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stringlengths 2
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stringlengths 13
225
| code
stringlengths 0
18.3M
| file_length
int64 0
18.3M
| avg_line_length
float64 0
1.36M
| max_line_length
int64 0
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| extension_type
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|---|---|---|---|---|---|---|
CIF-HieraDist
|
CIF-HieraDist-main/examples/speech_recognition/new/decoders/base_decoder.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import itertools as it
from typing import Any, Dict, List
import torch
from fairseq.data.dictionary import Dictionary
from fairseq.models.fairseq_model import FairseqModel
class BaseDecoder:
def __init__(self, tgt_dict: Dictionary) -> None:
self.tgt_dict = tgt_dict
self.vocab_size = len(tgt_dict)
self.blank = (
tgt_dict.index("<ctc_blank>")
if "<ctc_blank>" in tgt_dict.indices
else tgt_dict.bos()
)
if "<sep>" in tgt_dict.indices:
self.silence = tgt_dict.index("<sep>")
elif "|" in tgt_dict.indices:
self.silence = tgt_dict.index("|")
else:
self.silence = tgt_dict.eos()
def generate(
self, models: List[FairseqModel], sample: Dict[str, Any], **unused
) -> List[List[Dict[str, torch.LongTensor]]]:
encoder_input = {
k: v for k, v in sample["net_input"].items() if k != "prev_output_tokens"
}
emissions = self.get_emissions(models, encoder_input)
return self.decode(emissions)
def get_emissions(
self,
models: List[FairseqModel],
encoder_input: Dict[str, Any],
) -> torch.FloatTensor:
model = models[0]
encoder_out = model(**encoder_input)
if hasattr(model, "get_logits"):
emissions = model.get_logits(encoder_out)
else:
emissions = model.get_normalized_probs(encoder_out, log_probs=True)
return emissions.transpose(0, 1).float().cpu().contiguous()
def get_tokens(self, idxs: torch.IntTensor) -> torch.LongTensor:
idxs = (g[0] for g in it.groupby(idxs))
idxs = filter(lambda x: x != self.blank, idxs)
return torch.LongTensor(list(idxs))
def decode(
self,
emissions: torch.FloatTensor,
) -> List[List[Dict[str, torch.LongTensor]]]:
raise NotImplementedError
| 2,093
| 32.238095
| 85
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/examples/speech_recognition/new/decoders/viterbi_decoder.py
|
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
from typing import List, Dict
from .base_decoder import BaseDecoder
class ViterbiDecoder(BaseDecoder):
def decode(
self,
emissions: torch.FloatTensor,
) -> List[List[Dict[str, torch.LongTensor]]]:
def get_pred(e):
toks = e.argmax(dim=-1).unique_consecutive()
return toks[toks != self.blank]
return [[{"tokens": get_pred(x), "score": 0}] for x in emissions]
| 641
| 24.68
| 73
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/examples/speech_recognition/new/decoders/__init__.py
| 0
| 0
| 0
|
py
|
|
CIF-HieraDist
|
CIF-HieraDist-main/examples/speech_recognition/new/decoders/flashlight_decoder.py
|
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import gc
import os.path as osp
import warnings
from collections import deque, namedtuple
from typing import Any, Dict, Tuple
import numpy as np
import torch
from fairseq import tasks
from fairseq.data.dictionary import Dictionary
from fairseq.dataclass.utils import convert_namespace_to_omegaconf
from fairseq.models.fairseq_model import FairseqModel
from fairseq.utils import apply_to_sample
from omegaconf import open_dict, OmegaConf
from typing import List
from .decoder_config import FlashlightDecoderConfig
from .base_decoder import BaseDecoder
try:
from flashlight.lib.text.decoder import (
LM,
CriterionType,
DecodeResult,
KenLM,
LexiconDecoder,
LexiconDecoderOptions,
LexiconFreeDecoder,
LexiconFreeDecoderOptions,
LMState,
SmearingMode,
Trie,
)
from flashlight.lib.text.dictionary import create_word_dict, load_words
except ImportError:
warnings.warn(
"flashlight python bindings are required to use this functionality. "
"Please install from "
"https://github.com/facebookresearch/flashlight/tree/master/bindings/python"
)
LM = object
LMState = object
class KenLMDecoder(BaseDecoder):
def __init__(self, cfg: FlashlightDecoderConfig, tgt_dict: Dictionary) -> None:
super().__init__(tgt_dict)
self.nbest = cfg.nbest
self.unitlm = cfg.unitlm
if cfg.lexicon:
self.lexicon = load_words(cfg.lexicon)
self.word_dict = create_word_dict(self.lexicon)
self.unk_word = self.word_dict.get_index("<unk>")
self.lm = KenLM(cfg.lmpath, self.word_dict)
self.trie = Trie(self.vocab_size, self.silence)
start_state = self.lm.start(False)
for word, spellings in self.lexicon.items():
word_idx = self.word_dict.get_index(word)
_, score = self.lm.score(start_state, word_idx)
for spelling in spellings:
spelling_idxs = [tgt_dict.index(token) for token in spelling]
assert (
tgt_dict.unk() not in spelling_idxs
), f"{word} {spelling} {spelling_idxs}"
self.trie.insert(spelling_idxs, word_idx, score)
self.trie.smear(SmearingMode.MAX)
self.decoder_opts = LexiconDecoderOptions(
beam_size=cfg.beam,
beam_size_token=cfg.beamsizetoken or len(tgt_dict),
beam_threshold=cfg.beamthreshold,
lm_weight=cfg.lmweight,
word_score=cfg.wordscore,
unk_score=cfg.unkweight,
sil_score=cfg.silweight,
log_add=False,
criterion_type=CriterionType.CTC,
)
self.decoder = LexiconDecoder(
self.decoder_opts,
self.trie,
self.lm,
self.silence,
self.blank,
self.unk_word,
[],
self.unitlm,
)
else:
assert self.unitlm, "Lexicon-free decoding requires unit LM"
d = {w: [[w]] for w in tgt_dict.symbols}
self.word_dict = create_word_dict(d)
self.lm = KenLM(cfg.lmpath, self.word_dict)
self.decoder_opts = LexiconFreeDecoderOptions(
beam_size=cfg.beam,
beam_size_token=cfg.beamsizetoken or len(tgt_dict),
beam_threshold=cfg.beamthreshold,
lm_weight=cfg.lmweight,
sil_score=cfg.silweight,
log_add=False,
criterion_type=CriterionType.CTC,
)
self.decoder = LexiconFreeDecoder(
self.decoder_opts, self.lm, self.silence, self.blank, []
)
def get_timesteps(self, token_idxs: List[int]) -> List[int]:
"""Returns frame numbers corresponding to every non-blank token.
Parameters
----------
token_idxs : List[int]
IDs of decoded tokens.
Returns
-------
List[int]
Frame numbers corresponding to every non-blank token.
"""
timesteps = []
for i, token_idx in enumerate(token_idxs):
if token_idx == self.blank:
continue
if i == 0 or token_idx != token_idxs[i - 1]:
timesteps.append(i)
return timesteps
def decode(
self,
emissions: torch.FloatTensor,
) -> List[List[Dict[str, torch.LongTensor]]]:
B, T, N = emissions.size()
hypos = []
for b in range(B):
emissions_ptr = emissions.data_ptr() + 4 * b * emissions.stride(0)
results = self.decoder.decode(emissions_ptr, T, N)
nbest_results = results[: self.nbest]
hypos.append(
[
{
"tokens": self.get_tokens(result.tokens),
"score": result.score,
"timesteps": self.get_timesteps(result.tokens),
"words": [
self.word_dict.get_entry(x) for x in result.words if x >= 0
],
}
for result in nbest_results
]
)
return hypos
FairseqLMState = namedtuple(
"FairseqLMState",
[
"prefix",
"incremental_state",
"probs",
],
)
class FairseqLM(LM):
def __init__(self, dictionary: Dictionary, model: FairseqModel) -> None:
super().__init__()
self.dictionary = dictionary
self.model = model
self.unk = self.dictionary.unk()
self.save_incremental = False # this currently does not work properly
self.max_cache = 20_000
if torch.cuda.is_available():
model.cuda()
model.eval()
model.make_generation_fast_()
self.states = {}
self.stateq = deque()
def start(self, start_with_nothing: bool) -> LMState:
state = LMState()
prefix = torch.LongTensor([[self.dictionary.eos()]])
incremental_state = {} if self.save_incremental else None
with torch.no_grad():
res = self.model(prefix.cuda(), incremental_state=incremental_state)
probs = self.model.get_normalized_probs(res, log_probs=True, sample=None)
if incremental_state is not None:
incremental_state = apply_to_sample(lambda x: x.cpu(), incremental_state)
self.states[state] = FairseqLMState(
prefix.numpy(), incremental_state, probs[0, -1].cpu().numpy()
)
self.stateq.append(state)
return state
def score(
self,
state: LMState,
token_index: int,
no_cache: bool = False,
) -> Tuple[LMState, int]:
"""
Evaluate language model based on the current lm state and new word
Parameters:
-----------
state: current lm state
token_index: index of the word
(can be lexicon index then you should store inside LM the
mapping between indices of lexicon and lm, or lm index of a word)
Returns:
--------
(LMState, float): pair of (new state, score for the current word)
"""
curr_state = self.states[state]
def trim_cache(targ_size: int) -> None:
while len(self.stateq) > targ_size:
rem_k = self.stateq.popleft()
rem_st = self.states[rem_k]
rem_st = FairseqLMState(rem_st.prefix, None, None)
self.states[rem_k] = rem_st
if curr_state.probs is None:
new_incremental_state = (
curr_state.incremental_state.copy()
if curr_state.incremental_state is not None
else None
)
with torch.no_grad():
if new_incremental_state is not None:
new_incremental_state = apply_to_sample(
lambda x: x.cuda(), new_incremental_state
)
elif self.save_incremental:
new_incremental_state = {}
res = self.model(
torch.from_numpy(curr_state.prefix).cuda(),
incremental_state=new_incremental_state,
)
probs = self.model.get_normalized_probs(
res, log_probs=True, sample=None
)
if new_incremental_state is not None:
new_incremental_state = apply_to_sample(
lambda x: x.cpu(), new_incremental_state
)
curr_state = FairseqLMState(
curr_state.prefix, new_incremental_state, probs[0, -1].cpu().numpy()
)
if not no_cache:
self.states[state] = curr_state
self.stateq.append(state)
score = curr_state.probs[token_index].item()
trim_cache(self.max_cache)
outstate = state.child(token_index)
if outstate not in self.states and not no_cache:
prefix = np.concatenate(
[curr_state.prefix, torch.LongTensor([[token_index]])], -1
)
incr_state = curr_state.incremental_state
self.states[outstate] = FairseqLMState(prefix, incr_state, None)
if token_index == self.unk:
score = float("-inf")
return outstate, score
def finish(self, state: LMState) -> Tuple[LMState, int]:
"""
Evaluate eos for language model based on the current lm state
Returns:
--------
(LMState, float): pair of (new state, score for the current word)
"""
return self.score(state, self.dictionary.eos())
def empty_cache(self) -> None:
self.states = {}
self.stateq = deque()
gc.collect()
class FairseqLMDecoder(BaseDecoder):
def __init__(self, cfg: FlashlightDecoderConfig, tgt_dict: Dictionary) -> None:
super().__init__(tgt_dict)
self.nbest = cfg.nbest
self.unitlm = cfg.unitlm
self.lexicon = load_words(cfg.lexicon) if cfg.lexicon else None
self.idx_to_wrd = {}
checkpoint = torch.load(cfg.lmpath, map_location="cpu")
if "cfg" in checkpoint and checkpoint["cfg"] is not None:
lm_args = checkpoint["cfg"]
else:
lm_args = convert_namespace_to_omegaconf(checkpoint["args"])
if not OmegaConf.is_dict(lm_args):
lm_args = OmegaConf.create(lm_args)
with open_dict(lm_args.task):
lm_args.task.data = osp.dirname(cfg.lmpath)
task = tasks.setup_task(lm_args.task)
model = task.build_model(lm_args.model)
model.load_state_dict(checkpoint["model"], strict=False)
self.trie = Trie(self.vocab_size, self.silence)
self.word_dict = task.dictionary
self.unk_word = self.word_dict.unk()
self.lm = FairseqLM(self.word_dict, model)
if self.lexicon:
start_state = self.lm.start(False)
for i, (word, spellings) in enumerate(self.lexicon.items()):
if self.unitlm:
word_idx = i
self.idx_to_wrd[i] = word
score = 0
else:
word_idx = self.word_dict.index(word)
_, score = self.lm.score(start_state, word_idx, no_cache=True)
for spelling in spellings:
spelling_idxs = [tgt_dict.index(token) for token in spelling]
assert (
tgt_dict.unk() not in spelling_idxs
), f"{spelling} {spelling_idxs}"
self.trie.insert(spelling_idxs, word_idx, score)
self.trie.smear(SmearingMode.MAX)
self.decoder_opts = LexiconDecoderOptions(
beam_size=cfg.beam,
beam_size_token=cfg.beamsizetoken or len(tgt_dict),
beam_threshold=cfg.beamthreshold,
lm_weight=cfg.lmweight,
word_score=cfg.wordscore,
unk_score=cfg.unkweight,
sil_score=cfg.silweight,
log_add=False,
criterion_type=CriterionType.CTC,
)
self.decoder = LexiconDecoder(
self.decoder_opts,
self.trie,
self.lm,
self.silence,
self.blank,
self.unk_word,
[],
self.unitlm,
)
else:
assert self.unitlm, "Lexicon-free decoding requires unit LM"
d = {w: [[w]] for w in tgt_dict.symbols}
self.word_dict = create_word_dict(d)
self.lm = KenLM(cfg.lmpath, self.word_dict)
self.decoder_opts = LexiconFreeDecoderOptions(
beam_size=cfg.beam,
beam_size_token=cfg.beamsizetoken or len(tgt_dict),
beam_threshold=cfg.beamthreshold,
lm_weight=cfg.lmweight,
sil_score=cfg.silweight,
log_add=False,
criterion_type=CriterionType.CTC,
)
self.decoder = LexiconFreeDecoder(
self.decoder_opts, self.lm, self.silence, self.blank, []
)
def decode(
self,
emissions: torch.FloatTensor,
) -> List[List[Dict[str, torch.LongTensor]]]:
B, T, N = emissions.size()
hypos = []
def make_hypo(result: DecodeResult) -> Dict[str, Any]:
hypo = {
"tokens": self.get_tokens(result.tokens),
"score": result.score,
}
if self.lexicon:
hypo["words"] = [
self.idx_to_wrd[x] if self.unitlm else self.word_dict[x]
for x in result.words
if x >= 0
]
return hypo
for b in range(B):
emissions_ptr = emissions.data_ptr() + 4 * b * emissions.stride(0)
results = self.decoder.decode(emissions_ptr, T, N)
nbest_results = results[: self.nbest]
hypos.append([make_hypo(result) for result in nbest_results])
self.lm.empty_cache()
return hypos
| 14,748
| 33.141204
| 88
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/examples/speech_recognition/kaldi/kaldi_initializer.py
|
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from dataclasses import dataclass
import hydra
from hydra.core.config_store import ConfigStore
import logging
from omegaconf import MISSING, OmegaConf
import os
import os.path as osp
from pathlib import Path
import subprocess
from typing import Optional
from fairseq.data.dictionary import Dictionary
from fairseq.dataclass import FairseqDataclass
script_dir = Path(__file__).resolve().parent
config_path = script_dir / "config"
logger = logging.getLogger(__name__)
@dataclass
class KaldiInitializerConfig(FairseqDataclass):
data_dir: str = MISSING
fst_dir: Optional[str] = None
in_labels: str = MISSING
out_labels: Optional[str] = None
wav2letter_lexicon: Optional[str] = None
lm_arpa: str = MISSING
kaldi_root: str = MISSING
blank_symbol: str = "<s>"
silence_symbol: Optional[str] = None
def create_units(fst_dir: Path, in_labels: str, vocab: Dictionary) -> Path:
in_units_file = fst_dir / f"kaldi_dict.{in_labels}.txt"
if not in_units_file.exists():
logger.info(f"Creating {in_units_file}")
with open(in_units_file, "w") as f:
print("<eps> 0", file=f)
i = 1
for symb in vocab.symbols[vocab.nspecial :]:
if not symb.startswith("madeupword"):
print(f"{symb} {i}", file=f)
i += 1
return in_units_file
def create_lexicon(
cfg: KaldiInitializerConfig,
fst_dir: Path,
unique_label: str,
in_units_file: Path,
out_words_file: Path,
) -> (Path, Path):
disambig_in_units_file = fst_dir / f"kaldi_dict.{cfg.in_labels}_disambig.txt"
lexicon_file = fst_dir / f"kaldi_lexicon.{unique_label}.txt"
disambig_lexicon_file = fst_dir / f"kaldi_lexicon.{unique_label}_disambig.txt"
if (
not lexicon_file.exists()
or not disambig_lexicon_file.exists()
or not disambig_in_units_file.exists()
):
logger.info(f"Creating {lexicon_file} (in units file: {in_units_file})")
assert cfg.wav2letter_lexicon is not None or cfg.in_labels == cfg.out_labels
if cfg.wav2letter_lexicon is not None:
lm_words = set()
with open(out_words_file, "r") as lm_dict_f:
for line in lm_dict_f:
lm_words.add(line.split()[0])
num_skipped = 0
total = 0
with open(cfg.wav2letter_lexicon, "r") as w2l_lex_f, open(
lexicon_file, "w"
) as out_f:
for line in w2l_lex_f:
items = line.rstrip().split("\t")
assert len(items) == 2, items
if items[0] in lm_words:
print(items[0], items[1], file=out_f)
else:
num_skipped += 1
logger.debug(
f"Skipping word {items[0]} as it was not found in LM"
)
total += 1
if num_skipped > 0:
logger.warning(
f"Skipped {num_skipped} out of {total} words as they were not found in LM"
)
else:
with open(in_units_file, "r") as in_f, open(lexicon_file, "w") as out_f:
for line in in_f:
symb = line.split()[0]
if symb != "<eps>" and symb != "<ctc_blank>" and symb != "<SIL>":
print(symb, symb, file=out_f)
lex_disambig_path = (
Path(cfg.kaldi_root) / "egs/wsj/s5/utils/add_lex_disambig.pl"
)
res = subprocess.run(
[lex_disambig_path, lexicon_file, disambig_lexicon_file],
check=True,
capture_output=True,
)
ndisambig = int(res.stdout)
disamib_path = Path(cfg.kaldi_root) / "egs/wsj/s5/utils/add_disambig.pl"
res = subprocess.run(
[disamib_path, "--include-zero", in_units_file, str(ndisambig)],
check=True,
capture_output=True,
)
with open(disambig_in_units_file, "wb") as f:
f.write(res.stdout)
return disambig_lexicon_file, disambig_in_units_file
def create_G(
kaldi_root: Path, fst_dir: Path, lm_arpa: Path, arpa_base: str
) -> (Path, Path):
out_words_file = fst_dir / f"kaldi_dict.{arpa_base}.txt"
grammar_graph = fst_dir / f"G_{arpa_base}.fst"
if not grammar_graph.exists() or not out_words_file.exists():
logger.info(f"Creating {grammar_graph}")
arpa2fst = kaldi_root / "src/lmbin/arpa2fst"
subprocess.run(
[
arpa2fst,
"--disambig-symbol=#0",
f"--write-symbol-table={out_words_file}",
lm_arpa,
grammar_graph,
],
check=True,
)
return grammar_graph, out_words_file
def create_L(
kaldi_root: Path,
fst_dir: Path,
unique_label: str,
lexicon_file: Path,
in_units_file: Path,
out_words_file: Path,
) -> Path:
lexicon_graph = fst_dir / f"L.{unique_label}.fst"
if not lexicon_graph.exists():
logger.info(f"Creating {lexicon_graph} (in units: {in_units_file})")
make_lex = kaldi_root / "egs/wsj/s5/utils/make_lexicon_fst.pl"
fstcompile = kaldi_root / "tools/openfst-1.6.7/bin/fstcompile"
fstaddselfloops = kaldi_root / "src/fstbin/fstaddselfloops"
fstarcsort = kaldi_root / "tools/openfst-1.6.7/bin/fstarcsort"
def write_disambig_symbol(file):
with open(file, "r") as f:
for line in f:
items = line.rstrip().split()
if items[0] == "#0":
out_path = str(file) + "_disamig"
with open(out_path, "w") as out_f:
print(items[1], file=out_f)
return out_path
return None
in_disambig_sym = write_disambig_symbol(in_units_file)
assert in_disambig_sym is not None
out_disambig_sym = write_disambig_symbol(out_words_file)
assert out_disambig_sym is not None
try:
with open(lexicon_graph, "wb") as out_f:
res = subprocess.run(
[make_lex, lexicon_file], capture_output=True, check=True
)
assert len(res.stderr) == 0, res.stderr.decode("utf-8")
res = subprocess.run(
[
fstcompile,
f"--isymbols={in_units_file}",
f"--osymbols={out_words_file}",
"--keep_isymbols=false",
"--keep_osymbols=false",
],
input=res.stdout,
capture_output=True,
)
assert len(res.stderr) == 0, res.stderr.decode("utf-8")
res = subprocess.run(
[fstaddselfloops, in_disambig_sym, out_disambig_sym],
input=res.stdout,
capture_output=True,
check=True,
)
res = subprocess.run(
[fstarcsort, "--sort_type=olabel"],
input=res.stdout,
capture_output=True,
check=True,
)
out_f.write(res.stdout)
except subprocess.CalledProcessError as e:
logger.error(f"cmd: {e.cmd}, err: {e.stderr.decode('utf-8')}")
os.remove(lexicon_graph)
raise
except AssertionError:
os.remove(lexicon_graph)
raise
return lexicon_graph
def create_LG(
kaldi_root: Path,
fst_dir: Path,
unique_label: str,
lexicon_graph: Path,
grammar_graph: Path,
) -> Path:
lg_graph = fst_dir / f"LG.{unique_label}.fst"
if not lg_graph.exists():
logger.info(f"Creating {lg_graph}")
fsttablecompose = kaldi_root / "src/fstbin/fsttablecompose"
fstdeterminizestar = kaldi_root / "src/fstbin/fstdeterminizestar"
fstminimizeencoded = kaldi_root / "src/fstbin/fstminimizeencoded"
fstpushspecial = kaldi_root / "src/fstbin/fstpushspecial"
fstarcsort = kaldi_root / "tools/openfst-1.6.7/bin/fstarcsort"
try:
with open(lg_graph, "wb") as out_f:
res = subprocess.run(
[fsttablecompose, lexicon_graph, grammar_graph],
capture_output=True,
check=True,
)
res = subprocess.run(
[
fstdeterminizestar,
"--use-log=true",
],
input=res.stdout,
capture_output=True,
)
res = subprocess.run(
[fstminimizeencoded],
input=res.stdout,
capture_output=True,
check=True,
)
res = subprocess.run(
[fstpushspecial],
input=res.stdout,
capture_output=True,
check=True,
)
res = subprocess.run(
[fstarcsort, "--sort_type=ilabel"],
input=res.stdout,
capture_output=True,
check=True,
)
out_f.write(res.stdout)
except subprocess.CalledProcessError as e:
logger.error(f"cmd: {e.cmd}, err: {e.stderr.decode('utf-8')}")
os.remove(lg_graph)
raise
return lg_graph
def create_H(
kaldi_root: Path,
fst_dir: Path,
disambig_out_units_file: Path,
in_labels: str,
vocab: Dictionary,
blk_sym: str,
silence_symbol: Optional[str],
) -> (Path, Path, Path):
h_graph = (
fst_dir / f"H.{in_labels}{'_' + silence_symbol if silence_symbol else ''}.fst"
)
h_out_units_file = fst_dir / f"kaldi_dict.h_out.{in_labels}.txt"
disambig_in_units_file_int = Path(str(h_graph) + "isym_disambig.int")
disambig_out_units_file_int = Path(str(disambig_out_units_file) + ".int")
if (
not h_graph.exists()
or not h_out_units_file.exists()
or not disambig_in_units_file_int.exists()
):
logger.info(f"Creating {h_graph}")
eps_sym = "<eps>"
num_disambig = 0
osymbols = []
with open(disambig_out_units_file, "r") as f, open(
disambig_out_units_file_int, "w"
) as out_f:
for line in f:
symb, id = line.rstrip().split()
if line.startswith("#"):
num_disambig += 1
print(id, file=out_f)
else:
if len(osymbols) == 0:
assert symb == eps_sym, symb
osymbols.append((symb, id))
i_idx = 0
isymbols = [(eps_sym, 0)]
imap = {}
for i, s in enumerate(vocab.symbols):
i_idx += 1
isymbols.append((s, i_idx))
imap[s] = i_idx
fst_str = []
node_idx = 0
root_node = node_idx
special_symbols = [blk_sym]
if silence_symbol is not None:
special_symbols.append(silence_symbol)
for ss in special_symbols:
fst_str.append("{} {} {} {}".format(root_node, root_node, ss, eps_sym))
for symbol, _ in osymbols:
if symbol == eps_sym or symbol.startswith("#"):
continue
node_idx += 1
# 1. from root to emitting state
fst_str.append("{} {} {} {}".format(root_node, node_idx, symbol, symbol))
# 2. from emitting state back to root
fst_str.append("{} {} {} {}".format(node_idx, root_node, eps_sym, eps_sym))
# 3. from emitting state to optional blank state
pre_node = node_idx
node_idx += 1
for ss in special_symbols:
fst_str.append("{} {} {} {}".format(pre_node, node_idx, ss, eps_sym))
# 4. from blank state back to root
fst_str.append("{} {} {} {}".format(node_idx, root_node, eps_sym, eps_sym))
fst_str.append("{}".format(root_node))
fst_str = "\n".join(fst_str)
h_str = str(h_graph)
isym_file = h_str + ".isym"
with open(isym_file, "w") as f:
for sym, id in isymbols:
f.write("{} {}\n".format(sym, id))
with open(h_out_units_file, "w") as f:
for sym, id in osymbols:
f.write("{} {}\n".format(sym, id))
with open(disambig_in_units_file_int, "w") as f:
disam_sym_id = len(isymbols)
for _ in range(num_disambig):
f.write("{}\n".format(disam_sym_id))
disam_sym_id += 1
fstcompile = kaldi_root / "tools/openfst-1.6.7/bin/fstcompile"
fstaddselfloops = kaldi_root / "src/fstbin/fstaddselfloops"
fstarcsort = kaldi_root / "tools/openfst-1.6.7/bin/fstarcsort"
try:
with open(h_graph, "wb") as out_f:
res = subprocess.run(
[
fstcompile,
f"--isymbols={isym_file}",
f"--osymbols={h_out_units_file}",
"--keep_isymbols=false",
"--keep_osymbols=false",
],
input=str.encode(fst_str),
capture_output=True,
check=True,
)
res = subprocess.run(
[
fstaddselfloops,
disambig_in_units_file_int,
disambig_out_units_file_int,
],
input=res.stdout,
capture_output=True,
check=True,
)
res = subprocess.run(
[fstarcsort, "--sort_type=olabel"],
input=res.stdout,
capture_output=True,
check=True,
)
out_f.write(res.stdout)
except subprocess.CalledProcessError as e:
logger.error(f"cmd: {e.cmd}, err: {e.stderr.decode('utf-8')}")
os.remove(h_graph)
raise
return h_graph, h_out_units_file, disambig_in_units_file_int
def create_HLGa(
kaldi_root: Path,
fst_dir: Path,
unique_label: str,
h_graph: Path,
lg_graph: Path,
disambig_in_words_file_int: Path,
) -> Path:
hlga_graph = fst_dir / f"HLGa.{unique_label}.fst"
if not hlga_graph.exists():
logger.info(f"Creating {hlga_graph}")
fsttablecompose = kaldi_root / "src/fstbin/fsttablecompose"
fstdeterminizestar = kaldi_root / "src/fstbin/fstdeterminizestar"
fstrmsymbols = kaldi_root / "src/fstbin/fstrmsymbols"
fstrmepslocal = kaldi_root / "src/fstbin/fstrmepslocal"
fstminimizeencoded = kaldi_root / "src/fstbin/fstminimizeencoded"
try:
with open(hlga_graph, "wb") as out_f:
res = subprocess.run(
[
fsttablecompose,
h_graph,
lg_graph,
],
capture_output=True,
check=True,
)
res = subprocess.run(
[fstdeterminizestar, "--use-log=true"],
input=res.stdout,
capture_output=True,
check=True,
)
res = subprocess.run(
[fstrmsymbols, disambig_in_words_file_int],
input=res.stdout,
capture_output=True,
check=True,
)
res = subprocess.run(
[fstrmepslocal],
input=res.stdout,
capture_output=True,
check=True,
)
res = subprocess.run(
[fstminimizeencoded],
input=res.stdout,
capture_output=True,
check=True,
)
out_f.write(res.stdout)
except subprocess.CalledProcessError as e:
logger.error(f"cmd: {e.cmd}, err: {e.stderr.decode('utf-8')}")
os.remove(hlga_graph)
raise
return hlga_graph
def create_HLa(
kaldi_root: Path,
fst_dir: Path,
unique_label: str,
h_graph: Path,
l_graph: Path,
disambig_in_words_file_int: Path,
) -> Path:
hla_graph = fst_dir / f"HLa.{unique_label}.fst"
if not hla_graph.exists():
logger.info(f"Creating {hla_graph}")
fsttablecompose = kaldi_root / "src/fstbin/fsttablecompose"
fstdeterminizestar = kaldi_root / "src/fstbin/fstdeterminizestar"
fstrmsymbols = kaldi_root / "src/fstbin/fstrmsymbols"
fstrmepslocal = kaldi_root / "src/fstbin/fstrmepslocal"
fstminimizeencoded = kaldi_root / "src/fstbin/fstminimizeencoded"
try:
with open(hla_graph, "wb") as out_f:
res = subprocess.run(
[
fsttablecompose,
h_graph,
l_graph,
],
capture_output=True,
check=True,
)
res = subprocess.run(
[fstdeterminizestar, "--use-log=true"],
input=res.stdout,
capture_output=True,
check=True,
)
res = subprocess.run(
[fstrmsymbols, disambig_in_words_file_int],
input=res.stdout,
capture_output=True,
check=True,
)
res = subprocess.run(
[fstrmepslocal],
input=res.stdout,
capture_output=True,
check=True,
)
res = subprocess.run(
[fstminimizeencoded],
input=res.stdout,
capture_output=True,
check=True,
)
out_f.write(res.stdout)
except subprocess.CalledProcessError as e:
logger.error(f"cmd: {e.cmd}, err: {e.stderr.decode('utf-8')}")
os.remove(hla_graph)
raise
return hla_graph
def create_HLG(
kaldi_root: Path,
fst_dir: Path,
unique_label: str,
hlga_graph: Path,
prefix: str = "HLG",
) -> Path:
hlg_graph = fst_dir / f"{prefix}.{unique_label}.fst"
if not hlg_graph.exists():
logger.info(f"Creating {hlg_graph}")
add_self_loop = script_dir / "add-self-loop-simple"
kaldi_src = kaldi_root / "src"
kaldi_lib = kaldi_src / "lib"
try:
if not add_self_loop.exists():
fst_include = kaldi_root / "tools/openfst-1.6.7/include"
add_self_loop_src = script_dir / "add-self-loop-simple.cc"
subprocess.run(
[
"c++",
f"-I{kaldi_src}",
f"-I{fst_include}",
f"-L{kaldi_lib}",
add_self_loop_src,
"-lkaldi-base",
"-lkaldi-fstext",
"-o",
add_self_loop,
],
check=True,
)
my_env = os.environ.copy()
my_env["LD_LIBRARY_PATH"] = f"{kaldi_lib}:{my_env['LD_LIBRARY_PATH']}"
subprocess.run(
[
add_self_loop,
hlga_graph,
hlg_graph,
],
check=True,
capture_output=True,
env=my_env,
)
except subprocess.CalledProcessError as e:
logger.error(f"cmd: {e.cmd}, err: {e.stderr.decode('utf-8')}")
raise
return hlg_graph
def initalize_kaldi(cfg: KaldiInitializerConfig) -> Path:
if cfg.fst_dir is None:
cfg.fst_dir = osp.join(cfg.data_dir, "kaldi")
if cfg.out_labels is None:
cfg.out_labels = cfg.in_labels
kaldi_root = Path(cfg.kaldi_root)
data_dir = Path(cfg.data_dir)
fst_dir = Path(cfg.fst_dir)
fst_dir.mkdir(parents=True, exist_ok=True)
arpa_base = osp.splitext(osp.basename(cfg.lm_arpa))[0]
unique_label = f"{cfg.in_labels}.{arpa_base}"
with open(data_dir / f"dict.{cfg.in_labels}.txt", "r") as f:
vocab = Dictionary.load(f)
in_units_file = create_units(fst_dir, cfg.in_labels, vocab)
grammar_graph, out_words_file = create_G(
kaldi_root, fst_dir, Path(cfg.lm_arpa), arpa_base
)
disambig_lexicon_file, disambig_L_in_units_file = create_lexicon(
cfg, fst_dir, unique_label, in_units_file, out_words_file
)
h_graph, h_out_units_file, disambig_in_units_file_int = create_H(
kaldi_root,
fst_dir,
disambig_L_in_units_file,
cfg.in_labels,
vocab,
cfg.blank_symbol,
cfg.silence_symbol,
)
lexicon_graph = create_L(
kaldi_root,
fst_dir,
unique_label,
disambig_lexicon_file,
disambig_L_in_units_file,
out_words_file,
)
lg_graph = create_LG(
kaldi_root, fst_dir, unique_label, lexicon_graph, grammar_graph
)
hlga_graph = create_HLGa(
kaldi_root, fst_dir, unique_label, h_graph, lg_graph, disambig_in_units_file_int
)
hlg_graph = create_HLG(kaldi_root, fst_dir, unique_label, hlga_graph)
# for debugging
# hla_graph = create_HLa(kaldi_root, fst_dir, unique_label, h_graph, lexicon_graph, disambig_in_units_file_int)
# hl_graph = create_HLG(kaldi_root, fst_dir, unique_label, hla_graph, prefix="HL_looped")
# create_HLG(kaldi_root, fst_dir, "phnc", h_graph, prefix="H_looped")
return hlg_graph
@hydra.main(config_path=config_path, config_name="kaldi_initializer")
def cli_main(cfg: KaldiInitializerConfig) -> None:
container = OmegaConf.to_container(cfg, resolve=True, enum_to_str=True)
cfg = OmegaConf.create(container)
OmegaConf.set_struct(cfg, True)
initalize_kaldi(cfg)
if __name__ == "__main__":
logging.root.setLevel(logging.INFO)
logging.basicConfig(level=logging.INFO)
try:
from hydra._internal.utils import (
get_args,
) # pylint: disable=import-outside-toplevel
cfg_name = get_args().config_name or "kaldi_initializer"
except ImportError:
logger.warning("Failed to get config name from hydra args")
cfg_name = "kaldi_initializer"
cs = ConfigStore.instance()
cs.store(name=cfg_name, node=KaldiInitializerConfig)
cli_main()
| 23,437
| 32.723741
| 115
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/examples/speech_recognition/kaldi/kaldi_decoder.py
|
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from concurrent.futures import ThreadPoolExecutor
import logging
from omegaconf import MISSING
import os
import torch
from typing import Optional
import warnings
from dataclasses import dataclass
from fairseq.dataclass import FairseqDataclass
from .kaldi_initializer import KaldiInitializerConfig, initalize_kaldi
logger = logging.getLogger(__name__)
@dataclass
class KaldiDecoderConfig(FairseqDataclass):
hlg_graph_path: Optional[str] = None
output_dict: str = MISSING
kaldi_initializer_config: Optional[KaldiInitializerConfig] = None
acoustic_scale: float = 0.5
max_active: int = 10000
beam_delta: float = 0.5
hash_ratio: float = 2.0
is_lattice: bool = False
lattice_beam: float = 10.0
prune_interval: int = 25
determinize_lattice: bool = True
prune_scale: float = 0.1
max_mem: int = 0
phone_determinize: bool = True
word_determinize: bool = True
minimize: bool = True
num_threads: int = 1
class KaldiDecoder(object):
def __init__(
self,
cfg: KaldiDecoderConfig,
beam: int,
nbest: int = 1,
):
try:
from kaldi.asr import FasterRecognizer, LatticeFasterRecognizer
from kaldi.base import set_verbose_level
from kaldi.decoder import (
FasterDecoder,
FasterDecoderOptions,
LatticeFasterDecoder,
LatticeFasterDecoderOptions,
)
from kaldi.lat.functions import DeterminizeLatticePhonePrunedOptions
from kaldi.fstext import read_fst_kaldi, SymbolTable
except:
warnings.warn(
"pykaldi is required for this functionality. Please install from https://github.com/pykaldi/pykaldi"
)
# set_verbose_level(2)
self.acoustic_scale = cfg.acoustic_scale
self.nbest = nbest
if cfg.hlg_graph_path is None:
assert (
cfg.kaldi_initializer_config is not None
), "Must provide hlg graph path or kaldi initializer config"
cfg.hlg_graph_path = initalize_kaldi(cfg.kaldi_initializer_config)
assert os.path.exists(cfg.hlg_graph_path), cfg.hlg_graph_path
if cfg.is_lattice:
self.dec_cls = LatticeFasterDecoder
opt_cls = LatticeFasterDecoderOptions
self.rec_cls = LatticeFasterRecognizer
else:
assert self.nbest == 1, "nbest > 1 requires lattice decoder"
self.dec_cls = FasterDecoder
opt_cls = FasterDecoderOptions
self.rec_cls = FasterRecognizer
self.decoder_options = opt_cls()
self.decoder_options.beam = beam
self.decoder_options.max_active = cfg.max_active
self.decoder_options.beam_delta = cfg.beam_delta
self.decoder_options.hash_ratio = cfg.hash_ratio
if cfg.is_lattice:
self.decoder_options.lattice_beam = cfg.lattice_beam
self.decoder_options.prune_interval = cfg.prune_interval
self.decoder_options.determinize_lattice = cfg.determinize_lattice
self.decoder_options.prune_scale = cfg.prune_scale
det_opts = DeterminizeLatticePhonePrunedOptions()
det_opts.max_mem = cfg.max_mem
det_opts.phone_determinize = cfg.phone_determinize
det_opts.word_determinize = cfg.word_determinize
det_opts.minimize = cfg.minimize
self.decoder_options.det_opts = det_opts
self.output_symbols = {}
with open(cfg.output_dict, "r") as f:
for line in f:
items = line.rstrip().split()
assert len(items) == 2
self.output_symbols[int(items[1])] = items[0]
logger.info(f"Loading FST from {cfg.hlg_graph_path}")
self.fst = read_fst_kaldi(cfg.hlg_graph_path)
self.symbol_table = SymbolTable.read_text(cfg.output_dict)
self.executor = ThreadPoolExecutor(max_workers=cfg.num_threads)
def generate(self, models, sample, **unused):
"""Generate a batch of inferences."""
