer = dict()
if langs and len(langs) == 2:
self.src_lang, self.tgt_lang = langs
else:
raise ValueError(
f"arg `langs` needs to be a list of 2 langs, e.g. ['en', 'ru'], but got {langs}. "
"Usually that means that tokenizer can't find a mapping for the given model path "
"in VOCAB_MAP, and other maps of this tokenizer."
)
with open(src_vocab_file, encoding="utf-8") as src_vocab_handle:
self.encoder = json.load(src_vocab_handle)
with open(tgt_vocab_file, encoding="utf-8") as tgt_vocab_handle:
tgt_vocab = json.load(tgt_vocab_handle)
self.decoder = {v: k for k, v in tgt_vocab.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 = {}
def get_vocab(self):
return self.get_src_vocab()
@property
def s_vocab(self):
return self.s_src_vocab
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
return self.cache_moses_punct_normalizer[lang].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
return self.cache_moses_tokenizer[lang].tokenize(
text, aggressive_dash_splits=True, return_str=False, escape=True
)
def moses_detokenize(self, tokens, lang):
if lang not in self.cache_moses_tokenizer:
moses_detokenizer = sm.MosesDetokenizer(lang=self.tgt_lang)
self.cache_moses_detokenizer[lang] = moses_detokenizer
return self.cache_moses_detokenizer[lang].detokenize(tokens)
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
@property
def s_src_vocab(self):
return len(self.encoder)
@property
def s_tgt_vocab(self):
return len(self.decoder)
def get_src_vocab(self):
return dict(self.encoder, **self.added_tokens_encoder)
def get_tgt_vocab(self):
return dict(self.decoder, **self.added_tokens_decoder)
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):
lang = self.src_lang
if self.do_lower_case:
text = text.lower()
if bypass_tokenizer:
text = text.split()
else:
text = self.moses_pipeline(text, lang=lang)
text = self.moses_tokenize(text, lang=lang)
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):
tokens = [t.replace(" ", "").replace("</w>", " ") for t in tokens]
tokens = "".join(tokens).split()
text = self.moses_detokenize(tokens, self.tgt_lang)
return text
def build_inputs_with_special_tokens(self, toks_0, toks_1=None):
sep = [self.SEP]
if toks_1 is None:
return toks_0 + sep
return 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 ([0] * len(toks_0)) + [1] + ([0] * len(toks_1)) + [1]
return ([0] * len(toks_0)) + [1]
def create_token_type_ids_from_sequences(self, toks_0, toks_1=None):
sep = [self.SEP]
if toks_1 is None:
return len(toks_0 + sep) * [0]
return len(toks_0 + sep) * [0] + len(toks_1 + sep) * [1]
def save_vocabulary(self, dir, pre=None):
src_vocab_file = os.path.join(
dir,
(pre + "-" if pre else "") + VOCAB_FS["src_vocab_file"],
)
tgt_vocab_file = os.path.join(
dir,
(pre + "-" if pre else "") + VOCAB_FS["tgt_vocab_file"],
)
merges_file = os.path.join(
dir,
(pre + "-" if pre else "") + VOCAB_FS["merges_file"],
)
with open(src_vocab_file, "w", encoding="utf-8") as f:
f.write(json.dumps(self.encoder, ensure_ascii=False))
with open(tgt_vocab_file, "w", encoding="utf-8") as f:
tgt_vocab = {v: k for k, v in self.decoder.items()}
f.write(json.dumps(tgt_vocab, ensure_ascii=False))
index = 0
with open(merges_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 {merges_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 src_vocab_file, tgt_vocab_file, merges_file
|
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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"]}
|
33,467
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/run/xlate.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 text translation
import logging
import numpy as np
import random
import torch
from datasets import load_metric
from torch.utils.data import DataLoader
from transformers import (
AutoModelForSeq2SeqLM,
DataCollatorForSeq2Seq,
MBartTokenizer,
MBartTokenizerFast,
default_data_collator,
)
from .params import TRAIN, EVAL, ALL
from .runner import Runner as Base
log = logging.getLogger(__name__)
def postproc(xs, ls):
xs = [x.strip() for x in xs]
ls = [[x.strip()] for x in ls]
return xs, ls
class Runner(Base):
AutoModel = AutoModelForSeq2SeqLM
@property
def tokenizer(self):
if self._tokenizer is None:
ps = self.params
t = super().tokenizer
if isinstance(t, (MBartTokenizer, MBartTokenizerFast)):
if ps.source_lang is not None:
t.src_lang = ps.source_lang
if ps.target_lang is not None:
t.tgt_lang = ps.target_lang
self.source_lang = ps.source_lang.split("_")[0]
self.target_lang = ps.target_lang.split("_")[0]
self.prefix = ps.source_prefix if ps.source_prefix is not None else ""
return self._tokenizer
@property
def model(self):
if self._model is None:
ps = self.params
t, m = self.tokenizer, super().model
if m.config.dec_START is None and isinstance(t, (MBartTokenizer, MBartTokenizerFast)):
assert (
ps.target_lang is not None and ps.source_lang is not None
), "mBart needs --target_lang and --source_lang"
if isinstance(t, MBartTokenizer):
m.config.dec_START = t.lang_code_to_id[ps.target_lang]
else:
m.config.dec_START = t.convert_tokens_to_ids(ps.target_lang)
if m.config.dec_START is None:
raise ValueError("Needs `config.dec_START`")
@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, t = self.params, self.tokenizer
ins = [x[self.source_lang] for x in xs["translation"]]
targets = [x[self.target_lang] for x in xs["translation"]]
ins = [self.prefix + x for x in ins]
ys = t(ins, max_len=ps.max_source_length, padding=ps.padding, truncation=True)
with t.as_target_tokenizer():
ls = t(targets, max_len=ps.max_target_length, padding=ps.padding, truncation=True)
if self.padding == "max_len" and ps.ignore_pad_token_for_loss:
ls["input_ids"] = [[(y if y != t.PAD else -100) for y in x] for x in ls["input_ids"]]
ys["labels"] = ls["input_ids"]
return ys
@property
def loaders(self):
if self._loaders is None:
ps, t = self.params, self.tokenizer
if ps.pad_to_max_length:
c = default_data_collator
else:
c = DataCollatorForSeq2Seq(
t,
model=self.model,
label_pad_token_id=-100 if ps.ignore_pad_token_for_loss else t.PAD,
pad_to_multiple_of=8 if self.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("sacrebleu")
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)
ys = mgr.pad_across_processes(ys, dim=1, PAD=t.PAD)
ls = xs["labels"]
if not ps.pad_to_max_length:
ls = mgr.pad_across_processes(xs["labels"], dim=1, PAD=t.PAD)
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, t.PAD)
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()["score"]
mgr.print(f"epoch {e}: bleu: {y}")
def main():
x = Runner()
x.dataset
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 xlate.py \
--model_name Helsinki-NLP/opus-mt-en-ro \
--source_lang en \
--target_lang ro \
--dataset_name wmt16 \
--dataset_config ro-en \
--out_dir ~/tmp/tst-translation
accelerate launch xlate.py \
--model_name Helsinki-NLP/opus-mt-en-ro \
--source_lang en \
--target_lang ro \
--dataset_name wmt16 \
--dataset_config ro-en \
--out_dir ~/tmp/tst-translation
"""
|
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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/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"]}
|
33,468
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/tokens/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.utils import AddedToken
from .gpt2 import Tokenizer as GPT2
VOCAB_FS = {"vocab_file": "vocab.json", "merges_file": "merges.txt"}
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 Tokenizer(GPT2):
vocab_fs = VOCAB_FS
vocab_map = VOCAB_MAP
input_caps = INPUT_CAPS
model_input_names = ["input_ids", "attention_mask", "token_type_ids"]
def __init__(
self,
vocab_file,
merges_file,
errors="replace",
bos="[CLS]",
eos="[SEP]",
sep="[SEP]",
cls="[CLS]",
unk="[UNK]",
pad="[PAD]",
msk="[MASK]",
add_prefix_space=False,
**kw,
):
bos = AddedToken(bos, lstrip=False, rstrip=False) if isinstance(bos, str) else bos
eos = AddedToken(eos, lstrip=False, rstrip=False) if isinstance(eos, str) else eos
sep = AddedToken(sep, lstrip=False, rstrip=False) if isinstance(sep, str) else sep
cls = AddedToken(cls, lstrip=False, rstrip=False) if isinstance(cls, str) else cls
unk = AddedToken(unk, lstrip=False, rstrip=False) if isinstance(unk, str) else unk
pad = AddedToken(pad, lstrip=False, rstrip=False) if isinstance(pad, str) else pad
msk = AddedToken(msk, lstrip=True, rstrip=False) if isinstance(msk, str) else msk
super().__init__(
vocab_file=vocab_file,
merges_file=merges_file,
errors=errors,
bos=bos,
eos=eos,
unk=unk,
sep=sep,
cls=cls,
pad=pad,
msk=msk,
add_prefix_space=add_prefix_space,
**kw,
)
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 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 [1] + ([0] * len(toks_0)) + [1]
return [1] + ([0] * len(toks_0)) + [1] + ([0] * len(toks_1)) + [1]
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]
def prepare_for_tokenization(self, text, is_split_into_words=False, **kw):
add_prefix_space = kw.pop("add_prefix_space", self.add_prefix_space)
if (is_split_into_words or add_prefix_space) and (len(text) > 0 and not text[0].isspace()):
text = " " + text
return (text, kw)
|
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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"]}
|
33,469
|
quantapix/qnarre
|
refs/heads/main
|
/tools/triton/python/triton/ops/blocksparse/softmax.py
|
import torch
import triton
import triton.language as tl
def num_warps(n):
if n <= 128:
return 1
if n <= 256:
return 2
if n <= 512:
return 4
if n <= 4096:
return 8
return 16
@triton.jit
def _blocksparse_softmax_fwd(
Out, A, stride_xz, LUT,
R, extent, stride_zr, stride_hr, # relative attention
scale, is_causal,
ROW_SIZE: tl.constexpr,
BLOCK_SIZE: tl.constexpr,
IS_DENSE: tl.constexpr,
):
h = tl.program_id(0)
m = tl.program_id(1)
z = tl.program_id(2)
# create index ranges
hm = h * tl.num_programs(1) + m
lane_n = tl.arange(0, ROW_SIZE) % BLOCK_SIZE
block_n = tl.arange(0, ROW_SIZE) // BLOCK_SIZE
# extract information from LUT
header = LUT + (hm // BLOCK_SIZE) * 2
size = tl.load(header + 0)
offset = tl.load(header + 1)
# pointer offset
off_a = z * stride_xz
off_a += (offset + block_n) * BLOCK_SIZE * BLOCK_SIZE # block indx
off_a += (m % BLOCK_SIZE) * BLOCK_SIZE # row indx
# do not need to read column indices in the dense case
if IS_DENSE:
ns = tl.arange(0, ROW_SIZE)
else:
off_lut = offset + 2 * tl.num_programs(0) * tl.num_programs(1) // BLOCK_SIZE
start_n = tl.load(LUT + off_lut + block_n, mask=block_n < size, other=0)
ns = start_n * BLOCK_SIZE + lane_n
# load X
mask = block_n < size
a = tl.load(A + off_a + lane_n, mask=mask, other=-float("inf"))
a = a.to(tl.float32)
# compute
out = a
out *= scale
# apply relative attention
if R is not None:
R += z * stride_zr
R += h * stride_hr
off_lo = (extent - m - 1) + ns
mask_lo = (off_lo >= 0) & (off_lo < extent)
rel_logits = tl.load(R + m * extent + off_lo, mask=mask_lo, other=0.0)
out += rel_logits
out = out.to(tl.float32)
# apply causal mask
out = tl.where((ns > m) & is_causal, -float("inf"), out)
# computation
out = tl.softmax(out)
# write-back
tl.store(Out + off_a + lane_n, out, mask=mask)
@triton.jit
def _blocksparse_softmax_bwd(
DA, stride_zdx,
DOut, stride_zdout,
Out, stride_zout,
scale,
LUT,
DR, extent, stride_zr, stride_hr, stride_er,
is_causal,
ROW_SIZE: tl.constexpr,
BLOCK_SIZE: tl.constexpr,
IS_DENSE: tl.constexpr,
):
h = tl.program_id(0)
m = tl.program_id(1)
z = tl.program_id(2)
# create index ranges
hm = h * tl.num_programs(1) + m
lane_n = tl.arange(0, ROW_SIZE) % BLOCK_SIZE
block_n = tl.arange(0, ROW_SIZE) // BLOCK_SIZE
# extract information from LUT
header = LUT + (hm // BLOCK_SIZE) * 2
size = tl.load(header + 0)
offset = tl.load(header + 1)
# row-col offset
off_mn = (offset + block_n) * BLOCK_SIZE * BLOCK_SIZE
off_mn += (m % BLOCK_SIZE) * BLOCK_SIZE
mask = block_n < size
# pointers
As = Out + z * stride_zout + off_mn
DOuts = DOut + z * stride_zdout + off_mn
# do not need to read column indices in the dense case
if IS_DENSE:
ns = tl.arange(0, ROW_SIZE)
else:
off_lut = offset + 2 * tl.num_programs(0) * tl.num_programs(1) // BLOCK_SIZE
start_n = tl.load(LUT + off_lut + block_n, mask=mask, other=0)
ns = start_n * BLOCK_SIZE + lane_n
# load data
a = tl.load(As + lane_n, mask=mask, other=0.0)
a = a.to(tl.float32)
dout = tl.load(DOuts + lane_n, mask=mask, other=0.0)
dout = dout.to(tl.float32)
# compute
a = tl.where((ns > m) & is_causal & (a == a), 0., a)
da = a * (dout - tl.sum(a * dout, 0))
# apply relative attention
if DR is not None:
DR += z * stride_zr
DR += h * stride_hr
off_lo = (extent - m - 1) + ns
mask_lo = (off_lo >= 0) & (off_lo < extent) & mask
tl.store(DR + m * extent + off_lo, da, mask=mask_lo)
da = da * scale
# convert da
# write-back
DAs = DA + z * stride_zdx + off_mn
tl.store(DAs + lane_n, da, mask=mask)
class _softmax(torch.autograd.Function):
@staticmethod
def make_lut(layout, block, device):
_empty = torch.tensor([], dtype=torch.int64, device=layout.device)
sizes = _empty.clone()
# sizes along rows
for h in range(layout.shape[0]):
sizes = torch.cat((sizes, layout[h, :, :].sum(-1)))
total_sizes = sizes * block
# offsets in block format
offsets = torch.zeros_like(sizes)
offsets[1:] = torch.cumsum(sizes[:-1], dim=0)
# block indices
columns = layout.nonzero(as_tuple=False)[:, 2]
header = torch.stack((sizes, offsets), dim=1).view(-1)
lut = torch.cat((header, columns)).type(torch.int32).to(device)
return lut, int(total_sizes.max())
@staticmethod
def forward(
ctx, a, scale, rel_logits, is_causal,
spdims, block, lut, maxlut, is_dense
):
if scale is not None and isinstance(scale, torch.Tensor):
assert scale.device.type == "cpu"
scale = scale.item()
M = a.shape[0]
grid = [spdims[0], spdims[1] * block, M]
rel_shape = (1, 1, 1, 1) if rel_logits is None else rel_logits.shape
rel_strides = (1, 1, 1, 1) if rel_logits is None else rel_logits.stride()
# enqueue kernel
out = torch.empty_like(a)
_blocksparse_softmax_fwd[grid](
out, a, a.stride(0), lut,
rel_logits, rel_shape[-1], rel_strides[0], rel_strides[1], # relative attn
scale,
is_causal,
BLOCK_SIZE=block,
ROW_SIZE=triton.next_power_of_2(maxlut),
IS_DENSE=is_dense,
num_warps=num_warps(maxlut)
)
# save to context
# ctx.mark_dirty(x)
ctx.save_for_backward(out, lut)
ctx.spdims = spdims
ctx.block = block
ctx.maxlut = maxlut
ctx.scale = scale
ctx.rel_shape = rel_shape
ctx.rel_strides = rel_strides
ctx.rel_dtype = a.dtype
ctx.is_dense = is_dense
ctx.is_causal = is_causal
return out
@staticmethod
def backward(ctx, dout):
# retrieve from context
out, lut = ctx.saved_tensors
# relative logits gradients
dr = None
if ctx.needs_input_grad[3]:
dr = torch.zeros(ctx.rel_shape, dtype=ctx.rel_dtype, device=out.device)
# run kernel
M = out.shape[0]
grid = (ctx.spdims[0], ctx.spdims[1] * ctx.block, M)
da = torch.empty_like(dout)
_blocksparse_softmax_bwd[grid](
da, da.stride(0),
dout, dout.stride(0),
out, out.stride(0),
ctx.scale,
lut,
dr, ctx.rel_shape[-1], ctx.rel_strides[0], ctx.rel_strides[1], ctx.rel_strides[2],
ctx.is_causal,
BLOCK_SIZE=ctx.block,
ROW_SIZE=triton.next_power_of_2(ctx.maxlut),
IS_DENSE=ctx.is_dense,
num_warps=num_warps(ctx.maxlut)
)
return (da, None, None, dr, None,
None, None, None, None, None,
None,
None, None, None,
None,
None, None, None
)
class softmax:
def __init__(self, layout, block, device, is_dense=False):
self.spdims = layout.shape
self.layout = layout
self.block = block
self.lut, self.maxlut = _softmax.make_lut(self.layout, self.block, device)
self.is_dense = is_dense
def __call__(self, a, *, scale=1.0, rel_logits=None, is_causal=False):
if rel_logits is not None and rel_logits.dtype != a.dtype:
raise ValueError(f"relative position embedding must be {a.dtype}")
a = _softmax.apply(
a, scale, rel_logits, is_causal,
self.spdims, self.block, self.lut, self.maxlut, self.is_dense,
)
return a
|
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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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"/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"]}
|
33,470
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/dataset/xnli.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 csv
import datasets as ds
from os.path import join
_URL = "https://dl.fbaipublicfiles.com/XNLI/"
_URLS = {
"train": _URL + "XNLI-MT-1.0.zip",
"valid": _URL + "XNLI-1.0.zip",
}
_LANGS = ("de", "en")
class Xnli(ds.GeneratorBasedBuilder):
BUILDER_CONFIGS = [ds.BuilderConfig(name=x, version=ds.Version("1.1.0")) for x in _LANGS]
def _info(self):
return ds.DatasetInfo(
description="",
citation="",
homepage="",
license="",
features=ds.Features(
{
"premise": ds.Value("string"),
"hypothesis": ds.Value("string"),
"label": ds.ClassLabel(names=["entailment", "neutral", "contradiction"]),
}
),
)
def _split_generators(self, mgr):
fs = mgr.download_and_extract(_URLS)
t = join(fs["train"], "XNLI-MT-1.0", "multinli")
v = join(fs["valid"], "XNLI-1.0")
return [
ds.SplitGenerator(
name=ds.Split.TRAIN,
gen_kw={
"filepaths": join(t, f"multinli.train.{self.config.name}.tsv"),
"data_format": "XNLI-MT",
},
),
ds.SplitGenerator(
name=ds.Split.TEST,
gen_kw={"filepaths": [join(v, "xnli.test.tsv")], "data_format": "XNLI"},
),
ds.SplitGenerator(
name=ds.Split.VALIDATION,
gen_kw={"filepaths": [join(v, "xnli.dev.tsv")], "data_format": "XNLI"},
),
]
def _generate_examples(self, fmt, fs):
if fmt == "XNLI-MT":
for i, path in enumerate(fs):
f = open(path, encoding="utf-8")
r = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
for j, x in enumerate(r):
k = str(i) + "_" + str(j)
yield k, {
"premise": x["premise"],
"hypothesis": x["hypo"],
"label": x["label"].replace("contradictory", "contradiction"),
}
else:
for path in fs:
with open(path, encoding="utf-8") as f:
r = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
for x in r:
if x["language"] == self.config.name:
yield x["pairID"], {
"premise": x["sentence1"],
"hypothesis": x["sentence2"],
"label": x["gold_label"],
}
|
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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"]}
|
33,471
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/tokens/fast/splinter.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 normalizers
from ....tokens.fast import PreTrainedTokenizerFast
from ..splinter import Tokenizer as Splinter
VOCAB_FS = {"vocab_file": "vocab.txt"}
VOCAB_MAP = {
"vocab_file": {
"tau/splinter-base": "https://huggingface.co/tau/splinter-base/resolve/main/vocab.txt",
"tau/splinter-base-qass": "https://huggingface.co/tau/splinter-base-qass/resolve/main/vocab.txt",
"tau/splinter-large": "https://huggingface.co/tau/splinter-large/resolve/main/vocab.txt",
"tau/splinter-large-qass": "https://huggingface.co/tau/splinter-large-qass/resolve/main/vocab.txt",
}
}
INPUT_CAPS = {
"tau/splinter-base": 512,
"tau/splinter-base-qass": 512,
"tau/splinter-large": 512,
"tau/splinter-large-qass": 512,
}
PRETRAINED_INIT_CONFIGURATION = {
"tau/splinter-base": {"do_lower_case": False},
"tau/splinter-base-qass": {"do_lower_case": False},
"tau/splinter-large": {"do_lower_case": False},
"tau/splinter-large-qass": {"do_lower_case": False},
}
class Tokenizer(PreTrainedTokenizerFast):
vocab_fs = VOCAB_FS
vocab_map = VOCAB_MAP
pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
input_caps = INPUT_CAPS
slow_tokenizer_class = Splinter
def __init__(
self,
vocab_file=None,
tokenizer_file=None,
do_lower_case=True,
unk="[UNK]",
sep="[SEP]",
pad="[PAD]",
cls="[CLS]",
msk="[MASK]",
question_token="[QUESTION]",
tokenize_chinese_chars=True,
strip_accents=None,
**kw,
):
super().__init__(
vocab_file,
tokenizer_file=tokenizer_file,
do_lower_case=do_lower_case,
unk=unk,
sep=sep,
pad=pad,
cls=cls,
msk=msk,
tokenize_chinese_chars=tokenize_chinese_chars,
strip_accents=strip_accents,
additional_special_tokens=(question_token,),
**kw,
)
pre_tok_state = json.loads(self.backend_tokenizer.normalizer.__getstate__())
if (
pre_tok_state.get("lowercase", do_lower_case) != do_lower_case
or pre_tok_state.get("strip_accents", strip_accents) != strip_accents
):
pre_tok_class = getattr(normalizers, pre_tok_state.pop("type"))
pre_tok_state["lowercase"] = do_lower_case
pre_tok_state["strip_accents"] = strip_accents
self.backend_tokenizer.normalizer = pre_tok_class(**pre_tok_state)
self.do_lower_case = do_lower_case
@property
def question_token_id(self):
return self.convert_tokens_to_ids(self.question_token)
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]
suff = [self.question_token_id] + [self.convert_tokens_to_ids(".")]
if self.padding_side == "right":
return cls + toks_0 + suff + sep + toks_1 + sep
else:
return cls + toks_0 + sep + toks_1 + suff + sep
def create_token_type_ids_from_sequences(self, toks_0, toks_1=None):
sep = [self.sep_token_id]
cls = [self.cls_token_id]
suff = [self.question_token_id] + [self.convert_tokens_to_ids(".")]
if toks_1 is None:
return len(cls + toks_0 + sep) * [0]
if self.padding_side == "right":
return len(cls + toks_0 + suff + sep) * [0] + len(toks_1 + sep) * [1]
else:
return len(cls + toks_0 + sep) * [0] + len(toks_1 + suff + sep) * [1]
def save_vocabulary(self, dir, pre=None):
return tuple(self._tokenizer.model.save(dir, name=pre))
|
{"/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"]}
|
33,472
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/models/old/trafo.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.attention import Attend
from qnarre.core.base import Hypers, Module, Linear
from qnarre.core.mlp import MLP
from qnarre.core.deduce import Deduce, Search
from qnarre.core.norm import PreProc, PostProc
from qnarre.core.embed import TokEmbed, TypEmbed, PosEmbed, PosTiming
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):
src = torch.Input(shape=(ps.len_src,), dtype="int32")
typ = torch.Input(shape=(ps.len_src,), dtype="int32")
hint = torch.Input(shape=(ps.len_tgt,), dtype="int32")
tgt = torch.Input(shape=(ps.len_tgt,), dtype="int32")
ins = [src, typ, hint, tgt]
outs = [Trafo(ps)(ins)]
m = torch.Model(name="TrafoModel", inputs=ins, outputs=outs)
return m
class Trafo(Module):
hs = Hypers(
[
"beam_size",
"drop_hidden",
"len_src",
"len_tgt",
"num_toks",
"pos_type",
"n_typ",
],
{},
)
typ_embed = pos_embed = enc_stack = dec_stack = pos_x_b = pos_p_b = None
def __init__(self, dim_out=None, hs=[], **kw):
if dim_out is not None:
kw.update(dim_out=dim_out)
super().__init__([self.hs] + hs, **kw)
cfg = self.cfg
kw.update(hs=hs)
self.tok_embed = TokEmbed(**kw)
if cfg.n_typ:
self.typ_embed = TypEmbed(**kw)
if cfg.pos_type == "embed":
self.pos_embed = PosEmbed(**kw)
elif cfg.pos_type == "timing":
self.pos_embed = PosTiming(**kw)
else:
assert cfg.pos_type == "relative"
self.pre = PreProc(**kw)
self.post = PostProc(**kw)
self.enc_stack = EncStack(self, **kw)
self.dec_stack = DecStack(self, **kw)
self.deduce = Deduce(self, **kw)
self.search = Search(self, **kw)
self.out = Linear(cfg.num_toks, **kw)
def forward(self, x, training=None):
src, typ, hint, tgt = x
ctx = None
if src is not None:
y = self.embed(src, typ)
ctx = self.enc_stack([y])
if hint is not None:
y = self.embed(hint)
ctx = self.dec_stack([y, ctx])
if training is not None:
out = self.deduce([ctx, tgt])
else:
# out = self.search([tgt, ctx])
pass
return out
def embed(self, x, typ=None):
y = self.tok_embed(x)
if self.typ_embed and typ is not None:
y = self.typ_embed([y, typ])
if self.pos_embed:
y = self.pos_embed(y)
return y
class Stack(Module):
def __init__(self, owner, ps=None, **kw):
super().__init__(ps, **kw)
self.pre = owner.pre
self.post = owner.post
class EncStack(Stack):
hs = Hypers(["n_encoders"], {})
def __init__(self, ps, owner, **kw):
super().__init__(ps, owner, **kw)
cfg = self.cfg
n = cfg.n_encoders
self.encs = [Encoder(ps, owner, f"enc_{i}") for i in range(n)]
def forward(self, x):
x = x[0]
y = self.pre([x, x])
for e in self.encs:
y = e([y])
y = self.post([x, y])
return y
class DecStack(Stack):
hs = Hypers(["num_dec_lays"], {})
def __init__(self, ps, owner, **kw):
super().__init__(ps, owner, **kw)
cfg = self.cfg
n = cfg.num_dec_lays
self.decs = [Decoder(ps, owner, f"dec_{i}") for i in range(n)]
def forward(self, x):
x, ctx = x
"""
cfg = self.cfg
if ps.causal_refl:
if ps.prepend_mode == 'prepend_inputs_full_attention':
y = torch.cumsum(torch.cumsum(rb, axis=1), axis=1)
y2 = torch.expand_dims(y, axis=1)
y = torch.greater(y2, torch.expand_dims(y, axis=2))
b = torch.expand_dims(torch.cast(y, torch.floatx()) * -1e9, axis=1)
else:
ln = torch.int_shape(x)[1]
sh = (1, 1, ln, ln)
b = U.ones_band_part(ln, ln, -1, 0, out_shape=sh)
b = -1e9 * (1.0 - b)
"""
y = self.pre([x, x])
for d in self.decs:
y = d([y, ctx])
y = self.post([x, y])
return y
class Encoder(Module):
hs = Hypers(
["len_mem"],
{},
)
mem = None
def __init__(self, owner, ps=None, name="enc", **kw):
super().__init__(ps, name=name, **kw)
self.refl = Attend(owner, ps, name=name + "_refl")
self.ffnet = MLP(owner, ps, name=name + "_ffnet")
mlen = self.cfg.len_mem
if mlen:
s = input_shape[0]
s = s[:1] + (mlen,) + s[2:]
self.mem = self.add_resource(self.name + "_mem", s)
def forward(self, x):
x = x[0]
y = self.reflect(x)
y = self.ffnet(y)
return y
def reflect(self, x):
m = self.mem
if m is None:
y = self.refl([x])
else:
y = self.refl([x, m])
i = self.cfg.len_mem
self.mem.assign(torch.concat([m, x], axis=1)[:, -i:])
return y
class Decoder(Encoder):
def __init__(self, owner, ps=None, name="dec", **kw):
super().__init__(owner, ps, name, **kw)
self.attn = Attend(owner, ps, name=name + "_attn")
def forward(self, x):
x, ctx = x
y = self.reflect(x)
if ctx is not None:
y = self.attn([y, ctx])
y = self.ffnet(y)
return y
|
{"/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"]}
|
33,473
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/tokens/fast/led.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.utils import PaddingStrategy
from .bart import Tokenizer as BartFast
from ..led import Tokenizer as LED
VOCAB_MAP = {
"vocab_file": {
"allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json",
},
"merges_file": {
"allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt",
},
"tokenizer_file": {
"allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json",
},
}
INPUT_CAPS = {
"allenai/led-base-16384": 16384,
}
class Tokenizer(BartFast):
vocab_map = VOCAB_MAP
input_caps = INPUT_CAPS
slow_tokenizer_class = LED
def _pad(
self,
encoded_inputs,
max_length=None,
padding_strategy=PaddingStrategy.DO_NOT_PAD,
pad_to_multiple_of=None,
return_attention_mask=None,
):
encoded_inputs = super()._pad(
encoded_inputs=encoded_inputs,
max_length=max_length,
padding_strategy=padding_strategy,
pad_to_multiple_of=pad_to_multiple_of,
return_attention_mask=return_attention_mask,
)
if return_attention_mask is None:
return_attention_mask = "attention_mask" in self.model_input_names
if return_attention_mask and "global_attention_mask" in encoded_inputs:
required_input = encoded_inputs[self.model_input_names[0]]
needs_to_be_padded = len(encoded_inputs["global_attention_mask"]) != len(required_input)
if needs_to_be_padded:
difference = len(required_input) - len(encoded_inputs["global_attention_mask"])
if self.padding_side == "right":
encoded_inputs["global_attention_mask"] = (
encoded_inputs["global_attention_mask"] + [-1] * difference
)
else:
assert self.padding_side == "left"
encoded_inputs["global_attention_mask"] = [-1] * difference + encoded_inputs[
"global_attention_mask"
]
return encoded_inputs
|
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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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|
33,474
|
quantapix/qnarre
|
refs/heads/main
|
/tools/triton/python/triton/runtime/cache.py
|
import json
import os
import random
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, Optional
def default_cache_dir():
return os.path.join(Path.home(), ".triton", "cache")
class CacheManager(ABC):
def __init__(self, key):
pass
@abstractmethod
def get_file(self, filename) -> Optional[str]:
pass
@abstractmethod
def has_file(self, filename) -> bool:
pass
@abstractmethod
def put(self, data, filename, binary=True) -> str:
pass
@abstractmethod
def get_group(self, filename: str) -> Optional[Dict[str, str]]:
pass
@abstractmethod
def put_group(self, filename: str, group: Dict[str, str]):
pass
class FileCacheManager(CacheManager):
def __init__(self, key):
self.key = key
self.lock_path = None
# create cache directory if it doesn't exist
self.cache_dir = os.environ.get('TRITON_CACHE_DIR', default_cache_dir())
if self.cache_dir:
self.cache_dir = os.path.join(self.cache_dir, self.key)
self.lock_path = os.path.join(self.cache_dir, "lock")
os.makedirs(self.cache_dir, exist_ok=True)
def _make_path(self, filename) -> str:
return os.path.join(self.cache_dir, filename)
def has_file(self, filename):
if not self.cache_dir:
return False
return os.path.exists(self._make_path(filename))
def get_file(self, filename) -> Optional[str]:
if self.has_file(filename):
return self._make_path(filename)
else:
return None
def get_group(self, filename: str) -> Optional[Dict[str, str]]:
grp_filename = f"__grp__{filename}"
if not self.has_file(grp_filename):
return None
grp_filepath = self._make_path(grp_filename)
with open(grp_filepath) as f:
grp_data = json.load(f)
child_paths = grp_data.get("child_paths", None)
# Invalid group data.
if child_paths is None:
return None
result = {}
for c in child_paths:
p = self._make_path(c)
if not os.path.exists(p):
raise Exception(f"Group file {p} does not exist from group {grp_filename} ")
result[c] = p
return result
# Note a group of pushed files as being part of a group
def put_group(self, filename: str, group: Dict[str, str]):
if not self.cache_dir:
return
grp_contents = json.dumps({"child_paths": sorted(list(group.keys()))})
grp_filename = f"__grp__{filename}"
return self.put(grp_contents, grp_filename, binary=False)
def put(self, data, filename, binary=True) -> str:
if not self.cache_dir:
return
binary = isinstance(data, bytes)
if not binary:
data = str(data)
assert self.lock_path is not None
filepath = self._make_path(filename)
# Random ID to avoid any collisions
rnd_id = random.randint(0, 1000000)
# we use the PID incase a bunch of these around so we can see what PID made it
pid = os.getpid()
# use tempfile to be robust against program interruptions
temp_path = f"{filepath}.tmp.pid_{pid}_{rnd_id}"
mode = "wb" if binary else "w"
with open(temp_path, mode) as f:
f.write(data)
# Replace is guaranteed to be atomic on POSIX systems if it succeeds
# so filepath cannot see a partial write
os.replace(temp_path, filepath)
return filepath
__cache_cls = FileCacheManager
__cache_cls_nme = "DEFAULT"
def get_cache_manager(key) -> CacheManager:
import os
user_cache_manager = os.environ.get("TRITON_CACHE_MANAGER", None)
global __cache_cls
global __cache_cls_nme
if user_cache_manager is not None and user_cache_manager != __cache_cls_nme:
import importlib
module_path, clz_nme = user_cache_manager.split(":")
module = importlib.import_module(module_path)
__cache_cls = getattr(module, clz_nme)
__cache_cls_nme = user_cache_manager
return __cache_cls(key)
|
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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"], 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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", 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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"]}
|
33,475
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/models/realm.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 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
from ...pytorch_utils import (
apply_chunking_to_forward,
)
log = logging.get_logger(__name__)
LIST = [
"google/realm-cc-news-pretrained-embedder",
"google/realm-cc-news-pretrained-encoder",
"google/realm-cc-news-pretrained-scorer",
"google/realm-cc-news-pretrained-openqa",
"google/realm-orqa-nq-openqa",
"google/realm-orqa-nq-reader",
"google/realm-orqa-wq-openqa",
"google/realm-orqa-wq-reader",
]
# Copied from transformers.models.bert.modeling_bert.BertEmbeddings with Bert->Realm
class RealmEmbeddings(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)
# position_ids (1, len position emb) is contiguous in memory and exported when serialized
self.pos_type = getattr(config, "pos_type", "absolute")
self.register_buffer("position_ids", torch.arange(config.n_pos).expand((1, -1)))
self.register_buffer(
"token_type_ids",
torch.zeros(self.position_ids.size(), dtype=torch.long),
persistent=False,
)
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:
if hasattr(self, "token_type_ids"):
buffered_token_type_ids = self.token_type_ids[:, :seq_length]
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(
input_shape[0], seq_length
)
token_type_ids = buffered_token_type_ids_expanded
else:
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
if self.pos_type == "absolute":
position_embeddings = self.position_embeddings(position_ids)
embeddings += position_embeddings
embeddings = self.norm(embeddings)
embeddings = self.drop(embeddings)
return embeddings
# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->Realm
class RealmSelfAttention(qc.Module):
def __init__(self, config, pos_type=None):
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.pos_type = pos_type or getattr(config, "pos_type", "absolute")
if self.pos_type == "relative_key" or self.pos_type == "relative_key_query":
self.n_pos = config.n_pos
self.distance_embedding = qc.Embed(2 * config.n_pos - 1, self.attention_head_size)
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)
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
if self.pos_type == "relative_key" or self.pos_type == "relative_key_query":
seq_length = hiddens.size()[1]
position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hiddens.device).view(
-1, 1
)
position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hiddens.device).view(
1, -1
)
distance = position_ids_l - position_ids_r
positional_embedding = self.distance_embedding(distance + self.n_pos - 1)
positional_embedding = positional_embedding.to(
dtype=query_layer.dtype
) # fp16 compatibility
if self.pos_type == "relative_key":
relative_position_scores = torch.einsum(
"bhld,lrd->bhlr", query_layer, positional_embedding
)
attention_scores = attention_scores + relative_position_scores
elif self.pos_type == "relative_key_query":
relative_position_scores_query = torch.einsum(
"bhld,lrd->bhlr", query_layer, positional_embedding
)
relative_position_scores_key = torch.einsum(
"bhrd,lrd->bhlr", key_layer, positional_embedding
)
attention_scores = (
attention_scores + relative_position_scores_query + relative_position_scores_key
)
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 RealmModel 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->Realm
class RealmSelfOutput(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
# Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->Realm
class Attention(qc.Module):
def __init__(self, config, pos_type=None):
super().__init__()
self.self = RealmSelfAttention(config, pos_type=pos_type)
self.output = RealmSelfOutput(config)
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->Realm
class RealmIntermediate(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->Realm
class RealmOutput(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
# Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->Realm
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, pos_type="absolute")
self.intermediate = RealmIntermediate(config)
self.output = RealmOutput(config)
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
def feed_forward_chunk(self, attention_output):
intermediate_output = self.intermediate(attention_output)
layer_output = self.output(intermediate_output, attention_output)
return layer_output
# Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->Realm
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,
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,
):
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,
)
@dataclass
class RealmEmbedderOutput(ModelOutput):
projected_score = None
hiddens = None
attns = None
@dataclass
class RealmScorerOutput(ModelOutput):
relevance_score = None
query_score = None
candidate_score = None
@dataclass
class RealmReaderOutput(ModelOutput):
loss = None
retriever_loss = None
reader_loss = None
retriever_correct = None
reader_correct = None
block_idx = None
candidate = None
start_pos = None
end_pos = None
hiddens = None
attns = None
@dataclass
class RealmForOpenQAOutput(ModelOutput):
reader_output = None
predicted_answer_ids = None
class RealmPredictionHeadTransform(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 RealmLMPredictionHead(qc.Module):
def __init__(self, config):
super().__init__()
self.transform = RealmPredictionHeadTransform(config)
self.decoder = qc.Linear(config.d_model, config.s_vocab, bias=False)
self.bias = nn.Parameter(torch.zeros(config.s_vocab))
self.decoder.bias = self.bias
def forward(self, x):
y = self.transform(x)
y = self.decoder(y)
return y
class RealmOnlyMLMHead(qc.Module):
def __init__(self, config):
super().__init__()
self.predictions = RealmLMPredictionHead(config)
def forward(self, sequence_output):
prediction_scores = self.predictions(sequence_output)
return prediction_scores
class RealmScorerProjection(qc.Module):
def __init__(self, config):
super().__init__()
self.predictions = RealmLMPredictionHead(config)
self.dense = qc.Linear(config.d_model, config.retriever_proj_size)
self.norm = qc.LayerNorm(config.retriever_proj_size, eps=config.eps)
def forward(self, hiddens):
hiddens = self.dense(hiddens)
hiddens = self.norm(hiddens)
return hiddens
class RealmReaderProjection(qc.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.dense_intermediate = qc.Linear(config.d_model, config.span_hidden_size * 2)
self.dense_output = qc.Linear(config.span_hidden_size, 1)
self.layer_normalization = qc.LayerNorm(
config.span_hidden_size, eps=config.reader_layer_norm_eps
)
self.relu = nn.ReLU()
def forward(self, hiddens, block_mask):
def span_candidates(masks):
_, max_sequence_len = masks.shape
def _spans_given_width(width):
current_starts = torch.arange(max_sequence_len - width + 1, device=masks.device)
current_ends = torch.arange(width - 1, max_sequence_len, device=masks.device)
return current_starts, current_ends
starts, ends = zip(
*(_spans_given_width(w + 1) for w in range(self.config.max_span_width))
)
# [num_spans]
starts = torch.cat(starts, 0)
ends = torch.cat(ends, 0)
# [num_retrievals, num_spans]
start_masks = torch.index_select(masks, dim=-1, index=starts)
end_masks = torch.index_select(masks, dim=-1, index=ends)
span_masks = start_masks * end_masks
return starts, ends, span_masks
def mask_to_score(mask):
return (1.0 - mask.type(torch.float32)) * -10000.0
# [reader_beam_size, max_sequence_len, span_hidden_size * 2]
hiddens = self.dense_intermediate(hiddens)
# [reader_beam_size, max_sequence_len, span_hidden_size]
start_projection, end_projection = hiddens.chunk(2, dim=-1)
candidate_starts, candidate_ends, candidate_mask = span_candidates(block_mask)
candidate_start_projections = torch.index_select(
start_projection, dim=1, index=candidate_starts
)
candidate_end_projections = torch.index_select(end_projection, dim=1, index=candidate_ends)
candidate_hidden = candidate_start_projections + candidate_end_projections
# [reader_beam_size, num_candidates, span_hidden_size]
candidate_hidden = self.relu(candidate_hidden)
# [reader_beam_size, num_candidates, span_hidden_size]
candidate_hidden = self.layer_normalization(candidate_hidden)
# [reader_beam_size, num_candidates]
reader_logits = self.dense_output(candidate_hidden).squeeze(-1)
# [reader_beam_size, num_candidates]
reader_logits += mask_to_score(candidate_mask)
return reader_logits, candidate_starts, candidate_ends
class Model(PreTrained):
def __init__(self, config, add_pooling_layer=True):
super().__init__(config)
self.config = config
self.embeddings = RealmEmbeddings(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:
if hasattr(self.embeddings, "token_type_ids"):
buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(
batch_size, seq_length
)
token_type_ids = buffered_token_type_ids_expanded
else:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
extended_attention_mask = self.get_extended_attention_mask(
attention_mask, input_shape, device
)
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 RealmEmbedder(PreTrained):
def __init__(self, config):
super().__init__(config)
self.realm = Model(self.config)
self.cls = RealmScorerProjection(self.config)
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,
):
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
realm_outputs = self.realm(
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,
)
# [batch_size, d_model]
pools = realm_outputs[1]
# [batch_size, retriever_proj_size]
projected_score = self.cls(pools)
if not return_dict:
return (projected_score,) + realm_outputs[2:4]
else:
return RealmEmbedderOutput(
projected_score=projected_score,
hiddens=realm_outputs.hiddens,
attns=realm_outputs.attns,
)
class RealmScorer(PreTrained):
def __init__(self, config, query_embedder=None):
super().__init__(config)
self.embedder = RealmEmbedder(self.config)
self.query_embedder = query_embedder if query_embedder is not None else self.embedder
self.post_init()
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
candidate_input_ids=None,
candidate_attention_mask=None,
candidate_token_type_ids=None,
candidate_inputs_embeds=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
if input_ids is None and inputs_embeds is None:
raise ValueError("You have to specify either input_ids or input_embeds.")
if candidate_input_ids is None and candidate_inputs_embeds is None:
raise ValueError(
"You have to specify either candidate_input_ids or candidate_inputs_embeds."
)
query_outputs = self.query_embedder(
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,
)
# [batch_size * num_candidates, candidate_seq_len]
(
flattened_input_ids,
flattened_attention_mask,
flattened_token_type_ids,
) = self._flatten_inputs(
candidate_input_ids, candidate_attention_mask, candidate_token_type_ids
)
candidate_outputs = self.embedder(
flattened_input_ids,
attention_mask=flattened_attention_mask,
token_type_ids=flattened_token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=candidate_inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
# [batch_size, retriever_proj_size]
query_score = query_outputs[0]
# [batch_size * num_candidates, retriever_proj_size]
candidate_score = candidate_outputs[0]
# [batch_size, num_candidates, retriever_proj_size]
candidate_score = candidate_score.view(
-1, self.config.num_candidates, self.config.retriever_proj_size
)
# [batch_size, num_candidates]
relevance_score = torch.einsum("BD,BND->BN", query_score, candidate_score)
if not return_dict:
return relevance_score, query_score, candidate_score
return RealmScorerOutput(
relevance_score=relevance_score,
query_score=query_score,
candidate_score=candidate_score,
)
class RealmKnowledgeAugEncoder(PreTrained):
def __init__(self, config):
super().__init__(config)
self.realm = Model(self.config)
self.cls = RealmOnlyMLMHead(self.config)
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
relevance_score=None,
labels=None,
mlm_mask=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
(
flattened_input_ids,
flattened_attention_mask,
flattened_token_type_ids,
) = self._flatten_inputs(input_ids, attention_mask, token_type_ids)
joint_outputs = self.realm(
flattened_input_ids,
attention_mask=flattened_attention_mask,
token_type_ids=flattened_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,
)
# [batch_size * num_candidates, joint_seq_len, d_model]
joint_output = joint_outputs[0]
# [batch_size * num_candidates, joint_seq_len, s_vocab]
prediction_scores = self.cls(joint_output)
# [batch_size, num_candidates]
candidate_score = relevance_score
masked_lm_loss = None
if labels is not None:
if candidate_score is None:
raise ValueError(
"You have to specify `relevance_score` when `labels` is specified in order to compute loss."
)
batch_size, seq_length = labels.size()
if mlm_mask is None:
mlm_mask = torch.ones_like(labels, dtype=torch.float32)
else:
mlm_mask = mlm_mask.type(torch.float32)
# Compute marginal log-likelihood
loss_fct = CrossEntropyLoss(reduction="none") # -100 index = padding token
# [batch_size * num_candidates * joint_seq_len, s_vocab]
mlm_logits = prediction_scores.view(-1, self.config.s_vocab)
# [batch_size * num_candidates * joint_seq_len]
mlm_targets = labels.tile(1, self.config.num_candidates).view(-1)
# [batch_size, num_candidates, joint_seq_len]
masked_lm_log_prob = -loss_fct(mlm_logits, mlm_targets).view(
batch_size, self.config.num_candidates, seq_length
)
# [batch_size, num_candidates, 1]
candidate_log_prob = candidate_score.log_softmax(-1).unsqueeze(-1)
# [batch_size, num_candidates, joint_seq_len]
joint_gold_log_prob = candidate_log_prob + masked_lm_log_prob
# [batch_size, joint_seq_len]
marginal_gold_log_probs = joint_gold_log_prob.logsumexp(1)
# []
masked_lm_loss = -torch.nansum(
torch.sum(marginal_gold_log_probs * mlm_mask) / torch.sum(mlm_mask)
)
if not return_dict:
output = (prediction_scores,) + joint_outputs[2:4]
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
return MaskedLMOutput(
loss=masked_lm_loss,
logits=prediction_scores,
hiddens=joint_outputs.hiddens,
attns=joint_outputs.attns,
)
class RealmReader(PreTrained):
_keys_to_ignore_on_load_unexpected = [r"pooler", "cls"]
def __init__(self, config):
super().__init__(config)
self.n_labels = config.n_labels
self.realm = Model(config)
self.cls = RealmOnlyMLMHead(config)
self.qa_outputs = RealmReaderProjection(config)
self.post_init()
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
relevance_score=None,
block_mask=None,
start_positions=None,
end_positions=None,
has_answers=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
if relevance_score is None:
raise ValueError("You have to specify `relevance_score` to calculate logits and loss.")
if block_mask is None:
raise ValueError(
"You have to specify `block_mask` to separate question block and evidence block."
)
if token_type_ids.size(1) < self.config.max_span_width:
raise ValueError(
"The input sequence length must be greater than or equal to config.max_span_width."
)
outputs = self.realm(
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,
)
# [reader_beam_size, joint_seq_len, d_model]
sequence_output = outputs[0]
# [reader_beam_size, num_candidates], [num_candidates], [num_candidates]
reader_logits, candidate_starts, candidate_ends = self.qa_outputs(
sequence_output, block_mask[0 : self.config.reader_beam_size]
)
# [searcher_beam_size, 1]
retriever_logits = torch.unsqueeze(relevance_score[0 : self.config.reader_beam_size], -1)
# [reader_beam_size, num_candidates]
reader_logits += retriever_logits
# []
predicted_block_index = torch.argmax(torch.max(reader_logits, dim=1).values)
# []
predicted_candidate = torch.argmax(torch.max(reader_logits, dim=0).values)
# [1]
predicted_start = torch.index_select(candidate_starts, dim=0, index=predicted_candidate)
# [1]
predicted_end = torch.index_select(candidate_ends, dim=0, index=predicted_candidate)
total_loss = None
retriever_loss = None
reader_loss = None
retriever_correct = None
reader_correct = None
if start_positions is not None and end_positions is not None and has_answers is not None:
def compute_correct_candidates(
candidate_starts, candidate_ends, gold_starts, gold_ends
):
"""Compute correct span."""
# [reader_beam_size, num_answers, num_candidates]
is_gold_start = torch.eq(
torch.unsqueeze(torch.unsqueeze(candidate_starts, 0), 0),
torch.unsqueeze(gold_starts, -1),
)
is_gold_end = torch.eq(
torch.unsqueeze(torch.unsqueeze(candidate_ends, 0), 0),
torch.unsqueeze(gold_ends, -1),
)
# [reader_beam_size, num_candidates]
return torch.any(torch.logical_and(is_gold_start, is_gold_end), 1)
def marginal_log_loss(logits, is_correct):
"""Loss based on the negative marginal log-likelihood."""
def mask_to_score(mask):
return (1.0 - mask.type(torch.float32)) * -10000.0
# []
log_numerator = torch.logsumexp(logits + mask_to_score(is_correct), dim=-1)
log_denominator = torch.logsumexp(logits, dim=-1)
return log_denominator - log_numerator
# sometimes the start/end positions are outside our model inputs, we ignore these terms
# `-1` is reserved for no answer.
ignored_index = sequence_output.size(1)
start_positions = start_positions.clamp(-1, ignored_index)
end_positions = end_positions.clamp(-1, ignored_index)
retriever_correct = has_answers
any_retriever_correct = torch.any(retriever_correct)
reader_correct = compute_correct_candidates(
candidate_starts=candidate_starts,
candidate_ends=candidate_ends,
gold_starts=start_positions[0 : self.config.reader_beam_size],
gold_ends=end_positions[0 : self.config.reader_beam_size],
)
any_reader_correct = torch.any(reader_correct)
retriever_loss = marginal_log_loss(relevance_score, retriever_correct)
reader_loss = marginal_log_loss(reader_logits.view(-1), reader_correct.view(-1))
retriever_loss *= any_retriever_correct.type(torch.float32)
reader_loss *= any_reader_correct.type(torch.float32)
total_loss = (retriever_loss + reader_loss).mean()
if not return_dict:
output = (
predicted_block_index,
predicted_candidate,
predicted_start,
predicted_end,
) + outputs[2:]
return (
(
(total_loss, retriever_loss, reader_loss, retriever_correct, reader_correct)
+ output
)
if total_loss is not None
else output
)
return RealmReaderOutput(
loss=total_loss,
retriever_loss=retriever_loss,
reader_loss=reader_loss,
retriever_correct=retriever_correct,
reader_correct=reader_correct,
block_idx=predicted_block_index,
candidate=predicted_candidate,
start_pos=predicted_start,
end_pos=predicted_end,
hiddens=outputs.hiddens,
attns=outputs.attns,
)
class ForQA(PreTrained):
def __init__(self, config, retriever=None):
super().__init__(config)
self.embedder = RealmEmbedder(config)
self.reader = RealmReader(config)
self.register_buffer(
"block_emb",
torch.zeros(()).new_empty(
size=(config.num_block_records, config.retriever_proj_size),
dtype=torch.float32,
device=torch.device("cpu"),
),
)
self.retriever = retriever
@property
def searcher_beam_size(self):
if self.training:
return self.config.searcher_beam_size
return self.config.reader_beam_size
def block_embedding_to(self, device):
self.block_emb = self.block_emb.to(device)
def forward(
self,
input_ids,
attention_mask=None,
token_type_ids=None,
answer_ids=None,
return_dict=None,
):
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if input_ids is not None and input_ids.shape[0] != 1:
raise ValueError("The batch_size of the inputs must be 1.")
question_outputs = self.embedder(
input_ids=input_ids,
token_type_ids=token_type_ids,
attention_mask=attention_mask,
return_dict=True,
)
question_projection = question_outputs[0]
batch_scores = torch.einsum(
"BD,QD->QB", self.block_emb, question_projection.to(self.block_emb.device)
)
_, retrieved_block_ids = torch.topk(batch_scores, k=self.searcher_beam_size, dim=-1)
retrieved_block_ids = retrieved_block_ids.squeeze()
retrieved_block_emb = torch.index_select(self.block_emb, dim=0, index=retrieved_block_ids)
has_answers, start_pos, end_pos, concat_inputs = self.retriever(
retrieved_block_ids.cpu(), input_ids, answer_ids, max_length=self.config.reader_seq_len
)
concat_inputs = concat_inputs.to(self.reader.device)
block_mask = concat_inputs.special_tokens_mask.type(torch.bool).to(
device=self.reader.device
)
block_mask.logical_not_().logical_and_(concat_inputs.token_type_ids.type(torch.bool))
if has_answers is not None:
has_answers = torch.tensor(has_answers, dtype=torch.bool, device=self.reader.device)
start_pos = torch.tensor(start_pos, dtype=torch.long, device=self.reader.device)
end_pos = torch.tensor(end_pos, dtype=torch.long, device=self.reader.device)
retrieved_logits = torch.einsum(
"D,BD->B", question_projection.squeeze(), retrieved_block_emb.to(self.reader.device)
)
reader_output = self.reader(
input_ids=concat_inputs.input_ids[0 : self.config.reader_beam_size],
attention_mask=concat_inputs.attention_mask[0 : self.config.reader_beam_size],
token_type_ids=concat_inputs.token_type_ids[0 : self.config.reader_beam_size],
relevance_score=retrieved_logits,
block_mask=block_mask,
has_answers=has_answers,
start_positions=start_pos,
end_positions=end_pos,
return_dict=True,
)
predicted_block = concat_inputs.input_ids[reader_output.block_idx]
predicted_answer_ids = predicted_block[reader_output.start_pos : reader_output.end_pos + 1]
if not return_dict:
return reader_output, predicted_answer_ids
return RealmForOpenQAOutput(
reader_output=reader_output,
predicted_answer_ids=predicted_answer_ids,
)
|
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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"]}
|
33,476
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/models/convbert.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.convbert import PreTrained
from torch.nn import CrossEntropyLoss
from ...modeling_utils import SequenceSummary
from ...pytorch_utils import (
apply_chunking_to_forward,
)
from . import bert
log = logging.get_logger(__name__)
class ForChoice(PreTrained):
def __init__(self, config):
super().__init__(config)
self.model = Model(config)
self.sequence_summary = SequenceSummary(config)
self.classifier = qc.Linear(config.d_model, 1)
self.post_init()
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.model(
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,
)
sequence_output = outputs[0]
pooled_output = self.sequence_summary(sequence_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[1:]
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 ForMasked(PreTrained):
def __init__(self, **kw):
super().__init__(**kw)
cfg = self.get_cfg(kw)
self.model = Model(**kw)
self.proj = Predictor(cfg.d_embed, **kw)
forward = qf.forward_masked
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, **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 = qc.Linear(cfg.d_model, cfg.n_labels, **kw)
forward = qf.forward_qa
class PredictionHeadTransform(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):
super().__init__(config)
self.embeddings = Embed(config)
if config.d_embed != config.d_model:
self.embeddings_project = qc.Linear(config.d_embed, config.d_model)
self.encoder = Encoder(config)
self.config = config
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()
batch_size, seq_length = input_shape
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)
if token_type_ids is None:
if hasattr(self.embeddings, "token_type_ids"):
buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(
batch_size, seq_length
)
token_type_ids = buffered_token_type_ids_expanded
else:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, 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)
hiddens = self.embeddings(
input_ids=input_ids,
position_ids=position_ids,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
)
if hasattr(self, "embeddings_project"):
hiddens = self.embeddings_project(hiddens)
hiddens = self.encoder(
hiddens,
attention_mask=extended_attention_mask,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
return hiddens
class Group(qc.Module):
def __init__(self, input_size, output_size, n_groups):
super().__init__()
self.input_size = input_size
self.output_size = output_size
self.n_groups = n_groups
self.group_in_dim = self.input_size // self.n_groups
self.group_out_dim = self.output_size // self.n_groups
self.weight = nn.Parameter(
torch.empty(self.n_groups, self.group_in_dim, self.group_out_dim)
)
self.bias = nn.Parameter(torch.empty(output_size))
def forward(self, hiddens):
batch_size = list(hiddens.size())[0]
x = torch.reshape(hiddens, [-1, self.n_groups, self.group_in_dim])
x = x.permute(1, 0, 2)
x = torch.matmul(x, self.weight)
x = x.permute(1, 0, 2)
x = torch.reshape(x, [batch_size, -1, self.output_size])
x = x + self.bias
return x
class Intermediate(qc.Module):
def __init__(self, cfg):
super().__init__()
if cfg.n_groups == 1:
self.dense = qc.Linear(cfg.d_model, cfg.d_ff)
else:
self.dense = Group(
input_size=cfg.d_model,
output_size=cfg.d_ff,
n_groups=cfg.n_groups,
)
self.act = qu.activation(cfg.act)
def forward(self, x):
y = self.dense(x)
y = self.act(y)
return y
class Output(qc.Module):
def __init__(self, config):
super().__init__()
if config.n_groups == 1:
self.dense = qc.Linear(config.d_ff, config.d_model)
else:
self.dense = Group(
input_size=config.d_ff,
output_size=config.d_model,
n_groups=config.n_groups,
)
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.is_decoder = config.is_decoder
self.add_cross_attention = config.add_cross_attention
if self.add_cross_attention:
assert self.is_decoder
self.crossattention = Attention(config)
self.intermediate = Intermediate(config)
self.output = Output(config)
def forward(
self,
hiddens,
attention_mask=None,
head_mask=None,
enc_hiddens=None,
encoder_attention_mask=None,
output_attentions=False,
):
self_attention_outputs = self.attention(
hiddens,
attention_mask,
head_mask,
output_attentions=output_attentions,
)
attention_output = self_attention_outputs[0]
outputs = self_attention_outputs[1:]
if self.is_decoder and enc_hiddens is not None:
if not hasattr(self, "crossattention"):
raise AttributeError(
f"If `enc_hiddens` are passed, {self} has to be instantiated with cross-attention layers by setting `config.add_cross_attention=True`"
)
cross_attention_outputs = self.crossattention(
attention_output,
encoder_attention_mask,
head_mask,
enc_hiddens,
output_attentions,
)
attention_output = cross_attention_outputs[0]
outputs = outputs + cross_attention_outputs[1:]
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
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,
hiddens,
attention_mask=None,
head_mask=None,
enc_hiddens=None,
encoder_attention_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 = () if output_attentions and self.config.add_cross_attention 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
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),
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,
output_attentions,
)
hiddens = layer_outputs[0]
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,
all_hidden_states,
all_self_attentions,
all_cross_attentions,
]
if v is not None
)
return qo.BaseWithCrossAttentions(
y=hiddens,
hiddens=all_hidden_states,
attns=all_self_attentions,
crosses=all_cross_attentions,
)
class Attention(qc.Module):
def __init__(self, config):
super().__init__()
self.self = SelfAttention(config)
self.output = SelfOutput(config)
def forward(
self,
hiddens,
attention_mask=None,
head_mask=None,
enc_hiddens=None,
output_attentions=False,
):
self_outputs = self.self(
hiddens,
attention_mask,
head_mask,
enc_hiddens,
output_attentions,
)
attention_output = self.output(self_outputs[0], hiddens)
outputs = (attention_output,) + self_outputs[1:]
return outputs
class SelfAttention(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})"
)
new_num_attention_heads = config.n_heads // config.head_ratio
if new_num_attention_heads < 1:
self.head_ratio = config.n_heads
self.n_heads = 1
else:
self.n_heads = new_num_attention_heads
self.head_ratio = config.head_ratio
self.conv_kernel_size = config.conv_kernel_size
if config.d_model % self.n_heads != 0:
raise ValueError("d_model should be divisible by n_heads")
self.attention_head_size = 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.key_conv_attn_layer = Conv1D(
config, config.d_model, self.all_head_size, self.conv_kernel_size
)
self.conv_kernel_layer = qc.Linear(self.all_head_size, self.n_heads * self.conv_kernel_size)
self.conv_out_layer = qc.Linear(config.d_model, self.all_head_size)
self.unfold = nn.Unfold(
kernel_size=[self.conv_kernel_size, 1],
padding=[int((self.conv_kernel_size - 1) / 2), 0],
)
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,
enc_hiddens=None,
output_attentions=False,
):
mixed_query_layer = self.query(hiddens)
batch_size = hiddens.size(0)
# If this is instantiated as a cross-attention module, the keys
# and values come from an encoder; the attention mask needs to be
# such that the encoder's padding tokens are not attended to.
if enc_hiddens is not None:
mixed_key_layer = self.key(enc_hiddens)
mixed_value_layer = self.value(enc_hiddens)
else:
mixed_key_layer = self.key(hiddens)
mixed_value_layer = self.value(hiddens)
mixed_key_conv_attn_layer = self.key_conv_attn_layer(hiddens.transpose(1, 2))
mixed_key_conv_attn_layer = mixed_key_conv_attn_layer.transpose(1, 2)
query_layer = self.transpose_for_scores(mixed_query_layer)
key_layer = self.transpose_for_scores(mixed_key_layer)
value_layer = self.transpose_for_scores(mixed_value_layer)
conv_attn_layer = torch.multiply(mixed_key_conv_attn_layer, mixed_query_layer)
conv_kernel_layer = self.conv_kernel_layer(conv_attn_layer)
conv_kernel_layer = torch.reshape(conv_kernel_layer, [-1, self.conv_kernel_size, 1])
conv_kernel_layer = torch.softmax(conv_kernel_layer, dim=1)
conv_out_layer = self.conv_out_layer(hiddens)
conv_out_layer = torch.reshape(conv_out_layer, [batch_size, -1, self.all_head_size])
conv_out_layer = conv_out_layer.transpose(1, 2).contiguous().unsqueeze(-1)
conv_out_layer = F.unfold(
conv_out_layer,
kernel_size=[self.conv_kernel_size, 1],
dilation=1,
padding=[(self.conv_kernel_size - 1) // 2, 0],
stride=1,
)
conv_out_layer = conv_out_layer.transpose(1, 2).reshape(
batch_size, -1, self.all_head_size, self.conv_kernel_size
)
conv_out_layer = torch.reshape(
conv_out_layer, [-1, self.attention_head_size, self.conv_kernel_size]
)
conv_out_layer = torch.matmul(conv_out_layer, conv_kernel_layer)
conv_out_layer = torch.reshape(conv_out_layer, [-1, self.all_head_size])
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:
attention_scores = attention_scores + attention_mask
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
context_layer = torch.matmul(attention_probs, value_layer)
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
conv_out = torch.reshape(
conv_out_layer, [batch_size, -1, self.n_heads, self.attention_head_size]
)
context_layer = torch.cat([context_layer, conv_out], 2)
new_context_layer_shape = context_layer.size()[:-2] + (
self.head_ratio * self.all_head_size,
)
context_layer = context_layer.view(*new_context_layer_shape)
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
return outputs
class SelfOutput(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 Embed(qc.Module):
def __init__(self, config):
super().__init__()
self.word_embeddings = qc.Embed(config.s_vocab, config.d_embed, padding_idx=config.PAD)
self.position_embeddings = qc.Embed(config.n_pos, config.d_embed)
self.token_type_embeddings = qc.Embed(config.n_typ, config.d_embed)
self.norm = qc.LayerNorm(config.d_embed, eps=config.eps)
self.drop = qc.Dropout(config.drop)
self.register_buffer("position_ids", torch.arange(config.n_pos).expand((1, -1)))
self.register_buffer(
"token_type_ids",
torch.zeros(self.position_ids.size(), dtype=torch.long),
persistent=False,
)
def forward(self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None):
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 token_type_ids is None:
if hasattr(self, "token_type_ids"):
buffered_token_type_ids = self.token_type_ids[:, :seq_length]
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(
input_shape[0], seq_length
)
token_type_ids = buffered_token_type_ids_expanded
else:
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
class Conv1D(qc.Module):
def __init__(self, config, input_filters, output_filters, kernel_size, **kw):
super().__init__()
self.depthwise = qc.Conv1d(
input_filters,
input_filters,
kernel_size=kernel_size,
groups=input_filters,
padding=kernel_size // 2,
bias=False,
)
self.pointwise = qc.Conv1d(input_filters, output_filters, kernel_size=1, bias=False)
self.bias = nn.Parameter(torch.zeros(output_filters, 1))
self.depthwise.weight.data.normal_(mean=0.0, std=config.init_range)
self.pointwise.weight.data.normal_(mean=0.0, std=config.init_range)
def forward(self, hiddens):
x = self.depthwise(hiddens)
x = self.pointwise(x)
x += self.bias
return x
LIST = [
"YituTech/conv-bert-base",
"YituTech/conv-bert-medium-small",
"YituTech/conv-bert-small",
]
|
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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"]}
|
33,477
|
quantapix/qnarre
|
refs/heads/main
|
/tools/triton/python/test/unit/operators/test_inductor.py
|
import torch
import triton
import triton.language as tl
def test_normalization_with_remat():
@triton.jit
def triton_(in_out_ptr0, in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, in_ptr3, xnumel, rnumel, XBLOCK: tl.constexpr, RBLOCK: tl.constexpr):
xnumel = 512
rnumel = 4096
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
rbase = tl.arange(0, RBLOCK)[None, :]
x3 = xindex
x0 = xindex % 64
tmp1 = tl.load(in_ptr0 + (x0), xmask)
tmp3 = tl.load(in_ptr1 + (x0), xmask)
tmp11 = tl.load(in_ptr2 + (x0), xmask)
tmp13 = tl.load(in_ptr3 + (x0), xmask)
_tmp17 = tl.zeros([XBLOCK, RBLOCK], tl.float32) + 0
for roffset in range(0, rnumel, RBLOCK):
rindex = roffset + rbase
rmask = rindex < rnumel
r2 = rindex
tmp0 = tl.load(in_out_ptr0 + (r2 + (4096 * x3)), rmask & xmask, eviction_policy='evict_last', other=0)
tmp2 = tmp0 - tmp1
tmp4 = 1e-05
tmp5 = tmp3 + tmp4
tmp6 = tl.sqrt(tmp5)
tmp7 = 1 / tmp6
tmp8 = 1.0
tmp9 = tmp7 * tmp8
tmp10 = tmp2 * tmp9
tmp12 = tmp10 * tmp11
tmp14 = tmp12 + tmp13
_tmp17 = tl.where(rmask & xmask, _tmp17 + tmp14, _tmp17)
tl.store(in_out_ptr0 + (r2 + (4096 * x3) + tl.zeros([XBLOCK, RBLOCK], tl.int32)), tmp14, rmask & xmask)
tmp17 = tl.sum(_tmp17, 1)[:, None]
tmp18 = 4096.0
tmp19 = tmp17 / tmp18
tl.store(in_out_ptr1 + (x3 + tl.zeros([XBLOCK, 1], tl.int32)), tmp19, xmask)
torch.manual_seed(123)
buf14 = torch.rand(8, 64, 64, 64, device="cuda")
buf16 = torch.rand(8, 1, 64, device="cuda")
arg114_1 = torch.rand(64, device="cuda")
arg115_1 = torch.rand(64, device="cuda")
arg8_1 = torch.rand(64, device="cuda")
arg9_1 = torch.rand(64, device="cuda")
triton_[(512,)](buf14, buf16, arg114_1, arg115_1, arg8_1, arg9_1, 512, 4096, 1, 2048)
torch.testing.assert_allclose(buf16.mean().item(), buf14.mean().item(), atol=1e-7, rtol=0)
def test_avg_pool_bw():
@triton.jit
def triton_(in_ptr0, out_ptr0, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
x1 = (xindex // 8) % 8
x0 = xindex % 8
x2 = (xindex // 64)
x5 = xindex
tmp0 = (-1) + x1
tmp1 = (-1) + x0
tmp2 = 2 + x1
tmp3 = 2 + x0
tmp4 = 0
tmp5 = tl.where(tmp0 != tmp0, tmp0, tl.where(tmp0 > tmp4, tmp0, tmp4))
tmp6 = tl.where(tmp1 != tmp1, tmp1, tl.where(tmp1 > tmp4, tmp1, tmp4))
tmp7 = 8
tmp8 = tl.where(tmp2 != tmp2, tmp2, tl.where(tmp2 < tmp7, tmp2, tmp7))
tmp9 = tl.where(tmp3 != tmp3, tmp3, tl.where(tmp3 < tmp7, tmp3, tmp7))
tmp10 = tmp5 + tmp4
tmp11 = tmp6 + tmp4
tmp12 = 1
tmp13 = tmp8 - tmp12
tmp14 = tl.where(tmp10 != tmp10, tmp10, tl.where(tmp10 < tmp13, tmp10, tmp13))
tmp15 = tmp9 - tmp12
tmp16 = tl.where(tmp11 != tmp11, tmp11, tl.where(tmp11 < tmp15, tmp11, tmp15))
tmp17 = tl.load(in_ptr0 + (tmp16 + (8 * tmp14) + (64 * x2)), None).to(tl.float32)
tmp18 = tmp17 / 9
tmp19 = tmp10 < tmp8
tmp20 = tmp11 < tmp9
tmp21 = tmp19 & tmp20
tmp22 = 0.0
tmp23 = tl.where(tmp21, tmp18, tmp22)
tmp24 = tmp6 + tmp12
tmp25 = tl.where(tmp24 != tmp24, tmp24, tl.where(tmp24 < tmp15, tmp24, tmp15))
tmp26 = tl.load(in_ptr0 + (tmp25 + (8 * tmp14) + (64 * x2)), None).to(tl.float32)
tmp27 = tmp26 / 9
tmp28 = tmp24 < tmp9
tmp29 = tmp19 & tmp28
tmp30 = tmp23 + tmp27
tmp31 = tl.where(tmp29, tmp30, tmp23)
tmp32 = 2
tmp33 = tmp6 + tmp32
tmp34 = tl.where(tmp33 != tmp33, tmp33, tl.where(tmp33 < tmp15, tmp33, tmp15))
tmp35 = tl.load(in_ptr0 + (tmp34 + (8 * tmp14) + (64 * x2)), None).to(tl.float32)
tmp36 = tmp35 / 9
tmp37 = tmp33 < tmp9
tmp38 = tmp19 & tmp37
tmp39 = tmp31 + tmp36
tmp40 = tl.where(tmp38, tmp39, tmp31)
tmp41 = tmp5 + tmp12
tmp42 = tl.where(tmp41 != tmp41, tmp41, tl.where(tmp41 < tmp13, tmp41, tmp13))
tmp43 = tl.load(in_ptr0 + (tmp16 + (8 * tmp42) + (64 * x2)), None).to(tl.float32)
tmp44 = tmp43 / 9
tmp45 = tmp41 < tmp8
tmp46 = tmp45 & tmp20
tmp47 = tmp40 + tmp44
tmp48 = tl.where(tmp46, tmp47, tmp40)
tmp49 = tl.load(in_ptr0 + (tmp25 + (8 * tmp42) + (64 * x2)), None).to(tl.float32)
tmp50 = tmp49 / 9
tmp51 = tmp45 & tmp28
tmp52 = tmp48 + tmp50
tmp53 = tl.where(tmp51, tmp52, tmp48)
tmp54 = tl.load(in_ptr0 + (tmp34 + (8 * tmp42) + (64 * x2)), None).to(tl.float32)
tmp55 = tmp54 / 9
tmp56 = tmp45 & tmp37
tmp57 = tmp53 + tmp55
tmp58 = tl.where(tmp56, tmp57, tmp53)
tmp59 = tmp5 + tmp32
tmp60 = tl.where(tmp59 != tmp59, tmp59, tl.where(tmp59 < tmp13, tmp59, tmp13))
tmp61 = tl.load(in_ptr0 + (tmp16 + (8 * tmp60) + (64 * x2)), None).to(tl.float32)
tmp62 = tmp61 / 9
tmp63 = tmp59 < tmp8
tmp64 = tmp63 & tmp20
tmp65 = tmp58 + tmp62
tmp66 = tl.where(tmp64, tmp65, tmp58)
tmp67 = tl.load(in_ptr0 + (tmp25 + (8 * tmp60) + (64 * x2)), None).to(tl.float32)
tmp68 = tmp67 / 9
tmp69 = tmp63 & tmp28
tmp70 = tmp66 + tmp68
tmp71 = tl.where(tmp69, tmp70, tmp66)
tmp72 = tl.load(in_ptr0 + (tmp34 + (8 * tmp60) + (64 * x2)), None).to(tl.float32)
tmp73 = tmp72 / 9
tmp74 = tmp63 & tmp37
tmp75 = tmp71 + tmp73
tmp76 = tl.where(tmp74, tmp75, tmp71)
tl.store(out_ptr0 + (x5 + tl.zeros([XBLOCK], tl.int32)), tmp76, None)
inp = torch.ones(8, 2048, 8, 8, device="cuda", dtype=torch.half)
out = torch.ones_like(inp) * 3
numel = inp.numel()
triton_[(numel // 1024,)](inp, out, 1024)
out_ref = torch.ones_like(inp)
out_ref[:, :, 1:7, 0::7] = 2 / 3
out_ref[:, :, 0::7, 1:7] = 2 / 3
out_ref[:, :, 0::7, 0::7] = 4 / 9
torch.testing.assert_allclose(out, out_ref)
|
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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"]}
|
33,478
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/models/data2vec.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.data2vec import PreTrained
from torch.nn import CrossEntropyLoss
log = logging.get_logger(__name__)
class ForCausal(PreTrained):
_keys_to_ignore_on_save = [r"lm_head.decoder.weight", r"lm_head.decoder.bias"]
_keys_to_ignore_on_load_missing = [
r"position_ids",
r"lm_head.decoder.weight",
r"lm_head.decoder.bias",
]
_keys_to_ignore_on_load_unexpected = [r"pooler"]
def __init__(self, config):
super().__init__(config)
if not config.is_decoder:
log.warning("If you want to use `Model` as a standalone, add `is_decoder=True.`")
self.data2vec_text = Model(config, add_pooling_layer=False)
self.lm_head = Predictor(config)
self.update_keys_to_ignore(config, ["lm_head.decoder.weight"])
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,
labels=None,
caches=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
if labels is not None:
y_cache = False
outputs = self.data2vec_text(
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.lm_head(sequence_output)
lm_loss = None
if labels is not None:
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.data2vec_text = Model(config)
self.drop = qc.Dropout(config.drop)
self.classifier = qc.Linear(config.d_model, 1)
self.post_init()
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.data2vec_text(
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 ForMasked(PreTrained):
def __init__(self, **kw):
super().__init__(**kw)
cfg = self.get_cfg(kw)
self.model = Model(**kw)
self.proj = Predictor(cfg.d_embed, **kw)
forward = qf.forward_masked
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)
cfg = 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(add_pool=False, **kw)
self.proj = qc.Linear(cfg.d_model, cfg.n_labels, **kw)
forward = qf.forward_qa
class Model(PreTrained):
def __init__(self, config, add_pooling_layer=True):
super().__init__(config)
self.config = config
self.embeddings = Embed(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:
if hasattr(self.embeddings, "token_type_ids"):
buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(
batch_size, seq_length
)
token_type_ids = buffered_token_type_ids_expanded
else:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
extended_attention_mask = self.get_extended_attention_mask(
attention_mask, input_shape, device
)
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,
)
# Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->Data2VecText
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, pos_type="absolute")
self.intermediate = Intermediate(config)
self.output = Output(config)
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_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 self.is_decoder:
outputs = self_attention_outputs[1:-1]
present_key_value = self_attention_outputs[-1]
else:
outputs = self_attention_outputs[1:]
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_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]
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 self.is_decoder:
outputs = outputs + (present_key_value,)
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
# Copied from transformers.models.bert.modeling_bert.BertIntermediate
class Intermediate(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 Output(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
# Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->Data2VecText
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,
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,
):
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.BertAttention with Bert->Data2VecText
class Attention(qc.Module):
def __init__(self, config, pos_type=None):
super().__init__()
self.self = SelfAttention(config, pos_type=pos_type)
self.output = SelfOutput(config)
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:]
return outputs
# Copied from transformers.models.roberta.modeling_roberta.RobertaSelfAttention with Roberta->Data2VecText
class SelfAttention(qc.Module):
def __init__(self, config, pos_type=None):
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.pos_type = pos_type or getattr(config, "pos_type", "absolute")
if self.pos_type == "relative_key" or self.pos_type == "relative_key_query":
self.n_pos = config.n_pos
self.distance_embedding = qc.Embed(2 * config.n_pos - 1, self.attention_head_size)
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))
if self.pos_type == "relative_key" or self.pos_type == "relative_key_query":
seq_length = hiddens.size()[1]
position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hiddens.device).view(
-1, 1
)
position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hiddens.device).view(
1, -1
)
distance = position_ids_l - position_ids_r
positional_embedding = self.distance_embedding(distance + self.n_pos - 1)
positional_embedding = positional_embedding.to(
dtype=query_layer.dtype
) # fp16 compatibility
if self.pos_type == "relative_key":
relative_position_scores = torch.einsum(
"bhld,lrd->bhlr", query_layer, positional_embedding
)
attention_scores = attention_scores + relative_position_scores
elif self.pos_type == "relative_key_query":
relative_position_scores_query = torch.einsum(
"bhld,lrd->bhlr", query_layer, positional_embedding
)
relative_position_scores_key = torch.einsum(
"bhrd,lrd->bhlr", key_layer, positional_embedding
)
attention_scores = (
attention_scores + relative_position_scores_query + relative_position_scores_key
)
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 Data2VecTextModel 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
class SelfOutput(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
# Copied from transformers.models.roberta.modeling_roberta.RobertaEmbeddings with Roberta->Data2VecText
class Embed(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.pos_type = getattr(config, "pos_type", "absolute")
self.register_buffer("position_ids", torch.arange(config.n_pos).expand((1, -1)))
self.register_buffer(
"token_type_ids",
torch.zeros(self.position_ids.size(), dtype=torch.long),
persistent=False,
)
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,
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
)
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 token_type_ids is None:
if hasattr(self, "token_type_ids"):
buffered_token_type_ids = self.token_type_ids[:, :seq_length]
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(
input_shape[0], seq_length
)
token_type_ids = buffered_token_type_ids_expanded
else:
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
if self.pos_type == "absolute":
position_embeddings = self.position_embeddings(position_ids)
embeddings += 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)
LIST = [
"facebook/data2vec-text-base",
]
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
|
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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": 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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"]}
|
33,479
|
quantapix/qnarre
|
refs/heads/main
|
/tools/triton/python/triton/language/math.py
|
import functools
import os
from . import core
@functools.lru_cache()
def libdevice_path():
import torch
third_party_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "third_party")
if torch.version.hip is None:
default = os.path.join(third_party_dir, "cuda", "lib", "libdevice.10.bc")
else:
default = ''
return os.getenv("TRITON_LIBDEVICE_PATH", default)
@core.extern
def clz(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("int32"),): ("__nv_clz", core.dtype("int32")),
(core.dtype("int64"),): ("__nv_clzll", core.dtype("int32")),
}, is_pure=True, _builder=_builder)
@core.extern
def popc(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("int32"),): ("__nv_popc", core.dtype("int32")),
(core.dtype("int64"),): ("__nv_popcll", core.dtype("int32")),
}, is_pure=True, _builder=_builder)
@core.extern
def byte_perm(arg0, arg1, arg2, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, arg1, arg2, ],
{(core.dtype("int32"), core.dtype("int32"), core.dtype("int32"),): ("__nv_byte_perm", core.dtype("int32")),
}, is_pure=True, _builder=_builder)
@core.extern
def min(arg0, arg1, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, arg1, ],
{(core.dtype("int32"), core.dtype("int32"),): ("__nv_min", core.dtype("int32")),
(core.dtype("uint32"), core.dtype("uint32"),): ("__nv_umin", core.dtype("uint32")),
(core.dtype("int64"), core.dtype("int64"),): ("__nv_llmin", core.dtype("int64")),
(core.dtype("uint64"), core.dtype("uint64"),): ("__nv_ullmin", core.dtype("uint64")),
(core.dtype("fp32"), core.dtype("fp32"),): ("__nv_fminf", core.dtype("fp32")),
(core.dtype("fp64"), core.dtype("fp64"),): ("__nv_fmin", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def max(arg0, arg1, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, arg1, ],
{(core.dtype("int32"), core.dtype("int32"),): ("__nv_max", core.dtype("int32")),
(core.dtype("uint32"), core.dtype("uint32"),): ("__nv_umax", core.dtype("uint32")),
(core.dtype("int64"), core.dtype("int64"),): ("__nv_llmax", core.dtype("int64")),
(core.dtype("uint64"), core.dtype("uint64"),): ("__nv_ullmax", core.dtype("uint64")),
(core.dtype("fp32"), core.dtype("fp32"),): ("__nv_fmaxf", core.dtype("fp32")),
(core.dtype("fp64"), core.dtype("fp64"),): ("__nv_fmax", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def mulhi(arg0, arg1, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, arg1, ],
{(core.dtype("int32"), core.dtype("int32"),): ("__nv_mulhi", core.dtype("int32")),
(core.dtype("uint32"), core.dtype("uint32"),): ("__nv_umulhi", core.dtype("uint32")),
(core.dtype("int64"), core.dtype("int64"),): ("__nv_mul64hi", core.dtype("int64")),
(core.dtype("uint64"), core.dtype("uint64"),): ("__nv_umul64hi", core.dtype("uint64")),
}, is_pure=True, _builder=_builder)
@core.extern
def mul24(arg0, arg1, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, arg1, ],
{(core.dtype("int32"), core.dtype("int32"),): ("__nv_mul24", core.dtype("int32")),
(core.dtype("uint32"), core.dtype("uint32"),): ("__nv_umul24", core.dtype("uint32")),
}, is_pure=True, _builder=_builder)
@core.extern
def brev(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("int32"),): ("__nv_brev", core.dtype("int32")),
(core.dtype("int64"),): ("__nv_brevll", core.dtype("int64")),
}, is_pure=True, _builder=_builder)
@core.extern
def sad(arg0, arg1, arg2, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, arg1, arg2, ],
{(core.dtype("int32"), core.dtype("int32"), core.dtype("uint32"),): ("__nv_sad", core.dtype("int32")),
(core.dtype("uint32"), core.dtype("uint32"), core.dtype("uint32"),): ("__nv_usad", core.dtype("uint32")),
}, is_pure=True, _builder=_builder)
@core.extern
def abs(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("int32"),): ("__nv_abs", core.dtype("int32")),
(core.dtype("int64"),): ("__nv_llabs", core.dtype("int64")),
(core.dtype("fp32"),): ("__nv_fabsf", core.dtype("fp32")),
(core.dtype("fp64"),): ("__nv_fabs", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def floor(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_floorf", core.dtype("fp32")),
(core.dtype("fp64"),): ("__nv_floor", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def rcp64h(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp64"),): ("__nv_rcp64h", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def rsqrt(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_rsqrtf", core.dtype("fp32")),
(core.dtype("fp64"),): ("__nv_rsqrt", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def ceil(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp64"),): ("__nv_ceil", core.dtype("fp64")),
(core.dtype("fp32"),): ("__nv_ceilf", core.dtype("fp32")),
}, is_pure=True, _builder=_builder)
@core.extern
def trunc(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp64"),): ("__nv_trunc", core.dtype("fp64")),
(core.dtype("fp32"),): ("__nv_truncf", core.dtype("fp32")),
}, is_pure=True, _builder=_builder)
@core.extern
def exp2(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_exp2f", core.dtype("fp32")),
(core.dtype("fp64"),): ("__nv_exp2", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def saturatef(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_saturatef", core.dtype("fp32")),
}, is_pure=True, _builder=_builder)
@core.extern
def fma_rn(arg0, arg1, arg2, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, arg1, arg2, ],
{(core.dtype("fp32"), core.dtype("fp32"), core.dtype("fp32"),): ("__nv_fmaf_rn", core.dtype("fp32")),
(core.dtype("fp64"), core.dtype("fp64"), core.dtype("fp64"),): ("__nv_fma_rn", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def fma_rz(arg0, arg1, arg2, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, arg1, arg2, ],
{(core.dtype("fp32"), core.dtype("fp32"), core.dtype("fp32"),): ("__nv_fmaf_rz", core.dtype("fp32")),
(core.dtype("fp64"), core.dtype("fp64"), core.dtype("fp64"),): ("__nv_fma_rz", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def fma_rd(arg0, arg1, arg2, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, arg1, arg2, ],
{(core.dtype("fp32"), core.dtype("fp32"), core.dtype("fp32"),): ("__nv_fmaf_rd", core.dtype("fp32")),
(core.dtype("fp64"), core.dtype("fp64"), core.dtype("fp64"),): ("__nv_fma_rd", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def fma_ru(arg0, arg1, arg2, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, arg1, arg2, ],
{(core.dtype("fp32"), core.dtype("fp32"), core.dtype("fp32"),): ("__nv_fmaf_ru", core.dtype("fp32")),
(core.dtype("fp64"), core.dtype("fp64"), core.dtype("fp64"),): ("__nv_fma_ru", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def fast_dividef(arg0, arg1, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, arg1, ],
{(core.dtype("fp32"), core.dtype("fp32"),): ("__nv_fast_fdividef", core.dtype("fp32")),
}, is_pure=True, _builder=_builder)
@core.extern
def div_rn(arg0, arg1, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, arg1, ],
{(core.dtype("fp32"), core.dtype("fp32"),): ("__nv_fdiv_rn", core.dtype("fp32")),
(core.dtype("fp64"), core.dtype("fp64"),): ("__nv_ddiv_rn", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def div_rz(arg0, arg1, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, arg1, ],
{(core.dtype("fp32"), core.dtype("fp32"),): ("__nv_fdiv_rz", core.dtype("fp32")),
(core.dtype("fp64"), core.dtype("fp64"),): ("__nv_ddiv_rz", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def div_rd(arg0, arg1, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, arg1, ],
{(core.dtype("fp32"), core.dtype("fp32"),): ("__nv_fdiv_rd", core.dtype("fp32")),
(core.dtype("fp64"), core.dtype("fp64"),): ("__nv_ddiv_rd", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def div_ru(arg0, arg1, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, arg1, ],
{(core.dtype("fp32"), core.dtype("fp32"),): ("__nv_fdiv_ru", core.dtype("fp32")),
(core.dtype("fp64"), core.dtype("fp64"),): ("__nv_ddiv_ru", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def rcp_rn(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_frcp_rn", core.dtype("fp32")),
(core.dtype("fp64"),): ("__nv_drcp_rn", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def rcp_rz(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_frcp_rz", core.dtype("fp32")),
(core.dtype("fp64"),): ("__nv_drcp_rz", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def rcp_rd(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_frcp_rd", core.dtype("fp32")),
(core.dtype("fp64"),): ("__nv_drcp_rd", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def rcp_ru(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_frcp_ru", core.dtype("fp32")),
(core.dtype("fp64"),): ("__nv_drcp_ru", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def sqrt_rn(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_fsqrt_rn", core.dtype("fp32")),
(core.dtype("fp64"),): ("__nv_dsqrt_rn", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def sqrt_rz(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_fsqrt_rz", core.dtype("fp32")),
(core.dtype("fp64"),): ("__nv_dsqrt_rz", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def sqrt_rd(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_fsqrt_rd", core.dtype("fp32")),
(core.dtype("fp64"),): ("__nv_dsqrt_rd", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def sqrt_ru(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_fsqrt_ru", core.dtype("fp32")),
(core.dtype("fp64"),): ("__nv_dsqrt_ru", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def sqrt(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_sqrtf", core.dtype("fp32")),
(core.dtype("fp64"),): ("__nv_sqrt", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def add_rn(arg0, arg1, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, arg1, ],
{(core.dtype("fp64"), core.dtype("fp64"),): ("__nv_dadd_rn", core.dtype("fp64")),
(core.dtype("fp32"), core.dtype("fp32"),): ("__nv_fadd_rn", core.dtype("fp32")),
}, is_pure=True, _builder=_builder)
@core.extern
def add_rz(arg0, arg1, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, arg1, ],
{(core.dtype("fp64"), core.dtype("fp64"),): ("__nv_dadd_rz", core.dtype("fp64")),
(core.dtype("fp32"), core.dtype("fp32"),): ("__nv_fadd_rz", core.dtype("fp32")),
}, is_pure=True, _builder=_builder)
@core.extern
def add_rd(arg0, arg1, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, arg1, ],
{(core.dtype("fp64"), core.dtype("fp64"),): ("__nv_dadd_rd", core.dtype("fp64")),
(core.dtype("fp32"), core.dtype("fp32"),): ("__nv_fadd_rd", core.dtype("fp32")),
}, is_pure=True, _builder=_builder)
@core.extern
def add_ru(arg0, arg1, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, arg1, ],
{(core.dtype("fp64"), core.dtype("fp64"),): ("__nv_dadd_ru", core.dtype("fp64")),
(core.dtype("fp32"), core.dtype("fp32"),): ("__nv_fadd_ru", core.dtype("fp32")),
}, is_pure=True, _builder=_builder)
@core.extern
def mul_rn(arg0, arg1, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, arg1, ],
{(core.dtype("fp64"), core.dtype("fp64"),): ("__nv_dmul_rn", core.dtype("fp64")),
(core.dtype("fp32"), core.dtype("fp32"),): ("__nv_fmul_rn", core.dtype("fp32")),
}, is_pure=True, _builder=_builder)
@core.extern
def mul_rz(arg0, arg1, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, arg1, ],
{(core.dtype("fp64"), core.dtype("fp64"),): ("__nv_dmul_rz", core.dtype("fp64")),
(core.dtype("fp32"), core.dtype("fp32"),): ("__nv_fmul_rz", core.dtype("fp32")),
}, is_pure=True, _builder=_builder)
@core.extern
def mul_rd(arg0, arg1, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, arg1, ],
{(core.dtype("fp64"), core.dtype("fp64"),): ("__nv_dmul_rd", core.dtype("fp64")),
(core.dtype("fp32"), core.dtype("fp32"),): ("__nv_fmul_rd", core.dtype("fp32")),
}, is_pure=True, _builder=_builder)
@core.extern
def mul_ru(arg0, arg1, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, arg1, ],
{(core.dtype("fp64"), core.dtype("fp64"),): ("__nv_dmul_ru", core.dtype("fp64")),
(core.dtype("fp32"), core.dtype("fp32"),): ("__nv_fmul_ru", core.dtype("fp32")),
}, is_pure=True, _builder=_builder)
@core.extern
def double2float_rn(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp64"),): ("__nv_double2float_rn", core.dtype("fp32")),
}, is_pure=True, _builder=_builder)
@core.extern
def double2float_rz(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp64"),): ("__nv_double2float_rz", core.dtype("fp32")),
}, is_pure=True, _builder=_builder)
@core.extern
def double2float_rd(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp64"),): ("__nv_double2float_rd", core.dtype("fp32")),
}, is_pure=True, _builder=_builder)
@core.extern
def double2float_ru(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp64"),): ("__nv_double2float_ru", core.dtype("fp32")),
}, is_pure=True, _builder=_builder)
@core.extern
def double2int_rn(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp64"),): ("__nv_double2int_rn", core.dtype("int32")),
}, is_pure=True, _builder=_builder)
@core.extern
def double2int_rz(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp64"),): ("__nv_double2int_rz", core.dtype("int32")),
}, is_pure=True, _builder=_builder)
@core.extern
def double2int_rd(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp64"),): ("__nv_double2int_rd", core.dtype("int32")),
}, is_pure=True, _builder=_builder)
@core.extern
def double2int_ru(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp64"),): ("__nv_double2int_ru", core.dtype("int32")),
}, is_pure=True, _builder=_builder)
@core.extern
def double2uint_rn(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp64"),): ("__nv_double2uint_rn", core.dtype("int32")),
}, is_pure=True, _builder=_builder)
@core.extern
def double2uint_rz(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp64"),): ("__nv_double2uint_rz", core.dtype("int32")),
}, is_pure=True, _builder=_builder)
@core.extern
def double2uint_rd(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp64"),): ("__nv_double2uint_rd", core.dtype("int32")),
}, is_pure=True, _builder=_builder)
@core.extern
def double2uint_ru(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp64"),): ("__nv_double2uint_ru", core.dtype("int32")),
}, is_pure=True, _builder=_builder)
@core.extern
def int2double_rn(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("int32"),): ("__nv_int2double_rn", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def uint2double_rn(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("uint32"),): ("__nv_uint2double_rn", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def float2int_rn(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_float2int_rn", core.dtype("int32")),
}, is_pure=True, _builder=_builder)
@core.extern
def float2int_rz(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_float2int_rz", core.dtype("int32")),
}, is_pure=True, _builder=_builder)
@core.extern
def float2int_rd(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_float2int_rd", core.dtype("int32")),
}, is_pure=True, _builder=_builder)
@core.extern
def float2int_ru(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_float2int_ru", core.dtype("int32")),
}, is_pure=True, _builder=_builder)
@core.extern
def float2uint_rn(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_float2uint_rn", core.dtype("int32")),
}, is_pure=True, _builder=_builder)
@core.extern
def float2uint_rz(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_float2uint_rz", core.dtype("int32")),
}, is_pure=True, _builder=_builder)
@core.extern
def float2uint_rd(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_float2uint_rd", core.dtype("int32")),
}, is_pure=True, _builder=_builder)
@core.extern
def float2uint_ru(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_float2uint_ru", core.dtype("int32")),
}, is_pure=True, _builder=_builder)
@core.extern
def int2float_rn(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("int32"),): ("__nv_int2float_rn", core.dtype("fp32")),
}, is_pure=True, _builder=_builder)
@core.extern
def int2float_rz(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("int32"),): ("__nv_int2float_rz", core.dtype("fp32")),
}, is_pure=True, _builder=_builder)
@core.extern
def int2float_rd(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("int32"),): ("__nv_int2float_rd", core.dtype("fp32")),
}, is_pure=True, _builder=_builder)
@core.extern
def int2float_ru(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("int32"),): ("__nv_int2float_ru", core.dtype("fp32")),
}, is_pure=True, _builder=_builder)
@core.extern
def uint2float_rn(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("uint32"),): ("__nv_uint2float_rn", core.dtype("fp32")),
}, is_pure=True, _builder=_builder)
@core.extern
def uint2float_rz(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("uint32"),): ("__nv_uint2float_rz", core.dtype("fp32")),
}, is_pure=True, _builder=_builder)
@core.extern
def uint2float_rd(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("uint32"),): ("__nv_uint2float_rd", core.dtype("fp32")),
}, is_pure=True, _builder=_builder)
@core.extern
def uint2float_ru(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("uint32"),): ("__nv_uint2float_ru", core.dtype("fp32")),
}, is_pure=True, _builder=_builder)
@core.extern
def hiloint2double(arg0, arg1, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, arg1, ],
{(core.dtype("int32"), core.dtype("int32"),): ("__nv_hiloint2double", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def double2loint(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp64"),): ("__nv_double2loint", core.dtype("int32")),
}, is_pure=True, _builder=_builder)
@core.extern
def double2hiint(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp64"),): ("__nv_double2hiint", core.dtype("int32")),
}, is_pure=True, _builder=_builder)
@core.extern
def float2ll_rn(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_float2ll_rn", core.dtype("int64")),
}, is_pure=True, _builder=_builder)
@core.extern
def float2ll_rz(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_float2ll_rz", core.dtype("int64")),
}, is_pure=True, _builder=_builder)
@core.extern
def float2ll_rd(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_float2ll_rd", core.dtype("int64")),
}, is_pure=True, _builder=_builder)
@core.extern
def float2ll_ru(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_float2ll_ru", core.dtype("int64")),
}, is_pure=True, _builder=_builder)
@core.extern
def float2ull_rn(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_float2ull_rn", core.dtype("int64")),
}, is_pure=True, _builder=_builder)
@core.extern
def float2ull_rz(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_float2ull_rz", core.dtype("int64")),
}, is_pure=True, _builder=_builder)
@core.extern
def float2ull_rd(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_float2ull_rd", core.dtype("int64")),
}, is_pure=True, _builder=_builder)
@core.extern
def float2ull_ru(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_float2ull_ru", core.dtype("int64")),
}, is_pure=True, _builder=_builder)
@core.extern
def double2ll_rn(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp64"),): ("__nv_double2ll_rn", core.dtype("int64")),
}, is_pure=True, _builder=_builder)
@core.extern
def double2ll_rz(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp64"),): ("__nv_double2ll_rz", core.dtype("int64")),
}, is_pure=True, _builder=_builder)
@core.extern
def double2ll_rd(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp64"),): ("__nv_double2ll_rd", core.dtype("int64")),
}, is_pure=True, _builder=_builder)
@core.extern
def double2ll_ru(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp64"),): ("__nv_double2ll_ru", core.dtype("int64")),
}, is_pure=True, _builder=_builder)
@core.extern
def double2ull_rn(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp64"),): ("__nv_double2ull_rn", core.dtype("int64")),
}, is_pure=True, _builder=_builder)
@core.extern
def double2ull_rz(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp64"),): ("__nv_double2ull_rz", core.dtype("int64")),
}, is_pure=True, _builder=_builder)
@core.extern
def double2ull_rd(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp64"),): ("__nv_double2ull_rd", core.dtype("int64")),
}, is_pure=True, _builder=_builder)
@core.extern
def double2ull_ru(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp64"),): ("__nv_double2ull_ru", core.dtype("int64")),
}, is_pure=True, _builder=_builder)
@core.extern
def ll2float_rn(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("int64"),): ("__nv_ll2float_rn", core.dtype("fp32")),
}, is_pure=True, _builder=_builder)
@core.extern
def ll2float_rz(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("int64"),): ("__nv_ll2float_rz", core.dtype("fp32")),
}, is_pure=True, _builder=_builder)
@core.extern
def ll2float_rd(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("int64"),): ("__nv_ll2float_rd", core.dtype("fp32")),
}, is_pure=True, _builder=_builder)
@core.extern
def ll2float_ru(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("int64"),): ("__nv_ll2float_ru", core.dtype("fp32")),
}, is_pure=True, _builder=_builder)
@core.extern
def ull2float_rn(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("uint64"),): ("__nv_ull2float_rn", core.dtype("fp32")),
}, is_pure=True, _builder=_builder)
@core.extern
def ull2float_rz(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("uint64"),): ("__nv_ull2float_rz", core.dtype("fp32")),
}, is_pure=True, _builder=_builder)
@core.extern
def ull2float_rd(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("uint64"),): ("__nv_ull2float_rd", core.dtype("fp32")),
}, is_pure=True, _builder=_builder)
@core.extern
def ull2float_ru(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("uint64"),): ("__nv_ull2float_ru", core.dtype("fp32")),
}, is_pure=True, _builder=_builder)
@core.extern
def ll2double_rn(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("int64"),): ("__nv_ll2double_rn", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def ll2double_rz(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("int64"),): ("__nv_ll2double_rz", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def ll2double_rd(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("int64"),): ("__nv_ll2double_rd", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def ll2double_ru(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("int64"),): ("__nv_ll2double_ru", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def ull2double_rn(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("uint64"),): ("__nv_ull2double_rn", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def ull2double_rz(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("uint64"),): ("__nv_ull2double_rz", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def ull2double_rd(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("uint64"),): ("__nv_ull2double_rd", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def ull2double_ru(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("uint64"),): ("__nv_ull2double_ru", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def int_as_float(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("int32"),): ("__nv_int_as_float", core.dtype("fp32")),
}, is_pure=True, _builder=_builder)
@core.extern
def float_as_int(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_float_as_int", core.dtype("int32")),
}, is_pure=True, _builder=_builder)
@core.extern
def uint_as_float(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("uint32"),): ("__nv_uint_as_float", core.dtype("fp32")),
}, is_pure=True, _builder=_builder)
@core.extern
def float_as_uint(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_float_as_uint", core.dtype("int32")),
}, is_pure=True, _builder=_builder)
@core.extern
def longlong_as_double(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("int64"),): ("__nv_longlong_as_double", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def double_as_longlong(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp64"),): ("__nv_double_as_longlong", core.dtype("int64")),
}, is_pure=True, _builder=_builder)
@core.extern
def fast_sinf(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_fast_sinf", core.dtype("fp32")),
}, is_pure=True, _builder=_builder)
@core.extern
def fast_cosf(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_fast_cosf", core.dtype("fp32")),
}, is_pure=True, _builder=_builder)
@core.extern
def fast_log2f(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_fast_log2f", core.dtype("fp32")),
}, is_pure=True, _builder=_builder)
@core.extern
def fast_logf(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_fast_logf", core.dtype("fp32")),
}, is_pure=True, _builder=_builder)
@core.extern
def fast_expf(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_fast_expf", core.dtype("fp32")),
}, is_pure=True, _builder=_builder)
@core.extern
def fast_tanf(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_fast_tanf", core.dtype("fp32")),
}, is_pure=True, _builder=_builder)
@core.extern
def fast_exp10f(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_fast_exp10f", core.dtype("fp32")),
}, is_pure=True, _builder=_builder)
@core.extern
def fast_log10f(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_fast_log10f", core.dtype("fp32")),
}, is_pure=True, _builder=_builder)
@core.extern
def fast_powf(arg0, arg1, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, arg1, ],
{(core.dtype("fp32"), core.dtype("fp32"),): ("__nv_fast_powf", core.dtype("fp32")),
}, is_pure=True, _builder=_builder)
@core.extern
def hadd(arg0, arg1, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, arg1, ],
{(core.dtype("int32"), core.dtype("int32"),): ("__nv_hadd", core.dtype("int32")),
(core.dtype("uint32"), core.dtype("uint32"),): ("__nv_uhadd", core.dtype("uint32")),
}, is_pure=True, _builder=_builder)
@core.extern
def rhadd(arg0, arg1, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, arg1, ],
{(core.dtype("int32"), core.dtype("int32"),): ("__nv_rhadd", core.dtype("int32")),
(core.dtype("uint32"), core.dtype("uint32"),): ("__nv_urhadd", core.dtype("uint32")),
}, is_pure=True, _builder=_builder)
@core.extern
def sub_rn(arg0, arg1, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, arg1, ],
{(core.dtype("fp32"), core.dtype("fp32"),): ("__nv_fsub_rn", core.dtype("fp32")),
(core.dtype("fp64"), core.dtype("fp64"),): ("__nv_dsub_rn", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def sub_rz(arg0, arg1, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, arg1, ],
{(core.dtype("fp32"), core.dtype("fp32"),): ("__nv_fsub_rz", core.dtype("fp32")),
(core.dtype("fp64"), core.dtype("fp64"),): ("__nv_dsub_rz", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def sub_rd(arg0, arg1, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, arg1, ],
{(core.dtype("fp32"), core.dtype("fp32"),): ("__nv_fsub_rd", core.dtype("fp32")),
(core.dtype("fp64"), core.dtype("fp64"),): ("__nv_dsub_rd", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def sub_ru(arg0, arg1, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, arg1, ],
{(core.dtype("fp32"), core.dtype("fp32"),): ("__nv_fsub_ru", core.dtype("fp32")),
(core.dtype("fp64"), core.dtype("fp64"),): ("__nv_dsub_ru", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def rsqrt_rn(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_frsqrt_rn", core.dtype("fp32")),
}, is_pure=True, _builder=_builder)
@core.extern
def ffs(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("int32"),): ("__nv_ffs", core.dtype("int32")),
(core.dtype("int64"),): ("__nv_ffsll", core.dtype("int32")),
}, is_pure=True, _builder=_builder)
@core.extern
def rint(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_rintf", core.dtype("fp32")),
(core.dtype("fp64"),): ("__nv_rint", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def llrint(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_llrintf", core.dtype("int64")),
(core.dtype("fp64"),): ("__nv_llrint", core.dtype("int64")),
}, is_pure=True, _builder=_builder)
@core.extern
def nearbyint(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_nearbyintf", core.dtype("fp32")),
(core.dtype("fp64"),): ("__nv_nearbyint", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def isnan(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_isnanf", core.dtype("int32")),
(core.dtype("fp64"),): ("__nv_isnand", core.dtype("int32")),
}, is_pure=True, _builder=_builder)
@core.extern
def signbit(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_signbitf", core.dtype("int32")),
(core.dtype("fp64"),): ("__nv_signbitd", core.dtype("int32")),
}, is_pure=True, _builder=_builder)
@core.extern
def copysign(arg0, arg1, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, arg1, ],
{(core.dtype("fp32"), core.dtype("fp32"),): ("__nv_copysignf", core.dtype("fp32")),
(core.dtype("fp64"), core.dtype("fp64"),): ("__nv_copysign", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def finitef(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_finitef", core.dtype("int32")),
}, is_pure=True, _builder=_builder)
@core.extern
def isinf(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_isinff", core.dtype("int32")),
(core.dtype("fp64"),): ("__nv_isinfd", core.dtype("int32")),
}, is_pure=True, _builder=_builder)
@core.extern
def nextafter(arg0, arg1, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, arg1, ],
{(core.dtype("fp32"), core.dtype("fp32"),): ("__nv_nextafterf", core.dtype("fp32")),
(core.dtype("fp64"), core.dtype("fp64"),): ("__nv_nextafter", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def sin(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_sinf", core.dtype("fp32")),
(core.dtype("fp64"),): ("__nv_sin", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def cos(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_cosf", core.dtype("fp32")),
(core.dtype("fp64"),): ("__nv_cos", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def sinpi(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_sinpif", core.dtype("fp32")),
(core.dtype("fp64"),): ("__nv_sinpi", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def cospi(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_cospif", core.dtype("fp32")),
(core.dtype("fp64"),): ("__nv_cospi", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def tan(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_tanf", core.dtype("fp32")),
(core.dtype("fp64"),): ("__nv_tan", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def log2(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_log2f", core.dtype("fp32")),
(core.dtype("fp64"),): ("__nv_log2", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def exp(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_expf", core.dtype("fp32")),
(core.dtype("fp64"),): ("__nv_exp", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def exp10(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_exp10f", core.dtype("fp32")),
(core.dtype("fp64"),): ("__nv_exp10", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def cosh(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_coshf", core.dtype("fp32")),
(core.dtype("fp64"),): ("__nv_cosh", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def sinh(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_sinhf", core.dtype("fp32")),
(core.dtype("fp64"),): ("__nv_sinh", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def tanh(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_tanhf", core.dtype("fp32")),
(core.dtype("fp64"),): ("__nv_tanh", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def atan2(arg0, arg1, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, arg1, ],
{(core.dtype("fp32"), core.dtype("fp32"),): ("__nv_atan2f", core.dtype("fp32")),
(core.dtype("fp64"), core.dtype("fp64"),): ("__nv_atan2", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def atan(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_atanf", core.dtype("fp32")),
(core.dtype("fp64"),): ("__nv_atan", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def asin(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_asinf", core.dtype("fp32")),
(core.dtype("fp64"),): ("__nv_asin", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def acos(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_acosf", core.dtype("fp32")),
(core.dtype("fp64"),): ("__nv_acos", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def log(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_logf", core.dtype("fp32")),
(core.dtype("fp64"),): ("__nv_log", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def log10(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_log10f", core.dtype("fp32")),
(core.dtype("fp64"),): ("__nv_log10", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def log1p(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_log1pf", core.dtype("fp32")),
(core.dtype("fp64"),): ("__nv_log1p", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def acosh(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_acoshf", core.dtype("fp32")),
(core.dtype("fp64"),): ("__nv_acosh", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def asinh(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_asinhf", core.dtype("fp32")),
(core.dtype("fp64"),): ("__nv_asinh", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def atanh(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_atanhf", core.dtype("fp32")),
(core.dtype("fp64"),): ("__nv_atanh", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def expm1(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_expm1f", core.dtype("fp32")),
(core.dtype("fp64"),): ("__nv_expm1", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def hypot(arg0, arg1, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, arg1, ],
{(core.dtype("fp32"), core.dtype("fp32"),): ("__nv_hypotf", core.dtype("fp32")),
(core.dtype("fp64"), core.dtype("fp64"),): ("__nv_hypot", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def rhypot(arg0, arg1, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, arg1, ],
{(core.dtype("fp32"), core.dtype("fp32"),): ("__nv_rhypotf", core.dtype("fp32")),
(core.dtype("fp64"), core.dtype("fp64"),): ("__nv_rhypot", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def norm3d(arg0, arg1, arg2, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, arg1, arg2, ],
{(core.dtype("fp32"), core.dtype("fp32"), core.dtype("fp32"),): ("__nv_norm3df", core.dtype("fp32")),
(core.dtype("fp64"), core.dtype("fp64"), core.dtype("fp64"),): ("__nv_norm3d", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def rnorm3d(arg0, arg1, arg2, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, arg1, arg2, ],
{(core.dtype("fp32"), core.dtype("fp32"), core.dtype("fp32"),): ("__nv_rnorm3df", core.dtype("fp32")),
(core.dtype("fp64"), core.dtype("fp64"), core.dtype("fp64"),): ("__nv_rnorm3d", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def norm4d(arg0, arg1, arg2, arg3, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, arg1, arg2, arg3, ],
{(core.dtype("fp32"), core.dtype("fp32"), core.dtype("fp32"), core.dtype("fp32"),): ("__nv_norm4df", core.dtype("fp32")),
(core.dtype("fp64"), core.dtype("fp64"), core.dtype("fp64"), core.dtype("fp64"),): ("__nv_norm4d", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def rnorm4d(arg0, arg1, arg2, arg3, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, arg1, arg2, arg3, ],
{(core.dtype("fp32"), core.dtype("fp32"), core.dtype("fp32"), core.dtype("fp32"),): ("__nv_rnorm4df", core.dtype("fp32")),
(core.dtype("fp64"), core.dtype("fp64"), core.dtype("fp64"), core.dtype("fp64"),): ("__nv_rnorm4d", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def cbrt(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_cbrtf", core.dtype("fp32")),
(core.dtype("fp64"),): ("__nv_cbrt", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def rcbrt(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_rcbrtf", core.dtype("fp32")),
(core.dtype("fp64"),): ("__nv_rcbrt", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def j0(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_j0f", core.dtype("fp32")),
(core.dtype("fp64"),): ("__nv_j0", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def j1(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_j1f", core.dtype("fp32")),
(core.dtype("fp64"),): ("__nv_j1", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def y0(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_y0f", core.dtype("fp32")),
(core.dtype("fp64"),): ("__nv_y0", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def y1(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_y1f", core.dtype("fp32")),
(core.dtype("fp64"),): ("__nv_y1", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def yn(arg0, arg1, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, arg1, ],
{(core.dtype("int32"), core.dtype("fp32"),): ("__nv_ynf", core.dtype("fp32")),
(core.dtype("int32"), core.dtype("fp64"),): ("__nv_yn", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def jn(arg0, arg1, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, arg1, ],
{(core.dtype("int32"), core.dtype("fp32"),): ("__nv_jnf", core.dtype("fp32")),
(core.dtype("int32"), core.dtype("fp64"),): ("__nv_jn", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def cyl_bessel_i0(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_cyl_bessel_i0f", core.dtype("fp32")),
(core.dtype("fp64"),): ("__nv_cyl_bessel_i0", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def cyl_bessel_i1(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_cyl_bessel_i1f", core.dtype("fp32")),
(core.dtype("fp64"),): ("__nv_cyl_bessel_i1", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def erf(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_erff", core.dtype("fp32")),
(core.dtype("fp64"),): ("__nv_erf", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def erfinv(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_erfinvf", core.dtype("fp32")),
(core.dtype("fp64"),): ("__nv_erfinv", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def erfc(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_erfcf", core.dtype("fp32")),
(core.dtype("fp64"),): ("__nv_erfc", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def erfcx(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_erfcxf", core.dtype("fp32")),
(core.dtype("fp64"),): ("__nv_erfcx", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def erfcinv(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_erfcinvf", core.dtype("fp32")),
(core.dtype("fp64"),): ("__nv_erfcinv", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def normcdfinv(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_normcdfinvf", core.dtype("fp32")),
(core.dtype("fp64"),): ("__nv_normcdfinv", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def normcdf(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_normcdff", core.dtype("fp32")),
(core.dtype("fp64"),): ("__nv_normcdf", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def lgamma(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_lgammaf", core.dtype("fp32")),
(core.dtype("fp64"),): ("__nv_lgamma", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def ldexp(arg0, arg1, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, arg1, ],
{(core.dtype("fp32"), core.dtype("int32"),): ("__nv_ldexpf", core.dtype("fp32")),
(core.dtype("fp64"), core.dtype("int32"),): ("__nv_ldexp", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def scalbn(arg0, arg1, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, arg1, ],
{(core.dtype("fp32"), core.dtype("int32"),): ("__nv_scalbnf", core.dtype("fp32")),
(core.dtype("fp64"), core.dtype("int32"),): ("__nv_scalbn", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def fmod(arg0, arg1, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, arg1, ],
{(core.dtype("fp32"), core.dtype("fp32"),): ("__nv_fmodf", core.dtype("fp32")),
(core.dtype("fp64"), core.dtype("fp64"),): ("__nv_fmod", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def remainder(arg0, arg1, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, arg1, ],
{(core.dtype("fp32"), core.dtype("fp32"),): ("__nv_remainderf", core.dtype("fp32")),
(core.dtype("fp64"), core.dtype("fp64"),): ("__nv_remainder", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def fma(arg0, arg1, arg2, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, arg1, arg2, ],
{(core.dtype("fp32"), core.dtype("fp32"), core.dtype("fp32"),): ("__nv_fmaf", core.dtype("fp32")),
(core.dtype("fp64"), core.dtype("fp64"), core.dtype("fp64"),): ("__nv_fma", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def pow(arg0, arg1, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, arg1, ],
{(core.dtype("fp32"), core.dtype("int32"),): ("__nv_powif", core.dtype("fp32")),
(core.dtype("fp64"), core.dtype("int32"),): ("__nv_powi", core.dtype("fp64")),
(core.dtype("fp32"), core.dtype("fp32"),): ("__nv_powf", core.dtype("fp32")),
(core.dtype("fp64"), core.dtype("fp64"),): ("__nv_pow", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def tgamma(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_tgammaf", core.dtype("fp32")),
(core.dtype("fp64"),): ("__nv_tgamma", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def round(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_roundf", core.dtype("fp32")),
(core.dtype("fp64"),): ("__nv_round", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def llround(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_llroundf", core.dtype("int64")),
(core.dtype("fp64"),): ("__nv_llround", core.dtype("int64")),
}, is_pure=True, _builder=_builder)
@core.extern
def fdim(arg0, arg1, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, arg1, ],
{(core.dtype("fp32"), core.dtype("fp32"),): ("__nv_fdimf", core.dtype("fp32")),
(core.dtype("fp64"), core.dtype("fp64"),): ("__nv_fdim", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def ilogb(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_ilogbf", core.dtype("int32")),
(core.dtype("fp64"),): ("__nv_ilogb", core.dtype("int32")),
}, is_pure=True, _builder=_builder)
@core.extern
def logb(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp32"),): ("__nv_logbf", core.dtype("fp32")),
(core.dtype("fp64"),): ("__nv_logb", core.dtype("fp64")),
}, is_pure=True, _builder=_builder)
@core.extern
def isfinited(arg0, _builder=None):
return core.extern_elementwise("libdevice", libdevice_path(), [arg0, ],
{(core.dtype("fp64"),): ("__nv_isfinited", core.dtype("int32")),
}, is_pure=True, _builder=_builder)
|
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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", "/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"]}
|
33,480
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/config/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.
# =============================================================================
from ... import core as qc
class PreTrained(qc.PreTrained):
hs = qc.Hypers(
kw=dict(
act_fun="gelu",
d_dec_ffn=4096,
d_enc_ffn=4096,
d_model=1024,
dec_START=0,
drop_act=0.0,
drop_attn=0.0,
drop_dec=0.0,
drop_enc=0.0,
drop_proj=0.0,
drop=0.1,
EOS=1,
forced_EOS=1,
grad_checkpoint=True,
init_std=0.02,
is_enc_dec=True,
model_type="pegasus",
n_dec_heads=16,
n_dec_lays=12,
n_enc_heads=16,
n_enc_lays=12,
n_pos=1024,
PAD=0,
s_vocab=50265,
scale=False,
y_cache=True,
),
)
def __init__(self, **kw):
super().__init__(**kw)
def _init_weights(self, module):
std = self.cfg.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, SinusoidalPositionalEmbedding):
pass
elif isinstance(module, qc.Embedding):
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_grad_checkpoint(self, module, value=False):
if isinstance(module, (Decoder, Encoder)):
module.grad_checkpoint = value
@property
def n_heads(self):
return self.n_enc_heads
MAP = {
"google/pegasus-large": dict(
act_fun="relu",
add_bias_logits=False,
add_final_norm=True,
archs=["ForCondGen"],
BOS=0,
drop_act=0.1,
drop_attn=0.1,
extra_pos_embeddings=1,
force_bos_token_to_be_generated=False,
grad_checkpoint=False,
id2label={"0": "LABEL_0", "1": "LABEL_1", "2": "LABEL_2"},
label2id={"LABEL_0": 0, "LABEL_1": 1, "LABEL_2": 2},
len_penalty=0.8,
max_len=256,
n_beams=8,
n_dec_lays=16,
n_enc_lays=16,
n_lays=16,
name_or_path="google/pegasus-large",
normalize_embedding=False,
pre_norm=True,
s_vocab=96103,
scale=True,
static_position_embeddings=True,
task_params=dict(
sum_aeslc=dict(
len_penalty=0.6,
max_len=32,
n_pos=512,
),
sum_arxiv=dict(
len_penalty=0.8,
max_len=256,
n_pos=1024,
),
sum_big_patent=dict(
len_penalty=0.7,
max_len=256,
n_pos=1024,
),
sum_billsum=dict(
len_penalty=0.6,
max_len=256,
n_pos=1024,
),
sum_cnn_dailymail=dict(
len_penalty=0.8,
max_len=128,
n_pos=1024,
),
sum_gigaword=dict(
len_penalty=0.6,
max_len=32,
n_pos=128,
),
sum_large=dict(
len_penalty=0.8,
max_len=256,
n_pos=1024,
),
sum_multi_news=dict(
len_penalty=0.8,
max_len=256,
n_pos=1024,
),
sum_newsroom=dict(
len_penalty=0.8,
max_len=128,
n_pos=512,
),
sum_pubmed=dict(
len_penalty=0.8,
max_len=256,
n_pos=1024,
),
sum_reddit_tifu=dict(
len_penalty=0.6,
max_len=128,
n_pos=512,
),
sum_wikihow=dict(
len_penalty=0.6,
max_len=256,
n_pos=512,
),
sum_xsum=dict(
len_penalty=0.8,
max_len=64,
n_pos=512,
),
),
)
}
|
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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"]}
|
33,481
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/dataset/wiki_summary.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 csv
import datasets as ds
_ID = "id"
_LINK = "link"
_TITLE = "title"
_ARTICLE = "article"
_HIGHLIGHTS = "highlights"
_TRAIN = "https://drive.google.com/u/0/uc?id=1-CaP3xHgZxOGjQ3pXC5tr9YnIajmel-t&export=download"
_TEST = "https://drive.google.com/u/0/uc?id=1-9G4yYP6YO8oMA-o4cTe9NJpEyr7x5jg&export=download"
_VALID = "https://drive.google.com/u/0/uc?id=1-2g2gkDeNaN-vth-8Mgit_ovmSkVh91u&export=download"
class WikiSummary(ds.GeneratorBasedBuilder):
VERSION = ds.Version("1.1.0")
def _info(self):
return ds.DatasetInfo(
description="",
citation="",
homepage="",
license="",
features=ds.Features(
{k: ds.Value("string") for k in [_ID, _LINK, _TITLE, _ARTICLE, _HIGHLIGHTS]}
),
)
def _split_generators(self, mgr):
train = mgr.download_and_extract(_TRAIN)
test = mgr.download_and_extract(_TEST)
valid = mgr.download_and_extract(_VALID)
return [
ds.SplitGenerator(name=ds.Split.TRAIN, gen_kw={"filepath": train}),
ds.SplitGenerator(name=ds.Split.TEST, gen_kw={"filepath": test}),
ds.SplitGenerator(name=ds.Split.VALIDATION, gen_kw={"filepath": valid}),
]
def _generate_examples(self, path):
with open(path, encoding="utf8") as f:
r = csv.reader(
f,
quotechar='"',
delimiter="\t",
quoting=csv.QUOTE_ALL,
skipinitialspace=True,
)
for i, row in enumerate(r):
if len(row) == 5:
yield i, {
_ID: row[0],
_LINK: row[1],
_TITLE: row[2],
_ARTICLE: row[3],
_HIGHLIGHTS: row[4],
}
|
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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/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"]}
|
33,482
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/run/beam.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 XLNet for question answering with beam search
import collections
import json
import logging
import numpy as np
import os
import torch
from tqdm.auto import tqdm
from transformers import (
XLNetConfig,
XLNetTokenizerFast,
XLNetForQuestionAnswering,
EvalPrediction,
)
from .params import EVAL, TEST, EACH
from .qa import Runner as Base
from .utils import init_array
log = logging.getLogger(__name__)
class Runner(Base):
AutoConfig = XLNetConfig
AutoTokenizer = XLNetTokenizerFast
AutoModel = XLNetForQuestionAnswering
def prep_for_train(self, xs):
ps, pad_on_right = self.params, self.pad_on_right
q_col, c_col, a_col = self.cols[EACH]
xs[q_col] = [x.lstrip() for x in xs[q_col]]
ys = self.tokenizer(
xs[q_col if pad_on_right else c_col],
xs[c_col if pad_on_right else q_col],
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,
return_special_tokens_mask=True,
return_token_type_ids=True,
padding="max_len",
)
map = ys.pop("overflow_to_sample_mapping")
specials = ys.pop("special_tokens_mask")
ys["start_positions"] = []
ys["end_positions"] = []
ys["is_impossible"] = []
ys["cls_index"] = []
ys["p_mask"] = []
for i, offs in enumerate(ys.pop("offset_mapping")):
ins = ys["input_ids"][i]
cls = ins.index(self.tokenizer.cls_token_id)
ys["cls_index"].append(cls)
ids = ys["typ_ids"][i]
for k, s in enumerate(specials[i]):
if s:
ids[k] = 3
por = 1 if pad_on_right else 0
ys["p_mask"].append(
[
0.0 if (not specials[i][k] and s == por) or k == cls else 1.0
for k, s in enumerate(ids)
]
)
ans = xs[a_col][map[i]]
if len(ans["answer_start"]) == 0:
ys["start_positions"].append(cls)
ys["end_positions"].append(cls)
ys["is_impossible"].append(1.0)
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)
ys["is_impossible"].append(1.0)
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)
ys["is_impossible"].append(0.0)
return ys
def prep_for_eval(self, xs):
ps, pad_on_right = self.params, self.pad_on_right
q_col, c_col, _ = self.cols[EACH]
xs[q_col] = [q.lstrip() for q in xs[q_col]]
ys = self.tokenizer(
xs[q_col if pad_on_right else c_col],
xs[c_col if pad_on_right else q_col],
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,
return_special_tokens_mask=True,
return_token_type_ids=True,
padding="max_len",
)
map = ys.pop("overflow_to_sample_mapping")
specials = ys.pop("special_tokens_mask")
ys["example_id"] = []
ys["cls_index"] = []
ys["p_mask"] = []
for i, ins in enumerate(ys["input_ids"]):
cls = ins.index(self.tokenizer.cls_token_id)
ys["cls_index"].append(cls)
ids = ys["typ_ids"][i]
for k, s in enumerate(specials[i]):
if s:
ids[k] = 3
por = 1 if pad_on_right else 0
ys["p_mask"].append(
[
0.0 if (not specials[i][k] and s == por) or k == cls else 1.0
for k, s in enumerate(ids)
]
)
ys["example_id"].append(xs["id"][map[i]])
ys["offset_mapping"][i] = [
(o if ids[k] == por else None) for k, o in enumerate(ys["offset_mapping"][i])
]
return ys
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}")
all_start_top_log_probs = []
all_start_top_index = []
all_end_top_log_probs = []
all_end_top_index = []
all_cls_logits = []
for xs in src:
with torch.no_grad():
ys = self.model(**xs)
start_top_log_probs = ys.start_top_log_probs
start_top_index = ys.start_top_index
end_top_log_probs = ys.end_top_log_probs
end_top_index = ys.end_top_index
cls_logits = ys.cls_logits
if not ps.pad_to_max_length:
start_top_log_probs = mgr.pad_across_processes(
start_top_log_probs, dim=1, PAD=-100
)
start_top_index = mgr.pad_across_processes(start_top_index, dim=1, PAD=-100)
end_top_log_probs = mgr.pad_across_processes(end_top_log_probs, dim=1, PAD=-100)
end_top_index = mgr.pad_across_processes(end_top_index, dim=1, PAD=-100)
cls_logits = mgr.pad_across_processes(cls_logits, dim=1, PAD=-100)
all_start_top_log_probs.append(mgr.gather(start_top_log_probs).cpu().numpy())
all_start_top_index.append(mgr.gather(start_top_index).cpu().numpy())
all_end_top_log_probs.append(mgr.gather(end_top_log_probs).cpu().numpy())
all_end_top_index.append(mgr.gather(end_top_index).cpu().numpy())
all_cls_logits.append(mgr.gather(cls_logits).cpu().numpy())
l = max([x.shape[1] for x in all_end_top_log_probs])
start_top_log_probs_concat = init_array(all_start_top_log_probs, ds, l)
start_top_index_concat = init_array(all_start_top_index, ds, l)
end_top_log_probs_concat = init_array(all_end_top_log_probs, ds, l)
end_top_index_concat = init_array(all_end_top_index, ds, l)
cls_logits_concat = np.concatenate(all_cls_logits, axis=0)
del start_top_log_probs
del start_top_index
del end_top_log_probs
del end_top_index
del cls_logits
outputs_numpy = (
start_top_log_probs_concat,
start_top_index_concat,
end_top_log_probs_concat,
end_top_index_concat,
cls_logits_concat,
)
y = self.post_proc(self.evals, ds, outputs_numpy)
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, diff = proc_preds(
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,
start_n_top=self.model.config.start_n_top,
end_n_top=self.model.config.end_n_top,
out_dir=ps.out_dir,
prefix=stage,
)
if ps.version_2_with_negative:
ys = [
{"id": k, "prediction_text": v, "no_answer_probability": diff[k]}
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 pred(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}")
all_start_top_log_probs = []
all_start_top_index = []
all_end_top_log_probs = []
all_end_top_index = []
all_cls_logits = []
for xs in src:
with torch.no_grad():
ys = self.model(**xs)
start_top_log_probs = ys.start_top_log_probs
start_top_index = ys.start_top_index
end_top_log_probs = ys.end_top_log_probs
end_top_index = ys.end_top_index
cls_logits = ys.cls_logits
if not ps.pad_to_max_length:
start_top_log_probs = mgr.pad_across_processes(
start_top_log_probs, dim=1, PAD=-100
)
start_top_index = mgr.pad_across_processes(start_top_index, dim=1, PAD=-100)
end_top_log_probs = mgr.pad_across_processes(
end_top_log_probs, dim=1, PAD=-100
)
end_top_index = mgr.pad_across_processes(end_top_index, dim=1, PAD=-100)
cls_logits = mgr.pad_across_processes(cls_logits, dim=1, PAD=-100)
all_start_top_log_probs.append(mgr.gather(start_top_log_probs).cpu().numpy())
all_start_top_index.append(mgr.gather(start_top_index).cpu().numpy())
all_end_top_log_probs.append(mgr.gather(end_top_log_probs).cpu().numpy())
all_end_top_index.append(mgr.gather(end_top_index).cpu().numpy())
all_cls_logits.append(mgr.gather(cls_logits).cpu().numpy())
l = max([x.shape[1] for x in all_end_top_log_probs])
start_top_log_probs_concat = init_array(all_start_top_log_probs, ds, l)
start_top_index_concat = init_array(all_start_top_index, ds, l)
end_top_log_probs_concat = init_array(all_end_top_log_probs, ds, l)
end_top_index_concat = init_array(all_end_top_index, ds, l)
cls_logits_concat = np.concatenate(all_cls_logits, axis=0)
del start_top_log_probs
del start_top_index
del end_top_log_probs
del end_top_index
del cls_logits
outputs_numpy = (
start_top_log_probs_concat,
start_top_index_concat,
end_top_log_probs_concat,
end_top_index_concat,
cls_logits_concat,
)
y = self.post_proc(self.preds, ds, outputs_numpy)
y = self.metric.compute(predictions=y.predictions, references=y.label_ids)
log.info(f"Prediction metrics: {y}")
def proc_preds(
examples,
features,
predictions,
version_2_with_negative=False,
n_best_size=20,
max_answer_length=30,
start_n_top=5,
end_n_top=5,
out_dir=None,
prefix=None,
log_level=logging.WARNING,
):
if len(predictions) != 5:
raise ValueError("`predictions` should be a tuple with five elements.")
start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_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()
scores_diff_json = collections.OrderedDict() if version_2_with_negative else None
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_score = None
prelim_predictions = []
for feature_index in feature_indices:
start_log_prob = start_top_log_probs[feature_index]
start_indexes = start_top_index[feature_index]
end_log_prob = end_top_log_probs[feature_index]
end_indexes = end_top_index[feature_index]
feature_null_score = cls_logits[feature_index]
offset_mapping = features[feature_index]["offset_mapping"]
token_is_max_context = features[feature_index].get("token_is_max_context", None)
if min_null_score is None or feature_null_score < min_null_score:
min_null_score = feature_null_score
for i in range(start_n_top):
for j in range(end_n_top):
start_index = int(start_indexes[i])
j_index = i * end_n_top + j
end_index = int(end_indexes[j_index])
if (
start_index >= len(offset_mapping)
or end_index >= len(offset_mapping)
or offset_mapping[start_index] is None
or offset_mapping[end_index] is None
):
continue
if end_index < start_index or end_index - start_index + 1 > max_answer_length:
continue
if token_is_max_context is not None and not token_is_max_context.get(
str(start_index), False
):
continue
prelim_predictions.append(
{
"offsets": (
offset_mapping[start_index][0],
offset_mapping[end_index][1],
),
"score": start_log_prob[i] + end_log_prob[j_index],
"start_log_prob": start_log_prob[i],
"end_log_prob": end_log_prob[j_index],
}
)
predictions = sorted(prelim_predictions, key=lambda x: x["score"], reverse=True)[
:n_best_size
]
context = example["context"]
for pred in predictions:
offsets = pred.pop("offsets")
pred["text"] = context[offsets[0] : offsets[1]]
if len(predictions) == 0:
predictions.insert(
0, {"text": "", "start_logit": -1e-6, "end_logit": -1e-6, "score": -2e-6}
)
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
all_predictions[example["id"]] = predictions[0]["text"]
if version_2_with_negative:
scores_diff_json[example["id"]] = float(min_null_score)
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, scores_diff_json
def main():
x = Runner()
x.dataset
x.cols
x.config
x.tokenizer
x.model
x.loaders
x.prepare()
x.train()
x.eval()
x.pred()
x.save()
if __name__ == "__main__":
main()
|
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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"]}
|
33,483
|
quantapix/qnarre
|
refs/heads/main
|
/tools/triton/python/triton/compiler/compiler.py
|
from __future__ import annotations
import functools
import hashlib
import json
import os
import re
import subprocess
import tempfile
from collections import namedtuple
from pathlib import Path
from typing import Any, Tuple
import triton
import triton._C.libtriton.triton as _triton
from ..runtime import driver
# TODO: runtime.errors
from ..runtime.autotuner import OutOfResources
from ..runtime.cache import get_cache_manager
from ..tools.disasm import extract
from .code_generator import ast_to_ttir
from .make_launcher import make_stub
def inline_triton_ir(mod):
pm = _triton.ir.pass_manager(mod.context)
pm.enable_debug()
pm.add_inliner_pass()
pm.run(mod)
return mod
def ttir_compute_capability_rewrite(mod, arch):
# For hardware without support, we must rewrite all load/store
# with block (tensor) pointers into tensors of pointers
pm = _triton.ir.pass_manager(mod.context)
pm.enable_debug()
if _is_cuda(arch):
pm.add_rewrite_tensor_pointer_pass(arch)
pm.run(mod)
return mod
def optimize_ttir(mod, arch):
mod = inline_triton_ir(mod)
mod = ttir_compute_capability_rewrite(mod, arch)
pm = _triton.ir.pass_manager(mod.context)
pm.enable_debug()
pm.add_inliner_pass()
pm.add_triton_combine_pass()
pm.add_canonicalizer_pass()
pm.add_cse_pass()
pm.add_licm_pass()
pm.add_symbol_dce_pass()
pm.run(mod)
return mod
def ttir_to_ttgir(mod, num_warps):
pm = _triton.ir.pass_manager(mod.context)
pm.add_convert_triton_to_tritongpu_pass(num_warps)
pm.run(mod)
return mod
def optimize_ttgir(mod, num_stages, arch):
pm = _triton.ir.pass_manager(mod.context)
pm.enable_debug()
pm.add_tritongpu_coalesce_pass()
pm.add_tritongpu_remove_layout_conversions_pass()
if isinstance(arch, int):
pm.add_tritongpu_accelerate_matmul_pass(arch)
pm.add_tritongpu_remove_layout_conversions_pass()
pm.add_tritongpu_optimize_dot_operands_pass()
pm.add_tritongpu_pipeline_pass(num_stages)
pm.add_tritongpu_prefetch_pass()
pm.add_tritongpu_optimize_dot_operands_pass()
pm.add_tritongpu_remove_layout_conversions_pass()
pm.add_tritongpu_decompose_conversions_pass()
pm.add_tritongpu_reorder_instructions_pass()
pm.add_cse_pass()
pm.add_symbol_dce_pass()
pm.run(mod)
return mod
def _add_external_libs(mod, libs):
for name, path in libs.items():
if len(name) == 0 or len(path) == 0:
return
_triton.add_external_libs(mod, list(libs.keys()), list(libs.values()))
def ttgir_to_llir(mod, extern_libs, arch):
if extern_libs:
_add_external_libs(mod, extern_libs)
# TODO: separate tritongpu_to_llvmir for different backends
if _is_cuda(arch):
return _triton.translate_triton_gpu_to_llvmir(mod, arch, False)
else:
return _triton.translate_triton_gpu_to_llvmir(mod, 0, True)
# PTX translation
@functools.lru_cache()
def ptx_get_version(cuda_version) -> int:
'''
Get the highest PTX version supported by the current CUDA driver.
'''
assert isinstance(cuda_version, str)
major, minor = map(int, cuda_version.split('.'))
if major == 12:
return 80 + minor
if major == 11:
return 70 + minor
if major == 10:
return 63 + minor
raise RuntimeError("Triton only support CUDA 10.0 or higher")
@functools.lru_cache()
def path_to_ptxas():
base_dir = os.path.join(os.path.dirname(__file__), os.pardir)
paths = [
os.environ.get("TRITON_PTXAS_PATH", ""),
os.path.join(base_dir, "third_party", "cuda", "bin", "ptxas")
]
for ptxas in paths:
if os.path.exists(ptxas) and os.path.isfile(ptxas):
result = subprocess.check_output([ptxas, "--version"], stderr=subprocess.STDOUT)
if result is not None:
version = re.search(r".*release (\d+\.\d+).*", result.decode("utf-8"), flags=re.MULTILINE)
if version is not None:
return ptxas, version.group(1)
raise RuntimeError("Cannot find ptxas")
def llir_to_ptx(mod: Any, arch: int, ptx_version: int = None) -> str:
'''
Translate TritonGPU module to PTX code.
:param mod: a TritonGPU dialect module
:return: PTX code
'''
if ptx_version is None:
_, cuda_version = path_to_ptxas()
ptx_version = ptx_get_version(cuda_version)
return _triton.translate_llvmir_to_ptx(mod, arch, ptx_version)
def ptx_to_cubin(ptx: str, arch: int):
'''
Compile TritonGPU module to cubin.
:param ptx: ptx code
:param compute_capability: compute capability
:return: str
'''
ptxas, _ = path_to_ptxas()
return _triton.compile_ptx_to_cubin(ptx, ptxas, arch)
# AMDGCN translation
def get_amdgcn_bitcode_paths(arch):
gpu_arch_agnostic_bitcode_libraries = ["opencl.bc",
"ocml.bc",
"ockl.bc",
"oclc_finite_only_off.bc",
"oclc_daz_opt_off.bc",
"oclc_correctly_rounded_sqrt_on.bc",
"oclc_unsafe_math_off.bc",
"oclc_wavefrontsize64_on.bc"]
gfx_arch = arch[1]
gfx_arch_id = re.search('gfx(\\w+)', gfx_arch).group(1).strip()
gpu_arch_specific_bitcode_library = 'oclc_isa_version_' + gfx_arch_id + ".bc"
bitcode_path_dir = os.path.join(Path(__file__).parent.resolve(), "third_party/rocm/lib/bitcode/")
amdgcn_bitcode_paths = {}
i = 1
for bc_lib in gpu_arch_agnostic_bitcode_libraries:
bc_path = bitcode_path_dir + bc_lib
if os.path.exists(bc_path):
amdgcn_bitcode_paths['library_' + str(i)] = bc_path
i += 1
bc_gfx_path = bitcode_path_dir + gpu_arch_specific_bitcode_library
if os.path.exists(bc_gfx_path):
amdgcn_bitcode_paths['library_' + str(i)] = bc_gfx_path
return amdgcn_bitcode_paths
def get_amdgpu_arch_fulldetails():
"""
get the amdgpu fulll ISA details for compiling:
i.e., arch_triple: amdgcn-amd-amdhsa; arch_name: gfx906; arch_features: sramecc+:xnack-
"""
try:
# TODO: package rocm.cc with Triton
rocm_path_dir = os.getenv("ROCM_PATH", default="/opt/rocm")
rocminfo = subprocess.check_output(rocm_path_dir + '/bin/rocminfo').decode()
gfx_arch_details = re.search('amd.*', rocminfo).group(0).strip().split('--')
arch_triple = gfx_arch_details[0]
arch_name_features = gfx_arch_details[1].split(':')
arch_name = arch_name_features[0]
arch_features = ""
if (len(arch_name_features) == 3):
arch_features = "+" + re.search('\\w+', arch_name_features[1]).group(0) + ","\
"-" + re.search('\\w+', arch_name_features[2]).group(0)
return [arch_triple, arch_name, arch_features]
except BaseException:
return None
def llir_to_amdgcn_and_hsaco(mod: Any, gfx_arch: str, gfx_triple: str, gfx_features: str) -> Tuple[str, str]:
'''
Translate TritonGPU module to HSACO code based on full details of gpu architecture.
:param mod: a TritonGPU dialect module
:return:
- AMDGCN code
- Path to HSACO object
'''
return _triton.translate_llvmir_to_hsaco(mod, gfx_arch, gfx_triple, gfx_features)
# ------------------------------------------------------------------------------
# compiler
# ------------------------------------------------------------------------------
def get_kernel_name(src: str, pattern: str) -> str:
'''
Get kernel name from PTX code.
This Kernel name is required when launching the kernel.
'''
# There is a name mangling in PTX codegen, so the original kernel names in Triton IR are not available in PTX/cubin.
assert src
for line in src.split('\n'):
line = line.strip()
if line.startswith(pattern):
return line.split()[-1]
def convert_type_repr(x):
match = re.search(r'!tt\.ptr<(.*)>', x)
if match is not None:
return '*' + convert_type_repr(match.group(1))
return x
def make_hash(fn, arch, **kwargs):
if isinstance(fn, triton.runtime.JITFunction):
configs = kwargs["configs"]
signature = kwargs["signature"]
constants = kwargs.get("constants", dict())
num_warps = kwargs.get("num_warps", 4)
num_stages = kwargs.get("num_stages", 3)
debug = kwargs.get("debug", False)
# Get unique key for the compiled code
get_conf_key = lambda conf: (sorted(conf.divisible_by_16), sorted(conf.equal_to_1))
configs_key = [get_conf_key(conf) for conf in configs]
key = f"{fn.cache_key}-{''.join(signature.values())}-{configs_key}-{constants}-{num_warps}-{num_stages}-{debug}-{arch}"
return hashlib.md5(key.encode("utf-8")).hexdigest()
assert isinstance(fn, str)
return hashlib.md5((Path(fn).read_text() + triton.runtime.jit.version_key()).encode("utf-8")).hexdigest()
# - ^\s*tt\.func\s+ : match the start of the string, any leading whitespace, the keyword func,
# and any following whitespace
# - (public\s+)? : optionally match the keyword public and any following whitespace
# - (@\w+) : match an @ symbol followed by one or more word characters
# (letters, digits, or underscores), and capture it as group 1 (the function name)
# - (\((?:%\w+: \S+(?: \{\S+ = \S+ : \S+\})?(?:, )?)*\)) : match a pair of parentheses enclosing
# zero or more arguments separated by commas, and capture it as group 2 (the argument list)
mlir_prototype_pattern = r'^\s*tt\.func\s+(?:public\s+)?(@\w+)(\((?:%\w+: \S+(?: \{\S+ = \S+ : \S+\})?(?:, )?)*\))\s*\{\s*$'
ptx_prototype_pattern = r"\.(?:visible|extern)\s+\.(?:entry|func)\s+(\w+)\s*\(([^)]*)\)"
prototype_pattern = {
"ttir": mlir_prototype_pattern,
"ttgir": mlir_prototype_pattern,
"ptx": ptx_prototype_pattern,
}
mlir_arg_type_pattern = r'%\w+: ([^,^\)\s]+)(?: \{\S+ = \S+ : \S+\})?,?'
ptx_arg_type_pattern = r"\.param\s+\.(\w+)"
arg_type_pattern = {
"ttir": mlir_arg_type_pattern,
"ttgir": mlir_arg_type_pattern,
"ptx": ptx_arg_type_pattern,
}
ttgir_num_warps_pattern = r'"triton_gpu.num-warps"\s?=\s?(\d+)\s?:'
def _get_jsonable_constants(constants):
def _is_jsonable(x):
try:
json.dumps(x)
return True
except (TypeError, OverflowError):
return False
serialized_constants = {}
for constant in constants:
if _is_jsonable(constants[constant]):
serialized_constants[constant] = constants[constant]
return serialized_constants
def parse_mlir_module(path, context):
module = _triton.ir.parse_mlir_module(path, context)
# module takes ownership of the context
module.context = context
return module
instance_descriptor = namedtuple("instance_descriptor", ["divisible_by_16", "equal_to_1"], defaults=[set(), set()])
# TODO: architecture descriptor class
def _is_cuda(arch):
return isinstance(arch, int)
def get_architecture_descriptor(capability):
try:
import torch
except ImportError:
raise ImportError("Triton requires PyTorch to be installed")
if capability is None:
if torch.version.hip is None:
device = triton.runtime.jit.get_current_device()
capability = triton.runtime.jit.get_device_capability(device)
capability = capability[0] * 10 + capability[1]
else:
capability = get_amdgpu_arch_fulldetails()
return capability
def add_rocm_stages(arch, extern_libs, stages):
extern_libs.update(get_amdgcn_bitcode_paths(arch))
for key in list(extern_libs):
if extern_libs[key] == '' or extern_libs[key] is None:
extern_libs.pop(key)
gfx_arch_full_details = arch
gfx_arch = os.environ.get('MI_GPU_ARCH', gfx_arch_full_details[1])
if gfx_arch is None:
raise RuntimeError('gfx_arch is None (not specified)')
stages["amdgcn"] = (lambda path: Path(path).read_text(),
lambda src: llir_to_amdgcn_and_hsaco(src, gfx_arch,
gfx_arch_full_details[0],
gfx_arch_full_details[2]))
def add_cuda_stages(arch, extern_libs, stages):
stages["ptx"] = (lambda path: Path(path).read_text(),
lambda src: llir_to_ptx(src, arch))
stages["cubin"] = (lambda path: Path(path).read_bytes(),
lambda src: ptx_to_cubin(src, arch))
def compile(fn, **kwargs):
arch = get_architecture_descriptor(kwargs.get("cc", None))
is_cuda = _is_cuda(arch)
context = _triton.ir.context()
asm = dict()
constants = kwargs.get("constants", dict())
num_warps = kwargs.get("num_warps", 4)
num_stages = kwargs.get("num_stages", 3 if is_cuda and arch >= 75 else 2)
extern_libs = kwargs.get("extern_libs", dict())
if extern_libs is None:
extern_libs = dict()
debug = kwargs.get("debug", False)
# build compilation stages
stages = dict()
stages["ast"] = (lambda path: fn, None)
stages["ttir"] = (lambda path: parse_mlir_module(path, context),
lambda src: optimize_ttir(ast_to_ttir(src, signature, configs[0], constants, debug=debug), arch))
stages["ttgir"] = (lambda path: parse_mlir_module(path, context),
lambda src: optimize_ttgir(ttir_to_ttgir(src, num_warps), num_stages, arch))
stages["llir"] = (lambda path: Path(path).read_text(),
lambda src: ttgir_to_llir(src, extern_libs, arch))
if is_cuda:
add_cuda_stages(arch, extern_libs, stages)
else:
add_rocm_stages(arch, extern_libs, stages)
# find out the signature of the function
if isinstance(fn, triton.runtime.JITFunction):
configs = kwargs.get("configs", None)
signature = kwargs["signature"]
if configs is None:
configs = [instance_descriptor()]
assert len(configs) == 1
kwargs["configs"] = configs
name = fn.__name__
first_stage = 0
if isinstance(signature, str):
signature = {k: v.strip() for k, v in enumerate(signature.split(","))}
kwargs["signature"] = signature
else:
assert isinstance(fn, str)
_, ir = os.path.basename(fn).split(".")
src = Path(fn).read_text()
import re
match = re.search(prototype_pattern[ir], src, re.MULTILINE)
name, signature = match.group(1), match.group(2)
types = re.findall(arg_type_pattern[ir], signature)
if ir == 'ttgir':
num_warps_matches = re.findall(ttgir_num_warps_pattern, src)
assert len(num_warps_matches) == 1, "Expected exactly one match for num_warps"
assert "num_warps" not in kwargs or int(num_warps_matches[0]) == num_warps, "num_warps in ttgir does not match num_warps in compile"
num_warps = int(num_warps_matches[0])
param_tys = [convert_type_repr(ty) for ty in types]
signature = {k: v for k, v in enumerate(param_tys)}
first_stage = list(stages.keys()).index(ir)
# cache manager
so_path = make_stub(name, signature, constants)
# create cache manager
fn_cache_manager = get_cache_manager(make_hash(fn, arch, **kwargs))
# determine name and extension type of provided function
if isinstance(fn, triton.runtime.JITFunction):
name, ext = fn.__name__, "ast"
else:
name, ext = os.path.basename(fn).split(".")
# load metadata if any
metadata = None
metadata_filename = f"{name}.json"
# The group is addressed by the metadata
metadata_group = fn_cache_manager.get_group(
metadata_filename
) or {}
metadata_path = metadata_group.get(metadata_filename)
if metadata_path is not None:
with open(metadata_path) as f:
metadata = json.load(f)
else:
metadata = {"num_warps": num_warps,
"num_stages": num_stages,
"constants": _get_jsonable_constants(constants),
"debug": debug}
if ext == "ptx":
assert "shared" in kwargs, "ptx compilation must provide shared memory size"
metadata["shared"] = kwargs["shared"]
first_stage = list(stages.keys()).index(ext)
asm = dict()
module = fn
# run compilation pipeline and populate metadata
for ir, (parse, compile_kernel) in list(stages.items())[first_stage:]:
ir_filename = f"{name}.{ir}"
if ir == ext:
next_module = parse(fn)
else:
path = metadata_group.get(ir_filename)
if path is None:
next_module = compile_kernel(module)
if ir == "amdgcn":
extra_file_name = f"{name}.hsaco_path"
metadata_group[ir_filename] = fn_cache_manager.put(next_module[0], ir_filename)
metadata_group[extra_file_name] = fn_cache_manager.put(next_module[1], extra_file_name)
else:
metadata_group[ir_filename] = fn_cache_manager.put(next_module, ir_filename)
fn_cache_manager.put(next_module, ir_filename)
else:
if ir == "amdgcn":
extra_file_name = f"{name}.hsaco_path"
hasco_path = metadata_group.get(extra_file_name)
assert hasco_path is not None, "Expected to have hsaco_path in metadata when we have the amdgcn"
next_module = (parse(path), parse(hasco_path))
else:
next_module = parse(path)
if ir == "cubin":
asm[ir] = next_module
elif ir == "amdgcn":
asm[ir] = str(next_module[0])
else:
asm[ir] = str(next_module)
if ir == "llir" and "shared" not in metadata:
metadata["shared"] = _triton.get_shared_memory_size(module)
if ir == "ptx":
metadata["name"] = get_kernel_name(next_module, pattern='// .globl')
if ir == "amdgcn":
metadata["name"] = get_kernel_name(next_module[0], pattern='.globl')
asm["hsaco_path"] = next_module[1]
module = next_module
# write-back metadata, if it didn't come from the cache
if metadata_path is None:
metadata_group[metadata_filename] = fn_cache_manager.put(json.dumps(metadata), metadata_filename, binary=False)
fn_cache_manager.put_group(metadata_filename, metadata_group)
# return handle to compiled kernel
return CompiledKernel(fn, so_path, metadata, asm)
class CompiledKernel:
# Hooks for external tools to monitor the execution of triton kernels
launch_enter_hook = None
launch_exit_hook = None
def __init__(self, fn, so_path, metadata, asm):
# initialize launcher
import importlib.util
spec = importlib.util.spec_from_file_location("__triton_launcher", so_path)
mod = importlib.util.module_from_spec(spec)
self.fn = fn
spec.loader.exec_module(mod)
self.c_wrapper = getattr(mod, "launch")
# initialize metadata
self.shared = metadata["shared"]
self.num_warps = metadata["num_warps"]
self.num_stages = metadata["num_stages"]
self.constants = metadata["constants"]
# initialize asm dict
self.asm = asm
# binaries are lazily initialized
# because it involves doing runtime things
# (e.g., checking amount of shared memory on current device)
self.metadata = metadata
self.cu_module = None
self.cu_function = None
def _init_handles(self):
if self.cu_module is not None:
return
device = triton.runtime.jit.get_current_device()
bin_path = {
driver.HIP: "hsaco_path",
driver.CUDA: "cubin"
}[driver.backend]
max_shared = driver.utils.get_device_properties(device)["max_shared_mem"]
if self.shared > max_shared:
raise OutOfResources(self.shared, max_shared, "shared memory")
mod, func, n_regs, n_spills = driver.utils.load_binary(self.metadata["name"], self.asm[bin_path], self.shared, device)
self.n_spills = n_spills
self.n_regs = n_regs
self.cu_module = mod
self.cu_function = func
def __getattribute__(self, name):
if name == 'c_wrapper':
self._init_handles()
return super().__getattribute__(name)
def __getitem__(self, grid):
self._init_handles()
def runner(*args, stream=None):
if stream is None:
stream = triton.runtime.jit.get_cuda_stream()
self.c_wrapper(grid[0], grid[1], grid[2], self.num_warps, self.shared, stream, self.cu_function,
CompiledKernel.launch_enter_hook, CompiledKernel.launch_exit_hook, self, *args)
return runner
def get_sass(self, fun=None):
if 'sass' in self.asm:
return self.asm['sass']
fd, path = tempfile.mkstemp()
try:
with open(fd, 'wb') as cubin:
cubin.write(self.asm['cubin'])
self.sass = extract(path, fun)
finally:
os.remove(path)
self.asm['sass'] = self.sass
return self.sass
|
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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"]}
|
33,484
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/dataset/conllpp.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 = "https://github.com/ZihanWangKi/CrossWeigh/raw/master/data/"
_URLS = {
"train": f"{_URL}conllpp_train.txt",
"valid": f"{_URL}conllpp_dev.txt",
"test": f"{_URL}conllpp_test.txt",
}
class Conllpp(ds.GeneratorBasedBuilder):
BUILDER_CONFIGS = [ds.BuilderConfig(name="conllpp", version=ds.Version("1.0.0"))]
def _info(self):
return ds.DatasetInfo(
description="",
citation="",
homepage="",
license="",
features=ds.Features(
{
"id": ds.Value("string"),
"tokens": ds.Sequence(ds.Value("string")),
"pos_tags": ds.Sequence(
ds.features.ClassLabel(
names=[
'"',
"''",
"#",
"$",
"(",
")",
",",
".",
":",
"``",
"CC",
"CD",
"DT",
"EX",
"FW",
"IN",
"JJ",
"JJR",
"JJS",
"LS",
"MD",
"NN",
"NNP",
"NNPS",
"NNS",
"NN|SYM",
"PDT",
"POS",
"PRP",
"PRP$",
"RB",
"RBR",
"RBS",
"RP",
"SYM",
"TO",
"UH",
"VB",
"VBD",
"VBG",
"VBN",
"VBP",
"VBZ",
"WDT",
"WP",
"WP$",
"WRB",
]
)
),
"chunk_tags": ds.Sequence(
ds.features.ClassLabel(
names=[
"O",
"B-ADJP",
"I-ADJP",
"B-ADVP",
"I-ADVP",
"B-CONJP",
"I-CONJP",
"B-INTJ",
"I-INTJ",
"B-LST",
"I-LST",
"B-NP",
"I-NP",
"B-PP",
"I-PP",
"B-PRT",
"I-PRT",
"B-SBAR",
"I-SBAR",
"B-UCP",
"I-UCP",
"B-VP",
"I-VP",
]
)
),
"ner_tags": ds.Sequence(
ds.features.ClassLabel(
names=[
"O",
"B-PER",
"I-PER",
"B-ORG",
"I-ORG",
"B-LOC",
"I-LOC",
"B-MISC",
"I-MISC",
]
)
),
}
),
)
def _split_generators(self, mgr):
fs = mgr.download_and_extract(_URLS)
return [
ds.SplitGenerator(name=ds.Split.TRAIN, gen_kw={"filepath": fs["train"]}),
ds.SplitGenerator(name=ds.Split.VALIDATION, gen_kw={"filepath": fs["dev"]}),
ds.SplitGenerator(name=ds.Split.TEST, gen_kw={"filepath": fs["test"]}),
]
def _generate_examples(self, path):
with open(path, encoding="utf-8") as f:
i = 0
ts = []
pos = []
chunks = []
ners = []
for line in f:
if line.startswith("-DOCSTART-") or line == "" or line == "\n":
if ts:
yield i, {
"id"(i),
"tokens": ts,
"pos_tags": pos,
"chunk_tags": chunks,
"ner_tags": ners,
}
i += 1
ts = []
pos = []
chunks = []
ners = []
else:
splits = line.split(" ")
ts.append(splits[0])
pos.append(splits[1])
chunks.append(splits[2])
ners.append(splits[3].rstrip())
if ts:
yield i, {
"id"(i),
"tokens": ts,
"pos_tags": pos,
"chunk_tags": chunks,
"ner_tags": ners,
}
|
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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"]}
|
33,485
|
quantapix/qnarre
|
refs/heads/main
|
/tools/triton/python/triton/debugger/memory_map.py
|
import dataclasses
from triton.debugger import torch_wrapper
torch = torch_wrapper.torch
@dataclasses.dataclass
class RegisteredStorage:
storage: torch.Storage
dtype: torch.dtype
size: int
ptr: int
@property
def end_ptr(self) -> int:
return self.ptr + self.size
@property
def access_tensor(self) -> torch.Tensor:
return torch.tensor(self.storage, dtype=self.dtype, device=self.storage.device)
def ensure_immutable(self):
assert self.storage.data_ptr() == self.ptr and self.storage.size() == self.size
class MemoryMap:
storages: [RegisteredStorage]
def __init__(self):
self.storages = []
def _get_registered_storage(self, pointer: torch.Tensor):
max_pointer = torch.max(pointer).item()
min_pointer = torch.min(pointer).item()
registered_storage = next(
filter(
lambda registered: min_pointer >= registered.ptr and max_pointer < registered.end_ptr, self.storages
),
None,
)
if registered_storage is None:
raise Exception("Storage not found or pointers spanning multiple tensors")
registered_storage.ensure_immutable()
return registered_storage
def add_tensor(self, t: torch.Tensor):
storage = t.untyped_storage()
self.storages.append(RegisteredStorage(storage, t.dtype, storage.size(), storage.data_ptr()))
return t.data_ptr()
def load(
self,
pointer: torch.Tensor,
mask: torch.Tensor = None,
other=0.0,
):
assert pointer.is_cuda
assert 0 < pointer.dim() < 3
assert pointer.dtype == torch.int64
if mask is None:
mask = torch.ones_like(pointer).bool()
assert mask.is_cuda
assert 0 < mask.dim() < 3
assert mask.dtype == torch.bool
mask = mask.expand(pointer.size())
if torch.all(~mask):
# Todo: The type is wrong here, we can't determine the correct type
return torch.full_like(pointer, fill_value=other, dtype=torch.float16, device="cuda")
registered_storage = self._get_registered_storage(pointer[mask])
access_tensor = registered_storage.access_tensor
index_tensor = pointer - registered_storage.ptr
block = torch.full_like(pointer, fill_value=other, dtype=access_tensor.dtype, device="cuda")
block[mask] = access_tensor[index_tensor[mask]]
return block
def store(self, pointer: torch.Tensor, value: torch.Tensor, mask=None):
assert 0 < pointer.dim() < 3
assert pointer.dtype == torch.int64
if mask is None:
mask = torch.ones_like(pointer).bool()
assert 0 < mask.dim() < 3
assert mask.dtype == torch.bool
mask = mask.expand(pointer.size())
if torch.all(~mask):
return
registered_storage = self._get_registered_storage(pointer[mask])
access_tensor = registered_storage.access_tensor
index_tensor = pointer - registered_storage.ptr
access_tensor[index_tensor[mask]] = value[mask].to(access_tensor.dtype)
|
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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"]}
|
33,486
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/base/doc/graph.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 networkx as nx
from .base import Record
feeder = 0, 1
bridger = 1, 1
sinker = 1, 0
class DiGraph(nx.DiGraph):
def empty_recs(self):
for m, d in dict(self.nodes(data=True)).items():
if d.get('empty', False):
yield m
def linked_recs(self, kind):
i, o = kind
ms = (m for m, d in self.in_degree() if d == i)
for m in (m for m, d in self.out_degree(ms) if d == o):
if m in self:
if self.in_degree(m) == i and self.out_degree(m) == o:
p = self.predecessors(m)[0] if i else None
s = self.successors(m)[0] if o else None
yield p, m, s
def purge_recs(self):
if self.size():
for m in self.nodes():
if not self.degree(m):
self.remove_node(m)
def remove_msg(self, msg):
if msg in self:
for p in self.predecessors(msg):
for s in self.successors(msg):
self.add_edge(p, s)
self.remove_node(msg)
class Graphs:
@classmethod
def init_class(cls):
for g in cls._graphs:
setattr(cls, '_' + g, None)
def make_getter(name):
n = '_' + name
def get(self):
if getattr(self, n) is None:
setattr(self, n, DiGraph())
return getattr(self, n)
return get
setattr(cls, g, property(make_getter(g)))
def __init__(self, seed=(), **kw):
super().__init__(**kw)
for i in seed:
self.add_item(i)
@property
def graphs(self):
return (getattr(self, n) for n in self._graphs)
def msg_attrs(self, txt, kind, **kw):
kw.update(empty=not bool(txt), kind=kind)
return kw
def add_item(self, item, cntr, **kw):
f, s, k = item
if issubclass(k, Record):
self.record.add_node(f, **self.msg_attrs(s, k, **kw))
cntr.incr('record')
else:
getattr(self, k.label).add_edge(f, s)
cntr.incr(k.label)
def check(self):
pass
def grow_from(self, src, adjs=None, **kw):
for i in src:
self.add_item(i, **kw)
if adjs:
self.adjust_from(adjs)
self.check()
def purge_empty(self, cntr, **_):
for m in self.record.empty_recs():
for g in self.graphs:
g.remove_msg(m)
cntr.incr('d')
self.check()
"""
@property
def comps(self):
return nx.weakly_connected_components(self.nxdg)
def roots(self, comp):
return sorted(n for n, d in self.nxdg.in_degree(comp) if not d)
def nodes(self, root):
return nx.dfs_preorder_nodes(self.nxdg, root)
def merge(self, other):
return self
def init_from(self, src):
pass
def adjust_from(self, src):
pass
import contextlib as cl
@cl.contextmanager
def graph(path, directed=True, **kw):
g = nx.DiGraph(**kw) if directed else nx.Graph(**kw)
yield g
a = nx.nx_agraph.to_agraph(g)
#p = str(path)
# g.write_dot(p)
#g = gv.AGraph()
# g.read(p)
a.draw(str(path.with_suffix('.png')), prog="neato")
import pygraphviz as gv
A = gv.AGraph()
A.node_attr['style'] = 'filled'
A.node_attr['shape'] = 'circle'
A.node_attr['fixedsize'] = 'true'
A.node_attr['fontcolor'] = '#FFFFFF'
for i in range(16):
A.add_edge(0, i)
n = A.get_node(i)
n.attr['fillcolor'] = "#%2x0000" % (i * 16)
n.attr['height'] = "%s" % (i / 16.0 + 0.5)
n.attr['width'] = "%s" % (i / 16.0 + 0.5)
print(A.string())
A.write("/tmp/star.dot")
print("Wrote star.dot")
A.draw('/tmp/star.png', prog="circo")
print("Wrote star.png")
"""
|
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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/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/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"]}
|
33,487
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/base/activism.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
from .judgment import Judgment
class Activism(Node):
claims = judgments = None
def __init__(self, text=None, judgments=None, authority=None, **kw):
super().__init__(**kw)
if self.claims is None:
self.claims, self.judgments = [], []
if text:
for k in ('factor', 'bias', 'weight'):
kw.pop(k, None)
self.claims.append(Claim(text=text, **kw))
if judgments:
js = (j.strip() for j in judgments.split('|') if ':' in j)
self.judgments.extend(Judgment.create(name=j) for j in js if j)
if authority:
self.authority = Authority.create(name=authority)
@property
def weight(self):
cs = tuple(c.weight for c in self.claims)
js = tuple(j.weight for j in self.judgments)
return self.partial(cs, js) + self.bias
@property
def turmoil(self):
return self.weight
@property
def value(self):
t = self.turmoil
return '{} {}: T{}'.format(super().value, self.authority.agency, t)
@property
def fields(self):
fs = super().fields
fs['Activism'] = self.name
ls = []
for c in self.claims:
fs2 = c.fields
fs2.update(fs)
fs2['Turmoil'] = self.partial(c.weight)
ls.append(fs2)
for j in sorted(self.judgments, key=lambda j: j.sequence):
fs2 = c.fields
fs2['Topic'] = fs['Topic']
fs2['Narrative'] = fs['Narrative']
fs2['Activism'] = fs['Activism']
fs2['Turmoil'] = self.partial(j.weight)
ls.append(fs2)
return ls
class Exclude(Activism):
sign = '@x'
class Insinuate(Activism):
sign = '@i'
class Polarize(Activism):
sign = '@o'
class Recast(Activism):
sign = '@r'
class Elevate(Activism):
sign = '@e'
class Victimize(Activism):
sign = '@v'
class Exploit(Activism):
sign = '@t'
class Perpetuate(Activism):
sign = '@p'
|
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|
33,488
|
quantapix/qnarre
|
refs/heads/main
|
/tools/triton/python/test/unit/operators/test_flash_attention.py
|
import pytest
import torch
import triton
import triton.ops
@pytest.mark.parametrize('Z, H, N_CTX, D_HEAD', [(4, 48, 1024, 64)])
@pytest.mark.parametrize('dtype', [torch.float16, torch.bfloat16])
def test_op(Z, H, N_CTX, D_HEAD, dtype):
capability = torch.cuda.get_device_capability()
if capability[0] < 8:
pytest.skip("Flash attention only supported for compute capability < 80")
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).to(dtype)
# 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 = triton.ops.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
atol = 1e-1 if dtype == torch.bfloat16 else 1e-2
torch.testing.assert_allclose(ref_out, tri_out, atol=atol, rtol=0)
torch.testing.assert_allclose(ref_dv, tri_dv, atol=atol, rtol=0)
torch.testing.assert_allclose(ref_dk, tri_dk, atol=atol, rtol=0)
torch.testing.assert_allclose(ref_dq, tri_dq, atol=atol, rtol=0)
|
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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"]}
|
33,489
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/models/gptj.py
|
import warnings
from typing import Optional, Tuple, Union
import torch
import torch.fx
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import (
BaseModelOutputWithPast,
CausalLMOutputWithPast,
QuestionAnsweringModelOutput,
SequenceClassifierOutputWithPast,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
is_torch_fx_proxy,
logging,
)
from ...utils.model_parallel_utils import assert_device_map, get_device_map
from .configuration_gptj import GPTJConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "hf-internal-testing/tiny-random-gptj"
_REAL_CHECKPOINT_FOR_DOC = "EleutherAI/gpt-j-6B"
_CONFIG_FOR_DOC = "GPTJConfig"
GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST = [
"EleutherAI/gpt-j-6B",
# See all GPT-J models at https://huggingface.co/models?filter=gptj
]
def create_sinusoidal_positions(num_pos: int, dim: int) -> torch.Tensor:
inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2) / dim))
sinusoid_inp = torch.einsum(
"i , j -> i j", torch.arange(num_pos, dtype=torch.float), inv_freq
).float()
return torch.cat((torch.sin(sinusoid_inp), torch.cos(sinusoid_inp)), dim=1)
@torch.fx.wrap
def get_embed_positions(embed_positions, position_ids):
return embed_positions.to(position_ids.device).repeat(position_ids.shape[0], 1, 1)
def rotate_every_two(x) -> torch.Tensor:
x1 = x[:, :, :, ::2]
x2 = x[:, :, :, 1::2]
x = torch.stack((-x2, x1), dim=-1)
return x.flatten(-2) # in einsum notation: rearrange(x, '... d j -> ... (d j)')
def apply_rotary_pos_emb(tensor, sin, cos) -> torch.Tensor:
sin = torch.repeat_interleave(sin[:, :, None, :], 2, 3)
cos = torch.repeat_interleave(cos[:, :, None, :], 2, 3)
return (tensor * cos) + (rotate_every_two(tensor) * sin)
class GPTJAttention(nn.Module):
def __init__(self, config):
super().__init__()
max_positions = config.max_position_embeddings
self.register_buffer(
"bias",
torch.tril(torch.ones((max_positions, max_positions), dtype=torch.bool)).view(
1, 1, max_positions, max_positions
),
)
self.register_buffer("masked_bias", torch.tensor(-1e9))
self.attn_dropout = nn.Dropout(config.attn_pdrop)
self.resid_dropout = nn.Dropout(config.resid_pdrop)
self.embed_dim = config.hidden_size
self.num_attention_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_attention_heads
if self.head_dim * self.num_attention_heads != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_attention_heads (got `embed_dim`: {self.embed_dim} and"
f" `num_attention_heads`: {self.num_attention_heads})."
)
self.scale_attn = torch.sqrt(torch.tensor(self.head_dim, dtype=torch.float32)).to(
torch.get_default_dtype()
)
self.k_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False)
self.v_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False)
self.q_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False)
self.out_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False)
self.rotary_dim = config.rotary_dim
pos_embd_dim = self.rotary_dim or self.embed_dim
self.embed_positions = create_sinusoidal_positions(max_positions, pos_embd_dim)
def _split_heads(self, tensor, num_attention_heads, attn_head_size, rotary):
"""
Splits hidden dim into attn_head_size and num_attention_heads
"""
new_shape = tensor.size()[:-1] + (num_attention_heads, attn_head_size)
tensor = tensor.view(new_shape)
if rotary:
return tensor
if len(tensor.shape) == 5:
return tensor.permute(
0, 1, 3, 2, 4
) # (batch, blocks, head, block_length, head_features)
elif len(tensor.shape) == 4:
return tensor.permute(0, 2, 1, 3) # (batch, head, seq_length, head_features)
else:
raise ValueError(
f"Input tensor rank should be one of [4, 5], but is: {len(tensor.shape)}"
)
def _merge_heads(self, tensor, num_attention_heads, attn_head_size):
"""
Merges attn_head_size dim and num_attn_heads dim into hidden dim
"""
if len(tensor.shape) == 5:
tensor = tensor.permute(0, 1, 3, 2, 4).contiguous()
elif len(tensor.shape) == 4:
tensor = tensor.permute(0, 2, 1, 3).contiguous()
else:
raise ValueError(
f"Input tensor rank should be one of [4, 5], but is: {len(tensor.shape)}"
)
new_shape = tensor.size()[:-2] + (num_attention_heads * attn_head_size,)
return tensor.view(new_shape)
def _attn(
self,
query,
key,
value,
attention_mask=None,
head_mask=None,
):
# compute causal mask from causal mask buffer
query_length, key_length = query.size(-2), key.size(-2)
causal_mask = self.bias[:, :, key_length - query_length : key_length, :key_length]
# Keep the attention weights computation in fp32 to avoid overflow issues
query = query.to(torch.float32)
key = key.to(torch.float32)
attn_weights = torch.matmul(query, key.transpose(-1, -2))
mask_value = torch.finfo(attn_weights.dtype).min
# Need to be a tensor, otherwise we get error: `RuntimeError: expected scalar type float but found double`.
# Need to be on the same device, otherwise `RuntimeError: ..., x and y to be on the same device`
mask_value = torch.tensor(mask_value, dtype=attn_weights.dtype).to(attn_weights.device)
attn_weights = torch.where(causal_mask, attn_weights, mask_value)
attn_weights = attn_weights / self.scale_attn
if attention_mask is not None:
# Apply the attention mask
attn_weights = attn_weights + attention_mask
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
attn_weights = attn_weights.to(value.dtype)
attn_weights = self.attn_dropout(attn_weights)
# Mask heads if we want to
if head_mask is not None:
attn_weights = attn_weights * head_mask
attn_output = torch.matmul(attn_weights, value)
return attn_output, attn_weights
def _get_embed_positions(self, position_ids):
embed_positions = self.embed_positions
if embed_positions.device != position_ids.device:
embed_positions = embed_positions.to(position_ids.device)
self.embed_positions = embed_positions
return embed_positions.repeat(position_ids.shape[0], 1, 1)
def forward(
self,
hidden_states: torch.FloatTensor,
layer_past=None,
attention_mask=None,
position_ids=None,
head_mask=None,
use_cache=False,
output_attentions=False,
) -> Union[
Tuple[torch.Tensor, Tuple[torch.Tensor]],
Optional[Tuple[torch.Tensor, Tuple[torch.Tensor], Tuple[torch.Tensor, ...]]],
]:
query = self.q_proj(hidden_states)
key = self.k_proj(hidden_states)
value = self.v_proj(hidden_states)
query = self._split_heads(query, self.num_attention_heads, self.head_dim, True)
key = self._split_heads(key, self.num_attention_heads, self.head_dim, True)
value = self._split_heads(value, self.num_attention_heads, self.head_dim, False)
if is_torch_fx_proxy(position_ids):
# The logic to conditionally copy to GPU could not be traced, so we do this
# every time in the torch.fx case
embed_positions = get_embed_positions(self.embed_positions, position_ids)
else:
embed_positions = self._get_embed_positions(position_ids)
repeated_position_ids = position_ids.unsqueeze(-1).repeat(1, 1, embed_positions.shape[-1])
sincos = torch.gather(embed_positions, 1, repeated_position_ids)
sin, cos = torch.split(sincos, sincos.shape[-1] // 2, dim=-1)
if self.rotary_dim is not None:
k_rot = key[:, :, :, : self.rotary_dim]
k_pass = key[:, :, :, self.rotary_dim :]
q_rot = query[:, :, :, : self.rotary_dim]
q_pass = query[:, :, :, self.rotary_dim :]
k_rot = apply_rotary_pos_emb(k_rot, sin, cos)
q_rot = apply_rotary_pos_emb(q_rot, sin, cos)
key = torch.cat([k_rot, k_pass], dim=-1)
query = torch.cat([q_rot, q_pass], dim=-1)
else:
key = apply_rotary_pos_emb(key, sin, cos)
query = apply_rotary_pos_emb(query, sin, cos)
key = key.permute(0, 2, 1, 3)
query = query.permute(0, 2, 1, 3)
if layer_past is not None:
past_key = layer_past[0]
past_value = layer_past[1]
key = torch.cat((past_key, key), dim=-2)
value = torch.cat((past_value, value), dim=-2)
if use_cache is True:
present = (key, value)
else:
present = None
# compute self-attention: V x Softmax(QK^T)
attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask)
attn_output = self._merge_heads(attn_output, self.num_attention_heads, self.head_dim)
attn_output = self.out_proj(attn_output)
attn_output = self.resid_dropout(attn_output)
outputs = (attn_output, present)
if output_attentions:
outputs += (attn_weights,)
return outputs # a, present, (attentions)
class GPTJMLP(nn.Module):
def __init__(self, intermediate_size, config): # in MLP: intermediate_size= 4 * embed_dim
super().__init__()
embed_dim = config.n_embd
self.fc_in = nn.Linear(embed_dim, intermediate_size)
self.fc_out = nn.Linear(intermediate_size, embed_dim)
self.act = ACT2FN[config.activation_function]
self.dropout = nn.Dropout(config.resid_pdrop)
def forward(self, hidden_states: Optional[torch.FloatTensor]) -> torch.FloatTensor:
hidden_states = self.fc_in(hidden_states)
hidden_states = self.act(hidden_states)
hidden_states = self.fc_out(hidden_states)
hidden_states = self.dropout(hidden_states)
return hidden_states
class GPTJBlock(nn.Module):
def __init__(self, config):
super().__init__()
inner_dim = config.n_inner if config.n_inner is not None else 4 * config.n_embd
self.ln_1 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
self.attn = GPTJAttention(config)
self.mlp = GPTJMLP(inner_dim, config)
def forward(
self,
hidden_states: Optional[torch.FloatTensor],
layer_past=None,
attention_mask=None,
position_ids=None,
head_mask=None,
use_cache=False,
output_attentions=False,
) -> Union[Tuple[torch.Tensor], Optional[Tuple[torch.Tensor, Tuple[torch.FloatTensor, ...]]]]:
residual = hidden_states
hidden_states = self.ln_1(hidden_states)
attn_outputs = self.attn(
hidden_states=hidden_states,
layer_past=layer_past,
attention_mask=attention_mask,
position_ids=position_ids,
head_mask=head_mask,
use_cache=use_cache,
output_attentions=output_attentions,
)
attn_output = attn_outputs[0] # output_attn: a, present, (attentions)
outputs = attn_outputs[1:]
feed_forward_hidden_states = self.mlp(hidden_states)
hidden_states = attn_output + feed_forward_hidden_states + residual
if use_cache:
outputs = (hidden_states,) + outputs
else:
outputs = (hidden_states,) + outputs[1:]
return outputs # hidden_states, present, (attentions)
class GPTJPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = GPTJConfig
base_model_prefix = "transformer"
is_parallelizable = True
supports_gradient_checkpointing = True
_no_split_modules = ["GPTJBlock"]
def __init__(self, *inputs, **kw):
super().__init__(*inputs, **kw)
def _init_weights(self, module):
"""Initialize the weights."""
if isinstance(module, (nn.Linear,)):
# Slightly different from Mesh Transformer JAX which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
def _set_gradient_checkpointing(self, module, value=False):
if isinstance(module, GPTJModel):
module.gradient_checkpointing = value
class GPTJModel(GPTJPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.embed_dim = config.n_embd
self.vocab_size = config.vocab_size
self.wte = nn.Embedding(config.vocab_size, self.embed_dim)
self.drop = nn.Dropout(config.embd_pdrop)
self.h = nn.ModuleList([GPTJBlock(config) for _ in range(config.n_layer)])
self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
# Model parallel
self.model_parallel = False
self.device_map = None
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def parallelize(self, device_map=None):
warnings.warn(
"`GPTJModel.parallelize` is deprecated and will be removed in v5 of Transformers, you should load your"
" model with `device_map='balanced'` in the call to `from_pretrained`. You can also provide your own"
" `device_map` but it needs to be a dictionary module_name to device, so for instance {'h.0': 0, 'h.1': 1,"
" ...}",
FutureWarning,
)
# Check validity of device_map
self.device_map = (
get_device_map(len(self.h), range(torch.cuda.device_count()))
if device_map is None
else device_map
)
assert_device_map(self.device_map, len(self.h))
self.model_parallel = True
self.first_device = (
"cpu" if "cpu" in self.device_map.keys() else "cuda:" + str(min(self.device_map.keys()))
)
self.last_device = "cuda:" + str(max(self.device_map.keys()))
self.wte = self.wte.to(self.first_device)
# Load onto devices
for k, v in self.device_map.items():
for block in v:
cuda_device = "cuda:" + str(k)
self.h[block] = self.h[block].to(cuda_device)
# ln_f to last
self.ln_f = self.ln_f.to(self.last_device)
def deparallelize(self):
warnings.warn(
"Like `parallelize`, `deparallelize` is deprecated and will be removed in v5 of Transformers.",
FutureWarning,
)
self.model_parallel = False
self.device_map = None
self.first_device = "cpu"
self.last_device = "cpu"
self.wte = self.wte.to("cpu")
for index in range(len(self.h)):
self.h[index] = self.h[index].to("cpu")
self.ln_f = self.ln_f.to("cpu")
torch.cuda.empty_cache()
def get_input_embeddings(self):
return self.wte
def set_input_embeddings(self, new_embeddings):
self.wte = new_embeddings
def forward(
self,
input_ids=None,
past_key_values=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
) -> Union[Tuple, BaseModelOutputWithPast]:
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
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
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()
input_ids = input_ids.view(-1, input_shape[-1])
batch_size = input_ids.shape[0]
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
batch_size = inputs_embeds.shape[0]
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 token_type_ids is not None:
token_type_ids = token_type_ids.view(-1, input_shape[-1])
if position_ids is not None:
position_ids = position_ids.view(-1, input_shape[-1]).long()
if past_key_values is None:
past_length = 0
past_key_values = tuple([None] * len(self.h))
else:
past_length = past_key_values[0][0].size(-2)
if position_ids is None:
position_ids = torch.arange(
past_length, input_shape[-1] + past_length, dtype=torch.long, device=device
)
position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
# Attention mask.
if attention_mask is not None:
if batch_size <= 0:
raise ValueError("batch_size has to be defined and > 0")
attention_mask = attention_mask.view(batch_size, -1)
# We create a 3D attention mask from a 2D tensor mask.
# Sizes are [batch_size, 1, 1, to_seq_length]
# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
# this attention mask is more simple than the triangular masking of causal attention
# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
attention_mask = attention_mask[:, None, None, :]
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
# masked positions, this operation will create a tensor which is 0.0 for
# positions we want to attend and the dtype's smallest value for masked positions.
# Since we are adding it to the raw scores before the softmax, this is
# effectively the same as removing these entirely.
attention_mask = attention_mask.to(dtype=self.dtype) # fp16 compatibility
attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x num_attention_heads x N x N
# head_mask has shape n_layer x batch x num_attention_heads x N x N
head_mask = self.get_head_mask(head_mask, self.config.n_layer)
if inputs_embeds is None:
inputs_embeds = self.wte(input_ids)
hidden_states = inputs_embeds
if token_type_ids is not None:
token_type_embeds = self.wte(token_type_ids)
hidden_states = hidden_states + token_type_embeds
hidden_states = self.drop(hidden_states)
output_shape = input_shape + (hidden_states.size(-1),)
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
presents = () if use_cache else None
all_self_attentions = () if output_attentions else None
all_hidden_states = () if output_hidden_states else None
for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
# Model parallel
if self.model_parallel:
torch.cuda.set_device(hidden_states.device)
# Ensure layer_past is on same device as hidden_states (might not be correct)
if layer_past is not None:
layer_past = tuple(
past_state.to(hidden_states.device) for past_state in layer_past
)
# Ensure that attention_mask is always on the same device as hidden_states
if attention_mask is not None:
attention_mask = attention_mask.to(hidden_states.device)
if isinstance(head_mask, torch.Tensor):
head_mask = head_mask.to(hidden_states.device)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if self.gradient_checkpointing and self.training:
def create_custom_forward(module):
def custom_forward(*inputs):
# None for past_key_value
return module(*inputs, use_cache, output_attentions)
return custom_forward
outputs = torch.utils.checkpoint.checkpoint(
create_custom_forward(block),
hidden_states,
None,
attention_mask,
position_ids,
head_mask[i],
)
else:
outputs = block(
hidden_states=hidden_states,
layer_past=layer_past,
attention_mask=attention_mask,
position_ids=position_ids,
head_mask=head_mask[i],
use_cache=use_cache,
output_attentions=output_attentions,
)
hidden_states = outputs[0]
if use_cache is True:
presents = presents + (outputs[1],)
if output_attentions:
all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
# Model Parallel: If it's the last layer for that device, put things on the next device
if self.model_parallel:
for k, v in self.device_map.items():
if i == v[-1] and "cuda:" + str(k) != self.last_device:
hidden_states = hidden_states.to("cuda:" + str(k + 1))
hidden_states = self.ln_f(hidden_states)
hidden_states = hidden_states.view(output_shape)
# Add last hidden state
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(
v
for v in [hidden_states, presents, all_hidden_states, all_self_attentions]
if v is not None
)
return BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=presents,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
)
class GPTJForCausalLM(GPTJPreTrainedModel):
_keys_to_ignore_on_load_missing = [r"h\.\d+\.attn\.masked_bias", r"h\.\d+\.attn\.bias"]
def __init__(self, config):
super().__init__(config)
self.transformer = GPTJModel(config)
self.lm_head = nn.Linear(config.n_embd, config.vocab_size)
# Model parallel
self.model_parallel = False
self.device_map = None
# Initialize weights and apply final processing
self.post_init()
def parallelize(self, device_map=None):
warnings.warn(
"`GPTJForCausalLM.parallelize` is deprecated and will be removed in v5 of Transformers, you should load"
" your model with `device_map='balanced'` in the call to `from_pretrained`. You can also provide your own"
" `device_map` but it needs to be a dictionary module_name to device, so for instance {'transformer.h.0':"
" 0, 'transformer.h.1': 1, ...}",
FutureWarning,
)
self.device_map = (
get_device_map(len(self.transformer.h), range(torch.cuda.device_count()))
if device_map is None
else device_map
)
assert_device_map(self.device_map, len(self.transformer.h))
self.transformer.parallelize(self.device_map)
self.lm_head = self.lm_head.to(self.transformer.first_device)
self.model_parallel = True
def deparallelize(self):
warnings.warn(
"Like `parallelize`, `deparallelize` is deprecated and will be removed in v5 of Transformers.",
FutureWarning,
)
self.transformer.deparallelize()
self.transformer = self.transformer.to("cpu")
self.lm_head = self.lm_head.to("cpu")
self.model_parallel = False
torch.cuda.empty_cache()
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def prepare_inputs_for_generation(
self, input_ids, past_key_values=None, inputs_embeds=None, **kw
):
token_type_ids = kw.get("token_type_ids", None)
# only last token for inputs_ids if past is defined in kw
if past_key_values:
input_ids = input_ids[:, -1].unsqueeze(-1)
if token_type_ids is not None:
token_type_ids = token_type_ids[:, -1].unsqueeze(-1)
attention_mask = kw.get("attention_mask", None)
position_ids = kw.get("position_ids", None)
if attention_mask is not None and position_ids is None:
# create position_ids on the fly for batch generation
position_ids = attention_mask.long().cumsum(-1) - 1
position_ids.masked_fill_(attention_mask == 0, 1)
if past_key_values:
position_ids = position_ids[:, -1].unsqueeze(-1)
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
if inputs_embeds is not None and past_key_values is None:
model_inputs = {"inputs_embeds": inputs_embeds}
else:
model_inputs = {"input_ids": input_ids}
model_inputs.update(
{
"past_key_values": past_key_values,
"use_cache": kw.get("use_cache"),
"position_ids": position_ids,
"attention_mask": attention_mask,
"token_type_ids": token_type_ids,
}
)
return model_inputs
def forward(
self,
input_ids=None,
past_key_values=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
) -> Union[Tuple, CausalLMOutputWithPast]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
`labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
transformer_outputs = self.transformer(
input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = transformer_outputs[0]
# Set device for model parallelism
if self.model_parallel:
torch.cuda.set_device(self.transformer.first_device)
hidden_states = hidden_states.to(self.lm_head.weight.device)
# make sure sampling in fp16 works correctly and
# compute loss in fp32 to match with mesh-tf version
# https://github.com/EleutherAI/gpt-neo/blob/89ce74164da2fb16179106f54e2269b5da8db333/models/gpt2/gpt2.py#L179
lm_logits = self.lm_head(hidden_states).to(torch.float32)
loss = None
if labels is not None:
# Shift so that tokens < n predict n
shift_logits = lm_logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = CrossEntropyLoss()
loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
loss = loss.to(hidden_states.dtype)
if not return_dict:
output = (lm_logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return CausalLMOutputWithPast(
loss=loss,
logits=lm_logits,
past_key_values=transformer_outputs.past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
@staticmethod
def _reorder_cache(
past_key_values: Tuple[Tuple[torch.Tensor]], beam_idx
) -> Tuple[Tuple[torch.Tensor]]:
"""
This function is used to re-order the `past_key_values` cache if [`~PretrainedModel.beam_search`] or
[`~PretrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct
beam_idx at every generation step.