# model.forward normally channels prev_output_tokens into the decoder
# separately, but SequenceGenerator directly calls model.encoder
encoder_input = {
k: v for k, v in sample["net_input"].items() if k != "prev_output_tokens"
}
emissions, padding = self.get_emissions(models, encoder_input)
return self.decode(emissions, padding)
def get_emissions(self, models, encoder_input):
"""Run encoder and normalize emissions"""
model = models[0]
all_encoder_out = [m(**encoder_input) for m in models]
if len(all_encoder_out) > 1:
if "encoder_out" in all_encoder_out[0]:
encoder_out = {
"encoder_out": sum(e["encoder_out"] for e in all_encoder_out)
/ len(all_encoder_out),
"encoder_padding_mask": all_encoder_out[0]["encoder_padding_mask"],
}
padding = encoder_out["encoder_padding_mask"]
else:
encoder_out = {
"logits": sum(e["logits"] for e in all_encoder_out)
/ len(all_encoder_out),
"padding_mask": all_encoder_out[0]["padding_mask"],
}
padding = encoder_out["padding_mask"]
else:
encoder_out = all_encoder_out[0]
padding = (
encoder_out["padding_mask"]
if "padding_mask" in encoder_out
else encoder_out["encoder_padding_mask"]
)
if hasattr(model, "get_logits"):
emissions = model.get_logits(encoder_out, normalize=True)
else:
emissions = model.get_normalized_probs(encoder_out, log_probs=True)
return (
emissions.cpu().float().transpose(0, 1),
padding.cpu() if padding is not None and padding.any() else None,
)
def decode_one(self, logits, padding):
from kaldi.matrix import Matrix
decoder = self.dec_cls(self.fst, self.decoder_options)
asr = self.rec_cls(
decoder, self.symbol_table, acoustic_scale=self.acoustic_scale
)
if padding is not None:
logits = logits[~padding]
mat = Matrix(logits.numpy())
out = asr.decode(mat)
if self.nbest > 1:
from kaldi.fstext import shortestpath
from kaldi.fstext.utils import (
convert_compact_lattice_to_lattice,
convert_lattice_to_std,
convert_nbest_to_list,
get_linear_symbol_sequence,
)
lat = out["lattice"]
sp = shortestpath(lat, nshortest=self.nbest)
sp = convert_compact_lattice_to_lattice(sp)
sp = convert_lattice_to_std(sp)
seq = convert_nbest_to_list(sp)
results = []
for s in seq:
_, o, w = get_linear_symbol_sequence(s)
words = list(self.output_symbols[z] for z in o)
results.append(
{
"tokens": words,
"words": words,
"score": w.value,
"emissions": logits,
}
)
return results
else:
words = out["text"].split()
return [
{
"tokens": words,
"words": words,
"score": out["likelihood"],
"emissions": logits,
}
]
def decode(self, emissions, padding):
if padding is None:
padding = [None] * len(emissions)
ret = list(
map(
lambda e, p: self.executor.submit(self.decode_one, e, p),
emissions,
padding,
)
)
return ret
| 8,264
| 32.872951
| 116
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/examples/speech_recognition/kaldi/__init__.py
| 0
| 0
| 0
|
py
|
|
CIF-HieraDist
|
CIF-HieraDist-main/examples/speech_recognition/utils/wer_utils.py
|
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from __future__ import absolute_import, division, print_function, unicode_literals
import re
from collections import deque
from enum import Enum
import numpy as np
"""
Utility modules for computation of Word Error Rate,
Alignments, as well as more granular metrics like
deletion, insersion and substitutions.
"""
class Code(Enum):
match = 1
substitution = 2
insertion = 3
deletion = 4
class Token(object):
def __init__(self, lbl="", st=np.nan, en=np.nan):
if np.isnan(st):
self.label, self.start, self.end = "", 0.0, 0.0
else:
self.label, self.start, self.end = lbl, st, en
class AlignmentResult(object):
def __init__(self, refs, hyps, codes, score):
self.refs = refs # std::deque<int>
self.hyps = hyps # std::deque<int>
self.codes = codes # std::deque<Code>
self.score = score # float
def coordinate_to_offset(row, col, ncols):
return int(row * ncols + col)
def offset_to_row(offset, ncols):
return int(offset / ncols)
def offset_to_col(offset, ncols):
return int(offset % ncols)
def trimWhitespace(str):
return re.sub(" +", " ", re.sub(" *$", "", re.sub("^ *", "", str)))
def str2toks(str):
pieces = trimWhitespace(str).split(" ")
toks = []
for p in pieces:
toks.append(Token(p, 0.0, 0.0))
return toks
class EditDistance(object):
def __init__(self, time_mediated):
self.time_mediated_ = time_mediated
self.scores_ = np.nan # Eigen::Matrix<float, Eigen::Dynamic, Eigen::Dynamic>
self.backtraces_ = (
np.nan
) # Eigen::Matrix<size_t, Eigen::Dynamic, Eigen::Dynamic> backtraces_;
self.confusion_pairs_ = {}
def cost(self, ref, hyp, code):
if self.time_mediated_:
if code == Code.match:
return abs(ref.start - hyp.start) + abs(ref.end - hyp.end)
elif code == Code.insertion:
return hyp.end - hyp.start
elif code == Code.deletion:
return ref.end - ref.start
else: # substitution
return abs(ref.start - hyp.start) + abs(ref.end - hyp.end) + 0.1
else:
if code == Code.match:
return 0
elif code == Code.insertion or code == Code.deletion:
return 3
else: # substitution
return 4
def get_result(self, refs, hyps):
res = AlignmentResult(refs=deque(), hyps=deque(), codes=deque(), score=np.nan)
num_rows, num_cols = self.scores_.shape
res.score = self.scores_[num_rows - 1, num_cols - 1]
curr_offset = coordinate_to_offset(num_rows - 1, num_cols - 1, num_cols)
while curr_offset != 0:
curr_row = offset_to_row(curr_offset, num_cols)
curr_col = offset_to_col(curr_offset, num_cols)
prev_offset = self.backtraces_[curr_row, curr_col]
prev_row = offset_to_row(prev_offset, num_cols)
prev_col = offset_to_col(prev_offset, num_cols)
res.refs.appendleft(curr_row - 1) # Note: this was .push_front() in C++
res.hyps.appendleft(curr_col - 1)
if curr_row - 1 == prev_row and curr_col == prev_col:
res.codes.appendleft(Code.deletion)
elif curr_row == prev_row and curr_col - 1 == prev_col:
res.codes.appendleft(Code.insertion)
else:
# assert(curr_row - 1 == prev_row and curr_col - 1 == prev_col)
ref_str = refs[res.refs[0]].label
hyp_str = hyps[res.hyps[0]].label
if ref_str == hyp_str:
res.codes.appendleft(Code.match)
else:
res.codes.appendleft(Code.substitution)
confusion_pair = "%s -> %s" % (ref_str, hyp_str)
if confusion_pair not in self.confusion_pairs_:
self.confusion_pairs_[confusion_pair] = 1
else:
self.confusion_pairs_[confusion_pair] += 1
curr_offset = prev_offset
return res
def align(self, refs, hyps):
if len(refs) == 0 and len(hyps) == 0:
return np.nan
# NOTE: we're not resetting the values in these matrices because every value
# will be overridden in the loop below. If this assumption doesn't hold,
# be sure to set all entries in self.scores_ and self.backtraces_ to 0.
self.scores_ = np.zeros((len(refs) + 1, len(hyps) + 1))
self.backtraces_ = np.zeros((len(refs) + 1, len(hyps) + 1))
num_rows, num_cols = self.scores_.shape
for i in range(num_rows):
for j in range(num_cols):
if i == 0 and j == 0:
self.scores_[i, j] = 0.0
self.backtraces_[i, j] = 0
continue
if i == 0:
self.scores_[i, j] = self.scores_[i, j - 1] + self.cost(
None, hyps[j - 1], Code.insertion
)
self.backtraces_[i, j] = coordinate_to_offset(i, j - 1, num_cols)
continue
if j == 0:
self.scores_[i, j] = self.scores_[i - 1, j] + self.cost(
refs[i - 1], None, Code.deletion
)
self.backtraces_[i, j] = coordinate_to_offset(i - 1, j, num_cols)
continue
# Below here both i and j are greater than 0
ref = refs[i - 1]
hyp = hyps[j - 1]
best_score = self.scores_[i - 1, j - 1] + (
self.cost(ref, hyp, Code.match)
if (ref.label == hyp.label)
else self.cost(ref, hyp, Code.substitution)
)
prev_row = i - 1
prev_col = j - 1
ins = self.scores_[i, j - 1] + self.cost(None, hyp, Code.insertion)
if ins < best_score:
best_score = ins
prev_row = i
prev_col = j - 1
delt = self.scores_[i - 1, j] + self.cost(ref, None, Code.deletion)
if delt < best_score:
best_score = delt
prev_row = i - 1
prev_col = j
self.scores_[i, j] = best_score
self.backtraces_[i, j] = coordinate_to_offset(
prev_row, prev_col, num_cols
)
return self.get_result(refs, hyps)
class WERTransformer(object):
def __init__(self, hyp_str, ref_str, verbose=True):
self.ed_ = EditDistance(False)
self.id2oracle_errs_ = {}
self.utts_ = 0
self.words_ = 0
self.insertions_ = 0
self.deletions_ = 0
self.substitutions_ = 0
self.process(["dummy_str", hyp_str, ref_str])
if verbose:
print("'%s' vs '%s'" % (hyp_str, ref_str))
self.report_result()
def process(self, input): # std::vector<std::string>&& input
if len(input) < 3:
print(
"Input must be of the form <id> ... <hypo> <ref> , got ",
len(input),
" inputs:",
)
return None
# Align
# std::vector<Token> hyps;
# std::vector<Token> refs;
hyps = str2toks(input[-2])
refs = str2toks(input[-1])
alignment = self.ed_.align(refs, hyps)
if alignment is None:
print("Alignment is null")
return np.nan
# Tally errors
ins = 0
dels = 0
subs = 0
for code in alignment.codes:
if code == Code.substitution:
subs += 1
elif code == Code.insertion:
ins += 1
elif code == Code.deletion:
dels += 1
# Output
row = input
row.append(str(len(refs)))
row.append(str(ins))
row.append(str(dels))
row.append(str(subs))
# print(row)
# Accumulate
kIdIndex = 0
kNBestSep = "/"
pieces = input[kIdIndex].split(kNBestSep)
if len(pieces) == 0:
print(
"Error splitting ",
input[kIdIndex],
" on '",
kNBestSep,
"', got empty list",
)
return np.nan
id = pieces[0]
if id not in self.id2oracle_errs_:
self.utts_ += 1
self.words_ += len(refs)
self.insertions_ += ins
self.deletions_ += dels
self.substitutions_ += subs
self.id2oracle_errs_[id] = [ins, dels, subs]
else:
curr_err = ins + dels + subs
prev_err = np.sum(self.id2oracle_errs_[id])
if curr_err < prev_err:
self.id2oracle_errs_[id] = [ins, dels, subs]
return 0
def report_result(self):
# print("---------- Summary ---------------")
if self.words_ == 0:
print("No words counted")
return
# 1-best
best_wer = (
100.0
* (self.insertions_ + self.deletions_ + self.substitutions_)
/ self.words_
)
print(
"\tWER = %0.2f%% (%i utts, %i words, %0.2f%% ins, "
"%0.2f%% dels, %0.2f%% subs)"
% (
best_wer,
self.utts_,
self.words_,
100.0 * self.insertions_ / self.words_,
100.0 * self.deletions_ / self.words_,
100.0 * self.substitutions_ / self.words_,
)
)
def wer(self):
if self.words_ == 0:
wer = np.nan
else:
wer = (
100.0
* (self.insertions_ + self.deletions_ + self.substitutions_)
/ self.words_
)
return wer
def stats(self):
if self.words_ == 0:
stats = {}
else:
wer = (
100.0
* (self.insertions_ + self.deletions_ + self.substitutions_)
/ self.words_
)
stats = dict(
{
"wer": wer,
"utts": self.utts_,
"numwords": self.words_,
"ins": self.insertions_,
"dels": self.deletions_,
"subs": self.substitutions_,
"confusion_pairs": self.ed_.confusion_pairs_,
}
)
return stats
def calc_wer(hyp_str, ref_str):
t = WERTransformer(hyp_str, ref_str, verbose=0)
return t.wer()
def calc_wer_stats(hyp_str, ref_str):
t = WERTransformer(hyp_str, ref_str, verbose=0)
return t.stats()
def get_wer_alignment_codes(hyp_str, ref_str):
"""
INPUT: hypothesis string, reference string
OUTPUT: List of alignment codes (intermediate results from WER computation)
"""
t = WERTransformer(hyp_str, ref_str, verbose=0)
return t.ed_.align(str2toks(ref_str), str2toks(hyp_str)).codes
def merge_counts(x, y):
# Merge two hashes which have 'counts' as their values
# This can be used for example to merge confusion pair counts
# conf_pairs = merge_counts(conf_pairs, stats['confusion_pairs'])
for k, v in y.items():
if k not in x:
x[k] = 0
x[k] += v
return x
| 11,842
| 30.002618
| 86
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/examples/speech_recognition/data/collaters.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
This module contains collection of classes which implement
collate functionalities for various tasks.
Collaters should know what data to expect for each sample
and they should pack / collate them into batches
"""
from __future__ import absolute_import, division, print_function, unicode_literals
import numpy as np
import torch
from fairseq.data import data_utils as fairseq_data_utils
class Seq2SeqCollater(object):
"""
Implements collate function mainly for seq2seq tasks
This expects each sample to contain feature (src_tokens) and
targets.
This collator is also used for aligned training task.
"""
def __init__(
self,
feature_index=0,
label_index=1,
pad_index=1,
eos_index=2,
move_eos_to_beginning=True,
):
self.feature_index = feature_index
self.label_index = label_index
self.pad_index = pad_index
self.eos_index = eos_index
self.move_eos_to_beginning = move_eos_to_beginning
def _collate_frames(self, frames):
"""Convert a list of 2d frames into a padded 3d tensor
Args:
frames (list): list of 2d frames of size L[i]*f_dim. Where L[i] is
length of i-th frame and f_dim is static dimension of features
Returns:
3d tensor of size len(frames)*len_max*f_dim where len_max is max of L[i]
"""
len_max = max(frame.size(0) for frame in frames)
f_dim = frames[0].size(1)
res = frames[0].new(len(frames), len_max, f_dim).fill_(0.0)
for i, v in enumerate(frames):
res[i, : v.size(0)] = v
return res
def collate(self, samples):
"""
utility function to collate samples into batch for speech recognition.
"""
if len(samples) == 0:
return {}
# parse samples into torch tensors
parsed_samples = []
for s in samples:
# skip invalid samples
if s["data"][self.feature_index] is None:
continue
source = s["data"][self.feature_index]
if isinstance(source, (np.ndarray, np.generic)):
source = torch.from_numpy(source)
target = s["data"][self.label_index]
if isinstance(target, (np.ndarray, np.generic)):
target = torch.from_numpy(target).long()
elif isinstance(target, list):
target = torch.LongTensor(target)
parsed_sample = {"id": s["id"], "source": source, "target": target}
parsed_samples.append(parsed_sample)
samples = parsed_samples
id = torch.LongTensor([s["id"] for s in samples])
frames = self._collate_frames([s["source"] for s in samples])
# sort samples by descending number of frames
frames_lengths = torch.LongTensor([s["source"].size(0) for s in samples])
frames_lengths, sort_order = frames_lengths.sort(descending=True)
id = id.index_select(0, sort_order)
frames = frames.index_select(0, sort_order)
target = None
target_lengths = None
prev_output_tokens = None
if samples[0].get("target", None) is not None:
ntokens = sum(len(s["target"]) for s in samples)
target = fairseq_data_utils.collate_tokens(
[s["target"] for s in samples],
self.pad_index,
self.eos_index,
left_pad=False,
move_eos_to_beginning=False,
)
target = target.index_select(0, sort_order)
target_lengths = torch.LongTensor(
[s["target"].size(0) for s in samples]
).index_select(0, sort_order)
prev_output_tokens = fairseq_data_utils.collate_tokens(
[s["target"] for s in samples],
self.pad_index,
self.eos_index,
left_pad=False,
move_eos_to_beginning=self.move_eos_to_beginning,
)
prev_output_tokens = prev_output_tokens.index_select(0, sort_order)
else:
ntokens = sum(len(s["source"]) for s in samples)
batch = {
"id": id,
"ntokens": ntokens,
"net_input": {"src_tokens": frames, "src_lengths": frames_lengths},
"target": target,
"target_lengths": target_lengths,
"nsentences": len(samples),
}
if prev_output_tokens is not None:
batch["net_input"]["prev_output_tokens"] = prev_output_tokens
return batch
| 4,796
| 35.340909
| 84
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/examples/speech_recognition/data/replabels.py
|
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Replabel transforms for use with flashlight's ASG criterion.
"""
def replabel_symbol(i):
"""
Replabel symbols used in flashlight, currently just "1", "2", ...
This prevents training with numeral tokens, so this might change in the future
"""
return str(i)
def pack_replabels(tokens, dictionary, max_reps):
"""
Pack a token sequence so that repeated symbols are replaced by replabels
"""
if len(tokens) == 0 or max_reps <= 0:
return tokens
replabel_value_to_idx = [0] * (max_reps + 1)
for i in range(1, max_reps + 1):
replabel_value_to_idx[i] = dictionary.index(replabel_symbol(i))
result = []
prev_token = -1
num_reps = 0
for token in tokens:
if token == prev_token and num_reps < max_reps:
num_reps += 1
else:
if num_reps > 0:
result.append(replabel_value_to_idx[num_reps])
num_reps = 0
result.append(token)
prev_token = token
if num_reps > 0:
result.append(replabel_value_to_idx[num_reps])
return result
def unpack_replabels(tokens, dictionary, max_reps):
"""
Unpack a token sequence so that replabels are replaced by repeated symbols
"""
if len(tokens) == 0 or max_reps <= 0:
return tokens
replabel_idx_to_value = {}
for i in range(1, max_reps + 1):
replabel_idx_to_value[dictionary.index(replabel_symbol(i))] = i
result = []
prev_token = -1
for token in tokens:
try:
for _ in range(replabel_idx_to_value[token]):
result.append(prev_token)
prev_token = -1
except KeyError:
result.append(token)
prev_token = token
return result
| 1,970
| 26.760563
| 82
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/examples/speech_recognition/data/data_utils.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
def calc_mean_invstddev(feature):
if len(feature.size()) != 2:
raise ValueError("We expect the input feature to be 2-D tensor")
mean = feature.mean(0)
var = feature.var(0)
# avoid division by ~zero
eps = 1e-8
if (var < eps).any():
return mean, 1.0 / (torch.sqrt(var) + eps)
return mean, 1.0 / torch.sqrt(var)
def apply_mv_norm(features):
# If there is less than 2 spectrograms, the variance cannot be computed (is NaN)
# and normalization is not possible, so return the item as it is
if features.size(0) < 2:
return features
mean, invstddev = calc_mean_invstddev(features)
res = (features - mean) * invstddev
return res
def lengths_to_encoder_padding_mask(lengths, batch_first=False):
"""
convert lengths (a 1-D Long/Int tensor) to 2-D binary tensor
Args:
lengths: a (B, )-shaped tensor
Return:
max_length: maximum length of B sequences
encoder_padding_mask: a (max_length, B) binary mask, where
[t, b] = 0 for t < lengths[b] and 1 otherwise
TODO:
kernelize this function if benchmarking shows this function is slow
"""
max_lengths = torch.max(lengths).item()
bsz = lengths.size(0)
encoder_padding_mask = torch.arange(
max_lengths
).to( # a (T, ) tensor with [0, ..., T-1]
lengths.device
).view( # move to the right device
1, max_lengths
).expand( # reshape to (1, T)-shaped tensor
bsz, -1
) >= lengths.view( # expand to (B, T)-shaped tensor
bsz, 1
).expand(
-1, max_lengths
)
if not batch_first:
return encoder_padding_mask.t(), max_lengths
else:
return encoder_padding_mask, max_lengths
def encoder_padding_mask_to_lengths(
encoder_padding_mask, max_lengths, batch_size, device
):
"""
convert encoder_padding_mask (2-D binary tensor) to a 1-D tensor
Conventionally, encoder output contains a encoder_padding_mask, which is
a 2-D mask in a shape (T, B), whose (t, b) element indicate whether
encoder_out[t, b] is a valid output (=0) or not (=1). Occasionally, we
need to convert this mask tensor to a 1-D tensor in shape (B, ), where
[b] denotes the valid length of b-th sequence
Args:
encoder_padding_mask: a (T, B)-shaped binary tensor or None; if None,
indicating all are valid
Return:
seq_lengths: a (B,)-shaped tensor, where its (b, )-th element is the
number of valid elements of b-th sequence
max_lengths: maximum length of all sequence, if encoder_padding_mask is
not None, max_lengths must equal to encoder_padding_mask.size(0)
batch_size: batch size; if encoder_padding_mask is
not None, max_lengths must equal to encoder_padding_mask.size(1)
device: which device to put the result on
"""
if encoder_padding_mask is None:
return torch.Tensor([max_lengths] * batch_size).to(torch.int32).to(device)
assert encoder_padding_mask.size(0) == max_lengths, "max_lengths does not match"
assert encoder_padding_mask.size(1) == batch_size, "batch_size does not match"
return max_lengths - torch.sum(encoder_padding_mask, dim=0)
| 3,429
| 32.960396
| 84
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/examples/speech_recognition/data/asr_dataset.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
import numpy as np
from fairseq.data import FairseqDataset
from . import data_utils
from .collaters import Seq2SeqCollater
class AsrDataset(FairseqDataset):
"""
A dataset representing speech and corresponding transcription.
Args:
aud_paths: (List[str]): A list of str with paths to audio files.
aud_durations_ms (List[int]): A list of int containing the durations of
audio files.
tgt (List[torch.LongTensor]): A list of LongTensors containing the indices
of target transcriptions.
tgt_dict (~fairseq.data.Dictionary): target vocabulary.
ids (List[str]): A list of utterance IDs.
speakers (List[str]): A list of speakers corresponding to utterances.
num_mel_bins (int): Number of triangular mel-frequency bins (default: 80)
frame_length (float): Frame length in milliseconds (default: 25.0)
frame_shift (float): Frame shift in milliseconds (default: 10.0)
"""
def __init__(
self,
aud_paths,
aud_durations_ms,
tgt,
tgt_dict,
ids,
speakers,
num_mel_bins=80,
frame_length=25.0,
frame_shift=10.0,
):
assert frame_length > 0
assert frame_shift > 0
assert all(x > frame_length for x in aud_durations_ms)
self.frame_sizes = [
int(1 + (d - frame_length) / frame_shift) for d in aud_durations_ms
]
assert len(aud_paths) > 0
assert len(aud_paths) == len(aud_durations_ms)
assert len(aud_paths) == len(tgt)
assert len(aud_paths) == len(ids)
assert len(aud_paths) == len(speakers)
self.aud_paths = aud_paths
self.tgt_dict = tgt_dict
self.tgt = tgt
self.ids = ids
self.speakers = speakers
self.num_mel_bins = num_mel_bins
self.frame_length = frame_length
self.frame_shift = frame_shift
self.s2s_collater = Seq2SeqCollater(
0,
1,
pad_index=self.tgt_dict.pad(),
eos_index=self.tgt_dict.eos(),
move_eos_to_beginning=True,
)
def __getitem__(self, index):
import torchaudio
import torchaudio.compliance.kaldi as kaldi
tgt_item = self.tgt[index] if self.tgt is not None else None
path = self.aud_paths[index]
if not os.path.exists(path):
raise FileNotFoundError("Audio file not found: {}".format(path))
sound, sample_rate = torchaudio.load_wav(path)
output = kaldi.fbank(
sound,
num_mel_bins=self.num_mel_bins,
frame_length=self.frame_length,
frame_shift=self.frame_shift,
)
output_cmvn = data_utils.apply_mv_norm(output)
return {"id": index, "data": [output_cmvn.detach(), tgt_item]}
def __len__(self):
return len(self.aud_paths)
def collater(self, samples):
"""Merge a list of samples to form a mini-batch.