"""
return tuple(
tuple(
past_state.index_select(0, beam_idx.to(past_state.device))
for past_state in layer_past
)
for layer_past in past_key_values
)
class GPTJForSequenceClassification(GPTJPreTrainedModel):
_keys_to_ignore_on_load_missing = [
r"h\.\d+\.attn\.masked_bias",
r"h\.\d+\.attn\.bias",
r"lm_head.weight",
]
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.transformer = GPTJModel(config)
self.score = nn.Linear(config.n_embd, self.num_labels, bias=False)
# Model parallel
self.model_parallel = False
self.device_map = None
# Initialize weights and apply final processing
self.post_init()
def forward(
self,
input_ids=None,
past_key_values=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
) -> Union[Tuple, SequenceClassifierOutputWithPast]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
transformer_outputs = self.transformer(
input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = transformer_outputs[0]
logits = self.score(hidden_states)
if input_ids is not None:
batch_size = input_ids.shape[0]
else:
batch_size = inputs_embeds.shape[0]
if self.config.pad_token_id is None and batch_size != 1:
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
if self.config.pad_token_id is None:
sequence_lengths = -1
else:
if input_ids is not None:
sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(
logits.device
)
else:
sequence_lengths = -1
logger.warning(
f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
"unexpected if using padding tokens in conjunction with `inputs_embeds.`"
)
pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
loss = None
if labels is not None:
labels = labels.to(pooled_logits.device)
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (
labels.dtype == torch.long or labels.dtype == torch.int
):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(pooled_logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(pooled_logits, labels)
if not return_dict:
output = (pooled_logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutputWithPast(
loss=loss,
logits=pooled_logits,
past_key_values=transformer_outputs.past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
class GPTJForQuestionAnswering(GPTJPreTrainedModel):
_keys_to_ignore_on_load_missing = [
r"h\.\d+\.attn\.masked_bias",
r"h\.\d+\.attn\.bias",
r"lm_head.weight",
]
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.transformer = GPTJModel(config)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
# Model parallel
self.model_parallel = False
self.device_map = None
# 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,
head_mask=None,
inputs_embeds=None,
start_positions=None,
end_positions=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
) -> Union[Tuple, QuestionAnsweringModelOutput]:
r"""
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.transformer(
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,
)
sequence_output = outputs[0]
logits = self.qa_outputs(sequence_output)
start_logits, end_logits = logits.split(1, dim=-1)
start_logits = start_logits.squeeze(-1).contiguous()
end_logits = end_logits.squeeze(-1).contiguous()
total_loss = None
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
end_positions = end_positions.squeeze(-1)
# sometimes the start/end positions are outside our model inputs, we ignore these terms
ignored_index = start_logits.size(1)
start_positions = start_positions.clamp(0, ignored_index)
end_positions = end_positions.clamp(0, ignored_index)
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
start_loss = loss_fct(start_logits, start_positions)
end_loss = loss_fct(end_logits, end_positions)
total_loss = (start_loss + end_loss) / 2
if not return_dict:
output = (start_logits, end_logits) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
return QuestionAnsweringModelOutput(
loss=total_loss,
start_logits=start_logits,
end_logits=end_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
|
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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"], 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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"]}
|
33,490
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/core/params.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 argparse
from transformers import MODEL_MAPPING, SchedulerType
TRAIN = "train"
EVAL = "validation"
TEST = "test"
ALL = "all"
EACH = "each"
LABEL = "label"
MODEL_CLASSES = list(MODEL_MAPPING.keys())
MODEL_TYPES = tuple(c.model_type for c in MODEL_CLASSES)
LR_TYPES = [
"linear",
"cosine",
"cosine_with_restarts",
"polynomial",
"constant",
"constant_with_warmup",
]
question_answering_column_name_mapping = {
"squad_v2": ("question", "context", "answer"),
}
def parse_params(xs):
x = argparse.ArgumentParser()
x.add_argument("--answer_column", type=str, default="answers")
x.add_argument("--block_size", type=int, default=None)
x.add_argument("--cache_dir", type=str, default=None)
x.add_argument("--config_name", type=str, default=None)
x.add_argument("--config_overrides", type=str, default=None)
x.add_argument("--context_column", type=str, default="context")
x.add_argument("--cuda", action="store_true")
x.add_argument("--dataset_config", type=str, default=None)
x.add_argument("--dataset_name", type=str, default=None)
x.add_argument("--debug", action="store_true")
x.add_argument("--do_eval", action="store_true")
x.add_argument("--do_test", action="store_true")
x.add_argument("--do_train", action="store_true")
x.add_argument("--doc_stride", type=int, default=128)
x.add_argument("--eval_batch_size", type=int, default=8)
x.add_argument("--eval_file", type=str, default=None)
x.add_argument("--feature_extractor", type=str, default=None)
x.add_argument("--grad_accumulation_steps", type=int, default=1)
x.add_argument("--hub_model_id", type=str)
x.add_argument("--hub_token", type=str)
x.add_argument("--ignore_pad_token_for_loss", type=bool, default=True)
x.add_argument("--label_all_tokens", action="store_true")
x.add_argument("--label_column", type=str, default="label")
x.add_argument("--language", type=str, default=None)
x.add_argument("--line_by_line", type=bool, default=False)
x.add_argument("--lower_case", type=bool, default=False)
x.add_argument("--lr_scheduler", type=SchedulerType, default="linear", choices=LR_TYPES)
x.add_argument("--lr", type=float, default=5e-5)
x.add_argument("--max_answer_length", type=int, default=30)
x.add_argument("--max_duration", type=float, default=20.0)
x.add_argument("--max_eval_samples", type=int, default=None)
x.add_argument("--max_len", type=int, default=128)
x.add_argument("--max_seq_length", type=int, default=384) # 512
x.add_argument("--max_source_length", type=int, default=1024)
x.add_argument("--max_span_length", type=int, default=5)
x.add_argument("--max_target_length", type=int, default=128)
x.add_argument("--max_test_samples", type=int, default=None)
x.add_argument("--max_train_samples", type=int, default=None)
x.add_argument("--max_train_steps", type=int, default=None)
x.add_argument("--min_duration", type=float, default=0.0)
x.add_argument("--mlm_probability", type=float, default=0.15)
x.add_argument("--model_name", type=str, required=True)
x.add_argument("--model_type", type=str, default=None, choices=MODEL_TYPES)
x.add_argument("--model_version", type=str, default="main")
x.add_argument("--n_best_size", type=int, default=20)
x.add_argument("--no_keep_linebreaks", action="store_true")
x.add_argument("--null_score_diff_threshold", type=float, default=0.0)
x.add_argument("--n_beams", type=int, default=None)
x.add_argument("--num_warmup_steps", type=int, default=0)
x.add_argument("--num_workers", type=int, default=4)
x.add_argument("--out_dir", type=str, default=None)
x.add_argument("--overwrite_cache", type=bool, default=False)
x.add_argument("--pad_to_max_length", action="store_true")
x.add_argument("--plm_probability", type=float, default=1 / 6)
x.add_argument("--push_to_hub", action="store_true")
x.add_argument("--question_column", type=str, default="question")
x.add_argument("--return_entity_metrics", action="store_true")
x.add_argument("--seed", type=int, default=55)
x.add_argument("--source_lang", type=str, default=None)
x.add_argument("--source_prefix", type=str, default=None)
x.add_argument("--split_percent", default=5)
x.add_argument("--summary_column", type=str, default=None)
x.add_argument("--target_lang", type=str, default=None)
x.add_argument("--test_file", type=str, default=None)
x.add_argument("--test_with_gen", type=bool, default=True)
x.add_argument("--text_column", type=str, default="text")
x.add_argument("--tokenizer_name", type=str, default=None)
x.add_argument("--train_batch_size", type=int, default=8)
x.add_argument("--train_epochs", type=int, default=3)
x.add_argument("--train_file", type=str, default=None)
x.add_argument("--train_language", type=str, default=None)
x.add_argument("--use_auth_token", type=bool, default=False)
x.add_argument("--use_fast_tokenizer", type=bool, default=True)
x.add_argument("--use_slow_tokenizer", action="store_true")
x.add_argument("--val_max_target_length", type=int, default=None)
x.add_argument("--version_2_with_negative", type=bool, default=False)
x.add_argument("--weight_decay", type=float, default=0.0)
for n, kw in xs:
x.add_argument(n, **kw)
y = x.parse_args()
if (
y.dataset_name is None
and y.train_file is None
and y.eval_file is None
and y.test_file is None
):
raise ValueError("Need either a dataset name or a train/eval/test file")
else:
if y.train_file is not None:
y = y.train_file.split(".")[-1]
assert y in ["csv", "json", "txt"], "`train_file` should be a csv or a json file"
if y.eval_file is not None:
y = y.eval_file.split(".")[-1]
assert y in ["csv", "json", "txt"], "`eval_file` should be a csv or a json file"
if y.test_file is not None:
y = y.test_file.split(".")[-1]
assert y in ["csv", "json", "txt"], "`test_file` should be a csv or a json file"
if y.push_to_hub:
assert y.output_dir is not None, "Need an `output_dir` for repo with `--push_to_hub`"
return y
|
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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"]}
|
33,491
|
quantapix/qnarre
|
refs/heads/main
|
/tools/triton/python/triton/tools/disasm.py
|
# MIT License
# Copyright (c) 2020 Da Yan @ HKUST
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
import re
import subprocess
FLINE_RE = re.compile(r'\s*/\*\w{4}\*/\s*([^;]*;)\s*/\* 0x(\w{16}) \*/\s*')
SLINE_RE = re.compile(r'\s*/\* 0x(\w{16}) \*/\s*')
FNAME_RE = re.compile(r'\s*Function : (\w+)\s*')
BRA_RE = re.compile(r'(.*BRA(?:\.U)? )(0x\w+);')
def parseCtrl(sline):
enc = int(SLINE_RE.match(sline).group(1), 16)
stall = (enc >> 41) & 0xf
yld = (enc >> 45) & 0x1
wrtdb = (enc >> 46) & 0x7
readb = (enc >> 49) & 0x7
watdb = (enc >> 52) & 0x3f
yld_str = 'Y' if yld == 0 else '-'
wrtdb_str = '-' if wrtdb == 7 else str(wrtdb)
readb_str = '-' if readb == 7 else str(readb)
watdb_str = '--' if watdb == 0 else f'{watdb:02d}'
return f'{watdb_str}:{readb_str}:{wrtdb_str}:{yld_str}:{stall:x}'
def processSassLines(fline, sline, labels):
asm = FLINE_RE.match(fline).group(1)
# Remove tailing space
if asm.endswith(" ;"):
asm = asm[:-2] + ";"
ctrl = parseCtrl(sline)
# BRA target address
if BRA_RE.match(asm) is not None:
target = int(BRA_RE.match(asm).group(2), 16)
if target in labels:
pass
else:
labels[target] = len(labels)
return (f'{ctrl}', f'{asm}')
def extract(file_path, fun):
if fun is None:
sass_str = subprocess.check_output(["cuobjdump", "-sass", file_path])
else:
sass_str = subprocess.check_output(["cuobjdump", "-fun", fun, "-sass", file_path])
sass_lines = sass_str.splitlines()
line_idx = 0
while line_idx < len(sass_lines):
line = sass_lines[line_idx].decode()
# format:
# function : <function_name>
# .headerflags: ...
# /*0000*/ asmstr /*0x...*/
# /*0x...*/
# Looking for new function header (function: <name>)
while FNAME_RE.match(line) is None:
line_idx += 1
if line_idx < len(sass_lines):
line = sass_lines[line_idx].decode()
else:
return
fname = FNAME_RE.match(line).group(1)
ret = ''
ret += f'Function:{fname}\n'
line_idx += 2 # bypass .headerflags
line = sass_lines[line_idx].decode()
# Remapping address to label
labels = {} # address -> label_idx
# store sass asm in buffer and them print them (for labels)
# (ctrl, asm)
asm_buffer = []
while FLINE_RE.match(line) is not None:
# First line (Offset ASM Encoding)
fline = sass_lines[line_idx].decode()
line_idx += 1
# Second line (Encoding)
sline = sass_lines[line_idx].decode()
line_idx += 1
asm_buffer.append(processSassLines(fline, sline, labels))
# peek the next line
line = sass_lines[line_idx].decode()
# Print sass
# label naming convention: LBB#i
for idx, (ctrl, asm) in enumerate(asm_buffer):
# Print label if this is BRA target
offset = idx * 16
if offset in labels:
label_name = f'LBB{labels[offset]}'
ret += f'{label_name}:\n'
ret += ctrl + '\t'
# if this is BRA, remap offset to label
if BRA_RE.match(asm):
target = int(BRA_RE.match(asm).group(2), 16)
target_name = f'LBB{labels[target]}'
asm = BRA_RE.sub(rf'\1{target_name};', asm)
ret += asm + '\n'
ret += '\n'
return ret
|
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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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"/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"], "/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"]}
|
33,492
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/core/mlp.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 core as qc
from . import utils as qu
class Llama(qc.Module):
hs = qc.Hypers({"act", "d_ff", "d_model", "drop"}, {})
def __init__(self, d_ff=None, ps={}, hs=[], **kw):
if d_ff is not None:
kw.update(d_ff=d_ff)
super().__init__(ps, [self.hs] + hs, **kw)
cfg = self.get_cfg(kw)
self.gate_proj = qc.Linear(cfg.d_model, cfg.d_ff, bias=False, **kw)
self.down_proj = qc.Linear(cfg.d_ff, cfg.d_model, bias=False, **kw)
self.up_proj = nn.Linear(cfg.d_model, cfg.d_ff, bias=False, **kw)
self.act = qu.activation(cfg.act)
def forward(self, x):
return self.down_proj(self.act(self.gate_proj(x)) * self.up_proj(x))
class GPT(qc.Module):
hs = qc.Hypers({"act", "d_ff", "d_model", "drop"}, {})
def __init__(self, d_ff=None, ps={}, hs=[], **kw):
if d_ff is not None:
kw.update(d_ff=d_ff)
super().__init__(ps, [self.hs] + hs, **kw)
cfg = self.get_cfg(kw)
self.lin = qc.Conv1D(cfg.d_ff, cfg.d_model, **kw)
self.proj = qc.Conv1D(cfg.d_model, cfg.d_ff, **kw)
self.act = qu.activation(cfg.act)
self.drop = nn.Dropout(cfg.drop, **kw)
def forward(self, x):
y = self.lin(x)
y = self.act(y)
y = self.proj(y)
y = self.drop(y)
return y
class MLP(qc.Module):
hs = qc.Hypers(
{"act", "chunk_ff", "d_ff", "d_model", "drop", "eps"},
{"len_dim": 1},
)
def __init__(self, act=None, drop=None, eps=None, ps={}, hs=[], **kw):
if act is not None:
kw.update(act=act)
if drop is not None:
kw.update(drop=drop)
if eps is not None:
kw.update(eps=eps)
super().__init__(ps, [self.hs] + hs, **kw)
cfg = self.get_cfg(kw)
self.lin = qc.Linear(cfg.d_model, cfg.d_ff, **kw)
self.act = None if cfg.act is None else qu.activation(cfg.act)
self.proj = qc.Linear(cfg.d_ff, cfg.d_model, **kw)
self.drop = None if cfg.drop is None else qc.Dropout(cfg.drop, **kw)
self.norm = None if cfg.eps is None else qc.LayerNorm(cfg.d_model, cfg.eps, **kw)
def forward(self, *xs):
cfg = self.cfg
chunk, dim = cfg.chunk_ff, cfg.len_dim
assert len(xs) > 0
if chunk > 0:
shape = xs[0].shape[dim]
for x in xs:
assert x.shape[dim] == shape
assert xs[0].shape[dim] % chunk == 0
n = xs[0].shape[dim] // chunk
ys = tuple(x.chunk(n, dim=dim) for x in xs)
ys = tuple(self.chunker(*y) for y in zip(*ys))
return torch.cat(ys, dim=dim)
return self.chunker(*xs)
def chunker(self, x):
y = self.lin(x)
if self.act:
y = self.act(y)
# if self.drop:
# y = self.drop(y)
y = self.proj(y)
if self.drop:
y = self.drop(y)
if self.norm:
y = self.norm(x + y)
return y
class Predictor(qc.Module):
hs = qc.Hypers({"d_model", "d_lin", "eps", "s_vocab"}, {"act": "gelu"})
def __init__(self, d_lin=None, act=None, ps={}, hs=[], **kw):
if d_lin is not None:
kw.update(d_lin=d_lin)
if act is not None:
kw.update(act=act)
super().__init__(ps, [self.hs] + hs, **kw)
cfg = self.get_cfg(kw)
m = cfg.d_model
n = cfg.d_lin or m
self.lin = qc.Linear(m, n, **kw)
self.act = qu.activation(cfg.act)
self.norm = qc.LayerNorm(n, cfg.eps, **kw)
self.proj = qc.Linear(n, cfg.s_vocab, bias=False, **kw)
self.bias = nn.Parameter(torch.zeros(cfg.s_vocab))
self.proj.bias = self.bias
def forward(self, x):
y = self.lin(x)
y = self.act(y)
y = self.norm(y)
y = self.proj(y)
return y
class Classifier(qc.Module):
hs = qc.Hypers({"d_model", "d_lin", "drop", "drop_proj", "n_labels"}, {"act": "tanh"})
def __init__(self, d_lin=None, act=None, **kw):
if d_lin is not None:
kw.update(d_lin=d_lin)
if act is not None:
kw.update(act=act)
super().__init__(**kw)
cfg = self.get_cfg(kw)
if cfg.d_lin is None:
self.proj = qc.Linear(cfg.d_model, cfg.n_labels, **kw)
else:
self.lin = qc.Linear(cfg.d_model, cfg.d_lin, **kw)
self.act = qu.activation(cfg.act)
self.proj = qc.Linear(cfg.d_lin, cfg.n_labels, **kw)
p = cfg.drop_proj if cfg.drop_proj is not None else cfg.drop
self.drop = None if p is None else qc.Dropout(p, **kw)
def forward(self, x):
y = x # [:, 0, :] take <s> token (equiv. to [CLS])
if self.cfg.d_lin is not None:
if self.drop:
y = self.drop(y)
y = self.lin(y)
y = self.act(y)
if self.drop:
y = self.drop(y)
y = self.proj(y)
return y
class Pool(qc.Module):
hs = qc.Hypers(["d_model"], {})
def __init__(self, ps={}, hs=[], **kw):
super().__init__(ps, [self.hs] + hs, **kw)
cfg = self.get_cfg(kw)
self.lin = qc.Linear(cfg.d_model, cfg.d_model, **kw)
self.act = nn.Tanh()
def forward(self, x):
y = self.lin(x[:, 0])
y = self.act(y)
return y
class PoolBeg(qc.Module):
def __init__(self, cfg):
super().__init__()
self.proj = qc.Linear(cfg.d_model, 1)
def forward(self, x, mask=None):
y = self.proj(x).squeeze(-1)
if mask is not None:
if self.get_param_dtype() == torch.float16:
y = y * (1 - mask) - 65500 * mask
else:
y = y * (1 - mask) - 1e30 * mask
return y
class PoolEnd(qc.Module):
def __init__(self, cfg):
super().__init__()
self.ff = qc.Linear(cfg.d_model * 2, cfg.d_model)
self.act = nn.Tanh()
self.norm = qc.LayerNorm(cfg.d_model, cfg.eps)
self.proj = qc.Linear(cfg.d_model, 1)
def forward(self, x, x_beg=None, beg_pos=None, mask=None):
assert x_beg is not None or beg_pos is not None
if beg_pos is not None:
slen, hsz = x.shape[-2:]
beg_pos = beg_pos[:, None, None].expand(-1, -1, hsz)
x_beg = x.gather(-2, beg_pos)
x_beg = x_beg.expand(-1, slen, -1)
y = self.ff(torch.cat([x, x_beg], dim=-1))
y = self.act(y)
y = self.norm(y)
y = self.proj(y).squeeze(-1)
if mask is not None:
if self.get_param_dtype() == torch.float16:
y = y * (1 - mask) - 65500 * mask
else:
y = y * (1 - mask) - 1e30 * mask
return y
class PoolProj(qc.Module):
def __init__(self, cfg):
super().__init__()
self.ff = qc.Linear(cfg.d_model * 2, cfg.d_model)
self.act = nn.Tanh()
self.proj = qc.Linear(cfg.d_model, 1, bias=False)
def forward(self, x, x_beg=None, beg_pos=None, idx=None):
hsz = x.shape[-1]
assert x_beg is not None or beg_pos is not None
if beg_pos is not None:
beg_pos = beg_pos[:, None, None].expand(-1, -1, hsz)
x_beg = x.gather(-2, beg_pos).squeeze(-2)
if idx is not None:
idx = idx[:, None, None].expand(-1, -1, hsz)
cls_token_state = x.gather(-2, idx).squeeze(-2)
else:
cls_token_state = x[:, -1, :]
y = self.ff(torch.cat([x_beg, cls_token_state], dim=-1))
y = self.act(y)
y = self.proj(y).squeeze(-1)
return y
class Positionwise(qc.Module):
hs = qc.Hypers({"d_ff", "d_model", "drop"}, {"eps": 1e-5, "pre_norm": False})
def __init__(self, ps={}, hs=[], **kw):
super().__init__(ps, [self.hs] + hs, **kw)
cfg = self.get_cfg(kw)
m, f = cfg.d_model, cfg.d_ff
self.ff = nn.Sequential(
qc.Linear(m, f, **kw),
nn.ReLU(inplace=True),
qc.Dropout(cfg.drop, **kw),
qc.Linear(f, m, **kw),
qc.Dropout(cfg.drop, **kw),
)
self.norm = qc.LayerNorm(m, **kw)
def forward(self, x):
if self.cfg.pre_norm:
return x + self.ff(self.norm(x))
return self.norm(x + self.ff(x))
|
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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"], 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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"]}
|
33,493
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/base/doc/exporter.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 markdown
import pathlib as pth
from .base import config
markdown_settings = {
'extension_configs': {
'markdown.extensions.codehilite': {
'css_class': 'highlight'
},
'markdown.extensions.extra': {},
'markdown.extensions.meta': {},
},
'extensions': [
'markdown.extensions.codehilite', 'markdown.extensions.extra',
'markdown.extensions.meta', 'markdown.extensions.toc',
'markdown.extensions.fenced_code'
],
'output_format':
'html5',
}
class Exporter:
_topic = None
_subject = None
html_frame = None
markdown = markdown.Markdown(**markdown_settings)
@classmethod
def frame(cls):
if not cls.html_frame:
t = pth.Path(config.web_templates + 'frame.html').read_text()
t = t.replace(r'{% endblock %}', '')
fb, fe = t.split(r'{% block frame_content %}')
cls.html_frame = (fb, r'<div class="container">', link_begin,
link_title, link_end, r'</div>', fe)
return cls.html_frame
def __init__(self, **kw):
super().__init__(**kw)
def mboxer(self, ctype=config.HTML, **kw):
yield from self.hdr.mboxer(**kw)
yield 'subject', self.subject(**kw)
if ctype == config.PLAIN:
yield 'text/' + ctype, '\n'.join(self.plainer(**kw))
else:
yield 'text/' + ctype, '\n'.join(self.htmer(self.frame(), **kw))
def plainer(self, **kw):
yield self.text(**kw)
def htmer(self, frame=None, **kw):
if frame:
yield frame[0]
yield frame[1]
yield from self.hdr.htmer(None, frame, **kw)
yield self.markdown.reset().convert(self.text(**kw))
if frame:
yield frame[-3]
yield frame[-2]
yield frame[-1]
def blogger(self, **kw):
yield from self.hdr.blogger(**kw)
yield self.text(**kw)
yield from self.hdr.footer(**kw)
link_begin = """
<div class="row {}">
<div class="col-10">
<div class="card with-margin" style="background-color: #{};">
<div class="card-block">
"""
link_title = """
<h6 class="text-muted">{} <strong>{}:</strong></h6>
"""
link_end = """
</div>
</div>
</div>
</div>
"""
|
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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"], 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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"]}
|
33,494
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/tokens/fast/fnet.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 shutil import copyfile
from ....tokens.utils import AddedToken
from ....tokens.fast import PreTrainedTokenizerFast
from ....tokens.utils import is_sentencepiece_available
if is_sentencepiece_available():
from ..fnet import Tokenizer as FNet
else:
FNet = None
VOCAB_FS = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}
VOCAB_MAP = {
"vocab_file": {
"google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/spiece.model",
"google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/spiece.model",
},
"tokenizer_file": {
"google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json",
"google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json",
},
}
INPUT_CAPS = {
"google/fnet-base": 512,
"google/fnet-large": 512,
}
SPIECE_UNDERLINE = "▁"
class Tokenizer(PreTrainedTokenizerFast):
vocab_fs = VOCAB_FS
vocab_map = VOCAB_MAP
input_caps = INPUT_CAPS
model_input_names = ["input_ids", "token_type_ids"]
slow_tokenizer_class = FNet
def __init__(
self,
vocab_file=None,
tokenizer_file=None,
do_lower_case=False,
remove_space=True,
keep_accents=True,
unk="<unk>",
sep="[SEP]",
pad="<pad>",
cls="[CLS]",
msk="[MASK]",
**kw,
):
msk = (
AddedToken(msk, lstrip=True, rstrip=False, normalized=False)
if isinstance(msk, str)
else msk
)
super().__init__(
vocab_file,
tokenizer_file=tokenizer_file,
do_lower_case=do_lower_case,
remove_space=remove_space,
keep_accents=keep_accents,
unk=unk,
sep=sep,
pad=pad,
cls=cls,
msk=msk,
**kw,
)
self.do_lower_case = do_lower_case
self.remove_space = remove_space
self.keep_accents = keep_accents
self.vocab_file = vocab_file
self.can_save_slow_tokenizer = False if not self.vocab_file else True
def build_inputs_with_special_tokens(self, toks_0, toks_1=None):
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if toks_1 is None:
return cls + toks_0 + sep
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) * [0] + len(toks_1 + sep) * [1]
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):
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"], 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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"]}
|
33,495
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/tokens/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 collections
import os
import rjieba
from tokenizers import normalizers
from ...tokens.utils import PreTrainedTokenizer
from .bert import BasicTokenizer, WordpieceTokenizer, load_vocab
VOCAB_FS = {"vocab_file": "vocab.txt"}
VOCAB_MAP = {
"vocab_file": {
"junnyu/roformer_chinese_small": "https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt",
"junnyu/roformer_chinese_base": "https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt",
"junnyu/roformer_chinese_char_small": "https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt",
"junnyu/roformer_chinese_char_base": "https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt",
"junnyu/roformer_small_discriminator": "https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt",
"junnyu/roformer_small_generator": "https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt",
}
}
INPUT_CAPS = {
"junnyu/roformer_chinese_small": 1536,
"junnyu/roformer_chinese_base": 1536,
"junnyu/roformer_chinese_char_small": 512,
"junnyu/roformer_chinese_char_base": 512,
"junnyu/roformer_small_discriminator": 128,
"junnyu/roformer_small_generator": 128,
}
PRETRAINED_INIT_CONFIGURATION = {
"junnyu/roformer_chinese_small": {"do_lower_case": True},
"junnyu/roformer_chinese_base": {"do_lower_case": True},
"junnyu/roformer_chinese_char_small": {"do_lower_case": True},
"junnyu/roformer_chinese_char_base": {"do_lower_case": True},
"junnyu/roformer_small_discriminator": {"do_lower_case": True},
"junnyu/roformer_small_generator": {"do_lower_case": True},
}
class Tokenizer(PreTrainedTokenizer):
vocab_fs = VOCAB_FS
vocab_map = VOCAB_MAP
input_caps = INPUT_CAPS
pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
def __init__(
self,
vocab_file,
do_lower_case=True,
do_basic_tokenize=True,
never_split=None,
unk="[UNK]",
sep="[SEP]",
pad="[PAD]",
cls="[CLS]",
msk="[MASK]",
tokenize_chinese_chars=True,
strip_accents=None,
**kw,
):
super().__init__(
do_lower_case=do_lower_case,
do_basic_tokenize=do_basic_tokenize,
never_split=never_split,
unk=unk,
sep=sep,
pad=pad,
cls=cls,
msk=msk,
tokenize_chinese_chars=tokenize_chinese_chars,
strip_accents=strip_accents,
**kw,
)
if not os.path.isfile(vocab_file):
raise ValueError(
f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained "
"model use `tokenizer = AutoTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
)
self.vocab = load_vocab(vocab_file)
self.ids_to_tokens = collections.OrderedDict(
[(ids, tok) for tok, ids in self.vocab.items()]
)
self.do_basic_tokenize = do_basic_tokenize
if do_basic_tokenize:
self.basic_tokenizer = BasicTokenizer(
do_lower_case=do_lower_case,
never_split=never_split,
tokenize_chinese_chars=tokenize_chinese_chars,
strip_accents=strip_accents,
)
self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk=self.unk)
self.jieba = rjieba
@property
def do_lower_case(self):
return self.basic_tokenizer.do_lower_case
@property
def s_vocab(self):
return len(self.vocab)
def __getstate__(self):
state = self.__dict__.copy()
state["jieba"] = None
return state
def __setstate__(self, d):
self.__dict__ = d
self.jieba = rjieba
def get_vocab(self):
return dict(self.vocab, **self.added_tokens_encoder)
def _tokenize(self, text, use_jieba=True):
split_tokens = []
if use_jieba:
for wholword in self.jieba.cut(text, False):
if wholword in self.vocab:
split_tokens.append(wholword)
else:
# use bert tokenizer to _tokenize
char_list = self._tokenize(wholword, use_jieba=False)
split_tokens.extend(char_list)
else:
if self.do_basic_tokenize:
for token in self.basic_tokenizer.tokenize(
text, never_split=self.all_special_tokens
):
if token in self.basic_tokenizer.never_split:
split_tokens.append(token)
else:
split_tokens += self.wordpiece_tokenizer.tokenize(token)
else:
split_tokens = self.wordpiece_tokenizer.tokenize(text)
return split_tokens
def _convert_token_to_id(self, token):
return self.vocab.get(token, self.vocab.get(self.unk))
def _convert_id_to_token(self, index):
return self.ids_to_tokens.get(index, self.unk)
def convert_tokens_to_string(self, tokens):
out_string = " ".join(tokens).replace(" ##", "").strip()
return out_string
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 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_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) * [0] + len(toks_1 + sep) * [1]
def save_vocabulary(self, dir, pre=None):
index = 0
if os.path.isdir(dir):
vocab_file = os.path.join(
dir,
(pre + "-" if pre else "") + VOCAB_FS["vocab_file"],
)
else:
vocab_file = (pre + "-" if pre else "") + dir
with open(vocab_file, "w", encoding="utf-8") as writer:
for token, token_index in sorted(self.vocab.items(), key=lambda kv: kv[1]):
if index != token_index:
logger.warning(
f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
" Please check that the vocabulary is not corrupted!"
)
index = token_index
writer.write(token + "\n")
index += 1
return (vocab_file,)
class JiebaPreTokenizer:
def __init__(self, vocab):
self.vocab = vocab
self.normalizers = normalizers.BertNormalizer(
clean_text=False,
handle_chinese_chars=True,
strip_accents=False,
lowercase=False,
)
self.jieba = rjieba
def jieba_split(self, i, normalized_string):
splits = []
for token, start, end in self.jieba.tokenize(str(normalized_string), hmm=False):
if token in self.vocab:
splits.append(normalized_string[start:end])
else:
token_list = self.normalizers.normalize_str(token).split()
for token in token_list:
if token:
end = start + len(token)
splits.append(normalized_string[start:end])
start = end
return splits
def pre_tokenize(self, pretok):
pretok.split(self.jieba_split)
|
{"/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", 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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"]}
|
33,496
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/convert/megatron.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.megatron import PreTrained
from ...models.megatron import ForPreTraining
import os
import re
import zipfile
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("Converting TensorFlow checkpoint from {}".format(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)
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)
if pointer.shape != array.shape:
raise ValueError(
f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched"
)
log.info("Initialize PyTorch weight {}".format(name))
pointer.data = torch.from_numpy(array)
return model
def recursive_print(name, val, spaces=0):
if name is None:
msg = None
else:
fmt = "." * max(0, spaces - 2) + "# {:" + str(50 - spaces) + "s}"
msg = fmt.format(name)
if isinstance(val, dict):
if msg is not None:
print(msg)
for k in val.keys():
recursive_print(k, val[k], spaces + 2)
elif isinstance(val, torch.Tensor):
print(msg, ":", val.size())
else:
print(msg, ":", val)
def fix_query_key_value_ordering(param, checkpoint_version, num_splits, n_heads, d_hidden):
input_shape = param.size()
if checkpoint_version == 1.0:
# version 1.0 stores [n_heads * d_hidden * num_splits, :]
saved_shape = (n_heads, d_hidden, num_splits) + input_shape[1:]
param = param.view(*saved_shape)
param = param.transpose(0, 2)
param = param.transpose(1, 2).contiguous()
elif checkpoint_version >= 2.0:
# other versions store [n_heads * num_splits * d_hidden, :]
saved_shape = (n_heads, num_splits, d_hidden) + input_shape[1:]
param = param.view(*saved_shape)
param = param.transpose(0, 1).contiguous()
param = param.view(*input_shape)
return param
def convert_megatron_checkpoint(args, input_state_dict, config):
output_state_dict = {}
ds_args = input_state_dict.get("args", None)
if ds_args is not None:
config.tokenizer_type = ds_args.tokenizer_type
config.s_vocab = ds_args.padded_vocab_size
config.n_pos = ds_args.n_pos
config.d_hidden = ds_args.d_hidden
config.n_lays = ds_args.n_lays
config.n_heads = ds_args.n_heads
config.d_ff = (
ds_args.ffn_hidden_size if "ffn_hidden_size" in ds_args else 4 * ds_args.d_hidden
)
heads = config.n_heads
hidden_size_per_head = config.d_hidden // heads
if "checkpoint_version" in input_state_dict.keys():
checkpoint_version = input_state_dict["checkpoint_version"]
else:
checkpoint_version = 0.0
# The model.
model = input_state_dict["model"]
# The language model.
lm = model["language_model"]
# The embeddings.
embeddings = lm["embedding"]
# The word embeddings.
word_embeddings = embeddings["word_embeddings"]["weight"]
# Truncate the embedding table to s_vocab rows.
word_embeddings = word_embeddings[: config.s_vocab, :]
# Store the word embeddings.
output_state_dict["bert.embeddings.word_embeddings.weight"] = word_embeddings
# The position embeddings.
pos_embeddings = embeddings["position_embeddings"]["weight"]
assert pos_embeddings.size(0) == config.n_pos and pos_embeddings.size(1) == config.d_hidden
# Store the position embeddings.
output_state_dict["bert.embeddings.position_embeddings.weight"] = pos_embeddings
# The token-type embeddings.
tokentype_embeddings = embeddings["tokentype_embeddings"]["weight"]
# Store the position embeddings.
output_state_dict["bert.embeddings.token_type_embeddings.weight"] = tokentype_embeddings
# The transformer.
transformer = lm["transformer"] if "transformer" in lm.keys() else lm["encoder"]
# The regex to extract layer names.
layer_re = re.compile("layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)")
# The simple map of names for "automated" rules.
megatron_to_transformers = {
"attention.dense": ".attention.output.dense.",
"mlp.dense_h_to_4h": ".intermediate.dense.",
"mlp.dense_4h_to_h": ".output.dense.",
}
# Keep track of the attention/query/value tensor.
attention_qkv_weight = None
# Extract the layers.
for key, val in transformer.items():
# Match the name.
m = layer_re.match(key)
# Stop if that's not a layer
if m is None:
break
# The index of the layer.
layer_idx = int(m.group(1))
# The name of the operation.
op_name = m.group(2)
# Is it a weight or a bias?
weight_or_bias = m.group(3)
# The name of the layer.
layer_name = f"bert.encoder.layer.{layer_idx}"
# For layernorm(s), simply store the layer norm.
if op_name.endswith("layernorm"):
ln_name = "attention.ln" if op_name.startswith("input") else "ln"
output_state_dict[layer_name + "." + ln_name + "." + weight_or_bias] = val
# Transpose the QKV matrix.
elif op_name == "attention.query_key_value" and weight_or_bias == "weight":
# Make sure the QKV pointer is nil.
assert attention_qkv_weight is None, ""
out_val = fix_query_key_value_ordering(
val, checkpoint_version, 3, heads, hidden_size_per_head
)
# Store the tensor as we need the bias as well to interleave QKV and biases.
attention_qkv_weight = out_val
# Transpose the bias.
elif op_name == "attention.query_key_value" and weight_or_bias == "bias":
# Make sure we read the weight tensor.
assert attention_qkv_weight is not None, ""
# Split the QKV matrix into Q, K and V. Megatron stores Q,K,V interleaved.
q = attention_qkv_weight[0 * config.d_hidden : 1 * config.d_hidden, :]
k = attention_qkv_weight[1 * config.d_hidden : 2 * config.d_hidden, :]
v = attention_qkv_weight[2 * config.d_hidden : 3 * config.d_hidden, :]
out_val = fix_query_key_value_ordering(
val, checkpoint_version, 3, heads, hidden_size_per_head
)
# Split the bias.
q_bias = out_val[0 * config.d_hidden : 1 * config.d_hidden]
k_bias = out_val[1 * config.d_hidden : 2 * config.d_hidden]
v_bias = out_val[2 * config.d_hidden : 3 * config.d_hidden]
# Store.
output_state_dict[f"{layer_name}.attention.self.query.weight"] = q
output_state_dict[f"{layer_name}.attention.self.query.bias"] = q_bias
output_state_dict[f"{layer_name}.attention.self.key.weight"] = k
output_state_dict[f"{layer_name}.attention.self.key.bias"] = k_bias
output_state_dict[f"{layer_name}.attention.self.value.weight"] = v
output_state_dict[f"{layer_name}.attention.self.value.bias"] = v_bias
# Clear the stored tensor.
attention_qkv_weight = None
# Copy weights and biases as is.
elif weight_or_bias in ["weight", "bias"]:
out_name = megatron_to_transformers[op_name]
output_state_dict[layer_name + out_name + weight_or_bias] = val
# The final layernorm.
output_state_dict["bert.encoder.ln.weight"] = transformer["final_layernorm.weight"]
output_state_dict["bert.encoder.ln.bias"] = transformer["final_layernorm.bias"]
# The pooler.
pooler = lm["pooler"]
# Store the matrix and the bias.
output_state_dict["bert.pooler.dense.weight"] = pooler["dense.weight"]
output_state_dict["bert.pooler.dense.bias"] = pooler["dense.bias"]
# The LM head from Megatron (for RACE).
lm_head = model["lm_head"]
# The transform matrix.
output_state_dict["cls.predictions.transform.dense.weight"] = lm_head["dense.weight"]
output_state_dict["cls.predictions.transform.dense.bias"] = lm_head["dense.bias"]
# The transform LN.
output_state_dict["cls.predictions.transform.LayerNorm.weight"] = lm_head["layernorm.weight"]
output_state_dict["cls.predictions.transform.LayerNorm.bias"] = lm_head["layernorm.bias"]
# For the decoder, we replicate the weights.
output_state_dict["cls.predictions.decoder.weight"] = word_embeddings
output_state_dict["cls.predictions.bias"] = lm_head["bias"]
# The classifier from Megatron (for MLNI).
binary_head = model["binary_head"]
# Store the classifier.
output_state_dict["cls.seq_relationship.weight"] = binary_head["weight"]
output_state_dict["cls.seq_relationship.bias"] = binary_head["bias"]
# It should be done!
return output_state_dict
def main():
parser = ArgumentParser()
parser.add_argument("--print-checkpoint-structure", action="store_true")
parser.add_argument(
"path_to_checkpoint", type=str, help="Path to the ZIP file containing the checkpoint"
)
parser.add_argument(
"--config_file",
default="",
type=str,
help="An optional config json file describing the pre-trained model.",
)
args = parser.parse_args()
basename = os.path.dirname(args.path_to_checkpoint)
print(f'Extracting PyTorch state dictionary from "{args.path_to_checkpoint}"')
if args.path_to_checkpoint.endswith(".zip"):
with zipfile.ZipFile(args.path_to_checkpoint, "r") as checkpoint:
with checkpoint.open("release/mp_rank_00/model_optim_rng.pt") as pytorch_dict:
input_state_dict = torch.load(pytorch_dict, map_location="cpu")
else:
input_state_dict = torch.load(args.path_to_checkpoint, map_location="cpu")
if args.config_file == "":
config = PreTrained()
config.s_vocab = input_state_dict["model"]["lm_head"]["bias"].numel()
else:
config = PreTrained.from_json_file(args.config_file)
print("Converting")
output_state_dict = convert_megatron_checkpoint(args, input_state_dict, config)
if args.print_checkpoint_structure:
recursive_print(None, output_state_dict)
print("Saving config")
config.save_pretrained(basename)
output_checkpoint_file = os.path.join(basename, "pytorch_model.bin")
print(f'Saving checkpoint to "{output_checkpoint_file}"')
torch.save(output_state_dict, output_checkpoint_file)
if __name__ == "__main__":
main()
|
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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", 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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"]}
|
33,497
|
quantapix/qnarre
|
refs/heads/main
|
/notebooks/old/src/ragged.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 tensorflow as tf
import dataset as qd
ks = tf.keras
kl = ks.layers
@tf.function
def adapter(d):
ds = tf.RaggedTensor.from_sparse(d['defs'])
ss = tf.fill([ds.nrows(), 1], qd.SEP)
os = tf.RaggedTensor.from_sparse(d['op'])
x = tf.concat([ds, ss, os], axis=1)
y = tf.RaggedTensor.from_sparse(d['res'])[:, :1].to_tensor()
return (x.flat_values, x.row_splits), y
def dset_for(ps):
ds = tf.data.TFRecordDataset(list(qd.files(ps)))
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)).map(qd.caster)
return ds.map(adapter)
class Embed(kl.Layer):
def __init__(self, ps):
super().__init__(dtype=tf.float32)
s = (ps.dim_vocab, ps.dim_hidden)
self.emb = self.add_weight(name='emb', shape=s)
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)
return y
class Reflect(kl.Layer):
def build(self, shape):
s = shape[-1]
self.scale = 1 / (s**0.5)
self.q = self.add_weight(name='q', shape=(s, s))
self.k = self.add_weight(name='k', shape=(s, s))
self.v = self.add_weight(name='v', shape=(s, s))
return super().build(shape)
def call(self, 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
class Expand(kl.Layer):
def __init__(self, ps):
super().__init__()
self.ps = ps
def call(self, x):
y = x.to_tensor()
s = tf.shape(y)[-2]
y = tf.pad(y, [[0, 0], [0, self.ps.len_max_input - s], [0, 0]])
return y
def model_for(ps):
x = [
ks.Input(shape=(), dtype='int32'), # , ragged=True)
ks.Input(shape=(), dtype='int64'),
]
y = Embed(ps)(x)
y = Reflect()(y)
y = Expand(ps)(y)
y = kl.Reshape((ps.len_max_input * ps.dim_hidden, ))(y)
y = kl.Dense(ps.dim_dense, activation='relu')(y)
y = kl.Dense(ps.dim_vocab, name='dbd', activation=None)(y)
m = ks.Model(inputs=x, outputs=y)
m.compile(optimizer=ps.optimizer, loss=ps.loss, metrics=[ps.metric])
print(m.summary())
return m
def main_eager(ps, ds, m):
def step(x, y):
with tf.GradientTape() as tape:
logits = m(x)
loss = ps.loss(y, logits)
loss += sum(m.losses)
xent = ps.metric(y, logits)
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.numpy(), ', xent:', xent.numpy())
params = dict(
dim_batch=2,
dim_dense=150,
dim_hidden=15,
dim_vocab=len(qd.vocab),
len_max_input=20,
loss=ks.losses.SparseCategoricalCrossentropy(from_logits=True),
metric=ks.metrics.SparseCategoricalCrossentropy(from_logits=True),
num_epochs=10,
num_shards=2,
optimizer=ks.optimizers.Adam(),
)
if __name__ == '__main__':
ps = qd.Params(**params)
# import advanced_tf.masking as qm
# qm.main_graph(ps, dset_for(ps), model_for(ps))
main_eager(ps, dset_for(ps), model_for(ps))
|
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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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"/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", 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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"]}
|
33,498
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/tokens/fast/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 json
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ....tokens.fast import PreTrainedTokenizerFast
from ..roformer import Tokenizer as RoFormer
from ..utils import JiebaPreTokenizer
VOCAB_FS = {"vocab_file": "vocab.txt"}
VOCAB_MAP = {
"vocab_file": {
"junnyu/roformer_chinese_small": "https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt",
"junnyu/roformer_chinese_base": "https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt",
"junnyu/roformer_chinese_char_small": "https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt",
"junnyu/roformer_chinese_char_base": "https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt",
"junnyu/roformer_small_discriminator": "https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt",
"junnyu/roformer_small_generator": "https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt",
}
}
INPUT_CAPS = {
"junnyu/roformer_chinese_small": 1536,
"junnyu/roformer_chinese_base": 1536,
"junnyu/roformer_chinese_char_small": 512,
"junnyu/roformer_chinese_char_base": 512,
"junnyu/roformer_small_discriminator": 128,
"junnyu/roformer_small_generator": 128,
}
PRETRAINED_INIT_CONFIGURATION = {
"junnyu/roformer_chinese_small": {"do_lower_case": True},
"junnyu/roformer_chinese_base": {"do_lower_case": True},
"junnyu/roformer_chinese_char_small": {"do_lower_case": True},
"junnyu/roformer_chinese_char_base": {"do_lower_case": True},
"junnyu/roformer_small_discriminator": {"do_lower_case": True},
"junnyu/roformer_small_generator": {"do_lower_case": True},
}
class Tokenizer(PreTrainedTokenizerFast):
vocab_fs = VOCAB_FS
vocab_map = VOCAB_MAP
input_caps = INPUT_CAPS
pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
slow_tokenizer_class = RoFormer
def __init__(
self,
vocab_file=None,
tokenizer_file=None,
do_lower_case=True,
unk="[UNK]",
sep="[SEP]",
pad="[PAD]",
cls="[CLS]",
msk="[MASK]",
tokenize_chinese_chars=True,
strip_accents=None,
**kw,
):
super().__init__(
vocab_file,
tokenizer_file=tokenizer_file,
do_lower_case=do_lower_case,
unk=unk,
sep=sep,
pad=pad,
cls=cls,
msk=msk,
tokenize_chinese_chars=tokenize_chinese_chars,
strip_accents=strip_accents,
**kw,
)
pre_tok_state = json.loads(self.backend_tokenizer.normalizer.__getstate__())
if (
pre_tok_state.get("lowercase", do_lower_case) != do_lower_case
or pre_tok_state.get("strip_accents", strip_accents) != strip_accents
):
pre_tok_class = getattr(normalizers, pre_tok_state.pop("type"))
pre_tok_state["lowercase"] = do_lower_case
pre_tok_state["strip_accents"] = strip_accents
self.backend_tokenizer.normalizer = pre_tok_class(**pre_tok_state)
self.do_lower_case = do_lower_case
def __getstate__(self):
state = self.__dict__.copy()
state["_tokenizer"].pre_tokenizer = BertPreTokenizer()
return state
def __setstate__(self, d):
self.__dict__ = d
vocab = self.__dict__["_tokenizer"].get_vocab()
self.__dict__["_tokenizer"].pre_tokenizer = PreTokenizer.custom(JiebaPreTokenizer(vocab))
def build_inputs_with_special_tokens(self, toks_0, toks_1=None):
y = [self.cls_token_id] + toks_0 + [self.sep_token_id]
if toks_1:
y += toks_1 + [self.sep_token_id]
return y
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) * [0] + len(toks_1 + sep) * [1]
def save_vocabulary(self, dir, pre=None):
return tuple(self._tokenizer.model.save(dir, name=pre))
def save_pretrained(self, dir, legacy_format=None, pre=None, push_to_hub=False, **kw):
self.backend_tokenizer.pre_tokenizer = BertPreTokenizer()
return super().save_pretrained(dir, legacy_format, pre, push_to_hub, **kw)
|
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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"]}
|
33,499
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/models/flash/bert.py
|
import re
import logging
from functools import partial
from collections.abc import Sequence
from collections import OrderedDict
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import BertConfig
from transformers.models.bert.modeling_bert import BaseModelOutputWithPoolingAndCrossAttentions
from transformers.models.bert.modeling_bert import BertForPreTrainingOutput
from einops import rearrange
from flash_attn.modules.mha import MHA
from flash_attn.modules.mlp import Mlp, FusedMLP
from flash_attn.modules.block import Block
from flash_attn.modules.embedding import BertEmbeddings
from flash_attn.bert_padding import unpad_input, pad_input
from flash_attn.bert_padding import index_first_axis, index_first_axis_residual
from flash_attn.utils.pretrained import state_dict_from_pretrained
try:
from flash_attn.ops.fused_dense import FusedDense
except ImportError:
FusedDense = None
try:
from flash_attn.ops.layer_norm import dropout_add_layer_norm, layer_norm
except ImportError:
dropout_add_layer_norm, layer_norm = None, None
try:
from flash_attn.losses.cross_entropy import CrossEntropyLoss
except ImportError:
CrossEntropyLoss = None
logger = logging.getLogger(__name__)
class BertPooler(nn.Module):
def __init__(self, cfg):
super().__init__()
fused_bias_fc = getattr(cfg, "fused_bias_fc", False)
if fused_bias_fc and FusedDense is None:
raise ImportError("fused_dense is not installed")
linear_cls = nn.Linear if not fused_bias_fc else FusedDense
self.dense = linear_cls(cfg.hidden_size, cfg.hidden_size)
self.activation = nn.Tanh()
def forward(self, x, pool=True):
# We "pool" the model by simply taking the hidden state corresponding
# to the first token.
first_token_tensor = x[:, 0] if pool else x
y = self.dense(first_token_tensor)
y = self.activation(y)
return y
class BertPredictionHeadTransform(nn.Module):
def __init__(self, cfg):
super().__init__()
fused_bias_fc = getattr(cfg, "fused_bias_fc", False)
if fused_bias_fc and FusedDense is None:
raise ImportError("fused_dense is not installed")
self.fused_dropout_add_ln = getattr(cfg, "fused_dropout_add_ln", False)
if self.fused_dropout_add_ln and layer_norm is None:
raise ImportError("dropout_add_layer_norm is not installed")
linear_cls = nn.Linear if not fused_bias_fc else FusedDense
self.dense = linear_cls(cfg.hidden_size, cfg.hidden_size)
approximate = "tanh" if cfg.hidden_act in ["gelu_new", "gelu_fast"] else "none"
self.transform_act_fn = nn.GELU(approximate=approximate)
self.layer_norm = nn.LayerNorm(cfg.hidden_size, eps=cfg.layer_norm_eps)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.transform_act_fn(hidden_states)
if not self.fused_dropout_add_ln:
hidden_states = self.layer_norm(hidden_states)
else:
hidden_states = layer_norm(
hidden_states, self.layer_norm.weight, self.layer_norm.bias, self.layer_norm.eps
)
return hidden_states
class BertLMPredictionHead(nn.Module):
def __init__(self, cfg):
super().__init__()
fused_bias_fc = getattr(cfg, "fused_bias_fc", False)
if fused_bias_fc and FusedDense is None:
raise ImportError("fused_dense is not installed")
linear_cls = nn.Linear if not fused_bias_fc else FusedDense
self.transform = BertPredictionHeadTransform(cfg)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.decoder = linear_cls(cfg.hidden_size, cfg.vocab_size, bias=True)
def forward(self, hidden_states):
hidden_states = self.transform(hidden_states)
hidden_states = self.decoder(hidden_states)
return hidden_states
class BertPreTrainingHeads(nn.Module):
def __init__(self, cfg):
super().__init__()
self.predictions = BertLMPredictionHead(cfg)
self.seq_relationship = nn.Linear(cfg.hidden_size, 2)
def forward(self, sequence_output, pooled_output):
prediction_scores = self.predictions(sequence_output)
seq_relationship_score = self.seq_relationship(pooled_output)
return prediction_scores, seq_relationship_score
class PreTrained(nn.Module):
def __init__(self, cfg, *inputs, **kwargs):
super().__init__()
if not isinstance(cfg, BertConfig):
raise ValueError(
"Parameter cfg in `{}(cfg)` should be an instance of class `BertConfig`. "
"To create a model from a Google pretrained model use "
"`model = {}.from_pretrained(PRETRAINED_MODEL_NAME)`".format(
self.__class__.__name__, self.__class__.__name__
)
)
self.cfg = cfg
@classmethod
def from_pretrained(cls, model_name, cfg, *inputs, **kwargs):
model = cls(cfg, *inputs, **kwargs)
load_return = model.load_state_dict(
remap_state_dict(state_dict_from_pretrained(model_name), cfg), strict=False
)
logger.info(load_return)
return model
class ForPreTraining(PreTrained):
def __init__(self, cfg):
super().__init__(cfg)
self.dense_seq_output = getattr(cfg, "dense_seq_output", False)
self.last_layer_subset = getattr(cfg, "last_layer_subset", False)
if self.last_layer_subset:
assert self.dense_seq_output, "last_layer_subset requires dense_seq_output"
use_xentropy = getattr(cfg, "use_xentropy", False)
if use_xentropy and CrossEntropyLoss is None:
raise ImportError("xentropy_cuda is not installed")
loss_cls = (
nn.CrossEntropyLoss
if not use_xentropy
else partial(CrossEntropyLoss, inplace_backward=True)
)
self.model = Model(cfg)
self.cls = BertPreTrainingHeads(cfg)
self.mlm_loss = loss_cls(ignore_index=0)
self.nsp_loss = loss_cls(ignore_index=-1)
# Initialize weights and apply final processing
self.apply(partial(_init_weights, initializer_range=cfg.initializer_range))
self.tie_weights()
def tie_weights(self):
self.cls.predictions.decoder.weight = self.model.emb.word_embeddings.weight
def forward(self, x, mask=None, labels=None, next_sentence_label=None, **kw):
masked_tokens_mask = labels > 0 if (self.last_layer_subset and labels is not None) else None
ys = self.model(
x,
mask=mask.bool() if mask is not None else None,
masked_tokens_mask=masked_tokens_mask,
**kw,
)
sequence_output, pooled_output = ys.last_hidden_state, ys.pooler_output
if self.dense_seq_output and labels is not None:
masked_token_idx = torch.nonzero(labels.flatten() > 0, as_tuple=False).flatten()
if not self.last_layer_subset:
sequence_output = index_first_axis(
rearrange(sequence_output, "b s d -> (b s) d"), masked_token_idx
)
prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output)
loss = None
if labels is not None and next_sentence_label is not None:
if self.dense_seq_output and labels is not None:
masked_lm_loss = self.mlm_loss(
prediction_scores, labels.flatten()[masked_token_idx]
)
else:
masked_lm_loss = self.mlm_loss(
rearrange(prediction_scores, "... v -> (...) v"),
rearrange(labels, "... -> (...)"),
)
next_sentence_loss = self.nsp_loss(
rearrange(seq_relationship_score, "... t -> (...) t"),
rearrange(next_sentence_label, "... -> (...)"),
)
loss = masked_lm_loss.float() + next_sentence_loss.float()
return BertForPreTrainingOutput(
loss=loss,
prediction_logits=prediction_scores,
seq_relationship_logits=seq_relationship_score,
)
class Model(PreTrained):
def __init__(self, cfg, add_pool=True):
super().__init__(cfg)
self.pad_vocab_size_multiple = getattr(cfg, "pad_vocab_size_multiple", 1)
if cfg.vocab_size % self.pad_vocab_size_multiple != 0:
cfg.vocab_size += self.pad_vocab_size_multiple - (
cfg.vocab_size % self.pad_vocab_size_multiple
)
self.fused_dropout_add_ln = getattr(cfg, "fused_dropout_add_ln", False)
if self.fused_dropout_add_ln and layer_norm is None:
raise ImportError("dropout_add_layer_norm is not installed")
assert cfg.position_embedding_type == "absolute"
assert cfg.hidden_act in ["gelu", "gelu_new", "gelu_fast"]
self.emb = BertEmbeddings(
cfg.hidden_size,
cfg.vocab_size,
cfg.max_position_embeddings,
cfg.type_vocab_size,
padding_idx=cfg.pad_token_id,
)
self.emb_drop = nn.Dropout(cfg.hidden_dropout_prob)
self.emb_ln = nn.LayerNorm(cfg.hidden_size, eps=cfg.layer_norm_eps)
self.enc = Encoder(cfg)
self.pool = BertPooler(cfg) if add_pool else None
self.apply(partial(_init_weights, initializer_range=cfg.initializer_range))
def forward(
self,
x,
position_ids=None,
token_type_ids=None,
mask=None,
masked_tokens_mask=None,
**kw,
):
ys = self.emb(x, **kw)
if not self.fused_dropout_add_ln:
ys = self.emb_ln(ys)
else:
ys = layer_norm(ys, self.emb_ln.weight, self.emb_ln.bias, self.emb_ln.eps)
ys = self.emb_drop(ys)
if masked_tokens_mask is not None:
batch_size, seqlen = x.shape[:2]
first_col_mask = torch.zeros(batch_size, seqlen, dtype=torch.bool, device=x.device)
first_col_mask[:, 0] = True
subset_mask = masked_tokens_mask | first_col_mask
else:
subset_mask = None
ys = self.enc(ys, key_padding_mask=mask, subset_mask=subset_mask)
if masked_tokens_mask is None:
pooled_output = self.pool(ys) if self.pool is not None else None
else:
if mask is not None:
subset_idx = subset_mask[mask]
pool_input = ys[first_col_mask[mask][subset_idx]]
ys = ys[masked_tokens_mask[mask][subset_idx]]
else:
pool_input = ys[first_col_mask[subset_mask]]
ys = ys[masked_tokens_mask[subset_mask]]
pooled_output = self.pool(pool_input, pool=False) if self.pool is not None else None
return BaseModelOutputWithPoolingAndCrossAttentions(
last_hidden_state=ys,
pooler_output=pooled_output,
)
class Encoder(nn.Module):
def __init__(self, cfg):
super().__init__()
self.use_flash_attn = getattr(cfg, "use_flash_attn", False)
self.lays = nn.ModuleList([create_block(cfg, layer_idx=i) for i in range(cfg.n_lays)])
def forward(self, hidden_states, key_padding_mask=None, subset_mask=None):
if key_padding_mask is None or not self.use_flash_attn:
mixer_kwargs = (
{"key_padding_mask": key_padding_mask} if key_padding_mask is not None else None
)
for layer in self.lays:
hidden_states = layer(hidden_states, mixer_kwargs=mixer_kwargs)
if subset_mask is not None:
hidden_states = hidden_states[subset_mask]
else:
b, seqlen = hidden_states.shape[:2]
hidden_states, indices, cu_seqlens, max_seqlen_in_batch = unpad_input(
hidden_states, key_padding_mask
)
mixer_kwargs = {"cu_seqlens": cu_seqlens, "max_seqlen": max_seqlen_in_batch}
if subset_mask is None:
for layer in self.lays:
hidden_states = layer(hidden_states, mixer_kwargs=mixer_kwargs)
hidden_states = pad_input(hidden_states, indices, b, seqlen)
else:
for layer in self.lays[:-1]:
hidden_states = layer(hidden_states, mixer_kwargs=mixer_kwargs)
if key_padding_mask is not None:
subset_idx = torch.nonzero(
subset_mask[key_padding_mask], as_tuple=False
).flatten()
subset_seqlens = (subset_mask & key_padding_mask).sum(dim=-1, dtype=torch.int32)
subset_cu_seqlens = F.pad(
torch.cumsum(subset_seqlens, dim=0, dtype=torch.torch.int32), (1, 0)
)
else:
subset_idx = torch.nonzero(subset_mask, as_tuple=False).flatten()
subset_seqlens = subset_mask.sum(dim=-1, dtype=torch.int32)
subset_cu_seqlens = F.pad(
torch.cumsum(subset_seqlens, dim=0, dtype=torch.torch.int32), (1, 0)
)
hidden_states_subset, hidden_states = index_first_axis_residual(
hidden_states, subset_idx
)
mixer_kwargs = {
"x_kv": hidden_states,
"cu_seqlens": subset_cu_seqlens,
"max_seqlen": max_seqlen_in_batch,
"cu_seqlens_k": cu_seqlens,
"max_seqlen_k": max_seqlen_in_batch,
}
hidden_states = self.lays[-1](hidden_states_subset, mixer_kwargs=mixer_kwargs)
return hidden_states
def create_mixer_cls(cfg, cross_attn=False, return_residual=False):
use_flash_attn = getattr(cfg, "use_flash_attn", False)
fused_bias_fc = getattr(cfg, "fused_bias_fc", False)
mixer_cls = partial(
MHA,
num_heads=cfg.num_attention_heads,
cross_attn=cross_attn,
dropout=cfg.attention_probs_dropout_prob,
causal=False,
fused_bias_fc=fused_bias_fc,
use_flash_attn=use_flash_attn,
return_residual=return_residual,
)
return mixer_cls
def create_mlp_cls(cfg, layer_idx=None, return_residual=False):
inner_dim = cfg.intermediate_size
fused_mlp = getattr(cfg, "fused_mlp", False)
if fused_mlp:
assert cfg.hidden_act in ["gelu_new", "gelu_fast"], (
"fused_mlp only " "supports approximate gelu"
)
if not fused_mlp:
approximate = "tanh" if cfg.hidden_act in ["gelu_new", "gelu_fast"] else "none"
mlp_cls = partial(
Mlp,
hidden_features=inner_dim,
activation=partial(F.gelu, approximate=approximate),
return_residual=return_residual,
)
else:
if FusedMLP is None:
raise ImportError("fused_dense is not installed")
mlp_checkpoint_lvl = getattr(cfg, "mlp_checkpoint_lvl", 0)
if isinstance(mlp_checkpoint_lvl, Sequence):
assert layer_idx is not None
mlp_checkpoint_lvl = mlp_checkpoint_lvl[layer_idx]
mlp_cls = partial(
FusedMLP,
hidden_features=inner_dim,
checkpoint_lvl=mlp_checkpoint_lvl,
return_residual=return_residual,
)
return mlp_cls
def create_block(cfg, layer_idx=None):
last_layer_subset = getattr(cfg, "last_layer_subset", False)
cross_attn = last_layer_subset and layer_idx == cfg.n_lays - 1
return_residual = not cross_attn
mixer_cls = create_mixer_cls(cfg, cross_attn, return_residual=return_residual)
mlp_cls = create_mlp_cls(cfg, layer_idx, return_residual=return_residual)
norm_cls = partial(nn.LayerNorm, eps=cfg.layer_norm_eps)
block = Block(
cfg.hidden_size,
mixer_cls,
mlp_cls,
norm_cls=norm_cls,
prenorm=False,
resid_dropout1=cfg.hidden_dropout_prob,
resid_dropout2=cfg.hidden_dropout_prob,
fused_dropout_add_ln=getattr(cfg, "fused_dropout_add_ln", False),
return_residual=return_residual,
)
return block
def _init_weights(module, initializer_range=0.02):
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 module.padding_idx is not None:
nn.init.zeros_(module.weight[module.padding_idx])
def remap_state_dict(state_dict, cfg):
# LayerNorm
def key_mapping_ln_gamma_beta(key):
key = re.sub(r"LayerNorm.gamma$", "LayerNorm.weight", key)
key = re.sub(r"LayerNorm.beta$", "LayerNorm.bias", key)
return key
state_dict = OrderedDict((key_mapping_ln_gamma_beta(k), v) for k, v in state_dict.items())
# Layers
def key_mapping_layers(key):
return re.sub(r"^bert.encoder.layer.", "bert.encoder.layers.", key)
state_dict = OrderedDict((key_mapping_layers(k), v) for k, v in state_dict.items())
# LayerNorm
def key_mapping_ln(key):
key = re.sub(r"^bert.embeddings.LayerNorm.", "bert.emb_ln.", key)
key = re.sub(
r"^bert.encoder.layers.(\d+).attention.output.LayerNorm.(weight|bias)",
r"bert.encoder.layers.\1.norm1.\2",
key,
)
key = re.sub(
r"^bert.encoder.layers.(\d+).output.LayerNorm.(weight|bias)",
r"bert.encoder.layers.\1.norm2.\2",
key,
)
key = re.sub(
r"^cls.predictions.transform.LayerNorm.(weight|bias)",
r"cls.predictions.transform.layer_norm.\1",
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"^bert.encoder.layers.(\d+).intermediate.dense.(weight|bias)",
r"bert.encoder.layers.\1.mlp.fc1.\2",
key,
)
key = re.sub(
r"^bert.encoder.layers.(\d+).output.dense.(weight|bias)",
r"bert.encoder.layers.\1.mlp.fc2.\2",
key,
)
return key
state_dict = OrderedDict((key_mapping_mlp(k), v) for k, v in state_dict.items())
# Attention
last_layer_subset = getattr(cfg, "last_layer_subset", False)
for d in range(cfg.n_lays):
Wq = state_dict.pop(f"bert.encoder.layers.{d}.attention.self.query.weight")
Wk = state_dict.pop(f"bert.encoder.layers.{d}.attention.self.key.weight")
Wv = state_dict.pop(f"bert.encoder.layers.{d}.attention.self.value.weight")
bq = state_dict.pop(f"bert.encoder.layers.{d}.attention.self.query.bias")
bk = state_dict.pop(f"bert.encoder.layers.{d}.attention.self.key.bias")
bv = state_dict.pop(f"bert.encoder.layers.{d}.attention.self.value.bias")
if not (last_layer_subset and d == cfg.n_lays - 1):
state_dict[f"bert.encoder.layers.{d}.mixer.Wqkv.weight"] = torch.cat(
[Wq, Wk, Wv], dim=0
)
state_dict[f"bert.encoder.layers.{d}.mixer.Wqkv.bias"] = torch.cat([bq, bk, bv], dim=0)
else:
state_dict[f"bert.encoder.layers.{d}.mixer.Wq.weight"] = Wq
state_dict[f"bert.encoder.layers.{d}.mixer.Wkv.weight"] = torch.cat([Wk, Wv], dim=0)
state_dict[f"bert.encoder.layers.{d}.mixer.Wq.bias"] = bq
state_dict[f"bert.encoder.layers.{d}.mixer.Wkv.bias"] = torch.cat([bk, bv], dim=0)
def key_mapping_attn(key):
return re.sub(
r"^bert.encoder.layers.(\d+).attention.output.dense.(weight|bias)",
r"bert.encoder.layers.\1.mixer.out_proj.\2",
key,
)
state_dict = OrderedDict((key_mapping_attn(k), v) for k, v in state_dict.items())
def key_mapping_decoder_bias(key):
return re.sub(r"^cls.predictions.bias", "cls.predictions.decoder.bias", key)
state_dict = OrderedDict((key_mapping_decoder_bias(k), v) for k, v in state_dict.items())
# Word embedding
pad_vocab_size_multiple = getattr(cfg, "pad_vocab_size_multiple", 1)
if pad_vocab_size_multiple > 1:
word_embeddings = state_dict["bert.embeddings.word_embeddings.weight"]
state_dict["bert.embeddings.word_embeddings.weight"] = F.pad(
word_embeddings, (0, 0, 0, cfg.vocab_size - word_embeddings.shape[0])
)
decoder_weight = state_dict["cls.predictions.decoder.weight"]
state_dict["cls.predictions.decoder.weight"] = F.pad(
decoder_weight, (0, 0, 0, cfg.vocab_size - decoder_weight.shape[0])
)
# If the vocab was padded, we want to set the decoder bias for those padded indices to be
# strongly negative (i.e. the decoder shouldn't predict those indices).
# TD [2022-05-09]: I don't think it affects the MLPerf training.
decoder_bias = state_dict["cls.predictions.decoder.bias"]
state_dict["cls.predictions.decoder.bias"] = F.pad(
decoder_bias, (0, cfg.vocab_size - decoder_bias.shape[0]), value=-100.0
)
return state_dict
|
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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"]}
|
33,500
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/core/runner.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
import logging
import math
import os
import transformers
from accelerate import Accelerator
from datasets import load_dataset
from huggingface_hub import Repository
from pathlib import Path
from torch.utils.data import DataLoader
from tqdm.auto import tqdm
from transformers.file_utils import get_full_repo_name
from transformers import (
CONFIG_MAPPING,
AdamW,
AutoConfig,
AutoModel,
AutoTokenizer,
default_data_collator,
get_scheduler,
set_seed,
)
from .params import parse_params, TRAIN, EVAL, TEST, ALL
log = logging.getLogger(__name__)
class Runner:
AutoModel = AutoModel
AutoConfig = AutoConfig
AutoTokenizer = AutoTokenizer
def __init__(self, xs=[]):
self.params = ps = parse_params(xs)
self.mgr = mgr = Accelerator()
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
log.info(mgr.state)
log.setLevel(logging.INFO if mgr.is_local_main_process else logging.ERROR)
if mgr.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
if ps.seed is not None:
set_seed(ps.seed)
if mgr.is_main_process:
if ps.push_to_hub:
if ps.hub_model_id is None:
x = get_full_repo_name(Path(ps.out_dir).name, token=ps.hub_token)
else:
x = ps.hub_model_id
self.repo = Repository(ps.out_dir, clone_from=x)
elif ps.out_dir is not None:
os.makedirs(ps.out_dir, exist_ok=True)
mgr.wait_for_everyone()
self.padding = "max_len" if ps.pad_to_max_length else False
@property
def dataset(self):
if self._dataset is None:
ps = self.params
if ps.dataset_name is not None:
y = load_dataset(ps.dataset_name, ps.dataset_config)
else:
x, xs = None, {}
if ps.test_file is not None:
xs[TEST] = x = ps.test_file
if ps.eval_file is not None:
xs[EVAL] = x = ps.eval_file
if ps.train_file is not None:
xs[TRAIN] = x = ps.train_file
y = load_dataset(x.split(".")[-1], data_files=xs) # field="data")
if ps.debug:
for k in y.keys():
y[k] = y[k].select(range(100))
self._dataset = y
return self._dataset
@property
def cols(self):
if self._cols is None:
cs = self.dataset[TRAIN].column_names
self._cols = {ALL: cs}
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 = self.AutoConfig.from_pretrained(x)
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")
y = self.AutoTokenizer.from_pretrained(x, use_fast=not ps.use_slow_tokenizer)
self._tokenizer = y
return self._tokenizer
@property
def model(self):
if self._model is None:
ps = self.params
if ps.model_name:
y = self.AutoModel.from_pretrained(
ps.model_name,
from_tf=bool(".ckpt" in ps.model_name),
config=self.config,
)
else:
log.info("Training new model")
y = self.AutoModel.from_config(self.config)
self._model = y
return self._model
@property
def eval_ds(self):
if self._eval_ds is None:
ps, ds = self.params, self.dataset
y = ds[EVAL]
if ps.max_eval_samples is not None:
y = y.select(range(ps.max_eval_samples))
self._eval_ds = y
return self._eval_ds
@property
def test_ds(self):
if self._test_ds is None:
ps, ds = self.params, self.dataset
y = ds[TEST]
if ps.max_test_samples is not None:
y = y.select(range(ps.max_test_samples))
self._test_ds = y
return self._test_ds
@property
def loaders(self):
if self._loaders is None:
ps = self.params
c = default_data_collator
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 optimizer(self):
if self._optimizer is None:
ps, m = self.params, self.model
ds = ["bias", "LayerNorm.weight"]
xs = [
{
"params": [p for n, p in m.named_parameters() if not any(d in n for d in ds)],
"weight_decay": ps.weight_decay,
},
{
"params": [p for n, p in m.named_parameters() if any(d in n for d in ds)],
"weight_decay": 0.0,
},
]
self._optimizer = AdamW(xs, lr=ps.lr)
return self._optimizer
@property
def scheduler(self):
if self._scheduler is None:
ps = self.params
self._scheduler = get_scheduler(
name=ps.lr_scheduler_type,
optimizer=self.optimizer,
num_warmup_steps=ps.num_warmup_steps,
num_training_steps=ps.max_train_steps,
)
return self._optimizer
def prepare(self):
m, mgr, ls = self.model, self.mgr, self.loaders
m.to(mgr.device)
t, e = ls[TRAIN], ls[EVAL]
self._model, self._optimizer, t, e = mgr.prepare(m, self.optimizer, t, e)
self._loaders = {TRAIN: t, EVAL: e}
def train(self):
ps, mgr, src = self.params, self.mgr, self.loaders[TRAIN]
x = math.ceil(len(src) / ps.grad_accumulation_steps)
if ps.max_train_steps is None:
ps.max_train_steps = ps.num_train_epochs * x
else:
ps.num_train_epochs = math.ceil(ps.max_train_steps / x)
m, o, s = self.model, self.optimizer, self.scheduler
b = ps.train_batch_size * mgr.num_processes * ps.grad_accumulation_steps
log.info("*** Training ***")
log.info(f" Num samples = {len(self.train_ds)}")
log.info(f" Num epochs = {ps.num_train_epochs}")
log.info(f" Batch size per device = {ps.train_batch_size}")
log.info(f" Batch size (w. parallel, distributed & accumulation) = {b}")
log.info(f" Grad accumulation steps = {ps.grad_accumulation_steps}")
log.info(f" Train steps = {ps.max_train_steps}")
n = 0
bar = tqdm(range(ps.max_train_steps), disable=not mgr.is_local_main_process)
for e in range(ps.num_train_epochs):
m.train()
for i, xs in enumerate(src):
ys = m(**xs)
mgr.backward(ys.loss / ps.grad_accumulation_steps)
if i % ps.grad_accumulation_steps == 0 or i == len(src) - 1:
o.step()
s.step()
o.zero_grad()
bar.update(1)
n += 1
if n >= ps.max_train_steps:
break
self.eval_epoch(e)
if ps.push_to_hub and e < ps.num_train_epochs - 1:
mgr.wait_for_everyone()
mgr.unwrap_model(m).save_pretrained(ps.out_dir, save_function=mgr.save)
if mgr.is_main_process:
self.tokenizer.save_pretrained(ps.out_dir)
self.repo.push_to_hub(commit_message=f"Training... epoch {e}", blocking=False)
def eval_epoch(self, _):
pass
def save(self):
ps, mgr = self.params, self.mgr
if ps.out_dir is not None:
mgr.wait_for_everyone()
mgr.unwrap_model(self.model).save_pretrained(ps.out_dir, save_function=mgr.save)
if mgr.is_main_process:
self.tokenizer.save_pretrained(ps.out_dir)
if ps.push_to_hub:
self.repo.push_to_hub(commit_message="End of training")
|
{"/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"]}
|
33,501
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/run/seq2seq.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 seq2seq models for question answering
import logging
import random
from datasets import load_metric
from torch.utils.data import DataLoader
from transformers import AutoModelForSeq2SeqLM, DataCollatorForSeq2Seq
from transformers.trainer_utils import EvalPrediction
from .params import TRAIN, EVAL, TEST, ALL, EACH
from .runner import Runner as Base
from .qa import Runner as QA
log = logging.getLogger(__name__)
NAMES = {
"squad_v2": ("question", "context", "answer"),
}
class Runner(Base):
AutoModel = AutoModelForSeq2SeqLM
@property
def cols(self):
if self._cols is None:
ps = self.params
if ps.do_train:
cs = self.dataset[TRAIN].column_names
elif ps.do_eval:
cs = self.dataset[EVAL].column_names
elif ps.do_test:
cs = self.dataset[TEST].column_names
else:
raise ValueError("There is nothing to do")
ns = NAMES.get(ps.dataset_name, None)
if ps.question_column is None:
q = ns[0] if ns is not None else cs[0]
else:
q = ps.question_column
if q not in cs:
raise ValueError(f"--question_column' needs to be in: {', '.join(cs)}")
if ps.context_column is None:
c = ns[1] if ns is not None else cs[1]
else:
c = ps.context_column
if c not in cs:
raise ValueError(f"--context_column' needs to be in: {', '.join(cs)}")
if ps.answer_column is None:
a = ns[2] if ns is not None else cs[2]
else:
a = ps.answer_column
if a not in cs:
raise ValueError(f"--answer_column' needs to be in: {', '.join(cs)}")
self._cols = {ALL: cs, EACH: [q, c, a]}
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 not x:
raise ValueError("Config from scratch is not supported")
if x:
y = self.AutoConfig.from_pretrained(
x,
cache_dir=ps.cache_dir,
revision=ps.model_version,
use_auth_token=True if ps.use_auth_token else None,
)
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")
y = self.AutoTokenizer.from_pretrained(
x,
cache_dir=ps.cache_dir,
use_fast=True,
revision=ps.model_version,
use_auth_token=True if ps.use_auth_token else None,
)
self._tokenizer = y
if ps.max_seq_length > y.model_max_length:
log.warning(f"Using max_seq_length={y.model_max_length}")
self.max_seq_length = min(ps.max_seq_length, y.model_max_length)
return self._tokenizer
@property
def model(self):
if self._model is None:
ps = self.params
if ps.model_name:
y = self.AutoModel.from_pretrained(
ps.model_name,
from_tf=bool(".ckpt" in ps.model_name),
config=self.config,
cache_dir=ps.cache_dir,
revision=ps.model_version,
use_auth_token=True if ps.use_auth_token else None,
)
else:
log.info("Training new model")
y = self.AutoModel.from_config(self.config)
self._model = y
if y.config.dec_START is None:
raise ValueError("Needs `config.dec_START`")
if ps.label_smoothing_factor > 0 and not hasattr(
y, "prepare_decoder_input_ids_from_labels"
):
log.warning("Needs `prepare_decoder_input_ids_from_labels` method for model")
return self._model
@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, t = self.params, self.tokenizer
ins, ans = self.prep_batch(xs)
y = t(ins, max_len=self.max_seq_length, padding=self.padding, truncation=True)
with t.as_target_tokenizer():
ls = t(ans, max_len=ps.max_answer_length, padding=self.padding, truncation=True)
if self.padding == "max_len" and ps.ignore_pad_token_for_loss:
ls["input_ids"] = [[(x if x != t.PAD else -100) for x in l] for l in ls["input_ids"]]
y["labels"] = ls["input_ids"]
return y
def prep_batch(self, xs):
q, c, a = self.cols[EACH]
ins = [
" ".join(["question:", q.lstrip(), "context:", c.lstrip()])
for q, c in zip(xs[q], xs[c])
]
ans = [a["text"][0] if len(a["text"]) > 0 else "" for a in xs[a]]
return ins, ans
@property
def eval_ds(self):
if self._eval_ds is None:
ps, mgr = self.params, self.mgr
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, t = self.params, self.tokenizer
ins, ans = self.prep_batch(xs)
y = t(
ins,
max_len=self.max_seq_length,
padding=self.padding,
truncation=True,
return_overflowing_tokens=True,
return_offsets_mapping=True,
)
with t.as_target_tokenizer():
ls = t(ans, max_len=ps.max_answer_length, padding=self.padding, truncation=True)
map = y.pop("overflow_to_sample_mapping")
y["example_id"] = []
for i in range(len(y["input_ids"])):
y["example_id"].append(xs["id"][map[i]])
if self.padding == "max_len" and ps.ignore_pad_token_for_loss:
ls["input_ids"] = [[(x if x != t.PAD else -100) for x in l] for l in ls["input_ids"]]
y["labels"] = ls["input_ids"]
return y
@property
def test_ds(self):
if self._test_ds is None:
ps, mgr = self.params, self.mgr
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 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 compute_metrics(self, p):
return self.metric.compute(predictions=p.predictions, references=p.label_ids)
@property
def loaders(self):
if self._loaders is None:
ps, t = self.params, self.tokenizer
c = DataCollatorForSeq2Seq(
t,
model=self.model,
label_pad_token_id=-100 if ps.ignore_pad_token_for_loss else t.PAD,
pad_to_multiple_of=8 if ps.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
def post_proc(self, xs, features, outs, stage="eval"):
ps = self.params
preds = outs.predictions
if isinstance(preds, tuple):
preds = preds[0]
preds = self.tokenizer.batch_decode(preds, skip_special_tokens=True)
map = {k: i for i, k in enumerate(xs["id"])}
feature_per_example = {map[x["example_id"]]: i for i, x in enumerate(features)}
ys = {}
for i, x in enumerate(xs):
ys[x["id"]] = preds[feature_per_example[i]]
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()]
ls = [{"id": x["id"], "answers": x[self.cols[EACH][2]]} for x in xs]
return EvalPrediction(predictions=ys, label_ids=ls)
def main():
x = Runner()
x.cols
x.dataset
x.config
x.tokenizer
x.model
x.model.resize_token_embeddings(len(x.tokenizer))
x.loaders
x.prepare()
x.train()
x.eval()
x.test()
x.save()
if __name__ == "__main__":
main()
|
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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"]}
|
33,502
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/tokens/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 itertools
import json
import os
import numpy as np
from .roberta import Tokenizer as Roberta
from ...tokens.utils import (
AddedToken,
BatchEncoding,
PaddingStrategy,
TruncationStrategy,
_is_tensorflow,
_is_torch,
to_py_obj,
)
from ...tokens.utils import is_tf_available, is_torch_available
VOCAB_FS = {
"vocab_file": "vocab.json",
"merges_file": "merges.txt",
"entity_vocab_file": "entity_vocab.json",
}
VOCAB_MAP = {
"vocab_file": {
"studio-ousia/luke-base": "https://huggingface.co/studio-ousia/luke-base/resolve/main/vocab.json",
"studio-ousia/luke-large": "https://huggingface.co/studio-ousia/luke-large/resolve/main/vocab.json",
},
"merges_file": {
"studio-ousia/luke-base": "https://huggingface.co/studio-ousia/luke-base/resolve/main/merges.txt",
"studio-ousia/luke-large": "https://huggingface.co/studio-ousia/luke-large/resolve/main/merges.txt",
},
"entity_vocab_file": {
"studio-ousia/luke-base": "https://huggingface.co/studio-ousia/luke-base/resolve/main/entity_vocab.json",
"studio-ousia/luke-large": "https://huggingface.co/studio-ousia/luke-large/resolve/main/entity_vocab.json",
},
}
INPUT_CAPS = {
"studio-ousia/luke-base": 512,
"studio-ousia/luke-large": 512,
}
class Tokenizer(Roberta):
vocab_fs = VOCAB_FS
vocab_map = VOCAB_MAP
input_caps = INPUT_CAPS
def __init__(
self,
vocab_file,
merges_file,
entity_vocab_file,
task=None,
max_entity_length=32,
max_mention_length=30,
entity_token_1="<ent>",
entity_token_2="<ent2>",
entity_unk_token="[UNK]",
entity_pad_token="[PAD]",
entity_mask_token="[MASK]",
entity_mask2_token="[MASK2]",
**kw,
):
entity_token_1 = (
AddedToken(entity_token_1, lstrip=False, rstrip=False)
if isinstance(entity_token_1, str)
else entity_token_1
)
entity_token_2 = (
AddedToken(entity_token_2, lstrip=False, rstrip=False)
if isinstance(entity_token_2, str)
else entity_token_2
)
kw["additional_special_tokens"] = kw.get("additional_special_tokens", [])
kw["additional_special_tokens"] += [entity_token_1, entity_token_2]
super().__init__(
vocab_file=vocab_file,
merges_file=merges_file,
task=task,
max_entity_length=32,
max_mention_length=30,
entity_token_1="<ent>",
entity_token_2="<ent2>",
entity_unk_token=entity_unk_token,
entity_pad_token=entity_pad_token,
entity_mask_token=entity_mask_token,
entity_mask2_token=entity_mask2_token,
**kw,
)
with open(entity_vocab_file, encoding="utf-8") as entity_vocab_handle:
self.entity_vocab = json.load(entity_vocab_handle)
for entity_special_token in [
entity_unk_token,
entity_pad_token,
entity_mask_token,
entity_mask2_token,
]:
if entity_special_token not in self.entity_vocab:
raise ValueError(
f"Specified entity special token ``{entity_special_token}`` is not found in entity_vocab. "
f"Probably an incorrect entity vocab file is loaded: {entity_vocab_file}."
)
self.entity_unk_token_id = self.entity_vocab[entity_unk_token]
self.entity_pad_token_id = self.entity_vocab[entity_pad_token]
self.entity_mask_token_id = self.entity_vocab[entity_mask_token]
self.entity_mask2_token_id = self.entity_vocab[entity_mask2_token]
self.task = task
if task is None or task == "entity_span_classification":
self.max_entity_length = max_entity_length
elif task == "entity_classification":
self.max_entity_length = 1
elif task == "entity_pair_classification":
self.max_entity_length = 2
else:
raise ValueError(
f"Task {task} not supported. Select task from ['entity_classification', 'entity_pair_classification', 'entity_span_classification'] only."
)
self.max_mention_length = max_mention_length
def __call__(
self,
text,
text_pair=None,
entity_spans=None,
entity_spans_pair=None,
entities=None,
entities_pair=None,
add_special_tokens=True,
padding=False,
truncation=False,
max_length=None,
max_entity_length=None,
stride=0,
is_split_into_words=False,
pad_to_multiple_of=None,
return_tensors=None,
return_token_type_ids=None,
return_attention_mask=None,
return_overflowing_tokens=False,
return_special_tokens_mask=False,
return_offsets_mapping=False,
return_length=False,
verbose=True,
**kw,
):
is_valid_single_text = isinstance(text, str)
is_valid_batch_text = isinstance(text, (list, tuple)) and (
len(text) == 0 or (isinstance(text[0], str))
)
if not (is_valid_single_text or is_valid_batch_text):
raise ValueError(
"text input must be of type `str` (single example) or `List[str]` (batch)."
)
is_valid_single_text_pair = isinstance(text_pair, str)
is_valid_batch_text_pair = isinstance(text_pair, (list, tuple)) and (
len(text_pair) == 0 or isinstance(text_pair[0], str)
)
if not (text_pair is None or is_valid_single_text_pair or is_valid_batch_text_pair):
raise ValueError(
"text_pair input must be of type `str` (single example) or `List[str]` (batch)."
)
is_batched = bool(isinstance(text, (list, tuple)))
if is_batched:
batch_text_or_text_pairs = list(zip(text, text_pair)) if text_pair is not None else text
if entities is None:
batch_entities_or_entities_pairs = None
else:
batch_entities_or_entities_pairs = (
list(zip(entities, entities_pair)) if entities_pair is not None else entities
)
if entity_spans is None:
batch_entity_spans_or_entity_spans_pairs = None
else:
batch_entity_spans_or_entity_spans_pairs = (
list(zip(entity_spans, entity_spans_pair))
if entity_spans_pair is not None
else entity_spans
)
return self.batch_encode_plus(
batch_text_or_text_pairs=batch_text_or_text_pairs,
batch_entity_spans_or_entity_spans_pairs=batch_entity_spans_or_entity_spans_pairs,
batch_entities_or_entities_pairs=batch_entities_or_entities_pairs,
add_special_tokens=add_special_tokens,
padding=padding,
truncation=truncation,
max_length=max_length,
max_entity_length=max_entity_length,
stride=stride,
is_split_into_words=is_split_into_words,
pad_to_multiple_of=pad_to_multiple_of,
return_tensors=return_tensors,
return_token_type_ids=return_token_type_ids,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
return_length=return_length,
verbose=verbose,
**kw,
)
else:
return self.encode_plus(
text=text,
text_pair=text_pair,
entity_spans=entity_spans,
entity_spans_pair=entity_spans_pair,
entities=entities,
entities_pair=entities_pair,
add_special_tokens=add_special_tokens,
padding=padding,
truncation=truncation,
max_length=max_length,
max_entity_length=max_entity_length,
stride=stride,
is_split_into_words=is_split_into_words,
pad_to_multiple_of=pad_to_multiple_of,
return_tensors=return_tensors,
return_token_type_ids=return_token_type_ids,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
return_length=return_length,
verbose=verbose,
**kw,
)
def _encode_plus(
self,
text,
text_pair=None,
entity_spans=None,
entity_spans_pair=None,
entities=None,
entities_pair=None,
add_special_tokens=True,
padding_strategy=PaddingStrategy.DO_NOT_PAD,
truncation_strategy=TruncationStrategy.DO_NOT_TRUNCATE,
max_length=None,
max_entity_length=None,
stride=0,
is_split_into_words=False,
pad_to_multiple_of=None,
return_tensors=None,
return_token_type_ids=None,
return_attention_mask=None,
return_overflowing_tokens=False,
return_special_tokens_mask=False,
return_offsets_mapping=False,
return_length=False,
verbose=True,
**kw,
):
if return_offsets_mapping:
raise NotImplementedError()
if is_split_into_words:
raise NotImplementedError()
(
first_ids,
second_ids,
first_entity_ids,
second_entity_ids,
first_entity_token_spans,
second_entity_token_spans,
) = self._create_input_sequence(
text=text,
text_pair=text_pair,
entities=entities,
entities_pair=entities_pair,
entity_spans=entity_spans,
entity_spans_pair=entity_spans_pair,
**kw,
)
return self.prepare_for_model(
first_ids,
pair_ids=second_ids,
entity_ids=first_entity_ids,
pair_entity_ids=second_entity_ids,
entity_token_spans=first_entity_token_spans,
pair_entity_token_spans=second_entity_token_spans,
add_special_tokens=add_special_tokens,
padding=padding_strategy.value,
truncation=truncation_strategy.value,
max_length=max_length,
max_entity_length=max_entity_length,
stride=stride,
pad_to_multiple_of=pad_to_multiple_of,
return_tensors=return_tensors,
prepend_batch_axis=True,
return_attention_mask=return_attention_mask,
return_token_type_ids=return_token_type_ids,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_length=return_length,
verbose=verbose,
)
def _batch_encode_plus(
self,
batch_text_or_text_pairs,
batch_entity_spans_or_entity_spans_pairs=None,
batch_entities_or_entities_pairs=None,
add_special_tokens=True,
padding_strategy=PaddingStrategy.DO_NOT_PAD,
truncation_strategy=TruncationStrategy.DO_NOT_TRUNCATE,
max_length=None,
max_entity_length=None,
stride=0,
is_split_into_words=False,
pad_to_multiple_of=None,
return_tensors=None,
return_token_type_ids=None,
return_attention_mask=None,
return_overflowing_tokens=False,
return_special_tokens_mask=False,
return_offsets_mapping=False,
return_length=False,
verbose=True,
**kw,
):
if return_offsets_mapping:
raise NotImplementedError()
if is_split_into_words:
raise NotImplementedError()
input_ids = []
entity_ids = []
entity_token_spans = []
for index, text_or_text_pair in enumerate(batch_text_or_text_pairs):
if not isinstance(text_or_text_pair, (list, tuple)):
text, text_pair = text_or_text_pair, None
else:
text, text_pair = text_or_text_pair
entities, entities_pair = None, None
if batch_entities_or_entities_pairs is not None:
entities_or_entities_pairs = batch_entities_or_entities_pairs[index]
if entities_or_entities_pairs:
if isinstance(entities_or_entities_pairs[0], str):
entities, entities_pair = entities_or_entities_pairs, None
else:
entities, entities_pair = entities_or_entities_pairs
entity_spans, entity_spans_pair = None, None
if batch_entity_spans_or_entity_spans_pairs is not None:
entity_spans_or_entity_spans_pairs = batch_entity_spans_or_entity_spans_pairs[index]
if len(entity_spans_or_entity_spans_pairs) > 0 and isinstance(
entity_spans_or_entity_spans_pairs[0], list
):
entity_spans, entity_spans_pair = entity_spans_or_entity_spans_pairs
else:
entity_spans, entity_spans_pair = entity_spans_or_entity_spans_pairs, None
(
first_ids,
second_ids,
first_entity_ids,
second_entity_ids,
first_entity_token_spans,
second_entity_token_spans,
) = self._create_input_sequence(
text=text,
text_pair=text_pair,
entities=entities,
entities_pair=entities_pair,
entity_spans=entity_spans,
entity_spans_pair=entity_spans_pair,
**kw,
)
input_ids.append((first_ids, second_ids))
entity_ids.append((first_entity_ids, second_entity_ids))
entity_token_spans.append((first_entity_token_spans, second_entity_token_spans))
batch_outputs = self._batch_prepare_for_model(
input_ids,
batch_entity_ids_pairs=entity_ids,
batch_entity_token_spans_pairs=entity_token_spans,
add_special_tokens=add_special_tokens,
padding_strategy=padding_strategy,
truncation_strategy=truncation_strategy,
max_length=max_length,
max_entity_length=max_entity_length,
stride=stride,
pad_to_multiple_of=pad_to_multiple_of,
return_attention_mask=return_attention_mask,
return_token_type_ids=return_token_type_ids,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_length=return_length,
return_tensors=return_tensors,
verbose=verbose,
)
return BatchEncoding(batch_outputs)
def _check_entity_input_format(self, entities, entity_spans):
if not isinstance(entity_spans, list):
raise ValueError("entity_spans should be given as a list")
elif len(entity_spans) > 0 and not isinstance(entity_spans[0], tuple):
raise ValueError(
"entity_spans should be given as a list of tuples "
"containing the start and end character indices"
)
if entities is not None:
if not isinstance(entities, list):
raise ValueError("If you specify entities, they should be given as a list")
if len(entities) > 0 and not isinstance(entities[0], str):
raise ValueError(
"If you specify entities, they should be given as a list of entity names"
)
if len(entities) != len(entity_spans):
raise ValueError(
"If you specify entities, entities and entity_spans must be the same length"
)
def _create_input_sequence(
self,
text,
text_pair=None,
entities=None,
entities_pair=None,
entity_spans=None,
entity_spans_pair=None,
**kw,
):
def get_input_ids(text):
tokens = self.tokenize(text, **kw)
return self.convert_tokens_to_ids(tokens)
def get_input_ids_and_entity_token_spans(text, entity_spans):
if entity_spans is None:
return get_input_ids(text), None
cur = 0
input_ids = []
entity_token_spans = [None] * len(entity_spans)
split_char_positions = sorted(frozenset(itertools.chain(*entity_spans)))
char_pos2token_pos = {}
for split_char_position in split_char_positions:
orig_split_char_position = split_char_position
if split_char_position > 0 and text[split_char_position - 1] == " ":
split_char_position -= 1
if cur != split_char_position:
input_ids += get_input_ids(text[cur:split_char_position])
cur = split_char_position
char_pos2token_pos[orig_split_char_position] = len(input_ids)
input_ids += get_input_ids(text[cur:])
entity_token_spans = [
(char_pos2token_pos[char_start], char_pos2token_pos[char_end])
for char_start, char_end in entity_spans
]
return input_ids, entity_token_spans
first_ids, second_ids = None, None
first_entity_ids, second_entity_ids = None, None
first_entity_token_spans, second_entity_token_spans = None, None
if self.task is None:
if entity_spans is None:
first_ids = get_input_ids(text)
else:
self._check_entity_input_format(entities, entity_spans)
first_ids, first_entity_token_spans = get_input_ids_and_entity_token_spans(
text, entity_spans
)
if entities is None:
first_entity_ids = [self.entity_mask_token_id] * len(entity_spans)
else:
first_entity_ids = [
self.entity_vocab.get(entity, self.entity_unk_token_id)
for entity in entities
]
if text_pair is not None:
if entity_spans_pair is None:
second_ids = get_input_ids(text_pair)
else:
self._check_entity_input_format(entities_pair, entity_spans_pair)
second_ids, second_entity_token_spans = get_input_ids_and_entity_token_spans(
text_pair, entity_spans_pair
)
if entities_pair is None:
second_entity_ids = [self.entity_mask_token_id] * len(entity_spans_pair)
else:
second_entity_ids = [
self.entity_vocab.get(entity, self.entity_unk_token_id)
for entity in entities_pair
]
elif self.task == "entity_classification":
if not (
isinstance(entity_spans, list)
and len(entity_spans) == 1
and isinstance(entity_spans[0], tuple)
):
raise ValueError(
"Entity spans should be a list containing a single tuple "
"containing the start and end character indices of an entity"
)
first_entity_ids = [self.entity_mask_token_id]
first_ids, first_entity_token_spans = get_input_ids_and_entity_token_spans(
text, entity_spans
)
entity_token_start, entity_token_end = first_entity_token_spans[0]
first_ids = (
first_ids[:entity_token_end]
+ [self.additional_special_tokens_ids[0]]
+ first_ids[entity_token_end:]
)
first_ids = (
first_ids[:entity_token_start]
+ [self.additional_special_tokens_ids[0]]
+ first_ids[entity_token_start:]
)
first_entity_token_spans = [(entity_token_start, entity_token_end + 2)]
elif self.task == "entity_pair_classification":
if not (
isinstance(entity_spans, list)
and len(entity_spans) == 2
and isinstance(entity_spans[0], tuple)
and isinstance(entity_spans[1], tuple)
):
raise ValueError(
"Entity spans should be provided as a list of two tuples, "
"each tuple containing the start and end character indices of an entity"
)
head_span, tail_span = entity_spans
first_entity_ids = [self.entity_mask_token_id, self.entity_mask2_token_id]
first_ids, first_entity_token_spans = get_input_ids_and_entity_token_spans(
text, entity_spans
)
head_token_span, tail_token_span = first_entity_token_spans
token_span_with_special_token_ids = [
(head_token_span, self.additional_special_tokens_ids[0]),
(tail_token_span, self.additional_special_tokens_ids[1]),
]
if head_token_span[0] < tail_token_span[0]:
first_entity_token_spans[0] = (head_token_span[0], head_token_span[1] + 2)
first_entity_token_spans[1] = (tail_token_span[0] + 2, tail_token_span[1] + 4)
token_span_with_special_token_ids = reversed(token_span_with_special_token_ids)
else:
first_entity_token_spans[0] = (head_token_span[0] + 2, head_token_span[1] + 4)
first_entity_token_spans[1] = (tail_token_span[0], tail_token_span[1] + 2)
for (
entity_token_start,
entity_token_end,
), special_token_id in token_span_with_special_token_ids:
first_ids = (
first_ids[:entity_token_end] + [special_token_id] + first_ids[entity_token_end:]
)
first_ids = (
first_ids[:entity_token_start]
+ [special_token_id]
+ first_ids[entity_token_start:]
)
elif self.task == "entity_span_classification":
if not (
isinstance(entity_spans, list)
and len(entity_spans) > 0
and isinstance(entity_spans[0], tuple)
):
raise ValueError(
"Entity spans should be provided as a list of tuples, "
"each tuple containing the start and end character indices of an entity"
)
first_ids, first_entity_token_spans = get_input_ids_and_entity_token_spans(
text, entity_spans
)
first_entity_ids = [self.entity_mask_token_id] * len(entity_spans)
else:
raise ValueError(f"Task {self.task} not supported")
return (
first_ids,
second_ids,
first_entity_ids,
second_entity_ids,
first_entity_token_spans,
second_entity_token_spans,
)
def _batch_prepare_for_model(
self,
batch_ids_pairs,
batch_entity_ids_pairs,
batch_entity_token_spans_pairs,
add_special_tokens=True,
padding_strategy=PaddingStrategy.DO_NOT_PAD,
truncation_strategy=TruncationStrategy.DO_NOT_TRUNCATE,
max_length=None,
max_entity_length=None,
stride=0,
pad_to_multiple_of=None,
return_tensors=None,
return_token_type_ids=None,
return_attention_mask=None,
return_overflowing_tokens=False,
return_special_tokens_mask=False,
return_length=False,
verbose=True,
):
batch_outputs = {}
for input_ids, entity_ids, entity_token_span_pairs in zip(
batch_ids_pairs, batch_entity_ids_pairs, batch_entity_token_spans_pairs
):
first_ids, second_ids = input_ids
first_entity_ids, second_entity_ids = entity_ids
first_entity_token_spans, second_entity_token_spans = entity_token_span_pairs
outputs = self.prepare_for_model(
first_ids,
second_ids,
entity_ids=first_entity_ids,
pair_entity_ids=second_entity_ids,
entity_token_spans=first_entity_token_spans,
pair_entity_token_spans=second_entity_token_spans,
add_special_tokens=add_special_tokens,
padding=PaddingStrategy.DO_NOT_PAD.value, # we pad in batch afterward
truncation=truncation_strategy.value,
max_length=max_length,
max_entity_length=max_entity_length,
stride=stride,
pad_to_multiple_of=None, # we pad in batch afterward
return_attention_mask=False, # we pad in batch afterward
return_token_type_ids=return_token_type_ids,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_length=return_length,
return_tensors=None, # We convert the whole batch to tensors at the end
prepend_batch_axis=False,
verbose=verbose,
)
for key, value in outputs.items():
if key not in batch_outputs:
batch_outputs[key] = []
batch_outputs[key].append(value)
batch_outputs = self.pad(
batch_outputs,
padding=padding_strategy.value,
max_length=max_length,
pad_to_multiple_of=pad_to_multiple_of,
return_attention_mask=return_attention_mask,
)
batch_outputs = BatchEncoding(batch_outputs, tensor_type=return_tensors)
return batch_outputs
def prepare_for_model(
self,
ids,
pair_ids=None,
entity_ids=None,
pair_entity_ids=None,
entity_token_spans=None,
pair_entity_token_spans=None,
add_special_tokens=True,
padding=False,
truncation=False,
max_length=None,
max_entity_length=None,
stride=0,
pad_to_multiple_of=None,
return_tensors=None,
return_token_type_ids=None,
return_attention_mask=None,
return_overflowing_tokens=False,
return_special_tokens_mask=False,
return_offsets_mapping=False,
return_length=False,
verbose=True,
prepend_batch_axis=False,
**kw,
):
(
padding_strategy,
truncation_strategy,
max_length,
kw,
) = self._get_padding_truncation_strategies(
padding=padding,
truncation=truncation,
max_length=max_length,
pad_to_multiple_of=pad_to_multiple_of,
verbose=verbose,
**kw,
)
# Compute lengths
pair = bool(pair_ids is not None)
len_ids = len(ids)
len_pair_ids = len(pair_ids) if pair else 0
if return_token_type_ids and not add_special_tokens:
raise ValueError(
"Asking to return token_type_ids while setting add_special_tokens to False "
"results in an undefined behavior. Please set add_special_tokens to True or "
"set return_token_type_ids to None."
)
if (
return_overflowing_tokens
and truncation_strategy == TruncationStrategy.LONGEST_FIRST
and pair_ids is not None
):
raise ValueError(
"Not possible to return overflowing tokens for pair of sequences with the "
"`longest_first`. Please select another truncation strategy than `longest_first`, "
"for instance `only_second` or `only_first`."
)
if return_token_type_ids is None:
return_token_type_ids = "token_type_ids" in self.model_input_names
if return_attention_mask is None:
return_attention_mask = "attention_mask" in self.model_input_names
encoded_inputs = {}
total_len = (
len_ids
+ len_pair_ids
+ (self.num_special_tokens_to_add(pair=pair) if add_special_tokens else 0)
)
# Truncation: Handle max sequence length and max_entity_length
overflowing_tokens = []
if (
truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE
and max_length
and total_len > max_length
):
ids, pair_ids, overflowing_tokens = self.truncate_sequences(
ids,
pair_ids=pair_ids,
num_tokens_to_remove=total_len - max_length,
truncation_strategy=truncation_strategy,
stride=stride,
)
if return_overflowing_tokens:
encoded_inputs["overflowing_tokens"] = overflowing_tokens
encoded_inputs["num_truncated_tokens"] = total_len - max_length
if add_special_tokens:
sequence = self.build_inputs_with_special_tokens(ids, pair_ids)
token_type_ids = self.create_token_type_ids_from_sequences(ids, pair_ids)
entity_token_offset = 1 # 1 * <s> token
pair_entity_token_offset = len(ids) + 3 # 1 * <s> token & 2 * <sep> tokens
else:
sequence = ids + pair_ids if pair else ids
token_type_ids = [0] * len(ids) + ([0] * len(pair_ids) if pair else [])
entity_token_offset = 0
pair_entity_token_offset = len(ids)
encoded_inputs["input_ids"] = sequence
if return_token_type_ids:
encoded_inputs["token_type_ids"] = token_type_ids
if return_special_tokens_mask:
if add_special_tokens:
encoded_inputs["special_tokens_mask"] = self.get_special_tokens_mask(ids, pair_ids)
else:
encoded_inputs["special_tokens_mask"] = [0] * len(sequence)
if not max_entity_length:
max_entity_length = self.max_entity_length
if entity_ids is not None:
total_entity_len = 0
num_invalid_entities = 0
valid_entity_ids = [
ent_id
for ent_id, span in zip(entity_ids, entity_token_spans)
if span[1] <= len(ids)
]
valid_entity_token_spans = [span for span in entity_token_spans if span[1] <= len(ids)]
total_entity_len += len(valid_entity_ids)
num_invalid_entities += len(entity_ids) - len(valid_entity_ids)
valid_pair_entity_ids, valid_pair_entity_token_spans = None, None
if pair_entity_ids is not None:
valid_pair_entity_ids = [
ent_id
for ent_id, span in zip(pair_entity_ids, pair_entity_token_spans)
if span[1] <= len(pair_ids)
]
valid_pair_entity_token_spans = [
span for span in pair_entity_token_spans if span[1] <= len(pair_ids)
]
total_entity_len += len(valid_pair_entity_ids)
num_invalid_entities += len(pair_entity_ids) - len(valid_pair_entity_ids)
if num_invalid_entities != 0:
logger.warning(
f"{num_invalid_entities} entities are ignored because their entity spans are invalid due to the truncation of input tokens"
)
if (
truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE
and total_entity_len > max_entity_length
):
(
valid_entity_ids,
valid_pair_entity_ids,
overflowing_entities,
) = self.truncate_sequences(
valid_entity_ids,
pair_ids=valid_pair_entity_ids,
num_tokens_to_remove=total_entity_len - max_entity_length,
truncation_strategy=truncation_strategy,
stride=stride,
)
valid_entity_token_spans = valid_entity_token_spans[: len(valid_entity_ids)]
if valid_pair_entity_token_spans is not None:
valid_pair_entity_token_spans = valid_pair_entity_token_spans[
: len(valid_pair_entity_ids)
]
if return_overflowing_tokens:
encoded_inputs["overflowing_entities"] = overflowing_entities
encoded_inputs["num_truncated_entities"] = total_entity_len - max_entity_length
final_entity_ids = (
valid_entity_ids + valid_pair_entity_ids
if valid_pair_entity_ids
else valid_entity_ids
)
encoded_inputs["entity_ids"] = list(final_entity_ids)
entity_position_ids = []
entity_start_positions = []
entity_end_positions = []
for (token_spans, offset) in (
(valid_entity_token_spans, entity_token_offset),
(valid_pair_entity_token_spans, pair_entity_token_offset),
):
if token_spans is not None:
for start, end in token_spans:
start += offset
end += offset
position_ids = list(range(start, end))[: self.max_mention_length]
position_ids += [-1] * (self.max_mention_length - end + start)
entity_position_ids.append(position_ids)
entity_start_positions.append(start)
entity_end_positions.append(end - 1)
encoded_inputs["entity_position_ids"] = entity_position_ids
if self.task == "entity_span_classification":
encoded_inputs["entity_start_positions"] = entity_start_positions
encoded_inputs["entity_end_positions"] = entity_end_positions
if return_token_type_ids:
encoded_inputs["entity_token_type_ids"] = [0] * len(encoded_inputs["entity_ids"])
self._eventual_warn_about_too_long_sequence(
encoded_inputs["input_ids"], max_length, verbose
)
if padding_strategy != PaddingStrategy.DO_NOT_PAD or return_attention_mask:
encoded_inputs = self.pad(
encoded_inputs,
max_length=max_length,
max_entity_length=max_entity_length,
padding=padding_strategy.value,
pad_to_multiple_of=pad_to_multiple_of,
return_attention_mask=return_attention_mask,
)
if return_length:
encoded_inputs["length"] = len(encoded_inputs["input_ids"])
batch_outputs = BatchEncoding(
encoded_inputs, tensor_type=return_tensors, prepend_batch_axis=prepend_batch_axis
)
return batch_outputs
def pad(
self,
encoded_inputs,
padding=True,
max_length=None,
max_entity_length=None,
pad_to_multiple_of=None,
return_attention_mask=None,
return_tensors=None,
verbose=True,
):
if isinstance(encoded_inputs, (list, tuple)) and isinstance(
encoded_inputs[0], (dict, BatchEncoding)
):
encoded_inputs = {
key: [example[key] for example in encoded_inputs]
for key in encoded_inputs[0].keys()
}
if self.model_input_names[0] not in encoded_inputs:
raise ValueError(
"You should supply an encoding or a list of encodings to this method "
f"that includes {self.model_input_names[0]}, but you provided {list(encoded_inputs.keys())}"
)
required_input = encoded_inputs[self.model_input_names[0]]
if not required_input:
if return_attention_mask:
encoded_inputs["attention_mask"] = []
return encoded_inputs
first_element = required_input[0]
if isinstance(first_element, (list, tuple)):
index = 0
while len(required_input[index]) == 0:
index += 1
if index < len(required_input):
first_element = required_input[index][0]
if not isinstance(first_element, (int, list, tuple)):
if is_tf_available() and _is_tensorflow(first_element):
return_tensors = "tf" if return_tensors is None else return_tensors
elif is_torch_available() and _is_torch(first_element):
return_tensors = "pt" if return_tensors is None else return_tensors
elif isinstance(first_element, np.ndarray):
return_tensors = "np" if return_tensors is None else return_tensors
else:
raise ValueError(
f"type of {first_element} unknown: {type(first_element)}. "
f"Should be one of a python, numpy, pytorch or tensorflow object."
)
for key, value in encoded_inputs.items():
encoded_inputs[key] = to_py_obj(value)
padding_strategy, _, max_length, _ = self._get_padding_truncation_strategies(
padding=padding, max_length=max_length, verbose=verbose
)
if max_entity_length is None:
max_entity_length = self.max_entity_length
required_input = encoded_inputs[self.model_input_names[0]]
if required_input and not isinstance(required_input[0], (list, tuple)):
encoded_inputs = self._pad(
encoded_inputs,
max_length=max_length,
max_entity_length=max_entity_length,
padding_strategy=padding_strategy,
pad_to_multiple_of=pad_to_multiple_of,
return_attention_mask=return_attention_mask,
)
return BatchEncoding(encoded_inputs, tensor_type=return_tensors)
batch_size = len(required_input)
if any(len(v) != batch_size for v in encoded_inputs.values()):
raise ValueError(
"Some items in the output dictionary have a different batch size than others."
)
if padding_strategy == PaddingStrategy.LONGEST:
max_length = max(len(inputs) for inputs in required_input)
max_entity_length = (
max(len(inputs) for inputs in encoded_inputs["entity_ids"])
if "entity_ids" in encoded_inputs
else 0
)
padding_strategy = PaddingStrategy.MAX_LENGTH
batch_outputs = {}
for i in range(batch_size):
inputs = dict((k, v[i]) for k, v in encoded_inputs.items())
outputs = self._pad(
inputs,
max_length=max_length,
max_entity_length=max_entity_length,
padding_strategy=padding_strategy,
pad_to_multiple_of=pad_to_multiple_of,
return_attention_mask=return_attention_mask,
)
for key, value in outputs.items():
if key not in batch_outputs:
batch_outputs[key] = []
batch_outputs[key].append(value)
return BatchEncoding(batch_outputs, tensor_type=return_tensors)
def _pad(
self,
encoded_inputs,
max_length=None,
max_entity_length=None,
padding_strategy=PaddingStrategy.DO_NOT_PAD,
pad_to_multiple_of=None,
return_attention_mask=None,
):
entities_provided = bool("entity_ids" in encoded_inputs)
if return_attention_mask is None:
return_attention_mask = "attention_mask" in self.model_input_names
if padding_strategy == PaddingStrategy.LONGEST:
max_length = len(encoded_inputs["input_ids"])
if entities_provided:
max_entity_length = len(encoded_inputs["entity_ids"])
if (
max_length is not None
and pad_to_multiple_of is not None
and (max_length % pad_to_multiple_of != 0)
):
max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
if (
entities_provided
and max_entity_length is not None
and pad_to_multiple_of is not None
and (max_entity_length % pad_to_multiple_of != 0)
):
max_entity_length = ((max_entity_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and (
len(encoded_inputs["input_ids"]) != max_length
or (entities_provided and len(encoded_inputs["entity_ids"]) != max_entity_length)
)
if return_attention_mask and "attention_mask" not in encoded_inputs:
encoded_inputs["attention_mask"] = [1] * len(encoded_inputs["input_ids"])
if (
entities_provided
and return_attention_mask
and "entity_attention_mask" not in encoded_inputs
):
encoded_inputs["entity_attention_mask"] = [1] * len(encoded_inputs["entity_ids"])
if needs_to_be_padded:
difference = max_length - len(encoded_inputs["input_ids"])
if entities_provided:
entity_difference = max_entity_length - len(encoded_inputs["entity_ids"])
if self.padding_side == "right":
if return_attention_mask:
encoded_inputs["attention_mask"] = (
encoded_inputs["attention_mask"] + [0] * difference
)
if entities_provided:
encoded_inputs["entity_attention_mask"] = (
encoded_inputs["entity_attention_mask"] + [0] * entity_difference
)
if "token_type_ids" in encoded_inputs:
encoded_inputs["token_type_ids"] = (
encoded_inputs["token_type_ids"] + [0] * difference
)
if entities_provided:
encoded_inputs["entity_token_type_ids"] = (
encoded_inputs["entity_token_type_ids"] + [0] * entity_difference
)
if "special_tokens_mask" in encoded_inputs:
encoded_inputs["special_tokens_mask"] = (
encoded_inputs["special_tokens_mask"] + [1] * difference
)
encoded_inputs["input_ids"] = encoded_inputs["input_ids"] + [self.PAD] * difference
if entities_provided:
encoded_inputs["entity_ids"] = (
encoded_inputs["entity_ids"]
+ [self.entity_pad_token_id] * entity_difference
)
encoded_inputs["entity_position_ids"] = (
encoded_inputs["entity_position_ids"]
+ [[-1] * self.max_mention_length] * entity_difference
)
if self.task == "entity_span_classification":
encoded_inputs["entity_start_positions"] = (
encoded_inputs["entity_start_positions"] + [0] * entity_difference
)
encoded_inputs["entity_end_positions"] = (
encoded_inputs["entity_end_positions"] + [0] * entity_difference
)
elif self.padding_side == "left":
if return_attention_mask:
encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs[
"attention_mask"
]
if entities_provided:
encoded_inputs["entity_attention_mask"] = [
0
] * entity_difference + encoded_inputs["entity_attention_mask"]
if "token_type_ids" in encoded_inputs:
encoded_inputs["token_type_ids"] = [0] * difference + encoded_inputs[
"token_type_ids"
]
if entities_provided:
encoded_inputs["entity_token_type_ids"] = [
0
] * entity_difference + encoded_inputs["entity_token_type_ids"]
if "special_tokens_mask" in encoded_inputs:
encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs[
"special_tokens_mask"
]
encoded_inputs["input_ids"] = [self.PAD] * difference + encoded_inputs["input_ids"]
if entities_provided:
encoded_inputs["entity_ids"] = [
self.entity_pad_token_id
] * entity_difference + encoded_inputs["entity_ids"]
encoded_inputs["entity_position_ids"] = [
[-1] * self.max_mention_length
] * entity_difference + encoded_inputs["entity_position_ids"]
if self.task == "entity_span_classification":
encoded_inputs["entity_start_positions"] = [
0
] * entity_difference + encoded_inputs["entity_start_positions"]
encoded_inputs["entity_end_positions"] = [
0
] * entity_difference + encoded_inputs["entity_end_positions"]
else:
raise ValueError("Invalid padding strategy:" + str(self.padding_side))
return encoded_inputs
def save_vocabulary(self, dir, pre=None):
vocab_file, merge_file = super().save_vocabulary(dir, pre)
entity_vocab_file = os.path.join(
dir,
(pre + "-" if pre else "") + VOCAB_FS["entity_vocab_file"],
)
with open(entity_vocab_file, "w", encoding="utf-8") as f:
f.write(json.dumps(self.entity_vocab, ensure_ascii=False))
return vocab_file, merge_file, entity_vocab_file
|
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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"]}
|
33,503
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/metric/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.
# =============================================================================
import bert_score
import datasets as ds
import functools
from contextlib import contextmanager
from packaging import version
@contextmanager
def filter_logging_context():
def filter_log(record):
return False if "This IS expected if you are initializing" in record.msg else True
logger = ds.utils.logging.get_logger("transformers.modeling_utils")
logger.addFilter(filter_log)
try:
yield
finally:
logger.removeFilter(filter_log)
class BERTScore(ds.Metric):
def _info(self):
return ds.MetricInfo(
description="",
citation="",
inputs_description="",
features=ds.Features(
{
"predictions": ds.Value("string", id="sequence"),
"references": ds.Sequence(ds.Value("string", id="sequence"), id="references"),
}
),
codebase_urls=[],
reference_urls=[],
)
def _compute(
self,
predictions,
references,
lang=None,
model_type=None,
n_lays=None,
verbose=False,
idf=False,
device=None,
batch_size=64,
nthreads=4,
all_layers=False,
rescale_with_baseline=False,
baseline_path=None,
use_fast_tokenizer=False,
):
get_hash = bert_score.utils.get_hash
scorer = bert_score.BERTScorer
if version.parse(bert_score.__version__) >= version.parse("0.3.10"):
get_hash = functools.partial(get_hash, use_fast_tokenizer=use_fast_tokenizer)
scorer = functools.partial(scorer, use_fast_tokenizer=use_fast_tokenizer)
elif use_fast_tokenizer:
raise ImportWarning(
"To use a fast tokenizer, the module `bert-score>=0.3.10` is required, and the current version of `bert-score` doesn't match this condition.\n"
'You can install it with `pip install "bert-score>=0.3.10"`.'
)
if model_type is None:
assert lang is not None, "either lang or model_type should be specified"
model_type = bert_score.utils.lang2model[lang.lower()]
if n_lays is None:
n_lays = bert_score.utils.model2layers[model_type]
hashcode = get_hash(
model=model_type,
n_lays=n_lays,
idf=idf,
rescale_with_baseline=rescale_with_baseline,
use_custom_baseline=baseline_path is not None,
)
with filter_logging_context():
if not hasattr(self, "cached_bertscorer") or self.cached_bertscorer.hash != hashcode:
self.cached_bertscorer = scorer(
model_type=model_type,
n_lays=n_lays,
batch_size=batch_size,
nthreads=nthreads,
all_layers=all_layers,
idf=idf,
device=device,
lang=lang,
rescale_with_baseline=rescale_with_baseline,
baseline_path=baseline_path,
)
(P, R, F) = self.cached_bertscorer.score(
cands=predictions,
refs=references,
verbose=verbose,
batch_size=batch_size,
)
return {
"precision": P.tolist(),
"recall": R.tolist(),
"f1": F.tolist(),
"hashcode": hashcode,
}
def add_batch(self, preds=None, refs=None, **kw):
if refs is not None:
refs = [[r] if isinstance(r, str) else r for r in refs]
super().add_batch(preds, refs, **kw)
def add(self, pred=None, ref=None, **kw):
if isinstance(ref, str):
ref = [ref]
super().add(pred, ref, **kw)
|
{"/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"], 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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"]}
|
33,504
|
quantapix/qnarre
|
refs/heads/main
|
/tools/triton/python/test/unit/language/test_block_pointer.py
|
import pytest
import torch
import triton
import triton.language as tl
@triton.jit
def block_copy_kernel(a_ptr, b_ptr, N, BLOCK_SIZE: tl.constexpr, padding_option: tl.constexpr):
pid = tl.program_id(0)
# We only copy half of the data to see if the padding works
a_block_ptr = tl.make_block_ptr(base=a_ptr, shape=(N // 2, ), strides=(1, ), offsets=(pid * BLOCK_SIZE, ),
block_shape=(BLOCK_SIZE, ), order=(0, ))
b_block_ptr = tl.make_block_ptr(base=b_ptr, shape=(N, ), strides=(1, ), offsets=(pid * BLOCK_SIZE, ),
block_shape=(BLOCK_SIZE, ), order=(0, ))
a = tl.load(a_block_ptr, boundary_check=(0, ), padding_option=padding_option)
tl.store(b_block_ptr, a, boundary_check=(0, ))
@pytest.mark.parametrize("dtype_str, n, padding_option",
[(dtype_str, n, padding) for dtype_str in ("bool", "int16", "float16")
for n in (64, 128, 256, 512, 1024)
for padding in ("zero", "nan")])
def test_block_copy(dtype_str, n, padding_option):
capability = torch.cuda.get_device_capability()
if capability[0] >= 9:
pytest.skip("Hopper support is working in progress")
dtype = getattr(torch, dtype_str)
if dtype_str in ("bool", "int16"):
if padding_option == "nan":
pytest.skip("Padding with NaN is not supported for integer types")
a = torch.randint(0, 2, (n, ), device="cuda", dtype=dtype)
else:
a = torch.randn((n, ), device="cuda", dtype=dtype)
b = torch.zeros((n, ), device="cuda", dtype=dtype)
grid = lambda meta: (triton.cdiv(n, meta["BLOCK_SIZE"]),)
block_copy_kernel[grid](a_ptr=a, b_ptr=b, N=n, BLOCK_SIZE=64, padding_option=padding_option)
assert torch.all(a[0: n // 2] == b[0: n // 2])
if padding_option == "zero":
assert torch.all(b[n // 2: n] == 0)
else:
assert torch.all(torch.isnan(b[n // 2: n]))
@triton.jit
def matmul_no_scf_with_advance_kernel(
a_ptr, b_ptr, c_ptr,
M, N, K,
stride_am, stride_ak,
stride_bk, stride_bn,
stride_cm, stride_cn,
BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr
):
offs_m = tl.arange(0, BLOCK_M)
offs_n = tl.arange(0, BLOCK_N)
a_block_ptr = tl.make_block_ptr(base=a_ptr, shape=(M, K), strides=(stride_am, stride_ak),
offsets=(0, 0), block_shape=(BLOCK_M, BLOCK_K), order=(1, 0))
b_block_ptr = tl.make_block_ptr(base=b_ptr, shape=(K, N), strides=(stride_bk, stride_bn),
offsets=(0, 0), block_shape=(BLOCK_K, BLOCK_N), order=(1, 0))
# Below two lines are just for testing negative offsets for the `advance` API, which could be removed
a_block_ptr = tl.advance(a_block_ptr, (BLOCK_M, -BLOCK_K))
a_block_ptr = tl.advance(a_block_ptr, (-BLOCK_M, BLOCK_K))
a = tl.load(a_block_ptr, boundary_check=(1, ), padding_option="zero")
b = tl.load(b_block_ptr, boundary_check=(0, ), padding_option="zero")
c = tl.dot(a, b)
c_ptrs = c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn
tl.store(c_ptrs, c)
@pytest.mark.parametrize("shape, num_warps", [
(shape, num_warps)
for shape in [
[64, 64, 16],
[64, 64, 32],
[64, 64, 64],
]
for num_warps in [4, 8]
])
def test_block_ptr_matmul_no_scf(shape, num_warps):
capability = torch.cuda.get_device_capability()
if capability[0] >= 9:
pytest.skip("Hopper support is working in progress")
m, n, k = shape
a = torch.randn((m, k), device="cuda", dtype=torch.float16)
b = torch.randn((k, n), device="cuda", dtype=torch.float16)
c = torch.empty((m, n), device="cuda", dtype=torch.float32)
grid = lambda META: (1, )
matmul_no_scf_with_advance_kernel[grid](a_ptr=a, b_ptr=b, c_ptr=c,
M=m, N=n, K=k,
stride_am=a.stride(0), stride_ak=a.stride(1),
stride_bk=b.stride(0), stride_bn=b.stride(1),
stride_cm=c.stride(0), stride_cn=c.stride(1),
BLOCK_M=m, BLOCK_N=n, BLOCK_K=k,
num_warps=num_warps)
golden = torch.matmul(a, b)
torch.testing.assert_allclose(c, golden)
|
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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"]}
|
33,505
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/base/doc/util/table.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 pathlib as pth
import collections.abc as abc
from .log import Logger
from .item import Item
from .tree import Tree
from .utils import scanner
log = Logger(__name__)
calc_dig = Item.calc_digest
class Table(abc.MutableMapping):
def __init__(self, base=None, trees=None, cols=None, **kw):
super().__init__()
self.base = base = pth.Path(base) if base else None
if not isinstance(cols, tuple):
cols = () if cols is None else (cols, )
def _cols():
cs = frozenset(cols)
ns = []
with os.scandir(base) as scan:
for e in scan:
if e.is_dir(follow_symlinks=False):
n = pth.Path(e.path).stem
if n not in cs:
ns.append(n)
ns.sort()
return (*cols, *ns)
self._cols = cols = _cols() if base is not None else cols
assert self._cols
if isinstance(trees, dict):
self._trees = trees
else:
if not isinstance(trees, tuple):
trees = () if trees is None else (trees, )
if base is not None:
def _trees():
ts = frozenset(trees)
ns = []
for c in cols:
p = base / c
if p.exists():
with os.scandir(p) as scan:
for e in scan:
if e.is_dir(follow_symlinks=False):
n = pth.Path(e.path).stem
if n not in ts:
ns.append(n)
ns.sort()
return (*trees, *ns)
trees = _trees()
self._trees = {n: Tree(n, **kw) for n in trees}
assert self._trees
def __bool__(self):
return True
__hash__ = None
def __eq__(self, other):
if isinstance(other, type(self)):
return (self._trees == other._trees and self._cols == other._cols)
return NotImplemented
def __len__(self):
return len(self._trees)
def __iter__(self):
return iter(self._trees)
def __getitem__(self, n):
return self._trees[n]
def __setitem__(self, n, tree):
self._trees[n] = tree
def __delitem__(self, n):
del self._trees[n]
def __repr__(self):
s = type(self).__name__
b = str(self.base) if self.base else None
s += "({}".format(repr(b))
s += ", {}".format(repr(self._trees))
s += ", {})".format(repr(self._cols))
return s
def stringer(self, indent=0, **kw):
for t in self._trees.values():
yield from t.stringer(indent=indent, **kw)
def walker(self, trees=None, cols=None, **_):
if trees is None:
trees = self._trees.values()
else:
trees = trees if isinstance(trees, tuple) else (trees, )
trees = [self._trees[t] for t in trees if t in self._trees]
if not trees:
log.warning('Empty trees list')
if cols is None:
cols = self._cols
else:
cols = cols if isinstance(cols, tuple) else (cols, )
cols = [c for c in cols if c in self._cols]
if not cols:
log.warning('Empty cols list')
for t in trees:
for c in cols:
yield (t, c)
def adjust_kw(self, kw):
kw['base'] = base = kw.get('base') or self.base
assert base.exists() and base.is_dir()
try:
kw['touch'] = [c for c in kw['touch'] if c in self._cols]
except KeyError:
pass
return base
async def import_rows(self, src, cols=None, **kw):
self.adjust_kw(kw)
for t, c in self.walker(**kw, cols=cols or self._cols[0]):
s = src / c if isinstance(cols, tuple) else src
s = t.appender(scanner(s, c), **kw, col=c)
await t.apply(calc_dig, **kw, src=s, col=c)
t.normalize((c, self._cols), **kw)
t.copy_items(c, **kw)
async def load_cols(self, cols=None, src=None, **kw):
base = self.adjust_kw(kw)
src = src or base
for t, c in self.walker(**kw, cols=cols):
s = src / c / t.name
s = t.appender(scanner(s, c), **kw, col=c)
await t.apply(calc_dig, **kw, src=s, col=c)
t.normalize(**kw)
t.copy_items(c, **kw)
def dump_cols(self, dst, cols=None, **kw):
self.adjust_kw(kw)
for t, c in self.walker(**kw, cols=cols):
t.copy_items(c, **kw, out=dst)
def clear_cols(self, cols=None, **kw):
self.adjust_kw(kw)
for t, c in self.walker(**kw, cols=cols):
t.clear_col(c, **kw)
async def check_items(self, **kw):
kw['path'] = self.adjust_kw(kw)
for t, c in self.walker(**kw):
if not await t.apply(calc_dig, check=True, **kw, col=c):
return False
return True
async def extract(self, col=None, **kw):
self.adjust_kw(kw)
col = col if col in self._cols else self._cols[-1]
for t, c in self.walker(**kw, cols=col):
s = t.extractor(**kw, col=c)
s = t.appender(s, **kw, col=c)
await t.apply(calc_dig, **kw, src=s, col=c)
t.normalize(**kw)
t.copy_items(c, **kw)
|
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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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"/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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|
33,506
|
quantapix/qnarre
|
refs/heads/main
|
/tools/triton/python/triton/language/standard.py
|
from __future__ import annotations
from ..runtime.jit import jit
from . import core
# -----------------------
# Standard library
# -----------------------
@jit
def cdiv(x, div):
"""
Computes the ceiling division of :code:`x` by :code:`div`
:param x: the input number
:type input: Block
:param div: the divisor
:param div: Block
"""
return (x + div - 1) // div
@jit
@core._add_math_1arg_docstr("sigmoid")
def sigmoid(x):
return 1 / (1 + core.exp(-x))
@jit
@core._add_math_1arg_docstr("softmax")
def softmax(x, ieee_rounding=False):
z = x - core.max(x, 0)
num = core.exp(z)
den = core.sum(num, 0)
return core.fdiv(num, den, ieee_rounding)
@jit
def ravel(x):
"""
Returns a contiguous flattened view of :code:`x`.
:param x: the input tensor
:type x: Block
"""
return core.view(x, [x.numel])
@jit
def swizzle2d(i, j, size_i, size_j, size_g):
"""
Transforms indices of a row-major size_i*size_j matrix into those
of one where indices are row major for each group of size_j rows.
For example, for size_i = size_j = 4 and size_g = 2, it will transform
[[0 , 1 , 2 , 3 ],
[4 , 5 , 6 , 7 ],
[8 , 9 , 10, 11],
[12, 13, 14, 15]]
into
[[0, 2, 4 , 6 ],
[1, 3, 5 , 7 ],
[8, 10, 12, 14],
[9, 11, 13, 15]]
"""
# "unrolled index in array"
ij = i * size_j + j
# number of elements in `size_g` groups
# of `size_j` columns
size_gj = size_g * size_j
# index of the group in which (i,j) is
group_id = ij // size_gj
# row-index of the first element of this group
off_i = group_id * size_g
# last group may have fewer rows
size_g = core.minimum(size_i - off_i, size_g)
# new row and column indices
new_i = off_i + (ij % size_g)
new_j = (ij % size_gj) // size_g
return new_i, new_j
@jit
def zeros(shape, dtype):
"""
Returns a tensor filled with the scalar value 0 for the given :code:`shape` and :code:`dtype`.
:param shape: Shape of the new array, e.g., (8, 16) or (8, )
:type shape: tuple of ints
:param dtype: Data-type of the new array, e.g., :code:`tl.float16`
:type dtype: DType
"""
return core.full(shape, 0, dtype)
@jit
def zeros_like(input):
return zeros(input.shape, input.dtype)
|
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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"]}
|
33,507
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/base/doc/filters.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
import pprint as pp
from .log import Logger
from .base import config
from .resource import Resource
from .date import Date # needed for repr reading
log = Logger(__name__)
class AdrFilter:
def __init__(self, incl=(), doms=(), locs=(), fulls=()):
super().__init__()
def init(ds, ss):
return {k: True if k in ss else False for k in set((*ds, *ss))}
self.incl = init(config.include_adrs, incl)
self.doms = init(config.exclude_doms, doms)
self.locs = init(config.exclude_locs, locs)
self.fulls = init(config.exclude_fulls, fulls)
def __repr__(self):
s = '{}('.format(type(self).__name__)
def keys(es):
return pp.pformat(tuple(sorted(k for k, v in es if v)), indent=4)
s += '{}, '.format(keys(self.incl.items()))
s += '{}, '.format(keys(self.doms.items()))
s += '{}, '.format(keys(self.locs.items()))
s += '{})'.format(keys(self.fulls.items()))
return s
def probe(self, adr):
if adr in self.incl:
self.incl[adr] = True
return True
if adr in self.fulls:
self.fulls[adr] = True
return False
ps = adr.split('@')
if len(ps) == 2:
l, ad = ps
ds = ad.split('.')
d2 = ds[-2] + '.' + ds[-1]
for d in (d2, ad):
if d in self.incl:
self.incl[d] = True
return True
if l in self.incl:
self.incl[l] = True
return True
for d in ('.' + ds[-1], d2, ad):
if d in self.doms:
self.doms[d] = True
return False
if l in self.locs:
self.locs[l] = True
return False
else:
log.info('Invalid address {}', adr)
class RAdrFilter:
def __init__(self, spec, **_):
super().__init__()
self.spec = spec
self._cspec = re.compile(spec, re.ASCII)
def __repr__(self):
return '{}({!r})'.format(type(self).__name__, self.spec)
def probe(self, adr):
if self._cspec.match(adr):
return False
class Filters(Resource):
_res_path = config.qnar_dst + 'filts/filters.qnr'
_flog = None
_adrs = None
@classmethod
def globals(cls):
return globals()
def __init__(self, specs=(), simple=None, **kw):
super().__init__(**kw)
self.extend(specs or config.exclude_specs)
self.simple = simple or AdrFilter()
def __repr__(self):
s = '{}('.format(type(self).__name__)
s += '{!r}, '.format(tuple(sorted(self.keys())))
s += '{})'.format(pp.pformat(self.simple, indent=4))
return s
@property
def flog(self):
if self._flog is None:
self._flog = Flog.create(self.base, self.realm)
self._flog.clear()
return self._flog
@property
def adrs(self):
if self._adrs is None:
self._adrs = FAdrs.create(self.base, self.realm)
self._adrs.clear()
return self._adrs
def extend(self, specs):
for s in specs:
s = s.lower()
self[s] = RAdrFilter(s)
def probe(self, adr):
r = self.simple.probe(adr)
if r is None:
for f in self.values():
r = f.probe(adr)
if r is not None:
return r
else:
return r
self.adrs.incr(adr)
def save(self, pref=None):
super().save(pref)
if self._flog:
self._flog.save(pref)
if self._adrs:
self._adrs.save(pref)
class Flog(Resource):
_res_path = config.qnar_dst + 'filts/flog.qnr'
@classmethod
def globals(cls):
return globals()
def __repr__(self):
es = pp.pformat(self._elems, indent=4)
return '{}({})'.format(type(self).__name__, es)
def append(self, cur, fields):
ls = self.setdefault(cur, [])
ls.append(fields)
class FAdrs(Resource):
_res_path = config.qnar_dst + 'filts/fadrs.qnr'
@classmethod
def globals(cls):
return globals()
def __repr__(self):
es = pp.pformat(self._elems, indent=4)
return '{}({})'.format(type(self).__name__, es)
def incr(self, adr):
self[adr] = self.setdefault(adr, 0) + 1
def splits(self):
ls = {}
ds = {}
for a, c in self.items():
l, d = a.split('@')
fs = d.split('.')
d = fs[-2] + '.' + fs[-1]
ls[l] = ls.setdefault(l, 0) + c
ds[d] = ds.setdefault(d, 0) + c
return ds, ls
if __name__ == '__main__':
from .args import MArgs
from .resource import resource
a = MArgs()
a = a.parse_args()
with resource(FAdrs.create(a.base, a.files[0])) as fa:
ds, ls = fa.splits()
for d, n in sorted(ds.items(), key=lambda x: x[0], reverse=False):
print("'" + d + "',")
for l, n in sorted(ls.items(), key=lambda x: x[0], reverse=False):
print("'" + l + "',")
|
{"/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"]}
|
33,508
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/convert/gpt_neo.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 re
import tensorflow as tf
import torch
from argparse import ArgumentParser
from os.path import abspath
from transformers.utils import logging
from ..config.gpt_neo import PreTrained
from ...models.gpt_neo import ForCausal
logging.set_verbosity_info()
log = logging.get_logger(__name__)
def load_src_weights(model, config, gpt_neo_checkpoint_path):
tf_path = abspath(gpt_neo_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:
if "global_step" not in name and "adam" not in name:
array = tf.train.load_variable(tf_path, name)
array = tf.dtypes.cast(array.squeeze(), tf.float32).numpy()
name = name.replace("attn/q", "attn/attention/q_proj/w")
name = name.replace("attn/k", "attn/attention/k_proj/w")
name = name.replace("attn/v", "attn/attention/v_proj/w")
name = name.replace("attn/o", "attn/attention/out_proj/w")
name = name.replace("norm_1", "ln_1")
name = name.replace("norm_2", "ln_2")
name = name.replace("attn/compute_output_bias/o_b", "attn/attention/out_proj/b")
name = name.replace("conv1d_main/c_fc/kernel", "c_fc/w")
name = name.replace("conv1d_main/c_fc/bias", "c_fc/b")
name = name.replace("conv1d_main/c_proj/kernel", "c_proj/w")
name = name.replace("conv1d_main/c_proj/bias", "c_proj/b")
names.append(name)
arrays.append(array)
for name, array in zip(names, arrays):
name = name[5:] # skip "gpt2/"
name = name.split("/")
pointer = model.transformer
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] == "w" or scope_names[0] == "g":
pointer = getattr(pointer, "weight")
elif scope_names[0] == "b":
pointer = getattr(pointer, "bias")
elif scope_names[0] == "wpe" or scope_names[0] == "wte":
pointer = getattr(pointer, scope_names[0])
pointer = getattr(pointer, "weight")
else:
pointer = getattr(pointer, scope_names[0])
if len(scope_names) >= 2:
num = int(scope_names[1])
pointer = pointer[num]
if name[-1] == "w" and name[-2] in [
"out_proj",
"k_proj",
"q_proj",
"v_proj",
"c_proj",
"c_fc",
]:
array = array.transpose()
if name == ["wte"]:
array = array[: config.s_vocab]
if pointer.shape != array.shape:
raise ValueError(
f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched {name}"
)
print(f"Initialize PyTorch weight {name}")
pointer.data = torch.from_numpy(array)
embs = model.transformer.wte.weight
lin = torch.nn.Linear(embs.size()[1], embs.size()[0], bias=False)
lin.weight = embs
model.set_output_embeddings(lin)
return model
def to_pytorch(tf_checkpoint_path, config_file, pytorch_dump_path):
config_json = json.load(open(config_file, "r"))
cfg = GPTNeoConfig(
d_hidden=config_json["n_embd"],
n_lays=config_json["n_lays"],
n_heads=config_json["n_heads"],
attention_types=config_json["attention_types"],
n_pos=config_json["n_pos"],
drop_resid=config_json["res_dropout"],
drop_embed=config_json["drop_embed"],
drop_attn=config_json["attn_dropout"],
)
print(f"Building from config: {cfg}")
m = ForCausal(cfg)
load_src_weights(m, cfg, tf_checkpoint_path)
print(f"Saving to: {pytorch_dump_path}")
m.save_pretrained(pytorch_dump_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": ["/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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"/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"]}
|
33,509
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/try/attention.py
|
import flash_attn_cuda
import math
import pytest
import torch
import torch.nn as nn
import torch.nn.functional as F
import triton
import triton.language as tl
from einops import rearrange, repeat
@triton.jit
def _fwd_kernel(
Q,
K,
V,
sm_scale,
# TMP,
L,
M,
Y,
Z,
H,
N_CTX,
BLOCK_M: tl.constexpr,
BLOCK_N: tl.constexpr,
BLOCK_K: tl.constexpr,
):
start = tl.program_id(0)
off = tl.program_id(1)
offs_d = tl.arange(0, BLOCK_K)
offs_m = start * BLOCK_M + tl.arange(0, BLOCK_M)
offs_n = tl.arange(0, BLOCK_N)
_, s_qh, s_qm, s_qk = Q.stride()
_, _, s_kn, s_kk = K.stride()
_, _, s_vk, _ = V.stride()
_, s_yh, s_ym, s_yn = Y.stride()
q = tl.load(Q + off * s_qh + offs_m[:, None] * s_qm + offs_d[None, :] * s_qk)
ks = K + off * s_qh + offs_n[None, :] * s_kn + offs_d[:, None] * s_kk
vs = V + off * s_qh + offs_n[:, None] * s_qm + offs_d[None, :] * s_qk
l = tl.zeros([BLOCK_M], dtype=tl.float32)
m = tl.zeros([BLOCK_M], dtype=tl.float32) - float("inf")
y = tl.zeros([BLOCK_M, BLOCK_K], dtype=tl.float32)
# ts = TMP + off * N_CTX + offs_m
for i in range(0, (start + 1) * BLOCK_M, BLOCK_N):
# i = tl.multiple_of(i, BLOCK_N)
k = tl.load(ks + i * s_kn)
qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)
qk += tl.dot(q, k) # , tl.trans(k)) , trans_b=True)
qk *= sm_scale
qk = tl.where(offs_m[:, None] >= (i + offs_n[None, :]), qk, float("-inf"))
m2 = tl.maximum(tl.max(qk, 1), m)
l *= tl.exp(m - m2)
p = tl.exp(qk - m2[:, None])
l2 = tl.sum(p, 1) + l
l3 = 1.0 / l2
p *= l3[:, None]
y *= (l * l3)[:, None]
v = tl.load(vs + i * s_vk)
p = p.to(Q.dtype.element_ty)
y += tl.dot(p, v)
l = l2
m = m2
m2 = tl.max(qk, 1)
p = tl.exp(qk - m2[:, None])
m3 = tl.maximum(m, m2)
alpha = tl.exp(m - m3)
beta = tl.exp(m2 - m3)
l2 = alpha * l + beta * tl.sum(p, 1)
p_scale = beta / l2
p = p * p_scale[:, None]
y_scale = l / l2 * alpha
tl.store(ts, y_scale)
y_scale = tl.load(ts) # BUG: have to store and immediately load
y = y * y_scale[:, None]
v = tl.load(vs + i * s_vk)
p = p.to(v.dtype)
y += tl.dot(p, v)
l = l2
m = m3
tl.store(L + off * N_CTX + offs_m, l)
tl.store(M + off * N_CTX + offs_m, m)
tl.store(Y + off * s_yh + offs_m[:, None] * s_ym + offs_d[None, :] * s_yn, y)
@triton.jit
def _bwd_prep(
Y,
DY,
L,
NewDY,
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)
y = tl.load(Y + off_m[:, None] * D_HEAD + off_n[None, :]).to(tl.float32)
dy = tl.load(DY + off_m[:, None] * D_HEAD + off_n[None, :]).to(tl.float32)
denom = tl.load(L + off_m).to(tl.float32)
dy = dy / denom[:, None]
delta = tl.sum(y * dy, axis=1)
tl.store(NewDY + off_m[:, None] * D_HEAD + off_n[None, :], dy)
tl.store(Delta + off_m, delta)
@triton.jit
def _bwd_kernel(
Q,
K,
V,
sm_scale,
Y,
DY,
DQ,
DK,
DV,
L,
M,
D,
Z,
H,
N_CTX,
num_block,
BLOCK_M: tl.constexpr,
BLOCK_N: tl.constexpr,
BLOCK_K: tl.constexpr,
):
o_zh = tl.program_id(0)
o_z = o_zh // H
o_h = o_zh % H
s_qz, s_qh, s_qm, s_qk = Q.stride()
_, _, s_kn, s_kk = K.stride()
off = o_z * s_qz + o_h * s_qh
offs_k = tl.arange(0, BLOCK_K)
for i in range(0, num_block):
i *= BLOCK_M
offs_m = i + tl.arange(0, BLOCK_M)
offs_n = i + tl.arange(0, BLOCK_M)
qs = Q + off + (offs_m[:, None] * s_qm + offs_k[None, :] * s_qk)
ks = K + off + (offs_n[:, None] * s_kn + offs_k[None, :] * s_kk)
vs = V + off + (offs_n[:, None] * s_qm + offs_k[None, :] * s_qk)
dqs = DQ + off + (offs_m[:, None] * s_qm + offs_k[None, :] * s_qk)
dys = DY + off + (offs_m[:, None] * s_qm + offs_k[None, :] * s_qk)
ds = D + o_zh * N_CTX
ms = M + o_zh * N_CTX
dv = tl.zeros([BLOCK_M, BLOCK_K], dtype=tl.float32)
dk = tl.zeros([BLOCK_M, BLOCK_K], dtype=tl.float32)
k = tl.load(ks)
v = tl.load(vs)
for j in range(i, num_block * BLOCK_M, BLOCK_M):
j += tl.arange(0, BLOCK_N)
q = tl.load(qs)
qk = tl.dot(q, tl.trans(k)) # , trans_b=True)
qk = tl.where(j[:, None] >= (offs_n[None, :]), qk, float("-inf"))
m = tl.load(ms + j)
p = tl.exp(qk * sm_scale - m[:, None])
dy = tl.load(dys)
dv += tl.dot(
tl.trans(p.to(Q.dtype.element_ty)), dy
) # p.to(dy.dtype), dy, trans_a=True)
dp = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32) - tl.load(ds + j)[:, None]
dp += tl.dot(dy, tl.trans(v)) # , trans_b=True)
ds = p * dp * sm_scale
dk += tl.dot(tl.trans(ds.to(Q.dtype.element_ty)), q) # ds.to(q.dtype), q, trans_a=True)
dq = tl.load(dqs) # , eviction_policy="evict_last")
dq += tl.dot(ds.to(Q.dtype.element_ty), k) # ds.to(k.dtype), k)
tl.store(dqs, dq) # , eviction_policy="evict_last")
qs += BLOCK_M * s_qm
dqs += BLOCK_M * s_qm
dys += BLOCK_M * s_qm
tl.store(DK + off + (offs_n[:, None] * s_kn + offs_k[None, :] * s_kk), dk)
tl.store(DV + off + (offs_n[:, None] * s_qm + offs_k[None, :] * s_qk), dv)
empty = torch.empty(128, device="cuda")
class _attention(torch.autograd.Function):
@staticmethod
def forward(ctx, q, k, v, sm_scale):
assert torch.cuda.get_device_capability()[0] > 7
BLOCK = 128
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}
y = 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
tmp = torch.empty(
(q.shape[0] * q.shape[1], q.shape[2]), device=q.device, dtype=torch.float32
)
_fwd_kernel[grid](
q,
k,
v,
sm_scale,
# tmp,
L,
m,
y,
q.shape[0],
q.shape[1],
q.shape[2],
BLOCK_M=BLOCK,
BLOCK_N=BLOCK,
BLOCK_K=Lk,
num_warps=num_warps,
num_stages=2, # =1,
)
ctx.save_for_backward(q, k, v, y, L, m)
ctx.grid = grid
ctx.sm_scale = sm_scale
ctx.BLOCK_M = BLOCK
ctx.BLOCK_N = BLOCK
ctx.BLOCK_K = Lk
return y
@staticmethod
def backward(ctx, dy):
q, k, v, y, l, m = ctx.saved_tensors
dq = torch.zeros_like(q, dtype=torch.float32)
dk = torch.empty_like(k)
dv = torch.empty_like(v)
dy = dy.contiguous()
dy_scaled = torch.empty_like(dy)
delta = torch.empty_like(l)
_bwd_prep[(ctx.grid[0] * ctx.grid[1],)](
y,
dy,
l,
dy_scaled,
delta,
BLOCK_M=ctx.BLOCK_M,
D_HEAD=ctx.BLOCK_K,
)
_bwd_kernel[(ctx.grid[1],)](
q,
k,
v,
ctx.sm_scale,
y,
dy_scaled,
dq,
dk,
dv,
l,
m,
delta,
q.shape[0],
q.shape[1],
q.shape[2],
ctx.grid[0],
BLOCK_M=ctx.BLOCK_M,
BLOCK_N=ctx.BLOCK_N,
BLOCK_K=ctx.BLOCK_K,
num_warps=8,
num_stages=1,
)
return dq, dk, dv, None # dq.to(q.dtype),
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
dy = torch.randn_like(q)
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)
y_ref = torch.matmul(p, v)
y_ref.backward(dy)
dv_ref, v.grad = v.grad.clone(), None
dk_ref, k.grad = k.grad.clone(), None
dq_ref, q.grad = q.grad.clone(), None
y_triton = attention(q, k, v, sm_scale)
y_triton.backward(dy)
dv_triton, v.grad = v.grad.clone(), None
dk_triton, k.grad = k.grad.clone(), None
dq_triton, q.grad = q.grad.clone(), None
assert torch.allclose(y_ref, y_triton, atol=1e-2, rtol=0)
assert torch.allclose(dv_ref, dv_triton, atol=1e-2, rtol=0)
assert torch.allclose(dk_ref, dk_triton, atol=1e-2, rtol=0)
assert torch.allclose(dq_ref, dq_triton, atol=1e-2, rtol=0)
BATCH, N_HEADS, N_CTX, D_HEAD = 4, 48, 4096, 64
@triton.testing.perf_report(
[
triton.testing.Benchmark(
x_names=["N_CTX"],
x_vals=[2**i for i in range(10, 14)],
line_arg="provider",
line_vals=["triton", "flash"],
line_names=["Triton", "Flash"],
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"]
]
)
def benchmark(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
f = lambda: attention(q, k, v, sm_scale)
if mode == "bwd":
y = f()
dy = torch.randn_like(y)
f = lambda: y.backward(dy, retain_graph=True)
else:
assert 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
)
f = lambda: flash_attn_func(qkv, cu_seqlens, 0.0, N_CTX, causal=True)
if mode == "bwd":
y = f()
dy = torch.randn_like(y)
f = lambda: y.backward(dy, retain_graph=True)
ms = triton.testing.do_bench(f, warmup=warmup, rep=rep)
return ms
# only works on post-Ampere GPUs right now
benchmark.run(save_path=".", print_data=True)
class IndexFirstAxis(torch.autograd.Function):
@staticmethod
def forward(ctx, x, indices):
ctx.save_for_backward(indices)
assert x.ndim >= 2
ctx.first_axis_dim, s = x.shape[0], x.shape[1:]
return torch.gather(
rearrange(x, "b ... -> b (...)"), 0, repeat(indices, "z -> z d", d=s.numel())
).reshape(-1, *s)
@staticmethod
def backward(ctx, x):
(indices,) = ctx.saved_tensors
assert x.ndim >= 2
s = x.shape[1:]
x = rearrange(x, "b ... -> b (...)")
y = torch.zeros(
[ctx.first_axis_dim, x.shape[1]],
device=x.device,
dtype=x.dtype,
)
y.scatter_(0, repeat(indices, "z -> z d", d=x.shape[1]), x)
return y.reshape(ctx.first_axis_dim, *s), None
index_first_axis = IndexFirstAxis.apply
class IndexPutFirstAxis(torch.autograd.Function):
@staticmethod
def forward(ctx, x, indices, first_axis_dim):
ctx.save_for_backward(indices)
assert indices.ndim == 1
assert x.ndim >= 2
y = torch.zeros(first_axis_dim, *x.shape[1:], device=x.device, dtype=x.dtype)
y[indices] = x
# y.scatter_(0, repeat(indices, 'z -> z d', d=x.shape[1]), x)
return y
@staticmethod
def backward(ctx, x):
(indices,) = ctx.saved_tensors
y = x[indices]
# y = torch.gather(x, 0, repeat(indices, 'z -> z d', d=x.shape[1]))
return y, None, None
index_put_first_axis = IndexPutFirstAxis.apply
class IndexFirstAxisResidual(torch.autograd.Function):
@staticmethod
def forward(ctx, x, indices):
ctx.save_for_backward(indices)
assert x.ndim >= 2
ctx.first_axis_dim, s = x.shape[0], x.shape[1:]
second_dim = s.numel()
y = x[indices]
return y, x.detach()
@staticmethod
def backward(ctx, x, grad_residual):
(indices,) = ctx.saved_tensors
assert x.ndim >= 2
s = x.shape[1:]
assert grad_residual.shape[1:] == s
y = grad_residual
# y[indices] += x
indices = indices.reshape(indices.shape[0], *((1,) * (x.ndim - 1)))
indices = indices.expand_as(x)
y.scatter_add_(0, indices, x)
return y.reshape(ctx.first_axis_dim, *s), None
index_first_axis_residual = IndexFirstAxisResidual.apply
def unpad_input(x, mask):
seqlens_in_batch = mask.sum(dim=-1, dtype=torch.int32)
indices = torch.nonzero(mask.flatten(), as_tuple=False).flatten()
max_seqlen_in_batch = seqlens_in_batch.max().item()
cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
return (
index_first_axis(rearrange(x, "b s ... -> (b s) ..."), indices),
indices,
cu_seqlens,
max_seqlen_in_batch,
)
def pad_input(x, indices, batch, seqlen):
dim = x.shape[-1]
# y = torch.zeros((batch * seqlen), dim, device=x.device, dtype=x.dtype)
# y[indices] = x
y = index_put_first_axis(x, indices, batch * seqlen)
return rearrange(y, "(b s) ... -> b s ...", b=batch)
def _get_block_size(device, head_dim, is_dropout):
assert head_dim % 8 == 0 and head_dim <= 128
return 256 if head_dim <= 64 else 128
def _flash_attn_forward(
q,
k,
v,
out,
cu_seqlens_q,
cu_seqlens_k,
max_seqlen_q,
max_seqlen_k,
dropout_p,
softmax_scale,
causal,
return_softmax,
num_splits=0,
generator=None,
):
softmax_lse, rng_state, *rest = flash_attn_cuda.fwd(
q,
k,
v,
out,
cu_seqlens_q,
cu_seqlens_k,
max_seqlen_q,
max_seqlen_k,
dropout_p,
softmax_scale,
False,
causal,
return_softmax,
num_splits,
generator,
)
# if out.isnan().any() or softmax_lse.isnan().any():
# breakpoint()
S_dmask = rest[0] if return_softmax else None
return out, softmax_lse, rng_state, S_dmask
def _flash_attn_backward(
dout,
q,
k,
v,
out,
softmax_lse,
dq,
dk,
dv,
cu_seqlens_q,
cu_seqlens_k,
max_seqlen_q,
max_seqlen_k,
dropout_p,
softmax_scale,
causal,
rng_state=None,
num_splits=0,
generator=None,
):
dout = dout.contiguous()
_, _, _, softmax_d = flash_attn_cuda.bwd(
dout,
q,
k,
v,
out,
softmax_lse,
dq,
dk,
dv,
cu_seqlens_q,
cu_seqlens_k,
max_seqlen_q,
max_seqlen_k,
dropout_p,
softmax_scale,
False,
causal,
num_splits,
generator,
rng_state,
)
# if dk.isnan().any() or dk.isnan().any() or dv.isnan().any() or softmax_d.isnan().any():
# breakpoint()
return dq, dk, dv, softmax_d
class FlashAttnQKVPackedFunc(torch.autograd.Function):
@staticmethod
def forward(
ctx,
qkv,
cu_seqlens,
max_seqlen,
dropout_p,
softmax_scale,
causal,
return_softmax,
deterministic,
):
if softmax_scale is None:
softmax_scale = qkv.shape[-1] ** (-0.5)
out, softmax_lse, rng_state, S_dmask = _flash_attn_forward(
qkv[:, 0],
qkv[:, 1],
qkv[:, 2],
torch.empty_like(qkv[:, 0]),
cu_seqlens,
cu_seqlens,
max_seqlen,
max_seqlen,
dropout_p,
softmax_scale,
causal=causal,
return_softmax=return_softmax,
)
ctx.save_for_backward(qkv, out, softmax_lse, cu_seqlens, rng_state)
ctx.dropout_p = dropout_p
ctx.max_seqlen = max_seqlen
ctx.softmax_scale = softmax_scale
ctx.causal = causal
ctx.deterministic = deterministic
return out if not return_softmax else (out, softmax_lse, S_dmask)
@staticmethod
def backward(ctx, dout, *args):
qkv, out, softmax_lse, cu_seqlens, rng_state = ctx.saved_tensors
dqkv = torch.empty_like(qkv)
_flash_attn_backward(
dout,
qkv[:, 0],
qkv[:, 1],
qkv[:, 2],
out,
softmax_lse,
dqkv[:, 0],
dqkv[:, 1],
dqkv[:, 2],
cu_seqlens,
cu_seqlens,
ctx.max_seqlen,
ctx.max_seqlen,
ctx.dropout_p,
ctx.softmax_scale,
ctx.causal,
rng_state=rng_state,
num_splits=1 if ctx.deterministic else 0,
)
return dqkv, None, None, None, None, None, None, None
class FlashAttnKVPackedFunc(torch.autograd.Function):
@staticmethod
def forward(
ctx,
q,
kv,
cu_seqlens_q,
cu_seqlens_k,
max_seqlen_q,
max_seqlen_k,
dropout_p,
softmax_scale,
causal,
return_softmax,
deterministic,
):
if softmax_scale is None:
softmax_scale = q.shape[-1] ** (-0.5)
out, softmax_lse, rng_state, S_dmask = _flash_attn_forward(
q,
kv[:, 0],
kv[:, 1],
torch.empty_like(q),
cu_seqlens_q,
cu_seqlens_k,
max_seqlen_q,
max_seqlen_k,
dropout_p,
softmax_scale,
causal=causal,
return_softmax=return_softmax,
)
ctx.save_for_backward(q, kv, out, softmax_lse, cu_seqlens_q, cu_seqlens_k, rng_state)
ctx.dropout_p = dropout_p
ctx.max_seqlen_q = max_seqlen_q
ctx.max_seqlen_k = max_seqlen_k
ctx.softmax_scale = softmax_scale
ctx.causal = causal
ctx.deterministic = deterministic
return out if not return_softmax else (out, softmax_lse, S_dmask)
@staticmethod
def backward(ctx, dout, *args):
q, kv, out, softmax_lse, cu_seqlens_q, cu_seqlens_k, rng_state = ctx.saved_tensors
dq = torch.empty_like(q)
dkv = torch.empty_like(kv)
_flash_attn_backward(
dout,
q,
kv[:, 0],
kv[:, 1],
out,
softmax_lse,
dq,
dkv[:, 0],
dkv[:, 1],
cu_seqlens_q,
cu_seqlens_k,
ctx.max_seqlen_q,
ctx.max_seqlen_k,
ctx.dropout_p,
ctx.softmax_scale,
ctx.causal,
rng_state=rng_state,
num_splits=1 if ctx.deterministic else 0,
)
return dq, dkv, None, None, None, None, None, None, None, None, None
class FlashAttnFunc(torch.autograd.Function):
@staticmethod
def forward(
ctx,
q,
k,
v,
cu_seqlens_q,
cu_seqlens_k,
max_seqlen_q,
max_seqlen_k,
dropout_p,
softmax_scale,
causal,
return_softmax,
deterministic,
):
if softmax_scale is None:
softmax_scale = q.shape[-1] ** (-0.5)
out, softmax_lse, rng_state, S_dmask = _flash_attn_forward(
q,
k,
v,
torch.empty_like(q),
cu_seqlens_q,
cu_seqlens_k,
max_seqlen_q,
max_seqlen_k,
dropout_p,
softmax_scale,
causal=causal,
return_softmax=return_softmax,
)
ctx.save_for_backward(q, k, v, out, softmax_lse, cu_seqlens_q, cu_seqlens_k, rng_state)
ctx.dropout_p = dropout_p
ctx.max_seqlen_q = max_seqlen_q
ctx.max_seqlen_k = max_seqlen_k
ctx.softmax_scale = softmax_scale
ctx.causal = causal
ctx.deterministic = deterministic
return out if not return_softmax else (out, softmax_lse, S_dmask)
@staticmethod
def backward(ctx, dout, *args):
q, k, v, out, softmax_lse, cu_seqlens_q, cu_seqlens_k, rng_state = ctx.saved_tensors
dq, dk, dv = torch.empty_like(q), torch.empty_like(k), torch.empty_like(v)
_flash_attn_backward(
dout,
q,
k,
v,
out,
softmax_lse,
dq,
dk,
dv,
cu_seqlens_q,
cu_seqlens_k,
ctx.max_seqlen_q,
ctx.max_seqlen_k,
ctx.dropout_p,
ctx.softmax_scale,
ctx.causal,
rng_state=rng_state,
num_splits=1 if ctx.deterministic else 0,
)
return dq, dk, dv, None, None, None, None, None, None, None, None, None
class FlashAttnQKVPackedSplitFunc(torch.autograd.Function):
@staticmethod
def forward(
ctx,
qkv,
cu_seqlens,
max_seqlen0,
max_seqlen1,
batch_size0,
dropout_p,
softmax_scale,
causal,
return_softmax,
deterministic,
):
# Save rng_state because the backward pass will regenerate the dropout mask
if dropout_p > 0:
rng_state0 = torch.cuda.get_rng_state()
generator1 = torch.Generator(device="cuda")
rng_state1 = generator1.get_state()
else:
rng_state0, generator1, rng_state1 = None, None, None
if softmax_scale is None:
softmax_scale = qkv.shape[-1] ** (-0.5)
out = torch.empty_like(qkv[:, 0])
_, softmax_lse0, S_dmask0 = _flash_attn_forward(
qkv[:, 0],
qkv[:, 1],
qkv[:, 2],
out,
cu_seqlens[: batch_size0 + 1],
cu_seqlens[: batch_size0 + 1],
max_seqlen0,
max_seqlen0,
dropout_p,
softmax_scale,
causal=causal,
return_softmax=return_softmax,
)
s = torch.cuda.Stream()
with torch.cuda.stream(s):
_, softmax_lse1, S_dmask1 = _flash_attn_forward(
qkv[:, 0],
qkv[:, 1],
qkv[:, 2],
out,
cu_seqlens[batch_size0:],
cu_seqlens[batch_size0:],
max_seqlen1,
max_seqlen1,
dropout_p,
softmax_scale,
causal=causal,
return_softmax=return_softmax,
generator=generator1,
)
torch.cuda.current_stream().wait_stream(s)
ctx.save_for_backward(
qkv, out, softmax_lse0, softmax_lse1, cu_seqlens, rng_state0, rng_state1
)
ctx.dropout_p = dropout_p
ctx.max_seqlen0 = max_seqlen0
ctx.max_seqlen1 = max_seqlen1
ctx.batch_size0 = batch_size0
ctx.softmax_scale = softmax_scale
ctx.causal = causal
ctx.deterministic = deterministic
if not return_softmax:
return out
else:
max_seqlen_q = max(softmax_lse0.shape[2], softmax_lse1.shape[2])
max_seqlen_k = max(S_dmask0.shape[3], S_dmask1.shape[3])
softmax_lse = torch.cat(
[
F.pad(softmax_lse0, (0, max_seqlen_q - softmax_lse0.shape[2])),
F.pad(softmax_lse1, (0, max_seqlen_q - softmax_lse1.shape[2])),
],
dim=0,
)
return out, softmax_lse, S_dmask0, S_dmask1
@staticmethod
def backward(ctx, dout, *args):
qkv, out, softmax_lse0, softmax_lse1, cu_seqlens, rng_state0, rng_state1 = ctx.saved_tensors
batch_size0 = ctx.batch_size0
if rng_state0 is not None:
cur_rng_state = torch.cuda.get_rng_state()
torch.cuda.set_rng_state(rng_state0)
if rng_state1 is not None:
generator1 = torch.Generator(device="cuda")
generator1.set_state(rng_state1)
else:
generator1 = None
dqkv = torch.empty_like(qkv)
_flash_attn_backward(
dout,
qkv[:, 0],
qkv[:, 1],
qkv[:, 2],
out,
softmax_lse0,
dqkv[:, 0],
dqkv[:, 1],
dqkv[:, 2],
cu_seqlens[: batch_size0 + 1],
cu_seqlens[: batch_size0 + 1],
ctx.max_seqlen0,
ctx.max_seqlen0,
ctx.dropout_p,
ctx.softmax_scale,
ctx.causal,
num_splits=1 if ctx.deterministic else 0,
)
s = torch.cuda.Stream()
with torch.cuda.stream(s):
_flash_attn_backward(
dout,
qkv[:, 0],
qkv[:, 1],
qkv[:, 2],
out,
softmax_lse1,
dqkv[:, 0],
dqkv[:, 1],
dqkv[:, 2],
cu_seqlens[batch_size0:],
cu_seqlens[batch_size0:],
ctx.max_seqlen1,
ctx.max_seqlen1,
ctx.dropout_p,
ctx.softmax_scale,
ctx.causal,
generator=generator1,
num_splits=1 if ctx.deterministic else 0,
)
torch.cuda.current_stream().wait_stream(s)
if rng_state0 is not None:
torch.cuda.set_rng_state(cur_rng_state)
return dqkv, None, None, None, None, None, None, None, None, None
def flash_attn_unpadded_qkvpacked_func(
qkv,
cu_seqlens,
max_seqlen,
dropout_p,
softmax_scale=None,
causal=False,
return_attn_probs=False,
deterministic=False,
):
return FlashAttnQKVPackedFunc.apply(
qkv,
cu_seqlens,
max_seqlen,
dropout_p,
softmax_scale,
causal,
return_attn_probs,
deterministic,
)
def flash_attn_unpadded_kvpacked_func(
q,
kv,
cu_seqlens_q,
cu_seqlens_k,
max_seqlen_q,
max_seqlen_k,
dropout_p,
softmax_scale=None,
causal=False,
return_attn_probs=False,
deterministic=False,
):
return FlashAttnKVPackedFunc.apply(
q,
kv,
cu_seqlens_q,
cu_seqlens_k,
max_seqlen_q,
max_seqlen_k,
dropout_p,
softmax_scale,
causal,
return_attn_probs,
deterministic,
)
def flash_attn_unpadded_func(
q,
k,
v,
cu_seqlens_q,
cu_seqlens_k,
max_seqlen_q,
max_seqlen_k,
dropout_p,
softmax_scale=None,
causal=False,
return_attn_probs=False,
deterministic=False,
):
return FlashAttnFunc.apply(
q,
k,
v,
cu_seqlens_q,
cu_seqlens_k,
max_seqlen_q,
max_seqlen_k,
dropout_p,
softmax_scale,
causal,
return_attn_probs,
deterministic,
)
def flash_attn_unpadded_qkvpacked_split_func(
qkv,
cu_seqlens,
max_seqlen0,
max_seqlen1,
batch_size0,
dropout_p,
softmax_scale=None,
causal=False,
return_attn_probs=False,
deterministic=False,
):
return FlashAttnQKVPackedSplitFunc.apply(
qkv,
cu_seqlens,
max_seqlen0,
max_seqlen1,
batch_size0,
dropout_p,
softmax_scale,
causal,
return_attn_probs,
deterministic,
)
def flash_attn_func(
qkv, cu_seqlens, dropout_p, max_s, softmax_scale=None, causal=False, return_attn_probs=False
):
return flash_attn_unpadded_qkvpacked_func(
qkv, cu_seqlens, max_s, dropout_p, softmax_scale, causal, return_attn_probs
)
class FlashAttention(nn.Module):
def __init__(self, softmax_scale=None, attention_dropout=0.0):
super().__init__()
self.softmax_scale = softmax_scale
self.dropout_p = attention_dropout
def forward(
self,
qkv,
key_padding_mask=None,
causal=False,
cu_seqlens=None,
max_s=None,
need_weights=False,
):
assert not need_weights
assert qkv.dtype in [torch.float16, torch.bfloat16]
assert qkv.is_cuda
if cu_seqlens is None:
batch_size = qkv.shape[0]
seqlen = qkv.shape[1]
if key_padding_mask is None:
qkv = rearrange(qkv, "b s ... -> (b s) ...")
max_s = seqlen
cu_seqlens = torch.arange(
0, (batch_size + 1) * seqlen, step=seqlen, dtype=torch.int32, device=qkv.device
)
output = flash_attn_unpadded_qkvpacked_func(
qkv,
cu_seqlens,
max_s,
self.dropout_p if self.training else 0.0,
softmax_scale=self.softmax_scale,
causal=causal,
)
output = rearrange(output, "(b s) ... -> b s ...", b=batch_size)
else:
nheads = qkv.shape[-2]
x = rearrange(qkv, "b s three h d -> b s (three h d)")
x_unpad, indices, cu_seqlens, max_s = unpad_input(x, key_padding_mask)
x_unpad = rearrange(x_unpad, "nnz (three h d) -> nnz three h d", three=3, h=nheads)
output_unpad = flash_attn_unpadded_qkvpacked_func(
x_unpad,
cu_seqlens,
max_s,
self.dropout_p if self.training else 0.0,
softmax_scale=self.softmax_scale,
causal=causal,
)
output = rearrange(
pad_input(
rearrange(output_unpad, "nnz h d -> nnz (h d)"), indices, batch_size, seqlen
),
"b s (h d) -> b s h d",
h=nheads,
)
else:
assert max_s is not None
output = flash_attn_unpadded_qkvpacked_func(
qkv,
cu_seqlens,
max_s,
self.dropout_p if self.training else 0.0,
softmax_scale=self.softmax_scale,
causal=causal,
)
return output, None
class FlashMHA(nn.Module):
def __init__(
self,
embed_dim,
num_heads,
bias=True,
batch_first=True,
attention_dropout=0.0,
causal=False,
device=None,
dtype=None,
) -> None:
assert batch_first
factory_kwargs = {"device": device, "dtype": dtype}
super().__init__()
self.embed_dim = embed_dim
self.causal = causal
self.num_heads = num_heads
assert self.embed_dim % num_heads == 0, "self.kdim must be divisible by num_heads"
self.head_dim = self.embed_dim // num_heads
assert (
self.head_dim % 8 == 0 and self.head_dim <= 128
), "Only support head_dim <= 128 and divisible by 8"
self.Wqkv = nn.Linear(embed_dim, 3 * embed_dim, bias=bias, **factory_kwargs)
self.inner_attn = FlashAttention(attention_dropout=attention_dropout)
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias, **factory_kwargs)
def forward(self, x, key_padding_mask=None, need_weights=False):
qkv = self.Wqkv(x)
qkv = rearrange(qkv, "b s (three h d) -> b s three h d", three=3, h=self.num_heads)
context, attn_weights = self.inner_attn(
qkv, key_padding_mask=key_padding_mask, need_weights=need_weights, causal=self.causal
)
return self.out_proj(rearrange(context, "b s h d -> b s (h d)")), attn_weights
|
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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"]}
|
33,510
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/models/ibert_quant_modules.py
|
import decimal
import numpy as np
import torch
from torch import nn
from torch.autograd import Function
from ...utils import logging
logger = logging.get_logger(__name__)
class QuantEmbedding(qc.Module):
def __init__(
self,
num_embeddings,
embedding_dim,
padding_idx=None,
max_norm=None,
norm_type=2.0,
scale_grad_by_freq=False,
sparse=False,
_weight=None,
weight_bit=8,
momentum=0.95,
quant_mode=False,
):
super().__init__()
self.num_ = num_embeddings
self.dim = embedding_dim
self.padding_idx = padding_idx
self.max_norm = max_norm
self.norm_type = norm_type
self.scale_grad_by_freq = scale_grad_by_freq
self.sparse = sparse
self.weight = nn.Parameter(torch.zeros([num_embeddings, embedding_dim]))
self.register_buffer("weight_scaling_factor", torch.zeros(1))
self.register_buffer("weight_integer", torch.zeros_like(self.weight))
self.weight_bit = weight_bit
self.momentum = momentum
self.quant_mode = quant_mode
self.percentile_mode = False
self.weight_function = SymmetricQuantFunction.apply
def forward(self, x, positions=None, incremental_state=None):
if not self.quant_mode:
return (
F.embedding(
x,
self.weight,
self.padding_idx,
self.max_norm,
self.norm_type,
self.scale_grad_by_freq,
self.sparse,
),
None,
)
w = self.weight
w_transform = w.data.detach()
w_min = w_transform.min().expand(1)
w_max = w_transform.max().expand(1)
self.weight_scaling_factor = symmetric_linear_quantization_params(
self.weight_bit, w_min, w_max, False
)
self.weight_integer = self.weight_function(
self.weight, self.weight_bit, self.percentile_mode, self.weight_scaling_factor
)
emb_int = F.embedding(
x,
self.weight_integer,
self.padding_idx,
self.max_norm,
self.norm_type,
self.scale_grad_by_freq,
self.sparse,
)
return emb_int * self.weight_scaling_factor, self.weight_scaling_factor
class QuantAct(qc.Module):
def __init__(
self,
activation_bit,
act_range_momentum=0.95,
per_channel=False,
channel_len=None,
quant_mode=False,
):
super().__init__()
self.activation_bit = activation_bit
self.act_range_momentum = act_range_momentum
self.quant_mode = quant_mode
self.per_channel = per_channel
self.percentile = False
self.act_function = SymmetricQuantFunction.apply
if not self.per_channel:
self.register_buffer("x_min", torch.zeros(1))
self.register_buffer("x_max", torch.zeros(1))
self.register_buffer("act_scaling_factor", torch.zeros(1))
self.x_min -= 1e-5
self.x_max += 1e-5
else:
raise NotImplementedError("per-channel mode is not currently supported for activation.")
def __repr__(self):
return (
f"{self.__class__.__name__}(activation_bit={self.activation_bit}, "
f"quant_mode: {self.activation_bit}, Act_min: {self.x_min.item():.2f}, "
f"Act_max: {self.x_max.item():.2f})"
)
def forward(
self,
x,
pre_act_scaling_factor=None,
identity=None,
identity_scaling_factor=None,
specified_min=None,
specified_max=None,
):
x_act = x if identity is None else identity + x
# collect running stats if training
if self.training:
assert not self.percentile, "percentile mode is not currently supported for activation."
assert (
not self.per_channel
), "per-channel mode is not currently supported for activation."