Args:
samples (List[int]): sample indices to collate
Returns:
dict: a mini-batch suitable for forwarding with a Model
"""
return self.s2s_collater.collate(samples)
def num_tokens(self, index):
return self.frame_sizes[index]
def size(self, index):
"""Return an example's size as a float or tuple. This value is used when
filtering a dataset with ``--max-positions``."""
return (
self.frame_sizes[index],
len(self.tgt[index]) if self.tgt is not None else 0,
)
def ordered_indices(self):
"""Return an ordered list of indices. Batches will be constructed based
on this order."""
return np.arange(len(self))
| 3,955
| 31.162602
| 82
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/examples/speech_recognition/data/__init__.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from .asr_dataset import AsrDataset
__all__ = [
"AsrDataset",
]
| 248
| 19.75
| 65
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/examples/speech_recognition/tasks/speech_recognition.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import json
import os
import re
import sys
import torch
from examples.speech_recognition.data import AsrDataset
from examples.speech_recognition.data.replabels import replabel_symbol
from fairseq.data import Dictionary
from fairseq.tasks import LegacyFairseqTask, register_task
def get_asr_dataset_from_json(data_json_path, tgt_dict):
"""
Parse data json and create dataset.
See scripts/asr_prep_json.py which pack json from raw files
Json example:
{
"utts": {
"4771-29403-0025": {
"input": {
"length_ms": 170,
"path": "/tmp/file1.flac"
},
"output": {
"text": "HELLO \n",
"token": "HE LLO",
"tokenid": "4815, 861"
}
},
"1564-142299-0096": {
...
}
}
"""
if not os.path.isfile(data_json_path):
raise FileNotFoundError("Dataset not found: {}".format(data_json_path))
with open(data_json_path, "rb") as f:
data_samples = json.load(f)["utts"]
assert len(data_samples) != 0
sorted_samples = sorted(
data_samples.items(),
key=lambda sample: int(sample[1]["input"]["length_ms"]),
reverse=True,
)
aud_paths = [s[1]["input"]["path"] for s in sorted_samples]
ids = [s[0] for s in sorted_samples]
speakers = []
for s in sorted_samples:
m = re.search("(.+?)-(.+?)-(.+?)", s[0])
speakers.append(m.group(1) + "_" + m.group(2))
frame_sizes = [s[1]["input"]["length_ms"] for s in sorted_samples]
tgt = [
[int(i) for i in s[1]["output"]["tokenid"].split(", ")]
for s in sorted_samples
]
# append eos
tgt = [[*t, tgt_dict.eos()] for t in tgt]
return AsrDataset(aud_paths, frame_sizes, tgt, tgt_dict, ids, speakers)
@register_task("speech_recognition")
class SpeechRecognitionTask(LegacyFairseqTask):
"""
Task for training speech recognition model.
"""
@staticmethod
def add_args(parser):
"""Add task-specific arguments to the parser."""
parser.add_argument("data", help="path to data directory")
parser.add_argument(
"--silence-token", default="\u2581", help="token for silence (used by w2l)"
)
parser.add_argument(
"--max-source-positions",
default=sys.maxsize,
type=int,
metavar="N",
help="max number of frames in the source sequence",
)
parser.add_argument(
"--max-target-positions",
default=1024,
type=int,
metavar="N",
help="max number of tokens in the target sequence",
)
def __init__(self, args, tgt_dict):
super().__init__(args)
self.tgt_dict = tgt_dict
@classmethod
def setup_task(cls, args, **kwargs):
"""Setup the task (e.g., load dictionaries)."""
dict_path = os.path.join(args.data, "dict.txt")
if not os.path.isfile(dict_path):
raise FileNotFoundError("Dict not found: {}".format(dict_path))
tgt_dict = Dictionary.load(dict_path)
if args.criterion == "ctc_loss":
tgt_dict.add_symbol("<ctc_blank>")
elif args.criterion == "asg_loss":
for i in range(1, args.max_replabel + 1):
tgt_dict.add_symbol(replabel_symbol(i))
print("| dictionary: {} types".format(len(tgt_dict)))
return cls(args, tgt_dict)
def load_dataset(self, split, combine=False, **kwargs):
"""Load a given dataset split.
Args:
split (str): name of the split (e.g., train, valid, test)
"""
data_json_path = os.path.join(self.args.data, "{}.json".format(split))
self.datasets[split] = get_asr_dataset_from_json(data_json_path, self.tgt_dict)
def build_generator(self, models, args, **unused):
w2l_decoder = getattr(args, "w2l_decoder", None)
if w2l_decoder == "viterbi":
from examples.speech_recognition.w2l_decoder import W2lViterbiDecoder
return W2lViterbiDecoder(args, self.target_dictionary)
elif w2l_decoder == "kenlm":
from examples.speech_recognition.w2l_decoder import W2lKenLMDecoder
return W2lKenLMDecoder(args, self.target_dictionary)
elif w2l_decoder == "fairseqlm":
from examples.speech_recognition.w2l_decoder import W2lFairseqLMDecoder
return W2lFairseqLMDecoder(args, self.target_dictionary)
else:
return super().build_generator(models, args)
@property
def target_dictionary(self):
"""Return the :class:`~fairseq.data.Dictionary` for the language
model."""
return self.tgt_dict
@property
def source_dictionary(self):
"""Return the source :class:`~fairseq.data.Dictionary` (if applicable
for this task)."""
return None
def max_positions(self):
"""Return the max speech and sentence length allowed by the task."""
return (self.args.max_source_positions, self.args.max_target_positions)
| 5,397
| 33.164557
| 87
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/examples/speech_recognition/tasks/__init__.py
|
import importlib
import os
for file in sorted(os.listdir(os.path.dirname(__file__))):
if file.endswith(".py") and not file.startswith("_"):
task_name = file[: file.find(".py")]
importlib.import_module("examples.speech_recognition.tasks." + task_name)
| 273
| 29.444444
| 81
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/scripts/count_docs.py
|
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Count the number of documents and average number of lines and tokens per
document in a large file. Documents should be separated by a single empty line.
"""
import argparse
import gzip
import sys
import numpy as np
def main():
parser = argparse.ArgumentParser()
parser.add_argument("input")
parser.add_argument("--gzip", action="store_true")
args = parser.parse_args()
def gopen():
if args.gzip:
return gzip.open(args.input, "r")
else:
return open(args.input, "r", encoding="utf-8")
num_lines = []
num_toks = []
with gopen() as h:
num_docs = 1
num_lines_in_doc = 0
num_toks_in_doc = 0
for i, line in enumerate(h):
if len(line.strip()) == 0: # empty line indicates new document
num_docs += 1
num_lines.append(num_lines_in_doc)
num_toks.append(num_toks_in_doc)
num_lines_in_doc = 0
num_toks_in_doc = 0
else:
num_lines_in_doc += 1
num_toks_in_doc += len(line.rstrip().split())
if i % 1000000 == 0:
print(i, file=sys.stderr, end="", flush=True)
elif i % 100000 == 0:
print(".", file=sys.stderr, end="", flush=True)
print(file=sys.stderr, flush=True)
print("found {} docs".format(num_docs))
print("average num lines per doc: {}".format(np.mean(num_lines)))
print("average num toks per doc: {}".format(np.mean(num_toks)))
if __name__ == "__main__":
main()
| 1,784
| 29.254237
| 79
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/scripts/read_binarized.py
|
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
from fairseq.data import Dictionary, data_utils, indexed_dataset
def get_parser():
parser = argparse.ArgumentParser(
description="writes text from binarized file to stdout"
)
# fmt: off
parser.add_argument('--dataset-impl', help='dataset implementation',
choices=indexed_dataset.get_available_dataset_impl())
parser.add_argument('--dict', metavar='FP', help='dictionary containing known words', default=None)
parser.add_argument('--input', metavar='FP', required=True, help='binarized file to read')
# fmt: on
return parser
def main():
parser = get_parser()
args = parser.parse_args()
dictionary = Dictionary.load(args.dict) if args.dict is not None else None
dataset = data_utils.load_indexed_dataset(
args.input,
dictionary,
dataset_impl=args.dataset_impl,
default="lazy",
)
for tensor_line in dataset:
if dictionary is None:
line = " ".join([str(int(x)) for x in tensor_line])
else:
line = dictionary.string(tensor_line)
print(line)
if __name__ == "__main__":
main()
| 1,370
| 26.979592
| 103
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/scripts/compare_namespaces.py
|
#!/usr/bin/env python
"""Helper script to compare two argparse.Namespace objects."""
from argparse import Namespace # noqa
def main():
ns1 = eval(input("Namespace 1: "))
ns2 = eval(input("Namespace 2: "))
def keys(ns):
ks = set()
for k in dir(ns):
if not k.startswith("_"):
ks.add(k)
return ks
k1 = keys(ns1)
k2 = keys(ns2)
def print_keys(ks, ns1, ns2=None):
for k in ks:
if ns2 is None:
print("{}\t{}".format(k, getattr(ns1, k, None)))
else:
print(
"{}\t{}\t{}".format(k, getattr(ns1, k, None), getattr(ns2, k, None))
)
print("Keys unique to namespace 1:")
print_keys(k1 - k2, ns1)
print()
print("Keys unique to namespace 2:")
print_keys(k2 - k1, ns2)
print()
print("Overlapping keys with different values:")
ks = [k for k in k1 & k2 if getattr(ns1, k, "None") != getattr(ns2, k, "None")]
print_keys(ks, ns1, ns2)
print()
if __name__ == "__main__":
main()
| 1,090
| 22.212766
| 88
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/scripts/split_train_valid_docs.py
|
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Split a large file into a train and valid set while respecting document
boundaries. Documents should be separated by a single empty line.
"""
import argparse
import random
import sys
def main():
parser = argparse.ArgumentParser()
parser.add_argument("input")
parser.add_argument("sample_output", help="train output file")
parser.add_argument("remainder_output", help="valid output file")
parser.add_argument("-k", type=int, help="remainder size")
parser.add_argument(
"--lines", action="store_true", help="split lines instead of docs"
)
args = parser.parse_args()
assert args.k is not None
sample = []
remainder = []
num_docs = [0]
def update_sample(doc):
if len(sample) < args.k:
sample.append(doc.copy())
else:
i = num_docs[0]
j = random.randrange(i + 1)
if j < args.k:
remainder.append(sample[j])
sample[j] = doc.copy()
else:
remainder.append(doc.copy())
num_docs[0] += 1
doc.clear()
with open(args.input, "r", encoding="utf-8") as h:
doc = []
for i, line in enumerate(h):
if line.strip() == "": # empty line indicates new document
update_sample(doc)
else:
doc.append(line)
if args.lines:
update_sample(doc)
if i % 1000000 == 0:
print(i, file=sys.stderr, end="", flush=True)
elif i % 100000 == 0:
print(".", file=sys.stderr, end="", flush=True)
if len(doc) > 0:
update_sample(doc)
print(file=sys.stderr, flush=True)
assert len(sample) == args.k
with open(args.sample_output, "w", encoding="utf-8") as out:
first = True
for doc in sample:
if not first and not args.lines:
out.write("\n")
first = False
for line in doc:
out.write(line)
with open(args.remainder_output, "w", encoding="utf-8") as out:
first = True
for doc in remainder:
if not first and not args.lines:
out.write("\n")
first = False
for line in doc:
out.write(line)
if __name__ == "__main__":
main()
| 2,551
| 28.333333
| 74
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/scripts/average_checkpoints.py
|
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import collections
import os
import re
import torch
from fairseq.file_io import PathManager
def average_checkpoints(inputs):
"""Loads checkpoints from inputs and returns a model with averaged weights.
Args:
inputs: An iterable of string paths of checkpoints to load from.
Returns:
A dict of string keys mapping to various values. The 'model' key
from the returned dict should correspond to an OrderedDict mapping
string parameter names to torch Tensors.
"""
params_dict = collections.OrderedDict()
params_keys = None
new_state = None
num_models = len(inputs)
for fpath in inputs:
with PathManager.open(fpath, "rb") as f:
state = torch.load(
f,
map_location=(
lambda s, _: torch.serialization.default_restore_location(s, "cpu")
),
)
# Copies over the settings from the first checkpoint
if new_state is None:
new_state = state
model_params = state["model"]
model_params_keys = list(model_params.keys())
if params_keys is None:
params_keys = model_params_keys
elif params_keys != model_params_keys:
raise KeyError(
"For checkpoint {}, expected list of params: {}, "
"but found: {}".format(f, params_keys, model_params_keys)
)
for k in params_keys:
p = model_params[k]
if isinstance(p, torch.HalfTensor):
p = p.float()
if k not in params_dict:
params_dict[k] = p.clone()
# NOTE: clone() is needed in case of p is a shared parameter
else:
params_dict[k] += p
averaged_params = collections.OrderedDict()
for k, v in params_dict.items():
averaged_params[k] = v
if averaged_params[k].is_floating_point():
averaged_params[k].div_(num_models)
else:
averaged_params[k] //= num_models
new_state["model"] = averaged_params
return new_state
def last_n_checkpoints(paths, n, update_based, upper_bound=None):
assert len(paths) == 1
path = paths[0]
if update_based:
pt_regexp = re.compile(r"checkpoint_\d+_(\d+)\.pt")
else:
pt_regexp = re.compile(r"checkpoint(\d+)\.pt")
files = PathManager.ls(path)
entries = []
for f in files:
m = pt_regexp.fullmatch(f)
if m is not None:
sort_key = int(m.group(1))
if upper_bound is None or sort_key <= upper_bound:
entries.append((sort_key, m.group(0)))
if len(entries) < n:
raise Exception(
"Found {} checkpoint files but need at least {}", len(entries), n
)
return [os.path.join(path, x[1]) for x in sorted(entries, reverse=True)[:n]]
def main():
parser = argparse.ArgumentParser(
description="Tool to average the params of input checkpoints to "
"produce a new checkpoint",
)
# fmt: off
parser.add_argument('--inputs', required=True, nargs='+',
help='Input checkpoint file paths.')
parser.add_argument('--output', required=True, metavar='FILE',
help='Write the new checkpoint containing the averaged weights to this path.')
num_group = parser.add_mutually_exclusive_group()
num_group.add_argument('--num-epoch-checkpoints', type=int,
help='if set, will try to find checkpoints with names checkpoint_xx.pt in the path specified by input, '
'and average last this many of them.')
num_group.add_argument('--num-update-checkpoints', type=int,
help='if set, will try to find checkpoints with names checkpoint_ee_xx.pt in the path specified by input, '
'and average last this many of them.')
parser.add_argument('--checkpoint-upper-bound', type=int,
help='when using --num-epoch-checkpoints, this will set an upper bound on which epoch to use, '
'when using --num-update-checkpoints, this will set an upper bound on which update to use'
'e.g., with --num-epoch-checkpoints=10 --checkpoint-upper-bound=50, checkpoints 41-50 would be averaged.'
'e.g., with --num-update-checkpoints=10 --checkpoint-upper-bound=50000, checkpoints 40500-50000 would be averaged assuming --save-interval-updates 500'
)
# fmt: on
args = parser.parse_args()
print(args)
num = None
is_update_based = False
if args.num_update_checkpoints is not None:
num = args.num_update_checkpoints
is_update_based = True
elif args.num_epoch_checkpoints is not None:
num = args.num_epoch_checkpoints
assert args.checkpoint_upper_bound is None or (
args.num_epoch_checkpoints is not None
or args.num_update_checkpoints is not None
), "--checkpoint-upper-bound requires --num-epoch-checkpoints or --num-update-checkpoints"
assert (
args.num_epoch_checkpoints is None or args.num_update_checkpoints is None
), "Cannot combine --num-epoch-checkpoints and --num-update-checkpoints"
if num is not None:
args.inputs = last_n_checkpoints(
args.inputs,
num,
is_update_based,
upper_bound=args.checkpoint_upper_bound,
)
print("averaging checkpoints: ", args.inputs)
new_state = average_checkpoints(args.inputs)
with PathManager.open(args.output, "wb") as f:
torch.save(new_state, f)
print("Finished writing averaged checkpoint to {}".format(args.output))
if __name__ == "__main__":
main()
| 6,021
| 36.874214
| 175
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/scripts/build_sym_alignment.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Use this script in order to build symmetric alignments for your translation
dataset.
This script depends on fast_align and mosesdecoder tools. You will need to
build those before running the script.
fast_align:
github: http://github.com/clab/fast_align
instructions: follow the instructions in README.md
mosesdecoder:
github: http://github.com/moses-smt/mosesdecoder
instructions: http://www.statmt.org/moses/?n=Development.GetStarted
The script produces the following files under --output_dir:
text.joined - concatenation of lines from the source_file and the
target_file.
align.forward - forward pass of fast_align.
align.backward - backward pass of fast_align.
aligned.sym_heuristic - symmetrized alignment.
"""
import argparse
import os
from itertools import zip_longest
def main():
parser = argparse.ArgumentParser(description="symmetric alignment builer")
# fmt: off
parser.add_argument('--fast_align_dir',
help='path to fast_align build directory')
parser.add_argument('--mosesdecoder_dir',
help='path to mosesdecoder root directory')
parser.add_argument('--sym_heuristic',
help='heuristic to use for symmetrization',
default='grow-diag-final-and')
parser.add_argument('--source_file',
help='path to a file with sentences '
'in the source language')
parser.add_argument('--target_file',
help='path to a file with sentences '
'in the target language')
parser.add_argument('--output_dir',
help='output directory')
# fmt: on
args = parser.parse_args()
fast_align_bin = os.path.join(args.fast_align_dir, "fast_align")
symal_bin = os.path.join(args.mosesdecoder_dir, "bin", "symal")
sym_fast_align_bin = os.path.join(
args.mosesdecoder_dir, "scripts", "ems", "support", "symmetrize-fast-align.perl"
)
# create joined file
joined_file = os.path.join(args.output_dir, "text.joined")
with open(args.source_file, "r", encoding="utf-8") as src, open(
args.target_file, "r", encoding="utf-8"
) as tgt:
with open(joined_file, "w", encoding="utf-8") as joined:
for s, t in zip_longest(src, tgt):
print("{} ||| {}".format(s.strip(), t.strip()), file=joined)
bwd_align_file = os.path.join(args.output_dir, "align.backward")
# run forward alignment
fwd_align_file = os.path.join(args.output_dir, "align.forward")
fwd_fast_align_cmd = "{FASTALIGN} -i {JOINED} -d -o -v > {FWD}".format(
FASTALIGN=fast_align_bin, JOINED=joined_file, FWD=fwd_align_file
)
assert os.system(fwd_fast_align_cmd) == 0
# run backward alignment
bwd_align_file = os.path.join(args.output_dir, "align.backward")
bwd_fast_align_cmd = "{FASTALIGN} -i {JOINED} -d -o -v -r > {BWD}".format(
FASTALIGN=fast_align_bin, JOINED=joined_file, BWD=bwd_align_file
)
assert os.system(bwd_fast_align_cmd) == 0
# run symmetrization
sym_out_file = os.path.join(args.output_dir, "aligned")
sym_cmd = "{SYMFASTALIGN} {FWD} {BWD} {SRC} {TGT} {OUT} {HEURISTIC} {SYMAL}".format(
SYMFASTALIGN=sym_fast_align_bin,
FWD=fwd_align_file,
BWD=bwd_align_file,
SRC=args.source_file,
TGT=args.target_file,
OUT=sym_out_file,
HEURISTIC=args.sym_heuristic,
SYMAL=symal_bin,
)
assert os.system(sym_cmd) == 0
if __name__ == "__main__":
main()
| 3,796
| 37.744898
| 88
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/scripts/spm_decode.py
|
#!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from __future__ import absolute_import, division, print_function, unicode_literals
import argparse
import sentencepiece as spm
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--model", required=True, help="sentencepiece model to use for decoding"
)
parser.add_argument("--input", required=True, help="input file to decode")
parser.add_argument("--input_format", choices=["piece", "id"], default="piece")
args = parser.parse_args()
sp = spm.SentencePieceProcessor()
sp.Load(args.model)
if args.input_format == "piece":
def decode(l):
return "".join(sp.DecodePieces(l))
elif args.input_format == "id":
def decode(l):
return "".join(sp.DecodeIds(l))
else:
raise NotImplementedError
def tok2int(tok):
# remap reference-side <unk> (represented as <<unk>>) to 0
return int(tok) if tok != "<<unk>>" else 0
with open(args.input, "r", encoding="utf-8") as h:
for line in h:
if args.input_format == "id":
print(decode(list(map(tok2int, line.rstrip().split()))))
elif args.input_format == "piece":
print(decode(line.rstrip().split()))
if __name__ == "__main__":
main()
| 1,504
| 26.87037
| 83
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/scripts/rm_pt.py
|
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import os
import re
import shutil
import sys
pt_regexp = re.compile(r"checkpoint(\d+|_\d+_\d+|_[a-z]+)\.pt")
pt_regexp_epoch_based = re.compile(r"checkpoint(\d+)\.pt")
pt_regexp_update_based = re.compile(r"checkpoint_\d+_(\d+)\.pt")
def parse_checkpoints(files):
entries = []
for f in files:
m = pt_regexp_epoch_based.fullmatch(f)
if m is not None:
entries.append((int(m.group(1)), m.group(0)))
else:
m = pt_regexp_update_based.fullmatch(f)
if m is not None:
entries.append((int(m.group(1)), m.group(0)))
return entries
def last_n_checkpoints(files, n):
entries = parse_checkpoints(files)
return [x[1] for x in sorted(entries, reverse=True)[:n]]
def every_n_checkpoints(files, n):
entries = parse_checkpoints(files)
return [x[1] for x in sorted(sorted(entries)[::-n])]
def main():
parser = argparse.ArgumentParser(
description=(
"Recursively delete checkpoint files from `root_dir`, "
"but preserve checkpoint_best.pt and checkpoint_last.pt"
)
)
parser.add_argument("root_dirs", nargs="*")
parser.add_argument(
"--save-last", type=int, default=0, help="number of last checkpoints to save"
)
parser.add_argument(
"--save-every", type=int, default=0, help="interval of checkpoints to save"
)
parser.add_argument(
"--preserve-test",
action="store_true",
help="preserve checkpoints in dirs that start with test_ prefix (default: delete them)",
)
parser.add_argument(
"--delete-best", action="store_true", help="delete checkpoint_best.pt"
)
parser.add_argument(
"--delete-last", action="store_true", help="delete checkpoint_last.pt"
)
parser.add_argument(
"--no-dereference", action="store_true", help="don't dereference symlinks"
)
args = parser.parse_args()
files_to_desymlink = []
files_to_preserve = []
files_to_delete = []
for root_dir in args.root_dirs:
for root, _subdirs, files in os.walk(root_dir):
if args.save_last > 0:
to_save = last_n_checkpoints(files, args.save_last)
else:
to_save = []
if args.save_every > 0:
to_save += every_n_checkpoints(files, args.save_every)
for file in files:
if not pt_regexp.fullmatch(file):
continue
full_path = os.path.join(root, file)
if (
not os.path.basename(root).startswith("test_") or args.preserve_test
) and (
(file == "checkpoint_last.pt" and not args.delete_last)
or (file == "checkpoint_best.pt" and not args.delete_best)
or file in to_save
):
if os.path.islink(full_path) and not args.no_dereference:
files_to_desymlink.append(full_path)
else:
files_to_preserve.append(full_path)
else:
files_to_delete.append(full_path)
if len(files_to_desymlink) == 0 and len(files_to_delete) == 0:
print("Nothing to do.")
sys.exit(0)
files_to_desymlink = sorted(files_to_desymlink)
files_to_preserve = sorted(files_to_preserve)
files_to_delete = sorted(files_to_delete)
print("Operations to perform (in order):")
if len(files_to_desymlink) > 0:
for file in files_to_desymlink:
print(" - preserve (and dereference symlink): " + file)
if len(files_to_preserve) > 0:
for file in files_to_preserve:
print(" - preserve: " + file)
if len(files_to_delete) > 0:
for file in files_to_delete:
print(" - delete: " + file)
while True:
resp = input("Continue? (Y/N): ")
if resp.strip().lower() == "y":
break
elif resp.strip().lower() == "n":
sys.exit(0)
print("Executing...")
if len(files_to_desymlink) > 0:
for file in files_to_desymlink:
realpath = os.path.realpath(file)
print("rm " + file)
os.remove(file)
print("cp {} {}".format(realpath, file))
shutil.copyfile(realpath, file)
if len(files_to_delete) > 0:
for file in files_to_delete:
print("rm " + file)
os.remove(file)
if __name__ == "__main__":
main()
| 4,740
| 32.387324
| 96
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/scripts/__init__.py
| 0
| 0
| 0
|
py
|
|
CIF-HieraDist
|
CIF-HieraDist-main/scripts/spm_train.py
|
#!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from __future__ import absolute_import, division, print_function, unicode_literals
import sys
import sentencepiece as spm
if __name__ == "__main__":
spm.SentencePieceTrainer.Train(" ".join(sys.argv[1:]))
| 431
| 24.411765
| 82
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/scripts/shard_docs.py
|
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Split a large file into shards while respecting document boundaries. Documents
should be separated by a single empty line.
"""
import argparse
import contextlib
def main():
parser = argparse.ArgumentParser()
parser.add_argument("input")
parser.add_argument("--num-shards", type=int)
args = parser.parse_args()
assert args.num_shards is not None and args.num_shards > 1
with open(args.input, "r", encoding="utf-8") as h:
with contextlib.ExitStack() as stack:
outputs = [
stack.enter_context(
open(args.input + ".shard" + str(i), "w", encoding="utf-8")
)
for i in range(args.num_shards)
]
doc = []
first_doc = [True] * args.num_shards
def output_doc(i):
if not first_doc[i]:
outputs[i].write("\n")
first_doc[i] = False
for line in doc:
outputs[i].write(line)
doc.clear()
num_docs = 0
for line in h:
if line.strip() == "": # empty line indicates new document
output_doc(num_docs % args.num_shards)
num_docs += 1
else:
doc.append(line)
output_doc(num_docs % args.num_shards)
if __name__ == "__main__":
main()
| 1,616
| 28.4
| 79
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/scripts/spm_encode.py
|
#!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from __future__ import absolute_import, division, print_function, unicode_literals
import argparse
import contextlib
import sys
import sentencepiece as spm
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--model", required=True, help="sentencepiece model to use for encoding"
)
parser.add_argument(
"--inputs", nargs="+", default=["-"], help="input files to filter/encode"
)
parser.add_argument(
"--outputs", nargs="+", default=["-"], help="path to save encoded outputs"
)
parser.add_argument("--output_format", choices=["piece", "id"], default="piece")
parser.add_argument(
"--min-len",
type=int,
metavar="N",
help="filter sentence pairs with fewer than N tokens",
)
parser.add_argument(
"--max-len",
type=int,
metavar="N",
help="filter sentence pairs with more than N tokens",
)
args = parser.parse_args()
assert len(args.inputs) == len(
args.outputs
), "number of input and output paths should match"
sp = spm.SentencePieceProcessor()
sp.Load(args.model)
if args.output_format == "piece":
def encode(l):
return sp.EncodeAsPieces(l)
elif args.output_format == "id":
def encode(l):
return list(map(str, sp.EncodeAsIds(l)))
else:
raise NotImplementedError
if args.min_len is not None or args.max_len is not None:
def valid(line):
return (args.min_len is None or len(line) >= args.min_len) and (
args.max_len is None or len(line) <= args.max_len
)
else:
def valid(lines):
return True
with contextlib.ExitStack() as stack:
inputs = [
stack.enter_context(open(input, "r", encoding="utf-8"))
if input != "-"
else sys.stdin
for input in args.inputs
]
outputs = [
stack.enter_context(open(output, "w", encoding="utf-8"))
if output != "-"
else sys.stdout
for output in args.outputs
]
stats = {
"num_empty": 0,
"num_filtered": 0,
}
def encode_line(line):
line = line.strip()
if len(line) > 0:
line = encode(line)
if valid(line):
return line
else:
stats["num_filtered"] += 1
else:
stats["num_empty"] += 1
return None
for i, lines in enumerate(zip(*inputs), start=1):
enc_lines = list(map(encode_line, lines))
if not any(enc_line is None for enc_line in enc_lines):
for enc_line, output_h in zip(enc_lines, outputs):
print(" ".join(enc_line), file=output_h)
if i % 10000 == 0:
print("processed {} lines".format(i), file=sys.stderr)
print("skipped {} empty lines".format(stats["num_empty"]), file=sys.stderr)
print("filtered {} lines".format(stats["num_filtered"]), file=sys.stderr)
if __name__ == "__main__":
main()
| 3,416
| 27.475
| 84
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/scripts/constraints/validate.py
|
#!/usr/bin/env python3
#
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import sys
"""Reads in a fairseq output file, and verifies that the constraints
(C- lines) are present in the output (the first H- line). Assumes that
constraints are listed prior to the first hypothesis.