x_min = x_act.data.min()
x_max = x_act.data.max()
assert (
x_max.isnan().sum() == 0 and x_min.isnan().sum() == 0
), "NaN detected when computing min/max of the activation"
# Initialization
if self.x_min.min() > -1.1e-5 and self.x_max.max() < 1.1e-5:
self.x_min = self.x_min + x_min
self.x_max = self.x_max + x_max
# exponential moving average (EMA)
# use momentum to prevent the quantized values change greatly every iteration
elif self.act_range_momentum == -1:
self.x_min = torch.min(self.x_min, x_min)
self.x_max = torch.max(self.x_max, x_max)
else:
self.x_min = self.x_min * self.act_range_momentum + x_min * (
1 - self.act_range_momentum
)
self.x_max = self.x_max * self.act_range_momentum + x_max * (
1 - self.act_range_momentum
)
if not self.quant_mode:
return x_act, None
x_min = self.x_min if specified_min is None else specified_min
x_max = self.x_max if specified_max is None else specified_max
self.act_scaling_factor = symmetric_linear_quantization_params(
self.activation_bit, x_min, x_max, per_channel=self.per_channel
)
if pre_act_scaling_factor is None:
# this is for the input quantization
quant_act_int = self.act_function(
x, self.activation_bit, self.percentile, self.act_scaling_factor
)
else:
quant_act_int = FixedPointMul.apply(
x,
pre_act_scaling_factor,
self.activation_bit,
self.act_scaling_factor,
identity,
identity_scaling_factor,
)
correct_output_scale = self.act_scaling_factor.view(-1)
return quant_act_int * correct_output_scale, self.act_scaling_factor
class QuantLinear(qc.Module):
def __init__(
self,
in_features,
out_features,
bias=True,
weight_bit=8,
bias_bit=32,
per_channel=False,
quant_mode=False,
):
super().__init__()
self.in_features = in_features
self.out_features = out_features
self.weight = nn.Parameter(torch.zeros([out_features, in_features]))
self.register_buffer("weight_integer", torch.zeros_like(self.weight))
self.register_buffer("fc_scaling_factor", torch.zeros(self.out_features))
if bias:
self.bias = nn.Parameter(torch.zeros(out_features))
self.register_buffer("bias_integer", torch.zeros_like(self.bias))
self.weight_bit = weight_bit
self.quant_mode = quant_mode
self.per_channel = per_channel
self.bias_bit = bias_bit
self.quant_mode = quant_mode
self.percentile_mode = False
self.weight_function = SymmetricQuantFunction.apply
def __repr__(self):
s = super().__repr__()
s = f"({s} weight_bit={self.weight_bit}, quant_mode={self.quant_mode})"
return s
def forward(self, x, prev_act_scaling_factor=None):
if not self.quant_mode:
return F.linear(x, weight=self.weight, bias=self.bias), None
# assert that prev_act_scaling_factor is a scalar tensor
assert prev_act_scaling_factor is not None and prev_act_scaling_factor.shape == (1,), (
"Input activation to the QuantLinear layer should be globally (non-channel-wise) quantized. "
"Please add a QuantAct layer with `per_channel = True` before this QuantAct layer"
)
w = self.weight
w_transform = w.data.detach()
if self.per_channel:
w_min, _ = torch.min(w_transform, dim=1, out=None)
w_max, _ = torch.max(w_transform, dim=1, out=None)
else:
w_min = w_transform.min().expand(1)
w_max = w_transform.max().expand(1)
self.fc_scaling_factor = symmetric_linear_quantization_params(
self.weight_bit, w_min, w_max, self.per_channel
)
self.weight_integer = self.weight_function(
self.weight, self.weight_bit, self.percentile_mode, self.fc_scaling_factor
)
bias_scaling_factor = self.fc_scaling_factor * prev_act_scaling_factor
if self.bias is not None:
self.bias_integer = self.weight_function(
self.bias, self.bias_bit, False, bias_scaling_factor
)
prev_act_scaling_factor = prev_act_scaling_factor.view(1, -1)
x_int = x / prev_act_scaling_factor
return (
F.linear(x_int, weight=self.weight_integer, bias=self.bias_integer)
* bias_scaling_factor,
bias_scaling_factor,
)
class IntGELU(qc.Module):
def __init__(self, quant_mode=True, force_dequant="none"):
super().__init__()
self.quant_mode = quant_mode
if force_dequant in ["nonlinear", "gelu"]:
logger.info("Force dequantize gelu")
self.quant_mode = False
if not self.quant_mode:
self.activation_fn = nn.GELU()
self.k = 1.4142
self.const = 14 # dummy integer constant
self.coeff = [-0.2888, -1.769, 1] # a(x+b)**2 + c
self.coeff[2] /= self.coeff[0]
def int_erf(self, x_int, scaling_factor):
b_int = torch.floor(self.coeff[1] / scaling_factor)
c_int = torch.floor(self.coeff[2] / scaling_factor**2)
sign = torch.sign(x_int)
abs_int = torch.min(torch.abs(x_int), -b_int)
y_int = sign * ((abs_int + b_int) ** 2 + c_int)
scaling_factor = scaling_factor**2 * self.coeff[0]
# avoid overflow
y_int = floor_ste.apply(y_int / 2**self.const)
scaling_factor = scaling_factor * 2**self.const
return y_int, scaling_factor
def forward(self, x, scaling_factor=None):
if not self.quant_mode:
return self.activation_fn(x), None
x_int = x / scaling_factor
sigmoid_int, sigmoid_scaling_factor = self.int_erf(x_int, scaling_factor / self.k)
shift_int = 1.0 // sigmoid_scaling_factor
x_int = x_int * (sigmoid_int + shift_int)
scaling_factor = scaling_factor * sigmoid_scaling_factor / 2
return x_int * scaling_factor, scaling_factor
class IntSoftmax(qc.Module):
def __init__(self, output_bit, quant_mode=False, force_dequant="none"):
super().__init__()
self.output_bit = output_bit
self.max_bit = 32
self.quant_mode = quant_mode
if force_dequant in ["nonlinear", "softmax"]:
logger.info("Force dequantize softmax")
self.quant_mode = False
self.act = QuantAct(16, quant_mode=self.quant_mode)
self.x0 = -0.6931 # -ln2
self.const = 30 # dummy integer constant
self.coef = [0.35815147, 0.96963238, 1.0] # ax**2 + bx + c
self.coef[1] /= self.coef[0]
self.coef[2] /= self.coef[0]
def int_polynomial(self, x_int, scaling_factor):
with torch.no_grad():
b_int = torch.floor(self.coef[1] / scaling_factor)
c_int = torch.floor(self.coef[2] / scaling_factor**2)
z = (x_int + b_int) * x_int + c_int
scaling_factor = self.coef[0] * scaling_factor**2
return z, scaling_factor
def int_exp(self, x_int, scaling_factor):
with torch.no_grad():
x0_int = torch.floor(self.x0 / scaling_factor)
x_int = torch.max(x_int, self.const * x0_int)
q = floor_ste.apply(x_int / x0_int)
r = x_int - x0_int * q
exp_int, exp_scaling_factor = self.int_polynomial(r, scaling_factor)
exp_int = torch.clamp(floor_ste.apply(exp_int * 2 ** (self.const - q)), min=0)
scaling_factor = exp_scaling_factor / 2**self.const
return exp_int, scaling_factor
def forward(self, x, scaling_factor):
if not self.quant_mode:
return F.softmax(x, dim=-1), None
x_int = x / scaling_factor
x_int_max, _ = x_int.max(dim=-1, keepdim=True)
x_int = x_int - x_int_max
exp_int, exp_scaling_factor = self.int_exp(x_int, scaling_factor)
# Avoid overflow
exp, exp_scaling_factor = self.act(exp_int, exp_scaling_factor)
exp_int = exp / exp_scaling_factor
exp_int_sum = exp_int.sum(dim=-1, keepdim=True)
factor = floor_ste.apply(2**self.max_bit / exp_int_sum)
exp_int = floor_ste.apply(exp_int * factor / 2 ** (self.max_bit - self.output_bit))
scaling_factor = 1 / 2**self.output_bit
return exp_int * scaling_factor, scaling_factor
class IntLayerNorm(qc.Module):
def __init__(self, normalized_shape, eps, output_bit=8, quant_mode=False, force_dequant="none"):
super().__init__()
self.normalized_shape = normalized_shape
self.eps = eps
self.weight = nn.Parameter(torch.zeros(normalized_shape))
self.bias = nn.Parameter(torch.zeros(normalized_shape))
self.quant_mode = quant_mode
if force_dequant in ["nonlinear", "layernorm"]:
logger.info("Force dequantize layernorm")
self.quant_mode = False
self.register_buffer("shift", torch.zeros(1))
self.output_bit = output_bit
self.max_bit = 32
self.dim_sqrt = None
self.activation = QuantAct(self.output_bit, quant_mode=self.quant_mode)
def set_shift(self, y_int):
with torch.no_grad():
y_sq_int = y_int**2
var_int = torch.sum(y_sq_int, axis=2, keepdim=True)
shift = (torch.log2(torch.sqrt(var_int / 2**self.max_bit)).ceil()).max()
shift_old = self.shift
self.shift = torch.max(self.shift, shift)
logger.info(f"Dynamic shift adjustment: {int(shift_old)} to {int(self.shift)}")
def overflow_fallback(self, y_int):
self.set_shift(y_int) # adjusts `self.shift`
y_int_shifted = floor_ste.apply(y_int / 2**self.shift)
y_sq_int = y_int_shifted**2
var_int = torch.sum(y_sq_int, axis=2, keepdim=True)
return var_int
def forward(self, x, scaling_factor=None):
if not self.quant_mode:
mean = x.mean(axis=2, keepdim=True)
y = x - mean
var = torch.mean(y**2, axis=2, keepdim=True)
x = y / torch.sqrt(self.eps + var)
x = x * self.weight + self.bias
return x, None
# compute sqrt of the feature dimension if it is the first run
if self.dim_sqrt is None:
n = torch.tensor(x.shape[2], dtype=torch.float)
self.dim_sqrt = torch.sqrt(n).to(x.device)
# Normalization: computes mean and variance(std)
x_int = x / scaling_factor
mean_int = round_ste.apply(x_int.mean(axis=2, keepdim=True))
y_int = x_int - mean_int
y_int_shifted = floor_ste.apply(y_int / 2**self.shift)
y_sq_int = y_int_shifted**2
var_int = torch.sum(y_sq_int, axis=2, keepdim=True)
# overflow handling in training time
if self.training:
# if overflow is detected
if var_int.max() >= 2**self.max_bit:
var_int = self.overflow_fallback(y_int)
assert var_int.max() < 2**self.max_bit + 0.1, (
"Error detected in overflow handling: "
"`var_int` exceeds `self.max_bit` (the maximum possible bit width)"
)
# To be replaced with integer-sqrt kernel that produces the same output
std_int = floor_ste.apply(torch.sqrt(var_int)) * 2**self.shift
factor = floor_ste.apply(2**31 / std_int)
y_int = floor_ste.apply(y_int * factor / 2)
scaling_factor = self.dim_sqrt / 2**30
# scaling and shifting
bias = self.bias.data.detach() / (self.weight.data.detach())
bias_int = floor_ste.apply(bias / scaling_factor)
y_int = y_int + bias_int
scaling_factor = scaling_factor * self.weight
x = y_int * scaling_factor
return x, scaling_factor
def get_percentile_min_max(input, lower_percentile, upper_percentile, output_tensor=False):
input_length = input.shape[0]
lower_index = round(input_length * (1 - lower_percentile * 0.01))
upper_index = round(input_length * upper_percentile * 0.01)
upper_bound = torch.kthvalue(input, k=upper_index).values
if lower_percentile == 0:
lower_bound = upper_bound * 0
# lower_index += 1
else:
lower_bound = -torch.kthvalue(-input, k=lower_index).values
if not output_tensor:
lower_bound = lower_bound.item()
upper_bound = upper_bound.item()
return lower_bound, upper_bound
def linear_quantize(input, scale, zero_point, inplace=False):
if len(input.shape) == 4:
scale = scale.view(-1, 1, 1, 1)
zero_point = zero_point.view(-1, 1, 1, 1)
# reshape scale and zeropoint for linear weights
elif len(input.shape) == 2:
scale = scale.view(-1, 1)
zero_point = zero_point.view(-1, 1)
else:
scale = scale.view(-1)
zero_point = zero_point.view(-1)
# quantized = float / scale + zero_point
if inplace:
input.mul_(1.0 / scale).add_(zero_point).round_()
return input
return torch.round(1.0 / scale * input + zero_point)
def symmetric_linear_quantization_params(
num_bits, saturation_min, saturation_max, per_channel=False
):
with torch.no_grad():
n = 2 ** (num_bits - 1) - 1
if per_channel:
scale, _ = torch.max(
torch.stack([saturation_min.abs(), saturation_max.abs()], dim=1), dim=1
)
scale = torch.clamp(scale, min=1e-8) / n
else:
scale = max(saturation_min.abs(), saturation_max.abs())
scale = torch.clamp(scale, min=1e-8) / n
return scale
class SymmetricQuantFunction(Function):
@staticmethod
def forward(ctx, x, k, percentile_mode, scale):
zero_point = torch.tensor(0.0).to(scale.device)
n = 2 ** (k - 1) - 1
new_quant_x = linear_quantize(x, scale, zero_point, inplace=False)
new_quant_x = torch.clamp(new_quant_x, -n, n - 1)
ctx.scale = scale
return new_quant_x
@staticmethod
def backward(ctx, grad_output):
scale = ctx.scale
if len(grad_output.shape) == 4:
scale = scale.view(-1, 1, 1, 1)
# reshape scale and zeropoint for linear weights
elif len(grad_output.shape) == 2:
scale = scale.view(-1, 1)
else:
scale = scale.view(-1)
return grad_output.clone() / scale, None, None, None, None
class floor_ste(Function):
@staticmethod
def forward(ctx, x):
return torch.floor(x)
@staticmethod
def backward(ctx, grad_output):
return grad_output.clone()
class round_ste(Function):
@staticmethod
def forward(ctx, x):
return torch.round(x)
@staticmethod
def backward(ctx, grad_output):
return grad_output.clone()
def batch_frexp(inputs, max_bit=31):
shape_of_input = inputs.size()
# trans the input to be a 1-d tensor
inputs = inputs.view(-1)
output_m, output_e = np.frexp(inputs.cpu().numpy())
tmp_m = []
for m in output_m:
int_m_shifted = int(
decimal.Decimal(m * (2**max_bit)).quantize(
decimal.Decimal("1"), rounding=decimal.ROUND_HALF_UP
)
)
tmp_m.append(int_m_shifted)
output_m = np.array(tmp_m)
output_e = float(max_bit) - output_e
return (
torch.from_numpy(output_m).to(inputs.device).view(shape_of_input),
torch.from_numpy(output_e).to(inputs.device).view(shape_of_input),
)
class FixedPointMul(Function):
@staticmethod
def forward(
ctx,
pre_act,
pre_act_scaling_factor,
bit_num,
z_scaling_factor,
identity=None,
identity_scaling_factor=None,
):
if len(pre_act_scaling_factor.shape) == 3:
reshape = lambda x: x # noqa: E731
else:
reshape = lambda x: x.view(1, 1, -1) # noqa: E731
ctx.identity = identity
n = 2 ** (bit_num - 1) - 1
with torch.no_grad():
pre_act_scaling_factor = reshape(pre_act_scaling_factor)
if identity is not None:
identity_scaling_factor = reshape(identity_scaling_factor)
ctx.z_scaling_factor = z_scaling_factor
z_int = torch.round(pre_act / pre_act_scaling_factor)
_A = pre_act_scaling_factor.type(torch.double)
_B = (z_scaling_factor.type(torch.float)).type(torch.double)
new_scale = _A / _B
new_scale = reshape(new_scale)
m, e = batch_frexp(new_scale)
output = z_int.type(torch.double) * m.type(torch.double)
output = torch.round(output / (2.0**e))
if identity is not None:
# needs addition of identity activation
wx_int = torch.round(identity / identity_scaling_factor)
_A = identity_scaling_factor.type(torch.double)
_B = (z_scaling_factor.type(torch.float)).type(torch.double)
new_scale = _A / _B
new_scale = reshape(new_scale)
m1, e1 = batch_frexp(new_scale)
output1 = wx_int.type(torch.double) * m1.type(torch.double)
output1 = torch.round(output1 / (2.0**e1))
output = output1 + output
return torch.clamp(output.type(torch.float), -n - 1, n)
@staticmethod
def backward(ctx, grad_output):
identity_grad = None
if ctx.identity is not None:
identity_grad = grad_output.clone() / ctx.z_scaling_factor
return (
grad_output.clone() / ctx.z_scaling_factor,
None,
None,
None,
None,
identity_grad,
None,
)
|
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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"]}
|
33,511
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/base/doc/section.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 .realm import Agent
from .part import Setting
from .meta import with_property
from .message import StoryPost, BlogPost
from .message import Note, Message, Chain, Letter, Doc
class Section:
incl = ()
excl = ()
hide = ()
_extent = ()
_groups = _subgroups = _parts = None
def __init__(self, extent, **kw):
super().__init__(**kw)
self.extent = extent
@property
def extent(self):
return self._extent
@extent.setter
def extent(self, ps):
if ps:
e = []
for p in ps:
c = type(p)
if issubclass(c, self.incl) and not issubclass(c, self.excl):
if not p.parent:
e.append(p)
self._extent = sorted(e)
else:
self.__dict__.pop('_extent', None)
self.__dict__.pop('_groups', None)
self.__dict__.pop('_subgroups', None)
self.__dict__.pop('_parts', None)
@property
def groups(self):
if self._groups is None:
self.setup()
return self._groups
@property
def subgroups(self):
if self._subgroups is None:
self.setup()
return self._subgroups
@property
def parts(self):
if self._parts is None:
self.setup()
return self._parts
def setup(self):
gs = []
sg_g = {}
ps_s = {}
ps = set()
for p in self.extent:
s = p.subgroup
if s:
g = s.group
if g and g.slug in self.hide:
p.hide = True
continue
if g and g not in ps:
gs.append(g)
ps.add(g)
if s not in ps:
sg_g.setdefault(g, []).append(s)
ps.add(s)
ps_s.setdefault(s, []).append(p)
self._groups = sorted(gs)
for g, ss in sg_g.items():
sg_g[g] = sorted(ss)
self._subgroups = sg_g if self._groups else sg_g.get(None, ())
# for s, ps in ps_s.items():
# ps_s[s] = sorted(ps)
self._parts = ps_s if self._subgroups else ps_s.get(None, ())
class Story(Section):
incl = (StoryPost, )
hide = ('about', 'blurbs')
class Blog(Section):
incl = (BlogPost, )
class Agents(Section):
incl = (Agent, )
class Docs(Section):
incl = (Note, Message, Chain, Letter, Doc)
excl = (StoryPost, BlogPost)
def update(self, settings):
pass
@with_property('settings', Setting.creator)
class Session:
def __init__(self, app, settings=(), **kw):
super().__init__(**kw)
self.parts_all = app.parts_all
self.story = app.story
self.blog = app.blog
self.agents = app.agents
self.docs = Docs(app.parts_flat)
self.settings = settings
def update(self):
self.docs.update(self.settings)
|
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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/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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["/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"], 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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"]}
|
33,512
|
quantapix/qnarre
|
refs/heads/main
|
/notebooks/old/src/trackable.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 tensorflow as tf
from datetime import datetime
from tensorflow.python.training.tracking import base
from tensorflow.python.training.tracking import tracking
def trackable(tr1, v):
c = tf.train.Checkpoint(tr1=tr1)
m = tf.train.CheckpointManager(c, '/tmp/q/trackable', max_to_keep=2)
p = m.latest_checkpoint
c.restore(p).expect_partial()
if p:
print(f'restored from: {p}')
print(f'others are: {m.checkpoints}')
else:
print('start from scratch')
print(f'value before: {v.numpy()}')
v.assign_add(1)
m.save()
def autotrackable(tr2, tracked, untracked):
c = tf.train.Checkpoint(tr2=tr2)
m = tf.train.CheckpointManager(c, '/tmp/q/trackable', max_to_keep=2)
p = m.latest_checkpoint
c.restore(p).expect_partial()
if p:
print(f'restored from: {p}')
print(f'values before: {tracked.numpy()}, {untracked.numpy()}')
tracked.assign_add(1000)
m.save()
print(f'value as saved: {tracked.numpy()}')
def listing():
c = tf.train.Checkpoint()
m = tf.train.CheckpointManager(c, '/tmp/q/trackable', max_to_keep=2)
p = m.latest_checkpoint
vs = tf.train.list_variables(p)
print(f'names and shapes list: {vs}')
n, _ = vs[-1]
v = tf.train.load_variable(p, n)
print(f'loaded value: {v} for name: {n}')
c = tf.train.load_checkpoint(p)
ts = c.get_variable_to_dtype_map()
ss = c.get_variable_to_shape_map()
print(f'checkpoint types: {ts} and shapes: {ss}')
def deleting(tr2):
c = tf.train.Checkpoint(tr2=tr2)
m = tf.train.CheckpointManager(c, '/tmp/q/trackable', max_to_keep=2)
c.restore(m.latest_checkpoint)
c.tr2.deleted = tf.Variable(-1)
m.save()
vs = tf.train.list_variables(m.latest_checkpoint)
print(f'list deleted: {vs}')
del c.tr2.deleted
m.save()
vs = tf.train.list_variables(m.latest_checkpoint)
print(f'deleted IS DELETED: {vs}')
def containers(tr3):
c = tf.train.Checkpoint(tr3=tr3)
m = tf.train.CheckpointManager(c, '/tmp/q/trackable', max_to_keep=2)
m.save()
vs = tf.train.list_variables(m.latest_checkpoint)
print(f'containers: {vs}')
def sharing(tr3):
c = tf.train.Checkpoint(tr3=tr3)
m = tf.train.CheckpointManager(c, '/tmp/q/trackable', max_to_keep=2)
c.restore(m.latest_checkpoint).assert_consumed()
v1 = tr3.br_list[0].v
v2 = tr3.br_list[1].v
vd1 = tr3.br_dict['br1'].v
vd2 = tr3.br_dict['br2'].v
vd3 = tr3.br_dict['br3'].v
print(f'all fives: {v1.numpy()}, {v2.numpy()}, {vd3.numpy()}')
print(f'shared too: {vd1.numpy()}, {vd2.numpy()}')
v1.assign_add(5)
v2.assign_add(5)
vd3.assign_add(5)
m.save()
vs = tf.train.list_variables(m.latest_checkpoint)
print(f'shared not repeated: {vs}')
v1.assign_add(-10)
v2.assign_add(-10)
vd3.assign_add(-10)
print(f'all zeros: {v1.numpy()}, {v2.numpy()}, {vd3.numpy()}')
print(f'shared too: {vd1.numpy()}, {vd2.numpy()}')
c2 = tf.train.Checkpoint(tr3=tr3)
m = tf.train.CheckpointManager(c2, '/tmp/q/trackable', max_to_keep=2)
c2.restore(m.latest_checkpoint).assert_consumed()
print(f'all tens: {v1.numpy()}, {v2.numpy()}, {vd3.numpy()}')
print(f'shared too: {vd1.numpy()}, {vd2.numpy()}')
class Module(tf.Module):
sub = None
def __init__(self, name=None):
super().__init__(name=name)
with self.name_scope:
self.v = tf.Variable(1, name='m_v')
def __str__(self):
s = f'n: {self.name}, v: {self.v.numpy()}'
if self.sub:
s += f', s: ({self.sub})'
return s
@tf.Module.with_name_scope
def __call__(self):
if self.sub is None:
y = tf.constant(100)
else:
y = self.sub()
y = tf.math.add(y, self.v)
self.v.assign(y)
return y
def modules(mod):
vs = [v.name for v in mod.variables]
ms = [m.name for m in mod.submodules]
print(f'mod variables: {vs}, submodules: {ms}')
c = tf.train.Checkpoint(module=mod)
m = tf.train.CheckpointManager(c, '/tmp/q/trackable', max_to_keep=2)
mod()
print(mod)
m.save()
mod()
print(mod)
p = m.latest_checkpoint
vs = tf.train.list_variables(p)
print(f'containers: {vs}')
c.restore(p)
print(f'restored: {mod}')
def graph(tracer):
s = datetime.now().strftime('%Y%m%d-%H%M%S')
d = f'/tmp/q/logs/func/{s}'
w = tf.summary.create_file_writer(d)
tf.summary.trace_on(graph=True) # , profiler=True)
tracer()
with w.as_default():
tf.summary.trace_export(name="trace", step=0, profiler_outdir=d)
class Layer(tf.keras.layers.Layer):
def __init__(self, sub=None, **kw):
super().__init__(**kw)
self.sub = sub
def __str__(self):
s = f'n: {self.name}, v: {self.v.numpy()}'
if self.sub:
s += f', s: ({self.sub})'
return s
def build(self, input_shape):
self.v = self.add_weight(name='l_v',
shape=[],
dtype=tf.int32,
initializer=tf.ones_initializer)
return super().build(input_shape)
def call(self, x):
if self.sub is None:
y = x
else:
y = self.sub(x)
y = tf.math.add(y, self.v)
self.v.assign(tf.reduce_sum(y))
return y
def models(mod, lay):
print(mod.summary())
vs = [v.name for v in mod.variables]
ts = [t.name for t in mod.trainable_variables]
ms = [m.name for m in mod.submodules]
print(f'lay variables: {vs}, trainables: {ts}, submodules: {ms}')
d = tf.constant([100, 100])
mod(d)
print(lay)
c = tf.train.Checkpoint(model=mod)
m = tf.train.CheckpointManager(c, '/tmp/q/trackable', max_to_keep=2)
m.save()
mod(d)
print(lay)
p = m.latest_checkpoint
vs = tf.train.list_variables(p)
print(f'containers: {vs}')
c.restore(p)
print(f'restored: {lay}')
def main(_):
tr1 = base.Trackable()
v = tf.Variable(1)
tr1._track_trackable(v, name='tr1_v')
for _ in range(3):
trackable(tr1, v)
tr2 = tracking.AutoTrackable()
tracked, untracked = tf.Variable(1000), tf.Variable(0)
tr2.v = tracked
with base.no_automatic_dependency_tracking_scope(tr2):
tr2.untracked = untracked
for _ in range(2):
autotrackable(tr2, tracked, untracked)
listing()
deleting(tr2)
tr3 = tracking.AutoTrackable()
br1 = tracking.AutoTrackable()
br1.v = tf.Variable(5)
br2 = tracking.AutoTrackable()
br2.v = tf.Variable(5)
tr3.br_list = [br1, br2]
br3 = tracking.AutoTrackable()
br3.v = tf.Variable(5)
tr3.br_dict = {'br3': br3}
containers(tr3)
tr3.br_dict = {'br1': br1, 'br2': br2, 'br3': br3}
sharing(tr3)
mod1 = Module('m1')
mod1.sub = Module('m2')
mod1.sub.sub = Module('m3')
modules(mod1)
# @tf.function
# def tracer1():
# return mod1()
# graph(tracer1)
ins = [tf.keras.Input(shape=(), dtype=tf.int32)]
lay = Layer(name='l1', sub=Layer(name='l2', sub=Layer(name='l3')))
outs = [lay(ins)]
mod2 = tf.keras.Model(name='m2', inputs=ins, outputs=outs)
models(mod2, lay)
@tf.function
def tracer2():
return mod2(tf.constant([100, 100]))
graph(tracer2)
if __name__ == '__main__':
from absl import app
app.run(main)
|
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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"], 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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"]}
|
33,513
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/config/big_bird.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(
{"drop_proj"},
dict(
act="gelu_new",
attn_type="block_sparse",
block_size=64,
BOS=1,
d_ff=3072,
d_model=768,
drop_attn=0.1,
drop=0.1,
EOS=2,
grad_checkpoint=True,
init_range=0.02,
is_enc_dec=False,
eps=1e-12,
model_type="big_bird",
n_heads=12,
n_lays=12,
n_pos=4096,
n_rand_blocks=3,
n_typ=2,
PAD=0,
pos_type="absolute",
rescale=False,
s_vocab=50358,
SEP=66,
use_bias=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, BigBirdEncoder):
module.grad_checkpoint = value
MAP = {
"google/bigbird-roberta-base": dict(
archs=["ForPreTraining"],
grad_checkpoint=False,
),
"google/bigbird-roberta-large": dict(
archs=["ForMasked"],
d_ff=4096,
d_model=1024,
grad_checkpoint=False,
n_heads=16,
n_lays=24,
),
"google/bigbird-base-trivia-itc": dict(
archs=["ForQA"],
grad_checkpoint=False,
n_typ=16,
),
}
|
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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/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"]}
|
33,514
|
quantapix/qnarre
|
refs/heads/main
|
/notebooks/old/src/masking.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
from datetime import datetime
import tensorflow as tf
import dataset as qd
ks = tf.keras
kl = ks.layers
@tf.function
def adapter(d, len_max_input):
ds = tf.RaggedTensor.from_sparse(d['defs'])
ss = tf.fill([ds.nrows(), 1], qd.SEP)
os = tf.RaggedTensor.from_sparse(d['op'])
x = tf.concat([ds, ss, os], axis=1).to_tensor()
x = tf.pad(x, [[0, 0], [0, len_max_input - tf.shape(x)[-1]]])
y = tf.RaggedTensor.from_sparse(d['res'])[:, :1].to_tensor()
return x, y
def dset_for(ps):
ds = tf.data.TFRecordDataset(list(qd.files(ps)))
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)).map(qd.caster)
return ds.map(lambda d: adapter(d, tf.constant(ps.len_max_input)))
class Layer(kl.Layer):
def __init__(self, **kw):
super().__init__(**kw)
self.supports_masking = True
class Masking(Layer):
def __init__(self):
super().__init__()
# self._compute_output_and_mask_jointly = True
def compute_mask(self, x, mask=None):
return tf.not_equal(x, 0)
def call(self, x):
# x._keras_mask = self.compute_mask(x)
return x
class Embed(Layer):
def __init__(self, ps):
super().__init__(dtype=tf.float32)
s = (ps.dim_vocab, ps.dim_hidden)
self.emb = self.add_weight(name='emb', shape=s)
def call(self, x, mask=None):
y = tf.nn.embedding_lookup(self.emb, x)
if mask is not None:
y *= tf.cast(mask, tf.float32)[:, :, None]
return y
class Reflect(Layer):
def build(self, shape):
s = shape[-1]
self.scale = 1 / (s**0.5)
self.q = self.add_weight(name='q', shape=(s, s))
self.k = self.add_weight(name='k', shape=(s, s))
self.v = self.add_weight(name='v', shape=(s, s))
return super().build(shape)
def call(self, x, mask=None):
q = tf.einsum('bsi,ij->bsj', x, self.q)
k = tf.einsum('bsi,ij->bsj', x, self.k)
y = tf.einsum('bsi,bzi->bsz', q, k) * self.scale
if mask is not None:
# tf.print(' *** applying mask')
m = tf.logical_not(mask)
m = tf.cast(m, tf.float32)[:, :, None]
y += m * -1e9
v = tf.einsum('bsi,ij->bsj', x, self.v)
y = tf.einsum('bsz,bzi->bsi', tf.nn.softmax(y), v)
return y
def model_for(ps):
x = ks.Input(shape=(ps.len_max_input, ), dtype='int32')
y = Masking()(x)
y = Embed(ps)(y)
y = Reflect()(y)
y = kl.Reshape((ps.len_max_input * ps.dim_hidden, ))(y)
y = kl.Dense(ps.dim_dense, activation='relu')(y)
y = kl.Dense(ps.dim_vocab, name='dbd', activation=None)(y)
m = ks.Model(inputs=x, outputs=y)
m.compile(optimizer=ps.optimizer, loss=ps.loss, metrics=[ps.metric])
print(m.summary())
return m
def main_graph(ps, ds, m):
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)
params = dict(
dim_batch=2,
dim_dense=150,
dim_hidden=15,
dim_vocab=len(qd.vocab),
len_max_input=20,
loss=ks.losses.SparseCategoricalCrossentropy(from_logits=True),
metric=ks.metrics.SparseCategoricalCrossentropy(from_logits=True),
num_epochs=10,
num_shards=2,
optimizer=ks.optimizers.Adam(),
)
if __name__ == '__main__':
ps = qd.Params(**params)
main_graph(ps, dset_for(ps), model_for(ps))
|
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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"]}
|
33,515
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/models/funnel.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
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.funnel import PreTrained
log = logging.get_logger(__name__)
from torch.nn import CrossEntropyLoss
LIST = [
"funnel-transformer/small", # B4-4-4H768
"funnel-transformer/small-base", # B4-4-4H768, no decoder
"funnel-transformer/medium", # B6-3x2-3x2H768
"funnel-transformer/medium-base", # B6-3x2-3x2H768, no decoder
"funnel-transformer/intermediate", # B6-6-6H768
"funnel-transformer/intermediate-base", # B6-6-6H768, no decoder
"funnel-transformer/large", # B8-8-8H1024
"funnel-transformer/large-base", # B8-8-8H1024, no decoder
"funnel-transformer/xlarge-base", # B10-10-10H1024
"funnel-transformer/xlarge", # B10-10-10H1024, no decoder
]
INF = 1e6
class FunnelEmbeddings(qc.Module):
def __init__(self, config):
super().__init__()
self.word_embeddings = qc.Embed(config.s_vocab, config.d_model, padding_idx=config.PAD)
self.layer_norm = qc.LayerNorm(config.d_model, eps=config.eps)
self.drop = qc.Dropout(config.drop)
def forward(self, input_ids=None, inputs_embeds=None):
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
embeddings = self.layer_norm(inputs_embeds)
embeddings = self.drop(embeddings)
return embeddings
class FunnelAttentionStructure(qc.Module):
cls_token_type_id = 2
def __init__(self, config):
super().__init__()
self.config = config
self.sin_dropout = qc.Dropout(config.drop)
self.cos_dropout = qc.Dropout(config.drop)
self.pooling_mult = None
def init_attention_inputs(
self,
inputs_embeds,
attention_mask=None,
token_type_ids=None,
):
self.pooling_mult = 1
self.seq_len = seq_len = inputs_embeds.size(1)
position_embeds = self.get_position_embeds(
seq_len, inputs_embeds.dtype, inputs_embeds.device
)
token_type_mat = (
self.token_type_ids_to_mat(token_type_ids) if token_type_ids is not None else None
)
cls_mask = (
F.pad(inputs_embeds.new_ones([seq_len - 1, seq_len - 1]), (1, 0, 1, 0))
if self.config.separate_cls
else None
)
return (position_embeds, token_type_mat, attention_mask, cls_mask)
def token_type_ids_to_mat(self, token_type_ids):
token_type_mat = token_type_ids[:, :, None] == token_type_ids[:, None]
# Treat <cls> as in the same segment as both A & B
cls_ids = token_type_ids == self.cls_token_type_id
cls_mat = cls_ids[:, :, None] | cls_ids[:, None]
return cls_mat | token_type_mat
def get_position_embeds(self, seq_len, dtype, device):
d_model = self.config.d_model
if self.config.attention_type == "factorized":
pos_seq = torch.arange(0, seq_len, 1.0, dtype=dtype, device=device)
freq_seq = torch.arange(0, d_model // 2, 1.0, dtype=dtype, device=device)
inv_freq = 1 / (10000 ** (freq_seq / (d_model // 2)))
sinusoid = pos_seq[:, None] * inv_freq[None]
sin_embed = torch.sin(sinusoid)
sin_embed_d = self.sin_dropout(sin_embed)
cos_embed = torch.cos(sinusoid)
cos_embed_d = self.cos_dropout(cos_embed)
# This is different from the formula on the paper...
phi = torch.cat([sin_embed_d, sin_embed_d], dim=-1)
psi = torch.cat([cos_embed, sin_embed], dim=-1)
pi = torch.cat([cos_embed_d, cos_embed_d], dim=-1)
omega = torch.cat([-sin_embed, cos_embed], dim=-1)
return (phi, pi, psi, omega)
else:
freq_seq = torch.arange(0, d_model // 2, 1.0, dtype=dtype, device=device)
inv_freq = 1 / (10000 ** (freq_seq / (d_model // 2)))
# Maximum relative positions for the first input
rel_pos_id = torch.arange(-seq_len * 2, seq_len * 2, 1.0, dtype=dtype, device=device)
zero_offset = seq_len * 2
sinusoid = rel_pos_id[:, None] * inv_freq[None]
sin_embed = self.sin_dropout(torch.sin(sinusoid))
cos_embed = self.cos_dropout(torch.cos(sinusoid))
pos_embed = torch.cat([sin_embed, cos_embed], dim=-1)
pos = torch.arange(0, seq_len, dtype=dtype, device=device)
pooled_pos = pos
position_embeds_list = []
for block_index in range(0, self.config.num_blocks):
if block_index == 0:
position_embeds_pooling = None
else:
pooled_pos = self.stride_pool_pos(pos, block_index)
# construct rel_pos_id
stride = 2 ** (block_index - 1)
rel_pos = self.relative_pos(pos, stride, pooled_pos, shift=2)
rel_pos = rel_pos[:, None] + zero_offset
rel_pos = rel_pos.expand(rel_pos.size(0), d_model)
position_embeds_pooling = torch.gather(pos_embed, 0, rel_pos)
# Second type
pos = pooled_pos
stride = 2**block_index
rel_pos = self.relative_pos(pos, stride)
rel_pos = rel_pos[:, None] + zero_offset
rel_pos = rel_pos.expand(rel_pos.size(0), d_model)
position_embeds_no_pooling = torch.gather(pos_embed, 0, rel_pos)
position_embeds_list.append([position_embeds_no_pooling, position_embeds_pooling])
return position_embeds_list
def stride_pool_pos(self, pos_id, block_index):
if self.config.separate_cls:
cls_pos = pos_id.new_tensor([-(2**block_index) + 1])
pooled_pos_id = pos_id[1:-1] if self.config.truncate_seq else pos_id[1:]
return torch.cat([cls_pos, pooled_pos_id[::2]], 0)
else:
return pos_id[::2]
def relative_pos(self, pos, stride, pooled_pos=None, shift=1):
if pooled_pos is None:
pooled_pos = pos
ref_point = pooled_pos[0] - pos[0]
num_remove = shift * len(pooled_pos)
max_dist = ref_point + num_remove * stride
min_dist = pooled_pos[0] - pos[-1]
return torch.arange(max_dist, min_dist - 1, -stride, dtype=torch.long, device=pos.device)
def stride_pool(self, tensor, axis):
if tensor is None:
return None
if isinstance(axis, (list, tuple)):
for ax in axis:
tensor = self.stride_pool(tensor, ax)
return tensor
if isinstance(tensor, (tuple, list)):
return type(tensor)(self.stride_pool(x, axis) for x in tensor)
axis %= tensor.ndim
axis_slice = (
slice(None, -1, 2)
if self.config.separate_cls and self.config.truncate_seq
else slice(None, None, 2)
)
enc_slice = [slice(None)] * axis + [axis_slice]
if self.config.separate_cls:
cls_slice = [slice(None)] * axis + [slice(None, 1)]
tensor = torch.cat([tensor[cls_slice], tensor], axis=axis)
return tensor[enc_slice]
def pool_tensor(
self,
tensor,
mode="mean",
stride=2,
):
if tensor is None:
return None
if isinstance(tensor, (tuple, list)):
return type(tensor)(self.pool_tensor(tensor, mode=mode, stride=stride) for x in tensor)
if self.config.separate_cls:
suffix = tensor[:, :-1] if self.config.truncate_seq else tensor
tensor = torch.cat([tensor[:, :1], suffix], dim=1)
ndim = tensor.ndim
if ndim == 2:
tensor = tensor[:, None, :, None]
elif ndim == 3:
tensor = tensor[:, None, :, :]
# Stride is applied on the second-to-last dimension.
stride = (stride, 1)
if mode == "mean":
tensor = F.avg_pool2d(tensor, stride, stride=stride, ceil_mode=True)
elif mode == "max":
tensor = F.max_pool2d(tensor, stride, stride=stride, ceil_mode=True)
elif mode == "min":
tensor = -F.max_pool2d(-tensor, stride, stride=stride, ceil_mode=True)
else:
raise NotImplementedError("The supported modes are 'mean', 'max' and 'min'.")
if ndim == 2:
return tensor[:, 0, :, 0]
elif ndim == 3:
return tensor[:, 0]
return tensor
def pre_attention_pooling(self, output, attention_inputs):
position_embeds, token_type_mat, attention_mask, cls_mask = attention_inputs
if self.config.pool_q_only:
if self.config.attention_type == "factorized":
position_embeds = self.stride_pool(position_embeds[:2], 0) + position_embeds[2:]
token_type_mat = self.stride_pool(token_type_mat, 1)
cls_mask = self.stride_pool(cls_mask, 0)
output = self.pool_tensor(output, mode=self.config.pooling_type)
else:
self.pooling_mult *= 2
if self.config.attention_type == "factorized":
position_embeds = self.stride_pool(position_embeds, 0)
token_type_mat = self.stride_pool(token_type_mat, [1, 2])
cls_mask = self.stride_pool(cls_mask, [1, 2])
attention_mask = self.pool_tensor(attention_mask, mode="min")
output = self.pool_tensor(output, mode=self.config.pooling_type)
attention_inputs = (position_embeds, token_type_mat, attention_mask, cls_mask)
return output, attention_inputs
def post_attention_pooling(self, attention_inputs):
position_embeds, token_type_mat, attention_mask, cls_mask = attention_inputs
if self.config.pool_q_only:
self.pooling_mult *= 2
if self.config.attention_type == "factorized":
position_embeds = position_embeds[:2] + self.stride_pool(position_embeds[2:], 0)
token_type_mat = self.stride_pool(token_type_mat, 2)
cls_mask = self.stride_pool(cls_mask, 1)
attention_mask = self.pool_tensor(attention_mask, mode="min")
attention_inputs = (position_embeds, token_type_mat, attention_mask, cls_mask)
return attention_inputs
def _relative_shift_gather(positional_attn, context_len, shift):
batch_size, n_heads, seq_len, max_rel_len = positional_attn.shape
positional_attn = torch.reshape(positional_attn, [batch_size, n_heads, max_rel_len, seq_len])
positional_attn = positional_attn[:, :, shift:, :]
positional_attn = torch.reshape(
positional_attn, [batch_size, n_heads, seq_len, max_rel_len - shift]
)
positional_attn = positional_attn[..., :context_len]
return positional_attn
class FunnelRelMultiheadAttention(qc.Module):
def __init__(self, config, block_index):
super().__init__()
self.config = config
self.block_index = block_index
d_model, n_heads, d_head = config.d_model, config.n_heads, config.d_head
self.drop = qc.Dropout(config.drop)
self.drop_attn = qc.Dropout(config.drop_attn)
self.q_head = qc.Linear(d_model, n_heads * d_head, bias=False)
self.k_head = qc.Linear(d_model, n_heads * d_head)
self.v_head = qc.Linear(d_model, n_heads * d_head)
self.r_w_bias = nn.Parameter(torch.zeros([n_heads, d_head]))
self.r_r_bias = nn.Parameter(torch.zeros([n_heads, d_head]))
self.r_kernel = nn.Parameter(torch.zeros([d_model, n_heads, d_head]))
self.r_s_bias = nn.Parameter(torch.zeros([n_heads, d_head]))
self.seg_embed = nn.Parameter(torch.zeros([2, n_heads, d_head]))
self.post_proj = qc.Linear(n_heads * d_head, d_model)
self.layer_norm = qc.LayerNorm(d_model, eps=config.eps)
self.scale = 1.0 / (d_head**0.5)
def relative_positional_attention(self, position_embeds, q_head, context_len, cls_mask=None):
if self.config.attention_type == "factorized":
phi, pi, psi, omega = position_embeds
# Shape n_heads x d_head
u = self.r_r_bias * self.scale
# Shape d_model x n_heads x d_head
w_r = self.r_kernel
# Shape batch_size x sea_len x n_heads x d_model
q_r_attention = torch.einsum("binh,dnh->bind", q_head + u, w_r)
q_r_attention_1 = q_r_attention * phi[:, None]
q_r_attention_2 = q_r_attention * pi[:, None]
# Shape batch_size x n_heads x seq_len x context_len
positional_attn = torch.einsum("bind,jd->bnij", q_r_attention_1, psi) + torch.einsum(
"bind,jd->bnij", q_r_attention_2, omega
)
else:
shift = 2 if q_head.shape[1] != context_len else 1
# Notations from the paper, appending A.2.1, final formula (https://arxiv.org/abs/2006.03236)
# Grab the proper positional encoding, shape max_rel_len x d_model
r = position_embeds[self.block_index][shift - 1]
# Shape n_heads x d_head
v = self.r_r_bias * self.scale
# Shape d_model x n_heads x d_head
w_r = self.r_kernel
# Shape max_rel_len x n_heads x d_model
r_head = torch.einsum("td,dnh->tnh", r, w_r)
# Shape batch_size x n_heads x seq_len x max_rel_len
positional_attn = torch.einsum("binh,tnh->bnit", q_head + v, r_head)
# Shape batch_size x n_heads x seq_len x context_len
positional_attn = _relative_shift_gather(positional_attn, context_len, shift)
if cls_mask is not None:
positional_attn *= cls_mask
return positional_attn
def relative_token_type_attention(self, token_type_mat, q_head, cls_mask=None):
"""Relative attention score for the token_type_ids"""
if token_type_mat is None:
return 0
batch_size, seq_len, context_len = token_type_mat.shape
# q_head has shape batch_size x seq_len x n_heads x d_head
# Shape n_heads x d_head
r_s_bias = self.r_s_bias * self.scale
# Shape batch_size x n_heads x seq_len x 2
token_type_bias = torch.einsum("bind,snd->bnis", q_head + r_s_bias, self.seg_embed)
# Shape batch_size x n_heads x seq_len x context_len
token_type_mat = token_type_mat[:, None].expand(
[batch_size, q_head.shape[2], seq_len, context_len]
)
# Shapes batch_size x n_heads x seq_len
diff_token_type, same_token_type = torch.split(token_type_bias, 1, dim=-1)
# Shape batch_size x n_heads x seq_len x context_len
token_type_attn = torch.where(
token_type_mat,
same_token_type.expand(token_type_mat.shape),
diff_token_type.expand(token_type_mat.shape),
)
if cls_mask is not None:
token_type_attn *= cls_mask
return token_type_attn
def forward(
self,
query,
key,
value,
attention_inputs,
output_attentions=False,
):
position_embeds, token_type_mat, attention_mask, cls_mask = attention_inputs
batch_size, seq_len, _ = query.shape
context_len = key.shape[1]
n_heads, d_head = self.config.n_heads, self.config.d_head
# Shape batch_size x seq_len x n_heads x d_head
q_head = self.q_head(query).view(batch_size, seq_len, n_heads, d_head)
# Shapes batch_size x context_len x n_heads x d_head
k_head = self.k_head(key).view(batch_size, context_len, n_heads, d_head)
v_head = self.v_head(value).view(batch_size, context_len, n_heads, d_head)
q_head = q_head * self.scale
# Shape n_heads x d_head
r_w_bias = self.r_w_bias * self.scale
# Shapes batch_size x n_heads x seq_len x context_len
content_score = torch.einsum("bind,bjnd->bnij", q_head + r_w_bias, k_head)
positional_attn = self.relative_positional_attention(
position_embeds, q_head, context_len, cls_mask
)
token_type_attn = self.relative_token_type_attention(token_type_mat, q_head, cls_mask)
# merge attention scores
attn_score = content_score + positional_attn + token_type_attn
# precision safe in case of mixed precision training
dtype = attn_score.dtype
attn_score = attn_score.float()
# perform masking
if attention_mask is not None:
attn_score = attn_score - INF * (1 - attention_mask[:, None, None].float())
# attention probability
attn_prob = torch.softmax(attn_score, dim=-1, dtype=dtype)
attn_prob = self.drop_attn(attn_prob)
# attention output, shape batch_size x seq_len x n_heads x d_head
attn_vec = torch.einsum("bnij,bjnd->bind", attn_prob, v_head)
# Shape shape batch_size x seq_len x d_model
attn_out = self.post_proj(attn_vec.reshape(batch_size, seq_len, n_heads * d_head))
attn_out = self.drop(attn_out)
output = self.layer_norm(query + attn_out)
return (output, attn_prob) if output_attentions else (output,)
class FunnelPositionwiseFFN(qc.Module):
def __init__(self, config):
super().__init__()
self.linear_1 = qc.Linear(config.d_model, config.d_inner)
self.act = qu.activation(config.act)
self.drop_act = qc.Dropout(config.drop_act)
self.linear_2 = qc.Linear(config.d_inner, config.d_model)
self.drop = qc.Dropout(config.drop)
self.norm = qc.LayerNorm(config.d_model, config.eps)
def forward(self, hidden):
h = self.linear_1(hidden)
h = self.act(h)
h = self.drop_act(h)
h = self.linear_2(h)
h = self.drop(h)
return self.norm(hidden + h)
class Layer(qc.Module):
def __init__(self, config, block_index):
super().__init__()
self.attention = FunnelRelMultiheadAttention(config, block_index)
self.ffn = FunnelPositionwiseFFN(config)
def forward(
self,
query,
key,
value,
attention_inputs,
output_attentions=False,
):
attn = self.attention(
query, key, value, attention_inputs, output_attentions=output_attentions
)
output = self.ffn(attn[0])
return (output, attn[1]) if output_attentions else (output,)
class Encoder(qc.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.attention_structure = FunnelAttentionStructure(config)
self.blocks = nn.ModuleList(
[
nn.ModuleList([Layer(config, block_index) for _ in range(block_size)])
for block_index, block_size in enumerate(config.block_sizes)
]
)
def forward(
self,
inputs_embeds,
attention_mask=None,
token_type_ids=None,
output_attentions=False,
output_hidden_states=False,
return_dict=True,
):
# The pooling is not implemented on long tensors, so we convert this mask.
attention_mask = attention_mask.type_as(inputs_embeds)
attention_inputs = self.attention_structure.init_attention_inputs(
inputs_embeds,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
)
hidden = inputs_embeds
all_hidden_states = (inputs_embeds,) if output_hidden_states else None
all_attentions = () if output_attentions else None
for block_index, block in enumerate(self.blocks):
pooling_flag = hidden.size(1) > (2 if self.config.separate_cls else 1)
pooling_flag = pooling_flag and block_index > 0
if pooling_flag:
pooled_model, attention_inputs = self.attention_structure.pre_attention_pooling(
hidden, attention_inputs
)
for layer_index, layer in enumerate(block):
for repeat_index in range(self.config.block_repeats[block_index]):
do_pooling = (repeat_index == 0) and (layer_index == 0) and pooling_flag
if do_pooling:
query = pooled_model
key = value = hidden if self.config.pool_q_only else pooled_model
else:
query = key = value = hidden
layer_output = layer(
query, key, value, attention_inputs, output_attentions=output_attentions
)
hidden = layer_output[0]
if do_pooling:
attention_inputs = self.attention_structure.post_attention_pooling(
attention_inputs
)
if output_attentions:
all_attentions = all_attentions + layer_output[1:]
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden,)
if not return_dict:
return tuple(v for v in [hidden, all_hidden_states, all_attentions] if v is not None)
return qo.Base(y=hidden, hiddens=all_hidden_states, attns=all_attentions)
def upsample(
x,
stride,
target_len,
separate_cls=True,
truncate_seq=False,
):
if stride == 1:
return x
if separate_cls:
cls = x[:, :1]
x = x[:, 1:]
output = torch.repeat_interleave(x, repeats=stride, dim=1)
if separate_cls:
if truncate_seq:
output = F.pad(output, (0, 0, 0, stride - 1, 0, 0))
output = output[:, : target_len - 1]
output = torch.cat([cls, output], dim=1)
else:
output = output[:, :target_len]
return output
class Decoder(qc.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.attention_structure = FunnelAttentionStructure(config)
self.layers = nn.ModuleList([Layer(config, 0) for _ in range(config.n_dec_lays)])
def forward(
self,
final_hidden,
first_block_hidden,
attention_mask=None,
token_type_ids=None,
output_attentions=False,
output_hidden_states=False,
return_dict=True,
):
upsampled_model = upsample(
final_hidden,
stride=2 ** (len(self.config.block_sizes) - 1),
target_len=first_block_hidden.shape[1],
separate_cls=self.config.separate_cls,
truncate_seq=self.config.truncate_seq,
)
hidden = upsampled_model + first_block_hidden
all_hidden_states = (hidden,) if output_hidden_states else None
all_attentions = () if output_attentions else None
attention_inputs = self.attention_structure.init_attention_inputs(
hidden,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
)
for layer in self.layers:
layer_output = layer(
hidden, hidden, hidden, attention_inputs, output_attentions=output_attentions
)
hidden = layer_output[0]
if output_attentions:
all_attentions = all_attentions + layer_output[1:]
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden,)
if not return_dict:
return tuple(v for v in [hidden, all_hidden_states, all_attentions] if v is not None)
return qo.Base(y=hidden, hiddens=all_hidden_states, attns=all_attentions)
class FunnelDiscriminatorPredictions(qc.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.dense = qc.Linear(config.d_model, config.d_model)
self.dense_prediction = qc.Linear(config.d_model, 1)
def forward(self, discriminator_hidden_states):
hiddens = self.dense(discriminator_hidden_states)
hiddens = qu.activation(self.config.act)(hiddens)
logits = self.dense_prediction(hiddens).squeeze()
return logits
class FunnelBaseModel(PreTrained):
def __init__(self, config):
super().__init__(config)
self.embeddings = FunnelEmbeddings(config)
self.encoder = Encoder(config)
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")
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)
if token_type_ids is None:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
# TODO: deal with head_mask
if inputs_embeds is None:
inputs_embeds = self.embeddings(input_ids)
encoder_outputs = self.encoder(
inputs_embeds,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
return encoder_outputs
class Model(PreTrained):
def __init__(self, config):
super().__init__(config)
self.config = config
self.embeddings = FunnelEmbeddings(config)
self.encoder = Encoder(config)
self.decoder = Decoder(config)
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=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")
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)
if token_type_ids is None:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
# TODO: deal with head_mask
if inputs_embeds is None:
inputs_embeds = self.embeddings(input_ids)
encoder_outputs = self.encoder(
inputs_embeds,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
output_attentions=output_attentions,
output_hidden_states=True,
return_dict=return_dict,
)
decoder_outputs = self.decoder(
final_hidden=encoder_outputs[0],
first_block_hidden=encoder_outputs[1][self.config.block_sizes[0]],
attention_mask=attention_mask,
token_type_ids=token_type_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
if not return_dict:
idx = 0
outputs = (decoder_outputs[0],)
if output_hidden_states:
idx += 1
outputs = outputs + (encoder_outputs[1] + decoder_outputs[idx],)
if output_attentions:
idx += 1
outputs = outputs + (encoder_outputs[2] + decoder_outputs[idx],)
return outputs
return qo.Base(
y=decoder_outputs[0],
hiddens=(encoder_outputs.hiddens + decoder_outputs.hiddens)
if output_hidden_states
else None,
attns=(encoder_outputs.attns + decoder_outputs.attns) if output_attentions else None,
)
class ForPreTraining(PreTrained):
def __init__(self, config):
super().__init__(config)
self.funnel = Model(config)
self.discriminator_predictions = FunnelDiscriminatorPredictions(config)
self.post_init()
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=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
discriminator_hidden_states = self.funnel(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
discriminator_sequence_output = discriminator_hidden_states[0]
logits = self.discriminator_predictions(discriminator_sequence_output)
loss = None
if labels is not None:
loss_fct = nn.BCEWithLogitsLoss()
if attention_mask is not None:
active_loss = attention_mask.view(-1, discriminator_sequence_output.shape[1]) == 1
active_logits = logits.view(-1, discriminator_sequence_output.shape[1])[active_loss]
active_labels = labels[active_loss]
loss = loss_fct(active_logits, active_labels.float())
else:
loss = loss_fct(
logits.view(-1, discriminator_sequence_output.shape[1]), labels.float()
)
if not return_dict:
output = (logits,) + discriminator_hidden_states[1:]
return ((loss,) + output) if loss is not None else output
return qo.WithLoss(
loss=loss,
logits=logits,
hiddens=discriminator_hidden_states.hiddens,
attns=discriminator_hidden_states.attns,
)
class ForMasked(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.s_vocab, **kw)
forward = qf.forward_masked
class ForChoice(PreTrained):
def __init__(self, config):
super().__init__(config)
self.funnel = FunnelBaseModel(config)
self.classifier = Classifier(config, 1)
self.post_init()
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=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
)
inputs_embeds = (
inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
if inputs_embeds is not None
else None
)
outputs = self.funnel(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
y = outputs[0]
pooled_output = y[:, 0]
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[1:]
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(**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(**kw)
self.proj = qc.Linear(cfg.d_model, cfg.n_labels, **kw)
forward = qf.forward_qa
|
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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"]}
|
33,516
|
quantapix/qnarre
|
refs/heads/main
|
/tools/triton/python/triton/common/__init__.py
|
from .build import _build
__all__ = ["_build"]
|
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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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"/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"]}
|
33,517
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/base/doc/qnn.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 .base import config
from .mboxes import Mboxes
from .log import Logger, start_stop_log
from .resource import resource
from .dispatch import Dispatch
# from .ptorch import TorchOne, TorchTwo
# from .tflow import Mnist
log = Logger(__name__)
class Qnn(Dispatch):
_res_path = config.qnar_dst + 'qnn.qnr'
_blog = 'blog'
_ctxt = None
@classmethod
def globals(cls):
return globals()
def setup(self, **kw):
# TorchOne().loop()
# TorchTwo().loop()
# Mnist().loop()
"""
with resource(self.ctxt) as ctxt:
kw.update(ctxt=ctxt)
with start_stop_log(log, 'Setting up Qnn'):
dst = '/' + self.realm
Mboxes(self.base).export_to(dst, **kw)
"""
def learn(self, **kw):
with resource(self.ctxt) as ctxt:
kw.update(ctxt=ctxt)
with start_stop_log(log, 'Setting up Qnn'):
dst = '/' + self.realm
Mboxes(self.base).export_to(dst, **kw)
def guess(self, **kw):
with resource(self.ctxt) as ctxt:
kw.update(ctxt=ctxt)
with start_stop_log(log, 'Setting up Qnn'):
dst = '/' + self.realm
Mboxes(self.base).export_to(dst, **kw)
|
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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"]}
|
33,518
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/base/doc/util/roster.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 shutil as sh
import filecmp as fc
import pathlib as pth
import collections as co
from hashlib import blake2b
from .log import Logger
from .base import config
from .counter import counters
from .resource import Resource, resource, Names
log = Logger(__name__)
def calc_digest(path, *, base=None, **_):
p = base / path if base else pth.Path(path)
if p.exists():
d, s = blake2b(digest_size=20), 0
with open(p, 'rb') as f:
for b in iter(lambda: f.read(65536), b''):
s += len(b)
d.update(b)
assert s == p.stat().st_size
return d.hexdigest(), s
log.warning("Cant't digest nonexistent file {}", p)
return None, None
class Entry(co.namedtuple('Entry', 'path digest size')):
__slots__ = ()
def __new__(cls, path, digest=None, size=None, **kw):
if not digest:
digest, size = calc_digest(path, **kw)
return super().__new__(cls, path, digest, size)
def __bool__(self):
return bool(self.path and self.digest is not None
and self.size is not None)
__hash__ = None
def __eq__(self, other):
if isinstance(other, type(self)):
d = self.digest
return (d and d == other.digest and self.size == other.size)
return NotImplemented
def __repr__(self):
s = "{}({!r}".format(type(self).__name__, str(self.path))
d = self.digest
if d:
s += ", {!r}, {}".format(d, self.size)
s += ")"
return s
def relative_to(self, path, base, **_):
try:
(base / self.path).relative_to(base / path)
except ValueError:
return False
return True
def check(self, **kw):
d = self.digest
if d:
d2, s = calc_digest(self.path, **kw)
if d2 == d and s == self.size:
return True
m = 'Mismatched digest for {}'
else:
m = 'No digest for {}'
log.info(m, self.path)
return False
def prune_dir(path, cntr=None, **_):
with os.scandir(path) as es:
for e in es:
p = pth.Path(e.path)
j = None
if p.name.startswith('.'):
if e.is_dir(follow_symlinks=False):
sh.rmtree(str(p))
elif p.suffix != '.qnr':
p.unlink()
log.info('Deleted {}', p)
j = '-'
elif e.is_dir(follow_symlinks=False):
prune_dir(p, cntr)
continue
if cntr:
cntr.incr(j)
try:
path.rmdir()
log.info('Deleted {}', path)
j = '-'
except:
j = None
if cntr:
cntr.incr(j)
class Roster(Resource):
_res_path = '.roster.qnr'
@classmethod
def globals(cls):
return globals()
def __init__(self, entries=None, **kw):
super().__init__(None, **kw)
self._expels = []
self._symlinks = []
if entries:
self.add_entry(entries)
def __repr__(self):
return '{}({!r})'.format(type(self).__name__, tuple(self.entries))
def __str__(self):
s = '{}:'.format(str(self.base))
for e in self.entries:
s += '\n{} {} {}'.format(str(e.path), str(e.digest), e.size)
return s
@property
def entries(self):
es = [e for e in self.values() if isinstance(e, Entry)]
return sorted(es, key=lambda x: x.path)
def adjust_kw(self, kw):
def _adjust(key, default):
v = kw.get(key)
v = pth.Path(v) if v else default
kw[key] = v
_adjust('base', self.base)
def entry_adder(self, entry, cntr, modify=False, expel=True, **kw):
if isinstance(entry, Entry):
assert entry
p, d, s = entry
k = d, s
if p in self:
ok = self[p]
if k != ok:
if modify:
log.info('Modifying digest for {}', p)
del self[ok]
self[p] = k
self[k] = entry
cntr.incr(modify)
return
else:
log.warning('Digest mismatch for {}', p)
cntr.incr()
else:
try:
o = self[k]
except KeyError:
self[p] = k
self[k] = entry
yield p
else:
log.info('Duplicates: {} and {}', o.path, p)
if expel:
self._expels.append((o, entry))
cntr.incr()
else:
for e in entry:
yield from self.entry_adder(e, cntr, modify, expel, **kw)
add_args = ((('scanned', '.'), ('added', '+')), 'Adding:')
def add_entry(self, entry, **kw):
with counters(self.add_args, kw) as cs:
for _ in self.entry_adder(entry, **kw):
cs.incr('+')
return cs
def path_adder(self, path, **kw):
self.adjust_kw(kw)
p = str(pth.Path(path).relative_to(kw['base']))
yield from self.entry_adder(Entry(p, **kw), **kw)
def walker(self, paths=(), **kw):
for e in self.entries:
if paths:
for p in paths:
if e.relative_to(p, **kw):
break
else:
continue
yield e
def scanner(self, root, cntr, **kw):
def _paths(path):
with os.scandir(path) as es:
for e in es:
p = pth.Path(e.path)
if not p.name.startswith('.'):
if e.is_dir(follow_symlinks=False):
yield from _paths(p)
continue
elif e.is_file(follow_symlinks=False):
yield p
continue
elif e.is_symlink():
log.info('Symlink {}', p)
self._symlinks.append(p)
else:
log.info('Ignoring dir entry {}', p)
cntr.incr()
if root.exists():
for p in _paths(root):
yield from self.path_adder(p, **kw, cntr=cntr)
scan_args = ((('scanned', '.'), ('added', '+')), 'Scanning:')
def scan(self, paths=(), **kw):
self.adjust_kw(kw)
b = kw['base']
with counters(self.scan_args, kw) as cs:
for p in paths or ('', ):
for _ in self.scanner(b / p, **kw):
cs.incr('+')
return cs
rescan_args = ((('scanned', '.'), ('added', '+'), ('removed', '-'),
('modified', 'm')), 'Rescanning:')
def rescanner(self, paths, cntr, **kw):
self.adjust_kw(kw)
b = kw['base']
es = [e for e in self.walker(paths, **kw) if not (b / e.path).exists()]
for p, d, s in es:
del self[p]
del self[(d, s)]
cntr.incr('-')
self._expels = []
for p in paths or ('', ):
for p in self.scanner(b / p, **kw, cntr=cntr, modify='m'):
yield p
def rescan(self, paths=(), **kw):
with counters(self.rescan_args, kw) as cs:
for _ in self.rescanner(paths, **kw):
cs.incr('+')
return cs
check_args = ((('passed', '.'), ('failed', 'F')), 'Checking:')
def check(self, paths=(), **kw):
self.adjust_kw(kw)
with counters(self.check_args, kw) as cs:
for e in self.walker(paths, **kw):
cs.incr('.' if e.check(**kw) else 'F')
return cs
def check_ok(self, paths=(), **kw):
return not self.check(paths, **kw)['F']
def rename_path(self, src, dst, cntr, cntr_key=None, **_):
if dst.exists():
log.warning("Can't move/rename, destination exists {}", dst)
cntr.incr('F')
else:
dst.parent.mkdir(parents=True, exist_ok=True)
src.rename(dst)
log.info('Moved/renamed {} to/as {}', src, dst)
cntr.incr(cntr_key)
expel_args = ((('scanned', '.'), ('expelled', 'e'), ('failed', 'F')),
'Expelling:')
def expel(self, ebase=None, **kw):
with counters(self.expel_args, kw) as cs:
self.adjust_kw(kw)
b = kw['base']
for o, d in self._expels:
op = b / o.path
dp = b / d.path
if fc.cmp(op, dp, shallow=False):
e = (ebase or (b.parent / 'expel')) / d.path
self.rename_path(dp, e, **kw, cntr_key='e')
else:
log.error('Duplicates compare failed {}, {}', op, dp)
cs.incr('F')
self._expels = []
return cs
def absorb_paths(self, paths=(), abase=None, **kw):
self.adjust_kw(kw)
b = kw['base']
ab = abase or (b.parent / 'absorb')
for p in paths or ('', ):
p = ab / p
if p.exists():
yield b, ab, p
absorb_args = ((('scanned', '.'), ('absorbed', 'a'), ('failed', 'F')),
'Absorbing:')
def absorb(self, paths=(), abase=None, **kw):
with counters(self.absorb_args, kw) as cs:
kw['expel'] = False
for b, ab, path in self.absorb_paths(paths, abase, **kw):
for p in [p for p in self.scanner(path, **kw, base=ab)]:
self.rename_path(ab / p, b / p, **kw, cntr_key='a')
prune_dir(path)
return cs
prune_args = ((('scanned', '.'), ('deleted', '-')), 'Pruning:')
def prune(self, paths=(), abase=None, **kw):
with counters(self.prune_args, kw) as cs:
for _, ab, p in self.absorb_paths(paths, abase, **kw):
prune_dir(p, **kw)
return cs
def namer(self, path, names, base, cntr, **_):
p = str(path)
if p not in names:
if (base / path).exists():
names[p] = np = p.lower().replace(' ', '-')
cntr.incr('.' if p == np else 'n')
path = path.parent
if path.name:
self.namer(path, names, base, cntr)
else:
cntr.incr('F')
names_args = ((('scanned', '.'), ('renamed', 'r'), ('normalized', 'n'),
('failed', 'F')), 'Naming:')
def names(self, paths=(), **kw):
with counters(self.names_args, kw) as cs:
self.adjust_kw(kw)
with resource(Names.create(kw['base'])) as ns:
ns.clear()
for e in self.walker(paths, **kw):
self.namer(pth.Path(e.path), ns, **kw)
return cs
rename_args = ((('scanned', '.'), ('added', '+'), ('removed', '-'),
('modified', 'm'), ('normalized', 'n'), ('renamed', 'r'),
('failed', 'F')), 'Renaming:')
def rename(self, paths=(), **kw):
with counters(self.rename_args, kw) as cs:
self.adjust_kw(kw)
b = kw['base']
with resource(Names.create(b)) as ns:
if ns:
for e in self.walker(paths, **kw):
p = e.path
try:
d = b / ns.pop(p)
except KeyError:
cs.incr()
continue
self.rename_path(b / p, d, **kw, cntr_key='r')
ps = paths or ('', )
for o in sorted(ns.keys(), reverse=True):
d = b / ns.pop(o)
o = b / o
if o.exists() and o.is_dir():
for p in ps:
try:
o.relative_to(b / p)
break
except ValueError:
continue
else:
cs.incr()
continue
self.rename_path(o, d, **kw, cntr_key='r')
else:
cs.incr()
for p in self.rescanner(paths, **kw):
self.namer(pth.Path(p), ns, **kw)
return cs
if __name__ == '__main__':
from .args import BArgs
a = BArgs()
a.add_argument('paths', nargs='*', help='Paths to follow')
a.add_argument('-u', '--prune', action=a.st, help='Prune absorb dir')
a.add_argument('-a', '--absorb', help='Path to absorb uniques from')
a.add_argument('-x', '--rename', action=a.st, help='Rename files')
a.add_argument('-R', '--rescan', action=a.st, help='Rescan base')
a.add_argument('-s', '--scan', action=a.st, help='Scan base')
a.add_argument('-e', '--expel', help='Path to expel duplicates to')
a.add_argument('-c', '--check', action=a.st, help='Check all digests')
a.add_argument('-n', '--names', action=a.st, help='Names of files')
a = a.parse_args()
r = Roster.create(a.base)
if a.prune:
abase = None if a.absorb is None or a.absorb == config.DEFAULT else a.absorb
r.prune(a.paths, abase=abase)
elif a.absorb:
abase = None if a.absorb == config.DEFAULT else a.absorb
r.absorb(a.paths, abase=abase)
elif a.rename:
r.rename(a.paths)
else:
if a.rescan:
r.rescan(a.paths)
elif a.scan:
r.scan(a.paths)
if a.expel:
ebase = None if a.expel == config.DEFAULT else a.expel
r.expel(ebase=ebase)
if a.check:
r.check_ok(a.paths)
if a.names:
r.names(a.paths)
r.save()
|
{"/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"]}
|
33,519
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/base/doc/report.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 csv
import pathlib as pth
from qnarre import load_from
class Report:
fields = (
'Activism',
'Agency',
'Author',
'Coherence',
'Credibility',
'Date',
'Fragment',
'Genre',
'Judgment',
'Kind',
'Loss',
'Name',
'Narrative',
'Page',
'Para',
'Reality',
'Source',
'Text',
'Title',
'Topic',
'Turmoil',
'Type',
)
exclude = ()
def __init__(self, dst):
self.csv = csv.DictWriter(dst, self.fields)
self.csv.writeheader()
def write(self, node):
if node.__class__ not in self.exclude:
ls = node.fields
if isinstance(ls, list):
for fs in ls:
self.csv.writerow(fs)
else:
self.csv.writerow(ls)
def report(root, **kw):
kw.update(root=root)
print('Loading from {}...'.format(str(root)))
ns = set(n for n in load_from(pth.Path('merged.org'), **kw).net.nodes())
for n in ns:
print(n)
print('...done')
print('Reporting...')
with open(root / 'merged.csv', 'w', newline='') as f:
r = Report(f)
for n in ns:
r.write(n)
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()
report(pth.Path.cwd() / (args.root or 'sample'))
|
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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"]}
|
33,520
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/models/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.
# =============================================================================
import copy
import math
import random
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 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
log = logging.get_logger(__name__)
LIST = [
"uclanlp/plbart-base",
"uclanlp/plbart-cs-java",
"uclanlp/plbart-multi_task-all",
]
# Copied from transformers.models.mbart.modeling_mbart.shift_tokens_right
def shift_tokens_right(input_ids, PAD):
prev_output_tokens = input_ids.clone()
if PAD is None:
raise ValueError("self.model.config.PAD has to be defined.")
# replace possible -100 values in labels by `PAD`
prev_output_tokens.masked_fill_(prev_output_tokens == -100, PAD)
index_of_eos = (prev_output_tokens.ne(PAD).sum(dim=1) - 1).unsqueeze(-1)
decoder_start_tokens = prev_output_tokens.gather(1, index_of_eos).squeeze()
prev_output_tokens[:, 1:] = prev_output_tokens[:, :-1].clone()
prev_output_tokens[:, 0] = decoder_start_tokens
return prev_output_tokens
# Copied from transformers.models.bart.modeling_bart.BartLearnedPositionalEmbedding with Bart->PLBart
class PLBartLearnedPositionalEmbedding(qc.Embed):
def __init__(self, num_embeddings, embedding_dim):
self.offset = 2
super().__init__(num_embeddings + self.offset, embedding_dim)
def forward(self, input_ids_shape, past_key_values_length=0):
"""`input_ids_shape` is expected to be [bsz x seqlen]."""
bsz, seq_len = input_ids_shape[:2]
positions = torch.arange(
past_key_values_length,
past_key_values_length + seq_len,
dtype=torch.long,
device=self.weight.device,
)
return super().forward(positions + self.offset)
# Copied from transformers.models.bart.modeling_bart.BartAttention with Bart->PLBart
class Attention(qc.Module):
def __init__(
self,
embed_dim,
n_heads,
drop: float = 0.0,
is_decoder=False,
bias=True,
):
super().__init__()
self.embed_dim = embed_dim
self.n_heads = n_heads
self.drop = drop
self.head_dim = embed_dim // n_heads
if (self.head_dim * n_heads) != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by n_heads (got `embed_dim`: {self.embed_dim}"
f" and `n_heads`: {n_heads})."
)
self.scaling = self.head_dim**-0.5
self.is_decoder = is_decoder
self.k_proj = qc.Linear(embed_dim, embed_dim, bias=bias)
self.v_proj = qc.Linear(embed_dim, embed_dim, bias=bias)
self.q_proj = qc.Linear(embed_dim, embed_dim, bias=bias)
self.out_proj = qc.Linear(embed_dim, embed_dim, bias=bias)
def _shape(self, tensor, seq_len, bsz):
return tensor.view(bsz, seq_len, self.n_heads, self.head_dim).transpose(1, 2).contiguous()
def forward(
self,
hiddens,
key_value_states=None,
past_key_value=None,
attention_mask=None,
layer_head_mask=None,
output_attentions=False,
):
is_cross_attention = key_value_states is not None
bsz, tgt_len, _ = hiddens.size()
# get query proj
query_states = self.q_proj(hiddens) * self.scaling
# get key, value proj
if is_cross_attention and past_key_value is not None:
# reuse k,v, crosses
key_states = past_key_value[0]
value_states = past_key_value[1]
elif is_cross_attention:
# crosses
key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
elif past_key_value is not None:
# reuse k, v, self_attention
key_states = self._shape(self.k_proj(hiddens), -1, bsz)
value_states = self._shape(self.v_proj(hiddens), -1, bsz)
key_states = torch.cat([past_key_value[0], key_states], dim=2)
value_states = torch.cat([past_key_value[1], value_states], dim=2)
else:
# self_attention
key_states = self._shape(self.k_proj(hiddens), -1, bsz)
value_states = self._shape(self.v_proj(hiddens), -1, bsz)
if self.is_decoder:
past_key_value = (key_states, value_states)
proj_shape = (bsz * self.n_heads, -1, self.head_dim)
query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
key_states = key_states.view(*proj_shape)
value_states = value_states.view(*proj_shape)
src_len = key_states.size(1)
attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
if attn_weights.size() != (bsz * self.n_heads, tgt_len, src_len):
raise ValueError(
f"Attention weights should be of size {(bsz * self.n_heads, tgt_len, src_len)}, but is {attn_weights.size()}"
)
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, tgt_len, src_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
)
attn_weights = attn_weights.view(bsz, self.n_heads, tgt_len, src_len) + attention_mask
attn_weights = attn_weights.view(bsz * self.n_heads, tgt_len, src_len)
attn_weights = F.softmax(attn_weights, dim=-1)
if layer_head_mask is not None:
if layer_head_mask.size() != (self.n_heads,):
raise ValueError(
f"Head mask for a single layer should be of size {(self.n_heads,)}, but is {layer_head_mask.size()}"
)
attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(
bsz, self.n_heads, tgt_len, src_len
)
attn_weights = attn_weights.view(bsz * self.n_heads, tgt_len, src_len)
if output_attentions:
attn_weights_reshaped = attn_weights.view(bsz, self.n_heads, tgt_len, src_len)
attn_weights = attn_weights_reshaped.view(bsz * self.n_heads, tgt_len, src_len)
else:
attn_weights_reshaped = None
attn_probs = F.drop(attn_weights, p=self.drop, training=self.training)
attn_output = torch.bmm(attn_probs, value_states)
if attn_output.size() != (bsz * self.n_heads, tgt_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz, self.n_heads, tgt_len, self.head_dim)}, but is {attn_output.size()}"
)
attn_output = attn_output.view(bsz, self.n_heads, tgt_len, self.head_dim)
attn_output = attn_output.transpose(1, 2)
attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)
attn_output = self.out_proj(attn_output)
return attn_output, attn_weights_reshaped, past_key_value
# Copied from transformers.models.bart.modeling_bart.BartEncoderLayer with Bart->PLBart
class EncLayer(qc.Module):
def __init__(self, config):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = Attention(
embed_dim=self.embed_dim,
n_heads=config.encoder_attention_heads,
drop=config.drop_attn,
)
self.self_attn_layer_norm = qc.LayerNorm(self.embed_dim)
self.drop = config.drop
self.activation_fn = qu.activation(config.act)
self.drop_act = config.drop_act
self.fc1 = qc.Linear(self.embed_dim, config.encoder_ffn_dim)
self.fc2 = qc.Linear(config.encoder_ffn_dim, self.embed_dim)
self.final_layer_norm = qc.LayerNorm(self.embed_dim)
def forward(
self,
hiddens,
attention_mask,
layer_head_mask,
output_attentions=False,
):
residual = hiddens
hiddens, attn_weights, _ = self.self_attn(
hiddens=hiddens,
attention_mask=attention_mask,
layer_head_mask=layer_head_mask,
output_attentions=output_attentions,
)
hiddens = F.drop(hiddens, p=self.drop, training=self.training)
hiddens = residual + hiddens
hiddens = self.self_attn_layer_norm(hiddens)
residual = hiddens
hiddens = self.activation_fn(self.fc1(hiddens))
hiddens = F.drop(hiddens, p=self.drop_act, training=self.training)
hiddens = self.fc2(hiddens)
hiddens = F.drop(hiddens, p=self.drop, training=self.training)
hiddens = residual + hiddens
hiddens = self.final_layer_norm(hiddens)
if hiddens.dtype == torch.float16 and (
torch.isinf(hiddens).any() or torch.isnan(hiddens).any()
):
clamp_value = torch.finfo(hiddens.dtype).max - 1000
hiddens = torch.clamp(hiddens, min=-clamp_value, max=clamp_value)
outputs = (hiddens,)
if output_attentions:
outputs += (attn_weights,)
return outputs
# Copied from transformers.models.bart.modeling_bart.BartDecoderLayer with Bart->PLBart
class DecLayer(qc.Module):
def __init__(self, config):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = Attention(
embed_dim=self.embed_dim,
n_heads=config.decoder_attention_heads,
drop=config.drop_attn,
is_decoder=True,
)
self.drop = config.drop
self.activation_fn = qu.activation(config.act)
self.drop_act = config.drop_act
self.self_attn_layer_norm = qc.LayerNorm(self.embed_dim)
self.encoder_attn = Attention(
self.embed_dim,
config.decoder_attention_heads,
drop=config.drop_attn,
is_decoder=True,
)
self.encoder_attn_layer_norm = qc.LayerNorm(self.embed_dim)
self.fc1 = qc.Linear(self.embed_dim, config.decoder_ffn_dim)
self.fc2 = qc.Linear(config.decoder_ffn_dim, self.embed_dim)
self.final_layer_norm = qc.LayerNorm(self.embed_dim)
def forward(
self,
hiddens,
attention_mask=None,
enc_hiddens=None,
encoder_attention_mask=None,
layer_head_mask=None,
cross_attn_layer_head_mask=None,
past_key_value=None,
output_attentions=False,
y_cache=True,
):
residual = hiddens
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
# add present self-attn cache to positions 1,2 of present_key_value tuple
hiddens, self_attn_weights, present_key_value = self.self_attn(
hiddens=hiddens,
past_key_value=self_attn_past_key_value,
attention_mask=attention_mask,
layer_head_mask=layer_head_mask,
output_attentions=output_attentions,
)
hiddens = F.drop(hiddens, p=self.drop, training=self.training)
hiddens = residual + hiddens
hiddens = self.self_attn_layer_norm(hiddens)
# Cross-Attention Block
cross_attn_present_key_value = None
cross_attn_weights = None
if enc_hiddens is not None:
residual = hiddens
# cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
hiddens, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn(
hiddens=hiddens,
key_value_states=enc_hiddens,
attention_mask=encoder_attention_mask,
layer_head_mask=cross_attn_layer_head_mask,
past_key_value=cross_attn_past_key_value,
output_attentions=output_attentions,
)
hiddens = F.drop(hiddens, p=self.drop, training=self.training)
hiddens = residual + hiddens
hiddens = self.encoder_attn_layer_norm(hiddens)
# add cross-attn to positions 3,4 of present_key_value tuple
present_key_value = present_key_value + cross_attn_present_key_value
# Fully Connected
residual = hiddens
hiddens = self.activation_fn(self.fc1(hiddens))
hiddens = F.drop(hiddens, p=self.drop_act, training=self.training)
hiddens = self.fc2(hiddens)
hiddens = F.drop(hiddens, p=self.drop, training=self.training)
hiddens = residual + hiddens
hiddens = self.final_layer_norm(hiddens)
outputs = (hiddens,)
if output_attentions:
outputs += (self_attn_weights, cross_attn_weights)
if y_cache:
outputs += (present_key_value,)
return outputs
class Encoder(PreTrained):
def __init__(self, config, embed_tokens=None):
super().__init__(config)
self.drop = config.drop
self.layerdrop = config.encoder_layerdrop
embed_dim = config.d_model
self.padding_idx = config.PAD
self.max_source_positions = config.n_pos
self.embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0
if embed_tokens is not None:
self.embed_tokens = embed_tokens
else:
self.embed_tokens = qc.Embed(config.s_vocab, embed_dim, self.padding_idx)
self.embed_positions = PLBartLearnedPositionalEmbedding(
config.n_pos,
embed_dim,
)
self.layers = nn.ModuleList([EncLayer(config) for _ in range(config.encoder_layers)])
self.layernorm_embedding = qc.LayerNorm(embed_dim)
self.gradient_checkpointing = False
def forward(
self,
input_ids=None,
attention_mask=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
# retrieve input_ids and inputs_embeds
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()
input_ids = input_ids.view(-1, input_shape[-1])
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")
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
embed_pos = self.embed_positions(input_shape)
hiddens = inputs_embeds + embed_pos
hiddens = self.layernorm_embedding(hiddens)
hiddens = F.drop(hiddens, p=self.drop, training=self.training)
# expand attention_mask
if attention_mask is not None:
attention_mask = qu.expand_mask(attention_mask, inputs_embeds.dtype)
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
# check if head_mask has a correct number of layers specified if desired
if head_mask is not None:
if head_mask.size()[0] != (len(self.layers)):
raise ValueError(
f"The head_mask should be specified for {len(self.layers)} layers, but it is for {head_mask.size()[0]}."
)
for idx, encoder_layer in enumerate(self.layers):
if output_hidden_states:
encoder_states = encoder_states + (hiddens,)
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
dropout_probability = random.uniform(0, 1)
if self.training and (dropout_probability < self.layerdrop): # skip the layer
layer_outputs = (None, None)
else:
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(encoder_layer),
hiddens,
attention_mask,
(head_mask[idx] if head_mask is not None else None),
)
else:
layer_outputs = encoder_layer(
hiddens,
attention_mask,
layer_head_mask=(head_mask[idx] if head_mask is not None else None),
output_attentions=output_attentions,
)
hiddens = layer_outputs[0]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
if output_hidden_states:
encoder_states = encoder_states + (hiddens,)
if not return_dict:
return tuple(v for v in [hiddens, encoder_states, all_attentions] if v is not None)
return qo.Base(y=hiddens, hiddens=encoder_states, attns=all_attentions)
# Copied from transformers.models.bart.modeling_bart.BartDecoder with Bart->PLBart
class Decoder(PreTrained):
def __init__(self, config, embed_tokens=None):
super().__init__(config)
self.drop = config.drop
self.layerdrop = config.decoder_layerdrop
self.padding_idx = config.PAD
self.max_target_positions = config.n_pos
self.embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
if embed_tokens is not None:
self.embed_tokens = embed_tokens
else:
self.embed_tokens = qc.Embed(config.s_vocab, config.d_model, self.padding_idx)
self.embed_positions = PLBartLearnedPositionalEmbedding(
config.n_pos,
config.d_model,
)
self.layers = nn.ModuleList([DecLayer(config) for _ in range(config.decoder_layers)])
self.layernorm_embedding = qc.LayerNorm(config.d_model)
self.gradient_checkpointing = False
def _prepare_decoder_attention_mask(
self, attention_mask, input_shape, inputs_embeds, past_key_values_length
):
combined_attention_mask = None
if input_shape[-1] > 1:
combined_attention_mask = qu.causal_mask(
input_shape, inputs_embeds.dtype, past_key_values_length=past_key_values_length
).to(self.device)
if attention_mask is not None:
expanded_attn_mask = qu.expand_mask(
attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
)
combined_attention_mask = (
expanded_attn_mask
if combined_attention_mask is None
else expanded_attn_mask + combined_attention_mask
)
return combined_attention_mask
def forward(
self,
input_ids=None,
attention_mask=None,
enc_hiddens=None,
encoder_attention_mask=None,
head_mask=None,
cross_attn_head_mask=None,
caches=None,
inputs_embeds=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
)
y_cache = y_cache if y_cache is not None else self.config.y_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# retrieve input_ids and inputs_embeds
if input_ids is not None and inputs_embeds is not None:
raise ValueError(
"You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time"
)
elif input_ids is not None:
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError(
"You have to specify either decoder_input_ids or decoder_inputs_embeds"
)
# past_key_values_length
past_key_values_length = caches[0][0].shape[2] if caches is not None else 0
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
attention_mask = self._prepare_decoder_attention_mask(
attention_mask, input_shape, inputs_embeds, past_key_values_length
)
# expand encoder attention mask
if enc_hiddens is not None and encoder_attention_mask is not None:
encoder_attention_mask = qu.expand_mask(
encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
)
# embed positions
positions = self.embed_positions(input_shape, past_key_values_length)
hiddens = inputs_embeds + positions
hiddens = self.layernorm_embedding(hiddens)
hiddens = F.drop(hiddens, p=self.drop, training=self.training)
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
all_cross_attentions = () if (output_attentions and enc_hiddens is not None) else None
next_decoder_cache = () if y_cache else None
# check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired
for attn_mask, mask_name in zip(
[head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]
):
if attn_mask is not None:
if attn_mask.size()[0] != (len(self.layers)):
raise ValueError(
"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for {head_mask.size()[0]}."
)
for idx, decoder_layer in enumerate(self.layers):
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
if output_hidden_states:
all_hidden_states += (hiddens,)
dropout_probability = random.uniform(0, 1)
if self.training and (dropout_probability < self.layerdrop):
continue
past_key_value = caches[idx] 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):
# None for past_key_value
return module(*inputs, output_attentions, y_cache)
return custom_forward
layer_outputs = torch.utils.checkpoint.checkpoint(
create_custom_forward(decoder_layer),
hiddens,
attention_mask,
enc_hiddens,
encoder_attention_mask,
head_mask[idx] if head_mask is not None else None,
cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None,
None,
)
else:
layer_outputs = decoder_layer(
hiddens,
attention_mask=attention_mask,
enc_hiddens=enc_hiddens,
encoder_attention_mask=encoder_attention_mask,
layer_head_mask=(head_mask[idx] if head_mask is not None else None),
cross_attn_layer_head_mask=(
cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None
),
past_key_value=past_key_value,
output_attentions=output_attentions,
y_cache=y_cache,
)
hiddens = layer_outputs[0]
if y_cache:
next_decoder_cache += (layer_outputs[3 if output_attentions else 1],)
if output_attentions:
all_self_attns += (layer_outputs[1],)
if enc_hiddens is not None:
all_cross_attentions += (layer_outputs[2],)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hiddens,)
next_cache = next_decoder_cache if y_cache else None
if not return_dict:
return tuple(
v
for v in [
hiddens,
next_cache,
all_hidden_states,
all_self_attns,
all_cross_attentions,
]
if v is not None
)
return qo.CachesCrosses(
y=hiddens,
caches=next_cache,
hiddens=all_hidden_states,
attns=all_self_attns,
crosses=all_cross_attentions,
)
class Model(PreTrained):
def __init__(self, config):
super().__init__(config)
padding_idx, s_vocab = config.PAD, config.s_vocab
self.shared = qc.Embed(s_vocab, config.d_model, padding_idx)
self.encoder = Encoder(config, self.shared)
self.decoder = Decoder(config, self.shared)
def forward(
self,
input_ids=None,
attention_mask=None,
decoder_input_ids=None,
decoder_attention_mask=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
encoder_outputs=None,
caches=None,
inputs_embeds=None,
decoder_inputs_embeds=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
)
y_cache = y_cache if y_cache is not None else self.config.y_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# different to other models, PLBart automatically creates decoder_input_ids from
# input_ids if no decoder_input_ids are provided
if decoder_input_ids is None and decoder_inputs_embeds is None:
decoder_input_ids = shift_tokens_right(input_ids, self.config.PAD)
if encoder_outputs is None:
encoder_outputs = self.encoder(
input_ids=input_ids,
attention_mask=attention_mask,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
# If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True
elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
encoder_outputs = BaseModelOutput(
y=encoder_outputs[0],
hiddens=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
attns=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
)
# decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn)
decoder_outputs = self.decoder(
input_ids=decoder_input_ids,
attention_mask=decoder_attention_mask,
enc_hiddens=encoder_outputs[0],
encoder_attention_mask=attention_mask,
head_mask=decoder_head_mask,
cross_attn_head_mask=cross_attn_head_mask,
caches=caches,
inputs_embeds=decoder_inputs_embeds,
y_cache=y_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
if not return_dict:
return decoder_outputs + encoder_outputs
return Seq2SeqModelOutput(
y=decoder_outputs.y,
caches=decoder_outputs.caches,
hiddens=decoder_outputs.hiddens,
attns=decoder_outputs.attns,
crosses=decoder_outputs.crosses,
enc_y=encoder_outputs.y,
enc_hiddens=encoder_outputs.hiddens,
enc_attns=encoder_outputs.attns,
)
class ForCondGen(PreTrained):
def __init__(self, config):
super().__init__(config)
self.model = Model(config)
self.register_buffer(
"final_logits_bias", torch.zeros((1, self.model.shared.num_embeddings))
)
self.lm_head = qc.Linear(config.d_model, self.model.shared.num_embeddings, bias=False)
def forward(
self,
input_ids=None,
attention_mask=None,
decoder_input_ids=None,
decoder_attention_mask=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
encoder_outputs=None,
caches=None,
inputs_embeds=None,
decoder_inputs_embeds=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
if labels is not None:
if decoder_input_ids is None:
decoder_input_ids = shift_tokens_right(labels, self.config.PAD)
outputs = self.model(
input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
encoder_outputs=encoder_outputs,
decoder_attention_mask=decoder_attention_mask,
head_mask=head_mask,
decoder_head_mask=decoder_head_mask,
cross_attn_head_mask=cross_attn_head_mask,
caches=caches,
inputs_embeds=inputs_embeds,
decoder_inputs_embeds=decoder_inputs_embeds,
y_cache=y_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
lm_logits = self.lm_head(outputs[0]) + self.final_logits_bias
masked_lm_loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.s_vocab), labels.view(-1))
if not return_dict:
output = (lm_logits,) + outputs[1:]
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
return Seq2SeqLMOutput(
loss=masked_lm_loss,
logits=lm_logits,
caches=outputs.caches,
hiddens=outputs.hiddens,
attns=outputs.attns,
crosses=outputs.crosses,
enc_y=outputs.enc_y,
enc_hiddens=outputs.enc_hiddens,
enc_attns=outputs.enc_attns,
)
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, "tanh", **kw)
forward = qf.forward_seq
def pre_proj(self, x, ys):
y = ys[0]
eos_m = x.eq(self.cfg.EOS)
assert len(torch.unique_consecutive(eos_m.sum(1))) <= 1
y = y[eos_m, :].view(y.size(0), -1, y.size(-1))
return y[:, -1, :]
# Copied from transformers.models.bart.modeling_bart.BartDecoderWrapper with Bart->PLBart
class PLBartDecoderWrapper(PreTrained):
def __init__(self, config):
super().__init__(config)
self.decoder = Decoder(config)
def forward(self, *args, **kw):
return self.decoder(*args, **kw)
# Copied from transformers.models.bart.modeling_bart.ForCausal with Bart->PLBart, facebook/bart-base->uclanlp/plbart-base
class ForCausal(PreTrained):
def __init__(self, config):
config = copy.deepcopy(config)
config.is_decoder = True
config.is_enc_dec = False
super().__init__(config)
self.model = PLBartDecoderWrapper(config)
self.lm_head = qc.Linear(config.d_model, config.s_vocab, bias=False)
def forward(
self,
input_ids=None,
attention_mask=None,
enc_hiddens=None,
encoder_attention_mask=None,
head_mask=None,
cross_attn_head_mask=None,
caches=None,
inputs_embeds=None,
labels=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
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = self.model.decoder(
input_ids=input_ids,
attention_mask=attention_mask,
enc_hiddens=enc_hiddens,
encoder_attention_mask=encoder_attention_mask,
head_mask=head_mask,
cross_attn_head_mask=cross_attn_head_mask,
caches=caches,
inputs_embeds=inputs_embeds,
y_cache=y_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
logits = self.lm_head(outputs[0])
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.config.s_vocab), labels.view(-1))
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return CausalLMOutputWithCrossAttentions(
loss=loss,
logits=logits,
caches=outputs.caches,
hiddens=outputs.hiddens,
attns=outputs.attns,
crosses=outputs.crosses,
)
|
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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"]}
|
33,521
|
quantapix/qnarre
|
refs/heads/main
|
/tools/triton/python/triton/debugger/torch_wrapper.py
|
try:
import torch as _torch
except ImportError:
_torch = None
class TorchWrapper:
"""
Helps in making torch an optional dependency
"""
def __getattr__(self, name):
if _torch is None:
raise ImportError("Triton requires PyTorch to be installed")
return getattr(_torch, name)
torch = TorchWrapper()
|
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|
33,522
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quantapix/qnarre
|
refs/heads/main
|
/qnarre/models/reformer.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 sys
import torch
import torch.utils.checkpoint
from collections import namedtuple
from functools import reduce
from operator import mul
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.autograd.function import Function
from torch.nn import CrossEntropyLoss
from ...pytorch_utils import apply_chunking_to_forward
log = logging.get_logger(__name__)
LIST = [
"google/reformer-crime-and-punishment",
"google/reformer-enwik8",
]
LSHSelfAttentionOutput = namedtuple(
"LSHSelfAttentionOutput", ["hiddens", "attention_probs", "buckets"]
)
LocalSelfAttentionOutput = namedtuple("LocalSelfAttentionOutput", ["hiddens", "attention_probs"])
AttentionOutput = namedtuple("AttentionOutput", ["hiddens", "attention_probs", "buckets"])
ReformerOutput = namedtuple(
"ReformerOutput", ["hiddens", "attn_output", "attention_probs", "buckets"]
)
ReformerBackwardOutput = namedtuple(
"ReformerBackwardOutput",
["attn_output", "hiddens", "grad_attn_output", "grad_model_states"],
)
ReformerEncoderOutput = namedtuple(
"ReformerEncoderOutput",
["hiddens", "all_hidden_states", "all_attentions", "caches"],
)
def _stable_argsort(vector, dim):
scale_offset = torch.arange(vector.shape[dim], device=vector.device).view(1, 1, -1)
scale_offset = scale_offset.expand(vector.shape)
scaled_vector = vector.shape[dim] * vector + (scale_offset % vector.shape[dim])
return torch.argsort(scaled_vector, dim=dim)
def _get_least_common_mult_chunk_len(config):
attn_types = config.attn_layers
attn_types_set = set(attn_types)
if len(attn_types_set) == 1 and attn_types[0] == "lsh":
return config.lsh_attn_chunk_length
elif len(attn_types_set) == 1 and attn_types[0] == "local":
return config.local_attn_chunk_length
elif len(attn_types_set) == 2 and attn_types_set == set(["lsh", "local"]):
return np.lcm(config.lsh_attn_chunk_length, config.local_attn_chunk_length)
else:
raise NotImplementedError(
f"Only attn layer types 'lsh' and 'local' exist, but `config.attn_layers`: {config.attn_layers}. Select "
"attn layer types from ['lsh', 'local'] only."
)
def _get_min_chunk_len(config):
attn_types = config.attn_layers
attn_types_set = set(attn_types)
if len(attn_types_set) == 1 and attn_types[0] == "lsh":
return config.lsh_attn_chunk_length
elif len(attn_types_set) == 1 and attn_types[0] == "local":
return config.local_attn_chunk_length
elif len(attn_types_set) == 2 and attn_types_set == set(["lsh", "local"]):
return min(config.lsh_attn_chunk_length, config.local_attn_chunk_length)
else:
raise NotImplementedError(
f"Only attn layer types 'lsh' and 'local' exist, but `config.attn_layers`: {config.attn_layers}. Select "
"attn layer types from ['lsh', 'local'] only."
)
class AxialPositionEmbeddings(qc.Module):
def __init__(self, config):
super().__init__()
self.axial_pos_shape = config.axial_pos_shape
self.axial_pos_embds_dim = config.axial_pos_embds_dim
self.drop = config.drop
self.least_common_mult_chunk_length = _get_least_common_mult_chunk_len(config)
self.weights = nn.ParameterList()
if sum(self.axial_pos_embds_dim) != config.d_model:
raise ValueError(
f"Make sure that config.axial_pos_embds factors: {self.axial_pos_embds_dim} sum to "
f"config.d_model: {config.d_model}"
)
for axis, axial_pos_embd_dim in enumerate(self.axial_pos_embds_dim):
ax_shape = [1] * len(self.axial_pos_shape)
ax_shape[axis] = self.axial_pos_shape[axis]
ax_shape = tuple(ax_shape) + (axial_pos_embd_dim,)
self.weights.append(nn.Parameter(torch.ones(ax_shape, dtype=torch.float32)))
def forward(self, position_ids):
batch_size = position_ids.shape[0]
sequence_length = position_ids.shape[1]
broadcasted_weights = [
weight.expand((batch_size,) + self.axial_pos_shape + weight.shape[-1:])
for weight in self.weights
]
if self.training is True:
if reduce(mul, self.axial_pos_shape) != sequence_length:
raise ValueError(
f"If training, make sure that config.axial_pos_shape factors: {self.axial_pos_shape} multiply to "
f"sequence length. Got prod({self.axial_pos_shape}) != sequence_length: {sequence_length}. "
f"You might want to consider padding your sequence length to {reduce(mul, self.axial_pos_shape)} "
"or changing config.axial_pos_shape."
)
if self.drop > 0:
weights = torch.cat(broadcasted_weights, dim=-1)
# permute weights so that 2D correctly drops dims 1 and 2
transposed_weights = weights.transpose(2, 1)
# drop entire matrix of last two dims (prev dims 1 and 2)
dropped_transposed_weights = F.dropout2d(
transposed_weights, p=self.drop, training=self.training
)
dropped_weights = dropped_transposed_weights.transpose(2, 1)
position_encodings = torch.reshape(
dropped_weights, (batch_size, sequence_length, -1)
)
else:
position_encodings = torch.cat(
[
torch.reshape(weight, (batch_size, sequence_length, -1))
for weight in broadcasted_weights
],
dim=-1,
)
else:
if reduce(mul, self.axial_pos_shape) < sequence_length:
raise ValueError(
f"Make sure that config.axial_pos_shape factors: {self.axial_pos_shape} multiply at least to "
f"max(sequence_length, least_common_mult_chunk_length): max({sequence_length}, "
f"{self.least_common_mult_chunk_length})."
)
# compute how many columns are needed
max_position_id = position_ids.max().item()
required_pos_encodings_columns = -(-(max_position_id + 1) // self.axial_pos_shape[1])
# cut to columns that are needed
position_encodings = torch.cat(
[weight[:, :required_pos_encodings_columns] for weight in broadcasted_weights],
dim=-1,
)
position_encodings = torch.reshape(
position_encodings, (batch_size, -1, position_encodings.shape[-1])
)
# select correct position encodings
position_encodings = torch.cat(
[
torch.index_select(position_encodings[i], 0, position_ids[i]).unsqueeze(0)
for i in range(batch_size)
],
dim=0,
)
return position_encodings
class PositionEmbeddings(qc.Module):
def __init__(self, config):
super().__init__()
self.drop = config.drop
self.embedding = qc.Embed(config.n_pos, config.d_model)
def forward(self, position_ids):
position_embeddings = self.embedding(position_ids)
position_embeddings = F.drop(position_embeddings, p=self.drop, training=self.training)
return position_embeddings
class ReformerEmbeddings(qc.Module):
def __init__(self, config):
super().__init__()
self.n_pos = config.n_pos
self.drop = config.drop
self.word_embeddings = qc.Embed(config.s_vocab, config.d_model)
self.position_embeddings = (
AxialPositionEmbeddings(config)
if config.axial_pos_embds
else PositionEmbeddings(config)
)
def forward(
self, input_ids=None, position_ids=None, inputs_embeds=None, start_idx_pos_encodings=0
):
if input_ids is not None:
input_shape = input_ids.size()
device = input_ids.device
else:
input_shape = inputs_embeds.size()[:-1]
device = inputs_embeds.device
seq_length = input_shape[1]
if position_ids is None:
position_ids = torch.arange(
start_idx_pos_encodings,
start_idx_pos_encodings + seq_length,
dtype=torch.long,
device=device,
)
position_ids = position_ids.unsqueeze(0).expand(input_shape)
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
if position_ids.shape[-1] > self.n_pos:
raise ValueError(
f"Sequence Length: {position_ids.shape[-1]} has to be less or equal than "
f"config.n_pos {self.n_pos}."
)
# drop
embeddings = F.drop(inputs_embeds, p=self.drop, training=self.training)
# add positional embeddings
position_embeddings = self.position_embeddings(position_ids)
embeddings = embeddings + position_embeddings
return embeddings
class EfficientAttentionMixin:
def _look_adjacent(self, vectors, num_chunks_before, num_chunks_after):
if num_chunks_before == 0 and num_chunks_after == 0:
return vectors
slices = []
for i in range(-num_chunks_before, num_chunks_after + 1):
if i == 0:
slices.append(vectors)
else:
slices.append(torch.cat([vectors[:, :, i:, ...], vectors[:, :, :i, ...]], dim=2))
return torch.cat(slices, dim=3)
def _split_hidden_size_dim(self, x, num_attn_heads, attn_head_size):
new_x_shape = x.size()[:-1] + (num_attn_heads, attn_head_size)
x = x.view(*new_x_shape)
return x.transpose(2, 1)
def _merge_hidden_size_dims(self, x, num_attn_heads, attn_head_size):
x = x.permute(0, 2, 1, 3)
return torch.reshape(x, (x.size()[0], -1, num_attn_heads * attn_head_size))
def _split_seq_length_dim_to(
self, vectors, dim_factor_1, dim_factor_2, num_attn_heads, attn_head_size=None
):
batch_size = vectors.shape[0]
split_dim_shape = (batch_size, num_attn_heads, dim_factor_1, dim_factor_2)
if len(vectors.shape) == 4:
return torch.reshape(vectors, split_dim_shape + (attn_head_size,))
elif len(vectors.shape) == 3:
return torch.reshape(vectors, split_dim_shape)
else:
raise ValueError(
f"Input vector rank should be one of [3, 4], but is: {len(vectors.shape)}"
)
class LSHSelfAttention(qc.Module, EfficientAttentionMixin):
def __init__(self, config):
super().__init__()
self.config = config
self.chunk_length = config.lsh_attn_chunk_length
self.num_hashes = config.num_hashes
self.num_buckets = config.num_buckets
self.num_chunks_before = config.lsh_num_chunks_before
self.num_chunks_after = config.lsh_num_chunks_after
self.hash_seed = config.hash_seed
self.is_decoder = config.is_decoder
self.n_pos = config.n_pos
self.drop = config.lsh_attention_probs_dropout_prob
self.n_heads = config.n_heads
self.attention_head_size = config.attention_head_size
self.all_head_size = self.n_heads * self.attention_head_size
self.d_model = config.d_model
# projection matrices
self.query_key = qc.Linear(self.d_model, self.all_head_size, bias=False)
self.value = qc.Linear(self.d_model, self.all_head_size, bias=False)
# save mask value here. Need fp32 and fp16 mask values
self.register_buffer("self_mask_value_float16", torch.tensor(-1e3))
self.register_buffer("self_mask_value_float32", torch.tensor(-1e5))
self.register_buffer("mask_value_float16", torch.tensor(-1e4))
self.register_buffer("mask_value_float32", torch.tensor(-1e9))
def forward(
self,
hiddens,
attention_mask=None,
head_mask=None,
num_hashes=None,
buckets=None,
caches=None,
y_cache=False,
output_attentions=False,
**kw,
):
sequence_length = hiddens.shape[1]
batch_size = hiddens.shape[0]
# num hashes can optionally be overwritten by user
num_hashes = num_hashes if num_hashes is not None else self.num_hashes
do_cached_attention = y_cache and caches[1] is not None
# check if cache shall be used and that hidden states are already cached
if do_cached_attention:
assert sequence_length == 1
past_buckets = caches[0]
past_states = caches[1]
# get query vector
query_vectors = self.query_key(hiddens)
query_vectors = self._split_hidden_size_dim(
query_vectors, self.n_heads, self.attention_head_size
)
if past_buckets is not None:
(
key_value_hidden_states,
sorted_bucket_idx,
buckets,
) = self._get_relevant_hid_states_and_buckets(
query_vectors=query_vectors,
attention_mask=attention_mask,
num_hashes=num_hashes,
hiddens=hiddens,
past_states=past_states,
past_buckets=past_buckets,
)
query_key_vectors = self._query_per_attn_head(key_value_hidden_states)
value_vectors = self._value_per_attn_head(key_value_hidden_states)
# split key & value vectors by num hashes to apply
# self attention on each separately
query_key_vectors = self._split_seq_length_dim_to(
query_key_vectors,
num_hashes,
-1,
self.n_heads,
self.attention_head_size,
)
value_vectors = self._split_seq_length_dim_to(
value_vectors,
num_hashes,
-1,
self.n_heads,
self.attention_head_size,
)
# repeat query vectors across hash dimension
query_vectors = query_vectors.unsqueeze(2).repeat(1, 1, num_hashes, 1, 1)
else:
key_value_hidden_states = torch.cat([past_states, hiddens], dim=1)
query_key_vectors = self.query_key(key_value_hidden_states)
value_vectors = self.value(key_value_hidden_states)
else:
# project hiddens to query_key and value
query_vectors = None
query_key_vectors = self.query_key(hiddens)
value_vectors = self.value(hiddens)
# if query key is not already split
if not do_cached_attention or past_buckets is None:
query_key_vectors = self._split_hidden_size_dim(
query_key_vectors, self.n_heads, self.attention_head_size
)
value_vectors = self._split_hidden_size_dim(
value_vectors, self.n_heads, self.attention_head_size
)
# cache buckets for next incremental decoding
if (
do_cached_attention
and past_buckets is None
and key_value_hidden_states.shape[1] >= self.chunk_length
):
buckets = self._hash_vectors(query_key_vectors, num_hashes, attention_mask)
# free memory
del hiddens
assert query_key_vectors.shape[-1] == self.attention_head_size
assert value_vectors.shape[-1] == self.attention_head_size
do_standard_self_attention = (sequence_length <= self.chunk_length) or (
y_cache and caches[1] is not None
)
# LSH attention only makes sense if chunked attention should be performed
if not do_standard_self_attention:
# set `num_buckets` on the fly, recommended way to do it
if self.num_buckets is None:
self._set_num_buckets(sequence_length)
# use cached buckets for backprop only
if buckets is None:
# hash query key vectors into buckets
buckets = self._hash_vectors(query_key_vectors, num_hashes, attention_mask)
else:
# make sure buckets has correct shape for LSH attention
buckets = buckets.view(batch_size, self.n_heads, num_hashes * sequence_length)
assert int(buckets.shape[-1]) == num_hashes * sequence_length
(
sorted_bucket_idx,
undo_sorted_bucket_idx,
) = self._get_sorted_bucket_idx_and_undo_sorted_bucket_idx(
sequence_length, buckets, num_hashes
)
# make sure bucket idx is not longer then sequence length
sorted_bucket_idx_per_hash = sorted_bucket_idx % sequence_length
# cluster query key value vectors according to hashed buckets
query_key_vectors = self._gather_by_expansion(
query_key_vectors, sorted_bucket_idx_per_hash, num_hashes
)
value_vectors = self._gather_by_expansion(
value_vectors, sorted_bucket_idx_per_hash, num_hashes
)
query_key_vectors = self._split_seq_length_dim_to(
query_key_vectors,
-1,
self.chunk_length,
self.n_heads,
self.attention_head_size,
)
value_vectors = self._split_seq_length_dim_to(
value_vectors,
-1,
self.chunk_length,
self.n_heads,
self.attention_head_size,
)
if self.chunk_length is None:
assert self.num_chunks_before == 0 and self.num_chunks_after == 0
elif do_cached_attention and past_buckets is not None:
# use max sequence length
sorted_bucket_idx_per_hash = sorted_bucket_idx
else:
# get sequence length indices
sorted_bucket_idx_per_hash = torch.arange(
sequence_length, device=query_key_vectors.device
).repeat(batch_size, self.n_heads, 1)
# scale key vectors
key_vectors = self._len_and_dim_norm(query_key_vectors)
# set query_vectors to query key vectors if LSH self attention
query_vectors = query_vectors if query_vectors is not None else query_key_vectors
# free memory
del query_key_vectors
# get attention probs
out_vectors, logits, attention_probs = self._attend(
query_vectors=query_vectors,
key_vectors=key_vectors,
value_vectors=value_vectors,
sorted_bucket_idx_per_hash=sorted_bucket_idx_per_hash,
attention_mask=attention_mask,
head_mask=head_mask,
do_standard_self_attention=do_standard_self_attention,
do_cached_attention=do_cached_attention,
)
# free memory
del key_vectors, value_vectors
# re-order out_vectors and logits
if not do_standard_self_attention:
# sort clusters back to correct ordering
out_vectors, logits = ReverseSort.apply(
out_vectors, logits, sorted_bucket_idx, undo_sorted_bucket_idx
)
if not do_standard_self_attention or (do_cached_attention and past_buckets is not None):
# sum up all hash rounds
if num_hashes > 1:
out_vectors = self._split_seq_length_dim_to(
out_vectors,
num_hashes,
sequence_length,
self.n_heads,
self.attention_head_size,
)
logits = self._split_seq_length_dim_to(
logits,
num_hashes,
sequence_length,
self.n_heads,
self.attention_head_size,
).unsqueeze(-1)
probs_vectors = torch.exp(logits - torch.logsumexp(logits, dim=2, keepdim=True))
out_vectors = torch.sum(out_vectors * probs_vectors, dim=2)
# free memory
del probs_vectors
# free memory
del logits
assert out_vectors.shape == (
batch_size,
self.n_heads,
sequence_length,
self.attention_head_size,
)
out_vectors = self._merge_hidden_size_dims(
out_vectors, self.n_heads, self.attention_head_size
)
if output_attentions is False:
attention_probs = ()
if buckets is not None:
buckets = buckets.view(batch_size, self.n_heads, num_hashes, -1)
return LSHSelfAttentionOutput(
hiddens=out_vectors, attention_probs=attention_probs, buckets=buckets
)
def _query_per_attn_head(self, hiddens):
per_head_query_key = self.query_key.weight.reshape(
self.n_heads, self.attention_head_size, self.d_model
).transpose(-2, -1)
# only relevant for inference and no bias => we can use einsum here
query_key_vectors = torch.einsum("balh,ahr->balr", hiddens, per_head_query_key)
return query_key_vectors
def _value_per_attn_head(self, hiddens):
per_head_value = self.value.weight.reshape(
self.n_heads, self.attention_head_size, self.d_model
).transpose(-2, -1)
# only relevant for inference and no bias => we can use einsum here
value_vectors = torch.einsum("balh,ahr->balr", hiddens, per_head_value)
return value_vectors
def _hash_vectors(self, vectors, num_hashes, attention_mask, increase_num_buckets=False):
batch_size = vectors.shape[0]
if isinstance(self.num_buckets, int):
assert self.num_buckets % 2 == 0
rotation_size = self.num_buckets
num_buckets = self.num_buckets
else:
# Factorize the hash if self.num_buckets is a list or tuple
rotation_size, num_buckets = 0, 1
for bucket_factor in self.num_buckets:
assert bucket_factor % 2 == 0
rotation_size = rotation_size + bucket_factor
num_buckets = num_buckets * bucket_factor
# remove gradient
vectors = vectors.detach()
if self.hash_seed is not None:
# for determinism
torch.manual_seed(self.hash_seed)
rotations_shape = (
self.n_heads,
vectors.shape[-1],
num_hashes,
rotation_size // 2,
)
# create a random self.attention_head_size x num_hashes x num_buckets/2
random_rotations = torch.randn(rotations_shape, device=vectors.device, dtype=vectors.dtype)
# Output dim: Batch_Size x Num_Attn_Heads x Num_Hashes x Seq_Len x Num_Buckets/2
rotated_vectors = torch.einsum("bmtd,mdhr->bmhtr", vectors, random_rotations)
if isinstance(self.num_buckets, int) or len(self.num_buckets) == 1:
rotated_vectors = torch.cat([rotated_vectors, -rotated_vectors], dim=-1)
buckets = torch.argmax(rotated_vectors, dim=-1)
else:
# Get the buckets for them and combine.
buckets, cur_sum, cur_product = None, 0, 1
for bucket_factor in self.num_buckets:
rotated_vectors_factor = rotated_vectors[
..., cur_sum : cur_sum + (bucket_factor // 2)
]
cur_sum = cur_sum + bucket_factor // 2
rotated_vectors_factor = torch.cat(
[rotated_vectors_factor, -rotated_vectors_factor], dim=-1
)
if buckets is None:
buckets = torch.argmax(rotated_vectors_factor, dim=-1)
else:
buckets = buckets + (cur_product * torch.argmax(rotated_vectors_factor, dim=-1))
cur_product = cur_product * bucket_factor
if attention_mask is not None and (
attention_mask.sum().item() < batch_size * attention_mask.shape[-1]
):
# add an extra bucket for padding tokens only
num_buckets = num_buckets + 1
# assign padding tokens extra bucket
buckets_mask = attention_mask.to(torch.uint8)[:, None, None, :].expand(buckets.shape)
buckets = torch.where(
buckets_mask,
buckets,
torch.tensor(num_buckets - 1, dtype=torch.long, device=buckets.device),
)
elif increase_num_buckets:
num_buckets = num_buckets + 1
# buckets is now (Batch_size x Num_Attn_Heads x Num_Hashes x Seq_Len).
# Next we add offsets so that bucket numbers from different hashing rounds don't overlap.
offsets = torch.arange(num_hashes, device=vectors.device)
offsets = (offsets * num_buckets).view((1, 1, -1, 1))
# expand to batch size and num attention heads
offsets = offsets.expand((batch_size, self.n_heads) + offsets.shape[-2:])
offset_buckets = (buckets + offsets).flatten(start_dim=2, end_dim=3)
return offset_buckets
def _get_sorted_bucket_idx_and_undo_sorted_bucket_idx(
self, sequence_length, buckets, num_hashes
):
# no gradients are needed
with torch.no_grad():
# hash-based sort
sorted_bucket_idx = _stable_argsort(buckets, dim=-1)
# create simple indices to scatter to, to have undo sort
indices = (
torch.arange(sorted_bucket_idx.shape[-1], device=buckets.device)
.view(1, 1, -1)
.expand(sorted_bucket_idx.shape)
)
# get undo sort
undo_sorted_bucket_idx = sorted_bucket_idx.new(*sorted_bucket_idx.size())
undo_sorted_bucket_idx.scatter_(-1, sorted_bucket_idx, indices)
return sorted_bucket_idx, undo_sorted_bucket_idx
def _set_num_buckets(self, sequence_length):
# `num_buckets` should be set to 2 * sequence_length // chunk_length as recommended in paper
num_buckets_pow_2 = (2 * (sequence_length // self.chunk_length)).bit_length() - 1
# make sure buckets are power of 2
num_buckets = 2**num_buckets_pow_2
# factorize `num_buckets` if `num_buckets` becomes too large
num_buckets_limit = 2 * max(
int((self.n_pos // self.chunk_length) ** (0.5)),
self.chunk_length,
)
if num_buckets > num_buckets_limit:
num_buckets = [
2 ** (num_buckets_pow_2 // 2),
2 ** (num_buckets_pow_2 - num_buckets_pow_2 // 2),
]
log.warning(
f"config.num_buckets is not set. Setting config.num_buckets to {num_buckets}..."
)
# set num buckets in config to be properly saved
self.config.num_buckets = num_buckets
self.num_buckets = num_buckets
def _attend(
self,
query_vectors,
key_vectors,
value_vectors,
sorted_bucket_idx_per_hash,
attention_mask,
head_mask,
do_standard_self_attention,
do_cached_attention,
):
# look at previous and following chunks if chunked attention
if not do_standard_self_attention:
key_vectors = self._look_adjacent(
key_vectors, self.num_chunks_before, self.num_chunks_after
)
value_vectors = self._look_adjacent(
value_vectors, self.num_chunks_before, self.num_chunks_after
)
# get logits and dots
query_key_dots = torch.matmul(query_vectors, key_vectors.transpose(-1, -2))
# free memory
del query_vectors, key_vectors
# if chunked attention split bucket idxs to query and key
if not do_standard_self_attention:
query_bucket_idx = self._split_seq_length_dim_to(
sorted_bucket_idx_per_hash, -1, self.chunk_length, self.n_heads
)
key_value_bucket_idx = self._look_adjacent(
query_bucket_idx, self.num_chunks_before, self.num_chunks_after
)
elif do_cached_attention and query_key_dots.ndim > 4:
key_value_bucket_idx = sorted_bucket_idx_per_hash
query_bucket_idx = (
key_value_bucket_idx.new_ones(key_value_bucket_idx.shape[:-1] + (1,))
* key_value_bucket_idx.max()
)
elif do_cached_attention and query_key_dots.ndim <= 4:
query_bucket_idx = (query_key_dots.shape[-1] - 1) * torch.ones_like(query_key_dots)[
:, :, :, -1
]
key_value_bucket_idx = torch.arange(
query_key_dots.shape[-1], dtype=torch.long, device=query_key_dots.device
)[None, None, :].expand(query_bucket_idx.shape[:2] + (-1,))
else:
query_bucket_idx = key_value_bucket_idx = sorted_bucket_idx_per_hash
# get correct mask values depending on precision
if query_key_dots.dtype == torch.float16:
self_mask_value = self.self_mask_value_float16.half()
mask_value = self.mask_value_float16.half()
else:
self_mask_value = self.self_mask_value_float32
mask_value = self.mask_value_float32
if not do_cached_attention:
mask = self._compute_attn_mask(
query_bucket_idx,
key_value_bucket_idx,
attention_mask,
query_key_dots.shape,
do_standard_self_attention,
)
if mask is not None:
query_key_dots = torch.where(mask, query_key_dots, mask_value)
# free memory
del mask
self_mask = torch.ne(query_bucket_idx.unsqueeze(-1), key_value_bucket_idx.unsqueeze(-2)).to(
query_bucket_idx.device
)
# apply self_mask
query_key_dots = torch.where(self_mask, query_key_dots, self_mask_value)
# free memory
del self_mask
logits = torch.logsumexp(query_key_dots, dim=-1, keepdim=True)
# dots shape is `[batch_size, num_attn_heads, num_hashes * seq_len // chunk_length, chunk_length, chunk_length * (1 + num_chunks_before + num_chunks_after)]`
attention_probs = torch.exp(query_key_dots - logits)
# free memory
del query_key_dots
# drop
attention_probs = F.drop(attention_probs, p=self.drop, training=self.training)
# Mask heads if we want to
if head_mask is not None:
attention_probs = attention_probs * head_mask
# attend values
out_vectors = torch.matmul(attention_probs, value_vectors)
# free memory
del value_vectors
# merge chunk length
if out_vectors.ndim > 4:
logits = logits.flatten(start_dim=2, end_dim=3).squeeze(-1)
out_vectors = out_vectors.flatten(start_dim=2, end_dim=3)
return out_vectors, logits, attention_probs
def _compute_attn_mask(
self,
query_indices,
key_indices,
attention_mask,
query_key_dot_shape,
do_standard_self_attention,
):
# attention mask for LSH
if attention_mask is not None:
# if chunked attention, the attention mask has to correspond to LSH order
attention_mask = attention_mask.to(torch.uint8)[:, None, :]
if not do_standard_self_attention:
# expand attn_mask to fit with key_value_bucket_idx shape
attention_mask = attention_mask[:, None, :]
attention_mask = attention_mask.expand(query_indices.shape[:-1] + (-1,))
# extract attention mask from LSH sorted key_indices
attention_mask = torch.gather(attention_mask, -1, key_indices)
attention_mask = attention_mask.unsqueeze(-2).expand(query_key_dot_shape)
# Causal mask
if self.is_decoder is True:
causal_mask = torch.ge(query_indices.unsqueeze(-1), key_indices.unsqueeze(-2)).to(
query_indices.device
)
# add attention mask if not None
if attention_mask is not None:
attention_mask = causal_mask * attention_mask
else:
attention_mask = causal_mask
return attention_mask
def _get_relevant_hid_states_and_buckets(
self, query_vectors, attention_mask, num_hashes, hiddens, past_states, past_buckets
):
# concat hidden states
hiddens = torch.cat([past_states, hiddens], dim=1)
# batch_size hidden
batch_size = hiddens.shape[0]
sequence_length = hiddens.shape[1]
# check if cached buckets include pad bucket
max_bucket = (
self.num_buckets if isinstance(self.num_buckets, int) else reduce(mul, self.num_buckets)
)
# if pad bucket was cached => need to increase num buckets for caching
increase_num_buckets = past_buckets.max() > num_hashes * max_bucket - 1
# retrieve query buckets
query_buckets = self._hash_vectors(
query_vectors, num_hashes, attention_mask, increase_num_buckets=increase_num_buckets
)
# concat buckets
concat_buckets = torch.cat([past_buckets, query_buckets.unsqueeze(-1)], dim=-1)
# hash-based sort
bucket_idx = _stable_argsort(concat_buckets, dim=-1)
# bucket_idx has shape: BatchSize x NumAttnHeads x NumHashes x SequenceLength
assert bucket_idx.shape == (
batch_size,
self.n_heads,
num_hashes,
sequence_length,
)
# find indices of new bucket indices
relevant_bucket_idx = (bucket_idx == (bucket_idx.shape[-1] - 1)).nonzero()
# expand relevant bucket indices to its chunks
relevant_bucket_idx_chunk = self._expand_to_indices_in_relevant_chunk(
relevant_bucket_idx, sequence_length
)
relevant_bucket_idx_chunk = bucket_idx[tuple(relevant_bucket_idx_chunk.transpose(0, 1))]
# adapt bucket_idx for batch and hidden states for index select
bucket_idx_batch_offset = sequence_length * (
batch_size
* torch.arange(
relevant_bucket_idx_chunk.shape[-1], device=hiddens.device, dtype=torch.long
)
// relevant_bucket_idx_chunk.shape[-1]
)
# add batch offset
relevant_bucket_idx_chunk_all_batch = relevant_bucket_idx_chunk + bucket_idx_batch_offset
hiddens = hiddens.reshape((-1, self.d_model))
# select all relevant hidden states
relevant_hidden_states = hiddens.index_select(0, relevant_bucket_idx_chunk_all_batch)
# reshape hidden states and bucket_idx to correct output
relevant_hidden_states = relevant_hidden_states.reshape(
batch_size, self.n_heads, -1, self.d_model
)
relevant_bucket_idx_chunk = relevant_bucket_idx_chunk.reshape(
batch_size, self.n_heads, num_hashes, -1
)
assert (
relevant_hidden_states.shape[2]
== (self.num_chunks_before + self.num_chunks_after + 1) * self.chunk_length * num_hashes
)
assert (
relevant_bucket_idx_chunk.shape[-1]
== (self.num_chunks_before + self.num_chunks_after + 1) * self.chunk_length
)
return relevant_hidden_states, relevant_bucket_idx_chunk, query_buckets
def _expand_to_indices_in_relevant_chunk(self, indices, sequence_length):
# get relevant indices of where chunk starts and its size
start_indices_chunk = (
(indices[:, -1] // self.chunk_length) - self.num_chunks_before
) * self.chunk_length
total_chunk_size = self.chunk_length * (1 + self.num_chunks_before + self.num_chunks_after)
# expand start indices and add correct chunk offset via arange
expanded_start_indices = start_indices_chunk.unsqueeze(-1).expand(
indices.shape[0], total_chunk_size
)
chunk_sequence_indices = expanded_start_indices + torch.arange(
total_chunk_size, device=indices.device, dtype=torch.long
).unsqueeze(0).expand(indices.shape[0], total_chunk_size)
# make sure that circular logic holds via % seq len
chunk_sequence_indices = chunk_sequence_indices.flatten() % sequence_length
# expand indices and set indices correctly
indices = (
indices.unsqueeze(1)
.expand((indices.shape[0], total_chunk_size, -1))
.flatten(0, 1)
.clone()
)
indices[:, -1] = chunk_sequence_indices
return indices
def _len_and_dim_norm(self, vectors):
vectors = self._len_norm(vectors)
vectors = vectors * torch.rsqrt(
torch.tensor(self.attention_head_size, device=vectors.device, dtype=vectors.dtype)
)
return vectors
def _len_norm(self, x, epsilon=1e-6):
variance = torch.mean(x**2, -1, keepdim=True)
norm_x = x * torch.rsqrt(variance + epsilon)
return norm_x
def _gather_by_expansion(self, vectors, idxs, num_hashes):
expanded_idxs = idxs.unsqueeze(-1).expand(-1, -1, -1, self.attention_head_size)
vectors = vectors.repeat(1, 1, num_hashes, 1)
return torch.gather(vectors, 2, expanded_idxs)
class ReverseSort(Function):
@staticmethod
def forward(ctx, out_vectors, logits, sorted_bucket_idx, undo_sorted_bucket_idx):
# save sorted_bucket_idx for backprop
with torch.no_grad():
ctx.sorted_bucket_idx = sorted_bucket_idx
# undo sort to have correct order for next layer
expanded_undo_sort_indices = undo_sorted_bucket_idx.unsqueeze(-1).expand(
out_vectors.shape
)
out_vectors = torch.gather(out_vectors, 2, expanded_undo_sort_indices)
logits = torch.gather(logits, 2, undo_sorted_bucket_idx)
return out_vectors, logits
@staticmethod
def backward(ctx, grad_out_vectors, grad_logits):
# get parameters saved in ctx
sorted_bucket_idx = ctx.sorted_bucket_idx
expanded_sort_indices = sorted_bucket_idx.unsqueeze(-1).expand(grad_out_vectors.shape)
# reverse sort of forward
grad_out_vectors = torch.gather(grad_out_vectors, 2, expanded_sort_indices)
grad_logits = torch.gather(grad_logits, 2, sorted_bucket_idx)
# return grad and `None` fillers for last 2 forward args
return grad_out_vectors, grad_logits, None, None
class LocalSelfAttention(qc.Module, EfficientAttentionMixin):
def __init__(self, config):
super().__init__()
self.n_heads = config.n_heads
self.chunk_length = config.local_attn_chunk_length
self.num_chunks_before = config.local_num_chunks_before
self.num_chunks_after = config.local_num_chunks_after
self.is_decoder = config.is_decoder
self.PAD = config.PAD
self.attention_head_size = config.attention_head_size
self.all_head_size = self.n_heads * self.attention_head_size
self.d_model = config.d_model
# projection matrices
self.query = qc.Linear(self.d_model, self.all_head_size, bias=False)
self.key = qc.Linear(self.d_model, self.all_head_size, bias=False)
self.value = qc.Linear(self.d_model, self.all_head_size, bias=False)
self.drop = config.local_attention_probs_dropout_prob
# save mask value here
self.register_buffer("mask_value_float16", torch.tensor(-1e4))
self.register_buffer("mask_value_float32", torch.tensor(-1e9))
def forward(
self,
hiddens,
attention_mask=None,
head_mask=None,
caches=None,
y_cache=False,
output_attentions=False,
**kw,
):
sequence_length = hiddens.shape[1]
batch_size = hiddens.shape[0]
# check if cache shall be used and that hidden states are already cached
if y_cache and caches[1] is not None:
assert caches[0] is None
key_value_hidden_states = self._retrieve_relevant_hidden_states(
caches[1], self.chunk_length, self.num_chunks_before
)
key_value_hidden_states = torch.cat([key_value_hidden_states, hiddens], dim=1)
# only query vector for last token
query_vectors = self.query(hiddens)
# compute key and value for relevant chunk
key_vectors = self.key(key_value_hidden_states)
value_vectors = self.value(key_value_hidden_states)
# free memory
del key_value_hidden_states
else:
# project hiddens to query, key and value
query_vectors = self.query(hiddens)
key_vectors = self.key(hiddens)
value_vectors = self.value(hiddens)
# split last dim into `config.n_heads` and `config.attention_head_size`
query_vectors = self._split_hidden_size_dim(
query_vectors, self.n_heads, self.attention_head_size
)
key_vectors = self._split_hidden_size_dim(
key_vectors, self.n_heads, self.attention_head_size
)
value_vectors = self._split_hidden_size_dim(
value_vectors, self.n_heads, self.attention_head_size
)
assert query_vectors.shape[-1] == self.attention_head_size
assert key_vectors.shape[-1] == self.attention_head_size
assert value_vectors.shape[-1] == self.attention_head_size
if self.chunk_length is None:
assert self.num_chunks_before == 0 and self.num_chunks_after == 0
# normalize key vectors
key_vectors = key_vectors / torch.sqrt(
torch.tensor(
self.attention_head_size, device=key_vectors.device, dtype=key_vectors.dtype
)
)
# get sequence length indices
indices = torch.arange(sequence_length, device=query_vectors.device).repeat(
batch_size, self.n_heads, 1
)
# if one should do normal n^2 self-attention
do_standard_self_attention = sequence_length <= self.chunk_length
# if input should be chunked
if not do_standard_self_attention:
# chunk vectors
# B x Num_Attn_Head x Seq_Len // chunk_len x chunk_len x attn_head_size
query_vectors = self._split_seq_length_dim_to(
query_vectors,
-1,
self.chunk_length,
self.n_heads,
self.attention_head_size,
)
key_vectors = self._split_seq_length_dim_to(
key_vectors,
-1,
self.chunk_length,
self.n_heads,
self.attention_head_size,
)
value_vectors = self._split_seq_length_dim_to(
value_vectors,
-1,
self.chunk_length,
self.n_heads,
self.attention_head_size,
)
# chunk indices
query_indices = self._split_seq_length_dim_to(
indices, -1, self.chunk_length, self.n_heads
)
key_indices = self._split_seq_length_dim_to(
indices, -1, self.chunk_length, self.n_heads
)
# append chunks before and after
key_vectors = self._look_adjacent(
key_vectors, self.num_chunks_before, self.num_chunks_after
)
value_vectors = self._look_adjacent(
value_vectors, self.num_chunks_before, self.num_chunks_after
)
key_indices = self._look_adjacent(
key_indices, self.num_chunks_before, self.num_chunks_after
)
else:
query_indices = key_indices = indices
# query-key matmul: QK^T
query_key_dots = torch.matmul(query_vectors, key_vectors.transpose(-1, -2))
# free memory
del query_vectors, key_vectors
mask = self._compute_attn_mask(
query_indices,
key_indices,
attention_mask,
query_key_dots.shape,
do_standard_self_attention,
)
if mask is not None:
# get mask tensor depending on half precision or not
if query_key_dots.dtype == torch.float16:
mask_value = self.mask_value_float16.half()
else:
mask_value = self.mask_value_float32
query_key_dots = torch.where(mask, query_key_dots, mask_value)
# free memory
del mask
# softmax
logits = torch.logsumexp(query_key_dots, dim=-1, keepdim=True)
attention_probs = torch.exp(query_key_dots - logits)
# free memory
del logits
# drop
attention_probs = F.drop(attention_probs, p=self.drop, training=self.training)
# Mask heads if we want to
if head_mask is not None:
attention_probs = attention_probs * head_mask
# attend values
out_vectors = torch.matmul(attention_probs, value_vectors)
# free memory
del value_vectors
# merge chunk length
if not do_standard_self_attention:
out_vectors = out_vectors.flatten(start_dim=2, end_dim=3)
assert out_vectors.shape == (
batch_size,
self.n_heads,
sequence_length,
self.attention_head_size,
)
out_vectors = self._merge_hidden_size_dims(
out_vectors, self.n_heads, self.attention_head_size
)
if output_attentions is False:
attention_probs = ()
return LocalSelfAttentionOutput(hiddens=out_vectors, attention_probs=attention_probs)
def _compute_attn_mask(
self,
query_indices,
key_indices,
attention_mask,
query_key_dots_shape,
do_standard_self_attention,
):
# chunk attention mask and look before and after
if attention_mask is not None:
attention_mask = attention_mask.to(torch.uint8)[:, None, :]
if not do_standard_self_attention:
attention_mask = self._split_seq_length_dim_to(
attention_mask, -1, self.chunk_length, 1
)
attention_mask = self._look_adjacent(
attention_mask, self.num_chunks_before, self.num_chunks_after
)
# create attn_mask
attention_mask = attention_mask.unsqueeze(-2).expand(query_key_dots_shape)
# Causal mask
if self.is_decoder is True:
causal_mask = torch.ge(query_indices.unsqueeze(-1), key_indices.unsqueeze(-2)).to(
query_indices.device
)
# add attention mask if not None
if attention_mask is not None:
attention_mask = causal_mask * attention_mask
else:
attention_mask = causal_mask
return attention_mask
@staticmethod
def _retrieve_relevant_hidden_states(previous_hidden_states, chunk_length, num_chunks_before):
start_position = (
(previous_hidden_states.shape[1] // chunk_length) - num_chunks_before
) * chunk_length
return previous_hidden_states[:, start_position:]
class ReformerSelfOutput(qc.Module):
def __init__(self, config):
super().__init__()
all_head_size = config.n_heads * config.attention_head_size
self.drop = config.drop
self.dense = qc.Linear(all_head_size, config.d_model, bias=False)
def forward(self, hiddens):
hiddens = self.dense(hiddens)
hiddens = F.drop(hiddens, p=self.drop, training=self.training)
return hiddens
class Attention(qc.Module):
def __init__(self, config, layer_id=0):
super().__init__()
self.layer_id = layer_id
self.attn_layers = config.attn_layers
self.layer_norm = qc.LayerNorm(config.d_model, eps=config.eps)
if len(set(self.attn_layers)) == 1 and self.attn_layers[0] == "lsh":
self.self_attention = LSHSelfAttention(config)
elif len(set(self.attn_layers)) == 1 and self.attn_layers[0] == "local":
self.self_attention = LocalSelfAttention(config)
elif len(set(self.attn_layers)) == 2 and set(self.attn_layers) == set(["lsh", "local"]):
# get correct attn layers
if self.attn_layers[self.layer_id] == "lsh":
self.self_attention = LSHSelfAttention(config)
else:
self.self_attention = LocalSelfAttention(config)
else:
raise NotImplementedError(
f"Only attn layer types 'lsh' and 'local' exist, but got `config.attn_layers`: {self.attn_layers}. "
"Select attn layer types from ['lsh', 'local'] only."
)
self.output = ReformerSelfOutput(config)
def forward(
self,
hiddens,
attention_mask=None,
head_mask=None,
num_hashes=None,
caches=None,
y_cache=False,
orig_sequence_length=None,
output_attentions=False,
buckets=None,
):
hiddens = self.layer_norm(hiddens)
# make sure cached hidden states is set to None for backward pass
if caches is not None:
caches_layer = caches[self.layer_id]
else:
caches_layer = None
# use cached buckets for backprob if buckets not None for LSHSelfAttention
self_attention_outputs = self.self_attention(
hiddens=hiddens,
head_mask=head_mask,
attention_mask=attention_mask,
num_hashes=num_hashes,
caches=caches_layer,
y_cache=y_cache,
output_attentions=output_attentions,
buckets=buckets,
)
# add buckets if necessary
if hasattr(self_attention_outputs, "buckets"):
buckets = self_attention_outputs.buckets
else:
buckets = None
# cache hidden states for future use
if y_cache:
if caches[self.layer_id][0] is None:
# padded input should not be cached
past_buckets = (
buckets[:, :, :, :orig_sequence_length]
if (buckets is not None and orig_sequence_length > 1)
else buckets
)
else:
past_buckets = torch.cat([caches[self.layer_id][0], buckets], dim=-1)
if caches[self.layer_id][1] is None:
# padded input should not be cached
past_states = hiddens[:, :orig_sequence_length]
else:
past_states = torch.cat([caches[self.layer_id][1], hiddens], dim=1)
caches[self.layer_id] = (past_buckets, past_states)
# compute attention feed forward output
attention_output = self.output(self_attention_outputs.hiddens)
return AttentionOutput(
hiddens=attention_output,
attention_probs=self_attention_outputs.attention_probs,
buckets=buckets,
)
class ReformerFeedForwardDense(qc.Module):
def __init__(self, cfg):
super().__init__()
self.drop = cfg.drop
self.act = qu.activation(cfg.act)
self.dense = qc.Linear(cfg.d_model, cfg.feed_forward_size)
def forward(self, x):
y = self.dense(x)
y = F.drop(y, p=self.drop, training=self.training)
y = self.act(y)
return y
class ReformerFeedForwardOutput(qc.Module):
def __init__(self, config):
super().__init__()
self.drop = config.drop
self.dense = qc.Linear(config.feed_forward_size, config.d_model)
def forward(self, x):
y = self.dense(x)
y = F.drop(y, p=self.drop, training=self.training)
return y
class ChunkReformerFeedForward(qc.Module):
def __init__(self, config):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.layer_norm = qc.LayerNorm(config.d_model, eps=config.eps)
self.dense = ReformerFeedForwardDense(config)
self.output = ReformerFeedForwardOutput(config)
def forward(self, attention_output):
return apply_chunking_to_forward(
self.forward_chunk,
self.chunk_size_feed_forward,
self.seq_len_dim,
attention_output,
)
def forward_chunk(self, hiddens):
hiddens = self.layer_norm(hiddens)
hiddens = self.dense(hiddens)
return self.output(hiddens)
class Layer(qc.Module):
def __init__(self, config, layer_id=0):
super().__init__()
self.attention = Attention(config, layer_id)
# drop requires to have the same
# seed for forward and backward pass
self.attention_seed = None
self.feed_forward_seed = None
self.feed_forward = ChunkReformerFeedForward(config)
def _init_attention_seed(self):
if hasattr(torch.cuda, "default_generators") and len(torch.cuda.default_generators) > 0:
# GPU
device_idx = torch.cuda.current_device()
self.attention_seed = torch.cuda.default_generators[device_idx].seed()
else:
# CPU
self.attention_seed = int(torch.seed() % sys.maxsize)
torch.manual_seed(self.attention_seed)
def _init_feed_forward_seed(self):
if hasattr(torch.cuda, "default_generators") and len(torch.cuda.default_generators) > 0:
# GPU
device_idx = torch.cuda.current_device()
self.feed_forward_seed = torch.cuda.default_generators[device_idx].seed()
else:
# CPU
self.feed_forward_seed = int(torch.seed() % sys.maxsize)
torch.manual_seed(self.feed_forward_seed)
def forward(
self,
prev_attn_output,
hiddens,
attention_mask=None,
head_mask=None,
num_hashes=None,
caches=None,
y_cache=False,
orig_sequence_length=None,
output_attentions=False,
):
with torch.no_grad():
if self.training:
self._init_attention_seed()
attn_outputs = self.attention(
hiddens=hiddens,
head_mask=head_mask,
attention_mask=attention_mask,
num_hashes=num_hashes,
caches=caches,
y_cache=y_cache,
orig_sequence_length=orig_sequence_length,
output_attentions=output_attentions,
)
attn_output = attn_outputs.hiddens
# Implementation of RevNet (see Fig. 6 in https://towardsdatascience.com/illustrating-the-reformer-393575ac6ba0)
# Y_1 = X_1 + f(X_2)
attn_output = prev_attn_output + attn_output
# free memory
del prev_attn_output
# every forward pass we sample a different seed
# for drop and save seed for forward fn in backward
# to have correct drop
if self.training:
self._init_feed_forward_seed()
# Y_2 = X_2 + g(Y_1)
hiddens = hiddens + self.feed_forward(attn_output)
return ReformerOutput(
attn_output=attn_output,
hiddens=hiddens,
attention_probs=attn_outputs.attention_probs,
buckets=attn_outputs.buckets,
)
def backward_pass(
self,
next_attn_output,
hiddens,
grad_attn_output,
grad_model_states,
attention_mask=None,
head_mask=None,
buckets=None,
):
assert self.training
with torch.enable_grad():
next_attn_output.requires_grad = True
# set seed to have correct drop
torch.manual_seed(self.feed_forward_seed)
# g(Y_1)
res_hidden_states = self.feed_forward(next_attn_output)
res_hidden_states.backward(grad_model_states, retain_graph=True)
with torch.no_grad():
# X_2 = Y_2 - g(Y_1)
hiddens = hiddens - res_hidden_states
del res_hidden_states
grad_attn_output = grad_attn_output + next_attn_output.grad
next_attn_output.grad = None
with torch.enable_grad():
hiddens.requires_grad = True
# set seed to have correct drop
torch.manual_seed(self.attention_seed)
# f(X_2)
# use cached buckets for backprob if buckets not None for LSHSelfAttention
output = self.attention(
hiddens=hiddens,
head_mask=head_mask,
attention_mask=attention_mask,
buckets=buckets,
).hiddens
output.backward(grad_attn_output, retain_graph=True)
with torch.no_grad():
# X_1 = Y_1 - f(X_2)
attn_output = next_attn_output - output
del output, next_attn_output
grad_model_states = grad_model_states + hiddens.grad
hiddens.grad = None
hiddens = hiddens.detach()
return ReformerBackwardOutput(
attn_output=attn_output,
hiddens=hiddens,
grad_attn_output=grad_attn_output,
grad_model_states=grad_model_states,
)
class _ReversibleFunction(Function):
@staticmethod
def forward(
ctx,
hiddens,
layers,
attention_mask,
head_mask,
num_hashes,
all_hidden_states,
all_attentions,
caches,
y_cache,
orig_sequence_length,
output_hidden_states,
output_attentions,
):
all_buckets = ()
# split duplicated tensor
hiddens, attn_output = torch.chunk(hiddens, 2, dim=-1)
for layer_id, (layer, layer_head_mask) in enumerate(zip(layers, head_mask)):
if output_hidden_states is True:
all_hidden_states.append(hiddens)
layer_outputs = layer(
prev_attn_output=attn_output,
hiddens=hiddens,
attention_mask=attention_mask,
head_mask=layer_head_mask,
num_hashes=num_hashes,
caches=caches,
y_cache=y_cache,
orig_sequence_length=orig_sequence_length,
output_attentions=output_attentions,
)
attn_output = layer_outputs.attn_output
hiddens = layer_outputs.hiddens
all_buckets = all_buckets + (layer_outputs.buckets,)
if output_attentions:
all_attentions.append(layer_outputs.attention_probs)
# Add last layer
if output_hidden_states is True:
all_hidden_states.append(hiddens)
# attach params to ctx for backward
ctx.save_for_backward(attn_output.detach(), hiddens.detach())
ctx.layers = layers
ctx.all_buckets = all_buckets
ctx.head_mask = head_mask
ctx.attention_mask = attention_mask
# Concatenate 2 RevNet outputs
return torch.cat([attn_output, hiddens], dim=-1)
@staticmethod
def backward(ctx, grad_model_states):
grad_attn_output, grad_model_states = torch.chunk(grad_model_states, 2, dim=-1)
# retrieve params from ctx for backward
attn_output, hiddens = ctx.saved_tensors
# create tuple
output = ReformerBackwardOutput(
attn_output=attn_output,
hiddens=hiddens,
grad_attn_output=grad_attn_output,
grad_model_states=grad_model_states,
)
# free memory
del grad_attn_output, grad_model_states, attn_output, hiddens
layers = ctx.layers
all_buckets = ctx.all_buckets
head_mask = ctx.head_mask
attention_mask = ctx.attention_mask
for idx, layer in enumerate(layers[::-1]):
# pop last buckets from stack
buckets = all_buckets[-1]
all_buckets = all_buckets[:-1]
# backprop
output = layer.backward_pass(
next_attn_output=output.attn_output,
hiddens=output.hiddens,
grad_attn_output=output.grad_attn_output,
grad_model_states=output.grad_model_states,
head_mask=head_mask[len(layers) - idx - 1],
attention_mask=attention_mask,
buckets=buckets,
)
assert all_buckets == (), "buckets have to be empty after backpropagation"
grad_model_states = torch.cat([output.grad_attn_output, output.grad_model_states], dim=-1)
# num of return vars has to match num of forward() args
# return gradient for hiddens arg and None for other args
return grad_model_states, None, None, None, None, None, None, None, None, None, None, None
class Encoder(qc.Module):
def __init__(self, config):
super().__init__()
self.drop = config.drop
self.layers = nn.ModuleList([Layer(config, i) for i in range(config.n_lays)])
# Reformer is using Rev Nets, thus last layer outputs are concatenated and
# Layer Norm is done over 2 * d_model
self.layer_norm = qc.LayerNorm(2 * config.d_model, eps=config.eps)
def forward(
self,
hiddens,
attention_mask=None,
head_mask=None,
num_hashes=None,
caches=None,
y_cache=False,
orig_sequence_length=None,
output_hidden_states=False,
output_attentions=False,
):
# hiddens and attention lists to be filled if wished
all_hidden_states = []
all_attentions = []
# init cached hidden states if necessary
if caches is None:
caches = [((None), (None)) for i in range(len(self.layers))]
# concat same tensor for reversible ResNet
hiddens = torch.cat([hiddens, hiddens], dim=-1)
hiddens = _ReversibleFunction.apply(
hiddens,
self.layers,
attention_mask,
head_mask,
num_hashes,
all_hidden_states,
all_attentions,
caches,
y_cache,
orig_sequence_length,
output_hidden_states,
output_attentions,
)
# Apply layer norm to concatenated hidden states
hiddens = self.layer_norm(hiddens)
# Apply drop
hiddens = F.drop(hiddens, p=self.drop, training=self.training)
return ReformerEncoderOutput(
hiddens=hiddens,
all_hidden_states=all_hidden_states,
all_attentions=all_attentions,
caches=caches,
)
class ReformerOnlyLMHead(qc.Module):
def __init__(self, config):
super().__init__()
self.seq_len_dim = 1
self.chunk_size_lm_head = config.chunk_size_lm_head
self.decoder = qc.Linear(2 * config.d_model, config.s_vocab, bias=False)
self.bias = nn.Parameter(torch.zeros(config.s_vocab))
self.decoder.bias = self.bias
def forward(self, hiddens):
return apply_chunking_to_forward(
self.forward_chunk, self.chunk_size_lm_head, self.seq_len_dim, hiddens
)
def forward_chunk(self, hiddens):
hiddens = self.decoder(hiddens)
return hiddens
def _tie_weights(self):
# To tie those two weights if they get disconnected (on TPU or when the bias is resized)
self.bias = self.decoder.bias
class Model(PreTrained):
def __init__(self, config):
super().__init__(config)
self.config = config
assert self.config.n_lays > 0
self.embeddings = ReformerEmbeddings(config)
self.encoder = Encoder(config)
def forward(
self,
input_ids=None,
attention_mask=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
num_hashes=None,
caches=None,
y_cache=None,
output_hidden_states=None,
output_attentions=None,
return_dict=None,
):
y_cache = y_cache if y_cache is not None else self.config.y_cache
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() # noqa: F841
device = input_ids.device
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1] # noqa: F841
device = inputs_embeds.device
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
assert len(input_shape) == 2
if caches is not None:
assert not self.training
# prepare head mask
head_mask = self.get_head_mask(head_mask, self.config.n_lays, is_attention_chunked=True)
# original sequence length for padding
orig_sequence_length = input_shape[-1]
# if needs padding
least_common_mult_chunk_length = _get_least_common_mult_chunk_len(self.config)
min_chunk_length = _get_min_chunk_len(self.config)
must_pad_to_match_chunk_length = (
input_shape[-1] % least_common_mult_chunk_length != 0
and input_shape[-1] > min_chunk_length
and caches is None
)
if must_pad_to_match_chunk_length:
padding_length = (
least_common_mult_chunk_length - input_shape[-1] % least_common_mult_chunk_length
)
if self.training is True:
raise ValueError(
f"If training, sequence length {input_shape[-1]} has to be a multiple of least common multiple "
f"chunk_length {least_common_mult_chunk_length}. Please consider padding the input to a length "
f"of {input_shape[-1] + padding_length}."
)
# pad input
(
input_ids,
inputs_embeds,
attention_mask,
position_ids,
input_shape,
) = self._pad_to_mult_of_chunk_length(
input_ids,
inputs_embeds=inputs_embeds,
attention_mask=attention_mask,
position_ids=position_ids,
input_shape=input_shape,
padding_length=padding_length,
padded_seq_length=least_common_mult_chunk_length,
device=device,
)
# start index for position encoding depends on incremental decoding
if caches is not None:
start_idx_pos_encodings = caches[0][1].shape[1]
else:
start_idx_pos_encodings = 0
embedding_output = self.embeddings(
input_ids=input_ids,
position_ids=position_ids,
inputs_embeds=inputs_embeds,
start_idx_pos_encodings=start_idx_pos_encodings,
)
encoder_outputs = self.encoder(
hiddens=embedding_output,
head_mask=head_mask,
attention_mask=attention_mask,
num_hashes=num_hashes,
caches=caches,
y_cache=y_cache,
orig_sequence_length=orig_sequence_length,
output_hidden_states=output_hidden_states,
output_attentions=output_attentions,
)
sequence_output = encoder_outputs.hiddens
# if padding was applied
if must_pad_to_match_chunk_length:
sequence_output = sequence_output[:, :orig_sequence_length]
caches = encoder_outputs.caches if y_cache else None
hiddens = encoder_outputs.all_hidden_states if output_hidden_states else None
attns = encoder_outputs.all_attentions if output_attentions else None
if not return_dict:
return tuple(v for v in [sequence_output, caches, hiddens, attns] if v is not None)
return qo.WithCaches(
y=sequence_output,
caches=caches,
hiddens=hiddens,
attns=attns,
)
def _pad_to_mult_of_chunk_length(
self,
input_ids,
inputs_embeds=None,
attention_mask=None,
position_ids=None,
input_shape=None,
padding_length=None,
padded_seq_length=None,
device=None,
):
log.info(
f"Input ids are automatically padded from {input_shape[-1]} to {input_shape[-1] + padding_length} to be a "
f"multiple of `config.chunk_length`: {padded_seq_length}"
)
padded_input_ids = torch.full(
(input_shape[0], padding_length),
self.config.PAD,
device=device,
dtype=torch.long,
)
# Extend `attention_mask`
if attention_mask is not None:
pad_attention_mask = torch.zeros(
input_shape[0], padding_length, device=device, dtype=attention_mask.dtype
)
attention_mask = torch.cat([attention_mask, pad_attention_mask], dim=-1)
else:
attention_mask = torch.cat(
[
torch.ones(input_shape, device=device, dtype=torch.uint8),
torch.zeros((input_shape[0], padding_length), device=device, dtype=torch.uint8),
],
dim=-1,
)
# Extend `input_ids` with padding to match least common multiple chunk_length
if input_ids is not None:
input_ids = torch.cat([input_ids, padded_input_ids], dim=-1)
input_shape = input_ids.size()
# Pad position ids if given
if position_ids is not None:
padded_position_ids = torch.arange(
input_shape[-1], padded_seq_length, dtype=torch.long, device=device
)
padded_position_ids = position_ids.unsqueeze(0).expand(
input_shape[0], padding_length
)
position_ids = torch.cat([position_ids, padded_position_ids], dim=-1)
# Extend `inputs_embeds` with padding to match least common multiple chunk_length
if inputs_embeds is not None:
padded_inputs_embeds = self.embeddings(padded_input_ids, position_ids)
inputs_embeds = torch.cat([inputs_embeds, padded_inputs_embeds], dim=-2)
input_shape = inputs_embeds.size()
return input_ids, inputs_embeds, attention_mask, position_ids, input_shape
class ReformerModelWithLMHead(PreTrained):
def __init__(self, config):
super().__init__(config)
assert config.is_decoder
assert "local" not in self.config.attn_layers or config.local_num_chunks_after == 0
assert "lsh" not in self.config.attn_layers or config.lsh_num_chunks_after == 0
self.reformer = Model(config)
self.lm_head = ReformerOnlyLMHead(config)
def forward(
self,
input_ids=None,
position_ids=None,
attention_mask=None,
head_mask=None,
inputs_embeds=None,
num_hashes=None,
caches=None,
y_cache=None,
output_hidden_states=None,
output_attentions=None,
return_dict=None,
labels=None,
):
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
reformer_outputs = self.reformer(
input_ids,
position_ids=position_ids,
attention_mask=attention_mask,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
num_hashes=num_hashes,
caches=caches,
y_cache=y_cache,
output_hidden_states=output_hidden_states,
output_attentions=output_attentions,
return_dict=return_dict,
)
sequence_output = reformer_outputs[0]
logits = self.lm_head(sequence_output)
loss = None
if labels is not None:
# Shift so that tokens < n predict n
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = CrossEntropyLoss()
loss = loss_fct(shift_logits.view(-1, self.config.s_vocab), shift_labels.view(-1))
if not return_dict:
output = (logits,) + reformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return qo.LossCaches(
loss=loss,
logits=logits,
caches=reformer_outputs.caches,
hiddens=reformer_outputs.hiddens,
attns=reformer_outputs.attns,
)
class ForMasked(PreTrained):
def __init__(self, **kw):
super().__init__(**kw)
cfg = self.get_cfg(kw)
assert not cfg.is_decoder
self.model = Model(**kw)
self.proj = ReformerOnlyLMHead(**kw)
forward = qf.forward_masked
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, "tanh", **kw, d_model=2 * cfg.d_model)
forward = qf.forward_seq
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/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", 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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"], 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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"]}
|
33,523
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/models/old/params.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 tensorflow as T
from absl import flags as F
from qnarre.core import utils as U
def load_flags():
# from official.utils.flags import core as fu
# fu.define_base()
# fu.define_performance(
# all_reduce_alg=True,
# dtype=False,
# inter_op=False,
# intra_op=False,
# max_train_steps=False,
# num_parallel_calls=False,
# synthetic_data=True,
# )
# fu.define_image()
# fu.define_benchmark()
# F.adopt_module_key_flags(fu)
F.DEFINE_bool("do_eval", None, "")
F.DEFINE_bool("do_train", None, "")
F.DEFINE_float("stop_threshold", None, "")
F.DEFINE_float("train_epochs", None, "")
F.DEFINE_integer("batch_size", None, "")
F.DEFINE_integer("checkpoint_steps", None, "")
F.DEFINE_integer("epochs_between_evals", None, "")
F.DEFINE_integer("eval_batch_size", None, "")
F.DEFINE_integer("eval_steps", None, "")
F.DEFINE_integer("iters_per_loop", None, "")
F.DEFINE_integer("train_steps", None, "")
F.DEFINE_integer("warmup_steps", None, "")
F.DEFINE_string("dir_data", None, "")
# F.DEFINE_string('log_dir', None, '')
F.DEFINE_string("dir_model", None, "")
F.DEFINE_string("model", None, "")
F.DEFINE_string("dir_save", None, "")
df = ["channels_first", "channels_last"]
F.DEFINE_enum("data_format", None, df, "")
def load_params():
f = "channels_first" if T.test.is_built_with_cuda() else "channels_last"
return U.Params(_params, data_format=F.FLAGS.data_format or f)
_params = dict(
layout=None,
features=None,
)
_params2 = dict(
epochs_between_evals=None,
# len_bucket_step=1.1,
# vocab_divisor=1,
adam_beta1=0.9,
adam_beta2=0.997, # 0.999
adam_eps=1e-9, # 1e-6
adamw_decay=0.0,
add_relative=False,
all_reduce_alg=None,
alpha=0.6,
attn_bdims="",
attn_type="dot_attn",
beam_size=4,
causal_self_attn=True,
clip_grad_norm=2.0, # 0.0 no gradient clipping,
compress_steps=0,
conv_first_kernel=3,
daisy_chain_vars=True,
data_format=None,
dataset=None,
dist_strategy=None,
drop_long_seqs=False,
ds_src_len=0,
ds_tgt_len=0,
dtype=None,
eval_frequency=100,
eval_steps=1,
extra_decode_len=50,
factored_logits=False,
ffn_bdims="",
ffn_layer="dense_dense",
fixed_batch_size=False,
full_predict=False,
gpu_thread_mode=None,
grad_noise_scale=0.0,
group_eps=1e-5,
heads_share_embed=False,
init_gain=1.5, # 1.0
initializer="uniform_unit_scaling", # 'orthogonal',
input_frames=1,
inter_op=None,
intra_op=None,
kernel_height=3,
kernel_width=1,
label_smoothing=0.1,
learn_rate=2e-4, # 2.0, squad 5e-6,
loss_scale=None,
lr_constant=0.1,
lr_schedule="constant*linear_warmup*rsqrt_decay",
lr_warmup_steps=200,
max_position=0,
max_train_steps=None,
min_len_bucket=8,
min_len=0,
mixed_precision_loss=32768,
mixed_precision_loss_scaler="exponential",
mlm_preds=20,
mlm_prob=0.15,
model=None,
multiply_mode="sqrt_depth",
no_data_parallel=False,
norm_eps=1e-6,
norm_type="layer", # 'batch', layer', 'noam', 'none'.
num_gpu=None,
n_groups=8,
num_parallel_calls=None,
num_sampled_classes=0,
opt_multistep_accumulate_steps=None,
opt_zero_grads=False,
overload_metric="",
pack_dataset=False,
pad_batch=False,
pad_remover=False, # True,
parallel_batches=None,
params_profile=None,
penalty=0.1,
post_cmd="dan",
pre_cmd="n",
prepend_mode="none",
prepost_bdims="",
private_threads=None,
prox_bias=False,
run_autoregressive=False,
sampl_gold_mixin_prob=0.5,
sampl_method="argmax", # 'argmax' or 'random'
sampl_prob=0.0,
sampl_temp=1.0,
sampl_warmup_steps=50000,
self_attn_type="dot_attn",
shared_embed=False,
shared_weights=True,
short_seq_prob=0.1,
shuffle_size=512,
split_tgts_chunk_len=0,
split_tgts_max_chunks=100,
split_to_len=0,
src_len=0,
steps_between_evals=None,
stop_threshold=None,
summarize_grads=False,
summarize_vars=False,
symbol_modality_shards=16,
synthetic_data=None,
target_frames=1,
tgt_len=0,
train_epochs=[],
train_steps=1000,
unidirectional_encoder=False,
use_custom_ops=True,
use_target_embed=True,
warm_start_from="",
warmup_steps=16000,
weight_decay=1e-6,
weight_noise=0.0,
weights_fn={},
)
|
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|
33,524
|
quantapix/qnarre
|
refs/heads/main
|
/tools/triton/python/tutorials/04-low-memory-dropout.py
|
"""
Low-Memory Dropout
==================
In this tutorial, you will write a memory-efficient implementation of dropout whose state
will be composed of a single int32 seed. This differs from more traditional implementations of dropout,
whose state is generally composed of a bit mask tensor of the same shape as the input.
In doing so, you will learn about:
* The limitations of naive implementations of Dropout with PyTorch.
* Parallel pseudo-random number generation in Triton.
"""
# %%
# Baseline
# --------
#
# The *dropout* operator was first introduced in [SRIVASTAVA2014]_ as a way to improve the performance
# of deep neural networks in low-data regime (i.e. regularization).
#
# It takes a vector as input and produces a vector of the same shape as output. Each scalar in the
# output has a probability :math:`p` of being changed to zero and otherwise it is copied from the input.
# This forces the network to perform well even when only :math:`1 - p` scalars from the input are available.
#
# At evaluation time we want to use the full power of the network so we set :math:`p=0`. Naively this would
# increase the norm of the output (which can be a bad thing, e.g. it can lead to artificial decrease
# in the output softmax temperature). To prevent this we multiply the output by :math:`\frac{1}{1 - p}`, which
# keeps the norm consistent regardless of the dropout probability.
#
# Let's first take a look at the baseline implementation.
import tabulate
import torch
import triton
import triton.language as tl
@triton.jit
def _dropout(
x_ptr, # pointer to the input
x_keep_ptr, # pointer to a mask of 0s and 1s
output_ptr, # pointer to the output
n_elements, # number of elements in the `x` tensor
p, # probability that an element of `x` is changed to zero
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
# Load data
x = tl.load(x_ptr + offsets, mask=mask)
x_keep = tl.load(x_keep_ptr + offsets, mask=mask)
# The line below is the crucial part, described in the paragraph above!
output = tl.where(x_keep, x / (1 - p), 0.0)
# Write-back output
tl.store(output_ptr + offsets, output, mask=mask)
def dropout(x, x_keep, p):
output = torch.empty_like(x)
assert x.is_contiguous()
n_elements = x.numel()
grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),)
_dropout[grid](x, x_keep, output, n_elements, p, BLOCK_SIZE=1024)
return output
# Input tensor
x = torch.randn(size=(10,)).cuda()
# Dropout mask
p = 0.5
x_keep = (torch.rand(size=(10,)) > p).to(torch.int32).cuda()
#
output = dropout(x, x_keep=x_keep, p=p)
print(tabulate.tabulate([
["input"] + x.tolist(),
["keep mask"] + x_keep.tolist(),
["output"] + output.tolist()
]))
# %%
# Seeded dropout
# --------------
#
# The above implementation of dropout works fine, but it can be a bit awkward to deal with. Firstly
# we need to store the dropout mask for backpropagation. Secondly, dropout state management can get
# very tricky when using recompute/checkpointing (e.g. see all the notes about `preserve_rng_state` in
# https://pytorch.org/docs/1.9.0/checkpoint.html). In this tutorial we'll describe an alternative implementation
# that (1) has a smaller memory footprint; (2) requires less data movement; and (3) simplifies the management
# of persisting randomness across multiple invocations of the kernel.
#
# Pseudo-random number generation in Triton is simple! In this tutorial we will use the
# :code:`triton.language.rand` function which generates a block of uniformly distributed :code:`float32`
# values in [0, 1), given a seed and a block of :code:`int32` offsets. But if you need it, Triton also provides
# other :ref:`random number generation strategies <Random Number Generation>`.
#
# .. note::
# Triton's implementation of PRNG is based on the Philox algorithm (described on [SALMON2011]_).
#
# Let's put it all together.
@triton.jit
def _seeded_dropout(
x_ptr,
output_ptr,
n_elements,
p,
seed,
BLOCK_SIZE: tl.constexpr,
):
# compute memory offsets of elements handled by this instance
pid = tl.program_id(axis=0)
block_start = pid * BLOCK_SIZE
offsets = block_start + tl.arange(0, BLOCK_SIZE)
# load data from x
mask = offsets < n_elements
x = tl.load(x_ptr + offsets, mask=mask)
# randomly prune it
random = tl.rand(seed, offsets)
x_keep = random > p
# write-back
output = tl.where(x_keep, x / (1 - p), 0.0)
tl.store(output_ptr + offsets, output, mask=mask)
def seeded_dropout(x, p, seed):
output = torch.empty_like(x)
assert x.is_contiguous()
n_elements = x.numel()
grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),)
_seeded_dropout[grid](x, output, n_elements, p, seed, BLOCK_SIZE=1024)
return output
x = torch.randn(size=(10,)).cuda()
# Compare this to the baseline - dropout mask is never instantiated!
output = seeded_dropout(x, p=0.5, seed=123)
output2 = seeded_dropout(x, p=0.5, seed=123)
output3 = seeded_dropout(x, p=0.5, seed=512)
print(tabulate.tabulate([
["input"] + x.tolist(),
["output (seed = 123)"] + output.tolist(),
["output (seed = 123)"] + output2.tolist(),
["output (seed = 512)"] + output3.tolist()
]))
# %%
# Et Voilà! We have a triton kernel that applies the same dropout mask provided the seed is the same!
# If you'd like explore further applications of pseudorandomness in GPU programming, we encourage you
# to explore the `triton/language/random` folder!
# %%
# Exercises
# ---------
#
# 1. Extend the kernel to operate over a matrix and use a vector of seeds - one per row.
# 2. Add support for striding.
# 3. (challenge) Implement a kernel for sparse Johnson-Lindenstrauss transform which generates the projection matrix one the fly each time using a seed.
# %%
# References
# ----------
#
# .. [SALMON2011] John K. Salmon, Mark A. Moraes, Ron O. Dror, and David E. Shaw, "Parallel Random Numbers: As Easy as 1, 2, 3", 2011
# .. [SRIVASTAVA2014] Nitish Srivastava and Geoffrey Hinton and Alex Krizhevsky and Ilya Sutskever and Ruslan Salakhutdinov, "Dropout: A Simple Way to Prevent Neural Networks from Overfitting", JMLR 2014
|
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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"]}
|
33,525
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/base/doc/images.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 pathlib as pth
from wand.image import Image
from .counter import counters
from .base import config, num_to_name, lister
class Images:
kind = 'png'
export_args = ((('scanned', '.'), ('split', '+'), ('missing', '-'),
('resized', '>'), ('failed', 'F')), 'Splitting:')
@staticmethod
def rename_all(path):
for i, p in enumerate(sorted(lister(path)), start=1):
s = p.suffix
d = p.with_name(num_to_name(i)).with_suffix(s)
p.rename(d)
@classmethod
def filepath(cls, path, i):
return path / '{}.{}'.format(num_to_name(i), cls.kind)
def __init__(self, src, dst, width=500, blur=1):
self.src = src
self.dst = dst
self.width = width
# self.blur = 5 if dst.endswith(config.OPEN) else blur
self.blur = blur
def resize(self, dst, img, i=1):
s = img.sequence
if s and len(s) > 1:
for p in s:
i = p.index + 1
with Image(image=p) as p:
p.alpha_channel = False
w = self.width
h = int(w / (p.width / p.height))
p.resize(width=w, height=h, blur=self.blur)
f = self.filepath(dst, i)
p.save(filename=str(f))
yield f
else:
img.alpha_channel = False
w = self.width
h = int(w / (img.width / img.height))
img.resize(width=self.width, height=h, blur=self.blur)
f = self.filepath(dst, i)
img.save(filename=str(f))
yield f
def split_pdf(self, src, dst):
with Image(filename=str(src), resolution=300) as pdf:
with pdf.convert(self.kind) as img:
yield from self.resize(dst, img)
def resize_all(self, src, dst):
for i, f in enumerate(sorted(str(p) for p in lister(src)), start=1):
with Image(filename=f) as img:
with img.convert(self.kind) as img:
yield from self.resize(dst, img, i)
class Pngs(Images):
def export_all(self, ctxt, **kw):
kw.update(ctxt=ctxt)
with counters(self.export_args, kw) as cs:
for p in ctxt.sources.keys():
s = (self.src / p).with_suffix('.pdf')
if s.exists():
d = (self.dst / p).with_suffix('.slides')
if d.exists():
cs.incr('.')
else:
d.mkdir(parents=True, exist_ok=True)
for _ in self.split_pdf(s, d):
pass
cs.incr('+')
else:
cs.incr('-')
return cs
class Jpgs(Images):
kind = 'jpeg'
def __init__(self, src, dst, width=1000, blur=1):
super().__init__(src, dst, width, blur)
def export_all(self, ctxt, **kw):
kw.update(ctxt=ctxt)
src = self.src / 'pictures'
with counters(self.export_args, kw) as cs:
for p in ctxt.sources.keys():
s = (src / p).with_suffix('.pdf')
if s.exists():
d = (self.dst / p).with_suffix('.slides')
if d.exists():
cs.incr('.')
else:
d.mkdir(parents=True, exist_ok=True)
for _ in self.split_pdf(s, d):
pass
cs.incr('+')
continue
else:
s = s.with_suffix('')
if s.exists() and s.is_dir():
d = (self.dst / p).with_suffix('.slides')
if d.exists():
cs.incr('.')
else:
d.mkdir(parents=True, exist_ok=True)
for _ in self.resize_all(s, d):
pass
cs.incr('>')
continue
cs.incr('-')
return cs
class Orgs(Pngs):
org_frame = ('', '') # None
@classmethod
def frame(cls):
if not cls.org_frame:
t = pth.Path(config.web_templates + 'frame.org').read_text()
cls.org_frame = t.split(r'{% block frame_content %}')
return cls.org_frame
def __init__(self, src, dst, width=750, blur=1):
super().__init__(src, dst, width, blur)
def export_all(self, ctxt, **kw):
kw.update(ctxt=ctxt)
with counters(self.export_args, kw) as cs:
for c in ('affidavits', 'hearings'):
# 'exhibits', 'messages',
# 'pictures', 'reports', 'submissions', 'trials',
# 'discoveries', 'financial', 'letters', 'orders',
# 'services', 'transcripts'):
for s in lister(self.src / c, suffs=('.pdf', )):
# print(s)
d = (self.dst / s.relative_to(self.src)).with_suffix('')
if d.exists():
cs.incr('.')
else:
b = d.parent
d.mkdir(parents=True, exist_ok=True)
f, e = self.frame()
for p in self.split_pdf(s, d):
p = p.relative_to(b)
f += '#+NAME: {}\n[[./{}]]\n'.format(
p.stem, str(p))
d.with_suffix('.org').write_text(f + e)
cs.incr('+')
for s in lister(self.src / c, suffs=('.org', )):
d = (self.dst / s.relative_to(self.src))
try:
d.unlink()
except FileNotFoundError:
pass
d.symlink_to(os.path.relpath(s, d.parent))
return cs
if __name__ == '__main__':
cwd = pth.Path.cwd()
Images.rename_all(cwd)
with os.scandir(cwd) as es:
for e in es:
p = pth.Path(e.path)
if p.is_dir():
Images.rename_all(p)
|
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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/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"]}
|
33,526
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/metric/glue.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 scipy.stats import pearsonr, spearmanr
from sklearn.metrics import f1_score, matthews_corrcoef
class Glue(ds.Metric):
def _info(self):
assert self.config_name in [
"sst2",
"mnli",
"mnli_mismatched",
"mnli_matched",
"cola",
"stsb",
"mrpc",
"qqp",
"qnli",
"rte",
"wnli",
"hans",
]
return ds.MetricInfo(
description="",
citation="",
inputs_description="",
features=ds.Features(
{
"predictions": ds.Value("int64" if self.config_name != "stsb" else "float32"),
"references": ds.Value("int64" if self.config_name != "stsb" else "float32"),
}
),
codebase_urls=[],
reference_urls=[],
format="numpy",
)
def _compute(self, preds, refs):
if self.config_name == "cola":
return {"matthews_correlation": matthews_corrcoef(refs, preds)}
elif self.config_name == "stsb":
return _pearson_and_spearman(preds, refs)
elif self.config_name in ["mrpc", "qqp"]:
return _acc_and_f1(preds, refs)
else:
assert self.config_name in [
"sst2",
"mnli",
"mnli_mismatched",
"mnli_matched",
"qnli",
"rte",
"wnli",
"hans",
]
return {"accuracy": _accuracy(preds, refs)}
def _accuracy(preds, refs):
return float((preds == refs).mean())
def _acc_and_f1(preds, refs):
acc = _accuracy(preds, refs)
f1 = float(f1_score(y_true=refs, y_pred=preds))
return {"accuracy": acc, "f1": f1}
def _pearson_and_spearman(preds, refs):
p = float(pearsonr(preds, refs)[0])
s = float(spearmanr(preds, refs)[0])
return {"pearson": p, "spearmanr": s}
|
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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"]}
|
33,527
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/models/yoso.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 os
import torch
import torch.utils.checkpoint
from torch.nn import functional as F
from packaging import version
from torch import nn
from torch.nn import CrossEntropyLoss
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.yoso import PreTrained
from ...pytorch_utils import (
apply_chunking_to_forward,
)
log = logging.get_logger(__name__)
LIST = [
"uw-madison/yoso-4096",
]
def load_cuda_kernels():
global lsh_cumulation
try:
from torch.utils.cpp_extension import load
def append_root(files):
src_folder = os.path.dirname(os.path.realpath(__file__))
return [os.path.join(src_folder, file) for file in files]
src_files = append_root(
[
"fast_lsh_cumulation_torch.cpp",
"fast_lsh_cumulation.cu",
"fast_lsh_cumulation_cuda.cu",
]
)
load("fast_lsh_cumulation", src_files, verbose=True)
import fast_lsh_cumulation as lsh_cumulation
return True
except Exception:
lsh_cumulation = None
return False
def to_contiguous(xs):
if isinstance(xs, list):
ys = []
for x in xs:
if not x.is_contiguous():
x = x.contiguous()
ys.append(x)
return ys
else:
if not xs.is_contiguous():
xs = xs.contiguous()
return xs
def normalize(xs):
if type(xs) is list:
ys = []
for x in xs:
ys.append(F.normalize(x, p=2, dim=-1))
return ys
else:
return F.normalize(xs, p=2, dim=-1)
def hashing(q, k, num_hash, hash_len):
if len(q.size()) != 3:
raise ValueError("Query has incorrect size.")
if len(k.size()) != 3:
raise ValueError("Key has incorrect size.")
rmat = torch.randn(q.size(0), q.size(2), num_hash * hash_len, device=q.device)
raise_pow = 2 ** torch.arange(hash_len, device=q.device)
q = torch.matmul(q, rmat).reshape(q.size(0), q.size(1), num_hash, hash_len)
k = torch.matmul(k, rmat).reshape(k.size(0), k.size(1), num_hash, hash_len)
q = (q > 0).int()
k = (k > 0).int()
y = torch.sum(q * raise_pow, dim=-1)
y = torch.sum(k * raise_pow, dim=-1)
return y.int(), y.int()
class YosoCumulation(torch.autograd.Function):
@staticmethod
def forward(ctx, query_mask, key_mask, query, key, value, config):
hash_code_len = config["hash_code_len"]
expectation = (
1 - torch.acos(torch.matmul(query, key.transpose(-1, -2))) / math.pi
) ** hash_code_len
expectation = expectation * query_mask[:, :, None] * key_mask[:, None, :]
cumulation_value = torch.matmul(expectation, value)
ctx.save_for_backward(query_mask, key_mask, expectation, query, key, value)
ctx.config = config
return cumulation_value
@staticmethod
def backward(ctx, grad):
grad = to_contiguous(grad)
query_mask, key_mask, expectation, query, key, value = ctx.saved_tensors
config = ctx.config
hash_code_len = config["hash_code_len"]
weighted_exp = torch.matmul(grad, value.transpose(-1, -2)) * expectation
grad_query = torch.matmul(weighted_exp, (hash_code_len / 2) * key)
grad_key = torch.matmul(weighted_exp.transpose(-1, -2), (hash_code_len / 2) * query)
grad_value = torch.matmul(expectation.transpose(-1, -2), grad)
return None, None, grad_query, grad_key, grad_value, None
class YosoLSHCumulation(torch.autograd.Function):
@staticmethod
def forward(ctx, query_mask, key_mask, query, key, value, config):
if query_mask.size(0) != key_mask.size(0):
raise ValueError("Query mask and Key mask differ in sizes in dimension 0")
if query_mask.size(0) != query.size(0):
raise ValueError("Query mask and Query differ in sizes in dimension 0")
if query_mask.size(0) != key.size(0):
raise ValueError("Query mask and Key differ in sizes in dimension 0")
if query_mask.size(0) != value.size(0):
raise ValueError("Query mask and Value mask differ in sizes in dimension 0")
if key.size(1) != value.size(1):
raise ValueError("Key and Value differ in sizes in dimension 1")
if query.size(2) != key.size(2):
raise ValueError("Query and Key differ in sizes in dimension 2")
query_mask, key_mask, query, key, value = to_contiguous(
[query_mask, key_mask, query, key, value]
)
use_cuda = query_mask.is_cuda
num_hash = config["num_hash"]
hash_code_len = config["hash_code_len"]
hashtable_capacity = int(2**hash_code_len)
if config["use_fast_hash"]:
query_hash_code, key_hash_code = lsh_cumulation.fast_hash(
query_mask, query, key_mask, key, num_hash, hash_code_len, use_cuda, 1
)
else:
query_hash_code, key_hash_code = hashing(query, key, num_hash, hash_code_len)
cumulation_value = lsh_cumulation.lsh_cumulation(
query_mask,
query_hash_code,
key_mask,
key_hash_code,
value,
hashtable_capacity,
use_cuda,
1,
)
ctx.save_for_backward(
query_mask, key_mask, query_hash_code, key_hash_code, query, key, value
)
ctx.config = config
return cumulation_value
@staticmethod
def backward(ctx, grad):
grad = to_contiguous(grad)
query_mask, key_mask, query_hash_code, key_hash_code, query, key, value = ctx.saved_tensors
config = ctx.config
use_cuda = grad.is_cuda
hash_code_len = config["hash_code_len"]
hashtable_capacity = int(2**hash_code_len)
if config["lsh_backward"]:
grad_value = lsh_cumulation.lsh_cumulation(
key_mask,
key_hash_code,
query_mask,
query_hash_code,
grad,
hashtable_capacity,
use_cuda,
1,
)
grad_query = lsh_cumulation.lsh_weighted_cumulation(
query_mask,
query_hash_code,
grad,
key_mask,
key_hash_code,
value,
(hash_code_len / 2) * key,
hashtable_capacity,
use_cuda,
4,
)
grad_key = lsh_cumulation.lsh_weighted_cumulation(
key_mask,
key_hash_code,
value,
query_mask,
query_hash_code,
grad,
(hash_code_len / 2) * query,
hashtable_capacity,
use_cuda,
4,
)
else:
expectation = (
1 - torch.acos(torch.matmul(query, key.transpose(-1, -2))) / math.pi
) ** hash_code_len
expectation = expectation * query_mask[:, :, None] * key_mask[:, None, :]
weighted_exp = torch.matmul(grad, value.transpose(-1, -2)) * expectation
grad_query = torch.matmul(weighted_exp, (hash_code_len / 2) * key)
grad_key = torch.matmul(weighted_exp.transpose(-1, -2), (hash_code_len / 2) * query)
grad_value = torch.matmul(expectation.transpose(-1, -2), grad)
return None, None, grad_query, grad_key, grad_value, None
# Copied from transformers.models.nystromformer.modeling_nystromformer.NystromformerEmbeddings
class YosoEmbeddings(qc.Module):
def __init__(self, config):
super().__init__()
self.tok = qc.Embed(config.s_vocab, config.d_model, padding_idx=config.PAD)
self.pos = qc.Embed(config.n_pos + 2, config.d_model)
self.typ = 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.register_buffer("position_ids", torch.arange(config.n_pos).expand((1, -1)) + 2)
self.pos_type = getattr(config, "pos_type", "absolute")
if version.parse(torch.__version__) > version.parse("1.6.0"):
self.register_buffer(
"token_type_ids",
torch.zeros(
self.position_ids.size(), dtype=torch.long, device=self.position_ids.device
),
persistent=False,
)
def forward(self, x=None, typ=None, pos=None, inputs_embeds=None):
if x is not None:
s = x.size()
else:
s = inputs_embeds.size()[:-1]
seq_length = s[1]
if pos is None:
pos = self.position_ids[:, :seq_length]
if typ is None:
if hasattr(self, "token_type_ids"):
buffered_token_type_ids = self.token_type_ids[:, :seq_length]
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(s[0], seq_length)
typ = buffered_token_type_ids_expanded
else:
typ = torch.zeros(s, dtype=torch.long, device=self.position_ids.device)
if inputs_embeds is None:
inputs_embeds = self.tok(x)
token_type_embeddings = self.typ(typ)
embeddings = inputs_embeds + token_type_embeddings
if self.pos_type == "absolute":
position_embeddings = self.pos(pos)
embeddings += position_embeddings
embeddings = self.norm(embeddings)
embeddings = self.drop(embeddings)
return embeddings
class YosoSelfAttention(qc.Module):
def __init__(self, config, pos_type=None):
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.pos_type = pos_type if pos_type is not None else config.pos_type
self.use_expectation = config.use_expectation
self.hash_code_len = config.hash_code_len
self.use_conv = config.conv_window is not None
self.use_fast_hash = config.use_fast_hash
self.num_hash = config.num_hash
self.lsh_backward = config.lsh_backward
self.lsh_config = {
"hash_code_len": self.hash_code_len,
"use_fast_hash": self.use_fast_hash,
"num_hash": self.num_hash,
"lsh_backward": self.lsh_backward,
}
if config.conv_window is not None:
self.conv = nn.Conv2d(
in_channels=config.n_heads,
out_channels=config.n_heads,
kernel_size=(config.conv_window, 1),
padding=(config.conv_window // 2, 0),
bias=False,
groups=config.n_heads,
)
def transpose_for_scores(self, layer):
new_layer_shape = layer.size()[:-1] + (self.n_heads, self.attention_head_size)
layer = layer.view(*new_layer_shape)
return layer.permute(0, 2, 1, 3)
def forward(self, hiddens, attention_mask=None, output_attentions=False):
mixed_query_layer = self.query(hiddens)
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.use_conv:
conv_value_layer = self.conv(value_layer * attention_mask[:, None, :, None])
batch_size, n_heads, seq_len, head_dim = query_layer.size()
query_layer = query_layer.reshape(batch_size * n_heads, seq_len, head_dim)
key_layer = key_layer.reshape(batch_size * n_heads, seq_len, head_dim)
value_layer = value_layer.reshape(batch_size * n_heads, seq_len, head_dim)
# revert changes made by get_extended_attention_mask
attention_mask = 1.0 + attention_mask / 10000.0
attention_mask = (
attention_mask.squeeze()
.repeat(1, n_heads, 1)
.reshape(batch_size * n_heads, seq_len)
.int()
)
# The CUDA kernels are most efficient with inputs whose size is a multiple of a GPU's warp size (32). Inputs
# smaller than this are padded with zeros.
gpu_warp_size = 32
if (not self.use_expectation) and head_dim < gpu_warp_size:
pad_size = batch_size * n_heads, seq_len, gpu_warp_size - head_dim
query_layer = torch.cat(
[
query_layer,
torch.zeros(pad_size, device=query_layer.device),
],
dim=-1,
)
key_layer = torch.cat(
[
key_layer,
torch.zeros(pad_size, device=key_layer.device),
],
dim=-1,
)
value_layer = torch.cat(
[
value_layer,
torch.zeros(pad_size, device=value_layer.device),
],
dim=-1,
)
if self.use_expectation or self.training:
query_layer, key_layer = normalize([query_layer, key_layer])
if self.use_expectation:
context_layer = YosoCumulation.apply(
attention_mask, attention_mask, query_layer, key_layer, value_layer, self.lsh_config
)
else:
context_layer = YosoLSHCumulation.apply(
attention_mask, attention_mask, query_layer, key_layer, value_layer, self.lsh_config
)
if (not self.use_expectation) and head_dim < gpu_warp_size:
context_layer = context_layer[:, :, :head_dim]
context_layer = normalize(context_layer)
context_layer = context_layer.reshape(batch_size, n_heads, seq_len, head_dim)
if self.use_conv:
context_layer += conv_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, context_layer) if output_attentions else (context_layer,)
return outputs
# Copied from transformers.models.bert.modeling_bert.BertSelfOutput
class YosoSelfOutput(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, pos_type=None):
super().__init__()
self.self = YosoSelfAttention(config, pos_type=pos_type)
self.output = YosoSelfOutput(config)
def forward(self, hiddens, attention_mask=None, output_attentions=False):
self_outputs = self.self(hiddens, attention_mask, 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
class YosoIntermediate(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 YosoOutput(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.add_cross_attention = config.add_cross_attention
self.intermediate = YosoIntermediate(config)
self.output = YosoOutput(config)
def forward(self, hiddens, attention_mask=None, output_attentions=False):
self_attention_outputs = self.attention(
hiddens, attention_mask, output_attentions=output_attentions
)
attention_output = self_attention_outputs[0]
outputs = self_attention_outputs[1:] # 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,
attention_output,
)
outputs = (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,
hiddens,
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
for i, layer_module in enumerate(self.layer):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hiddens,)
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),
hiddens,
attention_mask,
)
else:
layer_outputs = layer_module(hiddens, attention_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, all_hidden_states, all_self_attentions] if v is not None
)
return qo.BaseWithCrossAttentions(
y=hiddens,
hiddens=all_hidden_states,
attns=all_self_attentions,
)
class Model(PreTrained):
def __init__(self, config):
super().__init__(config)
self.config = config
self.embeddings = YosoEmbeddings(config)
self.encoder = Encoder(config)
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:
if hasattr(self.embeddings, "token_type_ids"):
buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(
batch_size, seq_length
)
token_type_ids = buffered_token_type_ids_expanded
else:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, 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,
token_type_ids=token_type_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]
if not return_dict:
return (sequence_output,) + encoder_outputs[1:]
return qo.BaseWithCrossAttentions(
y=sequence_output,
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(**kw)
self.proj = Predictor(cfg.d_model, **kw)
forward = qf.forward_masked
class ForChoice(PreTrained):
def __init__(self, config):
super().__init__(config)
self.yoso = Model(config)
self.pre_classifier = qc.Linear(config.d_model, config.d_model)
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.yoso(
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,
)
hidden_state = outputs[0] # (bs * num_choices, seq_len, dim)
pooled_output = hidden_state[:, 0] # (bs * num_choices, dim)
pooled_output = self.pre_classifier(pooled_output) # (bs * num_choices, dim)
pooled_output = nn.ReLU()(pooled_output) # (bs * num_choices, dim)
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[1:]
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(**kw)
self.proj = Classifier(cfg.d_model, **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):
kw.update(n_labels=2)
super().__init__(**kw)
cfg = self.get_cfg(kw)
self.model = Model(**kw)
self.proj = qc.Linear(cfg.d_model, cfg.n_labels, **kw)
forward = qf.forward_qa
|
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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"]}
|
33,528
|
quantapix/qnarre
|
refs/heads/main
|
/tools/triton/python/test/unit/language/test_subprocess.py
|
import os
import subprocess
import sys
import pytest
dir_path = os.path.dirname(os.path.realpath(__file__))
print_path = os.path.join(dir_path, "print_helper.py")
assert_path = os.path.join(dir_path, "assert_helper.py")
# TODO: bfloat16 after LLVM-15
func_types = ["device_assert", "assert", "static_assert"]
torch_types = ["int8", "uint8", "int16", "int32", "long", "float16", "float32", "float64"]
@pytest.mark.parametrize("func_type, data_type",
[("device_print", data_type) for data_type in torch_types] + [("print", "int32"), ("static_print", "int32")])
def test_print(func_type: str, data_type: str):
proc = subprocess.Popen([sys.executable, print_path, func_type, data_type], stdout=subprocess.PIPE, shell=False)
outs, _ = proc.communicate()
outs = outs.split()
new_lines = set()
for line in outs:
try:
value = line
if func_type != "static_print":
value = int(float(line))
new_lines.add(value)
except Exception as e:
print(e)
if func_type != "static_print":
for i in range(128):
assert i in new_lines
assert len(new_lines) == 128
else:
assert len(new_lines) == 1
@pytest.mark.parametrize("func_type", func_types)
def test_assert(func_type: str):
os.environ["TRITON_DEBUG"] = "1"
proc = subprocess.Popen([sys.executable, assert_path, func_type], stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=False)
_, errs = proc.communicate()
errs = errs.splitlines()
num_errs = 0
for err in errs:
if "x != 0" in err.decode("utf-8"):
num_errs += 1
os.environ["TRITON_DEBUG"] = "0"
if func_type != "static_assert":
assert num_errs == 127
else:
assert num_errs == 0
|
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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"]}
|
33,529
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/models/canine.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 copy
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.canine import PreTrained
log = logging.get_logger(__name__)
class ForChoice(PreTrained):
def __init__(self, config):
super().__init__(config)
self.canine = Model(config)
self.drop = qc.Dropout(config.drop)
self.classifier = qc.Linear(config.d_model, 1)
self.post_init()
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.canine(
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 ForQA(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, **kw)
forward = qf.forward_qa
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(**kw)
self.proj = Classifier(**kw)
forward = qf.forward_tok
class CaninePredictionHeadTransform(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 LMHead(qc.Module):
def __init__(self, config):
super().__init__()
self.transform = CaninePredictionHeadTransform(config)
self.decoder = qc.Linear(config.d_model, config.s_vocab, bias=False)
self.bias = nn.Parameter(torch.zeros(config.s_vocab))
self.decoder.bias = self.bias
def forward(self, x):
y = self.transform(x)
y = self.decoder(y)
return y
class CanineOnlyMLMHead(qc.Module):
def __init__(self, config):
super().__init__()
self.predictions = LMHead(config)
def forward(self, sequence_output):
prediction_scores = self.predictions(sequence_output)
return prediction_scores
class Model(PreTrained):
def __init__(self, config, add_pooling_layer=True):
super().__init__(config)
self.config = config
shallow_config = copy.deepcopy(config)
shallow_config.n_lays = 1
self.char_embeddings = Embed(config)
self.initial_char_encoder = Encoder(
shallow_config,
local=True,
always_attend_to_first_position=False,
first_position_attends_to_all=False,
attend_from_chunk_width=config.local_transformer_stride,
attend_from_chunk_stride=config.local_transformer_stride,
attend_to_chunk_width=config.local_transformer_stride,
attend_to_chunk_stride=config.local_transformer_stride,
)
self.chars_to_molecules = CharactersToMolecules(config)
self.encoder = Encoder(config)
self.projection = ConvProjection(config)
self.final_char_encoder = Encoder(shallow_config)
self.pool = Pool(config) if add_pooling_layer else None
self.post_init()
def _create_3d_attention_mask_from_input_mask(self, from_tensor, to_mask):
batch_size, from_seq_length = from_tensor.shape[0], from_tensor.shape[1]
to_seq_length = to_mask.shape[1]
to_mask = torch.reshape(to_mask, (batch_size, 1, to_seq_length)).float()
broadcast_ones = torch.ones(
size=(batch_size, from_seq_length, 1), dtype=torch.float32, device=to_mask.device
)
mask = broadcast_ones * to_mask
return mask
def _downsample_attention_mask(self, char_attention_mask, downsampling_rate):
batch_size, char_seq_len = char_attention_mask.shape
poolable_char_mask = torch.reshape(char_attention_mask, (batch_size, 1, char_seq_len))
pooled_molecule_mask = torch.nn.MaxPool1d(
kernel_size=downsampling_rate, stride=downsampling_rate
)(poolable_char_mask.float())
molecule_attention_mask = torch.squeeze(pooled_molecule_mask, dim=-1)
return molecule_attention_mask
def _repeat_molecules(self, molecules, char_seq_length):
rate = self.config.downsampling_rate
molecules_without_extra_cls = molecules[:, 1:, :]
repeated = torch.repeat_interleave(molecules_without_extra_cls, repeats=rate, dim=-2)
last_molecule = molecules[:, -1:, :]
remainder_length = torch.fmod(torch.tensor(char_seq_length), torch.tensor(rate)).item()
remainder_repeated = torch.repeat_interleave(
last_molecule,
# +1 molecule to compensate for truncation.
repeats=remainder_length + rate,
dim=-2,
)
# `repeated`: [batch_size, char_seq_len, molecule_hidden_size]
return torch.cat([repeated, remainder_repeated], dim=-2)
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
)
all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
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)
extended_attention_mask = self.get_extended_attention_mask(
attention_mask, input_shape, device
)
molecule_attention_mask = self._downsample_attention_mask(
attention_mask, downsampling_rate=self.config.downsampling_rate
)
extended_molecule_attention_mask = self.get_extended_attention_mask(
molecule_attention_mask, (batch_size, molecule_attention_mask.shape[-1]), device
)
head_mask = self.get_head_mask(head_mask, self.config.n_lays)
input_char_embeddings = self.char_embeddings(
input_ids=input_ids,
position_ids=position_ids,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
)
char_attention_mask = self._create_3d_attention_mask_from_input_mask(
input_ids, attention_mask
)
init_chars_encoder_outputs = self.initial_char_encoder(
input_char_embeddings,
attention_mask=char_attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
input_char_encoding = init_chars_encoder_outputs.y
init_molecule_encoding = self.chars_to_molecules(input_char_encoding)
encoder_outputs = self.encoder(
init_molecule_encoding,
attention_mask=extended_molecule_attention_mask,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
molecule_sequence_output = encoder_outputs[0]
pooled_output = self.pool(molecule_sequence_output) if self.pool is not None else None
repeated_molecules = self._repeat_molecules(
molecule_sequence_output, char_seq_length=input_shape[-1]
)
concat = torch.cat([input_char_encoding, repeated_molecules], dim=-1)
sequence_output = self.projection(concat)
final_chars_encoder_outputs = self.final_char_encoder(
sequence_output,
attention_mask=extended_attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
sequence_output = final_chars_encoder_outputs.y
if output_hidden_states:
deep_encoder_hidden_states = (
encoder_outputs.hiddens if return_dict else encoder_outputs[1]
)
all_hidden_states = (
all_hidden_states
+ init_chars_encoder_outputs.hiddens
+ deep_encoder_hidden_states
+ final_chars_encoder_outputs.hiddens
)
if output_attentions:
deep_encoder_self_attentions = (
encoder_outputs.attns if return_dict else encoder_outputs[-1]
)
all_self_attentions = (
all_self_attentions
+ init_chars_encoder_outputs.attns
+ deep_encoder_self_attentions
+ final_chars_encoder_outputs.attns
)
if not return_dict:
output = (sequence_output, pooled_output)
output += tuple(v for v in [all_hidden_states, all_self_attentions] if v is not None)
return output
return qo.WithPools(
y=sequence_output,
pools=pooled_output,
hiddens=all_hidden_states,
attns=all_self_attentions,
)
class Encoder(qc.Module):
def __init__(
self,
config,
local=False,
always_attend_to_first_position=False,
first_position_attends_to_all=False,
attend_from_chunk_width=128,
attend_from_chunk_stride=128,
attend_to_chunk_width=128,
attend_to_chunk_stride=128,
):
super().__init__()
self.config = config
self.layer = nn.ModuleList(
[
Layer(
config,
local,
always_attend_to_first_position,
first_position_attends_to_all,
attend_from_chunk_width,
attend_from_chunk_stride,
attend_to_chunk_width,
attend_to_chunk_stride,
)
for _ in range(config.n_lays)
]
)
self.gradient_checkpointing = False
def forward(
self,
hiddens,
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
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
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),
hiddens,
attention_mask,
layer_head_mask,
)
else:
layer_outputs = layer_module(
hiddens, 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, all_hidden_states, all_self_attentions] if v is not None
)
return qo.Base(
y=hiddens,
hiddens=all_hidden_states,
attns=all_self_attentions,
)
class Layer(qc.Module):
def __init__(
self,
config,
local,
always_attend_to_first_position,
first_position_attends_to_all,
attend_from_chunk_width,
attend_from_chunk_stride,
attend_to_chunk_width,
attend_to_chunk_stride,
):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = Attention(
config,
local,
always_attend_to_first_position,
first_position_attends_to_all,
attend_from_chunk_width,
attend_from_chunk_stride,
attend_to_chunk_width,
attend_to_chunk_stride,
)
self.intermediate = Intermediate(config)
self.output = Output(config)
def forward(
self,
hiddens,
attention_mask=None,
head_mask=None,
output_attentions=False,
):
self_attention_outputs = self.attention(
hiddens,
attention_mask,
head_mask,
output_attentions=output_attentions,
)
attention_output = self_attention_outputs[0]
outputs = self_attention_outputs[1:]
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
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 Attention(qc.Module):
def __init__(
self,
config,
local=False,
always_attend_to_first_position=False,
first_position_attends_to_all=False,
attend_from_chunk_width=128,
attend_from_chunk_stride=128,
attend_to_chunk_width=128,
attend_to_chunk_stride=128,
):
super().__init__()
self.self = SelfAttention(config)
self.output = SelfOutput(config)
self.local = local
assert attend_from_chunk_width >= attend_from_chunk_stride
assert attend_to_chunk_width >= attend_to_chunk_stride
self.always_attend_to_first_position = always_attend_to_first_position
self.first_position_attends_to_all = first_position_attends_to_all
self.attend_from_chunk_width = attend_from_chunk_width
self.attend_from_chunk_stride = attend_from_chunk_stride
self.attend_to_chunk_width = attend_to_chunk_width
self.attend_to_chunk_stride = attend_to_chunk_stride
def forward(
self,
hiddens,
attention_mask=None,
head_mask=None,
output_attentions=False,
):
if not self.local:
self_outputs = self.self(hiddens, hiddens, attention_mask, head_mask, output_attentions)
attention_output = self_outputs[0]
else:
from_seq_length = to_seq_length = hiddens.shape[1]
from_tensor = to_tensor = hiddens
from_chunks = []
if self.first_position_attends_to_all:
from_chunks.append((0, 1))
from_start = 1
else:
from_start = 0
for chunk_start in range(from_start, from_seq_length, self.attend_from_chunk_stride):
chunk_end = min(from_seq_length, chunk_start + self.attend_from_chunk_width)
from_chunks.append((chunk_start, chunk_end))
to_chunks = []
if self.first_position_attends_to_all:
to_chunks.append((0, to_seq_length))
for chunk_start in range(0, to_seq_length, self.attend_to_chunk_stride):
chunk_end = min(to_seq_length, chunk_start + self.attend_to_chunk_width)
to_chunks.append((chunk_start, chunk_end))
if len(from_chunks) != len(to_chunks):
raise ValueError(
f"Expected to have same number of `from_chunks` ({from_chunks}) and "
f"`to_chunks` ({from_chunks}). Check strides."