"""
constraints = []
found = 0
total = 0
for line in sys.stdin:
if line.startswith("C-"):
constraints.append(line.rstrip().split("\t")[1])
elif line.startswith("H-"):
text = line.split("\t")[2]
for constraint in constraints:
total += 1
if constraint in text:
found += 1
else:
print(f"No {constraint} in {text}", file=sys.stderr)
constraints = []
print(f"Found {found} / {total} = {100 * found / total:.1f}%")
| 931
| 25.628571
| 70
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/scripts/constraints/extract.py
|
#!/usr/bin/env python3
#
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""Extracts random constraints from reference files."""
import argparse
import random
import sys
from sacrebleu import extract_ngrams
def get_phrase(words, index, length):
assert index < len(words) - length + 1
phr = " ".join(words[index : index + length])
for i in range(index, index + length):
words.pop(index)
return phr
def main(args):
if args.seed:
random.seed(args.seed)
for line in sys.stdin:
constraints = []
def add_constraint(constraint):
constraints.append(constraint)
source = line.rstrip()
if "\t" in line:
source, target = line.split("\t")
if args.add_sos:
target = f"<s> {target}"
if args.add_eos:
target = f"{target} </s>"
if len(target.split()) >= args.len:
words = [target]
num = args.number
choices = {}
for i in range(num):
if len(words) == 0:
break
segmentno = random.choice(range(len(words)))
segment = words.pop(segmentno)
tokens = segment.split()
phrase_index = random.choice(range(len(tokens)))
choice = " ".join(
tokens[phrase_index : min(len(tokens), phrase_index + args.len)]
)
for j in range(
phrase_index, min(len(tokens), phrase_index + args.len)
):
tokens.pop(phrase_index)
if phrase_index > 0:
words.append(" ".join(tokens[0:phrase_index]))
if phrase_index + 1 < len(tokens):
words.append(" ".join(tokens[phrase_index:]))
choices[target.find(choice)] = choice
# mask out with spaces
target = target.replace(choice, " " * len(choice), 1)
for key in sorted(choices.keys()):
add_constraint(choices[key])
print(source, *constraints, sep="\t")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--number", "-n", type=int, default=1, help="number of phrases")
parser.add_argument("--len", "-l", type=int, default=1, help="phrase length")
parser.add_argument(
"--add-sos", default=False, action="store_true", help="add <s> token"
)
parser.add_argument(
"--add-eos", default=False, action="store_true", help="add </s> token"
)
parser.add_argument("--seed", "-s", default=0, type=int)
args = parser.parse_args()
main(args)
| 2,953
| 30.763441
| 88
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/test_inference_dropout.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
import unittest
from fairseq.dataclass.utils import convert_namespace_to_omegaconf
from fairseq.models.transformer import TransformerModel
from tests.test_sequence_generator import get_dummy_task_and_parser
class TestInferenceDropout(unittest.TestCase):
def setUp(self):
self.task, self.parser = get_dummy_task_and_parser()
TransformerModel.add_args(self.parser)
self.args = self.parser.parse_args([])
self.args.encoder_layers = 2
self.args.decoder_layers = 1
logging.disable(logging.CRITICAL)
def tearDown(self):
logging.disable(logging.NOTSET)
def test_sets_inference_dropout_to_true(self):
self.args.retain_dropout = True
self.transformer_model = TransformerModel.build_model(self.args, self.task)
cfg = convert_namespace_to_omegaconf(self.args)
self.transformer_model.prepare_for_inference_(cfg)
assert self.transformer_model.encoder.dropout_module.apply_during_inference
assert self.transformer_model.decoder.dropout_module.apply_during_inference
for layer in self.transformer_model.encoder.layers:
assert layer.dropout_module.apply_during_inference
def test_inference_dropout_false_by_default(self):
self.transformer_model = TransformerModel.build_model(self.args, self.task)
cfg = convert_namespace_to_omegaconf(self.args)
self.transformer_model.prepare_for_inference_(cfg)
assert not self.transformer_model.encoder.dropout_module.apply_during_inference
assert not self.transformer_model.decoder.dropout_module.apply_during_inference
for layer in self.transformer_model.encoder.layers:
assert not layer.dropout_module.apply_during_inference
for layer in self.transformer_model.decoder.layers:
assert not layer.dropout_module.apply_during_inference
def test_applies_training_mode(self):
self.transformer_model = TransformerModel.build_model(self.args, self.task)
assert self.transformer_model.encoder.dropout_module.training
for layer in self.transformer_model.encoder.layers:
assert layer.dropout_module.training
self.transformer_model.eval()
assert not self.transformer_model.decoder.dropout_module.training
for layer in self.transformer_model.encoder.layers:
assert not layer.dropout_module.training
def test_retain_modules(self):
self.args.retain_dropout = True
self.args.retain_dropout_modules = [
"TransformerEncoder",
"TransformerEncoderLayer",
]
self.transformer_model = TransformerModel.build_model(self.args, self.task)
cfg = convert_namespace_to_omegaconf(self.args)
self.transformer_model.prepare_for_inference_(cfg)
assert self.transformer_model.encoder.dropout_module.apply_during_inference
assert not self.transformer_model.decoder.dropout_module.apply_during_inference
for layer in self.transformer_model.decoder.layers:
assert not layer.dropout_module.apply_during_inference
| 3,308
| 45.605634
| 87
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/test_dataset.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
import unittest
from typing import Sequence
from fairseq.data import LanguagePairDataset, ListDataset, RoundRobinZipDatasets
from tests.test_train import mock_dict
def lang_pair_dataset(lengths: Sequence[int]) -> LanguagePairDataset:
tokens = [[i] * l for i, l in enumerate(lengths)]
return LanguagePairDataset(ListDataset(tokens), lengths, mock_dict())
def sample(id: int, length: int):
return {"id": id, "source": [id] * length, "target": None}
class TestDataset(unittest.TestCase):
def setUp(self):
logging.disable(logging.CRITICAL)
def tearDown(self):
logging.disable(logging.NOTSET)
def test_round_robin_zip_datasets(self):
long_dataset = lang_pair_dataset([10, 9, 8, 11])
short_dataset = lang_pair_dataset([11, 9])
dataset = RoundRobinZipDatasets({"a": long_dataset, "b": short_dataset})
# Dataset is now sorted by sentence length
dataset.ordered_indices()
assert dataset.longest_dataset is long_dataset
self.assertEqual(dict(dataset[0]), {"a": sample(2, 8), "b": sample(1, 9)})
# The item 2 of dataset 'a' is with item (2 % 2 = 0) of dataset 'b'
self.assertEqual(dict(dataset[2]), {"a": sample(0, 10), "b": sample(1, 9)})
def test_round_robin_zip_datasets_filtered(self):
long_dataset = lang_pair_dataset([10, 20, 8, 11, 1000, 7, 12])
short_dataset = lang_pair_dataset([11, 20, 9, 1000])
dataset = RoundRobinZipDatasets({"a": long_dataset, "b": short_dataset})
# Dataset is now sorted by sentence length
idx = dataset.ordered_indices()
idx, _ = dataset.filter_indices_by_size(idx, {"a": 19, "b": 900})
self.assertEqual(list(idx), [0, 1, 2, 3, 4])
self.assertEqual(dict(dataset[0]), {"a": sample(5, 7), "b": sample(2, 9)})
self.assertEqual(dict(dataset[2]), {"a": sample(0, 10), "b": sample(1, 20)})
self.assertEqual(dict(dataset[4]), {"a": sample(6, 12), "b": sample(0, 11)})
def test_round_robin_zip_datasets_filtered_with_tuple(self):
long_dataset = lang_pair_dataset([10, 20, 8, 11, 1000, 7, 12])
short_dataset = lang_pair_dataset([11, 20, 9, 1000])
dataset = RoundRobinZipDatasets({"a": long_dataset, "b": short_dataset})
# Dataset is now sorted by sentence length
idx = dataset.ordered_indices()
idx, _ = dataset.filter_indices_by_size(idx, 19)
self.assertEqual(list(idx), [0, 1, 2, 3, 4])
self.assertEqual(dict(dataset[0]), {"a": sample(5, 7), "b": sample(2, 9)})
self.assertEqual(dict(dataset[2]), {"a": sample(0, 10), "b": sample(2, 9)})
self.assertEqual(dict(dataset[4]), {"a": sample(6, 12), "b": sample(2, 9)})
| 2,916
| 42.537313
| 84
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/test_train.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import contextlib
import logging
import unittest
from io import StringIO
from unittest.mock import MagicMock, patch
import torch
from fairseq import checkpoint_utils, data
from omegaconf import OmegaConf
def mock_trainer(epoch, num_updates, iterations_in_epoch):
trainer = MagicMock()
trainer.load_checkpoint.return_value = {
"train_iterator": {
"epoch": epoch,
"iterations_in_epoch": iterations_in_epoch,
"shuffle": False,
},
}
trainer.get_num_updates.return_value = num_updates
return trainer
def mock_dict():
d = MagicMock()
d.pad.return_value = 1
d.eos.return_value = 2
d.unk.return_value = 3
return d
def get_trainer_and_epoch_itr(epoch, epoch_size, num_updates, iterations_in_epoch):
tokens = torch.LongTensor(list(range(epoch_size))).view(1, -1)
tokens_ds = data.TokenBlockDataset(
tokens,
sizes=[tokens.size(-1)],
block_size=1,
pad=0,
eos=1,
include_targets=False,
)
trainer = mock_trainer(epoch, num_updates, iterations_in_epoch)
dataset = data.LanguagePairDataset(
tokens_ds, tokens_ds.sizes, mock_dict(), shuffle=False
)
epoch_itr = data.EpochBatchIterator(
dataset=dataset,
collate_fn=dataset.collater,
batch_sampler=[[i] for i in range(epoch_size)],
)
return trainer, epoch_itr
def get_mock_cfg(finetune_from_model):
cfg_mock = OmegaConf.create(
{
"checkpoint": {
"save_dir": None,
"optimizer_overrides": "{}",
"reset_dataloader": False,
"reset_meters": False,
"reset_optimizer": False,
"reset_lr_scheduler": False,
"finetune_from_model": finetune_from_model,
"model_parallel_size": 1,
"restore_file": "checkpoint_last.pt",
},
"common": {
"model_parallel_size": 1,
},
}
)
return cfg_mock
class TestLoadCheckpoint(unittest.TestCase):
def setUp(self):
self.cfg_mock = get_mock_cfg(None)
self.patches = {
"os.makedirs": MagicMock(),
"os.path.join": MagicMock(),
"os.path.isfile": MagicMock(return_value=True),
"os.path.isabs": MagicMock(return_value=False),
"fairseq.file_io.PathManager.exists": MagicMock(return_value=False),
}
self.applied_patches = [patch(p, d) for p, d in self.patches.items()]
[p.start() for p in self.applied_patches]
logging.disable(logging.CRITICAL)
def tearDown(self):
patch.stopall()
logging.disable(logging.NOTSET)
def test_load_partial_checkpoint(self):
with contextlib.redirect_stdout(StringIO()):
trainer, epoch_itr = get_trainer_and_epoch_itr(2, 150, 200, 50)
trainer.get_train_iterator = MagicMock(return_value=epoch_itr)
_, epoch_itr = checkpoint_utils.load_checkpoint(
self.cfg_mock.checkpoint, trainer
)
self.assertEqual(epoch_itr.epoch, 2)
self.assertEqual(epoch_itr.iterations_in_epoch, 50)
itr = epoch_itr.next_epoch_itr(shuffle=False)
self.assertEqual(epoch_itr.epoch, 2)
self.assertEqual(epoch_itr.iterations_in_epoch, 50)
self.assertEqual(next(itr)["net_input"]["src_tokens"][0].item(), 50)
self.assertEqual(epoch_itr.iterations_in_epoch, 51)
for _ in range(150 - 52):
next(itr)
self.assertEqual(epoch_itr.iterations_in_epoch, 149)
self.assertTrue(itr.has_next())
next(itr)
self.assertFalse(itr.has_next())
itr = epoch_itr.next_epoch_itr(shuffle=False)
self.assertTrue(itr.has_next())
self.assertEqual(epoch_itr.epoch, 3)
self.assertEqual(epoch_itr.iterations_in_epoch, 0)
def test_load_full_checkpoint(self):
with contextlib.redirect_stdout(StringIO()):
trainer, epoch_itr = get_trainer_and_epoch_itr(2, 150, 300, 150)
trainer.get_train_iterator = MagicMock(return_value=epoch_itr)
_, epoch_itr = checkpoint_utils.load_checkpoint(
self.cfg_mock.checkpoint, trainer
)
itr = epoch_itr.next_epoch_itr(shuffle=False)
self.assertEqual(epoch_itr.epoch, 3)
self.assertEqual(epoch_itr.iterations_in_epoch, 0)
self.assertEqual(next(itr)["net_input"]["src_tokens"][0].item(), 0)
def test_load_no_checkpoint(self):
with contextlib.redirect_stdout(StringIO()):
trainer, epoch_itr = get_trainer_and_epoch_itr(1, 150, 0, 0)
trainer.get_train_iterator = MagicMock(return_value=epoch_itr)
self.patches["os.path.isfile"].return_value = False
_, epoch_itr = checkpoint_utils.load_checkpoint(
self.cfg_mock.checkpoint, trainer
)
itr = epoch_itr.next_epoch_itr(shuffle=False)
self.assertEqual(epoch_itr.epoch, 1)
self.assertEqual(epoch_itr.iterations_in_epoch, 0)
self.assertEqual(next(itr)["net_input"]["src_tokens"][0].item(), 0)
def test_finetune_from_model_args_conflict(self):
with contextlib.redirect_stdout(StringIO()):
trainer, epoch_itr = get_trainer_and_epoch_itr(1, 150, 0, 0)
trainer.get_train_iterator = MagicMock(return_value=epoch_itr)
for arg in [
"reset_optimizer",
"reset_lr_scheduler",
"reset_meters",
"reset_dataloader",
]:
with self.subTest(arg=arg):
cfg_mock = get_mock_cfg("/temp/checkpoint_pretrained.pt")
cfg_mock["checkpoint"][arg] = True
with self.assertRaises(Exception) as context:
_, _ = checkpoint_utils.load_checkpoint(
cfg_mock.checkpoint, trainer
)
self.assertTrue(
"--finetune-from-model can not be set together with either --reset-optimizer"
" or reset_lr_scheduler or reset_meters or reset_dataloader"
in str(context.exception)
)
def test_finetune_from_model(self):
with contextlib.redirect_stdout(StringIO()):
trainer, epoch_itr = get_trainer_and_epoch_itr(1, 150, 0, 0)
trainer.get_train_iterator = MagicMock(return_value=epoch_itr)
from_model_path = "/temp/checkpoint_pretrained.pt"
def mock_finetune_exist(path):
if path == from_model_path:
return True
else:
return False
self.patches[
"fairseq.file_io.PathManager.exists"
].side_effect = mock_finetune_exist
cfg_mock = get_mock_cfg(from_model_path)
cfg_mock.checkpoint.restore_file = "checkpoint_last.pt"
_, _ = checkpoint_utils.load_checkpoint(cfg_mock.checkpoint, trainer)
(
checkpoint_path,
reset_optimizer,
reset_lr_scheduler,
optimizer_overrides,
) = trainer.load_checkpoint.call_args[0]
reset_meters = trainer.load_checkpoint.call_args[1]["reset_meters"]
self.assertTrue(reset_optimizer)
self.assertTrue(reset_lr_scheduler)
self.assertTrue(reset_meters)
def test_finetune_from_model_resume(self):
with contextlib.redirect_stdout(StringIO()):
trainer, epoch_itr = get_trainer_and_epoch_itr(1, 150, 0, 0)
trainer.get_train_iterator = MagicMock(return_value=epoch_itr)
from_model_path = "/temp/checkpoint_pretrained.pt"
# launch second time
# both restore_file=checkpoint_last.pt and finetune_from_model are set
def mock_finetune_exist(path):
if path == from_model_path or path.endsWith("checkpoint_last.pt"):
return True
else:
return False
self.patches[
"fairseq.file_io.PathManager.exists"
].side_effect = mock_finetune_exist
cfg_mock = get_mock_cfg(from_model_path)
cfg_mock.checkpoint.restore_file = "checkpoint_last.pt"
_, _ = checkpoint_utils.load_checkpoint(cfg_mock.checkpoint, trainer)
(
checkpoint_path,
reset_optimizer,
reset_lr_scheduler,
optimizer_overrides,
) = trainer.load_checkpoint.call_args[0]
reset_meters = trainer.load_checkpoint.call_args[1]["reset_meters"]
self.assertFalse(reset_optimizer)
self.assertFalse(reset_lr_scheduler)
self.assertFalse(reset_meters)
if __name__ == "__main__":
unittest.main()
| 9,292
| 36.471774
| 101
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/test_iterators.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
from fairseq.data import iterators
class TestIterators(unittest.TestCase):
def test_counting_iterator_index(self, ref=None, itr=None):
# Test the indexing functionality of CountingIterator
if ref is None:
assert itr is None
ref = list(range(10))
itr = iterators.CountingIterator(ref)
else:
assert len(ref) == 10
assert itr is not None
self.assertTrue(itr.has_next())
self.assertEqual(itr.n, 0)
self.assertEqual(next(itr), ref[0])
self.assertEqual(itr.n, 1)
self.assertEqual(next(itr), ref[1])
self.assertEqual(itr.n, 2)
itr.skip(3)
self.assertEqual(itr.n, 5)
self.assertEqual(next(itr), ref[5])
itr.skip(2)
self.assertEqual(itr.n, 8)
self.assertEqual(list(itr), [ref[8], ref[9]])
self.assertFalse(itr.has_next())
def test_counting_iterator_length_mismatch(self):
ref = list(range(10))
# When the underlying iterable is longer than the CountingIterator,
# the remaining items in the iterable should be ignored
itr = iterators.CountingIterator(ref, total=8)
self.assertEqual(list(itr), ref[:8])
# When the underlying iterable is shorter than the CountingIterator,
# raise an IndexError when the underlying iterable is exhausted
itr = iterators.CountingIterator(ref, total=12)
self.assertRaises(IndexError, list, itr)
def test_counting_iterator_take(self):
# Test the "take" method of CountingIterator
ref = list(range(10))
itr = iterators.CountingIterator(ref)
itr.take(5)
self.assertEqual(len(itr), len(list(iter(itr))))
self.assertEqual(len(itr), 5)
itr = iterators.CountingIterator(ref)
itr.take(5)
self.assertEqual(next(itr), ref[0])
self.assertEqual(next(itr), ref[1])
itr.skip(2)
self.assertEqual(next(itr), ref[4])
self.assertFalse(itr.has_next())
def test_grouped_iterator(self):
# test correctness
x = list(range(10))
itr = iterators.GroupedIterator(x, 1)
self.assertEqual(list(itr), [[0], [1], [2], [3], [4], [5], [6], [7], [8], [9]])
itr = iterators.GroupedIterator(x, 4)
self.assertEqual(list(itr), [[0, 1, 2, 3], [4, 5, 6, 7], [8, 9]])
itr = iterators.GroupedIterator(x, 5)
self.assertEqual(list(itr), [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]])
# test the GroupIterator also works correctly as a CountingIterator
x = list(range(30))
ref = list(iterators.GroupedIterator(x, 3))
itr = iterators.GroupedIterator(x, 3)
self.test_counting_iterator_index(ref, itr)
def test_sharded_iterator(self):
# test correctness
x = list(range(10))
itr = iterators.ShardedIterator(x, num_shards=1, shard_id=0)
self.assertEqual(list(itr), x)
itr = iterators.ShardedIterator(x, num_shards=2, shard_id=0)
self.assertEqual(list(itr), [0, 2, 4, 6, 8])
itr = iterators.ShardedIterator(x, num_shards=2, shard_id=1)
self.assertEqual(list(itr), [1, 3, 5, 7, 9])
itr = iterators.ShardedIterator(x, num_shards=3, shard_id=0)
self.assertEqual(list(itr), [0, 3, 6, 9])
itr = iterators.ShardedIterator(x, num_shards=3, shard_id=1)
self.assertEqual(list(itr), [1, 4, 7, None])
itr = iterators.ShardedIterator(x, num_shards=3, shard_id=2)
self.assertEqual(list(itr), [2, 5, 8, None])
# test CountingIterator functionality
x = list(range(30))
ref = list(iterators.ShardedIterator(x, num_shards=3, shard_id=0))
itr = iterators.ShardedIterator(x, num_shards=3, shard_id=0)
self.test_counting_iterator_index(ref, itr)
def test_counting_iterator_buffered_iterator_take(self):
ref = list(range(10))
buffered_itr = iterators.BufferedIterator(2, ref)
itr = iterators.CountingIterator(buffered_itr)
itr.take(5)
self.assertEqual(len(itr), len(list(iter(itr))))
self.assertEqual(len(itr), 5)
buffered_itr = iterators.BufferedIterator(2, ref)
itr = iterators.CountingIterator(buffered_itr)
itr.take(5)
self.assertEqual(len(buffered_itr), 5)
self.assertEqual(len(list(iter(buffered_itr))), 5)
buffered_itr = iterators.BufferedIterator(2, ref)
itr = iterators.CountingIterator(buffered_itr)
itr.take(5)
self.assertEqual(next(itr), ref[0])
self.assertEqual(next(itr), ref[1])
itr.skip(2)
self.assertEqual(next(itr), ref[4])
self.assertFalse(itr.has_next())
self.assertRaises(StopIteration, next, buffered_itr)
ref = list(range(4, 10))
buffered_itr = iterators.BufferedIterator(2, ref)
itr = iterators.CountingIterator(buffered_itr, start=4)
itr.take(5)
self.assertEqual(len(itr), 5)
self.assertEqual(len(buffered_itr), 1)
self.assertEqual(next(itr), ref[0])
self.assertFalse(itr.has_next())
self.assertRaises(StopIteration, next, buffered_itr)
if __name__ == "__main__":
unittest.main()
| 5,433
| 38.376812
| 87
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/test_checkpoint_utils.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import contextlib
import logging
import os
import tempfile
import unittest
from io import StringIO
from unittest.mock import patch
from fairseq import checkpoint_utils
from omegaconf import OmegaConf
from tests.utils import (
create_dummy_data,
preprocess_translation_data,
train_translation_model,
)
class TestCheckpointUtils(unittest.TestCase):
def setUp(self):
logging.disable(logging.CRITICAL)
def tearDown(self):
logging.disable(logging.NOTSET)
@contextlib.contextmanager
def _train_transformer(self, seed, extra_args=None):
if extra_args is None:
extra_args = []
with tempfile.TemporaryDirectory(f"_train_transformer_seed{seed}") as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir)
train_translation_model(
data_dir,
"transformer_iwslt_de_en",
[
"--encoder-layers",
"3",
"--decoder-layers",
"3",
"--encoder-embed-dim",
"8",
"--decoder-embed-dim",
"8",
"--seed",
str(seed),
]
+ extra_args,
)
yield os.path.join(data_dir, "checkpoint_last.pt")
def test_load_model_ensemble_and_task(self):
# with contextlib.redirect_stdout(StringIO()):
with self._train_transformer(seed=123) as model1:
with self._train_transformer(seed=456) as model2:
ensemble, cfg, task = checkpoint_utils.load_model_ensemble_and_task(
filenames=[model1, model2]
)
self.assertEqual(len(ensemble), 2)
# after Transformer has been migrated to Hydra, this will probably
# become cfg.common.seed
self.assertEqual(ensemble[0].args.seed, 123)
self.assertEqual(ensemble[1].args.seed, 456)
# the task from the first model should be returned
self.assertTrue("seed123" in task.cfg.data)
# last cfg is saved
self.assertEqual(cfg.common.seed, 456)
def test_prune_state_dict(self):
with contextlib.redirect_stdout(StringIO()):
extra_args = ["--encoder-layerdrop", "0.01", "--decoder-layerdrop", "0.01"]
with self._train_transformer(seed=1, extra_args=extra_args) as model:
ensemble, cfg, task = checkpoint_utils.load_model_ensemble_and_task(
filenames=[model],
arg_overrides={
"encoder_layers_to_keep": "0,2",
"decoder_layers_to_keep": "1",
},
)
self.assertEqual(len(ensemble), 1)
self.assertEqual(len(ensemble[0].encoder.layers), 2)
self.assertEqual(len(ensemble[0].decoder.layers), 1)
def test_torch_persistent_save_async(self):
state_dict = {}
filename = "async_checkpoint.pt"
with patch(f"{checkpoint_utils.__name__}.PathManager.opena") as mock_opena:
with patch(
f"{checkpoint_utils.__name__}._torch_persistent_save"
) as mock_save:
checkpoint_utils.torch_persistent_save(
state_dict, filename, async_write=True
)
mock_opena.assert_called_with(filename, "wb")
mock_save.assert_called()
if __name__ == "__main__":
unittest.main()
| 3,834
| 34.183486
| 87
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/test_average_checkpoints.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import collections
import os
import shutil
import tempfile
import unittest
import numpy as np
import torch
from scripts.average_checkpoints import average_checkpoints
from torch import nn
class ModelWithSharedParameter(nn.Module):
def __init__(self):
super(ModelWithSharedParameter, self).__init__()
self.embedding = nn.Embedding(1000, 200)
self.FC1 = nn.Linear(200, 200)
self.FC2 = nn.Linear(200, 200)
# tie weight in FC2 to FC1
self.FC2.weight = nn.Parameter(self.FC1.weight)
self.FC2.bias = nn.Parameter(self.FC1.bias)
self.relu = nn.ReLU()
def forward(self, input):
return self.FC2(self.ReLU(self.FC1(input))) + self.FC1(input)
class TestAverageCheckpoints(unittest.TestCase):
def test_average_checkpoints(self):
params_0 = collections.OrderedDict(
[
("a", torch.DoubleTensor([100.0])),
("b", torch.FloatTensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])),
("c", torch.IntTensor([7, 8, 9])),
]
)
params_1 = collections.OrderedDict(
[
("a", torch.DoubleTensor([1.0])),
("b", torch.FloatTensor([[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]])),
("c", torch.IntTensor([2, 2, 2])),
]
)
params_avg = collections.OrderedDict(
[
("a", torch.DoubleTensor([50.5])),
("b", torch.FloatTensor([[1.0, 1.5, 2.0], [2.5, 3.0, 3.5]])),
# We expect truncation for integer division
("c", torch.IntTensor([4, 5, 5])),
]
)
fd_0, path_0 = tempfile.mkstemp()
fd_1, path_1 = tempfile.mkstemp()
torch.save(collections.OrderedDict([("model", params_0)]), path_0)
torch.save(collections.OrderedDict([("model", params_1)]), path_1)
output = average_checkpoints([path_0, path_1])["model"]
os.close(fd_0)
os.remove(path_0)
os.close(fd_1)
os.remove(path_1)
for (k_expected, v_expected), (k_out, v_out) in zip(
params_avg.items(), output.items()
):
self.assertEqual(
k_expected,
k_out,
"Key mismatch - expected {} but found {}. "
"(Expected list of keys: {} vs actual list of keys: {})".format(
k_expected, k_out, params_avg.keys(), output.keys()
),
)
np.testing.assert_allclose(
v_expected.numpy(),
v_out.numpy(),
err_msg="Tensor value mismatch for key {}".format(k_expected),
)
def test_average_checkpoints_with_shared_parameters(self):
def _construct_model_with_shared_parameters(path, value):
m = ModelWithSharedParameter()
nn.init.constant_(m.FC1.weight, value)
torch.save({"model": m.state_dict()}, path)
return m
tmpdir = tempfile.mkdtemp()
paths = []
path = os.path.join(tmpdir, "m1.pt")
m1 = _construct_model_with_shared_parameters(path, 1.0)
paths.append(path)
path = os.path.join(tmpdir, "m2.pt")
m2 = _construct_model_with_shared_parameters(path, 2.0)
paths.append(path)
path = os.path.join(tmpdir, "m3.pt")
m3 = _construct_model_with_shared_parameters(path, 3.0)
paths.append(path)
new_model = average_checkpoints(paths)
self.assertTrue(
torch.equal(
new_model["model"]["embedding.weight"],
(m1.embedding.weight + m2.embedding.weight + m3.embedding.weight) / 3.0,
)
)
self.assertTrue(
torch.equal(
new_model["model"]["FC1.weight"],
(m1.FC1.weight + m2.FC1.weight + m3.FC1.weight) / 3.0,
)
)
self.assertTrue(
torch.equal(
new_model["model"]["FC2.weight"],
(m1.FC2.weight + m2.FC2.weight + m3.FC2.weight) / 3.0,
)
)
shutil.rmtree(tmpdir)
if __name__ == "__main__":
unittest.main()
| 4,385
| 31.488889
| 88
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/test_reproducibility.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import contextlib
import json
import os
import tempfile
import unittest
from io import StringIO
import torch
from . import test_binaries
class TestReproducibility(unittest.TestCase):
def _test_reproducibility(
self,
name,
extra_flags=None,
delta=0.0001,
resume_checkpoint="checkpoint1.pt",
max_epoch=3,
):
def get_last_log_stats_containing_string(log_records, search_string):
for log_record in logs.records[::-1]:
if isinstance(log_record.msg, str) and search_string in log_record.msg:
return json.loads(log_record.msg)
if extra_flags is None:
extra_flags = []
with tempfile.TemporaryDirectory(name) as data_dir:
with self.assertLogs() as logs:
test_binaries.create_dummy_data(data_dir)
test_binaries.preprocess_translation_data(data_dir)
# train epochs 1 and 2 together
with self.assertLogs() as logs:
test_binaries.train_translation_model(
data_dir,
"fconv_iwslt_de_en",
[
"--dropout",
"0.0",
"--log-format",
"json",
"--log-interval",
"1",
"--max-epoch",
str(max_epoch),
]
+ extra_flags,
)
train_log = get_last_log_stats_containing_string(logs.records, "train_loss")
valid_log = get_last_log_stats_containing_string(logs.records, "valid_loss")
# train epoch 2, resuming from previous checkpoint 1
os.rename(
os.path.join(data_dir, resume_checkpoint),
os.path.join(data_dir, "checkpoint_last.pt"),
)
with self.assertLogs() as logs:
test_binaries.train_translation_model(
data_dir,
"fconv_iwslt_de_en",
[
"--dropout",
"0.0",
"--log-format",
"json",
"--log-interval",
"1",
"--max-epoch",
str(max_epoch),
]
+ extra_flags,
)
train_res_log = get_last_log_stats_containing_string(
logs.records, "train_loss"
)
valid_res_log = get_last_log_stats_containing_string(
logs.records, "valid_loss"
)
for k in ["train_loss", "train_ppl", "train_num_updates", "train_gnorm"]:
self.assertAlmostEqual(
float(train_log[k]), float(train_res_log[k]), delta=delta
)
for k in [
"valid_loss",
"valid_ppl",
"valid_num_updates",
"valid_best_loss",
]:
self.assertAlmostEqual(
float(valid_log[k]), float(valid_res_log[k]), delta=delta
)
def test_reproducibility(self):
self._test_reproducibility("test_reproducibility")
@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU")
def test_reproducibility_fp16(self):
self._test_reproducibility(
"test_reproducibility_fp16",
[
"--fp16",
"--fp16-init-scale",
"4096",
],
delta=0.011,
)
@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU")
def test_reproducibility_memory_efficient_fp16(self):
self._test_reproducibility(
"test_reproducibility_memory_efficient_fp16",
[
"--memory-efficient-fp16",
"--fp16-init-scale",
"4096",
],
)
@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU")
def test_reproducibility_amp(self):
self._test_reproducibility(
"test_reproducibility_amp",
[
"--amp",
"--fp16-init-scale",
"4096",
],
delta=0.011,
)
def test_mid_epoch_reproducibility(self):
self._test_reproducibility(
"test_mid_epoch_reproducibility",
["--save-interval-updates", "3"],
resume_checkpoint="checkpoint_1_3.pt",
max_epoch=1,
)
if __name__ == "__main__":
unittest.main()
| 4,906
| 31.496689
| 88
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/test_sequence_scorer.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import unittest
import tests.utils as test_utils
import torch
from fairseq.sequence_scorer import SequenceScorer
class TestSequenceScorer(unittest.TestCase):
def test_sequence_scorer(self):
# construct dummy dictionary
d = test_utils.dummy_dictionary(vocab_size=2)
self.assertEqual(d.pad(), 1)
self.assertEqual(d.eos(), 2)
self.assertEqual(d.unk(), 3)
eos = d.eos()
w1 = 4
w2 = 5
# construct dataloader
data = [
{
"source": torch.LongTensor([w1, w2, eos]),
"target": torch.LongTensor([w1, w2, w1, eos]),
},
{
"source": torch.LongTensor([w2, eos]),
"target": torch.LongTensor([w2, w1, eos]),
},
{
"source": torch.LongTensor([w2, eos]),
"target": torch.LongTensor([w2, eos]),
},
]
data_itr = test_utils.dummy_dataloader(data)
# specify expected output probabilities
args = argparse.Namespace()
unk = 0.0
args.beam_probs = [
# step 0:
torch.FloatTensor(
[
# eos w1 w2
[0.0, unk, 0.6, 0.4], # sentence 1
[0.0, unk, 0.4, 0.6], # sentence 2
[0.0, unk, 0.7, 0.3], # sentence 3
]
),
# step 1:
torch.FloatTensor(
[
# eos w1 w2
[0.0, unk, 0.2, 0.7], # sentence 1
[0.0, unk, 0.8, 0.2], # sentence 2
[0.7, unk, 0.1, 0.2], # sentence 3
]
),
# step 2:
torch.FloatTensor(
[
# eos w1 w2
[0.10, unk, 0.50, 0.4], # sentence 1
[0.15, unk, 0.15, 0.7], # sentence 2
[0.00, unk, 0.00, 0.0], # sentence 3
]
),
# step 3:
torch.FloatTensor(
[
# eos w1 w2
[0.9, unk, 0.05, 0.05], # sentence 1
[0.0, unk, 0.00, 0.0], # sentence 2
[0.0, unk, 0.00, 0.0], # sentence 3
]
),
]
expected_scores = [
[0.6, 0.7, 0.5, 0.9], # sentence 1
[0.6, 0.8, 0.15], # sentence 2
[0.3, 0.7], # sentence 3
]
task = test_utils.TestTranslationTask.setup_task(args, d, d)
model = task.build_model(args)
scorer = SequenceScorer(task.target_dictionary)
for sample in data_itr:
hypos = task.inference_step(scorer, [model], sample)
for id, hypos_id in zip(sample["id"].tolist(), hypos):
self.assertHypoTokens(hypos_id[0], data[id]["target"])
self.assertHypoScore(hypos_id[0], expected_scores[id])
def assertHypoTokens(self, hypo, tokens):
self.assertTensorEqual(hypo["tokens"], torch.LongTensor(tokens))
def assertHypoScore(self, hypo, pos_probs, normalized=True, lenpen=1.0):
pos_scores = torch.FloatTensor(pos_probs).log()
self.assertAlmostEqual(hypo["positional_scores"], pos_scores)
self.assertEqual(pos_scores.numel(), hypo["tokens"].numel())
score = pos_scores.sum()
if normalized:
score /= pos_scores.numel() ** lenpen
self.assertLess(abs(score - hypo["score"]), 1e-6)
def assertAlmostEqual(self, t1, t2):
self.assertEqual(t1.size(), t2.size(), "size mismatch")
self.assertLess((t1 - t2).abs().max(), 1e-4)
def assertTensorEqual(self, t1, t2):
self.assertEqual(t1.size(), t2.size(), "size mismatch")
self.assertEqual(t1.ne(t2).long().sum(), 0)
if __name__ == "__main__":
unittest.main()
| 4,150
| 33.305785
| 76
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/test_multi_corpus_dataset.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
from collections import OrderedDict
import torch
from fairseq.data import LanguagePairDataset, TokenBlockDataset
from fairseq.data.multi_corpus_dataset import MultiCorpusDataset
from tests.test_train import mock_dict
class TestMultiCorpusDataset(unittest.TestCase):
def setUp(self):
d = mock_dict()
tokens_1 = torch.LongTensor([i for i in range(1, 5000, 2)]).view(1, -1)
tokens_ds1 = TokenBlockDataset(
tokens_1,
sizes=[tokens_1.size(-1)],
block_size=1,
pad=0,
eos=1,
include_targets=False,
)
self.dataset_1 = LanguagePairDataset(
tokens_ds1, tokens_ds1.sizes, d, shuffle=False
)
tokens_2 = torch.LongTensor([i for i in range(0, 5000, 2)]).view(1, -1)
tokens_ds2 = TokenBlockDataset(
tokens_2,
sizes=[tokens_2.size(-1)],
block_size=1,
pad=0,
eos=1,
include_targets=False,
)
self.dataset_2 = LanguagePairDataset(
tokens_ds2, tokens_ds2.sizes, d, shuffle=False
)
def _test_sample_helper(
self,
distribution,
):
m = MultiCorpusDataset(
OrderedDict({0: self.dataset_1, 1: self.dataset_2}),
distribution=distribution,
seed=0,
sort_indices=True,
)
m.set_epoch(1)
indices = m.ordered_indices()
count_sample_from_first_dataset = 0
items = set()
for i in indices:
item = m[i]["source"].item()
if item % 2 == 1:
count_sample_from_first_dataset += 1
items.add(item)
sample_from_first_ds_percentage = (
1.0 * count_sample_from_first_dataset / len(indices)
)
self.assertLess(
abs(sample_from_first_ds_percentage - distribution[0]),
0.01,
)
self.assertEqual(
len(items),
int(
min(len(self.dataset_1), len(indices) * distribution[0])
+ min(len(self.dataset_1), len(indices) * distribution[1])
),
)
print(distribution)
def test_multi_corpus_dataset(self):
for distribution in [[0.5, 0.5], [0.1, 0.9], [0.9, 0.1], [0.0, 1.0]]:
self._test_sample_helper(distribution=distribution)
| 2,586
| 30.54878
| 79
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/test_dataclass_utils.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
from argparse import ArgumentParser
from dataclasses import dataclass, field
from fairseq.dataclass import FairseqDataclass
from fairseq.dataclass.utils import gen_parser_from_dataclass
@dataclass
class A(FairseqDataclass):
data: str = field(default="test", metadata={"help": "the data input"})
num_layers: int = field(default=200, metadata={"help": "more layers is better?"})
@dataclass
class B(FairseqDataclass):
bar: A = field(default=A())
foo: int = field(default=0, metadata={"help": "not a bar"})
@dataclass
class D(FairseqDataclass):
arch: A = field(default=A())
foo: int = field(default=0, metadata={"help": "not a bar"})
@dataclass
class C(FairseqDataclass):
data: str = field(default="test", metadata={"help": "root level data input"})
encoder: D = field(default=D())
decoder: A = field(default=A())
lr: int = field(default=0, metadata={"help": "learning rate"})
class TestDataclassUtils(unittest.TestCase):
def test_argparse_convert_basic(self):
parser = ArgumentParser()
gen_parser_from_dataclass(parser, A(), True)
args = parser.parse_args(["--num-layers", "10", "the/data/path"])
self.assertEqual(args.num_layers, 10)
self.assertEqual(args.data, "the/data/path")
def test_argparse_recursive(self):
parser = ArgumentParser()
gen_parser_from_dataclass(parser, B(), True)
args = parser.parse_args(["--num-layers", "10", "--foo", "10", "the/data/path"])
self.assertEqual(args.num_layers, 10)
self.assertEqual(args.foo, 10)
self.assertEqual(args.data, "the/data/path")
def test_argparse_recursive_prefixing(self):
self.maxDiff = None
parser = ArgumentParser()
gen_parser_from_dataclass(parser, C(), True, "")
args = parser.parse_args(
[
"--encoder-arch-data",
"ENCODER_ARCH_DATA",
"--encoder-arch-num-layers",
"10",
"--encoder-foo",
"10",
"--decoder-data",
"DECODER_DATA",
"--decoder-num-layers",
"10",
"--lr",
"10",
"the/data/path",
]
)
self.assertEqual(args.encoder_arch_data, "ENCODER_ARCH_DATA")
self.assertEqual(args.encoder_arch_num_layers, 10)
self.assertEqual(args.encoder_foo, 10)
self.assertEqual(args.decoder_data, "DECODER_DATA")
self.assertEqual(args.decoder_num_layers, 10)
self.assertEqual(args.lr, 10)
self.assertEqual(args.data, "the/data/path")
if __name__ == "__main__":
unittest.main()
| 2,896
| 31.920455
| 88
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/test_memory_efficient_fp16.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import logging
import unittest
import torch
from fairseq.optim.adam import FairseqAdam
from fairseq.optim.fp16_optimizer import MemoryEfficientFP16Optimizer
from omegaconf import OmegaConf
@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU")
class TestMemoryEfficientFP16(unittest.TestCase):
def setUp(self):
logging.disable(logging.CRITICAL)
def tearDown(self):
logging.disable(logging.NOTSET)
def test_load_state_dict(self):
# define simple FP16 model
model = torch.nn.Linear(5, 5).cuda().half()
params = list(model.parameters())
# initialize memory efficient FP16 optimizer
# with pseudo DictConfigs
optimizer = FairseqAdam(
cfg=OmegaConf.create(
vars(
argparse.Namespace(
adam_betas="(0.9, 0.999)",
adam_eps=1e-8,
weight_decay=0.0,
lr=[0.00001],
)
)
),
params=params,
)
me_optimizer = MemoryEfficientFP16Optimizer(
cfg=OmegaConf.create(
{
"common": vars(
argparse.Namespace(
fp16_init_scale=1,
fp16_scale_window=1,
fp16_scale_tolerance=1,
threshold_loss_scale=1,
min_loss_scale=1e-4,
)
)
}
),
params=params,
optimizer=optimizer,
)
# optimizer state is created in the first step
loss = model(torch.rand(5).cuda().half()).sum()
me_optimizer.backward(loss)
me_optimizer.step()
# reload state
state = me_optimizer.state_dict()
me_optimizer.load_state_dict(state)
for k, v in me_optimizer.optimizer.state.items():
self.assertTrue(k.dtype == torch.float16)
for v_i in v.values():
if torch.is_tensor(v_i):
self.assertTrue(v_i.dtype == torch.float32)
if __name__ == "__main__":
unittest.main()
| 2,452
| 30.050633
| 70
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/test_data_utils.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import numpy as np
from fairseq.data.data_utils_fast import batch_by_size_fn
from fairseq.data.data_utils_fast import batch_by_size_vec
class TestBatchBySize(unittest.TestCase):
@classmethod
def batch_by_size_baseline(
cls,
indices,
num_tokens_vec,
max_tokens,
max_sentences,
bsz_mult,
):
"""Simple, reliable and slow implementation of batch by size"""
batches = []
start = 0
while start < len(indices):
for end in range(start + 1, len(indices) + 1):
max_val = max(num_tokens_vec[pos] for pos in range(start, end))
sent_count = end - start
num_tokens = max_val * sent_count
overflow = num_tokens > max_tokens > 0 or sent_count > max_sentences > 0
terminate = overflow or end == len(indices)
if overflow:
sent_count -= 1
if terminate:
if sent_count > bsz_mult:
sent_count = sent_count - sent_count % bsz_mult
batches.append(indices[start : start + sent_count])
start = start + sent_count
break
return batches
@classmethod
def _get_error_message(
cls, max_sentences, max_tokens, bsz_mult, num_tokens_vec, validation, results
):
return f"""Reference batch_by_size implementation should produce
same output as the baseline method.
Params:
max_sentences={max_sentences},
max_tokens={max_tokens},
bsz_mult={bsz_mult},
num_tokens_vec={num_tokens_vec},
expected_batches={validation},
returned_batches={results}"""
def _compare_results(
self,
indices_len,
batch_by_size_impl,
max_sentences,
max_tokens,
bsz_mult,
num_tokens_vec,
):
indices = np.array(list(range(indices_len)))
validation = self.batch_by_size_baseline(
indices,
num_tokens_vec,
max_tokens=max_tokens,
max_sentences=max_sentences,
bsz_mult=bsz_mult,
)
results = batch_by_size_impl(
indices,
num_tokens_vec,
max_tokens=max_tokens,
max_sentences=max_sentences,
bsz_mult=bsz_mult,
)
error_msg = self._get_error_message(
max_sentences, max_tokens, bsz_mult, num_tokens_vec, validation, results
)
self.assertEqual(len(validation), len(results), error_msg)
for first, second in zip(validation, results):
self.assertTrue(np.array_equal(first, second), error_msg)
def _run_compare_with_baseline_sweep(self, batch_by_size_impl):
"""Compare reference batch_by_size implementation with batch_by_size_baseline
across a dense grid of hyperparam values"""
MAX_MAX_TOKENS = 10
NUM_TOKENS_VECS_COUNT = 5
for indices_len in [10, 11]: # try odd and even len of indices
for max_sentences in range(0, indices_len + 2):
for max_tokens in range(0, MAX_MAX_TOKENS):
for bsz_mult in range(1, max(MAX_MAX_TOKENS, indices_len) + 2):
for _ in range(NUM_TOKENS_VECS_COUNT):
num_tokens_vec = np.random.randint(
0, max_tokens + 1, size=indices_len
)
self._compare_results(
indices_len,
batch_by_size_impl,
max_sentences,
max_tokens,
bsz_mult,
num_tokens_vec,
)
class TestBatchBySizeVec(TestBatchBySize):
def test_compare_with_baseline(self):
self._run_compare_with_baseline_sweep(batch_by_size_vec)
class TestBatchBySizeFn(TestBatchBySize):
def test_compare_with_baseline(self):
def batch_by_size_fn_wrapper(
indices,
num_tokens_vec,
max_tokens,
max_sentences,
bsz_mult,
):
def num_tokens_fn(idx):
return num_tokens_vec[idx]
return batch_by_size_fn(
indices, num_tokens_fn, max_tokens, max_sentences, bsz_mult
)
self._run_compare_with_baseline_sweep(batch_by_size_fn_wrapper)
if __name__ == "__main__":
unittest.main()
| 4,866
| 34.525547
| 88
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/test_ema.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
from copy import deepcopy
from dataclasses import dataclass
from typing import Optional
import torch
from fairseq.models.ema import EMA
class DummyModule(torch.nn.Module):
def __init__(self) -> None:
"""LightningModule for testing purposes
Args:
epoch_min_loss_override (int, optional): Pass in an epoch that will be set to the minimum
validation loss for testing purposes (zero based). If None this is ignored. Defaults to None.
"""
super().__init__()
self.layer = torch.nn.Linear(in_features=32, out_features=2)
self.another_layer = torch.nn.Linear(in_features=2, out_features=2)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.layer(x)
return self.another_layer(x)
@dataclass
class EMAConfig(object):
ema_decay: float = 0.99
ema_start_update: int = 0
ema_fp32: bool = False
ema_seed_model: Optional[str] = None
class TestEMAGPU(unittest.TestCase):
def assertTorchAllClose(self, x, y, atol=1e-8, rtol=1e-5, msg=None):
diff = x.float() - y.float()
diff_norm = torch.norm(diff)
other_norm = torch.norm(y.float())
if msg is None:
msg = "|input - other| > {} + {} * |other|".format(atol, rtol)
self.assertLessEqual(
diff_norm,
atol + rtol * other_norm,
msg=msg,
)
def test_ema(self):
model = DummyModule()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
state = deepcopy(model.state_dict())
config = EMAConfig()
ema = EMA(model, config)
# set decay
ema._set_decay(config.ema_decay)
self.assertEqual(ema.get_decay(), config.ema_decay)
# get model
self.assertEqual(ema.get_model(), ema.model)
# Since fp32 params is not used, it should be of size 0
self.assertEqual(len(ema.fp32_params), 0)
# EMA step
x = torch.randn(32)
y = model(x)
loss = y.sum()
loss.backward()
optimizer.step()
ema.step(model)
ema_state_dict = ema.get_model().state_dict()
for key, param in model.state_dict().items():
prev_param = state[key]
ema_param = ema_state_dict[key]
if "version" in key:
# Do not decay a model.version pytorch param
continue
self.assertTorchAllClose(
ema_param,
config.ema_decay * prev_param + (1 - config.ema_decay) * param,
)
# Since fp32 params is not used, it should be of size 0
self.assertEqual(len(ema.fp32_params), 0)
# Load EMA into model
model2 = DummyModule()
ema.reverse(model2)
for key, param in model2.state_dict().items():
ema_param = ema_state_dict[key]
self.assertTrue(torch.allclose(ema_param, param))
def test_ema_fp32(self):
model = DummyModule().half()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
state = deepcopy(model.state_dict())
config = EMAConfig(ema_fp32=True)
ema = EMA(model, config)
x = torch.randn(32)
y = model(x.half())
loss = y.sum()
loss.backward()
optimizer.step()
ema.step(model)
for key, param in model.state_dict().items():
prev_param = state[key]
ema_param = ema.get_model().state_dict()[key]
if "version" in key:
# Do not decay a model.version pytorch param
continue
self.assertIn(key, ema.fp32_params)
# EMA update is done in fp32, and hence the EMA param must be
# closer to the EMA update done in fp32 than in fp16.
self.assertLessEqual(
torch.norm(
ema_param.float()
- (
config.ema_decay * prev_param.float()
+ (1 - config.ema_decay) * param.float()
)
.half()
.float()
),
torch.norm(
ema_param.float()
- (
config.ema_decay * prev_param + (1 - config.ema_decay) * param
).float()
),
)
self.assertTorchAllClose(
ema_param,
(
config.ema_decay * prev_param.float()
+ (1 - config.ema_decay) * param.float()
).half(),
)
def test_ema_fp16(self):
model = DummyModule().half()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
state = deepcopy(model.state_dict())
config = EMAConfig(ema_fp32=False)
ema = EMA(model, config)
# Since fp32 params is not used, it should be of size 0
self.assertEqual(len(ema.fp32_params), 0)
x = torch.randn(32)
y = model(x.half())
loss = y.sum()
loss.backward()
optimizer.step()
ema.step(model)
for key, param in model.state_dict().items():
prev_param = state[key]
ema_param = ema.get_model().state_dict()[key]
if "version" in key:
# Do not decay a model.version pytorch param
continue
# EMA update is done in fp16, and hence the EMA param must be
# closer to the EMA update done in fp16 than in fp32.
self.assertLessEqual(
torch.norm(
ema_param.float()
- (
config.ema_decay * prev_param + (1 - config.ema_decay) * param
).float()
),
torch.norm(
ema_param.float()
- (
config.ema_decay * prev_param.float()
+ (1 - config.ema_decay) * param.float()
)
.half()
.float()
),
)
self.assertTorchAllClose(
ema_param,
config.ema_decay * prev_param + (1 - config.ema_decay) * param,
)
# Since fp32 params is not used, it should be of size 0
self.assertEqual(len(ema.fp32_params), 0)
if __name__ == "__main__":
unittest.main()
| 6,675
| 30.342723
| 109
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/test_file_chunker_utils.py
|
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
import shutil
import tempfile
import unittest
from typing import Optional
class TestFileChunker(unittest.TestCase):
_tmpdir: Optional[str] = None
_tmpfile: Optional[str] = None
_line_content = "Hello, World\n"
_num_bytes = None
_num_lines = 200
_num_splits = 20
@classmethod
def setUpClass(cls) -> None:
cls._num_bytes = len(cls._line_content.encode("utf-8"))
cls._tmpdir = tempfile.mkdtemp()
with open(os.path.join(cls._tmpdir, "test.txt"), "w") as f:
cls._tmpfile = f.name
for _i in range(cls._num_lines):
f.write(cls._line_content)
f.flush()
@classmethod
def tearDownClass(cls) -> None:
# Cleanup temp working dir.
if cls._tmpdir is not None:
shutil.rmtree(cls._tmpdir) # type: ignore
def test_find_offsets(self):
from fairseq.file_chunker_utils import find_offsets
offsets = find_offsets(self._tmpfile, self._num_splits)
self.assertEqual(len(offsets), self._num_splits + 1)
(zero, *real_offsets, last) = offsets
self.assertEqual(zero, 0)
for i, o in enumerate(real_offsets):
self.assertEqual(
o,
self._num_bytes
+ ((i + 1) * self._num_bytes * self._num_lines / self._num_splits),
)
self.assertEqual(last, self._num_bytes * self._num_lines)
def test_readchunks(self):
from fairseq.file_chunker_utils import Chunker, find_offsets
offsets = find_offsets(self._tmpfile, self._num_splits)
for start, end in zip(offsets, offsets[1:]):
with Chunker(self._tmpfile, start, end) as lines:
all_lines = list(lines)
num_lines = self._num_lines / self._num_splits
self.assertAlmostEqual(
len(all_lines), num_lines, delta=1
) # because we split on the bites, we might end up with one more/less line in a chunk
self.assertListEqual(
all_lines, [self._line_content for _ in range(len(all_lines))]
)
| 2,283
| 34.6875
| 102
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/test_file_io.py
|
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
import shutil
import sys
import tempfile
import unittest
from typing import Optional
from unittest.mock import MagicMock
class TestFileIO(unittest.TestCase):
_tmpdir: Optional[str] = None
_tmpfile: Optional[str] = None
_tmpfile_contents = "Hello, World"
@classmethod
def setUpClass(cls) -> None:
cls._tmpdir = tempfile.mkdtemp()
with open(os.path.join(cls._tmpdir, "test.txt"), "w") as f:
cls._tmpfile = f.name
f.write(cls._tmpfile_contents)
f.flush()
@classmethod
def tearDownClass(cls) -> None:
# Cleanup temp working dir.
if cls._tmpdir is not None:
shutil.rmtree(cls._tmpdir) # type: ignore
def test_file_io(self):
from fairseq.file_io import PathManager
with PathManager.open(os.path.join(self._tmpdir, "test.txt"), "r") as f:
s = f.read()
self.assertEqual(s, self._tmpfile_contents)
def test_file_io_oss(self):
# Mock iopath to simulate oss environment.
sys.modules["iopath"] = MagicMock()
from fairseq.file_io import PathManager
with PathManager.open(os.path.join(self._tmpdir, "test.txt"), "r") as f:
s = f.read()
self.assertEqual(s, self._tmpfile_contents)
def test_file_io_async(self):
# ioPath `PathManager` is initialized after the first `opena` call.
try:
from fairseq.file_io import IOPathManager, PathManager
_asyncfile = os.path.join(self._tmpdir, "async.txt")
f = PathManager.opena(_asyncfile, "wb")
f.close()
finally:
self.assertTrue(PathManager.async_close())
| 1,819
| 29.333333
| 80
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/test_lstm_jitable.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import tempfile
import unittest
import torch
from fairseq.data.dictionary import Dictionary
from fairseq.models.lstm import LSTMModel
from fairseq.tasks.fairseq_task import LegacyFairseqTask
DEFAULT_TEST_VOCAB_SIZE = 100
class DummyTask(LegacyFairseqTask):
def __init__(self, args):
super().__init__(args)
self.dictionary = get_dummy_dictionary()
if getattr(self.args, "ctc", False):
self.dictionary.add_symbol("<ctc_blank>")
self.src_dict = self.dictionary
self.tgt_dict = self.dictionary
@property
def source_dictionary(self):
return self.src_dict
@property
def target_dictionary(self):
return self.dictionary
def get_dummy_dictionary(vocab_size=DEFAULT_TEST_VOCAB_SIZE):
dummy_dict = Dictionary()
# add dummy symbol to satisfy vocab size
for id, _ in enumerate(range(vocab_size)):
dummy_dict.add_symbol("{}".format(id), 1000)
return dummy_dict
def get_dummy_task_and_parser():
"""
to build a fariseq model, we need some dummy parse and task. This function
is used to create dummy task and parser to faciliate model/criterion test
Note: we use FbSpeechRecognitionTask as the dummy task. You may want
to use other task by providing another function
"""
parser = argparse.ArgumentParser(
description="test_dummy_s2s_task", argument_default=argparse.SUPPRESS
)
DummyTask.add_args(parser)
args = parser.parse_args([])
task = DummyTask.setup_task(args)
return task, parser
class TestJitLSTMModel(unittest.TestCase):
def _test_save_and_load(self, scripted_module):
with tempfile.NamedTemporaryFile() as f:
scripted_module.save(f.name)
torch.jit.load(f.name)
def assertTensorEqual(self, t1, t2):
t1 = t1[~torch.isnan(t1)] # can cause size mismatch errors if there are NaNs
t2 = t2[~torch.isnan(t2)]
self.assertEqual(t1.size(), t2.size(), "size mismatch")
self.assertEqual(t1.ne(t2).long().sum(), 0)
def test_jit_and_export_lstm(self):
task, parser = get_dummy_task_and_parser()
LSTMModel.add_args(parser)
args = parser.parse_args([])
args.criterion = ""
model = LSTMModel.build_model(args, task)
scripted_model = torch.jit.script(model)
self._test_save_and_load(scripted_model)
def test_assert_jit_vs_nonjit_(self):
task, parser = get_dummy_task_and_parser()
LSTMModel.add_args(parser)
args = parser.parse_args([])
args.criterion = ""
model = LSTMModel.build_model(args, task)
model.eval()
scripted_model = torch.jit.script(model)
scripted_model.eval()
idx = len(task.source_dictionary)
iter = 100
# Inject random input and check output
seq_len_tensor = torch.randint(1, 10, (iter,))
num_samples_tensor = torch.randint(1, 10, (iter,))
for i in range(iter):
seq_len = seq_len_tensor[i]
num_samples = num_samples_tensor[i]
src_token = (torch.randint(0, idx, (num_samples, seq_len)),)
src_lengths = torch.randint(1, seq_len + 1, (num_samples,))
src_lengths, _ = torch.sort(src_lengths, descending=True)
# Force the first sample to have seq_len
src_lengths[0] = seq_len
prev_output_token = (torch.randint(0, idx, (num_samples, 1)),)
result = model(src_token[0], src_lengths, prev_output_token[0], None)
scripted_result = scripted_model(
src_token[0], src_lengths, prev_output_token[0], None
)
self.assertTensorEqual(result[0], scripted_result[0])
self.assertTensorEqual(result[1], scripted_result[1])
if __name__ == "__main__":
unittest.main()
| 4,041
| 33.844828
| 85
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/test_multihead_attention.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import torch
from fairseq.modules.multihead_attention import MultiheadAttention
class TestMultiheadAttention(unittest.TestCase):
def test_append_prev_key_padding_mask(self):
bsz = 1
src_len = 4
cases = [
# no padding mask
(None, None, None),
# current padding mask only
(
torch.tensor([[1]]).bool(),
None,
torch.tensor([[0, 0, 0, 1]]).bool(),
),
# previous padding mask only
(
None,
torch.tensor([[0, 1, 0]]).bool(),
torch.tensor([[0, 1, 0, 0]]).bool(),
),
# both padding masks
(
torch.tensor([[1]]).bool(),
torch.tensor([[0, 1, 0]]).bool(),
torch.tensor([[0, 1, 0, 1]]).bool(),
),
# prev_key_padding_mask already full
(
torch.tensor([[0, 1, 0, 1]]).bool(),
None,
torch.tensor([[0, 1, 0, 1]]).bool(),
),
# key_padding_mask already full
(
None,
torch.tensor([[0, 1, 0, 1]]).bool(),
torch.tensor([[0, 1, 0, 1]]).bool(),
),
]
for c in cases:
key_padding_mask = MultiheadAttention._append_prev_key_padding_mask(
c[0],
c[1],
batch_size=bsz,
src_len=src_len,
static_kv=False,
)
if key_padding_mask is not None:
self.assertTrue(
torch.all(torch.eq(key_padding_mask, c[2])),
f"Unexpected resultant key padding mask: {key_padding_mask}"
f" given current: {c[0]} and previous: {c[1]}",
)
self.assertEqual(key_padding_mask.size(0), bsz)
self.assertEqual(key_padding_mask.size(1), src_len)
else:
self.assertIsNone(c[2])
if __name__ == "__main__":
unittest.main()
| 2,312
| 30.256757
| 80
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/utils.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import json
import os
import random
import sys
from io import StringIO
import torch
import torch.nn.functional as F
from fairseq import options, utils
from fairseq.data import Dictionary
from fairseq.data.language_pair_dataset import collate
from fairseq.models import (
FairseqEncoder,
FairseqEncoderDecoderModel,
FairseqIncrementalDecoder,
)
from fairseq.models.fairseq_encoder import EncoderOut
from fairseq.tasks import LegacyFairseqTask
from fairseq_cli import generate, interactive, preprocess, train, validate
import fairseq.distributed.utils as distributed_utils
from fairseq.dataclass.utils import convert_namespace_to_omegaconf
def dummy_dictionary(vocab_size, prefix="token_"):
d = Dictionary()
for i in range(vocab_size):
token = prefix + str(i)
d.add_symbol(token)
d.finalize(padding_factor=1) # don't add extra padding symbols
return d
def dummy_dataloader(
samples,
padding_idx=1,
eos_idx=2,
batch_size=None,
):
if batch_size is None:
batch_size = len(samples)
# add any missing data to samples
for i, sample in enumerate(samples):
if "id" not in sample:
sample["id"] = i
# create dataloader
dataset = TestDataset(samples)
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=batch_size,
collate_fn=(lambda samples: collate(samples, padding_idx, eos_idx)),
)
return iter(dataloader)
def sequence_generator_setup():
# construct dummy dictionary
d = dummy_dictionary(vocab_size=2)
eos = d.eos()
w1 = 4
w2 = 5
# construct source data
src_tokens = torch.LongTensor([[w1, w2, eos], [w1, w2, eos]])
src_lengths = torch.LongTensor([2, 2])
args = argparse.Namespace()
unk = 0.0
args.beam_probs = [
# step 0:
torch.FloatTensor(
[
# eos w1 w2
# sentence 1:
[0.0, unk, 0.9, 0.1], # beam 1
[0.0, unk, 0.9, 0.1], # beam 2
# sentence 2:
[0.0, unk, 0.7, 0.3],
[0.0, unk, 0.7, 0.3],
]
),
# step 1:
torch.FloatTensor(
[
# eos w1 w2 prefix
# sentence 1:
[1.0, unk, 0.0, 0.0], # w1: 0.9 (emit: w1 <eos>: 0.9*1.0)
[0.0, unk, 0.9, 0.1], # w2: 0.1
# sentence 2:
[0.25, unk, 0.35, 0.4], # w1: 0.7 (don't emit: w1 <eos>: 0.7*0.25)
[0.00, unk, 0.10, 0.9], # w2: 0.3
]
),
# step 2:
torch.FloatTensor(
[
# eos w1 w2 prefix
# sentence 1:
[0.0, unk, 0.1, 0.9], # w2 w1: 0.1*0.9
[
0.6,
unk,
0.2,
0.2,
], # w2 w2: 0.1*0.1 (emit: w2 w2 <eos>: 0.1*0.1*0.6)
# sentence 2:
[
0.60,
unk,
0.4,
0.00,
], # w1 w2: 0.7*0.4 (emit: w1 w2 <eos>: 0.7*0.4*0.6)
[0.01, unk, 0.0, 0.99], # w2 w2: 0.3*0.9
]
),
# step 3:
torch.FloatTensor(
[
# eos w1 w2 prefix
# sentence 1:
[
1.0,
unk,
0.0,
0.0,
], # w2 w1 w2: 0.1*0.9*0.9 (emit: w2 w1 w2 <eos>: 0.1*0.9*0.9*1.0)
[
1.0,
unk,
0.0,
0.0,
], # w2 w1 w1: 0.1*0.9*0.1 (emit: w2 w1 w1 <eos>: 0.1*0.9*0.1*1.0)
# sentence 2:
[
0.1,
unk,
0.5,
0.4,
], # w2 w2 w2: 0.3*0.9*0.99 (emit: w2 w2 w2 <eos>: 0.3*0.9*0.99*0.1)
[
1.0,
unk,
0.0,
0.0,
], # w1 w2 w1: 0.7*0.4*0.4 (emit: w1 w2 w1 <eos>: 0.7*0.4*0.4*1.0)
]
),
]
task = TestTranslationTask.setup_task(args, d, d)
model = task.build_model(args)
tgt_dict = task.target_dictionary
return tgt_dict, w1, w2, src_tokens, src_lengths, model
def create_dummy_data(data_dir, num_examples=100, maxlen=20, alignment=False):
def _create_dummy_data(filename):
data = torch.rand(num_examples * maxlen)
data = 97 + torch.floor(26 * data).int()
with open(os.path.join(data_dir, filename), "w") as h:
offset = 0
for _ in range(num_examples):
ex_len = random.randint(1, maxlen)
ex_str = " ".join(map(chr, data[offset : offset + ex_len]))
print(ex_str, file=h)
offset += ex_len
def _create_dummy_alignment_data(filename_src, filename_tgt, filename):
with open(os.path.join(data_dir, filename_src), "r") as src_f, open(
os.path.join(data_dir, filename_tgt), "r"
) as tgt_f, open(os.path.join(data_dir, filename), "w") as h:
for src, tgt in zip(src_f, tgt_f):
src_len = len(src.split())
tgt_len = len(tgt.split())
avg_len = (src_len + tgt_len) // 2
num_alignments = random.randint(avg_len // 2, 2 * avg_len)
src_indices = torch.floor(torch.rand(num_alignments) * src_len).int()
tgt_indices = torch.floor(torch.rand(num_alignments) * tgt_len).int()
ex_str = " ".join(
[
"{}-{}".format(src, tgt)
for src, tgt in zip(src_indices, tgt_indices)
]
)
print(ex_str, file=h)
_create_dummy_data("train.in")
_create_dummy_data("train.out")
_create_dummy_data("valid.in")
_create_dummy_data("valid.out")
_create_dummy_data("test.in")
_create_dummy_data("test.out")
if alignment:
_create_dummy_alignment_data("train.in", "train.out", "train.align")
_create_dummy_alignment_data("valid.in", "valid.out", "valid.align")
_create_dummy_alignment_data("test.in", "test.out", "test.align")
def preprocess_lm_data(data_dir):
preprocess_parser = options.get_preprocessing_parser()
preprocess_args = preprocess_parser.parse_args(
[
"--only-source",
"--trainpref",
os.path.join(data_dir, "train.out"),
"--validpref",
os.path.join(data_dir, "valid.out"),
"--testpref",
os.path.join(data_dir, "test.out"),
"--destdir",
data_dir,
]
)
preprocess.main(preprocess_args)
def preprocess_translation_data(data_dir, extra_flags=None):
preprocess_parser = options.get_preprocessing_parser()
preprocess_args = preprocess_parser.parse_args(
[
"--source-lang",
"in",
"--target-lang",
"out",
"--trainpref",
os.path.join(data_dir, "train"),
"--validpref",
os.path.join(data_dir, "valid"),
"--testpref",
os.path.join(data_dir, "test"),
"--thresholdtgt",
"0",
"--thresholdsrc",
"0",
"--destdir",
data_dir,
]
+ (extra_flags or []),
)
preprocess.main(preprocess_args)
def preprocess_summarization_data(data_dir, extra_flags=None):
preprocess_parser = options.get_preprocessing_parser()
preprocess_args = preprocess_parser.parse_args(
[
"--source-lang",
"in",
"--target-lang",
"out",
"--trainpref",
os.path.join(data_dir, "train"),
"--validpref",
os.path.join(data_dir, "valid"),
"--testpref",
os.path.join(data_dir, "test"),
"--thresholdtgt",
"0",
"--thresholdsrc",
"0",
"--joined-dictionary",
"--destdir",
data_dir,
]
+ (extra_flags or []),
)
preprocess.main(preprocess_args)
def create_laser_data_and_config_json(data_dir):
src_langs = ["de", "fr", "ru", "tr", "zh"]
tgt_langs = ["en", "es"]
config_json = {}
config_train_json = []
src_vocab = None
tgt_vocab = None
for src_lang in src_langs:
for tgt_lang in tgt_langs:
langpair_folder = f"{src_lang}-{tgt_lang}"
langpair_path = os.path.join(data_dir, langpair_folder)
os.mkdir(langpair_path)
create_dummy_data(langpair_path)
preprocess_translation_data(langpair_path, ["--dataset-impl", "cached"])
src_vocab = os.path.join(langpair_path, "dict.in.txt")
tgt_vocab = os.path.join(langpair_path, "dict.out.txt")
config_train_json.append(
{
"id": 0 if tgt_lang == "en" else 1,
"src": os.path.join(langpair_path, "train.in-out.in"),
"tgt": os.path.join(langpair_path, "train.in-out.out"),
}
)
config_json["src_vocab"] = src_vocab
config_json["tgt_vocab"] = tgt_vocab
config_json["train"] = config_train_json
with open(os.path.join(data_dir, "laserconfig.json"), "w") as config_file:
json.dump(config_json, config_file)
return config_file
def train_translation_model(
data_dir,
arch,
extra_flags=None,
task="translation",
run_validation=False,
lang_flags=None,
extra_valid_flags=None,
world_size=1,
):
if lang_flags is None:
lang_flags = [
"--source-lang",
"in",
"--target-lang",
"out",
]
train_parser = options.get_training_parser()
train_args = options.parse_args_and_arch(
train_parser,
[
"--task",
task,
data_dir,
"--save-dir",
data_dir,
"--arch",
arch,
"--optimizer",
"nag",
"--lr",
"0.05",
"--max-tokens",
"500",
"--max-epoch",
"1",
"--no-progress-bar",
"--distributed-world-size",
str(world_size),
"--num-workers",
"0",
]
+ lang_flags
+ (extra_flags or []),
)
cfg = convert_namespace_to_omegaconf(train_args)
distributed_utils.call_main(cfg, train.main)
if run_validation:
# test validation
validate_parser = options.get_validation_parser()
validate_args = options.parse_args_and_arch(
validate_parser,
[
"--task",
task,
data_dir,
"--path",
os.path.join(data_dir, "checkpoint_last.pt"),
"--valid-subset",
"valid",
"--max-tokens",
"500",
"--no-progress-bar",
"--num-workers",
"0",
]
+ lang_flags
+ (extra_valid_flags or []),
)
validate.main(validate_args)
def generate_main(data_dir, extra_flags=None, path=None):
if extra_flags is None:
extra_flags = [
"--print-alignment",
]
if path is None:
path = os.path.join(data_dir, "checkpoint_last.pt")
generate_parser = options.get_generation_parser()
generate_args = options.parse_args_and_arch(
generate_parser,
[
data_dir,
"--path",
path,
"--beam",
"3",
"--batch-size",
"64",
"--max-len-b",
"5",
"--gen-subset",
"valid",
"--no-progress-bar",
"--num-workers",
"0",
]
+ (extra_flags or []),
)
# evaluate model in batch mode
generate.main(generate_args)
# evaluate model interactively
generate_args.buffer_size = 0
generate_args.input = "-"
generate_args.batch_size = None
orig_stdin = sys.stdin
sys.stdin = StringIO("h e l l o\n")
interactive.main(generate_args)
sys.stdin = orig_stdin
class TestDataset(torch.utils.data.Dataset):
def __init__(self, data):
super().__init__()
self.data = data
self.sizes = None
def __getitem__(self, index):
return self.data[index]
def __len__(self):
return len(self.data)
class TestTranslationTask(LegacyFairseqTask):
def __init__(self, args, src_dict, tgt_dict, model):
super().__init__(args)
self.src_dict = src_dict
self.tgt_dict = tgt_dict
self.model = model
@classmethod
def setup_task(cls, args, src_dict=None, tgt_dict=None, model=None):
return cls(args, src_dict, tgt_dict, model)
def build_model(self, args):
return TestModel.build_model(args, self)
@property
def source_dictionary(self):
return self.src_dict
@property
def target_dictionary(self):
return self.tgt_dict
class TestModel(FairseqEncoderDecoderModel):
def __init__(self, encoder, decoder):
super().__init__(encoder, decoder)
@classmethod
def build_model(cls, args, task):
encoder = TestEncoder(args, task.source_dictionary)
decoder = TestIncrementalDecoder(args, task.target_dictionary)
return cls(encoder, decoder)
class TestEncoder(FairseqEncoder):
def __init__(self, args, dictionary):
super().__init__(dictionary)
self.args = args
def forward(self, src_tokens, src_lengths=None, **kwargs):
return EncoderOut(
encoder_out=src_tokens,
encoder_padding_mask=None,
encoder_embedding=None,
encoder_states=None,
src_tokens=None,
src_lengths=None,
)
def reorder_encoder_out(self, encoder_out, new_order):