)
attention_output_chunks = []
attention_probs_chunks = []
for (from_start, from_end), (to_start, to_end) in zip(from_chunks, to_chunks):
from_tensor_chunk = from_tensor[:, from_start:from_end, :]
to_tensor_chunk = to_tensor[:, to_start:to_end, :]
attention_mask_chunk = attention_mask[:, from_start:from_end, to_start:to_end]
if self.always_attend_to_first_position:
cls_attention_mask = attention_mask[:, from_start:from_end, 0:1]
attention_mask_chunk = torch.cat(
[cls_attention_mask, attention_mask_chunk], dim=2
)
cls_position = to_tensor[:, 0:1, :]
to_tensor_chunk = torch.cat([cls_position, to_tensor_chunk], dim=1)
attention_outputs_chunk = self.self(
from_tensor_chunk,
to_tensor_chunk,
attention_mask_chunk,
head_mask,
output_attentions,
)
attention_output_chunks.append(attention_outputs_chunk[0])
if output_attentions:
attention_probs_chunks.append(attention_outputs_chunk[1])
attention_output = torch.cat(attention_output_chunks, dim=1)
attention_output = self.output(attention_output, hiddens)
outputs = (attention_output,)
if not self.local:
outputs = outputs + self_outputs[1:]
else:
outputs = outputs + tuple(attention_probs_chunks)
return outputs
class Embed(qc.Module):
def __init__(self, config):
super().__init__()
self.config = config
shard_embedding_size = config.d_model // config.num_hash_functions
for i in range(config.num_hash_functions):
name = f"HashBucketCodepointEmbedder_{i}"
setattr(self, name, qc.Embed(config.num_hash_buckets, shard_embedding_size))
self.char_position_embeddings = qc.Embed(config.num_hash_buckets, 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.register_buffer("position_ids", torch.arange(config.n_pos).expand((1, -1)))
self.pos_type = getattr(config, "pos_type", "absolute")
def _hash_bucket_tensors(self, input_ids, num_hashes, num_buckets):
if num_hashes > len(_PRIMES):
raise ValueError(f"`num_hashes` must be <= {len(_PRIMES)}")
primes = _PRIMES[:num_hashes]
result_tensors = []
for prime in primes:
hashed = ((input_ids + 1) * prime) % num_buckets
result_tensors.append(hashed)
return result_tensors
def _embed_hash_buckets(self, input_ids, d_embed, num_hashes, num_buckets):
if d_embed % num_hashes != 0:
raise ValueError(f"Expected `d_embed` ({d_embed}) % `num_hashes` ({num_hashes}) == 0")
hash_bucket_tensors = self._hash_bucket_tensors(
input_ids, num_hashes=num_hashes, num_buckets=num_buckets
)
embedding_shards = []
for i, hash_bucket_ids in enumerate(hash_bucket_tensors):
name = f"HashBucketCodepointEmbedder_{i}"
shard_embeddings = getattr(self, name)(hash_bucket_ids)
embedding_shards.append(shard_embeddings)
return torch.cat(embedding_shards, dim=-1)
def forward(
self,
input_ids=None,
token_type_ids=None,
position_ids=None,
inputs_embeds=None,
):
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 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._embed_hash_buckets(
input_ids,
self.config.d_model,
self.config.num_hash_functions,
self.config.num_hash_buckets,
)
token_type_embeddings = self.token_type_embeddings(token_type_ids)
embeddings = inputs_embeds + token_type_embeddings
if self.pos_type == "absolute":
position_embeddings = self.char_position_embeddings(position_ids)
embeddings += position_embeddings
embeddings = self.norm(embeddings)
embeddings = self.drop(embeddings)
return embeddings
class CharactersToMolecules(qc.Module):
def __init__(self, config):
super().__init__()
self.conv = qc.Conv1d(
in_channels=config.d_model,
out_channels=config.d_model,
kernel_size=config.downsampling_rate,
stride=config.downsampling_rate,
)
self.act = qu.activation(config.act)
self.norm = qc.LayerNorm(config.d_model, eps=config.eps)
def forward(self, char_encoding):
cls_encoding = char_encoding[:, 0:1, :]
char_encoding = torch.transpose(char_encoding, 1, 2)
downsampled = self.conv(char_encoding)
downsampled = torch.transpose(downsampled, 1, 2)
downsampled = self.act(downsampled)
downsampled_truncated = downsampled[:, 0:-1, :]
result = torch.cat([cls_encoding, downsampled_truncated], dim=1)
result = self.norm(result)
return result
class ConvProjection(qc.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.conv = qc.Conv1d(
in_channels=config.d_model * 2,
out_channels=config.d_model,
kernel_size=config.upsampling_kernel_size,
stride=1,
)
self.act = qu.activation(config.act)
self.norm = qc.LayerNorm(config.d_model, eps=config.eps)
self.drop = qc.Dropout(config.drop)
def forward(self, inputs, final_seq_char_positions=None):
inputs = torch.transpose(inputs, 1, 2)
pad_total = self.config.upsampling_kernel_size - 1
pad_beg = pad_total // 2
pad_end = pad_total - pad_beg
pad = nn.ConstantPad1d((pad_beg, pad_end), 0)
result = self.conv(pad(inputs))
result = torch.transpose(result, 1, 2)
result = self.act(result)
result = self.norm(result)
result = self.drop(result)
final_char_seq = result
if final_seq_char_positions is not None:
raise NotImplementedError("ForMasked is currently not supported")
else:
query_seq = final_char_seq
return query_seq
class SelfAttention(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.pos_type = getattr(config, "pos_type", "absolute")
if self.pos_type == "relative_key" or self.pos_type == "relative_key_query":
self.n_pos = config.n_pos
self.distance_embedding = qc.Embed(2 * config.n_pos - 1, self.attention_head_size)
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,
from_tensor,
to_tensor,
attention_mask=None,
head_mask=None,
output_attentions=False,
):
mixed_query_layer = self.query(from_tensor)
key_layer = self.transpose_for_scores(self.key(to_tensor))
value_layer = self.transpose_for_scores(self.value(to_tensor))
query_layer = self.transpose_for_scores(mixed_query_layer)
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
if self.pos_type == "relative_key" or self.pos_type == "relative_key_query":
seq_length = from_tensor.size()[1]
position_ids_l = torch.arange(
seq_length, dtype=torch.long, device=from_tensor.device
).view(-1, 1)
position_ids_r = torch.arange(
seq_length, dtype=torch.long, device=from_tensor.device
).view(1, -1)
distance = position_ids_l - position_ids_r
positional_embedding = self.distance_embedding(distance + self.n_pos - 1)
positional_embedding = positional_embedding.to(
dtype=query_layer.dtype
) # fp16 compatibility
if self.pos_type == "relative_key":
relative_position_scores = torch.einsum(
"bhld,lrd->bhlr", query_layer, positional_embedding
)
attention_scores = attention_scores + relative_position_scores
elif self.pos_type == "relative_key_query":
relative_position_scores_query = torch.einsum(
"bhld,lrd->bhlr", query_layer, positional_embedding
)
relative_position_scores_key = torch.einsum(
"bhrd,lrd->bhlr", key_layer, positional_embedding
)
attention_scores = (
attention_scores + relative_position_scores_query + relative_position_scores_key
)
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
if attention_mask is not None:
if attention_mask.ndim == 3:
# if attention_mask is 3D, do the following:
attention_mask = torch.unsqueeze(attention_mask, dim=1)
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
# masked positions, this operation will create a tensor which is 0.0 for
# positions we want to attend and -10000.0 for masked positions.
attention_mask = (1.0 - attention_mask.float()) * -10000.0
# Apply the attention mask (precomputed for all layers in CanineModel 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,)
return outputs
class SelfOutput(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 Intermediate(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
class Output(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
LIST = ["google/canine-s", "google/canine-r"]
_PRIMES = [31, 43, 59, 61, 73, 97, 103, 113, 137, 149, 157, 173, 181, 193, 211, 223]
|
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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"]}
|
33,530
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/config/reformer.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 ... import core as qc
class PreTrained(qc.PreTrained):
hs = qc.Hypers(
[],
dict(
act="relu",
attention_head_size=64,
attn_layers=["local", "lsh", "local", "lsh", "local", "lsh"],
axial_norm_std=1.0,
axial_pos_embds_dim=[64, 192],
axial_pos_embds=True,
axial_pos_shape=[64, 64],
chunk_size_lm_head=0,
d_hidden=256,
drop_proj=None,
drop=0.05,
EOS=2,
eps=1e-12,
feed_forward_size=512,
hash_seed=None,
init_range=0.02,
is_decoder=False,
local_attention_probs_dropout_prob=0.05,
local_attn_chunk_length=64,
local_num_chunks_after=0,
local_num_chunks_before=1,
lsh_attention_probs_dropout_prob=0.0,
lsh_attn_chunk_length=64,
lsh_num_chunks_after=0,
lsh_num_chunks_before=1,
model_type="reformer",
n_heads=12,
n_pos=4096,
num_buckets=None,
num_hashes=1,
PAD=0,
s_vocab=320,
tie_word_embeddings=False,
y_cache=True,
),
)
@property
def dummy_inputs(self):
input_ids = torch.tensor(DUMMY_INPUTS)
input_mask = torch.tensor(DUMMY_MASK)
dummy_inputs = {
"input_ids": input_ids,
"attention_mask": input_mask,
}
return dummy_inputs
def _init_weights(self, module):
if isinstance(module, AxialPositionEmbeddings):
for weight in module.weights:
nn.init.normal_(weight, std=self.config.axial_norm_std)
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.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.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
MAP = {
"google/reformer-crime-and-punishment": "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/config.json",
"google/reformer-enwik8": "https://huggingface.co/google/reformer-enwik8/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", "/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"]}
|
33,531
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/run/trafo_std.py
|
import argparse
import time
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import TransformerEncoder, TransformerEncLayer
from io import open
from ..utils import Corpus
parser = argparse.ArgumentParser()
parser.add_argument("--checkpoint", type=str, default="./model.pt")
parser.add_argument("--outf", type=str, default="generated.txt")
parser.add_argument("--temperature", type=float, default=1.0)
parser.add_argument("--words", type=int, default="1000")
parser.add_argument("--bptt", type=int, default=35)
parser.add_argument("--clip", type=float, default=0.25)
parser.add_argument("--data", type=str, default="./data/wikitext-2")
parser.add_argument("--drop", type=float, default=0.2)
parser.add_argument("--dry-run", action="store_true")
parser.add_argument("--emsize", type=int, default=200)
parser.add_argument("--log-interval", type=int, default=200, metavar="N")
parser.add_argument("--nhead", type=int, default=2)
parser.add_argument("--nhid", type=int, default=200)
parser.add_argument("--nlayers", type=int, default=2)
parser.add_argument("--save", type=str, default="model.pt")
parser.add_argument("--tied", action="store_true")
args = parser.parse_args()
torch.manual_seed(args.seed)
device = torch.device("cuda" if args.cuda else "cpu")
corpus = Corpus(args.data)
class RNN(qc.Module):
def __init__(self, rnn_type, ntoken, ninp, nhid, nlayers, drop=0.5, tie_weights=False):
super().__init__()
self.ntoken = ntoken
self.drop = qc.Dropout(drop)
self.encoder = qc.Embed(ntoken, ninp)
if rnn_type in ["LSTM", "GRU"]:
self.rnn = getattr(nn, rnn_type)(ninp, nhid, nlayers, drop=drop)
else:
try:
nonlinearity = {"RNN_TANH": "tanh", "RNN_RELU": "relu"}[rnn_type]
except KeyError:
raise ValueError(
"""An invalid option for `--model` was supplied,
options are ['LSTM', 'GRU', 'RNN_TANH' or 'RNN_RELU']"""
)
self.rnn = nn.RNN(ninp, nhid, nlayers, nonlinearity=nonlinearity, drop=drop)
self.decoder = nn.Linear(nhid, ntoken)
if tie_weights:
if nhid != ninp:
raise ValueError("When using the tied flag, nhid must be equal to emsize")
self.decoder.weight = self.encoder.weight
self.init_weights()
self.rnn_type = rnn_type
self.nhid = nhid
self.nlayers = nlayers
def init_weights(self):
initrange = 0.1
nn.init.uniform_(self.encoder.weight, -initrange, initrange)
nn.init.zeros_(self.decoder.bias)
nn.init.uniform_(self.decoder.weight, -initrange, initrange)
def forward(self, input, hidden):
emb = self.drop(self.encoder(input))
output, hidden = self.rnn(emb, hidden)
output = self.drop(output)
decoded = self.decoder(output)
decoded = decoded.view(-1, self.ntoken)
return F.log_softmax(decoded, dim=1), hidden
def init_hidden(self, bsz):
weight = next(self.parameters())
if self.rnn_type == "LSTM":
return (
weight.new_zeros(self.nlayers, bsz, self.nhid),
weight.new_zeros(self.nlayers, bsz, self.nhid),
)
else:
return weight.new_zeros(self.nlayers, bsz, self.nhid)
class PositionalEncoding(qc.Module):
def __init__(self, d_hidden, drop=0.1, max_len=5000):
super(PositionalEncoding, self).__init__()
self.drop = qc.Dropout(p=drop)
pe = torch.zeros(max_len, d_hidden)
position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
div_term = torch.exp(torch.arange(0, d_hidden, 2).float() * (-math.log(10000.0) / d_hidden))
pe[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term)
pe = pe.unsqueeze(0).transpose(0, 1)
self.register_buffer("pe", pe)
def forward(self, x):
x = x + self.pe[: x.size(0), :]
return self.drop(x)
class Transformer(qc.Module):
def __init__(self, ntoken, ninp, nhead, nhid, nlayers, drop=0.5):
super().__init__()
self.model_type = "Transformer"
self.src_mask = None
self.pos_encoder = PositionalEncoding(ninp, drop)
n_enc_lays = TransformerEncLayer(ninp, nhead, nhid, drop)
self.transformer_encoder = TransformerEncoder(n_enc_lays, nlayers)
self.encoder = qc.Embed(ntoken, ninp)
self.ninp = ninp
self.decoder = nn.Linear(ninp, ntoken)
self.init_weights()
def _generate_square_subsequent_mask(self, sz):
mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1)
mask = mask.float().masked_fill(mask == 0, float("-inf")).masked_fill(mask == 1, float(0.0))
return mask
def init_weights(self):
initrange = 0.1
nn.init.uniform_(self.encoder.weight, -initrange, initrange)
nn.init.zeros_(self.decoder.bias)
nn.init.uniform_(self.decoder.weight, -initrange, initrange)
def forward(self, src, has_mask=True):
if has_mask:
device = src.device
if self.src_mask is None or self.src_mask.size(0) != len(src):
mask = self._generate_square_subsequent_mask(len(src)).to(device)
self.src_mask = mask
else:
self.src_mask = None
src = self.encoder(src) * math.sqrt(self.ninp)
src = self.pos_encoder(src)
output = self.transformer_encoder(src, self.src_mask)
output = self.decoder(output)
return F.log_softmax(output, dim=-1)
def batchify(data, bsz):
nbatch = data.size(0) // bsz
data = data.narrow(0, 0, nbatch * bsz)
data = data.view(bsz, -1).t().contiguous()
return data.to(device)
train_data = batchify(corpus.train, args.train_batch_size)
eval_data = batchify(corpus.eval, args.eval_batch_size)
test_data = batchify(corpus.test, args.eval_batch_size)
ntokens = len(corpus.dictionary)
if args.model_name == "Transformer":
model = Transformer(ntokens, args.emsize, args.nhead, args.nhid, args.nlayers, args.drop).to(
device
)
else:
model = RNN(
args.model_name, ntokens, args.emsize, args.nhid, args.nlayers, args.drop, args.tied
).to(device)
criterion = nn.NLLLoss()
def repackage_hidden(h):
if isinstance(h, torch.Tensor):
return h.detach()
else:
return tuple(repackage_hidden(v) for v in h)
def get_batch(source, i):
seq_len = min(args.bptt, len(source) - 1 - i)
data = source[i : i + seq_len]
target = source[i + 1 : i + 1 + seq_len].view(-1)
return data, target
def evaluate(data_source):
model.eval()
total_loss = 0.0
ntokens = len(corpus.dictionary)
if args.model_name != "Transformer":
hidden = model.init_hidden(args.eval_batch_size)
with torch.no_grad():
for i in range(0, data_source.size(0) - 1, args.bptt):
data, targets = get_batch(data_source, i)
if args.model_name == "Transformer":
output = model(data)
output = output.view(-1, ntokens)
else:
output, hidden = model(data, hidden)
hidden = repackage_hidden(hidden)
total_loss += len(data) * criterion(output, targets).item()
return total_loss / (len(data_source) - 1)
def train():
model.train()
total_loss = 0.0
start_time = time.time()
ntokens = len(corpus.dictionary)
if args.model_name != "Transformer":
hidden = model.init_hidden(args.train_batch_size)
for batch, i in enumerate(range(0, train_data.size(0) - 1, args.bptt)):
data, targets = get_batch(train_data, i)
model.zero_grad()
if args.model_name == "Transformer":
output = model(data)
output = output.view(-1, ntokens)
else:
hidden = repackage_hidden(hidden)
output, hidden = model(data, hidden)
loss = criterion(output, targets)
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip)
for p in model.parameters():
p.data.add_(p.grad, alpha=-lr)
total_loss += loss.item()
if batch % args.log_interval == 0 and batch > 0:
cur_loss = total_loss / args.log_interval
elapsed = time.time() - start_time
print(
"| epoch {:3d} | {:5d}/{:5d} batches | lr {:02.2f} | ms/batch {:5.2f} | "
"loss {:5.2f} | ppl {:8.2f}".format(
epoch,
batch,
len(train_data) // args.bptt,
lr,
elapsed * 1000 / args.log_interval,
cur_loss,
math.exp(cur_loss),
)
)
total_loss = 0
start_time = time.time()
if args.dry_run:
break
lr = args.lr
best_val_loss = None
try:
for epoch in range(1, args.train_epochs + 1):
epoch_start_time = time.time()
train()
val_loss = evaluate(eval_data)
print("-" * 89)
print(
"| end of epoch {:3d} | time: {:5.2f}s | valid loss {:5.2f} | "
"valid ppl {:8.2f}".format(
epoch, (time.time() - epoch_start_time), val_loss, math.exp(val_loss)
)
)
print("-" * 89)
if not best_val_loss or val_loss < best_val_loss:
with open(args.save, "wb") as f:
torch.save(model, f)
best_val_loss = val_loss
else:
lr /= 4.0
except KeyboardInterrupt:
print("-" * 89)
print("Exiting from training early")
with open(args.save, "rb") as f:
model = torch.load(f)
if args.model_name in ["RNN_TANH", "RNN_RELU", "LSTM", "GRU"]:
model.rnn.flatten_parameters()
test_loss = evaluate(test_data)
print("=" * 89)
print(
"| End of training | test loss {:5.2f} | test ppl {:8.2f}".format(
test_loss, math.exp(test_loss)
)
)
print("=" * 89)
if args.temperature < 1e-3:
parser.error("--temperature has to be greater or equal 1e-3.")
with open(args.checkpoint, "rb") as f:
model = torch.load(f, map_location=device)
model.eval()
corpus = Corpus(args.data)
ntokens = len(corpus.dictionary)
is_transformer_model = hasattr(model, "model_type") and model.model_type == "Transformer"
if not is_transformer_model:
hidden = model.init_hidden(1)
input = torch.randint(ntokens, (1, 1), dtype=torch.long).to(device)
with open(args.outf, "w") as outf:
with torch.no_grad():
for i in range(args.words):
if is_transformer_model:
output = model(input, False)
word_weights = output[-1].squeeze().div(args.temperature).exp().cpu()
word_idx = torch.multinomial(word_weights, 1)[0]
word_tensor = torch.Tensor([[word_idx]]).long().to(device)
input = torch.cat([input, word_tensor], 0)
else:
output, hidden = model(input, hidden)
word_weights = output.squeeze().div(args.temperature).exp().cpu()
word_idx = torch.multinomial(word_weights, 1)[0]
input.fill_(word_idx)
word = corpus.dictionary.idx2word[word_idx]
outf.write(word + ("\n" if i % 20 == 19 else " "))
if i % args.log_interval == 0:
print("| Generated {}/{} words".format(i, args.words))
"""
python main.py --cuda --train_epochs 6
python main.py --cuda --train_epochs 6 --tied
python main.py --cuda --tied
python main.py --cuda --train_epochs 6 --model Transformer --lr 5
python generate.py
python generate.py --cuda --model Transformer
"""
|
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|
33,532
|
quantapix/qnarre
|
refs/heads/main
|
/tools/triton/python/examples/empty.py
|
import torch
import triton
import triton.language as tl
@triton.jit
def kernel(X, stride_xm, stride_xn, BLOCK: tl.constexpr):
pass
X = torch.randn(1, device="cuda")
pgm = kernel[(1,)](X, 1, 1, BLOCK=1024)
|
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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"]}
|
33,533
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/base/doc/blog.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 filecmp as fl
import pathlib as pth
from .log import Logger
from .base import config
# from .mirror import copy
from .counter import counters
log = Logger(__name__)
SUFF = '.rst'
def copy(src, dst):
dst.parent.mkdir(parents=True, exist_ok=True)
if isinstance(src, pth.Path):
if dst.exists():
assert fl.cmp(str(src), str(dst), False)
else:
sh.copy2(str(src), str(dst))
else:
assert isinstance(src, str)
if dst.exists():
assert src == dst.read_text()
else:
dst.write_text(src)
class Blog:
def __init__(self, base):
self.base = base
populate_args = ((('chained', '.'), ('blogged', '+'), ('excluded', '-'),
('failed', 'F')), 'Populating:')
def populate(self, dst, ctxt, **kw):
kw.update(ctxt=ctxt)
dst = self.base / dst
with counters(self.export_args, kw) as cs:
for _, ms in ctxt.recs.chainer(**kw):
for m in ms:
a = '\n'.join(m.blogger(**kw))
(dst / m.slug).with_suffix(SUFF).write_text(a)
o = m.hdr.original
if o:
s = self.base / config.docs_src / m.source / o
copy(s, dst / s.name)
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"]}
|
33,534
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/dataset/big_patent.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 glob
import gzip
import json
import os
import datasets as ds
_URL = "https://drive.google.com/uc?export=download&id=1J3mucMFTWrgAYa3LuBZoLRR3CzzYD3fa"
_DOC = "description"
_SUM = "abstract"
_CPC = {
"a": "Human Necessities",
"b": "Performing Operations; Transporting",
"c": "Chemistry; Metallurgy",
"d": "Textiles; Paper",
"e": "Fixed Constructions",
"f": "Mechanical Engineering; Lightning; Heating; Weapons; Blasting",
"g": "Physics",
"h": "Electricity",
"y": "General tagging of new or cross-sectional technology",
}
class Config(ds.BuilderConfig):
def __init__(self, *xs, cpc=None, **kw):
super().__init__(*xs, version=ds.Version("1.0.0"), **kw)
self.cpc = cpc
class BigPatent(ds.GeneratorBasedBuilder):
BUILDER_CONFIGS = [Config(cpc=list(_CPC), name="all")] + [
Config(cpc=[k], name=k) for k, v in sorted(_CPC.items())
]
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):
p = mgr.download_and_extract(_URL)
ks = ["train", "valid", "test"]
fs = mgr.extract({k: os.path.join(p, "bigPatentData", k + ".tar.gz") for k in ks})
fs = {k: os.path.join(fs[k], k) for k in ks}
return [
ds.SplitGenerator(name=ds.Split.TRAIN, gen_kw={"path": fs["train"]}),
ds.SplitGenerator(name=ds.Split.VALIDATION, gen_kw={"path": fs["val"]}),
ds.SplitGenerator(name=ds.Split.TEST, gen_kw={"path": fs["test"]}),
]
def _generate_examples(self, path=None):
for c in self.config.cpc:
ns = glob.glob(os.path.join(path, c, "*"))
for n in sorted(ns):
with open(n, "rb") as f:
f = gzip.GzipFile(fileobj=f)
for r in f:
x = json.loads(r)
yield x["publication_number"], {_DOC: x[_DOC], _SUM: x[_SUM]}
|
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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/__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"]}
|
33,535
|
quantapix/qnarre
|
refs/heads/main
|
/tools/triton/python/examples/copy_strided.py
|
import triton
import triton.language as tl
# triton kernel
@triton.jit
def kernel(X, stride_xm,
Z, stride_zn,
BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr):
off_m = tl.arange(0, BLOCK_M)
off_n = tl.arange(0, BLOCK_N)
Xs = X + off_m[:, None] * stride_xm + off_n[None, :] * 1
Zs = Z + off_m[:, None] * 1 + off_n[None, :] * stride_zn
tl.store(Zs, tl.load(Xs))
ret = triton.compile(kernel, signature="*fp32,i32,*fp32,i32", constants={"BLOCK_M": 64, "BLOCK_N": 64})
print(ret.asm["ttgir"])
|
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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"]}
|
33,536
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/base/doc/util/doc.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 .log import Logger
from .header import Header
log = Logger(__name__)
class PackHeader(Header):
def __init__(self, hdr, **kw):
super().__init__({}, **kw)
self.extract(vars(hdr))
def merge(self, other):
super().merge(other)
if not self.subject and other.subject:
self.subject = other.subject
|
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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"]}
|
33,537
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/tokens/fast/funnel.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 BertFast
from ..funnel import Tokenizer as Funnel
VOCAB_FS = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
_model_names = [
"small",
"small-base",
"medium",
"medium-base",
"intermediate",
"intermediate-base",
"large",
"large-base",
"xlarge",
"xlarge-base",
]
VOCAB_MAP = {
"vocab_file": {
"funnel-transformer/small": "https://huggingface.co/funnel-transformer/small/resolve/main/vocab.txt",
"funnel-transformer/small-base": "https://huggingface.co/funnel-transformer/small-base/resolve/main/vocab.txt",
"funnel-transformer/medium": "https://huggingface.co/funnel-transformer/medium/resolve/main/vocab.txt",
"funnel-transformer/medium-base": "https://huggingface.co/funnel-transformer/medium-base/resolve/main/vocab.txt",
"funnel-transformer/intermediate": "https://huggingface.co/funnel-transformer/intermediate/resolve/main/vocab.txt",
"funnel-transformer/intermediate-base": "https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/vocab.txt",
"funnel-transformer/large": "https://huggingface.co/funnel-transformer/large/resolve/main/vocab.txt",
"funnel-transformer/large-base": "https://huggingface.co/funnel-transformer/large-base/resolve/main/vocab.txt",
"funnel-transformer/xlarge": "https://huggingface.co/funnel-transformer/xlarge/resolve/main/vocab.txt",
"funnel-transformer/xlarge-base": "https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/vocab.txt",
},
"tokenizer_file": {
"funnel-transformer/small": "https://huggingface.co/funnel-transformer/small/resolve/main/tokenizer.json",
"funnel-transformer/small-base": "https://huggingface.co/funnel-transformer/small-base/resolve/main/tokenizer.json",
"funnel-transformer/medium": "https://huggingface.co/funnel-transformer/medium/resolve/main/tokenizer.json",
"funnel-transformer/medium-base": "https://huggingface.co/funnel-transformer/medium-base/resolve/main/tokenizer.json",
"funnel-transformer/intermediate": "https://huggingface.co/funnel-transformer/intermediate/resolve/main/tokenizer.json",
"funnel-transformer/intermediate-base": "https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/tokenizer.json",
"funnel-transformer/large": "https://huggingface.co/funnel-transformer/large/resolve/main/tokenizer.json",
"funnel-transformer/large-base": "https://huggingface.co/funnel-transformer/large-base/resolve/main/tokenizer.json",
"funnel-transformer/xlarge": "https://huggingface.co/funnel-transformer/xlarge/resolve/main/tokenizer.json",
"funnel-transformer/xlarge-base": "https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/tokenizer.json",
},
}
INPUT_CAPS = {f"funnel-transformer/{name}": 512 for name in _model_names}
PRETRAINED_INIT_CONFIGURATION = {
f"funnel-transformer/{name}": {"do_lower_case": True} for name in _model_names
}
class Tokenizer(BertFast):
vocab_fs = VOCAB_FS
vocab_map = VOCAB_MAP
input_caps = INPUT_CAPS
pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
slow_tokenizer_class = Funnel
cls_token_type_id = 2
def __init__(
self,
vocab_file=None,
tokenizer_file=None,
do_lower_case=True,
unk="<unk>",
sep="<sep>",
pad="<pad>",
cls="<cls>",
msk="<mask>",
bos="<s>",
eos="</s>",
clean_text=True,
tokenize_chinese_chars=True,
strip_accents=None,
wordpieces_prefix="##",
**kw,
):
super().__init__(
vocab_file,
tokenizer_file=tokenizer_file,
do_lower_case=do_lower_case,
unk=unk,
sep=sep,
pad=pad,
cls=cls,
msk=msk,
bos=bos,
eos=eos,
clean_text=clean_text,
tokenize_chinese_chars=tokenize_chinese_chars,
strip_accents=strip_accents,
wordpieces_prefix=wordpieces_prefix,
**kw,
)
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) * [self.cls_token_type_id] + len(toks_0 + sep) * [0]
return (
len(cls) * [self.cls_token_type_id] + len(toks_0 + sep) * [0] + len(toks_1 + sep) * [1]
)
|
{"/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"], 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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"], 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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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|
33,538
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/try/softmax.py
|
# %%
import torch
import triton
import triton.language as tl
@torch.jit.script
def naive_softmax(x):
# read MN elements ; write M elements
x_max = x.max(dim=1)[0]
# read MN + M elements ; write MN elements
x2 = x - x_max[:, None]
# read MN elements ; write MN elements
nr = torch.exp(x2)
# read MN elements ; write M elements
dr = nr.sum(dim=1)
# read MN + M elements ; write MN elements
y = nr / dr[:, None]
# in total: read 5MN + 2M elements ; wrote 3MN + 2M elements
return y
# %%
@triton.jit
def softmax_kernel(y_ptr, x_ptr, x_stride, y_stride, n_cols, BLOCK: tl.constexpr):
pid = tl.program_id(0)
offsets = tl.arange(0, BLOCK)
x = x_ptr + pid * x_stride + offsets
x = tl.load(x, mask=offsets < n_cols, other=-float("inf"))
nr = tl.exp(x - tl.max(x, axis=0))
dr = tl.sum(nr, axis=0)
y = y_ptr + pid * y_stride + offsets
tl.store(y, nr / dr, mask=offsets < n_cols)
# %%
def softmax(x):
n_rows, n_cols = x.shape
BLOCK = triton.next_power_of_2(n_cols)
n_warps = 4
if BLOCK >= 2048:
n_warps = 8
if BLOCK >= 4096:
n_warps = 16
y = torch.empty_like(x)
softmax_kernel[(n_rows,)](
y,
x,
x.stride(0),
y.stride(0),
n_cols,
num_warps=n_warps,
BLOCK=BLOCK,
)
return y
# %%
torch.manual_seed(0)
x = torch.randn(1823, 781, device="cuda")
y_torch = torch.softmax(x, axis=1)
y_triton = softmax(x)
assert torch.allclose(y_triton, y_torch), (y_triton, y_torch)
# %%
@triton.testing.perf_report(
triton.testing.Benchmark(
x_names=["N"],
x_vals=[128 * i for i in range(2, 100)],
line_arg="provider",
line_vals=["triton", "torch-native", "torch-jit"],
line_names=["Triton", "Torch (native)", "Torch (jit)"],
styles=[("blue", "-"), ("green", "-"), ("green", "--")],
ylabel="GB/s",
plot_name="softmax-performance",
args={"M": 4096},
)
)
def benchmark(M, N, provider):
x = torch.randn(M, N, device="cuda", dtype=torch.float32)
quantiles = [0.5, 0.2, 0.8]
if provider == "torch-native":
ms, min_ms, max_ms = triton.testing.do_bench(
lambda: torch.softmax(x, axis=-1), quantiles=quantiles
)
if provider == "triton":
ms, min_ms, max_ms = triton.testing.do_bench(lambda: softmax(x), quantiles=quantiles)
if provider == "torch-jit":
ms, min_ms, max_ms = triton.testing.do_bench(lambda: naive_softmax(x), quantiles=quantiles)
gbps = lambda ms: 2 * x.nelement() * x.element_size() * 1e-9 / (ms * 1e-3)
return gbps(ms), gbps(max_ms), gbps(min_ms)
benchmark.run(show_plots=True, print_data=True)
# %%
|
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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/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"]}
|
33,539
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/run/xnli.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 multi-lingual models on XNLI (e.g. Bert, DistilBERT, XLM)
import logging
import random
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from transformers import (
CONFIG_MAPPING,
AutoModelForSequenceClassification,
DataCollatorWithPadding,
default_data_collator,
)
from .params import TRAIN, EVAL, TEST, ALL, EACH
from .runner import Runner as Base
log = logging.getLogger(__name__)
class Runner(Base):
AutoModel = AutoModelForSequenceClassification
@property
def dataset(self):
if self._dataset is None:
ps = self.params
y = {TRAIN: {}, EVAL: {}, TEST: {}}
if ps.do_train:
if ps.train_language is None:
y[TRAIN] = load_dataset(
"xnli", ps.language, split=TRAIN, cache_dir=ps.cache_dir
)
else:
y[TRAIN] = load_dataset(
"xnli", ps.train_language, split=TRAIN, cache_dir=ps.cache_dir
)
self.label_list = y.features["label"].names
if ps.do_eval:
y[EVAL] = load_dataset("xnli", ps.language, split=EVAL, cache_dir=ps.cache_dir)
self.label_list = y.features["label"].names
if ps.do_test:
y[TEST] = load_dataset("xnli", ps.language, split=TEST, cache_dir=ps.cache_dir)
self.label_list = y.features["label"].names
self._dataset = y
return self._dataset
@property
def cols(self):
if self._cols is None:
cs = self.dataset[TRAIN].column_names
t = "text" if "text" in cs else cs[0]
self._cols = {ALL: cs, EACH: [t]}
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 = self.AutoConfig.from_pretrained(
x,
n_labels=len(self.label_list),
finetune="xnli",
cache_dir=ps.cache_dir,
revision=ps.model_version,
use_auth_token=True if ps.use_auth_token else None,
)
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")
y = self.AutoTokenizer.from_pretrained(
x,
lower_case=ps.lower_case,
cache_dir=ps.cache_dir,
use_fast=ps.use_fast_tokenizer,
revision=ps.model_version,
use_auth_token=True if ps.use_auth_token else None,
)
self._tokenizer = y
return self._tokenizer
@property
def model(self):
if self._model is None:
ps = self.params
if ps.model_name:
y = self.AutoModel.from_pretrained(
ps.model_name,
from_tf=bool(".ckpt" in ps.model_name),
config=self.config,
cache_dir=ps.cache_dir,
revision=ps.model_version,
use_auth_token=True if ps.use_auth_token else None,
)
else:
log.info("Training new model")
y = self.AutoModel.from_config(self.config)
self._model = y
return self._model
@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,
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):
return self.tokenizer(
xs["premise"],
xs["hypothesis"],
padding=self.padding,
max_len=self.params.max_seq_length,
truncation=True,
)
@property
def eval_ds(self):
if self._eval_ds is None:
ps, mgr = self.params, self.mgr
y = super().eval_ds
with mgr.main_process_first():
y = y.map(
self.prep_for_train,
batched=True,
load_from_cache_file=not ps.overwrite_cache,
desc="Running tokenizer on eval dataset",
)
self._eval_ds = y
return self._eval_ds
@property
def test_ds(self):
if self._test_ds is None:
ps, mgr = self.params, self.mgr
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 = self.params
if ps.pad_to_max_length:
c = default_data_collator
elif ps.fp16:
c = DataCollatorWithPadding(self.tokenizer, pad_to_multiple_of=8)
else:
c = 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}
if ps.do_test:
p = DataLoader(self.test_ds, 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("xnli")
# 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)
return self._metric
def main():
x = Runner()
x.dataset
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 xnli.py \
--model_name bert-base-multilingual-cased \
--language de \
--train_language en \
--do_train \
--do_eval \
--train_batch_size 32 \
--train_epochs 2.0 \
--max_seq_length 128 \
--out_dir /tmp/debug_xnli/ \
--save_steps -1
"""
|
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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"]}
|
33,540
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/models/longformer.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 torch.nn import CrossEntropyLoss
from ...pytorch_utils import (
apply_chunking_to_forward,
)
LIST = [
"allenai/longformer-base-4096",
"allenai/longformer-large-4096",
"allenai/longformer-large-4096-finetuned-triviaqa",
"allenai/longformer-base-4096-extra.pos.embd.only",
"allenai/longformer-large-4096-extra.pos.embd.only",
]
def _get_question_end_index(input_ids, sep_token_id):
sep_token_indices = (input_ids == sep_token_id).nonzero()
batch_size = input_ids.shape[0]
assert sep_token_indices.shape[1] == 2, "`input_ids` should have two dimensions"
assert (
sep_token_indices.shape[0] == 3 * batch_size
), f"There should be exactly three separator tokens: {sep_token_id} in every sample for questions answering. You might also consider to set `global_attention_mask` manually in the forward function to avoid this error."
return sep_token_indices.view(batch_size, 3, 2)[:, 0, 1]
def _compute_global_attention_mask(input_ids, sep_token_id, before_sep_token=True):
question_end_index = _get_question_end_index(input_ids, sep_token_id)
question_end_index = question_end_index.unsqueeze(dim=1) # size: batch_size x 1
# bool attention mask with True in locations of global attention
attention_mask = torch.arange(input_ids.shape[1], device=input_ids.device)
if before_sep_token is True:
attention_mask = (attention_mask.expand_as(input_ids) < question_end_index).to(torch.uint8)
else:
# last token is separation token and should not be counted and in the middle are two separation tokens
attention_mask = (attention_mask.expand_as(input_ids) > (question_end_index + 1)).to(
torch.uint8
) * (attention_mask.expand_as(input_ids) < input_ids.shape[-1]).to(torch.uint8)
return 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
class LongformerEmbeddings(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 is not snake-cased to stick with TensorFlow model variable name and be able to load
# any TensorFlow checkpoint file
self.norm = qc.LayerNorm(config.d_model, eps=config.eps)
self.drop = qc.Dropout(config.drop)
# position_ids (1, len position emb) is contiguous in memory and exported when serialized
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 = 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]
seq_length = input_shape[1]
if position_ids is None:
position_ids = self.position_ids[:, :seq_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)
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 LongformerSelfAttention(qc.Module):
def __init__(self, config, layer_id):
super().__init__()
if config.d_model % config.n_heads != 0:
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.head_dim = int(config.d_model / config.n_heads)
self.embed_dim = config.d_model
self.query = qc.Linear(config.d_model, self.embed_dim)
self.key = qc.Linear(config.d_model, self.embed_dim)
self.value = qc.Linear(config.d_model, self.embed_dim)
# separate projection layers for tokens with global attention
self.query_global = qc.Linear(config.d_model, self.embed_dim)
self.key_global = qc.Linear(config.d_model, self.embed_dim)
self.value_global = qc.Linear(config.d_model, self.embed_dim)
self.drop = config.drop_attn
self.layer_id = layer_id
attention_window = config.attention_window[self.layer_id]
assert (
attention_window % 2 == 0
), f"`attention_window` for layer {self.layer_id} has to be an even value. Given {attention_window}"
assert (
attention_window > 0
), f"`attention_window` for layer {self.layer_id} has to be positive. Given {attention_window}"
self.one_sided_attn_window_size = attention_window // 2
def forward(
self,
hiddens,
attention_mask=None,
layer_head_mask=None,
is_index_masked=None,
is_index_global_attn=None,
is_global_attn=None,
output_attentions=False,
):
hiddens = hiddens.transpose(0, 1)
# project hidden states
query_vectors = self.query(hiddens)
key_vectors = self.key(hiddens)
value_vectors = self.value(hiddens)
seq_len, batch_size, embed_dim = hiddens.size()
assert (
embed_dim == self.embed_dim
), f"hiddens should have embed_dim = {self.embed_dim}, but has {embed_dim}"
# normalize query
query_vectors /= math.sqrt(self.head_dim)
query_vectors = query_vectors.view(
seq_len, batch_size, self.n_heads, self.head_dim
).transpose(0, 1)
key_vectors = key_vectors.view(seq_len, batch_size, self.n_heads, self.head_dim).transpose(
0, 1
)
attn_scores = self._sliding_chunks_query_key_matmul(
query_vectors, key_vectors, self.one_sided_attn_window_size
)
# values to pad for attention probs
remove_from_windowed_attention_mask = (attention_mask != 0)[:, :, None, None]
# cast to fp32/fp16 then replace 1's with -inf
float_mask = remove_from_windowed_attention_mask.type_as(query_vectors).masked_fill(
remove_from_windowed_attention_mask, -10000.0
)
# diagonal mask with zeros everywhere and -inf inplace of padding
diagonal_mask = self._sliding_chunks_query_key_matmul(
float_mask.new_ones(size=float_mask.size()), float_mask, self.one_sided_attn_window_size
)
# pad local attention probs
attn_scores += diagonal_mask
assert list(attn_scores.size()) == [
batch_size,
seq_len,
self.n_heads,
self.one_sided_attn_window_size * 2 + 1,
], f"local_attn_probs should be of size ({batch_size}, {seq_len}, {self.n_heads}, {self.one_sided_attn_window_size * 2 + 1}), but is of size {attn_scores.size()}"
# compute local attention probs from global attention keys and contact over window dim
if is_global_attn:
# compute global attn indices required through out forward fn
(
max_num_global_attn_indices,
is_index_global_attn_nonzero,
is_local_index_global_attn_nonzero,
is_local_index_no_global_attn_nonzero,
) = self._get_global_attn_indices(is_index_global_attn)
# calculate global attn probs from global key
global_key_attn_scores = self._concat_with_global_key_attn_probs(
query_vectors=query_vectors,
key_vectors=key_vectors,
max_num_global_attn_indices=max_num_global_attn_indices,
is_index_global_attn_nonzero=is_index_global_attn_nonzero,
is_local_index_global_attn_nonzero=is_local_index_global_attn_nonzero,
is_local_index_no_global_attn_nonzero=is_local_index_no_global_attn_nonzero,
)
# concat to local_attn_probs
# (batch_size, seq_len, n_heads, extra attention count + 2*window+1)
attn_scores = torch.cat((global_key_attn_scores, attn_scores), dim=-1)
# free memory
del global_key_attn_scores
attn_probs = F.softmax(
attn_scores, dim=-1, dtype=torch.float32
) # use fp32 for numerical stability
if layer_head_mask is not None:
assert layer_head_mask.size() == (
self.n_heads,
), f"Head mask for a single layer should be of size {(self.n_heads,)}, but is {layer_head_mask.size()}"
attn_probs = layer_head_mask.view(1, 1, -1, 1) * attn_probs
# softmax sometimes inserts NaN if all positions are masked, replace them with 0
attn_probs = torch.masked_fill(attn_probs, is_index_masked[:, :, None, None], 0.0)
attn_probs = attn_probs.type_as(attn_scores)
# free memory
del attn_scores
# apply drop
attn_probs = F.drop(attn_probs, p=self.drop, training=self.training)
value_vectors = value_vectors.view(
seq_len, batch_size, self.n_heads, self.head_dim
).transpose(0, 1)
# compute local attention output with global attention value and add
if is_global_attn:
# compute sum of global and local attn
attn_output = self._compute_attn_output_with_global_indices(
value_vectors=value_vectors,
attn_probs=attn_probs,
max_num_global_attn_indices=max_num_global_attn_indices,
is_index_global_attn_nonzero=is_index_global_attn_nonzero,
is_local_index_global_attn_nonzero=is_local_index_global_attn_nonzero,
)
else:
# compute local attn only
attn_output = self._sliding_chunks_matmul_attn_probs_value(
attn_probs, value_vectors, self.one_sided_attn_window_size
)
assert attn_output.size() == (
batch_size,
seq_len,
self.n_heads,
self.head_dim,
), "Unexpected size"
attn_output = (
attn_output.transpose(0, 1).reshape(seq_len, batch_size, embed_dim).contiguous()
)
# compute value for global attention and overwrite to attention output
# TODO: remove the redundant computation
if is_global_attn:
global_attn_output, global_attn_probs = self._compute_global_attn_output_from_hidden(
hiddens=hiddens,
max_num_global_attn_indices=max_num_global_attn_indices,
layer_head_mask=layer_head_mask,
is_local_index_global_attn_nonzero=is_local_index_global_attn_nonzero,
is_index_global_attn_nonzero=is_index_global_attn_nonzero,
is_local_index_no_global_attn_nonzero=is_local_index_no_global_attn_nonzero,
is_index_masked=is_index_masked,
)
# get only non zero global attn output
nonzero_global_attn_output = global_attn_output[
is_local_index_global_attn_nonzero[0], :, is_local_index_global_attn_nonzero[1]
]
# overwrite values with global attention
attn_output[is_index_global_attn_nonzero[::-1]] = nonzero_global_attn_output.view(
len(is_local_index_global_attn_nonzero[0]), -1
)
# The attention weights for tokens with global attention are
# just filler values, they were never used to compute the output.
# Fill with 0 now, the correct values are in 'global_attn_probs'.
attn_probs[is_index_global_attn_nonzero] = 0
outputs = (attn_output.transpose(0, 1),)
if output_attentions:
outputs += (attn_probs,)
return outputs + (global_attn_probs,) if (is_global_attn and output_attentions) else outputs
@staticmethod
def _pad_and_transpose_last_two_dims(hidden_states_padded, padding):
"""pads rows and then flips rows and columns"""
hidden_states_padded = F.pad(
hidden_states_padded, padding
) # padding value is not important because it will be overwritten
hidden_states_padded = hidden_states_padded.view(
*hidden_states_padded.size()[:-2],
hidden_states_padded.size(-1),
hidden_states_padded.size(-2),
)
return hidden_states_padded
@staticmethod
def _pad_and_diagonalize(chunked_model_states):
total_num_heads, num_chunks, window_overlap, hidden_dim = chunked_model_states.size()
chunked_model_states = F.pad(
chunked_model_states, (0, window_overlap + 1)
) # total_num_heads x num_chunks x window_overlap x (hidden_dim+window_overlap+1). Padding value is not important because it'll be overwritten
chunked_model_states = chunked_model_states.view(
total_num_heads, num_chunks, -1
) # total_num_heads x num_chunks x window_overlap*window_overlap+window_overlap
chunked_model_states = chunked_model_states[
:, :, :-window_overlap
] # total_num_heads x num_chunks x window_overlap*window_overlap
chunked_model_states = chunked_model_states.view(
total_num_heads, num_chunks, window_overlap, window_overlap + hidden_dim
)
chunked_model_states = chunked_model_states[:, :, :, :-1]
return chunked_model_states
@staticmethod
def _chunk(hiddens, window_overlap):
hiddens = hiddens.view(
hiddens.size(0),
hiddens.size(1) // (window_overlap * 2),
window_overlap * 2,
hiddens.size(2),
)
chunk_size = list(hiddens.size())
chunk_size[1] = chunk_size[1] * 2 - 1
chunk_stride = list(hiddens.stride())
chunk_stride[1] = chunk_stride[1] // 2
return hiddens.as_strided(size=chunk_size, stride=chunk_stride)
@staticmethod
def _mask_invalid_locations(input_tensor, affected_seq_len):
beginning_mask_2d = (
input_tensor.new_ones(affected_seq_len, affected_seq_len + 1).tril().flip(dims=[0])
)
beginning_mask = beginning_mask_2d[None, :, None, :]
ending_mask = beginning_mask.flip(dims=(1, 3))
beginning_input = input_tensor[:, :affected_seq_len, :, : affected_seq_len + 1]
beginning_mask = beginning_mask.expand(beginning_input.size())
beginning_input.masked_fill_(
beginning_mask == 1, -float("inf")
) # `== 1` converts to bool or uint8
ending_input = input_tensor[:, -affected_seq_len:, :, -(affected_seq_len + 1) :]
ending_mask = ending_mask.expand(ending_input.size())
ending_input.masked_fill_(
ending_mask == 1, -float("inf")
) # `== 1` converts to bool or uint8
def _sliding_chunks_query_key_matmul(self, query, key, window_overlap):
batch_size, seq_len, n_heads, head_dim = query.size()
assert (
seq_len % (window_overlap * 2) == 0
), f"Sequence length should be multiple of {window_overlap * 2}. Given {seq_len}"
assert query.size() == key.size()
chunks_count = seq_len // window_overlap - 1
# group batch_size and n_heads dimensions into one, then chunk seq_len into chunks of size window_overlap * 2
query = query.transpose(1, 2).reshape(batch_size * n_heads, seq_len, head_dim)
key = key.transpose(1, 2).reshape(batch_size * n_heads, seq_len, head_dim)
query = self._chunk(query, window_overlap)
key = self._chunk(key, window_overlap)
diagonal_chunked_attention_scores = torch.einsum(
"bcxd,bcyd->bcxy", (query, key)
) # multiply
# convert diagonals into columns
diagonal_chunked_attention_scores = self._pad_and_transpose_last_two_dims(
diagonal_chunked_attention_scores, padding=(0, 0, 0, 1)
)
diagonal_attention_scores = diagonal_chunked_attention_scores.new_empty(
(batch_size * n_heads, chunks_count + 1, window_overlap, window_overlap * 2 + 1)
)
# copy parts from diagonal_chunked_attention_scores into the combined matrix of attns
# - copying the main diagonal and the upper triangle
diagonal_attention_scores[:, :-1, :, window_overlap:] = diagonal_chunked_attention_scores[
:, :, :window_overlap, : window_overlap + 1
]
diagonal_attention_scores[:, -1, :, window_overlap:] = diagonal_chunked_attention_scores[
:, -1, window_overlap:, : window_overlap + 1
]
# - copying the lower triangle
diagonal_attention_scores[:, 1:, :, :window_overlap] = diagonal_chunked_attention_scores[
:, :, -(window_overlap + 1) : -1, window_overlap + 1 :
]
diagonal_attention_scores[
:, 0, 1:window_overlap, 1:window_overlap
] = diagonal_chunked_attention_scores[:, 0, : window_overlap - 1, 1 - window_overlap :]
# separate batch_size and n_heads dimensions again
diagonal_attention_scores = diagonal_attention_scores.view(
batch_size, n_heads, seq_len, 2 * window_overlap + 1
).transpose(2, 1)
self._mask_invalid_locations(diagonal_attention_scores, window_overlap)
return diagonal_attention_scores
def _sliding_chunks_matmul_attn_probs_value(self, attn_probs, value, window_overlap):
batch_size, seq_len, n_heads, head_dim = value.size()
assert seq_len % (window_overlap * 2) == 0
assert attn_probs.size()[:3] == value.size()[:3]
assert attn_probs.size(3) == 2 * window_overlap + 1
chunks_count = seq_len // window_overlap - 1
# group batch_size and n_heads dimensions into one, then chunk seq_len into chunks of size 2 window overlap
chunked_attn_probs = attn_probs.transpose(1, 2).reshape(
batch_size * n_heads,
seq_len // window_overlap,
window_overlap,
2 * window_overlap + 1,
)
# group batch_size and n_heads dimensions into one
value = value.transpose(1, 2).reshape(batch_size * n_heads, seq_len, head_dim)
# pad seq_len with w at the beginning of the sequence and another window overlap at the end
padded_value = F.pad(value, (0, 0, window_overlap, window_overlap), value=-1)
# chunk padded_value into chunks of size 3 window overlap and an overlap of size window overlap
chunked_value_size = (
batch_size * n_heads,
chunks_count + 1,
3 * window_overlap,
head_dim,
)
chunked_value_stride = padded_value.stride()
chunked_value_stride = (
chunked_value_stride[0],
window_overlap * chunked_value_stride[1],
chunked_value_stride[1],
chunked_value_stride[2],
)
chunked_value = padded_value.as_strided(
size=chunked_value_size, stride=chunked_value_stride
)
chunked_attn_probs = self._pad_and_diagonalize(chunked_attn_probs)
context = torch.einsum("bcwd,bcdh->bcwh", (chunked_attn_probs, chunked_value))
return context.view(batch_size, n_heads, seq_len, head_dim).transpose(1, 2)
@staticmethod
def _get_global_attn_indices(is_index_global_attn):
num_global_attn_indices = is_index_global_attn.long().sum(dim=1)
# max number of global attn indices in batch
max_num_global_attn_indices = num_global_attn_indices.max()
# indices of global attn
is_index_global_attn_nonzero = is_index_global_attn.nonzero(as_tuple=True)
# helper variable
is_local_index_global_attn = torch.arange(
max_num_global_attn_indices, device=is_index_global_attn.device
) < num_global_attn_indices.unsqueeze(dim=-1)
# location of the non-padding values within global attention indices
is_local_index_global_attn_nonzero = is_local_index_global_attn.nonzero(as_tuple=True)
# location of the padding values within global attention indices
is_local_index_no_global_attn_nonzero = (is_local_index_global_attn == 0).nonzero(
as_tuple=True
)
return (
max_num_global_attn_indices,
is_index_global_attn_nonzero,
is_local_index_global_attn_nonzero,
is_local_index_no_global_attn_nonzero,
)
def _concat_with_global_key_attn_probs(
self,
key_vectors,
query_vectors,
max_num_global_attn_indices,
is_index_global_attn_nonzero,
is_local_index_global_attn_nonzero,
is_local_index_no_global_attn_nonzero,
):
batch_size = key_vectors.shape[0]
# create only global key vectors
key_vectors_only_global = key_vectors.new_zeros(
batch_size, max_num_global_attn_indices, self.n_heads, self.head_dim
)
key_vectors_only_global[is_local_index_global_attn_nonzero] = key_vectors[
is_index_global_attn_nonzero
]
# (batch_size, seq_len, n_heads, max_num_global_attn_indices)
attn_probs_from_global_key = torch.einsum(
"blhd,bshd->blhs", (query_vectors, key_vectors_only_global)
)
attn_probs_from_global_key[
is_local_index_no_global_attn_nonzero[0], :, :, is_local_index_no_global_attn_nonzero[1]
] = -10000.0
return attn_probs_from_global_key
def _compute_attn_output_with_global_indices(
self,
value_vectors,
attn_probs,
max_num_global_attn_indices,
is_index_global_attn_nonzero,
is_local_index_global_attn_nonzero,
):
batch_size = attn_probs.shape[0]
# cut local attn probs to global only
attn_probs_only_global = attn_probs.narrow(-1, 0, max_num_global_attn_indices)
# get value vectors for global only
value_vectors_only_global = value_vectors.new_zeros(
batch_size, max_num_global_attn_indices, self.n_heads, self.head_dim
)
value_vectors_only_global[is_local_index_global_attn_nonzero] = value_vectors[
is_index_global_attn_nonzero
]
# use `matmul` because `einsum` crashes sometimes with fp16
# attn = torch.einsum('blhs,bshd->blhd', (selected_attn_probs, selected_v))
# compute attn output only global
attn_output_only_global = torch.matmul(
attn_probs_only_global.transpose(1, 2).clone(),
value_vectors_only_global.transpose(1, 2).clone(),
).transpose(1, 2)
# reshape attn probs
attn_probs_without_global = attn_probs.narrow(
-1, max_num_global_attn_indices, attn_probs.size(-1) - max_num_global_attn_indices
).contiguous()
# compute attn output with global
attn_output_without_global = self._sliding_chunks_matmul_attn_probs_value(
attn_probs_without_global, value_vectors, self.one_sided_attn_window_size
)
return attn_output_only_global + attn_output_without_global
def _compute_global_attn_output_from_hidden(
self,
hiddens,
max_num_global_attn_indices,
layer_head_mask,
is_local_index_global_attn_nonzero,
is_index_global_attn_nonzero,
is_local_index_no_global_attn_nonzero,
is_index_masked,
):
seq_len, batch_size = hiddens.shape[:2]
# prepare global hidden states
global_attn_hidden_states = hiddens.new_zeros(
max_num_global_attn_indices, batch_size, self.embed_dim
)
global_attn_hidden_states[is_local_index_global_attn_nonzero[::-1]] = hiddens[
is_index_global_attn_nonzero[::-1]
]
# global key, query, value
global_query_vectors_only_global = self.query_global(global_attn_hidden_states)
global_key_vectors = self.key_global(hiddens)
global_value_vectors = self.value_global(hiddens)
# normalize
global_query_vectors_only_global /= math.sqrt(self.head_dim)
# reshape
global_query_vectors_only_global = (
global_query_vectors_only_global.contiguous()
.view(max_num_global_attn_indices, batch_size * self.n_heads, self.head_dim)
.transpose(0, 1)
) # (batch_size * self.n_heads, max_num_global_attn_indices, head_dim)
global_key_vectors = (
global_key_vectors.contiguous()
.view(-1, batch_size * self.n_heads, self.head_dim)
.transpose(0, 1)
) # batch_size * self.n_heads, seq_len, head_dim)
global_value_vectors = (
global_value_vectors.contiguous()
.view(-1, batch_size * self.n_heads, self.head_dim)
.transpose(0, 1)
) # batch_size * self.n_heads, seq_len, head_dim)
# compute attn scores
global_attn_scores = torch.bmm(
global_query_vectors_only_global, global_key_vectors.transpose(1, 2)
)
assert list(global_attn_scores.size()) == [
batch_size * self.n_heads,
max_num_global_attn_indices,
seq_len,
], f"global_attn_scores have the wrong size. Size should be {(batch_size * self.n_heads, max_num_global_attn_indices, seq_len)}, but is {global_attn_scores.size()}."
global_attn_scores = global_attn_scores.view(
batch_size, self.n_heads, max_num_global_attn_indices, seq_len
)
global_attn_scores[
is_local_index_no_global_attn_nonzero[0], :, is_local_index_no_global_attn_nonzero[1], :
] = -10000.0
global_attn_scores = global_attn_scores.masked_fill(
is_index_masked[:, None, None, :],
-10000.0,
)
global_attn_scores = global_attn_scores.view(
batch_size * self.n_heads, max_num_global_attn_indices, seq_len
)
# compute global attn probs
global_attn_probs_float = F.softmax(
global_attn_scores, dim=-1, dtype=torch.float32
) # use fp32 for numerical stability
# apply layer head masking
if layer_head_mask is not None:
assert layer_head_mask.size() == (
self.n_heads,
), f"Head mask for a single layer should be of size {(self.n_heads,)}, but is {layer_head_mask.size()}"
global_attn_probs_float = layer_head_mask.view(
1, -1, 1, 1
) * global_attn_probs_float.view(
batch_size, self.n_heads, max_num_global_attn_indices, seq_len
)
global_attn_probs_float = global_attn_probs_float.view(
batch_size * self.n_heads, max_num_global_attn_indices, seq_len
)
global_attn_probs = F.drop(
global_attn_probs_float.type_as(global_attn_scores),
p=self.drop,
training=self.training,
)
# global attn output
global_attn_output = torch.bmm(global_attn_probs, global_value_vectors)
assert list(global_attn_output.size()) == [
batch_size * self.n_heads,
max_num_global_attn_indices,
self.head_dim,
], f"global_attn_output tensor has the wrong size. Size should be {(batch_size * self.n_heads, max_num_global_attn_indices, self.head_dim)}, but is {global_attn_output.size()}."
global_attn_probs = global_attn_probs.view(
batch_size, self.n_heads, max_num_global_attn_indices, seq_len
)
global_attn_output = global_attn_output.view(
batch_size, self.n_heads, max_num_global_attn_indices, self.head_dim
)
return global_attn_output, global_attn_probs
# Copied from transformers.models.bert.modeling_bert.BertSelfOutput
class LongformerSelfOutput(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, layer_id=0):
super().__init__()
self.self = LongformerSelfAttention(config, layer_id)
self.output = LongformerSelfOutput(config)
def forward(
self,
hiddens,
attention_mask=None,
layer_head_mask=None,
is_index_masked=None,
is_index_global_attn=None,
is_global_attn=None,
output_attentions=False,
):
self_outputs = self.self(
hiddens,
attention_mask=attention_mask,
layer_head_mask=layer_head_mask,
is_index_masked=is_index_masked,
is_index_global_attn=is_index_global_attn,
is_global_attn=is_global_attn,
output_attentions=output_attentions,
)
attn_output = self.output(self_outputs[0], hiddens)
outputs = (attn_output,) + self_outputs[1:]
return outputs
# Copied from transformers.models.bert.modeling_bert.BertIntermediate
class LongformerIntermediate(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 LongformerOutput(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, layer_id=0):
super().__init__()
self.attention = Attention(config, layer_id)
self.intermediate = LongformerIntermediate(config)
self.output = LongformerOutput(config)
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
def forward(
self,
hiddens,
attention_mask=None,
layer_head_mask=None,
is_index_masked=None,
is_index_global_attn=None,
is_global_attn=None,
output_attentions=False,
):
self_attn_outputs = self.attention(
hiddens,
attention_mask=attention_mask,
layer_head_mask=layer_head_mask,
is_index_masked=is_index_masked,
is_index_global_attn=is_index_global_attn,
is_global_attn=is_global_attn,
output_attentions=output_attentions,
)
attn_output = self_attn_outputs[0]
outputs = self_attn_outputs[1:]
layer_output = apply_chunking_to_forward(
self.ff_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attn_output
)
outputs = (layer_output,) + outputs
return outputs
def ff_chunk(self, attn_output):
intermediate_output = self.intermediate(attn_output)
layer_output = self.output(intermediate_output, attn_output)
return layer_output
class Encoder(qc.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([Layer(config, layer_id=i) for i in range(config.n_lays)])
self.gradient_checkpointing = False
def forward(
self,
hiddens,
attention_mask=None,
head_mask=None,
padding_len=0,
output_attentions=False,
output_hidden_states=False,
return_dict=True,
):
is_index_masked = attention_mask < 0
is_index_global_attn = attention_mask > 0
is_global_attn = is_index_global_attn.flatten().any().item()
all_hidden_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None # All local attns.
all_global_attentions = () if (output_attentions and is_global_attn) else None
# check if head_mask has a correct number of layers specified if desired
if head_mask is not None:
assert head_mask.size()[0] == (
len(self.layer)
), f"The head_mask should be specified for {len(self.layer)} layers, but it is for {head_mask.size()[0]}."
for idx, layer_module in enumerate(self.layer):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hiddens,)
if self.gradient_checkpointing and self.training:
def create_custom_forward(module):
def custom_forward(*inputs):
return module(*inputs, is_global_attn, output_attentions)
return custom_forward
layer_outputs = torch.utils.checkpoint.checkpoint(
create_custom_forward(layer_module),
hiddens,
attention_mask,
head_mask[idx] if head_mask is not None else None,
is_index_masked,
is_index_global_attn,
)
else:
layer_outputs = layer_module(
hiddens,
attention_mask=attention_mask,
layer_head_mask=head_mask[idx] if head_mask is not None else None,
is_index_masked=is_index_masked,
is_index_global_attn=is_index_global_attn,
is_global_attn=is_global_attn,
output_attentions=output_attentions,
)
hiddens = layer_outputs[0]
if output_attentions:
# bzs x seq_len x num_attn_heads x (num_global_attn + attention_window_len + 1) => bzs x num_attn_heads x seq_len x (num_global_attn + attention_window_len + 1)
all_attentions = all_attentions + (layer_outputs[1].transpose(1, 2),)
if is_global_attn:
# bzs x num_attn_heads x num_global_attn x seq_len => bzs x num_attn_heads x seq_len x num_global_attn
all_global_attentions = all_global_attentions + (
layer_outputs[2].transpose(2, 3),
)
# Add last layer
if output_hidden_states:
all_hidden_states = all_hidden_states + (hiddens,)
# undo padding
if padding_len > 0:
# unpad `hiddens` because the calling function is expecting a length == input_ids.size(1)
hiddens = hiddens[:, :-padding_len]
if output_hidden_states:
all_hidden_states = tuple([state[:, :-padding_len] for state in all_hidden_states])
if output_attentions:
all_attentions = tuple([state[:, :, :-padding_len, :] for state in all_attentions])
if not return_dict:
return tuple(
v
for v in [hiddens, all_hidden_states, all_attentions, all_global_attentions]
if v is not None
)
return qo.Base(
y=hiddens,
hiddens=all_hidden_states,
attns=all_attentions,
globals=all_global_attentions,
)
class Model(PreTrained):
def __init__(self, config, add_pooling_layer=True):
super().__init__(config)
self.config = config
if isinstance(config.attention_window, int):
assert config.attention_window % 2 == 0
assert config.attention_window > 0
config.attention_window = [
config.attention_window
] * config.n_lays # one value per layer
else:
assert len(config.attention_window) == config.n_lays
self.embeddings = LongformerEmbeddings(config)
self.encoder = Encoder(config)
self.pool = Pool(config) if add_pooling_layer else None
def _pad_to_window_size(
self,
input_ids,
attention_mask,
token_type_ids,
position_ids,
inputs_embeds,
PAD,
):
attention_window = (
self.config.attention_window
if isinstance(self.config.attention_window, int)
else max(self.config.attention_window)
)
assert attention_window % 2 == 0
input_shape = input_ids.shape if input_ids is not None else inputs_embeds.shape
batch_size, seq_len = input_shape[:2]
padding_len = (attention_window - seq_len % attention_window) % attention_window
if padding_len > 0:
log.info(
f"Input ids are automatically padded from {seq_len} to {seq_len + padding_len} to be a multiple of "
f"`config.attention_window`: {attention_window}"
)
if input_ids is not None:
input_ids = F.pad(input_ids, (0, padding_len), value=PAD)
if position_ids is not None:
# pad with position_id = PAD as in modeling_roberta.RobertaEmbeddings
position_ids = F.pad(position_ids, (0, padding_len), value=PAD)
if inputs_embeds is not None:
input_ids_padding = inputs_embeds.new_full(
(batch_size, padding_len),
self.config.PAD,
dtype=torch.long,
)
inputs_embeds_padding = self.embeddings(input_ids_padding)
inputs_embeds = torch.cat([inputs_embeds, inputs_embeds_padding], dim=-2)
attention_mask = F.pad(
attention_mask, (0, padding_len), value=False
) # no attention on the padding tokens
token_type_ids = F.pad(
token_type_ids, (0, padding_len), value=0
) # pad with token_type_id = 0
return padding_len, input_ids, attention_mask, token_type_ids, position_ids, inputs_embeds
def _merge_to_attention_mask(self, attention_mask, global_attention_mask):
if attention_mask is not None:
attention_mask = attention_mask * (global_attention_mask + 1)
else:
attention_mask = global_attention_mask + 1
return attention_mask
def forward(
self,
input_ids=None,
attention_mask=None,
global_attention_mask=None,
head_mask=None,
token_type_ids=None,
position_ids=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")
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)
if token_type_ids is None:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
# merge `global_attention_mask` and `attention_mask`
if global_attention_mask is not None:
attention_mask = self._merge_to_attention_mask(attention_mask, global_attention_mask)
(
padding_len,
input_ids,
attention_mask,
token_type_ids,
position_ids,
inputs_embeds,
) = self._pad_to_window_size(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
inputs_embeds=inputs_embeds,
PAD=self.config.PAD,
)
extended_attention_mask = self.get_extended_attention_mask(
attention_mask, input_shape, device
)[:, 0, 0, :]
embedding_output = 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,
attention_mask=extended_attention_mask,
head_mask=head_mask,
padding_len=padding_len,
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.WithPools(
y=sequence_output,
pools=pooled_output,
hiddens=encoder_outputs.hiddens,
attns=encoder_outputs.attns,
globals=encoder_outputs.globals,
)
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 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)
def forward(self, x, g_mask=None, **kw):
if g_mask is None:
g_mask = torch.zeros_like(x)
g_mask[:, 0] = 1
return qf.forward_seq(x, g_mask=g_mask, **kw)
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)
def forward(self, x, globals=None, **kw):
if globals is None:
assert x is not None
globals = _compute_global_attention_mask(x, self.cfg.sep_token_id)
return qf.forward_qa(x, globals=globals, **kw)
class ForChoice(PreTrained):
def __init__(self, config):
super().__init__(config)
self.longformer = 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,
global_attention_mask=None,
head_mask=None,
labels=None,
position_ids=None,
inputs_embeds=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if global_attention_mask is None and input_ids is not None:
log.info("Initializing global attention on multiple choice...")
global_attention_mask = torch.stack(
[
_compute_global_attention_mask(
input_ids[:, i], self.config.sep_token_id, before_sep_token=False
)
for i in range(num_choices)
],
dim=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_global_attention_mask = (
global_attention_mask.view(-1, global_attention_mask.size(-1))
if global_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.longformer(
flat_input_ids,
position_ids=flat_position_ids,
token_type_ids=flat_token_type_ids,
attention_mask=flat_attention_mask,
global_attention_mask=flat_global_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,
globals=outputs.globals,
)
|
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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"]}
|
33,541
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/config/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 ... import core as qc
class PreTrained(qc.PreTrained):
hs = qc.Hypers(
{"act_sum"},
dict(
act="gelu",
drop_attn=0.1,
drop_embed=0.1,
drop_sum_first=0.1,
drop=0.1,
init_range=0.02,
model_type="openai-gpt",
n_ctx=512,
n_embed=768,
n_heads=12,
n_lays=12,
n_pos=512,
eps=1e-5,
predict_special_tokens=True,
s_vocab=40478,
sum_proj=True,
sum_type="cls_index",
sum_use_proj=True,
),
)
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)
MAP = {
"openai-gpt": dict(
archs=["LMHead"],
n_special=0,
task_params={"text-generation": {"do_sample": True, "max_len": 50}},
)
}
|
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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/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"]}
|
33,542
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/base/doc/rectify.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
import codecs
import datetime as dt
import collections as col
def _handler(err):
c_map = {
b'\x87': '',
b'\xa3': '',
b'\xa5': '',
b'\xb4': '',
b'\xb5': '',
b'\xbc': '',
b'\xc1': '',
b'\xc7': '',
b'\xc9': '',
b'\xd2': '"',
b'\xd3': '"',
b'\xd4': "'",
b'\xd5': "'",
b'\xde': 'fi',
b'\xdf': 'fl',
b'\xe1': '',
}
k = err.object[err.start:err.end]
# print('***', k, k.hex())
if k in c_map:
# print('replacing {} with {}'.format(k, c_map[k]))
return c_map[k], err.end
print(err.object[err.start - 20:err.end + 20])
raise err
QNERR = 'qnerr'
codecs.register_error(QNERR, _handler)
flags = r'(?aim)'
mos = r'January|February|March|April|May|June|July|'
mos += r'August|September|October|November|December'
dp = r'(?P<dt>(?:' + mos + r') \d{1,2}, 20\d{2}),?'
dp = re.compile(flags + dp)
def days(txt):
for p in dp.split(txt):
for f in ('%B %d, %Y', ):
try:
d = dt.datetime.strptime(p, f)
p = '{0:%y-%m-%d}'.format(d)
break
except ValueError:
continue
yield p
mp = r'(?P<mo>(?:' + mos + r') of 20\d{2})'
mp = re.compile(flags + mp)
def months(txt):
for p in mp.split(txt):
for f in ('%B of %Y', ):
try:
d = dt.datetime.strptime(p, f)
p = '{0:%Y-%m}'.format(d)
break
except ValueError:
continue
yield p
s_map = col.OrderedDict()
s_map.update((
('Dr.', 'Dr '),
('Mr.', 'Mr '),
('Ms.', 'Ms '),
('Ofc.', 'Ofc '),
('Atty.', 'Atty '),
('Guardian ad litem', 'GAL'),
('Guardian ad Litem', 'GAL'),
('Guardian Ad Litem', 'GAL'),
('Department of Children and Family', 'DCF'),
('Department of Children and Families', 'DCF'),
('Concord Police Officers', 'Police'),
('Concord Police officers', 'Police'),
('Concord police officers', 'Police'),
('police officers', 'Police'),
('Concord Police Department', 'Police'),
('Concord District Court', 'District Court'),
('New Hampshire', 'NH'),
('the Commonwealth of Massachusetts', 'MA'),
('Massachusetts', 'MA'),
('Middlesex Probate and Family Court', 'Family Court'),
('Middlesex Probate & Family Court', 'Family Court'),
('Middlesex Division of the Probate and Family Court', 'Family Court'),
))
s_map.update((
('The Father', 'Dad'),
('the Father', 'Dad'),
('Father', 'Dad'),
('Imre Kifor', 'Dad'),
('Imre', 'Dad'),
('Barbara A.', 'Mom-B'),
('Barbara A', 'Mom-B'),
('Barbara', 'Mom-B'),
('Duchesne', 'Mom-B'),
('Ms Mom-B', 'Mom-B'),
('his former girlfriend', 'Mom-C'),
('Cynthia S.', 'Mom-C'),
('Cynthia S', 'Mom-C'),
('Cynthia', 'Mom-C'),
('Cyndi', 'Mom-C'),
('Cindy', 'Mom-C'),
('Oulton', 'Mom-C'),
('Ms Mom-C', 'Mom-C'),
('Twins', 'Kids-B'),
('twins', 'Kids-B'),
('Evan Kifor', 'Leon'),
('Evan', 'Leon'),
('Anna Kifor', 'Lisa'),
('Anna', 'Lisa'),
('Blake', 'Luke'),
('Belle', 'Lola'),
('Leon and Lisa', 'Kids-B'),
('Lisa and Leon', 'Kids-B'),
))
s_map.update((
('The Defendant', 'Dad'),
('the Defendant', 'Dad'),
('Defendant', 'Dad'),
('The Plaintiff', 'Mom-B'),
('the Plaintiff', 'Mom-B'),
('Plaintiff', 'Mom-B'),
('The children', 'children'),
('the children', 'children'),
('Children', 'Kids-B'),
('children', 'Kids-B'),
('Katie L. Lenihan, Esquire', 'Atty Lenihan'),
('Honorable Court', 'Court-B'),
('Sandy Mahoney', 'Ms Mahoney'),
))
s_map.update((
('Dad, Dad', 'Dad'),
('Dad Dad', 'Dad'),
('Mom-B, Mom-B', 'Mom-B'),
('Mom-B Mom-B', 'Mom-B'),
('Mom-C, Mom-C', 'Mom-C'),
('Mom-C Mom-C', 'Mom-C'),
('Kids-B, Kids-B', 'Kids-B'),
('Kids-B Kids-B', 'Kids-B'),
))
s_map.update((
('\r', '\n'),
('\t', ' '),
(':', ' '),
(';', ','),
(' ', ' '),
(' ', ' '),
(' \n', '\n'),
# ('?', '?.'),
# ('!', '!.'),
('..', '.'),
('.\n', '\n'),
(',\n', '\n'),
('. ', '\n'),
))
def rectify(txt):
# txt = ''.join(days(txt))
# txt = ''.join(months(txt))
# for k, v in s_map.items():
# txt = txt.replace(k, v)
return txt.strip()
def rectifier(txt):
for ln in txt.splitlines():
yield rectify(ln)
if __name__ == '__main__':
print(rectify('dsfaf sc casdf Febru 25, 2011, dfwec asef ef'))
print(rectify('dsfaf sc casdf February 25, 2011, dfwec asef ef'))
print(rectify('dsfaf February 25, 2011, dfwec June 1, 2009 ef'))
|
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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"]}
|
33,543
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/tokens/fast/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
from shutil import copyfile
from ....tokens.fast import PreTrainedTokenizerFast
from ..pegasus import Tokenizer as Pegasus
SPIECE_UNDERLINE = "▁"
VOCAB_FS = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}
VOCAB_MAP = {
"vocab_file": {
"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"
},
"tokenizer_file": {
"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"
},
}
INPUT_CAPS = {
"google/pegasus-xsum": 512,
}
class Tokenizer(PreTrainedTokenizerFast):
vocab_fs = VOCAB_FS
vocab_map = VOCAB_MAP
input_caps = INPUT_CAPS
slow_tokenizer_class = Pegasus
model_input_names = ["input_ids", "mask"]
def __init__(
self,
vocab_file=None,
tokenizer_file=None,
pad="<pad>",
eos="</s>",
unk="<unk>",
msk="<mask_2>",
mask_token_sent="<mask_1>",
additional_special_tokens=None,
offset=103,
**kw,
):
self.offset = offset
if additional_special_tokens is not None:
assert isinstance(additional_special_tokens, list)
additional_special_tokens_extended = (
([mask_token_sent] + additional_special_tokens)
if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
else additional_special_tokens
)
additional_special_tokens_extended += [
f"<unk_{i}>"
for i in range(len(additional_special_tokens_extended), self.offset - 1)
]
if len(set(additional_special_tokens_extended)) != len(
additional_special_tokens_extended
):
raise ValueError(
f"Please make sure that the provided additional_special_tokens do not contain an incorrectly shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."
)
additional_special_tokens = additional_special_tokens_extended
else:
additional_special_tokens = [mask_token_sent] if mask_token_sent is not None else []
additional_special_tokens += [f"<unk_{i}>" for i in range(2, self.offset)]
super().__init__(
vocab_file,
tokenizer_file=tokenizer_file,
pad=pad,
eos=eos,
unk=unk,
msk=msk,
mask_token_sent=mask_token_sent,
offset=offset,
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
def _special_token_mask(self, seq):
all_special_ids = set(self.all_special_ids)
all_special_ids.remove(self.unk_token_id)
assert all_special_ids == set(range(len(self.additional_special_tokens) + 3))
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 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]
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"], "/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"]}
|
33,544
|
quantapix/qnarre
|
refs/heads/main
|
/tools/triton/python/triton/common/build.py
|
import contextlib
import functools
import io
import os
import shutil
import subprocess
import sys
import sysconfig
import setuptools
# TODO: is_hip shouldn't be here
def is_hip():
import torch
return torch.version.hip is not None
@functools.lru_cache()
def libcuda_dirs():
locs = subprocess.check_output(["whereis", "libcuda.so"]).decode().strip().split()[1:]
return [os.path.dirname(loc) for loc in locs]
@functools.lru_cache()
def rocm_path_dir():
return os.getenv("ROCM_PATH", default="/opt/rocm")
@contextlib.contextmanager
def quiet():
old_stdout, old_stderr = sys.stdout, sys.stderr
sys.stdout, sys.stderr = io.StringIO(), io.StringIO()
try:
yield
finally:
sys.stdout, sys.stderr = old_stdout, old_stderr
def _build(name, src, srcdir):
if is_hip():
hip_lib_dir = os.path.join(rocm_path_dir(), "lib")
hip_include_dir = os.path.join(rocm_path_dir(), "include")
else:
cuda_lib_dirs = libcuda_dirs()
base_dir = os.path.join(os.path.dirname(__file__), os.path.pardir)
cuda_path = os.path.join(base_dir, "third_party", "cuda")
cu_include_dir = os.path.join(cuda_path, "include")
triton_include_dir = os.path.join(os.path.dirname(__file__), "include")
cuda_header = os.path.join(cu_include_dir, "cuda.h")
triton_cuda_header = os.path.join(triton_include_dir, "cuda.h")
if not os.path.exists(cuda_header) and os.path.exists(triton_cuda_header):
cu_include_dir = triton_include_dir
suffix = sysconfig.get_config_var('EXT_SUFFIX')
so = os.path.join(srcdir, '{name}{suffix}'.format(name=name, suffix=suffix))
# try to avoid setuptools if possible
cc = os.environ.get("CC")
if cc is None:
# TODO: support more things here.
clang = shutil.which("clang")
gcc = shutil.which("gcc")
cc = gcc if gcc is not None else clang
if cc is None:
raise RuntimeError("Failed to find C compiler. Please specify via CC environment variable.")