return EncoderOut(
encoder_out=encoder_out.encoder_out.index_select(0, new_order),
encoder_padding_mask=None,
encoder_embedding=None,
encoder_states=None,
src_tokens=None,
src_lengths=None,
)
class TestIncrementalDecoder(FairseqIncrementalDecoder):
def __init__(self, args, dictionary):
super().__init__(dictionary)
assert hasattr(args, "beam_probs") or hasattr(args, "probs")
args.max_decoder_positions = getattr(args, "max_decoder_positions", 100)
self.args = args
def forward(self, prev_output_tokens, encoder_out=None, incremental_state=None):
if incremental_state is not None:
prev_output_tokens = prev_output_tokens[:, -1:]
bbsz = prev_output_tokens.size(0)
vocab = len(self.dictionary)
src_len = encoder_out.encoder_out.size(1)
tgt_len = prev_output_tokens.size(1)
# determine number of steps
if incremental_state is not None:
# cache step number
step = utils.get_incremental_state(self, incremental_state, "step")
if step is None:
step = 0
utils.set_incremental_state(self, incremental_state, "step", step + 1)
steps = [step]
else:
steps = list(range(tgt_len))
# define output in terms of raw probs
if hasattr(self.args, "probs"):
assert (
self.args.probs.dim() == 3
), "expected probs to have size bsz*steps*vocab"
probs = self.args.probs.index_select(1, torch.LongTensor(steps))
else:
probs = torch.FloatTensor(bbsz, len(steps), vocab).zero_()
for i, step in enumerate(steps):
# args.beam_probs gives the probability for every vocab element,
# starting with eos, then unknown, and then the rest of the vocab
if step < len(self.args.beam_probs):
probs[:, i, self.dictionary.eos() :] = self.args.beam_probs[step]
else:
probs[:, i, self.dictionary.eos()] = 1.0
# random attention
attn = torch.rand(bbsz, tgt_len, src_len)
dev = prev_output_tokens.device
return probs.to(dev), {"attn": [attn.to(dev)]}
def get_normalized_probs(self, net_output, log_probs, _):
# the decoder returns probabilities directly
probs = net_output[0]
if log_probs:
return probs.log()
else:
return probs
def max_positions(self):
return self.args.max_decoder_positions
class TestReshapingEncoder(FairseqEncoder):
def __init__(self, args, dictionary):
super().__init__(dictionary)
self.args = args
def forward(self, src_tokens, src_lengths=None, **kwargs):
b_sz, t_sz = src_tokens.shape
padding_needed = t_sz % 2
x = src_tokens
if padding_needed > 0:
padding_needed = 2 - padding_needed
x = F.pad(x, (0, padding_needed))
return EncoderOut(
encoder_out=x.view(b_sz, -1, 2),
encoder_padding_mask=None,
encoder_embedding=None,
encoder_states=None,
src_tokens=None,
src_lengths=None,
)
def reorder_encoder_out(self, encoder_out, new_order):
return EncoderOut(
encoder_out=encoder_out.encoder_out.index_select(0, new_order),
encoder_padding_mask=None,
encoder_embedding=None,
encoder_states=None,
src_tokens=None,
src_lengths=None,
)
class TestReshapingModel(FairseqEncoderDecoderModel):
def __init__(self, encoder, decoder):
super().__init__(encoder, decoder)
@classmethod
def build_model(cls, args, task):
encoder = TestReshapingEncoder(args, task.source_dictionary)
decoder = TestIncrementalDecoder(args, task.target_dictionary)
return cls(encoder, decoder)
class TestAdditionalInputEncoder(FairseqEncoder):
def __init__(self, args, dictionary):
super().__init__(dictionary)
self.args = args
def forward(self, src_tokens, src_lengths=None, **kwargs):
assert "fancy_other_input" in kwargs
assert kwargs["fancy_other_input"] is not None
return EncoderOut(
encoder_out=src_tokens,
encoder_padding_mask=None,
encoder_embedding=None,
encoder_states=None,
src_tokens=None,
src_lengths=None,
)
def reorder_encoder_out(self, encoder_out, new_order):
return EncoderOut(
encoder_out=encoder_out.encoder_out.index_select(0, new_order),
encoder_padding_mask=None,
encoder_embedding=None,
encoder_states=None,
src_tokens=None,
src_lengths=None,
)
class TestAdditionalInputModel(FairseqEncoderDecoderModel):
def __init__(self, encoder, decoder):
super().__init__(encoder, decoder)
@classmethod
def build_model(cls, args, task):
encoder = TestAdditionalInputEncoder(args, task.source_dictionary)
decoder = TestIncrementalDecoder(args, task.target_dictionary)
return cls(encoder, decoder)
def forward(self, src_tokens, src_lengths, prev_output_tokens, **kwargs):
encoder_out = self.encoder(src_tokens, src_lengths=src_lengths, **kwargs)
decoder_out = self.decoder(
prev_output_tokens, encoder_out=encoder_out, **kwargs
)
return decoder_out
def train_language_model(
data_dir,
arch,
extra_flags=None,
run_validation=False,
extra_valid_flags=None,
task="language_modeling",
world_size=1,
):
train_parser = options.get_training_parser()
train_args = options.parse_args_and_arch(
train_parser,
[
"--task",
task,
data_dir,
"--arch",
arch,
"--optimizer",
"adam",
"--lr",
"0.0001",
"--max-tokens",
"500",
"--tokens-per-sample",
"500",
"--save-dir",
data_dir,
"--max-epoch",
"1",
"--no-progress-bar",
"--distributed-world-size",
str(world_size),
"--ddp-backend",
"no_c10d",
"--num-workers",
"0",
]
+ (extra_flags or []),
)
cfg = convert_namespace_to_omegaconf(train_args)
distributed_utils.call_main(cfg, train.main)
if run_validation:
# test validation
validate_parser = options.get_validation_parser()
validate_args = options.parse_args_and_arch(
validate_parser,
[
"--task",
task,
data_dir,
"--path",
os.path.join(data_dir, "checkpoint_last.pt"),
"--valid-subset",
"valid",
"--max-tokens",
"500",
"--no-progress-bar",
"--num-workers",
"0",
]
+ (extra_valid_flags or []),
)
validate.main(validate_args)
| 21,929
| 29.416089
| 86
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/test_binaries.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import contextlib
import logging
import json
import os
import random
import sys
import tempfile
import unittest
from io import StringIO
from typing import List, Dict
import torch
from fairseq import options
from fairseq_cli import eval_lm, train
from tests.utils import (
create_dummy_data,
generate_main,
preprocess_lm_data,
preprocess_summarization_data,
preprocess_translation_data,
create_laser_data_and_config_json,
train_translation_model,
train_language_model,
)
try:
import transformers # noqa
has_hf_transformers = True
except ImportError:
has_hf_transformers = False
class TestTranslation(unittest.TestCase):
def setUp(self):
logging.disable(logging.CRITICAL)
def tearDown(self):
logging.disable(logging.NOTSET)
def test_fconv(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_fconv") as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir)
train_translation_model(data_dir, "fconv_iwslt_de_en")
generate_main(data_dir)
def test_raw(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_fconv_raw") as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir, ["--dataset-impl", "raw"])
train_translation_model(
data_dir, "fconv_iwslt_de_en", ["--dataset-impl", "raw"]
)
generate_main(data_dir, ["--dataset-impl", "raw"])
def test_update_freq(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_update_freq") as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir)
train_translation_model(
data_dir, "fconv_iwslt_de_en", ["--update-freq", "3"]
)
generate_main(data_dir)
def test_max_positions(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_max_positions") as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir)
with self.assertRaises(Exception) as context:
train_translation_model(
data_dir,
"fconv_iwslt_de_en",
["--max-target-positions", "5"],
)
self.assertTrue(
"skip this example with --skip-invalid-size-inputs-valid-test"
in str(context.exception)
)
train_translation_model(
data_dir,
"fconv_iwslt_de_en",
[
"--max-target-positions",
"5",
"--skip-invalid-size-inputs-valid-test",
],
)
with self.assertRaises(Exception) as context:
generate_main(data_dir)
generate_main(data_dir, ["--skip-invalid-size-inputs-valid-test"])
def test_generation(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_sampling") as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir)
train_translation_model(data_dir, "fconv_iwslt_de_en")
generate_main(
data_dir,
[
"--sampling",
"--temperature",
"2",
"--beam",
"2",
"--nbest",
"2",
],
)
generate_main(
data_dir,
[
"--sampling",
"--sampling-topk",
"3",
"--beam",
"2",
"--nbest",
"2",
],
)
generate_main(
data_dir,
[
"--sampling",
"--sampling-topp",
"0.2",
"--beam",
"2",
"--nbest",
"2",
],
)
generate_main(
data_dir,
[
"--diversity-rate",
"0.5",
"--beam",
"6",
],
)
with self.assertRaises(ValueError):
generate_main(
data_dir,
[
"--diverse-beam-groups",
"4",
"--match-source-len",
],
)
generate_main(data_dir, ["--prefix-size", "2"])
generate_main(data_dir, ["--retain-dropout"])
def test_eval_bleu(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_eval_bleu") as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir)
train_translation_model(
data_dir,
"fconv_iwslt_de_en",
[
"--eval-bleu",
"--eval-bleu-print-samples",
"--eval-bleu-remove-bpe",
"--eval-bleu-detok",
"space",
"--eval-bleu-args",
'{"beam": 4, "min_len": 10}',
],
)
def test_lstm(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_lstm") as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir)
train_translation_model(
data_dir,
"lstm_wiseman_iwslt_de_en",
[
"--encoder-layers",
"2",
"--decoder-layers",
"2",
"--encoder-embed-dim",
"8",
"--decoder-embed-dim",
"8",
"--decoder-out-embed-dim",
"8",
],
)
generate_main(data_dir)
def test_lstm_bidirectional(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_lstm_bidirectional") as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir)
train_translation_model(
data_dir,
"lstm",
[
"--encoder-layers",
"2",
"--encoder-bidirectional",
"--encoder-hidden-size",
"16",
"--encoder-embed-dim",
"8",
"--decoder-embed-dim",
"8",
"--decoder-out-embed-dim",
"8",
"--decoder-layers",
"2",
],
)
generate_main(data_dir)
def test_transformer(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_transformer") as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir)
train_translation_model(
data_dir,
"transformer_iwslt_de_en",
[
"--encoder-layers",
"2",
"--decoder-layers",
"2",
"--encoder-embed-dim",
"8",
"--decoder-embed-dim",
"8",
],
run_validation=True,
)
generate_main(data_dir)
def test_multilingual_transformer(self):
# test with all combinations of encoder/decoder lang tokens
encoder_langtok_flags = [
[],
["--encoder-langtok", "src"],
["--encoder-langtok", "tgt"],
]
decoder_langtok_flags = [[], ["--decoder-langtok"]]
with contextlib.redirect_stdout(StringIO()):
for i in range(len(encoder_langtok_flags)):
for j in range(len(decoder_langtok_flags)):
enc_ltok_flag = encoder_langtok_flags[i]
dec_ltok_flag = decoder_langtok_flags[j]
with tempfile.TemporaryDirectory(
f"test_multilingual_transformer_{i}_{j}"
) as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir)
train_translation_model(
data_dir,
arch="multilingual_transformer",
task="multilingual_translation",
extra_flags=[
"--encoder-layers",
"2",
"--decoder-layers",
"2",
"--encoder-embed-dim",
"8",
"--decoder-embed-dim",
"8",
]
+ enc_ltok_flag
+ dec_ltok_flag,
lang_flags=["--lang-pairs", "in-out,out-in"],
run_validation=True,
extra_valid_flags=enc_ltok_flag + dec_ltok_flag,
)
generate_main(
data_dir,
extra_flags=[
"--task",
"multilingual_translation",
"--lang-pairs",
"in-out,out-in",
"--source-lang",
"in",
"--target-lang",
"out",
]
+ enc_ltok_flag
+ dec_ltok_flag,
)
@unittest.skipIf(
sys.platform.lower() == "darwin", "skip latent depth test on MacOS"
)
def test_multilingual_translation_latent_depth(self):
# test with latent depth in encoder, decoder, or both
encoder_latent_layer = [[], ["--encoder-latent-layer"]]
decoder_latent_layer = [[], ["--decoder-latent-layer"]]
with contextlib.redirect_stdout(StringIO()):
for i in range(len(encoder_latent_layer)):
for j in range(len(decoder_latent_layer)):
if i == 0 and j == 0:
continue
enc_ll_flag = encoder_latent_layer[i]
dec_ll_flag = decoder_latent_layer[j]
with tempfile.TemporaryDirectory(
f"test_multilingual_translation_latent_depth_{i}_{j}"
) as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(
data_dir, extra_flags=["--joined-dictionary"]
)
train_translation_model(
data_dir,
arch="latent_multilingual_transformer",
task="multilingual_translation_latent_depth",
extra_flags=[
"--user-dir",
"examples/latent_depth/latent_depth_src",
"--encoder-layers",
"2",
"--decoder-layers",
"2",
"--encoder-embed-dim",
"8",
"--decoder-embed-dim",
"8",
"--share-encoders",
"--share-decoders",
"--sparsity-weight",
"0.1",
]
+ enc_ll_flag
+ dec_ll_flag,
lang_flags=["--lang-pairs", "in-out,out-in"],
run_validation=True,
extra_valid_flags=[
"--user-dir",
"examples/latent_depth/latent_depth_src",
]
+ enc_ll_flag
+ dec_ll_flag,
)
generate_main(
data_dir,
extra_flags=[
"--user-dir",
"examples/latent_depth/latent_depth_src",
"--task",
"multilingual_translation_latent_depth",
"--lang-pairs",
"in-out,out-in",
"--source-lang",
"in",
"--target-lang",
"out",
]
+ enc_ll_flag
+ dec_ll_flag,
)
def test_translation_multi_simple_epoch(self):
# test with all combinations of encoder/decoder lang tokens
encoder_langtok_flags = [
[],
["--encoder-langtok", "src"],
["--encoder-langtok", "tgt"],
]
decoder_langtok_flags = [[], ["--decoder-langtok"]]
with contextlib.redirect_stdout(StringIO()):
for i in range(len(encoder_langtok_flags)):
for j in range(len(decoder_langtok_flags)):
enc_ltok_flag = encoder_langtok_flags[i]
dec_ltok_flag = decoder_langtok_flags[j]
with tempfile.TemporaryDirectory(
f"test_translation_multi_simple_epoch_{i}_{j}"
) as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(
data_dir, extra_flags=["--joined-dictionary"]
)
train_translation_model(
data_dir,
arch="transformer",
task="translation_multi_simple_epoch",
extra_flags=[
"--encoder-layers",
"2",
"--decoder-layers",
"2",
"--encoder-embed-dim",
"8",
"--decoder-embed-dim",
"8",
"--sampling-method",
"temperature",
"--sampling-temperature",
"1.5",
"--virtual-epoch-size",
"1000",
]
+ enc_ltok_flag
+ dec_ltok_flag,
lang_flags=["--lang-pairs", "in-out,out-in"],
run_validation=True,
extra_valid_flags=enc_ltok_flag + dec_ltok_flag,
)
generate_main(
data_dir,
extra_flags=[
"--task",
"translation_multi_simple_epoch",
"--lang-pairs",
"in-out,out-in",
"--source-lang",
"in",
"--target-lang",
"out",
]
+ enc_ltok_flag
+ dec_ltok_flag,
)
def test_translation_multi_simple_epoch_no_vepoch(self):
# test with all combinations of encoder/decoder lang tokens
with contextlib.redirect_stdout(StringIO()):
enc_ltok_flag = ["--encoder-langtok", "src"]
dec_ltok_flag = ["--decoder-langtok"]
with tempfile.TemporaryDirectory(
"test_translation_multi_simple_epoch_dict"
) as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir, extra_flags=[])
train_translation_model(
data_dir,
arch="transformer",
task="translation_multi_simple_epoch",
extra_flags=[
"--encoder-layers",
"2",
"--decoder-layers",
"2",
"--encoder-embed-dim",
"8",
"--decoder-embed-dim",
"8",
"--sampling-method",
"temperature",
"--sampling-temperature",
"1.5",
]
+ enc_ltok_flag
+ dec_ltok_flag,
lang_flags=["--lang-pairs", "in-out"],
run_validation=True,
extra_valid_flags=enc_ltok_flag + dec_ltok_flag,
)
generate_main(
data_dir,
extra_flags=[
"--task",
"translation_multi_simple_epoch",
"--lang-pairs",
"in-out",
"--source-lang",
"in",
"--target-lang",
"out",
]
+ enc_ltok_flag
+ dec_ltok_flag,
)
def test_translation_multi_simple_epoch_dicts(self):
# test with all combinations of encoder/decoder lang tokens
with contextlib.redirect_stdout(StringIO()):
enc_ltok_flag = ["--encoder-langtok", "src"]
dec_ltok_flag = ["--decoder-langtok"]
with tempfile.TemporaryDirectory(
"test_translation_multi_simple_epoch_dict"
) as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir, extra_flags=[])
train_translation_model(
data_dir,
arch="transformer",
task="translation_multi_simple_epoch",
extra_flags=[
"--encoder-layers",
"2",
"--decoder-layers",
"2",
"--encoder-embed-dim",
"8",
"--decoder-embed-dim",
"8",
"--sampling-method",
"temperature",
"--sampling-temperature",
"1.5",
"--virtual-epoch-size",
"1000",
]
+ enc_ltok_flag
+ dec_ltok_flag,
lang_flags=["--lang-pairs", "in-out"],
run_validation=True,
extra_valid_flags=enc_ltok_flag + dec_ltok_flag,
)
generate_main(
data_dir,
extra_flags=[
"--task",
"translation_multi_simple_epoch",
"--lang-pairs",
"in-out",
"--source-lang",
"in",
"--target-lang",
"out",
]
+ enc_ltok_flag
+ dec_ltok_flag,
)
def test_translation_multi_simple_epoch_src_tgt_dict_spec(self):
# test the specification of explicit --src-dict and --tgt-dict
with contextlib.redirect_stdout(StringIO()):
enc_ltok_flag = ["--encoder-langtok", "src"]
dec_ltok_flag = ["--decoder-langtok"]
with tempfile.TemporaryDirectory(
"test_translation_multi_simple_epoch_dict"
) as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir, extra_flags=[])
train_translation_model(
data_dir,
arch="transformer",
task="translation_multi_simple_epoch",
extra_flags=[
"--source-dict",
f"{data_dir}/dict.in.txt",
"--target-dict",
f"{data_dir}/dict.out.txt",
"--encoder-layers",
"2",
"--decoder-layers",
"2",
"--encoder-embed-dim",
"8",
"--decoder-embed-dim",
"8",
"--sampling-method",
"temperature",
"--sampling-temperature",
"1.5",
"--virtual-epoch-size",
"1000",
]
+ enc_ltok_flag
+ dec_ltok_flag,
lang_flags=["--lang-pairs", "in-out"],
run_validation=True,
extra_valid_flags=enc_ltok_flag + dec_ltok_flag,
)
generate_main(
data_dir,
extra_flags=[
"--task",
"translation_multi_simple_epoch",
"--lang-pairs",
"in-out",
"--source-lang",
"in",
"--target-lang",
"out",
]
+ enc_ltok_flag
+ dec_ltok_flag,
)
def test_transformer_cross_self_attention(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory(
"test_transformer_cross_self_attention"
) as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir)
train_translation_model(
data_dir,
"transformer_iwslt_de_en",
[
"--encoder-layers",
"2",
"--decoder-layers",
"2",
"--encoder-embed-dim",
"8",
"--decoder-embed-dim",
"8",
"--decoder-embed-dim",
"8",
"--no-cross-attention",
"--cross-self-attention",
],
run_validation=True,
)
generate_main(data_dir, extra_flags=[])
def test_transformer_pointer_generator(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory(
"test_transformer_pointer_generator"
) as data_dir:
create_dummy_data(data_dir)
preprocess_summarization_data(data_dir)
train_translation_model(
data_dir,
"transformer_pointer_generator",
extra_flags=[
"--user-dir",
"examples/pointer_generator/pointer_generator_src",
"--encoder-layers",
"2",
"--decoder-layers",
"2",
"--encoder-embed-dim",
"8",
"--decoder-embed-dim",
"8",
"--alignment-layer",
"-1",
"--alignment-heads",
"1",
"--source-position-markers",
"0",
],
run_validation=True,
extra_valid_flags=[
"--user-dir",
"examples/pointer_generator/pointer_generator_src",
],
)
generate_main(
data_dir,
extra_flags=[
"--user-dir",
"examples/pointer_generator/pointer_generator_src",
],
)
def test_lightconv(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_lightconv") as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir)
train_translation_model(
data_dir,
"lightconv_iwslt_de_en",
[
"--encoder-conv-type",
"lightweight",
"--decoder-conv-type",
"lightweight",
"--encoder-embed-dim",
"8",
"--decoder-embed-dim",
"8",
],
)
generate_main(data_dir)
def test_dynamicconv(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_dynamicconv") as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir)
train_translation_model(
data_dir,
"lightconv_iwslt_de_en",
[
"--encoder-conv-type",
"dynamic",
"--decoder-conv-type",
"dynamic",
"--encoder-embed-dim",
"8",
"--decoder-embed-dim",
"8",
],
)
generate_main(data_dir)
def test_cmlm_transformer(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_cmlm_transformer") as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir, ["--joined-dictionary"])
train_translation_model(
data_dir,
"cmlm_transformer",
[
"--apply-bert-init",
"--criterion",
"nat_loss",
"--noise",
"full_mask",
"--pred-length-offset",
"--length-loss-factor",
"0.1",
],
task="translation_lev",
)
generate_main(
data_dir,
[
"--task",
"translation_lev",
"--iter-decode-max-iter",
"9",
"--iter-decode-eos-penalty",
"0",
"--print-step",
],
)
def test_nonautoregressive_transformer(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory(
"test_nonautoregressive_transformer"
) as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir, ["--joined-dictionary"])
train_translation_model(
data_dir,
"nonautoregressive_transformer",
[
"--apply-bert-init",
"--src-embedding-copy",
"--criterion",
"nat_loss",
"--noise",
"full_mask",
"--pred-length-offset",
"--length-loss-factor",
"0.1",
],
task="translation_lev",
)
generate_main(
data_dir,
[
"--task",
"translation_lev",
"--iter-decode-max-iter",
"0",
"--iter-decode-eos-penalty",
"0",
"--print-step",
],
)
# def test_nat_crf_transformer(self):
# with contextlib.redirect_stdout(StringIO()):
# with tempfile.TemporaryDirectory('test_nat_crf_transformer') as data_dir:
# create_dummy_data(data_dir)
# preprocess_translation_data(data_dir, ['--joined-dictionary'])
# train_translation_model(data_dir, 'nacrf_transformer', [
# '--apply-bert-init', '--criterion',
# 'nat_loss', '--noise', 'full_mask', '--pred-length-offset',
# '--length-loss-factor', '0.1',
# '--word-ins-loss-factor', '0.5',
# '--crf-lowrank-approx', '1',
# '--crf-beam-approx', '1'
# ], task='translation_lev')
# generate_main(data_dir, [
# '--task', 'translation_lev',
# '--iter-decode-max-iter', '0',
# '--iter-decode-eos-penalty', '0',
# '--print-step',
# ])
def test_iterative_nonautoregressive_transformer(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory(
"test_iterative_nonautoregressive_transformer"
) as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir, ["--joined-dictionary"])
train_translation_model(
data_dir,
"iterative_nonautoregressive_transformer",
[
"--apply-bert-init",
"--src-embedding-copy",
"--criterion",
"nat_loss",
"--noise",
"full_mask",
"--stochastic-approx",
"--dae-ratio",
"0.5",
"--train-step",
"3",
],
task="translation_lev",
)
generate_main(
data_dir,
[
"--task",
"translation_lev",
"--iter-decode-max-iter",
"9",
"--iter-decode-eos-penalty",
"0",
"--print-step",
],
)
def test_insertion_transformer(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_insertion_transformer") as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir, ["--joined-dictionary"])
train_translation_model(
data_dir,
"insertion_transformer",
[
"--apply-bert-init",
"--criterion",
"nat_loss",
"--noise",
"random_mask",
],
task="translation_lev",
)
generate_main(
data_dir,
[
"--task",
"translation_lev",
"--iter-decode-max-iter",
"9",
"--iter-decode-eos-penalty",
"0",
"--print-step",
],
)
def test_mixture_of_experts(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_moe") as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir)
train_translation_model(
data_dir,
"transformer_iwslt_de_en",
[
"--task",
"translation_moe",
"--user-dir",
"examples/translation_moe/translation_moe_src",
"--method",
"hMoElp",
"--mean-pool-gating-network",
"--num-experts",
"3",
"--encoder-layers",
"2",
"--decoder-layers",
"2",
"--encoder-embed-dim",
"8",
"--decoder-embed-dim",
"8",
],
)
generate_main(
data_dir,
[
"--task",
"translation_moe",
"--user-dir",
"examples/translation_moe/translation_moe_src",
"--method",
"hMoElp",
"--mean-pool-gating-network",
"--num-experts",
"3",
"--gen-expert",
"0",
],
)
def test_alignment(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_alignment") as data_dir:
create_dummy_data(data_dir, alignment=True)
preprocess_translation_data(data_dir, ["--align-suffix", "align"])
train_translation_model(
data_dir,
"transformer_align",
[
"--encoder-layers",
"2",
"--decoder-layers",
"2",
"--encoder-embed-dim",
"8",
"--decoder-embed-dim",
"8",
"--load-alignments",
"--alignment-layer",
"1",
"--criterion",
"label_smoothed_cross_entropy_with_alignment",
],
run_validation=True,
)
generate_main(data_dir)
def test_laser_lstm(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_laser_lstm") as data_dir:
laser_config_file = create_laser_data_and_config_json(data_dir)
train_translation_model(
laser_config_file.name,
"laser_lstm",
[
"--user-dir",
"examples/laser/laser_src",
"--weighting-alpha",
"0.3",
"--encoder-bidirectional",
"--encoder-hidden-size",
"512",
"--encoder-layers",
"5",
"--decoder-layers",
"1",
"--encoder-embed-dim",
"320",
"--decoder-embed-dim",
"320",
"--decoder-lang-embed-dim",
"32",
"--save-dir",
data_dir,
"--disable-validation",
],
task="laser",
lang_flags=[],
)
def test_laser_transformer(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_laser_transformer") as data_dir:
laser_config_file = create_laser_data_and_config_json(data_dir)
train_translation_model(
laser_config_file.name,
"laser_transformer",
[
"--user-dir",
"examples/laser/laser_src",
"--weighting-alpha",
"0.3",
"--encoder-embed-dim",
"320",
"--decoder-embed-dim",
"320",
"--decoder-lang-embed-dim",
"32",
"--save-dir",
data_dir,
"--disable-validation",
],
task="laser",
lang_flags=[],
)
def test_alignment_full_context(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_alignment") as data_dir:
create_dummy_data(data_dir, alignment=True)
preprocess_translation_data(data_dir, ["--align-suffix", "align"])
train_translation_model(
data_dir,
"transformer_align",
[
"--encoder-layers",
"2",
"--decoder-layers",
"2",
"--encoder-embed-dim",
"8",
"--decoder-embed-dim",
"8",
"--load-alignments",
"--alignment-layer",
"1",
"--criterion",
"label_smoothed_cross_entropy_with_alignment",
"--full-context-alignment",
],
run_validation=True,
)
generate_main(data_dir)
def test_transformer_layerdrop(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_transformer_layerdrop") as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir)
train_translation_model(
data_dir,
"transformer_iwslt_de_en",
[
"--encoder-layers",
"3",
"--decoder-layers",
"3",
"--encoder-embed-dim",
"8",
"--decoder-embed-dim",
"8",
"--encoder-layerdrop",
"0.01",
"--decoder-layerdrop",
"0.01",
],
)
generate_main(data_dir)
generate_main(
data_dir,
[
"--model-overrides",
"{'encoder_layers_to_keep':'0,2','decoder_layers_to_keep':'1'}",
],
)
class TestStories(unittest.TestCase):
def setUp(self):
logging.disable(logging.CRITICAL)
def tearDown(self):
logging.disable(logging.NOTSET)
def test_fconv_self_att_wp(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_fconv_self_att_wp") as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir)
config = [
"--encoder-layers",
"[(128, 3)] * 2",
"--decoder-layers",
"[(128, 3)] * 2",
"--decoder-attention",
"True",
"--encoder-attention",
"False",
"--gated-attention",
"True",
"--self-attention",
"True",
"--project-input",
"True",
"--encoder-embed-dim",
"8",
"--decoder-embed-dim",
"8",
"--decoder-out-embed-dim",
"8",
"--multihead-self-attention-nheads",
"2",
]
train_translation_model(data_dir, "fconv_self_att_wp", config)
generate_main(data_dir)
# fusion model
os.rename(
os.path.join(data_dir, "checkpoint_last.pt"),
os.path.join(data_dir, "pretrained.pt"),
)
config.extend(
[
"--pretrained",
"True",
"--pretrained-checkpoint",
os.path.join(data_dir, "pretrained.pt"),
"--save-dir",
os.path.join(data_dir, "fusion_model"),
]
)
train_translation_model(data_dir, "fconv_self_att_wp", config)
class TestLanguageModeling(unittest.TestCase):
def setUp(self):
logging.disable(logging.CRITICAL)
def tearDown(self):
logging.disable(logging.NOTSET)
def test_fconv_lm(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_fconv_lm") as data_dir:
create_dummy_data(data_dir)
preprocess_lm_data(data_dir)
train_language_model(
data_dir,
"fconv_lm",
[
"--decoder-layers",
"[(850, 3)] * 2 + [(1024,4)]",
"--decoder-embed-dim",
"280",
"--optimizer",
"nag",
"--lr",
"0.1",
],
)
eval_lm_main(data_dir)
generate_main(
data_dir,
[
"--task",
"language_modeling",
"--sample-break-mode",
"eos",
"--tokens-per-sample",
"500",
],
)
def test_transformer_lm(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_transformer_lm") as data_dir:
create_dummy_data(data_dir)
preprocess_lm_data(data_dir)
train_language_model(
data_dir,
"transformer_lm",
["--add-bos-token", "--nval", "1"],
run_validation=True,
)
eval_lm_main(data_dir)
eval_lm_main(data_dir, extra_flags=["--context-window", "25"])
generate_main(
data_dir,
[
"--task",
"language_modeling",
"--sample-break-mode",
"eos",
"--tokens-per-sample",
"500",
],
)
def test_normformer_lm(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_transformer_lm") as data_dir:
create_dummy_data(data_dir)
preprocess_lm_data(data_dir)
train_language_model(
data_dir,
"transformer_lm",
[
"--add-bos-token",
"--nval",
"1",
"--scale-fc",
"--scale-heads",
"--scale-attn",
"--scale-fc",
],
run_validation=True,
)
eval_lm_main(data_dir)
eval_lm_main(data_dir, extra_flags=["--context-window", "25"])
generate_main(
data_dir,
[
"--task",
"language_modeling",
"--sample-break-mode",
"eos",
"--tokens-per-sample",
"500",
],
)
def test_transformer_lm_with_adaptive_softmax(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory(
"test_transformer_lm_with_adaptive_softmax"
) as data_dir:
create_dummy_data(data_dir)
preprocess_lm_data(data_dir)
train_language_model(
data_dir,
"transformer_lm",
[
"--add-bos-token",
"--criterion",
"adaptive_loss",
"--adaptive-softmax-cutoff",
"5,10,15",
],
run_validation=True,
)
eval_lm_main(data_dir)
generate_main(
data_dir,
[
"--task",
"language_modeling",
"--sample-break-mode",
"eos",
"--tokens-per-sample",
"500",
],
)
def test_lightconv_lm(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_lightconv_lm") as data_dir:
create_dummy_data(data_dir)
preprocess_lm_data(data_dir)
train_language_model(
data_dir,
"lightconv_lm",
["--add-bos-token"],
run_validation=True,
)
eval_lm_main(data_dir)
generate_main(
data_dir,
[
"--task",
"language_modeling",
"--sample-break-mode",
"eos",
"--tokens-per-sample",
"500",
],
)
def test_lstm_lm(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_lstm_lm") as data_dir:
create_dummy_data(data_dir)
preprocess_lm_data(data_dir)
train_language_model(
data_dir,
"lstm_lm",
["--add-bos-token"],
run_validation=True,
)
eval_lm_main(data_dir)
generate_main(
data_dir,
[
"--task",
"language_modeling",
"--sample-break-mode",
"eos",
"--tokens-per-sample",
"500",
],
)
def test_lstm_lm_residuals(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_lstm_lm_residuals") as data_dir:
create_dummy_data(data_dir)
preprocess_lm_data(data_dir)
train_language_model(
data_dir,
"lstm_lm",
["--add-bos-token", "--residuals"],
run_validation=True,
)
eval_lm_main(data_dir)
generate_main(
data_dir,
[
"--task",
"language_modeling",
"--sample-break-mode",
"eos",
"--tokens-per-sample",
"500",
],
)
@unittest.skipIf(not has_hf_transformers, "skip test if transformers is missing")
def test_transformer_xl_bptt_lm(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_transformer_xl_bptt_lm") as data_dir:
create_dummy_data(data_dir)
preprocess_lm_data(data_dir)
task_flags = [
"--user-dir",
"examples/truncated_bptt",
"--task",
"truncated_bptt_lm",
"--batch-size",
"2",
"--tokens-per-sample",
"50",
]
train_language_model(
data_dir=data_dir,
arch="transformer_xl",
extra_flags=task_flags
+ [
"--n-layer",
"2",
],
task="truncated_bptt_lm",
run_validation=True,
extra_valid_flags=task_flags,
)
eval_lm_main(data_dir, extra_flags=task_flags)
# Train with activation offloading
train_language_model(
data_dir=data_dir,
arch="transformer_xl",
extra_flags=task_flags
+ [
"--n-layer",
"2",
"--offload-activations",
],
task="truncated_bptt_lm",
run_validation=True,
extra_valid_flags=task_flags,
)
class TestMaskedLanguageModel(unittest.TestCase):
def setUp(self):
logging.disable(logging.CRITICAL)
def tearDown(self):
logging.disable(logging.NOTSET)
def test_legacy_masked_lm(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_legacy_mlm") as data_dir:
create_dummy_data(data_dir)
preprocess_lm_data(data_dir)
train_legacy_masked_language_model(data_dir, "masked_lm")
def test_roberta_masked_lm(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_roberta_mlm") as data_dir:
create_dummy_data(data_dir)
preprocess_lm_data(data_dir)
train_masked_lm(
data_dir, "roberta_base", extra_flags=["--encoder-layers", "2"]
)
def test_roberta_sentence_prediction(self):
num_classes = 3
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_roberta_head") as data_dir:
create_dummy_roberta_head_data(data_dir, num_classes=num_classes)
preprocess_lm_data(os.path.join(data_dir, "input0"))
preprocess_lm_data(os.path.join(data_dir, "label"))
train_roberta_head(data_dir, "roberta_base", num_classes=num_classes)
def test_roberta_regression_single(self):
num_classes = 1
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory(
"test_roberta_regression_single"
) as data_dir:
create_dummy_roberta_head_data(
data_dir, num_classes=num_classes, regression=True
)
preprocess_lm_data(os.path.join(data_dir, "input0"))
train_roberta_head(
data_dir,
"roberta_base",
num_classes=num_classes,
extra_flags=["--regression-target"],
)
def test_roberta_regression_multiple(self):
num_classes = 3
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory(
"test_roberta_regression_multiple"
) as data_dir:
create_dummy_roberta_head_data(
data_dir, num_classes=num_classes, regression=True
)
preprocess_lm_data(os.path.join(data_dir, "input0"))
train_roberta_head(
data_dir,
"roberta_base",
num_classes=num_classes,
extra_flags=["--regression-target"],
)
def test_linformer_roberta_masked_lm(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_linformer_roberta_mlm") as data_dir:
create_dummy_data(data_dir)
preprocess_lm_data(data_dir)
train_masked_lm(
data_dir,
"linformer_roberta_base",
extra_flags=[
"--user-dir",
"examples/linformer/linformer_src",
"--encoder-layers",
"2",
],
)
def test_linformer_roberta_sentence_prediction(self):
num_classes = 3
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_linformer_roberta_head") as data_dir:
create_dummy_roberta_head_data(data_dir, num_classes=num_classes)
preprocess_lm_data(os.path.join(data_dir, "input0"))
preprocess_lm_data(os.path.join(data_dir, "label"))
train_roberta_head(
data_dir,
"linformer_roberta_base",
num_classes=num_classes,
extra_flags=["--user-dir", "examples/linformer/linformer_src"],
)
def test_linformer_roberta_regression_single(self):
num_classes = 1
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory(
"test_linformer_roberta_regression_single"
) as data_dir:
create_dummy_roberta_head_data(
data_dir, num_classes=num_classes, regression=True
)
preprocess_lm_data(os.path.join(data_dir, "input0"))
train_roberta_head(
data_dir,
"linformer_roberta_base",
num_classes=num_classes,
extra_flags=[
"--regression-target",
"--user-dir",
"examples/linformer/linformer_src",
],
)
def test_linformer_roberta_regression_multiple(self):
num_classes = 3
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory(
"test_linformer_roberta_regression_multiple"
) as data_dir:
create_dummy_roberta_head_data(
data_dir, num_classes=num_classes, regression=True
)
preprocess_lm_data(os.path.join(data_dir, "input0"))
train_roberta_head(
data_dir,
"linformer_roberta_base",
num_classes=num_classes,
extra_flags=[
"--regression-target",
"--user-dir",
"examples/linformer/linformer_src",
],
)
def _test_pretrained_masked_lm_for_translation(self, learned_pos_emb, encoder_only):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_mlm") as data_dir:
create_dummy_data(data_dir)
preprocess_lm_data(data_dir)
train_legacy_masked_language_model(
data_dir,
arch="masked_lm",
extra_args=("--encoder-learned-pos",) if learned_pos_emb else (),
)
with tempfile.TemporaryDirectory(
"test_mlm_translation"
) as translation_dir:
create_dummy_data(translation_dir)
preprocess_translation_data(
translation_dir, extra_flags=["--joined-dictionary"]
)
# Train transformer with data_dir/checkpoint_last.pt
train_translation_model(
translation_dir,
arch="transformer_from_pretrained_xlm",
extra_flags=[
"--decoder-layers",
"1",
"--decoder-embed-dim",
"32",
"--decoder-attention-heads",
"1",
"--decoder-ffn-embed-dim",
"32",
"--encoder-layers",
"1",
"--encoder-embed-dim",
"32",
"--encoder-attention-heads",
"1",
"--encoder-ffn-embed-dim",
"32",
"--pretrained-xlm-checkpoint",
"{}/checkpoint_last.pt".format(data_dir),
"--activation-fn",
"gelu",
"--max-source-positions",
"500",
"--max-target-positions",
"500",
]
+ (
["--encoder-learned-pos", "--decoder-learned-pos"]
if learned_pos_emb
else []
)
+ (["--init-encoder-only"] if encoder_only else []),
task="translation_from_pretrained_xlm",
)
def test_pretrained_masked_lm_for_translation_learned_pos_emb(self):
self._test_pretrained_masked_lm_for_translation(True, False)
def test_pretrained_masked_lm_for_translation_sinusoidal_pos_emb(self):
self._test_pretrained_masked_lm_for_translation(False, False)
def test_pretrained_masked_lm_for_translation_encoder_only(self):
self._test_pretrained_masked_lm_for_translation(True, True)
def test_r4f_roberta(self):
num_classes = 3
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_r4f_roberta_head") as data_dir:
create_dummy_roberta_head_data(data_dir, num_classes=num_classes)
preprocess_lm_data(os.path.join(data_dir, "input0"))
preprocess_lm_data(os.path.join(data_dir, "label"))
train_roberta_head(
data_dir,
"roberta_base",
num_classes=num_classes,
extra_flags=[
"--user-dir",
"examples/rxf/rxf_src",
"--criterion",
"sentence_prediction_r3f",
"--spectral-norm-classification-head",
],
)
def train_legacy_masked_language_model(data_dir, arch, extra_args=()):
train_parser = options.get_training_parser()
# TODO: langs should be in and out right?
train_args = options.parse_args_and_arch(
train_parser,
[
"--task",
"cross_lingual_lm",
data_dir,
"--arch",
arch,
# Optimizer args
"--optimizer",
"adam",
"--lr-scheduler",
"reduce_lr_on_plateau",
"--lr-shrink",
"0.5",
"--lr",
"0.0001",
"--stop-min-lr",
"1e-09",
# dropout, attention args
"--dropout",
"0.1",
"--attention-dropout",
"0.1",
# MLM args
"--criterion",
"legacy_masked_lm_loss",
"--masked-lm-only",
"--monolingual-langs",
"in,out",
"--num-segment",
"5",
# Transformer args: use a small transformer model for fast training
"--encoder-layers",
"1",
"--encoder-embed-dim",
"32",
"--encoder-attention-heads",
"1",
"--encoder-ffn-embed-dim",
"32",
# Other training args
"--max-tokens",
"500",
"--tokens-per-sample",
"500",
"--save-dir",
data_dir,
"--max-epoch",
"1",
"--no-progress-bar",
"--distributed-world-size",
"1",
"--dataset-impl",
"raw",
"--num-workers",
"0",
]
+ list(extra_args),
)
train.main(train_args)
class TestOptimizers(unittest.TestCase):
def setUp(self):
logging.disable(logging.CRITICAL)
def tearDown(self):
logging.disable(logging.NOTSET)
def test_optimizers(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_optimizers") as data_dir:
# Use just a bit of data and tiny model to keep this test runtime reasonable
create_dummy_data(data_dir, num_examples=10, maxlen=5)
preprocess_translation_data(data_dir)
optimizers = ["adafactor", "adam", "nag", "adagrad", "sgd", "adadelta"]
last_checkpoint = os.path.join(data_dir, "checkpoint_last.pt")
for optimizer in optimizers:
if os.path.exists(last_checkpoint):
os.remove(last_checkpoint)
train_translation_model(
data_dir,
"lstm",
[
"--required-batch-size-multiple",
"1",
"--encoder-layers",
"1",
"--encoder-hidden-size",
"32",
"--decoder-layers",
"1",
"--optimizer",
optimizer,
],
)
generate_main(data_dir)
def read_last_log_entry(
logs: List[logging.LogRecord], logger_name: str
) -> Dict[str, float]:
for x in reversed(logs):
if x.name == logger_name:
return json.loads(x.message)
raise ValueError(f"No entries from {logger_name} found in captured logs")
class TestActivationCheckpointing(unittest.TestCase):
base_flags = [
"--encoder-layers",
"2",
"--decoder-layers",
"2",
"--encoder-embed-dim",
"8",
"--decoder-embed-dim",
"8",
"--restore-file",
"x.pt",
"--log-format",
"json",
"--log-interval",
"1",
"--max-update",
"2",
]
def _train(self, data_dir, extra_flags):
with self.assertLogs() as logs:
train_translation_model(
data_dir,
"transformer_iwslt_de_en",
self.base_flags + extra_flags,
run_validation=True,
extra_valid_flags=["--log-format", "json"],
)
return logs.records
def test_activation_offloading_does_not_change_metrics(self):
"""Neither ----checkpoint-activations nor --offload-activations should change loss"""
with tempfile.TemporaryDirectory("test_transformer_with_act_cpt") as data_dir:
with self.assertLogs():
create_dummy_data(data_dir, num_examples=20)
preprocess_translation_data(data_dir)
offload_logs = self._train(data_dir, ["--offload-activations"])
baseline_logs = self._train(data_dir, [])
assert len(baseline_logs) == len(offload_logs)
baseline_valid_stats = read_last_log_entry(baseline_logs, "valid")
offload_valid_stats = read_last_log_entry(offload_logs, "valid")
baseline_train_stats = read_last_log_entry(baseline_logs, "train")
offload_train_stats = read_last_log_entry(offload_logs, "train")
assert (
baseline_train_stats["train_loss"] == offload_train_stats["train_loss"]
)
assert (
baseline_valid_stats["valid_loss"] == offload_valid_stats["valid_loss"]
)
def test_activation_checkpointing_does_not_change_metrics(self):
"""--checkpoint-activations should not change loss"""
with tempfile.TemporaryDirectory("test_transformer_with_act_cpt") as data_dir:
with self.assertLogs():
create_dummy_data(data_dir, num_examples=20)
preprocess_translation_data(data_dir)
ckpt_logs = self._train(data_dir, ["--checkpoint-activations"])
baseline_logs = self._train(data_dir, [])
assert len(baseline_logs) == len(ckpt_logs)
baseline_train_stats = read_last_log_entry(baseline_logs, "train")
ckpt_train_stats = read_last_log_entry(ckpt_logs, "train")
assert baseline_train_stats["train_loss"] == ckpt_train_stats["train_loss"]
baseline_valid_stats = read_last_log_entry(baseline_logs, "valid")
ckpt_valid_stats = read_last_log_entry(ckpt_logs, "valid")
assert baseline_valid_stats["valid_loss"] == ckpt_valid_stats["valid_loss"]
def create_dummy_roberta_head_data(
data_dir, num_examples=100, maxlen=10, num_classes=2, regression=False
):
input_dir = "input0"
def _create_dummy_data(filename):
random_data = torch.rand(num_examples * maxlen)
input_data = 97 + torch.floor(26 * random_data).int()
if regression:
output_data = torch.rand((num_examples, num_classes))
else:
output_data = 1 + torch.floor(num_classes * torch.rand(num_examples)).int()
with open(os.path.join(data_dir, input_dir, filename + ".out"), "w") as f_in:
label_filename = filename + ".label" if regression else filename + ".out"
with open(os.path.join(data_dir, "label", label_filename), "w") as f_out:
offset = 0
for i in range(num_examples):
# write example input
ex_len = random.randint(1, maxlen)
ex_str = " ".join(map(chr, input_data[offset : offset + ex_len]))
print(ex_str, file=f_in)
# write example label
if regression:
class_str = " ".join(map(str, output_data[i].numpy()))
print(class_str, file=f_out)
else:
class_str = "class{}".format(output_data[i])
print(class_str, file=f_out)
offset += ex_len
os.mkdir(os.path.join(data_dir, input_dir))
os.mkdir(os.path.join(data_dir, "label"))
_create_dummy_data("train")
_create_dummy_data("valid")
_create_dummy_data("test")
def train_masked_lm(data_dir, arch, extra_flags=None):
train_parser = options.get_training_parser()
train_args = options.parse_args_and_arch(
train_parser,
[
"--task",
"masked_lm",
data_dir,
"--arch",
arch,
"--optimizer",
"adam",
"--lr",
"0.0001",
"--criterion",
"masked_lm",
"--batch-size",
"500",
"--save-dir",
data_dir,
"--max-epoch",
"1",
"--no-progress-bar",
"--distributed-world-size",
"1",
"--ddp-backend",
"no_c10d",
"--num-workers",
"0",
]
+ (extra_flags or []),
)
train.main(train_args)
def train_roberta_head(data_dir, arch, num_classes=2, extra_flags=None):
train_parser = options.get_training_parser()
train_args = options.parse_args_and_arch(
train_parser,
[
"--task",
"sentence_prediction",
data_dir,
"--arch",
arch,
"--encoder-layers",
"2",
"--num-classes",
str(num_classes),
"--optimizer",
"adam",
"--lr",
"0.0001",
"--criterion",
"sentence_prediction",
"--max-tokens",
"500",
"--max-positions",
"500",
"--batch-size",
"500",
"--save-dir",
data_dir,
"--max-epoch",
"1",
"--no-progress-bar",
"--distributed-world-size",
"1",
"--ddp-backend",
"no_c10d",
"--num-workers",
"0",
]
+ (extra_flags or []),
)
train.main(train_args)
def eval_lm_main(data_dir, extra_flags=None):
eval_lm_parser = options.get_eval_lm_parser()
eval_lm_args = options.parse_args_and_arch(
eval_lm_parser,
[
data_dir,
"--path",
os.path.join(data_dir, "checkpoint_last.pt"),
"--no-progress-bar",
"--num-workers",
"0",
]
+ (extra_flags or []),
)
eval_lm.main(eval_lm_args)
if __name__ == "__main__":
unittest.main()
| 73,337
| 37.437107
| 93
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/test_concat_dataset.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import torch
from fairseq.data import LanguagePairDataset, TokenBlockDataset
from fairseq.data.concat_dataset import ConcatDataset
from tests.test_train import mock_dict
class TestConcatDataset(unittest.TestCase):
def setUp(self):
d = mock_dict()
tokens_1 = torch.LongTensor([1]).view(1, -1)
tokens_ds1 = TokenBlockDataset(
tokens_1,
sizes=[tokens_1.size(-1)],
block_size=1,
pad=0,
eos=1,
include_targets=False,
)
self.dataset_1 = LanguagePairDataset(
tokens_ds1, tokens_ds1.sizes, d, shuffle=False
)
tokens_2 = torch.LongTensor([2]).view(1, -1)
tokens_ds2 = TokenBlockDataset(
tokens_2,
sizes=[tokens_2.size(-1)],
block_size=1,
pad=0,
eos=1,
include_targets=False,
)
self.dataset_2 = LanguagePairDataset(
tokens_ds2, tokens_ds2.sizes, d, shuffle=False
)
def test_concat_dataset_basics(self):
d = ConcatDataset([self.dataset_1, self.dataset_2])
assert len(d) == 2
assert d[0]["source"][0] == 1
assert d[1]["source"][0] == 2
d = ConcatDataset([self.dataset_1, self.dataset_2], sample_ratios=[1, 2])
assert len(d) == 3
assert d[0]["source"][0] == 1
assert d[1]["source"][0] == 2
assert d[2]["source"][0] == 2
d = ConcatDataset([self.dataset_1, self.dataset_2], sample_ratios=[2, 1])
assert len(d) == 3
assert d[0]["source"][0] == 1
assert d[1]["source"][0] == 1
assert d[2]["source"][0] == 2
| 1,866
| 30.644068
| 81
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/test_activation_checkpointing.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import torch
import torch.nn as nn
from fairseq.modules.checkpoint_activations import checkpoint_wrapper
from torch.utils.checkpoint import checkpoint
class Model(nn.Module):
def __init__(
self, use_pytorch_checkpoint=False, use_fairseq_checkpoint=False, **kwargs
):
super().__init__()
torch.manual_seed(0)
self.use_pytorch_checkpoint = use_pytorch_checkpoint
self.ffn = nn.Sequential(
nn.Linear(32, 128),
# add a Dropout layer to test RNG save/restore
nn.Dropout(p=0.5),
nn.Linear(128, 32),
)
if use_fairseq_checkpoint:
self.ffn = checkpoint_wrapper(self.ffn, **kwargs)
self.out = nn.Linear(32, 1)
def forward(self, x):
if self.use_pytorch_checkpoint:
x = checkpoint(self.ffn, x)
else:
x = self.ffn(x)
return self.out(x)
class TestActivationCheckpointing(unittest.TestCase):
def _test_checkpoint_wrapper(self, device, log_memory_usage=False):
def get_loss_and_gnorm(model):
torch.manual_seed(1)
input = torch.rand(2, 16, 32).requires_grad_(True).to(device)
model.zero_grad()
loss = model(input).sum()
loss.backward()
gnorm = torch.norm(
torch.stack([torch.norm(p.grad.detach()) for p in model.parameters()])
)
return {"loss": loss, "gnorm": gnorm}
model = Model().to(device)
no_cpt = get_loss_and_gnorm(model)
model = Model(use_pytorch_checkpoint=True).to(device)
pyt_cpt = get_loss_and_gnorm(model)
torch.testing.assert_allclose(no_cpt["loss"], pyt_cpt["loss"])
torch.testing.assert_allclose(no_cpt["gnorm"], pyt_cpt["gnorm"])
model = Model(use_fairseq_checkpoint=True).to(device)
fairseq_cpt = get_loss_and_gnorm(model)
torch.testing.assert_allclose(no_cpt["loss"], fairseq_cpt["loss"])
torch.testing.assert_allclose(no_cpt["gnorm"], fairseq_cpt["gnorm"])
model = Model(use_fairseq_checkpoint=True, offload_to_cpu=True).to(device)
fairseq_cpt_offload = get_loss_and_gnorm(model)
torch.testing.assert_allclose(no_cpt["loss"], fairseq_cpt_offload["loss"])
torch.testing.assert_allclose(no_cpt["gnorm"], fairseq_cpt_offload["gnorm"])
def test_checkpoint_wrapper_cpu(self):
self._test_checkpoint_wrapper(device=torch.device("cpu"))
@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU")
def test_checkpoint_wrapper_cuda(self):
self._test_checkpoint_wrapper(device=torch.device("cuda"))
if __name__ == "__main__":
unittest.main()
| 2,904
| 35.3125
| 86
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/test_noising.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
from typing import Dict, List
import tests.utils as test_utils
import torch
from fairseq import utils
from fairseq.data import (
Dictionary,
LanguagePairDataset,
TransformEosDataset,
data_utils,
noising,
)
class TestDataNoising(unittest.TestCase):
def _get_test_data_with_bpe_cont_marker(self, append_eos=True):
"""
Args:
append_eos: if True, each input sentence in the source tokens tensor
will have an EOS appended to the end.
Returns:
vocabs: BPE vocab with continuation markers as suffixes to denote
non-end of word tokens. This is the standard BPE format used in
fairseq's preprocessing.
x: input tensor containing numberized source tokens, with EOS at the
end if append_eos is true
src_lengths: and source lengths.
"""
vocab = Dictionary()
vocab.add_symbol("he@@")
vocab.add_symbol("llo")
vocab.add_symbol("how")
vocab.add_symbol("are")
vocab.add_symbol("y@@")
vocab.add_symbol("ou")
vocab.add_symbol("n@@")
vocab.add_symbol("ew")
vocab.add_symbol("or@@")
vocab.add_symbol("k")
src_tokens = [
["he@@", "llo", "n@@", "ew", "y@@", "or@@", "k"],
["how", "are", "y@@", "ou"],
]
x, src_lengths = x, src_lengths = self._convert_src_tokens_to_tensor(
vocab=vocab, src_tokens=src_tokens, append_eos=append_eos
)
return vocab, x, src_lengths
def _get_test_data_with_bpe_end_marker(self, append_eos=True):
"""
Args:
append_eos: if True, each input sentence in the source tokens tensor
will have an EOS appended to the end.
Returns:
vocabs: BPE vocab with end-of-word markers as suffixes to denote
tokens at the end of a word. This is an alternative to fairseq's
standard preprocessing framework and is not generally supported
within fairseq.
x: input tensor containing numberized source tokens, with EOS at the
end if append_eos is true
src_lengths: and source lengths.
"""
vocab = Dictionary()
vocab.add_symbol("he")
vocab.add_symbol("llo_EOW")
vocab.add_symbol("how_EOW")
vocab.add_symbol("are_EOW")
vocab.add_symbol("y")
vocab.add_symbol("ou_EOW")
vocab.add_symbol("n")
vocab.add_symbol("ew_EOW")
vocab.add_symbol("or")
vocab.add_symbol("k_EOW")
src_tokens = [
["he", "llo_EOW", "n", "ew_EOW", "y", "or", "k_EOW"],
["how_EOW", "are_EOW", "y", "ou_EOW"],
]
x, src_lengths = x, src_lengths = self._convert_src_tokens_to_tensor(
vocab=vocab, src_tokens=src_tokens, append_eos=append_eos
)
return vocab, x, src_lengths
def _get_test_data_with_word_vocab(self, append_eos=True):
"""
Args:
append_eos: if True, each input sentence in the source tokens tensor
will have an EOS appended to the end.
Returns:
vocabs: word vocab
x: input tensor containing numberized source tokens, with EOS at the
end if append_eos is true
src_lengths: and source lengths.