# This function was renamed and made public in Python 3.10
if hasattr(sysconfig, 'get_default_scheme'):
scheme = sysconfig.get_default_scheme()
else:
scheme = sysconfig._get_default_scheme()
# 'posix_local' is a custom scheme on Debian. However, starting Python 3.10, the default install
# path changes to include 'local'. This change is required to use triton with system-wide python.
if scheme == 'posix_local':
scheme = 'posix_prefix'
py_include_dir = sysconfig.get_paths(scheme=scheme)["include"]
if is_hip():
ret = subprocess.check_call([cc, src, f"-I{hip_include_dir}", f"-I{py_include_dir}", f"-I{srcdir}", "-shared", "-fPIC", f"-L{hip_lib_dir}", "-lamdhip64", "-o", so])
else:
cc_cmd = [cc, src, "-O3", f"-I{cu_include_dir}", f"-I{py_include_dir}", f"-I{srcdir}", "-shared", "-fPIC", "-lcuda", "-o", so]
cc_cmd += [f"-L{dir}" for dir in cuda_lib_dirs]
ret = subprocess.check_call(cc_cmd)
if ret == 0:
return so
# fallback on setuptools
extra_compile_args = []
library_dirs = cuda_lib_dirs
include_dirs = [srcdir, cu_include_dir]
libraries = ['cuda']
# extra arguments
extra_link_args = []
# create extension module
ext = setuptools.Extension(
name=name,
language='c',
sources=[src],
include_dirs=include_dirs,
extra_compile_args=extra_compile_args + ['-O3'],
extra_link_args=extra_link_args,
library_dirs=library_dirs,
libraries=libraries,
)
# build extension module
args = ['build_ext']
args.append('--build-temp=' + srcdir)
args.append('--build-lib=' + srcdir)
args.append('-q')
args = dict(
name=name,
ext_modules=[ext],
script_args=args,
)
with quiet():
setuptools.setup(**args)
return so
|
{"/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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|
33,545
|
quantapix/qnarre
|
refs/heads/main
|
/tools/triton/python/test/unit/language/test_annotations.py
|
from __future__ import annotations
import torch
import triton
import triton.language as tl
def test_annotations():
@triton.jit
def _kernel(X: torch.Tensor, N: int, BLOCK_SIZE: tl.constexpr):
pass
x = torch.empty(1, device='cuda')
_kernel[(1,)](x, x.shape[0], 32)
try:
_kernel[(1,)](x.shape[0], x.shape[0], 32)
except AttributeError:
pass
|
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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"]}
|
33,546
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/config/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 torch
from ... import core as qc
class PreTrained(qc.PreTrained):
hs = qc.Hypers(
{"n_dec_lays"},
dict(
d_ff=2048,
d_kv=64,
d_model=512,
drop_rate=0.1,
EOS=1,
eps=1e-6,
feed_forward_proj="relu",
grad_checkpoint=True,
init_factor=1.0,
is_enc_dec=True,
is_parallelizable=True,
model_type="t5",
n_heads=8,
n_lays=6,
PAD=0,
relative_attention_num_buckets=32,
s_vocab=32128,
y_cache=True,
),
)
def __init__(self, **kw):
self.n_dec_lays = n_dec_lays if n_dec_lays is not None else self.n_lays
super().__init__(PAD=PAD, EOS=EOS, is_enc_dec=is_enc_dec, **kw)
@property
def dummy_inputs(self):
input_ids = torch.tensor(DUMMY_INPUTS)
input_mask = torch.tensor(DUMMY_MASK)
dummy_inputs = {
"decoder_input_ids": input_ids,
"input_ids": input_ids,
"dec_m": input_mask,
}
return dummy_inputs
def _init_weights(self, module):
factor = self.cfg.initializer_factor # Used for testing weights initialization
if isinstance(module, LayerNorm):
module.weight.data.fill_(factor * 1.0)
elif isinstance(module, (Model, ForConditionalGeneration, EncoderModel)):
module.shared.weight.data.normal_(mean=0.0, std=factor * 1.0)
elif isinstance(module, DenseReluDense):
module.wi.weight.data.normal_(mean=0.0, std=factor * ((self.cfg.d_model) ** -0.5))
if hasattr(module.wi, "bias") and module.wi.bias is not None:
module.wi.bias.data.zero_()
module.wo.weight.data.normal_(mean=0.0, std=factor * ((self.cfg.d_ff) ** -0.5))
if hasattr(module.wo, "bias") and module.wo.bias is not None:
module.wo.bias.data.zero_()
elif isinstance(module, DenseGatedGeluDense):
module.wi_0.weight.data.normal_(mean=0.0, std=factor * ((self.cfg.d_model) ** -0.5))
if hasattr(module.wi_0, "bias") and module.wi_0.bias is not None:
module.wi_0.bias.data.zero_()
module.wi_1.weight.data.normal_(mean=0.0, std=factor * ((self.cfg.d_model) ** -0.5))
if hasattr(module.wi_1, "bias") and module.wi_1.bias is not None:
module.wi_1.bias.data.zero_()
module.wo.weight.data.normal_(mean=0.0, std=factor * ((self.cfg.d_ff) ** -0.5))
if hasattr(module.wo, "bias") and module.wo.bias is not None:
module.wo.bias.data.zero_()
elif isinstance(module, Attention):
d_model = self.cfg.d_model
key_value_proj_dim = self.cfg.d_kv
n_heads = self.cfg.n_heads
module.q.weight.data.normal_(
mean=0.0, std=factor * ((d_model * key_value_proj_dim) ** -0.5)
)
module.k.weight.data.normal_(mean=0.0, std=factor * (d_model**-0.5))
module.v.weight.data.normal_(mean=0.0, std=factor * (d_model**-0.5))
module.o.weight.data.normal_(
mean=0.0, std=factor * ((n_heads * key_value_proj_dim) ** -0.5)
)
if module.has_relative_attention_bias:
module.relative_attention_bias.weight.data.normal_(
mean=0.0, std=factor * ((d_model) ** -0.5)
)
def _set_grad_checkpoint(self, module, value=False):
if isinstance(module, (Attention, Stack)):
module.grad_checkpoint = value
def _shift_right(self, input_ids):
dec_START = self.cfg.dec_START
PAD = self.cfg.PAD
assert (
dec_START is not None
), "self.model.config.dec_START has to be defined. In it is usually set to the PAD. See docs for more information"
if is_torch_fx_proxy(input_ids):
shifted_input_ids = torch.full(input_ids.shape[:-1] + (1,), dec_START)
shifted_input_ids = torch.cat([shifted_input_ids, input_ids[..., :-1]], dim=-1)
else:
shifted_input_ids = input_ids.new_zeros(input_ids.shape)
shifted_input_ids[..., 1:] = input_ids[..., :-1].clone()
shifted_input_ids[..., 0] = dec_START
assert PAD is not None, "self.model.config.PAD has to be defined."
shifted_input_ids.masked_fill_(shifted_input_ids == -100, PAD)
assert torch.all(
shifted_input_ids >= 0
).item(), "Verify that `shifted_input_ids` has only positive values"
return shifted_input_ids
MAP = {
"t5-small": dict(
archs=["LMHead"],
dec_START=0,
n_pos=512,
eps=1e-06,
y_prev=True,
task_params=dict(
summarization=dict(
early_stop=True,
len_penalty=2.0,
max_len=200,
min_len=30,
s_no_repeat_ngram=3,
n_beams=4,
prefix="summarize: ",
),
translation_en_to_de=dict(
early_stop=True,
max_len=300,
n_beams=4,
prefix="translate English to German: ",
),
translation_en_to_ro=dict(
early_stop=True,
max_len=300,
n_beams=4,
prefix="translate English to Romanian: ",
),
),
),
"t5-base": dict(
archs=["LMHead"],
d_ff=3072,
d_model=768,
dec_START=0,
n_heads=12,
n_lays=12,
n_pos=512,
eps=1e-06,
y_prev=True,
task_params=dict(
summarization=dict(
early_stop=True,
len_penalty=2.0,
max_len=200,
min_len=30,
s_no_repeat_ngram=3,
n_beams=4,
prefix="summarize: ",
),
translation_en_to_de=dict(
early_stop=True,
max_len=300,
n_beams=4,
prefix="translate English to German: ",
),
translation_en_to_ro=dict(
early_stop=True,
max_len=300,
n_beams=4,
prefix="translate English to Romanian: ",
),
),
),
"t5-large": dict(
archs=["LMHead"],
d_ff=4096,
d_model=1024,
dec_START=0,
n_heads=16,
n_lays=24,
n_pos=512,
eps=1e-06,
y_prev=True,
task_params=dict(
summarization=dict(
early_stop=True,
len_penalty=2.0,
max_len=200,
min_len=30,
s_no_repeat_ngram=3,
n_beams=4,
prefix="summarize: ",
),
translation_en_to_de=dict(
early_stop=True,
max_len=300,
n_beams=4,
prefix="translate English to German: ",
),
translation_en_to_ro=dict(
early_stop=True,
max_len=300,
n_beams=4,
prefix="translate English to Romanian: ",
),
),
),
"t5-3b": dict(
archs=["LMHead"],
d_ff=16384,
d_model=1024,
d_kv=128,
dec_START=0,
n_heads=32,
n_lays=24,
n_pos=512,
eps=1e-06,
y_prev=True,
task_params=dict(
summarization=dict(
early_stop=True,
len_penalty=2.0,
max_len=200,
min_len=30,
s_no_repeat_ngram=3,
n_beams=4,
prefix="summarize: ",
),
translation_en_to_de=dict(
early_stop=True,
max_len=300,
n_beams=4,
prefix="translate English to German: ",
),
translation_en_to_ro=dict(
early_stop=True,
max_len=300,
n_beams=4,
prefix="translate English to Romanian: ",
),
),
),
"t5-11b": dict(
archs=["LMHead"],
d_ff=65536,
d_model=1024,
d_kv=128,
dec_START=0,
n_heads=128,
n_lays=24,
n_pos=512,
eps=1e-06,
y_prev=True,
task_params=dict(
summarization=dict(
early_stop=True,
len_penalty=2.0,
max_len=200,
min_len=30,
s_no_repeat_ngram=3,
n_beams=4,
prefix="summarize: ",
),
translation_en_to_de=dict(
early_stop=True,
max_len=300,
n_beams=4,
prefix="translate English to German: ",
),
translation_en_to_ro=dict(
early_stop=True,
max_len=300,
n_beams=4,
prefix="translate English to Romanian: ",
),
),
),
}
class Onnx:
@property
def inputs(self):
y = {
"input_ids": {0: "batch", 1: "encoder_sequence"},
"mask": {0: "batch", 1: "encoder_sequence"},
}
if self.use_past:
y["mask"][1] = "past_encoder_sequence + sequence"
y["decoder_input_ids"] = {0: "batch"}
y["dec_m"] = {
0: "batch",
1: "past_decoder_sequence + sequence",
}
else:
y["decoder_input_ids"] = {0: "batch", 1: "decoder_sequence"}
y["dec_m"] = {0: "batch", 1: "decoder_sequence"}
if self.use_past:
self.fill_with_past_key_values_(y, direction="inputs")
return y
@property
def default_onnx_opset(self):
return 13
|
{"/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"], 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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"]}
|
33,547
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/base/judgment.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
from .conflict import Conflict
from .conjecture import Dissent
class Judgment(Node):
claims = conflicts = dissents = None
def __init__(self,
text=None,
conflicts=None,
dissents=None,
authority=None,
**kw):
super().__init__(**kw)
if self.claims is None:
self.claims, self.conflicts, self.dissents = [], [], []
if text:
for k in ('factor', 'bias', 'weight'):
kw.pop(k, None)
self.claims.append(Claim(text=text, **kw))
if conflicts:
fs = (f.strip() for f in conflicts.split('|') if ':' in f)
self.conflicts.extend(Conflict.create(name=f) for f in fs if f)
if dissents:
ds = (d.strip() for d in dissents.split('|') if ':' in d)
self.dissents.extend(Dissent.create(name=d) for d in ds if d)
if authority:
self.authority = Authority.create(name=authority)
@property
def weight(self):
cs = tuple(c.weight for c in self.claims)
fs = tuple(f.weight for f in self.conflicts)
ds = tuple(d.weight for d in self.dissents)
return self.partial(cs, fs, ds) + self.bias
@property
def turmoil(self):
return self.weight
@property
def value(self):
t = self.turmoil
return '{} {}: T{}'.format(super().value, self.authority.agency, t)
@property
def fields(self):
fs = super().fields
fs['Judgment'] = self.name
ls = []
for c in self.claims:
fs2 = c.fields
fs2.update(fs)
fs2['Turmoil'] = self.partial(c.weight)
ls.append(fs2)
for f in sorted(self.conflicts, key=lambda f: f.sequence):
fs2 = f.fields
fs2['Topic'] = fs['Topic']
fs2['Narrative'] = fs['Narrative']
fs2['Judgment'] = fs['Judgment']
fs2['Turmoil'] = self.partial(f.weight)
ls.append(fs2)
for d in sorted(self.dissents, key=lambda d: d.sequence):
fs2 = d.fields
fs2['Topic'] = fs['Topic']
fs2['Narrative'] = fs['Narrative']
fs2['Judgment'] = fs['Judgment']
fs2['Turmoil'] = self.partial(d.weight)
ls.append(fs2)
return ls
class Validation(Judgment):
sign = '=v'
_factor = 0
class Confusion(Judgment):
sign = '=c'
_factor = 0.25
class Bias(Judgment):
sign = '=b'
_factor = 0.5
class Disregard(Judgment):
sign = '=g'
_factor = 0.75
class Fabrication(Judgment):
sign = '=f'
_factor = 1
|
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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"]}
|
33,548
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/models/splinter.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 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__)
LIST = [
"tau/splinter-base",
"tau/splinter-base-qass",
"tau/splinter-large",
"tau/splinter-large-qass",
]
class SplinterEmbeddings(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.register_buffer("position_ids", torch.arange(config.n_pos).expand((1, -1)))
self.pos_type = getattr(config, "pos_type", "absolute")
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
if self.pos_type == "absolute":
position_embeddings = self.position_embeddings(position_ids)
embeddings += position_embeddings
embeddings = self.norm(embeddings)
embeddings = self.drop(embeddings)
return embeddings
class SplinterSelfAttention(qc.Module):
def __init__(self, config, pos_type=None):
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.pos_type = pos_type or getattr(config, "pos_type", "absolute")
if self.pos_type == "relative_key" or self.pos_type == "relative_key_query":
self.n_pos = config.n_pos
self.distance_embedding = qc.Embed(2 * config.n_pos - 1, self.attention_head_size)
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:
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)
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
if self.pos_type == "relative_key" or self.pos_type == "relative_key_query":
seq_length = hiddens.size()[1]
position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hiddens.device).view(
-1, 1
)
position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hiddens.device).view(
1, -1
)
distance = position_ids_l - position_ids_r
positional_embedding = self.distance_embedding(distance + self.n_pos - 1)
positional_embedding = positional_embedding.to(
dtype=query_layer.dtype
) # fp16 compatibility
if self.pos_type == "relative_key":
relative_position_scores = torch.einsum(
"bhld,lrd->bhlr", query_layer, positional_embedding
)
attention_scores = attention_scores + relative_position_scores
elif self.pos_type == "relative_key_query":
relative_position_scores_query = torch.einsum(
"bhld,lrd->bhlr", query_layer, positional_embedding
)
relative_position_scores_key = torch.einsum(
"bhrd,lrd->bhlr", key_layer, positional_embedding
)
attention_scores = (
attention_scores + relative_position_scores_query + relative_position_scores_key
)
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
if attention_mask is not None:
attention_scores = attention_scores + attention_mask
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
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
class SplinterSelfOutput(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, pos_type=None):
super().__init__()
self.self = SplinterSelfAttention(config, pos_type=pos_type)
self.output = SplinterSelfOutput(config)
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
class SplinterIntermediate(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
class SplinterOutput(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, pos_type="absolute")
self.intermediate = SplinterIntermediate(config)
self.output = SplinterOutput(config)
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_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 self.is_decoder:
outputs = self_attention_outputs[1:-1]
present_key_value = self_attention_outputs[-1]
else:
outputs = self_attention_outputs[1:]
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_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]
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 self.is_decoder:
outputs = outputs + (present_key_value,)
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,
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,
):
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,
)
class Model(PreTrained):
def __init__(self, config):
super().__init__(config)
self.config = config
self.embeddings = SplinterEmbeddings(config)
self.encoder = Encoder(config)
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)
extended_attention_mask = self.get_extended_attention_mask(
attention_mask, input_shape, device
)
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]
if not return_dict:
return (sequence_output,) + encoder_outputs[1:]
return qo.CachesCrosses(
y=sequence_output,
caches=encoder_outputs.caches,
hiddens=encoder_outputs.hiddens,
attns=encoder_outputs.attns,
crosses=encoder_outputs.crosses,
)
class SplinterFullyConnectedLayer(qc.Module):
def __init__(self, input_dim, output_dim, act="gelu"):
super().__init__()
self.input_dim = input_dim
self.output_dim = output_dim
self.dense = qc.Linear(self.input_dim, self.output_dim)
self.act = qu.activation(act)
self.norm = qc.LayerNorm(self.output_dim)
def forward(self, inputs):
hiddens = self.dense(inputs)
hiddens = self.act(hiddens)
hiddens = self.norm(hiddens)
return hiddens
class QuestionAwareSpanSelectionHead(qc.Module):
def __init__(self, config):
super().__init__()
self.query_start_transform = SplinterFullyConnectedLayer(config.d_model, config.d_model)
self.query_end_transform = SplinterFullyConnectedLayer(config.d_model, config.d_model)
self.start_transform = SplinterFullyConnectedLayer(config.d_model, config.d_model)
self.end_transform = SplinterFullyConnectedLayer(config.d_model, config.d_model)
self.start_classifier = qc.Linear(config.d_model, config.d_model, bias=False)
self.end_classifier = qc.Linear(config.d_model, config.d_model, bias=False)
def forward(self, inputs, positions):
_, _, dim = inputs.size()
index = positions.unsqueeze(-1).repeat(1, 1, dim) # [batch_size, num_positions, dim]
gathered_reps = torch.gather(inputs, dim=1, index=index) # [batch_size, num_positions, dim]
query_start_reps = self.query_start_transform(
gathered_reps
) # [batch_size, num_positions, dim]
query_end_reps = self.query_end_transform(gathered_reps) # [batch_size, num_positions, dim]
start_reps = self.start_transform(inputs) # [batch_size, seq_length, dim]
end_reps = self.end_transform(inputs) # [batch_size, seq_length, dim]
hiddens = self.start_classifier(query_start_reps) # [batch_size, num_positions, dim]
start_reps = start_reps.permute(0, 2, 1) # [batch_size, dim, seq_length]
logits_beg = torch.matmul(hiddens, start_reps)
hiddens = self.end_classifier(query_end_reps)
end_reps = end_reps.permute(0, 2, 1)
logits_end = torch.matmul(hiddens, end_reps)
return logits_beg, logits_end
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)
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
start_positions=None,
end_positions=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
question_positions=None,
):
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
question_positions_were_none = False
if question_positions is None:
if input_ids is not None:
question_position_for_each_example = torch.argmax(
(torch.eq(input_ids, self.question_token_id)).int(), dim=-1
)
else:
question_position_for_each_example = torch.zeros(
inputs_embeds.size(0),
dtype=torch.long,
layout=inputs_embeds.layout,
device=inputs_embeds.device,
)
question_positions = question_position_for_each_example.unsqueeze(-1)
question_positions_were_none = True
outputs = self.splinter(
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,
)
sequence_output = outputs[0]
logits_beg, logits_end = self.splinter_qass(sequence_output, question_positions)
if question_positions_were_none:
logits_beg, logits_end = logits_beg.squeeze(1), logits_end.squeeze(1)
if attention_mask is not None:
logits_beg = logits_beg + (1 - attention_mask) * -10000.0
logits_end = logits_end + (1 - attention_mask) * -10000.0
total_loss = None
if start_positions is not None and end_positions is not None:
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
end_positions = end_positions.squeeze(-1)
ignored_index = logits_beg.size(1)
start_positions.clamp_(0, ignored_index)
end_positions.clamp_(0, ignored_index)
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
start_loss = loss_fct(logits_beg, start_positions)
end_loss = loss_fct(logits_end, end_positions)
total_loss = (start_loss + end_loss) / 2
if not return_dict:
output = (logits_beg, logits_end) + outputs[1:]
return ((total_loss,) + output) if total_loss is not None else output
return qo.LossQA(
loss=total_loss,
logits_beg=logits_beg,
logits_end=logits_end,
hiddens=outputs.hiddens,
attns=outputs.attns,
)
|
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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"]}
|
33,549
|
quantapix/qnarre
|
refs/heads/main
|
/tools/triton/python/test/unit/operators/test_blocksparse.py
|
import pytest
import torch
import triton
import triton.ops
def sparsify_tensor(x, mask, block):
ret = torch.empty((x.size(0), mask.sum(), block, block), dtype=x.dtype, device=x.device)
for idx, (h, i, j) in enumerate(zip(*mask.nonzero(as_tuple=True))):
ret[:, idx, :, :] = x[:, h, i * block:(i + 1) * block, j * block:(j + 1) * block]
return ret
def make_pair(shape, device="cuda", alpha=1e-2, beta=0., trans=False, data=None, dtype=torch.float32):
if data is None:
data = torch.randn(shape, dtype=torch.float32, requires_grad=True, device=device)
ref_ret = data
ref_ret = ref_ret * alpha + beta
ref_ret = ref_ret.half().to(dtype)
if trans:
ref_ret = ref_ret.t().requires_grad_()
ref_ret = ref_ret.detach().requires_grad_()
tri_ret = ref_ret.clone().detach().requires_grad_()
return ref_ret, tri_ret
def mask_tensor(x, mask, block, value=0):
ret = x.clone()
for h, i, j in zip(*(mask == 0).nonzero(as_tuple=True)):
ret[:, h, i * block:(i + 1) * block, j * block:(j + 1) * block] = value
return ret
@pytest.mark.parametrize("MODE", ["sdd", "dds", "dsd"])
@pytest.mark.parametrize("TRANS_A", [False, True])
@pytest.mark.parametrize("TRANS_B", [False, True])
@pytest.mark.parametrize("BLOCK", [16, 32, 64])
@pytest.mark.parametrize("DTYPE", [torch.float16])
def test_matmul(MODE, TRANS_A, TRANS_B, BLOCK, DTYPE, Z=3, H=2, M=512, N=384, K=256):
seed = 0
torch.manual_seed(seed)
is_sdd = MODE == "sdd"
is_dsd = MODE == "dsd"
is_dds = MODE == "dds"
do_sparsify = lambda x: sparsify_tensor(x, layout, BLOCK)
do_mask = lambda x: mask_tensor(x, layout, BLOCK)
# create inputs
# create op
a_shape = (Z, H, K, M) if TRANS_A else (Z, H, M, K)
b_shape = (Z, H, N, K) if TRANS_B else (Z, H, K, N)
c_shape = (Z, H, M, N)
shape = {
"sdd": (M, N),
"dsd": (a_shape[2], a_shape[3]),
"dds": (b_shape[2], b_shape[3]),
}[MODE]
layout = torch.randint(2, (H, shape[0] // BLOCK, shape[1] // BLOCK))
layout[1, 2, :] = 0
layout[1, :, 1] = 0
# create data
a_ref, a_tri = make_pair(a_shape, alpha=.1, dtype=DTYPE)
b_ref, b_tri = make_pair(b_shape, alpha=.1, dtype=DTYPE)
dc_ref, dc_tri = make_pair(c_shape, dtype=DTYPE)
# compute [torch]
dc_ref = do_mask(dc_ref) if is_sdd else dc_ref
a_ref = do_mask(a_ref) if is_dsd else a_ref
b_ref = do_mask(b_ref) if is_dds else b_ref
a_ref.retain_grad()
b_ref.retain_grad()
c_ref = torch.matmul(a_ref.transpose(2, 3) if TRANS_A else a_ref,
b_ref.transpose(2, 3) if TRANS_B else b_ref)
c_ref.backward(dc_ref)
c_ref = do_sparsify(c_ref) if is_sdd else c_ref
da_ref = do_sparsify(a_ref.grad) if is_dsd else a_ref.grad
db_ref = do_sparsify(b_ref.grad) if is_dds else b_ref.grad
# triton result
dc_tri = do_sparsify(dc_tri) if is_sdd else dc_tri
a_tri = do_sparsify(a_tri) if is_dsd else a_tri
b_tri = do_sparsify(b_tri) if is_dds else b_tri
a_tri.retain_grad()
b_tri.retain_grad()
op = triton.ops.blocksparse.matmul(layout, BLOCK, MODE, trans_a=TRANS_A, trans_b=TRANS_B, device="cuda")
c_tri = op(a_tri, b_tri)
c_tri.backward(dc_tri)
da_tri = a_tri.grad
db_tri = b_tri.grad
# compare
torch.testing.assert_allclose(c_ref, c_tri)
torch.testing.assert_allclose(da_ref, da_tri)
torch.testing.assert_allclose(db_ref, db_tri)
configs = [
(16, 256),
(32, 576),
(64, 1871),
(128, 2511),
]
@pytest.mark.parametrize("is_dense", [False, True])
@pytest.mark.parametrize("BLOCK, WIDTH", configs)
def test_softmax(BLOCK, WIDTH, is_dense, Z=2, H=2, is_causal=True, scale=0.4):
# set seed
torch.random.manual_seed(0)
Z, H, M, N = 2, 3, WIDTH, WIDTH
# initialize layout
# make sure each row has at least one non-zero element
layout = torch.randint(2, (H, M // BLOCK, N // BLOCK))
if is_dense:
layout[:] = 1
else:
layout[1, 2, :] = 0
layout[1, :, 1] = 0
# initialize data
a_shape = (Z, H, M, N)
a_ref, a_tri = make_pair(a_shape)
dout_ref, dout_tri = make_pair(a_shape)
# compute [torch]
a_ref = mask_tensor(a_ref, layout, BLOCK, value=float("-inf"))
a_ref.retain_grad()
at_mask = torch.ones((M, N), device="cuda")
if is_causal:
at_mask = torch.tril(at_mask)
M = at_mask[None, None, :, :] + torch.zeros_like(a_ref)
a_ref[M == 0] = float("-inf")
out_ref = torch.softmax(a_ref * scale, -1)
out_ref.backward(dout_ref)
out_ref = sparsify_tensor(out_ref, layout, BLOCK)
da_ref = sparsify_tensor(a_ref.grad, layout, BLOCK)
# compute [triton]
a_tri = sparsify_tensor(a_tri, layout, BLOCK)
a_tri.retain_grad()
dout_tri = sparsify_tensor(dout_tri, layout, BLOCK)
op = triton.ops.blocksparse.softmax(layout, BLOCK, device="cuda", is_dense=is_dense)
out_tri = op(a_tri, scale=scale, is_causal=is_causal)
out_tri.backward(dout_tri)
da_tri = a_tri.grad
# compare
torch.testing.assert_allclose(out_tri, out_ref)
torch.testing.assert_allclose(da_tri, da_ref)
@pytest.mark.parametrize("block", [16, 32, 64])
@pytest.mark.parametrize("dtype", [torch.float16, torch.float32])
def test_attention_fwd_bwd(
block,
dtype,
input_scale=1.0,
scale=1 / 8.0,
n_ctx=256,
batch_size=2,
n_heads=2,
):
capability = torch.cuda.get_device_capability()
if capability[0] < 7:
pytest.skip("Only test tl.dot() on devices with sm >= 70")
# inputs
qkv_shape = (batch_size, n_heads, n_ctx, 64)
qkvs = [
torch.nn.Parameter(input_scale * torch.randn(qkv_shape), requires_grad=True).to(dtype).cuda() for _ in range(3)
]
# Triton:
n_blocks = n_ctx // block
layout = torch.tril(torch.ones([n_heads, n_blocks, n_blocks], dtype=torch.long))
query, key, value = [x.clone() for x in qkvs]
query.retain_grad()
key.retain_grad()
value.retain_grad()
attn_out = triton_attention(layout, block, query=query, key=key, value=value, scale=scale)
# ad hoc loss
loss = (attn_out ** 2).mean()
loss.backward()
grads = [query.grad, key.grad, value.grad]
# Torch version:
torch_q, torch_k, torch_v = [x.clone() for x in qkvs]
attn_mask = torch.ones([n_ctx, n_ctx], device="cuda", dtype=dtype)
attn_mask = torch.tril(attn_mask, diagonal=0)
attn_mask = 1e6 * (-1 + (attn_mask.reshape((1, 1, n_ctx, n_ctx)).cuda()))
torch_q.retain_grad()
torch_k.retain_grad()
torch_v.retain_grad()
scores = scale * torch.einsum("bhsd,bhtd->bhst", torch_q, torch_k)
scores = scores + attn_mask
probs = torch.softmax(scores, dim=-1)
torch_attn_out = torch.einsum("bhst,bhtd->bhsd", probs, torch_v)
# ad hoc loss
torch_loss = (torch_attn_out ** 2).mean()
torch_loss.backward()
torch_grads = [torch_q.grad, torch_k.grad, torch_v.grad]
# comparison
# print(f"Triton loss {loss} and torch loss {torch_loss}. Also checking grads...")
torch.testing.assert_allclose(loss, torch_loss, atol=1e-3, rtol=0)
for g1, g2 in zip(grads, torch_grads):
torch.testing.assert_allclose(g1, g2)
@pytest.mark.parametrize("block", [16, 32, 64])
def triton_attention(
layout,
block: int,
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
scale: float,
):
sparse_dot_sdd_nt = triton.ops.blocksparse.matmul(layout, block, "sdd", trans_a=False, trans_b=True, device=value.device)
sparse_dot_dsd_nn = triton.ops.blocksparse.matmul(layout, block, "dsd", trans_a=False, trans_b=False, device=value.device)
sparse_softmax = triton.ops.blocksparse.softmax(layout, block, device=value.device)
w = sparse_dot_sdd_nt(query, key)
w = sparse_softmax(w, scale=scale, is_causal=True)
a = sparse_dot_dsd_nn(w, value)
return a
|
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|
33,550
|
quantapix/qnarre
|
refs/heads/main
|
/tools/triton/python/test/unit/operators/test_cross_entropy.py
|
import pytest
import torch
import triton
import triton.ops
@pytest.mark.parametrize("M, N, dtype, mode",
[
(M, N, dtype, mode) for M in [1024, 821]
for N in [512, 857, 1871, 2089, 8573, 31000]
for dtype in ['float16', 'float32']
for mode in ['forward', 'backward']
]
)
def test_op(M, N, dtype, mode):
capability = torch.cuda.get_device_capability()
if capability[0] < 8 and dtype == "bfloat16":
pytest.skip("Only test bfloat16 on devices with sm >= 80")
dtype = {'bfloat16': torch.bfloat16, 'float16': torch.float16, 'float32': torch.float32}[dtype]
# create inputs
x = torch.randn(M, N, dtype=dtype, device='cuda', requires_grad=True)
idx = 4 + torch.ones(M, dtype=torch.int64, device='cuda')
# forward pass
tt_y = triton.ops.cross_entropy(x, idx)
th_y = torch.nn.CrossEntropyLoss(reduction="none")(x, idx)
if mode == 'forward':
torch.testing.assert_allclose(th_y, tt_y)
# backward pass
elif mode == 'backward':
dy = torch.randn_like(tt_y)
# triton backward
tt_y.backward(dy)
tt_dx = x.grad.clone()
# torch backward
x.grad.zero_()
th_y.backward(dy)
th_dx = x.grad.clone()
torch.testing.assert_allclose(th_dx, tt_dx)
|
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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"]}
|
33,551
|
quantapix/qnarre
|
refs/heads/main
|
/tools/triton/python/triton/tools/build_extern.py
|
import argparse
import subprocess
from abc import ABC, abstractmethod
from typing import Dict, List, Optional
class Symbol:
_name: str
_op_name: str
_ret_type: str
_arg_names: List[str]
_arg_types: List[str]
def __init__(
self,
name: str,
op_name: str,
ret_type: str,
arg_names: List[str],
arg_types: List[str],
) -> None:
'''
A symbol is a function declaration.
:param name: name of the symbol
:param op_name: name of the operation
:param ret_type: return type of the operation
:param arg_names: names of the arguments
:param arg_types: types of the arguments
'''
self._name = name
self._op_name = op_name
self._ret_type = ret_type
self._arg_names = list(arg_names)
self._arg_types = list(arg_types)
@property
def name(self) -> str:
return self._name
@property
def op_name(self) -> str:
return self._op_name
@property
def ret_type(self) -> str:
return self._ret_type
@property
def arg_names(self) -> List[str]:
return self._arg_names
@property
def arg_types(self) -> List[str]:
return self._arg_types
def convert_type(type_str) -> Optional[str]:
if type_str == "i32":
return "int32"
elif type_str == "u32":
return "uint32"
elif type_str == "i64":
return "int64"
elif type_str == "u64":
return "uint64"
elif type_str == "float":
return "fp32"
elif type_str == "double":
return "fp64"
else:
# ignore other types, such as pointer types
return None
def to_unsigned(type_str) -> str:
if type_str == "int32":
return "uint32"
elif type_str == "int64":
return "uint64"
else:
return type_str
class ExternLibrary(ABC):
_name: str
_path: str
_symbols: Dict[str, Symbol]
_format: bool
_grouping: bool
def __init__(
self,
name: str,
path: str,
format: bool = True,
grouping: bool = True,
) -> None:
'''
Abstract class for extern library.
:param name: name of the library
:param path: path of the library
:param format: whether to format the generated stub file
'''
self._name = name
self._path = path
self._symbols = {}
self._format = format
self._grouping = grouping
@property
def name(self) -> str:
return self._name
@property
def path(self) -> str:
return self._path
@property
def symbols(self) -> Dict[str, Symbol]:
return self._symbols
@property
def grouping(self) -> bool:
return self._grouping
@abstractmethod
def parse_symbols(self, input_file) -> None:
pass
@abstractmethod
def _output_stubs(self) -> str:
pass
def generate_stub_file(self, output_dir) -> None:
file_str = self._output_stubs()
if file_str is None or len(file_str) == 0:
raise Exception("file_str is empty")
output_file = f"{output_dir}/{self._name}.py"
with open(output_file, "w") as f:
f.write(file_str)
f.close()
if self._format:
subprocess.Popen(["autopep8", "-a", "-r", "-i", output_file],
stdout=subprocess.PIPE).communicate()
subprocess.Popen(["isort", output_file], stdout=subprocess.PIPE).communicate()
class Libdevice(ExternLibrary):
_symbol_groups: Dict[str, List[Symbol]]
def __init__(self, path) -> None:
'''
Constructor for Libdevice.
:param path: path of the libdevice library
'''
super().__init__("libdevice", path)
self._symbol_groups = {}
self.is_pure = True
@staticmethod
def _extract_symbol(line) -> Optional[Symbol]:
# Extract symbols from line in the following format:
# "define [internal] <ret_type> @<name>(<arg_types>,)"
entries = line.split("@")
ret_str = entries[0]
func_str = entries[1]
# Get ret_type, skip internal symbols
ret_strs = ret_str.split()
if ret_strs[1] == "internal":
return None
ret_type = convert_type(ret_strs[1])
if ret_type is None:
return None
# Get function name
func_strs = func_str.split("(")
func_name = func_strs[0].replace("@", "")
op_name = func_name.replace("__nv_", "")
if 'ieee' in op_name:
return None
# Get arg_types
arg_strs = func_strs[1].split(",")
arg_types = []
arg_names = []
for i, arg_str in enumerate(arg_strs):
arg_type = convert_type(arg_str.split()[0])
if arg_type is None:
return None
arg_name = 'arg' + str(i)
arg_types.append(arg_type)
arg_names.append(arg_name)
if op_name == "sad":
# Special case for sad, where the last argument is an unsigned int
arg_types[-1] = to_unsigned(arg_types[-1])
elif op_name.startswith("u"):
# LLVM does not differentiate between signed and unsigned integer type.
# We have to convert the types to unsigned
ret_type = to_unsigned(ret_type)
for i, arg_type in enumerate(arg_types):
arg_types[i] = to_unsigned(arg_type)
return Symbol(func_name, op_name, ret_type, arg_names, arg_types)
def _group_symbols(self) -> None:
symbol_set = {}
for symbol in self._symbols.values():
op_name = symbol.op_name
symbol_set[op_name] = symbol
# Group functions together by renaming.
renaming = {
'llabs': 'abs', 'acosf': 'acos', 'acoshf': 'acosh',
'dadd_rd': 'add_rd', 'fadd_rd': 'add_rd', 'dadd_rn': 'add_rn',
'fadd_rn': 'add_rn', 'dadd_ru': 'add_ru', 'fadd_ru': 'add_ru',
'dadd_rz': 'add_rz', 'fadd_rz': 'add_rz', 'asinf': 'asin',
'asinhf': 'asinh', 'atanf': 'atan', 'atan2f': 'atan2',
'atanhf': 'atanh', 'brevll': 'brev', 'cbrtf': 'cbrt',
'ceilf': 'ceil', 'clzll': 'clz', 'copysignf': 'copysign',
'cosf': 'cos', 'coshf': 'cosh', 'cospif': 'cospi',
'cyl_bessel_i0f': 'cyl_bessel_i0', 'cyl_bessel_i1f': 'cyl_bessel_i1',
'fdiv_rd': 'div_rd', 'ddiv_rd': 'div_rd', 'fdiv_rn': 'div_rn',
'ddiv_rn': 'div_rn', 'fdiv_ru': 'div_ru', 'ddiv_ru': 'div_ru',
'fdiv_rz': 'div_rz', 'ddiv_rz': 'div_rz', 'erff': 'erf',
'erfcf': 'erfc', 'erfcinvf': 'erfcinv', 'erfcxf': 'erfcx',
'erfinvf': 'erfinv', 'expf': 'exp', 'exp10f': 'exp10',
'exp2f': 'exp2', 'expm1f': 'expm1', 'fabsf': 'abs',
'fabs': 'abs', 'fast_fdividef': 'fast_dividef',
'fdimf': 'fdim', 'ffsll': 'ffs', 'floorf': 'floor',
'fmaf': 'fma', 'fmaf_rd': 'fma_rd', 'fmaf_rn': 'fma_rn',
'fmaf_ru': 'fma_ru', 'fmaf_rz': 'fma_rz', 'fmodf': 'fmod',
'uhadd': 'hadd', 'hypotf': 'hypot', 'ilogbf': 'ilogb',
'isinff': 'isinf', 'isinfd': 'isinf', 'isnanf': 'isnan',
'isnand': 'isnan', 'j0f': 'j0', 'j1f': 'j1', 'jnf': 'jn',
'ldexpf': 'ldexp', 'lgammaf': 'lgamma', 'llrintf': 'llrint',
'llroundf': 'llround', 'logf': 'log', 'log10f': 'log10',
'log1pf': 'log1p', 'log2f': 'log2', 'logbf': 'logb',
'umax': 'max', 'llmax': 'max', 'ullmax': 'max', 'fmaxf': 'max',
'fmax': 'max', 'umin': 'min', 'llmin': 'min', 'ullmin': 'min',
'fminf': 'min', 'fmin': 'min', 'dmul_rd': 'mul_rd', 'fmul_rd': 'mul_rd',
'dmul_rn': 'mul_rn', 'fmul_rn': 'mul_rn', 'dmul_ru': 'mul_ru',
'fmul_ru': 'mul_ru', 'dmul_rz': 'mul_rz', 'fmul_rz': 'mul_rz',
'umul24': 'mul24', 'umulhi': 'mulhi', 'mul64hi': 'mulhi',
'umul64hi': 'mulhi', 'nearbyintf': 'nearbyint', 'nextafterf': 'nextafter',
'norm3df': 'norm3d', 'norm4df': 'norm4d', 'normcdff': 'normcdf',
'normcdfinvf': 'normcdfinv', 'popcll': 'popc', 'powif': 'pow', 'powi': 'pow',
'powf': 'pow', 'rcbrtf': 'rcbrt', 'frcp_rd': 'rcp_rd', 'drcp_rd': 'rcp_rd',
'frcp_rn': 'rcp_rn', 'drcp_rn': 'rcp_rn', 'frcp_ru': 'rcp_ru',
'drcp_ru': 'rcp_ru', 'frcp_rz': 'rcp_rz', 'drcp_rz': 'rcp_rz',
'remainderf': 'remainder', 'urhadd': 'rhadd', 'rhypotf': 'rhypot',
'rintf': 'rint', 'rnorm3df': 'rnorm3d', 'rnorm4df': 'rnorm4d',
'roundf': 'round', 'rsqrtf': 'rsqrt', 'frsqrt_rn': 'rsqrt_rn',
'usad': 'sad', 'scalbnf': 'scalbn', 'signbitf': 'signbit',
'signbitd': 'signbit', 'sinf': 'sin', 'sinhf': 'sinh',
'sinpif': 'sinpi', 'sqrtf': 'sqrt', 'fsqrt_rd': 'sqrt_rd',
'dsqrt_rd': 'sqrt_rd', 'fsqrt_rn': 'sqrt_rn', 'dsqrt_rn': 'sqrt_rn',
'fsqrt_ru': 'sqrt_ru', 'dsqrt_ru': 'sqrt_ru', 'fsqrt_rz': 'sqrt_rz',
'dsqrt_rz': 'sqrt_rz', 'fsub_rd': 'sub_rd', 'dsub_rd': 'sub_rd',
'fsub_rn': 'sub_rn', 'dsub_rn': 'sub_rn', 'fsub_ru': 'sub_ru',
'dsub_ru': 'sub_ru', 'fsub_rz': 'sub_rz', 'dsub_rz': 'sub_rz',
'tanf': 'tan', 'tanhf': 'tanh', 'tgammaf': 'tgamma', 'truncf': 'trunc',
'y0f': 'y0', 'y1f': 'y1', 'ynf': 'yn'
}
for symbol in self._symbols.values():
op_name = symbol.op_name
if op_name in renaming:
op_name = renaming[op_name]
symbol._op_name = op_name
if op_name in self._symbol_groups:
self._symbol_groups[op_name].append(symbol)
else:
self._symbol_groups[op_name] = [symbol]
def parse_symbols(self, input_file) -> None:
if len(self.symbols) > 0:
return
output = subprocess.check_output(["grep", "define", input_file]).decode().splitlines()
for line in output:
symbol = self._extract_symbol(line)
if symbol is None:
continue
self._symbols[symbol.name] = symbol
self._group_symbols()
def _output_stubs(self) -> str:
# Generate python functions in the following format:
# @extern.extern
# def <op_name>(<args>, _builder=None):
# arg_type_symbol_dict = {[arg_type]: {(symbol, ret_type)}}
# return core.extern_elementwise("libdevice", <path>, <args>, <arg_type_symbol_dict>, _builder)
import_str = "from . import core\n"
import_str += "import os\n"
import_str += "import functools\n"
header_str = ""
header_str += "@functools.lru_cache()\n"
header_str += "def libdevice_path():\n"
header_str += " import torch\n"
header_str += " third_party_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), \"..\", \"third_party\")\n"
header_str += " if torch.version.hip is None:\n"
header_str += " default = os.path.join(third_party_dir, \"cuda\", \"lib\", \"libdevice.10.bc\")\n"
header_str += " else:\n"
header_str += " default = ''\n"
header_str += " return os.getenv(\"TRITON_LIBDEVICE_PATH\", default)\n"
func_str = ""
for symbols in self._symbol_groups.values():
func_str += "@core.extern\n"
func_name_str = f"def {symbols[0].op_name}("
for arg_name in symbols[0].arg_names:
func_name_str += f"{arg_name}, "
func_name_str += "_builder=None):\n"
return_str = f"\treturn core.extern_elementwise(\"{self._name}\", libdevice_path(), ["
for arg_name in symbols[0].arg_names:
return_str += f"{arg_name}, "
return_str += "], \n"
arg_type_symbol_dict_str = "{"
for symbol in symbols:
arg_type_symbol_dict_str += "("
for arg_type in symbol.arg_types:
arg_type_symbol_dict_str += f'core.dtype("{arg_type}"),'
ret_type = f'core.dtype("{symbol.ret_type}")'
arg_type_symbol_dict_str += "): (\"" + symbol.name + "\", " + ret_type + "),\n"
arg_type_symbol_dict_str += "}"
return_str += arg_type_symbol_dict_str
return_str += f", is_pure={self.is_pure}"
return_str += ", _builder=_builder)\n"
func_str += func_name_str + return_str + "\n"
file_str = import_str + header_str + func_str
return file_str
class LLVMDisassembler:
_path: str
_ll_file: str
def __init__(self, path) -> None:
'''
Invoke llvm-dis to disassemble the given file.
:param path: path to llvm-dis
'''
self._path = path
self._ll_file = "/tmp/extern_lib.ll"
def disasm(self, lib_path: str) -> None:
subprocess.Popen([self._path, lib_path, "-o", self.ll_file],
stdout=subprocess.PIPE).communicate()
@property
def ll_file(self) -> str:
return self._ll_file
@property
def path(self) -> str:
return self._path
extern_libs = ["libdevice"]
def build(
llvm_dis_path: str,
lib_path: str,
lib_name: str,
output_dir: str,
) -> None:
'''
Interface function to build the library file.
:param llvm_dis_path: path to the llvm-dis binary
:param lib_path: path to the external library file
:param lib_name: name of the library
:param output_dir: path to the output directory
'''
if lib_name == "libdevice":
extern_lib = Libdevice(lib_path)
else:
raise Exception(f"Unknown extern library: {lib_name}")
llvm_disassembler = LLVMDisassembler(llvm_dis_path)
llvm_disassembler.disasm(lib_path)
extern_lib.parse_symbols(llvm_disassembler.ll_file)
extern_lib.generate_stub_file(output_dir)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--llvm-dis", dest="llvm_dis_path", help="Path to llvm-dis", default="llvm-dis")
parser.add_argument("--lib-path", dest="lib_path", help="Path to the extern library")
parser.add_argument("--lib-name", dest="lib_name", help="Name of the extern library")
parser.add_argument("--output", dest="output_dir", help="Output file path", default="/tmp/")
args = parser.parse_args()
build(args.llvm_dis_path, args.lib_path, args.lib_name, args.output_dir)
|
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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"]}
|
33,552
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/try/07-math-functions.py
|
"""
Libdevice (`tl.math`) function
==============================
Triton can invoke a custom function from an external library.
In this example, we will use the `libdevice` library (a.k.a `math` in triton) to apply `asin` on a tensor.
Please refer to https://docs.nvidia.com/cuda/libdevice-users-guide/index.html regarding the semantics of all available libdevice functions.
In `triton/language/math.py`, we try to aggregate functions with the same computation but different data types together.
For example, both `__nv_asin` and `__nvasinf` calculate the principal value of the arc sine of the input, but `__nv_asin` operates on `double` and `__nv_asinf` operates on `float`.
Using triton, you can simply call `tl.math.asin`.
Triton automatically selects the correct underlying device function to invoke based on input and output types.
"""
# %%
# asin Kernel
# ------------
import torch
import triton
import triton.language as tl
@triton.jit
def asin_kernel(
x_ptr,
y_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)
x = tl.math.asin(x)
tl.store(y_ptr + offsets, x, mask=mask)
# %%
# Using the default libdevice library path
# -----------------------------------------
# We can use the default libdevice library path encoded in `triton/language/math.py`
torch.manual_seed(0)
size = 98432
x = torch.rand(size, device='cuda')
output_triton = torch.zeros(size, device='cuda')
output_torch = torch.asin(x)
assert x.is_cuda and output_triton.is_cuda
n_elements = output_torch.numel()
grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),)
asin_kernel[grid](x, output_triton, n_elements, BLOCK_SIZE=1024)
print(output_torch)
print(output_triton)
print(
f'The maximum difference between torch and triton is '
f'{torch.max(torch.abs(output_torch - output_triton))}'
)
# %%
# Customize the libdevice library path
# -------------------------------------
# We can also customize the libdevice library path by passing the path to the `libdevice` library to the `asin` kernel.
output_triton = torch.empty_like(x)
asin_kernel[grid](x, output_triton, n_elements, BLOCK_SIZE=1024,
extern_libs={'libdevice': '/usr/local/cuda/nvvm/libdevice/libdevice.10.bc'})
print(output_torch)
print(output_triton)
print(
f'The maximum difference between torch and triton is '
f'{torch.max(torch.abs(output_torch - output_triton))}'
)
|
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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"]}
|
33,553
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/models/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 torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import functional as F
from transformers.utils import logging
from torch.utils.checkpoint import checkpoint
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
from dataclasses import dataclass
from ...generation_beam_search import BeamSearchScorer
from ...generation_logits_process import LogitsProcessorList
from ...generation_stopping_criteria import StoppingCriteriaList
from .configuration_rag import RagConfig
from .retrieval_rag import RagRetriever
log = logging.get_logger(__name__)
@dataclass
class RetrievAugLMMarginOutput(ModelOutput):
loss = None
logits = None
doc_scores = None
caches = None
retrieved_doc_embeds = None
retrieved_doc_ids = None
context_input_ids = None
context_attention_mask = None
question_encoder_last_hidden_state = None
question_enc_hidden_states = None
question_enc_attentions = None
generator_enc_last_hidden_state = None
generator_enc_hidden_states = None
generator_enc_attentions = None
generator_dec_hidden_states = None
generator_dec_attentions = None
generator_cross_attentions = None
@dataclass
class RetrievAugLMOutput(ModelOutput):
logits = None
doc_scores = None
caches = None
retrieved_doc_embeds = None
retrieved_doc_ids = None
context_input_ids = None
context_attention_mask = None
question_encoder_last_hidden_state = None
question_enc_hidden_states = None
question_enc_attentions = None
generator_enc_last_hidden_state = None
generator_enc_hidden_states = None
generator_enc_attentions = None
generator_dec_hidden_states = None
generator_dec_attentions = None
generator_cross_attentions = None
class Model(PreTrained):
def __init__(
self,
config=None,
question_encoder=None,
generator=None,
retriever=None,
**kw,
):
assert config is not None or (question_encoder is not None and generator is not None)
if config is None:
config = RagConfig.from_question_encoder_generator_configs(
question_encoder.config, generator.config, **kw
)
else:
assert isinstance(config, self.config_class)
super().__init__(config)
if question_encoder is None:
from ..auto.modeling_auto import AutoModel
question_encoder = AutoModel.from_config(config.question_encoder)
if generator is None:
from ..auto.modeling_auto import AutoModelForSeq2SeqLM
generator = AutoModelForSeq2SeqLM.from_config(config.generator)
self.retriever = retriever
if self.retriever is not None:
self.retriever = retriever
self.question_encoder = question_encoder
self.generator = generator
self.ctx_encoder = None
self.context_encoder_training = False
def forward(
self,
input_ids=None,
attention_mask=None,
encoder_outputs=None,
decoder_input_ids=None,
decoder_attention_mask=None,
caches=None,
doc_scores=None,
context_input_ids=None,
context_attention_mask=None,
y_cache=None,
output_attentions=None,
output_hidden_states=None,
output_retrieved=None,
n_docs=None,
):
n_docs = n_docs if n_docs is not None else self.config.n_docs
y_cache = y_cache if y_cache is not None else self.config.y_cache
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
)
output_retrieved = (
output_retrieved if output_retrieved is not None else self.config.output_retrieved
)
# whether retriever has to be used
has_to_retrieve = (
self.retriever is not None
and (context_input_ids is None or context_attention_mask is None or doc_scores is None)
and encoder_outputs is None
)
# encoder_outputs are pre-computed during RAG-token generation
if encoder_outputs is None:
if has_to_retrieve:
question_enc_outputs = self.question_encoder(
input_ids, attention_mask=attention_mask, return_dict=True
)
question_encoder_last_hidden_state = question_enc_outputs[
0
] # hidden states of question encoder
retriever_outputs = self.retriever(
input_ids,
question_encoder_last_hidden_state.cpu().detach().to(torch.float32).numpy(),
prefix=self.generator.config.prefix,
n_docs=n_docs,
return_tensors="pt",
)
if self.context_encoder_training:
(
context_input_ids,
context_attention_mask,
retrieved_doc_embeds,
retrived_doc_input_ids,
retrived_doc_attention_mask,
retrieved_doc_ids,
) = (
retriever_outputs["context_input_ids"],
retriever_outputs["context_attention_mask"],
retriever_outputs["retrieved_doc_embeds"],
retriever_outputs["tokenized_doc_ids"],
retriever_outputs["tokenized_doc_attention_mask"],
retriever_outputs["doc_ids"],
)
context_input_ids = context_input_ids.to(input_ids)
context_attention_mask = context_attention_mask.to(input_ids)
retrived_doc_input_ids = retrived_doc_input_ids.to(input_ids)
retrived_doc_attention_mask = retrived_doc_attention_mask.to(input_ids)
retrieved_doc_embeds = self.ctx_encoder(
retrived_doc_input_ids,
attention_mask=retrived_doc_attention_mask,
return_dict=True,
).pools
retrieved_doc_embeds = retrieved_doc_embeds.view(
-1, n_docs, question_encoder_last_hidden_state.shape[1]
) # reshaping
# compute doc_scores involving ctx_encoder
doc_scores = torch.bmm(
question_encoder_last_hidden_state.unsqueeze(1),
retrieved_doc_embeds.transpose(1, 2),
).squeeze(1)
else:
(
context_input_ids,
context_attention_mask,
retrieved_doc_embeds,
retrieved_doc_ids,
) = (
retriever_outputs["context_input_ids"],
retriever_outputs["context_attention_mask"],
retriever_outputs["retrieved_doc_embeds"],
retriever_outputs["doc_ids"],
)
# set to correct device
retrieved_doc_embeds = retrieved_doc_embeds.to(
question_encoder_last_hidden_state
)
context_input_ids = context_input_ids.to(input_ids)
context_attention_mask = context_attention_mask.to(input_ids)
# compute doc_scores
doc_scores = torch.bmm(
question_encoder_last_hidden_state.unsqueeze(1),
retrieved_doc_embeds.transpose(1, 2),
).squeeze(1)
else:
assert context_input_ids is not None
assert context_attention_mask is not None
assert doc_scores is not None
assert doc_scores is not None
assert (doc_scores.shape[1] % n_docs) == 0
# Decoder input without context documents
if decoder_input_ids is not None:
decoder_input_ids = decoder_input_ids.repeat_interleave(n_docs, dim=0)
if decoder_attention_mask is not None:
decoder_attention_mask = decoder_attention_mask.repeat_interleave(n_docs, dim=0)
gen_outputs = self.generator(
input_ids=context_input_ids,
attention_mask=context_attention_mask,
encoder_outputs=encoder_outputs,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
caches=caches,
y_cache=y_cache,
output_attentions=output_attentions,
return_dict=True,
)
if not has_to_retrieve:
question_encoder_last_hidden_state = None
question_enc_hidden_states = None
question_enc_attentions = None
retrieved_doc_embeds = None
retrieved_doc_ids = None
else:
question_enc_hidden_states = question_enc_outputs.hiddens
question_enc_attentions = question_enc_outputs.attns
if not has_to_retrieve or not output_retrieved:
# don't output retrieved docs
context_input_ids = (None,)
context_attention_mask = None
retrieved_doc_embeds = None
retrieved_doc_ids = None
return RetrievAugLMOutput(
logits=gen_outputs.logits,
doc_scores=doc_scores,
caches=gen_outputs.caches,
context_input_ids=context_input_ids,
context_attention_mask=context_attention_mask,
retrieved_doc_embeds=retrieved_doc_embeds,
retrieved_doc_ids=retrieved_doc_ids,
question_encoder_last_hidden_state=question_encoder_last_hidden_state,
question_enc_hidden_states=question_enc_hidden_states,
question_enc_attentions=question_enc_attentions,
generator_enc_last_hidden_state=gen_outputs.enc_y,
generator_enc_hidden_states=gen_outputs.enc_hiddens,
generator_enc_attentions=gen_outputs.enc_attns,
generator_dec_hidden_states=gen_outputs.hiddens,
generator_dec_attentions=gen_outputs.attns,
generator_cross_attentions=gen_outputs.crosses,
)
class RagSequenceForGeneration(PreTrained):
def __init__(
self,
config=None,
question_encoder=None,
generator=None,
retriever=None,
**kw,
):
assert config is not None or (question_encoder is not None and generator is not None)
if config is None:
config = RagConfig.from_question_encoder_generator_configs(
question_encoder.config, generator.config, **kw
)
super().__init__(config)
self.rag = Model(
config=config,
question_encoder=question_encoder,
generator=generator,
retriever=retriever,
)
def set_retriever(self, retriever: RagRetriever):
self.rag.retriever = retriever
def set_context_encoder_for_training(self, ctx_encoder):
self.rag.context_encoder_training = True
self.rag.ctx_encoder = ctx_encoder
def forward(
self,
input_ids=None,
attention_mask=None,
encoder_outputs=None,
decoder_input_ids=None,
decoder_attention_mask=None,
caches=None,
context_input_ids=None,
context_attention_mask=None,
doc_scores=None,
y_cache=None,
output_attentions=None,
output_hidden_states=None,
output_retrieved=None,
exclude_bos_score=None,
reduce_loss=None,
labels=None,
n_docs=None,
**kw, # needs kw for generation
):
n_docs = n_docs if n_docs is not None else self.config.n_docs
exclude_bos_score = (
exclude_bos_score if exclude_bos_score is not None else self.config.exclude_bos_score
)
reduce_loss = reduce_loss if reduce_loss is not None else self.config.reduce_loss
if labels is not None:
if decoder_input_ids is None:
decoder_input_ids = labels
y_cache = False
outputs = self.rag(
input_ids=input_ids,
attention_mask=attention_mask,
encoder_outputs=encoder_outputs,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
context_input_ids=context_input_ids,
context_attention_mask=context_attention_mask,
doc_scores=doc_scores,
caches=caches,
y_cache=y_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
output_retrieved=output_retrieved,
n_docs=n_docs,
)
loss = None
if labels is not None:
loss = self.get_nll(
outputs.logits,
outputs.doc_scores,
decoder_input_ids,
reduce_loss=reduce_loss,
epsilon=self.config.label_smoothing,
exclude_bos_score=exclude_bos_score,
n_docs=n_docs,
)
return RetrievAugLMMarginOutput(
loss=loss,
logits=outputs.logits,
doc_scores=outputs.doc_scores,
caches=outputs.caches,
context_input_ids=outputs.context_input_ids,
context_attention_mask=outputs.context_attention_mask,
retrieved_doc_embeds=outputs.retrieved_doc_embeds,
retrieved_doc_ids=outputs.retrieved_doc_ids,
question_encoder_last_hidden_state=outputs.question_encoder_last_hidden_state,
question_enc_hidden_states=outputs.question_enc_hidden_states,
question_enc_attentions=outputs.question_enc_attentions,
generator_enc_last_hidden_state=outputs.generator_enc_last_hidden_state,
generator_enc_hidden_states=outputs.generator_enc_hidden_states,
generator_enc_attentions=outputs.generator_enc_attentions,
generator_dec_hidden_states=outputs.generator_dec_hidden_states,
generator_dec_attentions=outputs.generator_dec_attentions,
generator_cross_attentions=outputs.generator_cross_attentions,
)
@property
def retriever(self):
return self.rag.retriever
@property
def generator(self):
return self.rag.generator
@property
def question_encoder(self):
return self.rag.question_encoder
@torch.no_grad()
def generate(
self,
input_ids=None,
attention_mask=None,
context_input_ids=None,
context_attention_mask=None,
doc_scores=None,
do_deduplication=None, # defaults to True
num_return_sequences=None, # defaults to 1
num_beams=None, # defaults to 1
n_docs=None,
**model_kw,
):
n_docs = n_docs if n_docs is not None else self.config.n_docs
do_deduplication = (
do_deduplication if do_deduplication is not None else self.config.do_deduplication
)
num_doc_return_sequences = (
num_return_sequences
if num_return_sequences is not None
else self.config.num_return_sequences
)
num_beams = num_beams if num_beams is not None else self.config.num_beams
assert input_ids is not None or context_input_ids is not None
if self.retriever is not None and context_input_ids is None:
question_hidden_states = self.question_encoder(
input_ids, attention_mask=attention_mask
)[0]
context_input_ids = self.retriever(
input_ids,
question_hidden_states.cpu().detach().to(torch.float32).numpy(),
prefix=self.generator.config.prefix,
n_docs=n_docs,
return_tensors="pt",
)["context_input_ids"]
# set to correct device
context_input_ids = context_input_ids.to(input_ids)
hypos = []
model_kw["num_beams"] = num_beams
model_kw["num_return_sequences"] = num_beams
model_kw["attention_mask"] = None
batch_size = (
input_ids.shape[0] if input_ids is not None else context_input_ids.shape[0] // n_docs
)
for index in range(batch_size):
# first, generate beams from documents:
generator_input_ids = context_input_ids[
index * n_docs : (index + 1) * n_docs
] # (n_docs, max_len)
output_sequences = self.generator.generate(
generator_input_ids,
**model_kw,
) # n_docs * n_beam, tgt_len
if do_deduplication:
# do_deduplication, max_output_len
output_sequences = torch.stack(
list({str(k.tolist()): k for k in output_sequences}.values())
)
num_candidates = output_sequences.shape[
0
] # after deduplication, this number can be less than n_docs*n_beam
# then, run model forwards to get nll scores:
if input_ids is not None:
new_input_ids = input_ids[index : index + 1].repeat(num_candidates, 1)
outputs = self(new_input_ids, labels=output_sequences, exclude_bos_score=True)
else: # input_ids is None, need context_input_ids/mask and doc_scores
assert context_attention_mask is not None
assert doc_scores is not None
individual_input_ids = generator_input_ids.repeat(
num_candidates, 1
) # (num_candidates*n_docs, max_len)
individual_attention_mask = context_attention_mask[
index * n_docs : (index + 1) * n_docs
]
individual_attention_mask = individual_attention_mask.repeat(num_candidates, 1)
individual_doc_scores = doc_scores[
index : (index + 1), :
] # doc_scores.shape = [batch, n_docs]
individual_doc_scores = individual_doc_scores.repeat(
num_candidates, 1
) # [num_candidates, n_docs]
outputs = self(
context_input_ids=individual_input_ids,
context_attention_mask=individual_attention_mask,
doc_scores=individual_doc_scores,
labels=output_sequences,
exclude_bos_score=True,
)
top_cand_inds = (-outputs["loss"]).topk(num_doc_return_sequences)[1]
# add hypothesis
hypos.append(output_sequences[top_cand_inds])
return self._cat_and_pad(hypos, PAD=self.config.generator.PAD)
def get_nll(
self,
seq_logits,
doc_scores,
target,
reduce_loss=False,
epsilon=0.0,
exclude_bos_score=False,
n_docs=None,
):
# shift tokens left
target = torch.cat(
[
target[:, 1:],
target.new(target.shape[0], 1).fill_(self.config.generator.PAD),
],
1,
)
n_docs = n_docs if n_docs is not None else self.config.n_docs
# BOS is None for T5
BOS = self.config.BOS or self.config.generator.BOS
use_bos = BOS is not None and target[:, 0].eq(BOS).all()
def _mask_pads(ll, smooth_obj):
pad_mask = target.eq(self.config.generator.PAD)
if pad_mask.any():
ll.masked_fill_(pad_mask, 0.0)
smooth_obj.masked_fill_(pad_mask, 0.0)
return ll.squeeze(-1), smooth_obj.squeeze(-1)
# seq_logits dim = (batch*n_docs, tgt_len , #vocabs)
seq_logprobs = F.log_softmax(seq_logits, dim=-1).view(
seq_logits.shape[0] // n_docs, n_docs, -1, seq_logits.size(-1)
) # batch_size x n_docs x tgt_len x #s_vocab
doc_logprobs = F.log_softmax(doc_scores, dim=1).unsqueeze(-1).unsqueeze(-1)
# RAG-sequence marginalization
first_token_scores = seq_logprobs[:, :, :1, :]
second_token_scores = seq_logprobs[:, :, 1:2, :]
remainder = seq_logprobs[:, :, 2:, :]
rag_logprobs = torch.cat(
[first_token_scores, second_token_scores + doc_logprobs, remainder], dim=2
)
# calculate loss
target = target.unsqueeze(1).unsqueeze(-1).repeat(1, n_docs, 1, 1)
assert target.dim() == rag_logprobs.dim()
ll = rag_logprobs.gather(dim=-1, index=target)
smooth_obj = rag_logprobs.sum(dim=-1, keepdim=True) # total sum of all (normalised) logits
ll, smooth_obj = _mask_pads(ll, smooth_obj)
# sum over tokens, exclude bos while scoring
ll = ll[:, :, 1:].sum(2) if exclude_bos_score and use_bos else ll.sum(2)
smooth_obj = smooth_obj.sum(2)
ll = ll.logsumexp(1) # logsumexp over docs
smooth_obj = smooth_obj.logsumexp(1)
nll_loss = -ll
smooth_loss = -smooth_obj
if reduce_loss:
nll_loss = nll_loss.sum()
smooth_loss = smooth_loss.sum()
eps_i = epsilon / rag_logprobs.size(-1)
loss = (1.0 - epsilon) * nll_loss + eps_i * smooth_loss
return loss
@staticmethod
def _cat_and_pad(tensors, PAD):
output = (
tensors[0]
.new(sum([t.shape[0] for t in tensors]), max([t.shape[1] for t in tensors]))
.fill_(PAD)
)
ind = 0
for t in tensors:
output[ind : ind + t.shape[0], : t.shape[1]] = t
ind += t.shape[0]
return output
class RagTokenForGeneration(PreTrained):
def __init__(
self,
config=None,
question_encoder=None,
generator=None,
retriever=None,
**kw,
):
assert config is not None or (question_encoder is not None and generator is not None)
if config is None:
config = RagConfig.from_question_encoder_generator_configs(
question_encoder.config, generator.config, **kw
)
super().__init__(config)
self.rag = Model(
config=config,
question_encoder=question_encoder,
generator=generator,
retriever=retriever,
)
def marginalize(self, seq_logits, doc_scores, n_docs=None):
n_docs = n_docs if n_docs is not None else self.config.n_docs
# RAG-token marginalization
seq_logprobs = F.log_softmax(seq_logits, dim=-1).view(
seq_logits.shape[0] // n_docs, n_docs, -1, seq_logits.size(-1)
)
doc_logprobs = torch.log_softmax(doc_scores, dim=1)
log_prob_sum = seq_logprobs + doc_logprobs.unsqueeze(-1).unsqueeze(-1)
return torch.logsumexp(log_prob_sum, dim=1)
def forward(
self,
input_ids=None,
attention_mask=None,
encoder_outputs=None,
decoder_input_ids=None,
decoder_attention_mask=None,
caches=None,
context_input_ids=None,
context_attention_mask=None,
doc_scores=None,
y_cache=None,
output_attentions=None,
output_hidden_states=None,
output_retrieved=None,
do_marginalize=None,
reduce_loss=None,
labels=None,
n_docs=None,
**kw, # needs kw for generation
):
n_docs = n_docs if n_docs is not None else self.config.n_docs
do_marginalize = (
do_marginalize if do_marginalize is not None else self.config.do_marginalize
)
reduce_loss = reduce_loss if reduce_loss is not None else self.config.reduce_loss
if labels is not None:
if decoder_input_ids is None:
decoder_input_ids = labels
y_cache = False
outputs = self.rag(
input_ids=input_ids,
attention_mask=attention_mask,
encoder_outputs=encoder_outputs,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
context_input_ids=context_input_ids,
context_attention_mask=context_attention_mask,
doc_scores=doc_scores,
caches=caches,
y_cache=y_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
output_retrieved=output_retrieved,
n_docs=n_docs,
)
loss = None
logits = outputs.logits
if labels is not None:
assert decoder_input_ids is not None
loss = self.get_nll(
outputs.logits,
outputs.doc_scores,
labels,
reduce_loss=reduce_loss,
epsilon=self.config.label_smoothing,
n_docs=n_docs,
)
if do_marginalize:
logits = self.marginalize(logits, outputs.doc_scores, n_docs)
return RetrievAugLMMarginOutput(
loss=loss,
logits=logits,
doc_scores=outputs.doc_scores,
caches=outputs.caches,
context_input_ids=outputs.context_input_ids,
context_attention_mask=outputs.context_attention_mask,
retrieved_doc_embeds=outputs.retrieved_doc_embeds,
retrieved_doc_ids=outputs.retrieved_doc_ids,
question_encoder_last_hidden_state=outputs.question_encoder_last_hidden_state,
question_enc_hidden_states=outputs.question_enc_hidden_states,
question_enc_attentions=outputs.question_enc_attentions,
generator_enc_last_hidden_state=outputs.generator_enc_last_hidden_state,
generator_enc_hidden_states=outputs.generator_enc_hidden_states,
generator_enc_attentions=outputs.generator_enc_attentions,
generator_dec_hidden_states=outputs.generator_dec_hidden_states,
generator_dec_attentions=outputs.generator_dec_attentions,
generator_cross_attentions=outputs.generator_cross_attentions,
)
@torch.no_grad()
def generate(
self,
input_ids=None,
attention_mask=None,
context_input_ids=None,
context_attention_mask=None,
doc_scores=None,
max_length=None,
min_length=None,
early_stopping=None,
y_cache=None,
num_beams=None,
num_beam_groups=None,
diversity_penalty=None,
BOS=None,
PAD=None,
EOS=None,
length_penalty=None,
no_repeat_ngram_size=None,
encoder_no_repeat_ngram_size=None,
repetition_penalty=None,
bad_words_ids=None,
num_return_sequences=None,
decoder_start_token_id=None,
n_docs=None,
prefix_allowed_tokens_fn=None,
logits_processor=LogitsProcessorList(),
renormalize_logits=None,
stopping_criteria=StoppingCriteriaList(),
forced_bos_token_id=None,
forced_eos_token_id=None,
remove_invalid_values=None,
exponential_decay_length_penalty=None,
**model_kw,
):
n_docs = n_docs if n_docs is not None else self.config.n_docs
num_beams = num_beams if num_beams is not None else self.config.num_beams
num_beam_groups = (
num_beam_groups if num_beam_groups is not None else self.config.num_beam_groups
)
max_length = max_length if max_length is not None else self.config.max_length
num_return_sequences = (
num_return_sequences
if num_return_sequences is not None
else self.config.num_return_sequences
)
BOS = BOS if BOS is not None else self.config.generator.BOS
EOS = EOS if EOS is not None else self.config.generator.EOS
PAD = PAD if PAD is not None else self.config.generator.PAD
y_cache = y_cache if y_cache is not None else self.config.y_cache
decoder_start_token_id = (
decoder_start_token_id
if decoder_start_token_id is not None
else self.config.generator.decoder_start_token_id
)
remove_invalid_values = (
remove_invalid_values
if remove_invalid_values is not None
else self.config.remove_invalid_values
)
exponential_decay_length_penalty = (
exponential_decay_length_penalty
if exponential_decay_length_penalty is not None
else self.config.exponential_decay_length_penalty
)
# retrieve docs
if self.retriever is not None and context_input_ids is None:
question_hidden_states = self.question_encoder(
input_ids, attention_mask=attention_mask
)[0]
out = self.retriever(
input_ids,
question_hidden_states.cpu().detach().to(torch.float32).numpy(),
prefix=self.generator.config.prefix,
n_docs=n_docs,
return_tensors="pt",
)
context_input_ids, context_attention_mask, retrieved_doc_embeds = (
out["context_input_ids"],
out["context_attention_mask"],
out["retrieved_doc_embeds"],
)
# set to correct device
retrieved_doc_embeds = retrieved_doc_embeds.to(question_hidden_states)
context_input_ids = context_input_ids.to(input_ids)
context_attention_mask = context_attention_mask.to(input_ids)
# compute doc_scores
doc_scores = torch.bmm(
question_hidden_states.unsqueeze(1), retrieved_doc_embeds.transpose(1, 2)
).squeeze(1)
assert (context_input_ids.shape[0] % n_docs) == 0
# batch_size
batch_size = context_input_ids.shape[0] // n_docs
encoder = self.rag.generator.get_encoder()
encoder_outputs = encoder(
input_ids=context_input_ids, attention_mask=context_attention_mask, return_dict=True
)
input_ids = torch.full(
(batch_size * num_beams, 1),
decoder_start_token_id,
dtype=torch.long,
device=next(self.parameters()).device,
)
input_ids_seq_length = input_ids.shape[-1]
y = encoder_outputs["y"]
def extend_enc_output(tensor, num_beams=None):
# split into `batch_size`, `num_beams`, `num_docs`
tensor = tensor[None, None, :].reshape((batch_size, 1, n_docs) + tensor.shape[1:])
# repeat same last hidden states over `num_beams` dimension
tensor = tensor.expand((batch_size, num_beams, n_docs) + tensor.shape[3:])
# merge `batch_size`, `num_beams`, `num_docs` dims again
return tensor.reshape((batch_size * num_beams * n_docs,) + tensor.shape[3:])
# correctly extend y and attention mask
context_attention_mask = extend_enc_output(context_attention_mask, num_beams=num_beams)
encoder_outputs["y"] = extend_enc_output(y, num_beams=num_beams)
doc_scores = doc_scores.repeat_interleave(num_beams, dim=0)
# define start_len & additional parameters
model_kw["doc_scores"] = doc_scores
model_kw["encoder_outputs"] = encoder_outputs
model_kw["attention_mask"] = context_attention_mask
model_kw["n_docs"] = n_docs
pre_processor = self._get_logits_processor(
repetition_penalty=repetition_penalty,
no_repeat_ngram_size=no_repeat_ngram_size,
encoder_no_repeat_ngram_size=encoder_no_repeat_ngram_size,
input_ids_seq_length=input_ids_seq_length,
encoder_input_ids=context_input_ids,
bad_words_ids=bad_words_ids,
min_length=min_length,
max_length=max_length,
EOS=EOS,
forced_bos_token_id=forced_bos_token_id,
forced_eos_token_id=forced_eos_token_id,
prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
num_beams=num_beams,
num_beam_groups=num_beam_groups,
diversity_penalty=diversity_penalty,
remove_invalid_values=remove_invalid_values,
exponential_decay_length_penalty=exponential_decay_length_penalty,
logits_processor=logits_processor,
renormalize_logits=renormalize_logits,
)
if num_beams == 1:
if num_return_sequences > 1:
raise ValueError(
f"num_return_sequences has to be 1, but is {num_return_sequences} when doing greedy search."
)
return self.greedy_search(
input_ids,
logits_processor=pre_processor,
max_length=max_length,
PAD=PAD,
EOS=EOS,
**model_kw,
)
elif num_beams > 1:
length_penalty = (
length_penalty if length_penalty is not None else self.config.length_penalty
)
early_stopping = (
early_stopping if early_stopping is not None else self.config.early_stopping
)
if num_return_sequences > num_beams:
raise ValueError(
"`num_return_sequences` has to be smaller or equal to `num_beams`."
)
beam_scorer = BeamSearchScorer(
batch_size=batch_size,
num_beams=num_beams,
device=self.device,
length_penalty=length_penalty,
do_early_stopping=early_stopping,
num_beam_hyps_to_keep=num_return_sequences,
)
return self.beam_search(
input_ids,
beam_scorer,
logits_processor=pre_processor,
max_length=max_length,
PAD=PAD,
EOS=EOS,
**model_kw,
)
else:
raise ValueError(
f"`num_beams` has to be an integer strictly superior to 0 (≥ 1), but is {num_beams}"
)
def shift_tokens_right(self, input_ids, start_token_id=None):
if start_token_id is None:
start_token_id = self.config.decoder_start_token_id
shifted_input_ids = input_ids.new_zeros(input_ids.shape)
shifted_input_ids[:, 1:] = input_ids[:, :-1].clone()
shifted_input_ids[:, 0] = start_token_id
return shifted_input_ids
def get_nll(self, seq_logits, doc_scores, target, reduce_loss=False, epsilon=0.0, n_docs=None):
n_docs = n_docs if n_docs is not None else self.config.n_docs
# shift tokens left
target = torch.cat(
[
target[:, 1:],
target.new(target.shape[0], 1).fill_(self.config.generator.PAD),
],
1,
)
def _mask_pads(ll, smooth_obj):
pad_mask = target.eq(self.config.generator.PAD)
if pad_mask.any():
ll.masked_fill_(pad_mask, 0.0)
smooth_obj.masked_fill_(pad_mask, 0.0)
return ll.squeeze(-1), smooth_obj.squeeze(-1)
rag_logprobs = self.marginalize(seq_logits, doc_scores, n_docs)
target = target.unsqueeze(-1)
assert target.dim() == rag_logprobs.dim()
ll = rag_logprobs.gather(dim=-1, index=target)
smooth_obj = rag_logprobs.sum(dim=-1, keepdim=True) # total sum of all (normalised) logits
ll, smooth_obj = _mask_pads(ll, smooth_obj)
ll = ll.sum(1) # sum over tokens
smooth_obj = smooth_obj.sum(1)
nll_loss = -ll
smooth_loss = -smooth_obj
if reduce_loss:
nll_loss = nll_loss.sum()
smooth_loss = smooth_loss.sum()
eps_i = epsilon / rag_logprobs.size(-1)
loss = (1.0 - epsilon) * nll_loss + eps_i * smooth_loss
return loss
|
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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"]}
|
33,554
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/convert/albert.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.albert import PreTrained
from ...models.albert import ForPreTraining
logging.set_verbosity_info()
log = logging.get_logger(__name__)
_SKIP = [
"adam_v",
"adam_m",
"AdamWeightDecayOptimizer",
"AdamWeightDecayOptimizer_1",
"global_step",
]
def load_src_weights(model, src_path):
src_path = abspath(src_path)
log.info(f"Loading from: {src_path}")
xs = tf.train.list_variables(src_path)
assert len(xs) > 0
ns, ws = _load_weights(xs, src_path)
for n, w in zip(ns, ws):
n = n.replace("module/", "")
n = n.replace("ffn_1", "ffn")
n = n.replace("bert/", "albert/")
n = n.replace("attention_1", "attention")
n = n.replace("transform/", "")
n = n.replace("LayerNorm_1", "full_layer_layer_norm")
n = n.replace("LayerNorm", "attention/LayerNorm")
n = n.replace("transformer/", "")
n = n.replace("intermediate/dense/", "")
n = n.replace("ffn/intermediate/output/dense/", "ffn_output/")
n = n.replace("/output/", "/")
n = n.replace("/self/", "/")
n = n.replace("pooler/dense", "pooler")
n = n.replace("cls/predictions", "predictions")
n = n.replace("predictions/attention", "predictions")
n = n.replace("embeddings/attention", "embeddings")
n = n.replace("inner_group_", "albert_layers/")
n = n.replace("group_", "albert_layer_groups/")
if len(n.split("/")) == 1 and ("output_bias" in n or "output_weights" in n):
n = "classifier/" + n
if "seq_relationship" in n:
n = n.replace("seq_relationship/output_", "sop_classifier/classifier/")
n = n.replace("weights", "weight")
ss = n.split("/")
if any(s in _SKIP for s in ss):
log.info(f"Skipping {'/'.join(ss)}")
continue
p = model
for s in ss:
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:
try:
p = getattr(p, scopes[0])
except AttributeError:
log.info(f"Skipping {'/'.join(ss)}")
continue
if len(scopes) >= 2:
p = p[int(scopes[1])]
if s[-11:] == "_embeddings":
p = getattr(p, "weight")
elif s == "kernel":
w = np.transpose(w)
assert p.shape == w.shape
p.data = torch.from_numpy(w)
return model
def _load_weights(xs, src_path):
ns = []
ws = {}
for n, shape in xs:
log.info(f"Loading TF weight {n} with shape {shape}")
ns.append(n)
ws[n] = tf.train.load_variable(src_path, n)
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 = ForPreTraining(cfg)
load_src_weights(m, src_path)
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)
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"], 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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"], "/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"]}
|
33,555
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/run/gen.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.
# =============================================================================
# conditional text generation with (GPT/GPT-2/CTRL/Transformer-XL/XLNet)
import argparse
import logging
import numpy as np
import torch
from transformers import (
CTRLLMHeadModel,
CTRLTokenizer,
GPT2LMHeadModel,
GPT2Tokenizer,
OpenAIGPTLMHeadModel,
OpenAIGPTTokenizer,
TransfoXLLMHeadModel,
TransfoXLTokenizer,
XLMTokenizer,
XLMWithLMHeadModel,
XLNetLMHeadModel,
XLNetTokenizer,
)
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
log = logging.getLogger(__name__)
MAX_LENGTH = int(10000)
MODEL_CLASSES = {
"gpt2": (GPT2LMHeadModel, GPT2Tokenizer),
"ctrl": (CTRLLMHeadModel, CTRLTokenizer),
"openai-gpt": (OpenAIGPTLMHeadModel, OpenAIGPTTokenizer),
"xlnet": (XLNetLMHeadModel, XLNetTokenizer),
"transfo-xl": (TransfoXLLMHeadModel, TransfoXLTokenizer),
"xlm": (XLMWithLMHeadModel, XLMTokenizer),
}
PREFIX = """In 1991, the remains of Russian Tsar Nicholas II and his family
(except for Alexei and Maria) are discovered.
The voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the
remainder of the story. 1883 Western Siberia,
a young Grigori Rasputin is asked by his father and a group of men to perform magic.
Rasputin has a vision and denounces one of the men as a horse thief. Although his
father initially slaps him for making such an accusation, Rasputin watches as the
man is chased outside and beaten. Twenty years later, Rasputin sees a vision of
the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,
with people, even a bishop, begging for his blessing. <eod> </s> <eos>"""
def adjust_length_to_model(x, lim):
if x < 0 and lim > 0:
x = lim
elif 0 < lim < x:
x = lim
elif x < 0:
x = MAX_LENGTH
return x
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--model_type", default=None, type=str, required=True)
parser.add_argument("--prompt", type=str, default="")
parser.add_argument("--length", type=int, default=20)
parser.add_argument("--stop_token", type=str, default=None)
parser.add_argument("--temperature", type=float, default=1.0)
parser.add_argument("--repetition_penalty", type=float, default=1.0)
parser.add_argument("--k", type=int, default=0)
parser.add_argument("--p", type=float, default=0.9)
parser.add_argument("--prefix", type=str, default="")
parser.add_argument("--padding_text", type=str, default="")
parser.add_argument("--xlm_language", type=str, default="")
parser.add_argument("--seed", type=int, default=42)
parser.add_argument("--no_cuda", action="store_true")
parser.add_argument("--num_return_sequences", type=int, default=1)
parser.add_argument("--fp16", action="store_true")
ps = parser.parse_args()
ps.device = torch.device("cuda" if torch.cuda.is_available() and not ps.no_cuda else "cpu")
ps.n_gpu = 0 if ps.no_cuda else torch.cuda.device_count()
log.warning(f"device: {ps.device}, n_gpu: {ps.n_gpu}, 16-bits training: {ps.fp16}")
def set_seed(ps):
np.random.seed(ps.seed)
torch.manual_seed(ps.seed)
if ps.n_gpu > 0:
torch.cuda.manual_seed_all(ps.seed)
def prepare_ctrl_input(ps, _, tokenizer, prompt):
if ps.temperature > 0.7:
log.info("CTRL typically works better with lower temperatures (and lower top_k).")
y = tokenizer.encode(prompt, add_special_tokens=False)
if not any(y[0] == x for x in tokenizer.control_codes.values()):
log.info(
"WARNING! You are not starting your generation from a control code so you won't get good results"
)
return prompt
def prepare_xlm_input(ps, model, tokenizer, prompt):
# kw = {"language": None, "MSK_TOK": None}
use_lang_emb = hasattr(model.config, "use_lang_emb") and model.config.use_lang_emb
if hasattr(model.config, "lang2id") and use_lang_emb:
ls = model.config.lang2id.keys()
if ps.xlm_language in ls:
l = ps.xlm_language
else:
l = None
while l not in ls:
l = input("Using XLM. Select language in " + str(list(ls)) + " >>> ")
model.config.LANG = model.config.lang2id[l]
# kw["language"] = tokenizer.lang2id[l]
return prompt
def prepare_xlnet_input(ps, _, tokenizer, prompt):
x = ps.prefix if ps.prefix else ps.padding_text if ps.padding_text else PREFIX
prompt = x + prompt
return prompt
def prepare_transfoxl_input(ps, _, tokenizer, prompt):
x = ps.prefix if ps.prefix else ps.padding_text if ps.padding_text else PREFIX
prompt = x + prompt
return prompt
PREPROCESSING_FUNCTIONS = {
"ctrl": prepare_ctrl_input,
"xlm": prepare_xlm_input,
"xlnet": prepare_xlnet_input,
"transfo-xl": prepare_transfoxl_input,
}
set_seed(ps)
try:
ps.model_type = ps.model_type.lower()
model_class, tokenizer_class = MODEL_CLASSES[ps.model_type]
except KeyError:
raise KeyError("Model {} is not supported")
tokenizer = tokenizer_class.from_pretrained(ps.model_name)
model = model_class.from_pretrained(ps.model_name)
model.to(ps.device)
if ps.fp16:
model.half()
ps.length = adjust_length_to_model(ps.length, lim=model.config.n_pos)
log.info(ps)
x = ps.prompt if ps.prompt else input("Model prompt >>> ")
if ps.model_type in PREPROCESSING_FUNCTIONS.keys():
prep = PREPROCESSING_FUNCTIONS.get(ps.model_type)
y = prep(ps, model, tokenizer, x)
if model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
kws = {"add_space_before_punct_symbol": True}
else:
kws = {}
prompt = tokenizer.encode(y, add_special_tokens=False, return_tensors="pt", **kws)
else:
prefix = ps.prefix if ps.prefix else ps.padding_text
prompt = tokenizer.encode(prefix + x, add_special_tokens=False, return_tensors="pt")
prompt = prompt.to(ps.device)
if prompt.size()[-1] == 0:
ins = None
else:
ins = prompt
out = model.generate(
input_ids=ins,
max_len=ps.length + len(prompt[0]),
temperature=ps.temperature,
top_k=ps.k,
top_p=ps.p,
repetition_penalty=ps.repetition_penalty,
do_sample=True,
num_return_sequences=ps.num_return_sequences,
)
if len(out.shape) > 2:
out.squeeze_()
ys = []
for i, x in enumerate(out):
print(f"=== GENERATED SEQUENCE {i + 1} ===")
x = x.tolist()
y = tokenizer.decode(x, clean_up_tokenization_spaces=True)
y = y[: y.find(ps.stop_token) if ps.stop_token else None]
y = x + y[len(tokenizer.decode(prompt[0], clean_up_tokenization_spaces=True)) :]
ys.append(y)
print(y)
return ys
if __name__ == "__main__":
main()
"""
python gen.py \
--model_type=gpt2 \
--model_name=gpt2
"""
|
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|
33,556
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/run/image.py
|
from dataclasses import dataclass, field
import datasets
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
CenterCrop,
Compose,
Normalize,
RandomHorizontalFlip,
RandomResizedCrop,
Resize,
ToTensor,
)
from transformers import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
AutoConfig,
AutoFeatureExtractor,
AutoModelForImageClassification,
Trainer,
)
MODEL_CONFIG_CLASSES = list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
def pil_loader(path):
with open(path, "rb") as f:
im = Image.open(f)
return im.convert("RGB")
add_argument("--dataset_name", type=str, default="nateraw/image-folder")
@dataclass
class DataTrainingArguments:
train_dir = field(default=None, metadata={"help": "A folder containing the training data."})
validation_dir = field(
default=None, metadata={"help": "A folder containing the validation data."}
)
train_val_split = field(
default=0.15, metadata={"help": "Percent to split off of train for validation."}
)
max_train_samples = field(
default=None,
metadata={
"help": "For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
},
)
max_eval_samples = field(
default=None,
metadata={
"help": "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
},
)
def __post_init__(self):
data_files = dict()
if self.train_dir is not None:
data_files["train"] = self.train_dir
if self.validation_dir is not None:
data_files["val"] = self.validation_dir
self.data_files = data_files if data_files else None
add_argument("--model_name", type=str, default="google/vit-base-patch16-224-in21k", required=True)
def collate_fn(examples):
pixel_values = torch.stack([example["pixel_values"] for example in examples])
labels = torch.tensor([example["labels"] for example in examples])
return {"pixel_values": pixel_values, "labels": labels}
def main():
ds = load_dataset(
data_args.dataset_name,
data_args.dataset_config,
data_files=data_args.data_files,
cache_dir=model_args.cache_dir,
task="image-classification",
)
# If we don't have a validation split, split off a percentage of train as validation.
data_args.train_val_split = None if "validation" in ds.keys() else data_args.train_val_split
if isinstance(data_args.train_val_split, float) and data_args.train_val_split > 0.0:
split = ds["train"].train_test_split(data_args.train_val_split)
ds["train"] = split["train"]
ds["validation"] = split["test"]
# Prepare label mappings.
# We'll include these in the model's config to get human readable labels in the Inference API.
labels = ds["train"].features["labels"].names
label2id, id2label = dict(), dict()
for i, label in enumerate(labels):
label2id[label] = str(i)
id2label[str(i)] = label
# Load the accuracy metric from the datasets package
metric = datasets.load_metric("accuracy")
# Define our 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):
"""Computes accuracy on a batch of predictions"""
return metric.compute(predictions=np.argmax(p.predictions, axis=1), references=p.label_ids)
config = AutoConfig.from_pretrained(
model_args.config_name or model_args.model_name,
n_labels=len(labels),
label2id=label2id,
id2label=id2label,
finetune="image-classification",
cache_dir=model_args.cache_dir,
revision=model_args.model_version,
use_auth_token=True if model_args.use_auth_token else None,
)
model = AutoModelForImageClassification.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,
)
feature_extractor = AutoFeatureExtractor.from_pretrained(
model_args.feature_extractor or model_args.model_name,
cache_dir=model_args.cache_dir,
revision=model_args.model_version,
use_auth_token=True if model_args.use_auth_token else None,
)
# Define torchvision transforms to be applied to each image.
normalize = Normalize(mean=feature_extractor.image_mean, std=feature_extractor.image_std)
_train_transforms = Compose(
[
RandomResizedCrop(feature_extractor.size),
RandomHorizontalFlip(),
ToTensor(),
normalize,
]
)
_val_transforms = Compose(
[
Resize(feature_extractor.size),
CenterCrop(feature_extractor.size),
ToTensor(),
normalize,
]
)
def train_transforms(example_batch):
"""Apply _train_transforms across a batch."""
example_batch["pixel_values"] = [
_train_transforms(pil_img.convert("RGB")) for pil_img in example_batch["image"]
]
return example_batch
def val_transforms(example_batch):
"""Apply _val_transforms across a batch."""
example_batch["pixel_values"] = [
_val_transforms(pil_img.convert("RGB")) for pil_img in example_batch["image"]
]
return example_batch
if training_args.do_train:
if "train" not in ds:
raise ValueError("--do_train requires a train dataset")
if data_args.max_train_samples is not None:
ds["train"] = (
ds["train"]
.shuffle(seed=training_args.seed)
.select(range(data_args.max_train_samples))
)
# Set the training transforms
ds["train"].set_transform(train_transforms)
if training_args.do_eval:
if "validation" not in ds:
raise ValueError("--do_eval requires a validation dataset")
if data_args.max_eval_samples is not None:
ds["validation"] = (
ds["validation"]
.shuffle(seed=training_args.seed)
.select(range(data_args.max_eval_samples))
)
# Set the validation transforms
ds["validation"].set_transform(val_transforms)
# Initalize our trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=ds["train"] if training_args.do_train else None,
eval_dataset=ds["validation"] if training_args.do_eval else None,
compute_metrics=compute_metrics,
tokenizer=feature_extractor,
data_collator=collate_fn,
)
# 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)
trainer.save_model()
trainer.log_metrics("train", train_result.metrics)
trainer.save_metrics("train", train_result.metrics)
trainer.save_state()
# Evaluation
if training_args.do_eval:
metrics = trainer.evaluate()
trainer.log_metrics("eval", metrics)
trainer.save_metrics("eval", metrics)
# Write model card and (optionally) push to hub
kw = {
"finetuned_from": model_args.model_name,
"tasks": "image-classification",
"dataset": data_args.dataset_name,
"tags": ["image-classification"],
}
if training_args.push_to_hub:
trainer.push_to_hub(**kw)
else:
trainer.create_model_card(**kw)
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"]}
|
33,557
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/convert/decision_transfo.py
|
# Copied from transformers.models.gpt2.modeling_gpt2.load_tf_weights_in_gpt2
def load_tf_weights_in_gpt2(model, config, gpt2_checkpoint_path):
tf_path = os.path.abspath(gpt2_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)
arrays.append(array.squeeze())
for name, array in zip(names, arrays):
name = name[6:] # skip "model/"
name = name.split("/")
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] == "w" or scope_names[0] == "g":
pointer = getattr(pointer, "weight")
elif scope_names[0] == "b":
pointer = getattr(pointer, "bias")
elif scope_names[0] == "wpe" or scope_names[0] == "wte":
pointer = getattr(pointer, scope_names[0])
pointer = getattr(pointer, "weight")
else:
pointer = getattr(pointer, scope_names[0])
if len(scope_names) >= 2:
num = int(scope_names[1])
pointer = pointer[num]
try:
assert (
pointer.shape == array.shape
), 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
|
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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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|
33,558
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/tokens/fast/gpt2.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
from ..gpt2 import Tokenizer as GPT2
VOCAB_FS = {
"vocab_file": "vocab.json",
"merges_file": "merges.txt",
"tokenizer_file": "tokenizer.json",
}
VOCAB_MAP = {
"vocab_file": {
"gpt2": "https://huggingface.co/gpt2/resolve/main/vocab.json",
"gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/vocab.json",
"gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/vocab.json",
"gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/vocab.json",
"distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/vocab.json",
},
"merges_file": {
"gpt2": "https://huggingface.co/gpt2/resolve/main/merges.txt",
"gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/merges.txt",
"gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/merges.txt",
"gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/merges.txt",
"distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/merges.txt",
},
"tokenizer_file": {
"gpt2": "https://huggingface.co/gpt2/resolve/main/tokenizer.json",
"gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json",
"gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/tokenizer.json",
"gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json",
"distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/tokenizer.json",
},
}
INPUT_CAPS = {
"gpt2": 1024,
"gpt2-medium": 1024,
"gpt2-large": 1024,
"gpt2-xl": 1024,
"distilgpt2": 1024,
}
class Tokenizer(PreTrainedTokenizerFast):
vocab_fs = VOCAB_FS
vocab_map = VOCAB_MAP
input_caps = INPUT_CAPS
model_input_names = ["input_ids", "mask"]
slow_tokenizer_class = GPT2
def __init__(
self,
vocab_file=None,
merges_file=None,
tokenizer_file=None,
unk="<|endoftext|>",
bos="<|endoftext|>",
eos="<|endoftext|>",
add_prefix_space=False,
**kw,
):
super().__init__(
vocab_file,
merges_file,
tokenizer_file=tokenizer_file,
unk=unk,
bos=bos,
eos=eos,
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 _batch_encode_plus(self, *args, **kw):
is_split_into_words = kw.get("is_split_into_words", False)
assert self.add_prefix_space or not is_split_into_words
return super()._batch_encode_plus(*args, **kw)
def _encode_plus(self, *args, **kw):
is_split_into_words = kw.get("is_split_into_words", False)
assert self.add_prefix_space or not is_split_into_words
return super()._encode_plus(*args, **kw)
def save_vocabulary(self, dir, pre=None):
return tuple(self._tokenizer.model.save(dir, name=pre))
def _build_conversation_input_ids(self, conversation):
ys = []
for is_user, text in conversation.iter_texts():
ys.extend(self.encode(text, add_special_tokens=False) + [self.EOS])
if len(ys) > self.model_max_length:
ys = ys[-self.model_max_length :]
return ys
|
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