"""
vocab = Dictionary()
vocab.add_symbol("hello")
vocab.add_symbol("how")
vocab.add_symbol("are")
vocab.add_symbol("you")
vocab.add_symbol("new")
vocab.add_symbol("york")
src_tokens = [
["hello", "new", "york", "you"],
["how", "are", "you", "new", "york"],
]
x, src_lengths = self._convert_src_tokens_to_tensor(
vocab=vocab, src_tokens=src_tokens, append_eos=append_eos
)
return vocab, x, src_lengths
def _convert_src_tokens_to_tensor(
self, vocab: Dictionary, src_tokens: List[List[str]], append_eos: bool
):
src_len = [len(x) for x in src_tokens]
# If we have to append EOS, we include EOS in counting src length
if append_eos:
src_len = [length + 1 for length in src_len]
x = torch.LongTensor(len(src_tokens), max(src_len)).fill_(vocab.pad())
for i in range(len(src_tokens)):
for j in range(len(src_tokens[i])):
x[i][j] = vocab.index(src_tokens[i][j])
if append_eos:
x[i][j + 1] = vocab.eos()
x = x.transpose(1, 0)
return x, torch.LongTensor(src_len)
def assert_eos_at_end(self, x, x_len, eos):
"""Asserts last token of every sentence in x is EOS"""
for i in range(len(x_len)):
self.assertEqual(
x[x_len[i] - 1][i],
eos,
(
"Expected eos (token id {eos}) at the end of sentence {i} "
"but got {other} instead"
).format(i=i, eos=eos, other=x[i][-1]),
)
def assert_word_dropout_correct(self, x, x_noised, x_len, l_noised):
# Expect only the first word (2 bpe tokens) of the first example
# was dropped out
self.assertEqual(x_len[0] - 2, l_noised[0])
for i in range(l_noised[0]):
self.assertEqual(x_noised[i][0], x[i + 2][0])
def test_word_dropout_with_eos(self):
vocab, x, x_len = self._get_test_data_with_bpe_cont_marker(append_eos=True)
with data_utils.numpy_seed(1234):
noising_gen = noising.WordDropout(vocab)
x_noised, l_noised = noising_gen.noising(x, x_len, 0.2)
self.assert_word_dropout_correct(
x=x, x_noised=x_noised, x_len=x_len, l_noised=l_noised
)
self.assert_eos_at_end(x=x_noised, x_len=l_noised, eos=vocab.eos())
def assert_word_blanking_correct(self, x, x_noised, x_len, l_noised, unk):
# Expect only the first word (2 bpe tokens) of the first example
# was blanked out
self.assertEqual(x_len[0], l_noised[0])
for i in range(l_noised[0]):
if i < 2:
self.assertEqual(x_noised[i][0], unk)
else:
self.assertEqual(x_noised[i][0], x[i][0])
def test_word_blank_with_eos(self):
vocab, x, x_len = self._get_test_data_with_bpe_cont_marker(append_eos=True)
with data_utils.numpy_seed(1234):
noising_gen = noising.WordDropout(vocab)
x_noised, l_noised = noising_gen.noising(x, x_len, 0.2, vocab.unk())
self.assert_word_blanking_correct(
x=x, x_noised=x_noised, x_len=x_len, l_noised=l_noised, unk=vocab.unk()
)
self.assert_eos_at_end(x=x_noised, x_len=l_noised, eos=vocab.eos())
def generate_unchanged_shuffle_map(self, length):
return {i: i for i in range(length)}
def assert_word_shuffle_matches_expected(
self,
x,
x_len,
max_shuffle_distance: int,
vocab: Dictionary,
expected_shufle_maps: List[Dict[int, int]],
expect_eos_at_end: bool,
bpe_end_marker=None,
):
"""
This verifies that with a given x, x_len, max_shuffle_distance, and
vocab, we get the expected shuffle result.
Args:
x: Tensor of shape (T x B) = (sequence_length, batch_size)
x_len: Tensor of length B = batch_size
max_shuffle_distance: arg to pass to noising
expected_shuffle_maps: List[mapping] where mapping is a
Dict[old_index, new_index], mapping x's elements from their
old positions in x to their new positions in x.
expect_eos_at_end: if True, check the output to make sure there is
an EOS at the end.
bpe_end_marker: str denoting the BPE end token. If this is not None, we
set the BPE cont token to None in the noising classes.
"""
bpe_cont_marker = None
if bpe_end_marker is None:
bpe_cont_marker = "@@"
with data_utils.numpy_seed(1234):
word_shuffle = noising.WordShuffle(
vocab, bpe_cont_marker=bpe_cont_marker, bpe_end_marker=bpe_end_marker
)
x_noised, l_noised = word_shuffle.noising(
x, x_len, max_shuffle_distance=max_shuffle_distance
)
# For every example, we have a different expected shuffle map. We check
# that each example is shuffled as expected according to each
# corresponding shuffle map.
for i in range(len(expected_shufle_maps)):
shuffle_map = expected_shufle_maps[i]
for k, v in shuffle_map.items():
self.assertEqual(x[k][i], x_noised[v][i])
# Shuffling should not affect the length of each example
for pre_shuffle_length, post_shuffle_length in zip(x_len, l_noised):
self.assertEqual(pre_shuffle_length, post_shuffle_length)
if expect_eos_at_end:
self.assert_eos_at_end(x=x_noised, x_len=l_noised, eos=vocab.eos())
def test_word_shuffle_with_eos(self):
vocab, x, x_len = self._get_test_data_with_bpe_cont_marker(append_eos=True)
# Assert word shuffle with max shuffle distance 0 causes input to be
# unchanged
self.assert_word_shuffle_matches_expected(
x=x,
x_len=x_len,
max_shuffle_distance=0,
vocab=vocab,
expected_shufle_maps=[
self.generate_unchanged_shuffle_map(example_len)
for example_len in x_len
],
expect_eos_at_end=True,
)
# Assert word shuffle with max shuffle distance 3 matches our expected
# shuffle order
self.assert_word_shuffle_matches_expected(
x=x,
x_len=x_len,
vocab=vocab,
max_shuffle_distance=3,
expected_shufle_maps=[
self.generate_unchanged_shuffle_map(x_len[0]),
{0: 0, 1: 3, 2: 1, 3: 2},
],
expect_eos_at_end=True,
)
def test_word_shuffle_with_eos_nonbpe(self):
"""The purpose of this is to test shuffling logic with word vocabs"""
vocab, x, x_len = self._get_test_data_with_word_vocab(append_eos=True)
# Assert word shuffle with max shuffle distance 0 causes input to be
# unchanged
self.assert_word_shuffle_matches_expected(
x=x,
x_len=x_len,
max_shuffle_distance=0,
vocab=vocab,
expected_shufle_maps=[
self.generate_unchanged_shuffle_map(example_len)
for example_len in x_len
],
expect_eos_at_end=True,
)
# Assert word shuffle with max shuffle distance 3 matches our expected
# shuffle order
self.assert_word_shuffle_matches_expected(
x=x,
x_len=x_len,
vocab=vocab,
max_shuffle_distance=3,
expected_shufle_maps=[
{0: 0, 1: 1, 2: 3, 3: 2},
{0: 0, 1: 2, 2: 1, 3: 3, 4: 4},
],
expect_eos_at_end=True,
)
def test_word_shuffle_without_eos(self):
"""Same result as word shuffle with eos except no EOS at end"""
vocab, x, x_len = self._get_test_data_with_bpe_cont_marker(append_eos=False)
# Assert word shuffle with max shuffle distance 0 causes input to be
# unchanged
self.assert_word_shuffle_matches_expected(
x=x,
x_len=x_len,
max_shuffle_distance=0,
vocab=vocab,
expected_shufle_maps=[
self.generate_unchanged_shuffle_map(example_len)
for example_len in x_len
],
expect_eos_at_end=False,
)
# Assert word shuffle with max shuffle distance 3 matches our expected
# shuffle order
self.assert_word_shuffle_matches_expected(
x=x,
x_len=x_len,
vocab=vocab,
max_shuffle_distance=3,
expected_shufle_maps=[
self.generate_unchanged_shuffle_map(x_len[0]),
{0: 0, 1: 3, 2: 1, 3: 2},
],
expect_eos_at_end=False,
)
def test_word_shuffle_without_eos_with_bpe_end_marker(self):
"""Same result as word shuffle without eos except using BPE end token"""
vocab, x, x_len = self._get_test_data_with_bpe_end_marker(append_eos=False)
# Assert word shuffle with max shuffle distance 0 causes input to be
# unchanged
self.assert_word_shuffle_matches_expected(
x=x,
x_len=x_len,
max_shuffle_distance=0,
vocab=vocab,
expected_shufle_maps=[
self.generate_unchanged_shuffle_map(example_len)
for example_len in x_len
],
expect_eos_at_end=False,
bpe_end_marker="_EOW",
)
# Assert word shuffle with max shuffle distance 3 matches our expected
# shuffle order
self.assert_word_shuffle_matches_expected(
x=x,
x_len=x_len,
vocab=vocab,
max_shuffle_distance=3,
expected_shufle_maps=[
self.generate_unchanged_shuffle_map(x_len[0]),
{0: 0, 1: 3, 2: 1, 3: 2},
],
expect_eos_at_end=False,
bpe_end_marker="_EOW",
)
def assert_no_eos_at_end(self, x, x_len, eos):
"""Asserts that the last token of each sentence in x is not EOS"""
for i in range(len(x_len)):
self.assertNotEqual(
x[x_len[i] - 1][i],
eos,
"Expected no eos (token id {eos}) at the end of sentence {i}.".format(
eos=eos, i=i
),
)
def test_word_dropout_without_eos(self):
"""Same result as word dropout with eos except no EOS at end"""
vocab, x, x_len = self._get_test_data_with_bpe_cont_marker(append_eos=False)
with data_utils.numpy_seed(1234):
noising_gen = noising.WordDropout(vocab)
x_noised, l_noised = noising_gen.noising(x, x_len, 0.2)
self.assert_word_dropout_correct(
x=x, x_noised=x_noised, x_len=x_len, l_noised=l_noised
)
self.assert_no_eos_at_end(x=x_noised, x_len=l_noised, eos=vocab.eos())
def test_word_blank_without_eos(self):
"""Same result as word blank with eos except no EOS at end"""
vocab, x, x_len = self._get_test_data_with_bpe_cont_marker(append_eos=False)
with data_utils.numpy_seed(1234):
noising_gen = noising.WordDropout(vocab)
x_noised, l_noised = noising_gen.noising(x, x_len, 0.2, vocab.unk())
self.assert_word_blanking_correct(
x=x, x_noised=x_noised, x_len=x_len, l_noised=l_noised, unk=vocab.unk()
)
self.assert_no_eos_at_end(x=x_noised, x_len=l_noised, eos=vocab.eos())
def _get_noising_dataset_batch(
self,
src_tokens_no_pad,
src_dict,
append_eos_to_tgt=False,
):
"""
Constructs a NoisingDataset and the corresponding
``LanguagePairDataset(NoisingDataset(src), src)``. If
*append_eos_to_tgt* is True, wrap the source dataset in
:class:`TransformEosDataset` to append EOS to the clean source when
using it as the target.
"""
src_dataset = test_utils.TestDataset(data=src_tokens_no_pad)
noising_dataset = noising.NoisingDataset(
src_dataset=src_dataset,
src_dict=src_dict,
seed=1234,
max_word_shuffle_distance=3,
word_dropout_prob=0.2,
word_blanking_prob=0.2,
noising_class=noising.UnsupervisedMTNoising,
)
tgt = src_dataset
language_pair_dataset = LanguagePairDataset(
src=noising_dataset, tgt=tgt, src_sizes=None, src_dict=src_dict
)
language_pair_dataset = TransformEosDataset(
language_pair_dataset,
src_dict.eos(),
append_eos_to_tgt=append_eos_to_tgt,
)
dataloader = torch.utils.data.DataLoader(
dataset=language_pair_dataset,
batch_size=2,
collate_fn=language_pair_dataset.collater,
)
denoising_batch_result = next(iter(dataloader))
return denoising_batch_result
def test_noising_dataset_with_eos(self):
src_dict, src_tokens, _ = self._get_test_data_with_bpe_cont_marker(
append_eos=True
)
# Format data for src_dataset
src_tokens = torch.t(src_tokens)
src_tokens_no_pad = []
for src_sentence in src_tokens:
src_tokens_no_pad.append(
utils.strip_pad(tensor=src_sentence, pad=src_dict.pad())
)
denoising_batch_result = self._get_noising_dataset_batch(
src_tokens_no_pad=src_tokens_no_pad, src_dict=src_dict
)
eos, pad = src_dict.eos(), src_dict.pad()
# Generated noisy source as source
expected_src = torch.LongTensor(
[[4, 5, 10, 11, 8, 12, 13, eos], [pad, pad, pad, 6, 8, 9, 7, eos]]
)
# Original clean source as target (right-padded)
expected_tgt = torch.LongTensor(
[[4, 5, 10, 11, 8, 12, 13, eos], [6, 7, 8, 9, eos, pad, pad, pad]]
)
generated_src = denoising_batch_result["net_input"]["src_tokens"]
tgt_tokens = denoising_batch_result["target"]
self.assertTensorEqual(expected_src, generated_src)
self.assertTensorEqual(expected_tgt, tgt_tokens)
def test_noising_dataset_without_eos(self):
"""
Similar to test noising dataset with eos except that we have to set
*append_eos_to_tgt* to ``True``.
"""
src_dict, src_tokens, _ = self._get_test_data_with_bpe_cont_marker(
append_eos=False
)
# Format data for src_dataset
src_tokens = torch.t(src_tokens)
src_tokens_no_pad = []
for src_sentence in src_tokens:
src_tokens_no_pad.append(
utils.strip_pad(tensor=src_sentence, pad=src_dict.pad())
)
denoising_batch_result = self._get_noising_dataset_batch(
src_tokens_no_pad=src_tokens_no_pad,
src_dict=src_dict,
append_eos_to_tgt=True,
)
eos, pad = src_dict.eos(), src_dict.pad()
# Generated noisy source as source
expected_src = torch.LongTensor(
[[4, 5, 10, 11, 8, 12, 13], [pad, pad, pad, 6, 8, 9, 7]]
)
# Original clean source as target (right-padded)
expected_tgt = torch.LongTensor(
[[4, 5, 10, 11, 8, 12, 13, eos], [6, 7, 8, 9, eos, pad, pad, pad]]
)
generated_src = denoising_batch_result["net_input"]["src_tokens"]
tgt_tokens = denoising_batch_result["target"]
self.assertTensorEqual(expected_src, generated_src)
self.assertTensorEqual(expected_tgt, tgt_tokens)
def assertTensorEqual(self, t1, t2):
self.assertEqual(t1.size(), t2.size(), "size mismatch")
self.assertEqual(t1.ne(t2).long().sum(), 0)
if __name__ == "__main__":
unittest.main()
| 19,813
| 36.314501
| 87
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/test_constraints.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import sys
import unittest
import torch
from fairseq.token_generation_constraints import *
def tensorize(constraints: List[List[int]]) -> torch.Tensor:
return [torch.tensor(x) for x in constraints]
class TestHelperRoutines(unittest.TestCase):
def setUp(self):
self.examples = [
([[]], torch.tensor([[0]])),
([[], []], torch.tensor([[0], [0]])),
([[torch.tensor([1, 2])], []], torch.tensor([[1, 1, 2, 0], [0, 0, 0, 0]])),
(
[
[
torch.tensor([3, 1, 2]),
torch.tensor([3]),
torch.tensor([4, 5, 6, 7]),
],
[],
[torch.tensor([1, 8, 9, 10, 1, 4, 11, 12])],
],
torch.tensor(
[
[3, 3, 1, 2, 0, 3, 0, 4, 5, 6, 7, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 8, 9, 10, 1, 4, 11, 12, 0, 0, 0],
]
),
),
]
def test_packing(self):
"""Ensures the list of lists of tensors gets packed correctly."""
for batch_constraints, expected_tensor in self.examples:
packed = pack_constraints(batch_constraints)
assert torch.equal(packed, expected_tensor)
class TestUnorderedConstraintState(unittest.TestCase):
def setUp(self):
# Tuples of (contraint set, expected printed graph, token counts per node)
self.examples = [
(
tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]),
"([None].False#6 ([1].True#4 ([2].False#1 [3].True#1) [3].True#1 [4].True#1) ([4].False#2 ([5].True#2 ([6].False#1 [7].True#1))))",
{1: 4, 2: 1, 3: 2, 4: 3, 5: 2, 6: 1, 7: 1},
),
([], "[None].False#0", {}),
(tensorize([[0]]), "([None].False#1 [0].True#1)", {0: 1}),
(
tensorize([[100000, 1, 2, 3, 4, 5]]),
"([None].False#1 ([100000].False#1 ([1].False#1 ([2].False#1 ([3].False#1 ([4].False#1 [5].True#1))))))",
{100000: 1, 1: 1, 2: 1, 3: 1, 4: 1, 5: 1},
),
(
tensorize([[1, 2], [1, 2]]),
"([None].False#2 ([1].False#2 [2].True#2))",
{1: 2, 2: 2},
),
(
tensorize([[1, 2], [3, 4]]),
"([None].False#2 ([1].False#1 [2].True#1) ([3].False#1 [4].True#1))",
{1: 1, 2: 1, 3: 1, 4: 1},
),
]
self.sequences = [
(
self.examples[0][0],
[],
{"bank": 0, "num_completed": 0, "finished": False, "is_root": True},
),
(
self.examples[0][0],
[1, 2],
{"bank": 2, "num_completed": 0, "finished": False, "is_root": False},
),
(
self.examples[0][0],
[1, 2, 94],
{"bank": 1, "num_completed": 1, "finished": False, "is_root": True},
),
(
self.examples[0][0],
[1, 3, 999, 1, 4],
{"bank": 4, "num_completed": 2, "finished": False, "is_root": False},
),
(
self.examples[0][0],
[1, 3, 999, 1, 4, 999],
{"bank": 4, "num_completed": 2, "finished": False, "is_root": True},
),
(
self.examples[0][0],
[4, 5, 6, 8],
{"bank": 2, "num_completed": 1, "finished": False, "is_root": True},
),
(
self.examples[0][0],
# Tricky, because in last three, goes down [1->4] branch, could miss [1] and [4->5]
# [[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]],
[1, 2, 3, 1, 3, 1, 4, 4, 5, 6, 7, 1, 4, 5],
{"bank": 14, "num_completed": 6, "finished": True, "is_root": False},
),
(
self.examples[0][0],
[1, 2, 3, 999, 1, 3, 1, 4, 4, 5, 6, 7, 1, 4, 5, 117],
{"bank": 14, "num_completed": 6, "finished": True, "is_root": True},
),
(
tensorize([[1], [2, 3]]),
# Should not be able to get credit for entering 1 a second time
[1, 1],
{"bank": 1, "num_completed": 1, "finished": False, "is_root": True},
),
(
self.examples[4][0],
[1, 2, 1, 2],
{"bank": 4, "num_completed": 2, "finished": True, "is_root": False},
),
(
self.examples[4][0],
[1, 2, 1, 2, 1],
{"bank": 4, "num_completed": 2, "finished": True, "is_root": True},
),
(
self.examples[5][0],
[1, 2, 3, 4, 5],
{"bank": 4, "num_completed": 2, "finished": True, "is_root": True},
),
]
def test_graphs(self):
"""
Test whether unordered graph systems are created correctly.
"""
for example in self.examples:
constraints, expected, gold_counts = example
c = ConstraintNode.create(constraints)
assert (
ConstraintNode.print_graph(c) == expected
), f"got {ConstraintNode.print_graph(c)}, expected {expected}"
assert (
c.token_counts() == gold_counts
), f"{c} got {c.token_counts()} wanted {gold_counts}"
def test_next_tokens(self):
"""
Tests that the set of next tokens is correct.
"""
for example in self.examples:
constraints, expected, gold_counts = example
root = ConstraintNode.create(constraints)
root_tokens = set(root.children.keys())
for sequence in constraints:
state = UnorderedConstraintState(root)
for token in sequence:
all_tokens = root_tokens.union(state.node.children.keys())
assert (
all_tokens == state.next_tokens()
), f"ALL {all_tokens} NEXT {state.next_tokens()}"
state = state.advance(token)
def test_sequences(self):
for constraints, tokens, expected in self.sequences:
state = UnorderedConstraintState.create(pack_constraints([constraints])[0])
for token in tokens:
state = state.advance(token)
result = {}
for attr in expected.keys():
result[attr] = getattr(state, attr)
assert (
result == expected
), f"TEST({tokens}) GOT: {result} WANTED: {expected}"
class TestOrderedConstraintState(unittest.TestCase):
def setUp(self):
self.sequences = [
(
tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]),
[],
{"bank": 0, "num_completed": 0, "finished": False, "is_root": True},
),
(
tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]),
[1, 2],
{"bank": 2, "num_completed": 0, "finished": False, "is_root": False},
),
(
tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]),
[1, 2, 94],
{"bank": 0, "num_completed": 0, "finished": False, "is_root": True},
),
(
tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]),
[1, 3, 999, 1, 4],
{"bank": 0, "num_completed": 0, "finished": False, "is_root": True},
),
(
tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]),
[1, 2, 3, 999, 999],
{"bank": 3, "num_completed": 1, "finished": False, "is_root": False},
),
(
tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]),
[1, 2, 3, 77, 1, 3, 1],
{"bank": 6, "num_completed": 2, "finished": False, "is_root": False},
),
(
tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]),
[1, 2, 3, 1, 3, 1, 4, 4, 5, 6, 7, 1, 4, 5],
{"bank": 14, "num_completed": 6, "finished": True, "is_root": False},
),
(
tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]),
[1, 2, 999, 1, 2, 3, 999, 1, 3, 1, 4, 4, 5, 6, 7, 1, 4, 5, 117],
{"bank": 14, "num_completed": 6, "finished": True, "is_root": False},
),
(
tensorize([[1], [2, 3]]),
[1, 1],
{"bank": 1, "num_completed": 1, "finished": False, "is_root": False},
),
(
tensorize([[1, 2], [1, 2]]),
[1, 2, 1, 2],
{"bank": 4, "num_completed": 2, "finished": True, "is_root": False},
),
(
tensorize([[1, 2], [1, 2]]),
[1, 2, 1, 2, 1],
{"bank": 4, "num_completed": 2, "finished": True, "is_root": False},
),
(
tensorize([[1, 2], [3, 4]]),
[1, 2, 3, 4, 5],
{"bank": 4, "num_completed": 2, "finished": True, "is_root": False},
),
]
def test_sequences(self):
for i, (constraints, tokens, expected) in enumerate(self.sequences):
state = OrderedConstraintState.create(pack_constraints([constraints])[0])
for token in tokens:
state = state.advance(token)
result = {}
for attr in expected.keys():
result[attr] = getattr(state, attr)
assert (
result == expected
), f"TEST({tokens}) GOT: {result} WANTED: {expected}"
if __name__ == "__main__":
unittest.main()
| 10,488
| 37.848148
| 147
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/test_sparse_multihead_attention.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import torch
from fairseq.modules.sparse_multihead_attention import SparseMultiheadAttention
class TestSparseMultiheadAttention(unittest.TestCase):
def test_sparse_multihead_attention(self):
attn_weights = torch.randn(1, 8, 8)
bidirectional_sparse_mask = torch.tensor(
[
[0, 0, 0, 0, 0, float("-inf"), float("-inf"), 0],
[0, 0, 0, 0, 0, float("-inf"), float("-inf"), 0],
[0, 0, 0, 0, 0, float("-inf"), float("-inf"), 0],
[0, 0, 0, 0, 0, float("-inf"), float("-inf"), 0],
[float("-inf"), float("-inf"), float("-inf"), 0, 0, 0, 0, 0],
[float("-inf"), float("-inf"), float("-inf"), 0, 0, 0, 0, 0],
[float("-inf"), float("-inf"), float("-inf"), 0, 0, 0, 0, 0],
[float("-inf"), float("-inf"), float("-inf"), 0, 0, 0, 0, 0],
]
)
bidirectional_attention = SparseMultiheadAttention(
16, 1, stride=4, expressivity=1, is_bidirectional=True
)
bidirectional_attention_sparse_mask = (
bidirectional_attention.buffered_sparse_mask(attn_weights, 8, 8)
)
torch.all(
torch.eq(bidirectional_attention_sparse_mask, bidirectional_sparse_mask)
)
sparse_mask = torch.tensor(
[
[
0,
float("-inf"),
float("-inf"),
float("-inf"),
float("-inf"),
float("-inf"),
float("-inf"),
float("-inf"),
],
[
0,
0,
float("-inf"),
float("-inf"),
float("-inf"),
float("-inf"),
float("-inf"),
float("-inf"),
],
[
0,
0,
0,
float("-inf"),
float("-inf"),
float("-inf"),
float("-inf"),
float("-inf"),
],
[
0,
0,
0,
0,
float("-inf"),
float("-inf"),
float("-inf"),
float("-inf"),
],
[0, 0, 0, 0, 0, float("-inf"), float("-inf"), float("-inf")],
[
float("-inf"),
float("-inf"),
float("-inf"),
0,
0,
0,
float("-inf"),
float("-inf"),
],
[
float("-inf"),
float("-inf"),
float("-inf"),
0,
0,
0,
0,
float("-inf"),
],
[float("-inf"), float("-inf"), float("-inf"), 0, 0, 0, 0, 0],
]
)
attention = SparseMultiheadAttention(
16, 1, stride=4, expressivity=1, is_bidirectional=False
)
attention_sparse_mask = attention.buffered_sparse_mask(attn_weights, 8, 8)
torch.all(torch.eq(attention_sparse_mask, sparse_mask))
if __name__ == "__main__":
unittest.main()
| 3,738
| 31.513043
| 84
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/test_export.py
|
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import tempfile
import unittest
import torch
from fairseq.data.dictionary import Dictionary
from fairseq.models.transformer import TransformerModel
from fairseq.modules import multihead_attention, sinusoidal_positional_embedding
from fairseq.tasks.fairseq_task import LegacyFairseqTask
DEFAULT_TEST_VOCAB_SIZE = 100
class DummyTask(LegacyFairseqTask):
def __init__(self, args):
super().__init__(args)
self.dictionary = get_dummy_dictionary()
if getattr(self.args, "ctc", False):
self.dictionary.add_symbol("<ctc_blank>")
self.src_dict = self.dictionary
self.tgt_dict = self.dictionary
@property
def source_dictionary(self):
return self.src_dict
@property
def target_dictionary(self):
return self.dictionary
def get_dummy_dictionary(vocab_size=DEFAULT_TEST_VOCAB_SIZE):
dummy_dict = Dictionary()
# add dummy symbol to satisfy vocab size
for id, _ in enumerate(range(vocab_size)):
dummy_dict.add_symbol("{}".format(id), 1000)
return dummy_dict
def get_dummy_task_and_parser():
"""
Return a dummy task and argument parser, which can be used to
create a model/criterion.
"""
parser = argparse.ArgumentParser(
description="test_dummy_s2s_task", argument_default=argparse.SUPPRESS
)
DummyTask.add_args(parser)
args = parser.parse_args([])
task = DummyTask.setup_task(args)
return task, parser
def _test_save_and_load(scripted_module):
with tempfile.NamedTemporaryFile() as f:
scripted_module.save(f.name)
torch.jit.load(f.name)
class TestExportModels(unittest.TestCase):
def test_export_multihead_attention(self):
module = multihead_attention.MultiheadAttention(embed_dim=8, num_heads=2)
scripted = torch.jit.script(module)
_test_save_and_load(scripted)
def test_incremental_state_multihead_attention(self):
module1 = multihead_attention.MultiheadAttention(embed_dim=8, num_heads=2)
module1 = torch.jit.script(module1)
module2 = multihead_attention.MultiheadAttention(embed_dim=8, num_heads=2)
module2 = torch.jit.script(module2)
state = {}
state = module1.set_incremental_state(state, "key", {"a": torch.tensor([1])})
state = module2.set_incremental_state(state, "key", {"a": torch.tensor([2])})
v1 = module1.get_incremental_state(state, "key")["a"]
v2 = module2.get_incremental_state(state, "key")["a"]
self.assertEqual(v1, 1)
self.assertEqual(v2, 2)
def test_positional_embedding(self):
module = sinusoidal_positional_embedding.SinusoidalPositionalEmbedding(
embedding_dim=8, padding_idx=1
)
scripted = torch.jit.script(module)
_test_save_and_load(scripted)
@unittest.skipIf(
torch.__version__ < "1.6.0", "Targeting OSS scriptability for the 1.6 release"
)
def test_export_transformer(self):
task, parser = get_dummy_task_and_parser()
TransformerModel.add_args(parser)
args = parser.parse_args([])
model = TransformerModel.build_model(args, task)
scripted = torch.jit.script(model)
_test_save_and_load(scripted)
@unittest.skipIf(
torch.__version__ < "1.6.0", "Targeting OSS scriptability for the 1.6 release"
)
def test_export_transformer_no_token_pos_emb(self):
task, parser = get_dummy_task_and_parser()
TransformerModel.add_args(parser)
args = parser.parse_args([])
args.no_token_positional_embeddings = True
model = TransformerModel.build_model(args, task)
scripted = torch.jit.script(model)
_test_save_and_load(scripted)
if __name__ == "__main__":
unittest.main()
| 4,002
| 32.082645
| 86
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/test_iopath.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
from unittest import mock
class TestIOPath(unittest.TestCase):
def test_no_iopath(self):
from .test_reproducibility import TestReproducibility
with mock.patch.dict("sys.modules", {"iopath": None}):
# reuse reproducibility tests, which are e2e tests that should cover
# most checkpoint related functionality
TestReproducibility._test_reproducibility(self, "test_reproducibility")
def test_no_supports_rename(self):
from .test_reproducibility import TestReproducibility
with mock.patch("fairseq.file_io.PathManager.supports_rename") as mock_fn:
mock_fn.return_value = False
TestReproducibility._test_reproducibility(self, "test_reproducibility")
if __name__ == "__main__":
unittest.main()
| 992
| 33.241379
| 83
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/test_resampling_dataset.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import collections
import unittest
import numpy as np
from fairseq.data import ListDataset, ResamplingDataset
class TestResamplingDataset(unittest.TestCase):
def setUp(self):
self.strings = ["ab", "c", "def", "ghij"]
self.weights = [4.0, 2.0, 7.0, 1.5]
self.size_ratio = 2
self.dataset = ListDataset(
self.strings, np.array([len(s) for s in self.strings])
)
def _test_common(self, resampling_dataset, iters):
assert len(self.dataset) == len(self.strings) == len(self.weights)
assert len(resampling_dataset) == self.size_ratio * len(self.strings)
results = {"ordered_by_size": True, "max_distribution_diff": 0.0}
totalfreqs = 0
freqs = collections.defaultdict(int)
for epoch_num in range(iters):
resampling_dataset.set_epoch(epoch_num)
indices = resampling_dataset.ordered_indices()
assert len(indices) == len(resampling_dataset)
prev_size = -1
for i in indices:
cur_size = resampling_dataset.size(i)
# Make sure indices map to same sequences within an epoch
assert resampling_dataset[i] == resampling_dataset[i]
# Make sure length of sequence is correct
assert cur_size == len(resampling_dataset[i])
freqs[resampling_dataset[i]] += 1
totalfreqs += 1
if prev_size > cur_size:
results["ordered_by_size"] = False
prev_size = cur_size
assert set(freqs.keys()) == set(self.strings)
for s, weight in zip(self.strings, self.weights):
freq = freqs[s] / totalfreqs
expected_freq = weight / sum(self.weights)
results["max_distribution_diff"] = max(
results["max_distribution_diff"], abs(expected_freq - freq)
)
return results
def test_resampling_dataset_batch_by_size_false(self):
resampling_dataset = ResamplingDataset(
self.dataset,
self.weights,
size_ratio=self.size_ratio,
batch_by_size=False,
seed=0,
)
results = self._test_common(resampling_dataset, iters=1000)
# For batch_by_size = False, the batches should be returned in
# arbitrary order of size.
assert not results["ordered_by_size"]
# Allow tolerance in distribution error of 2%.
assert results["max_distribution_diff"] < 0.02
def test_resampling_dataset_batch_by_size_true(self):
resampling_dataset = ResamplingDataset(
self.dataset,
self.weights,
size_ratio=self.size_ratio,
batch_by_size=True,
seed=0,
)
results = self._test_common(resampling_dataset, iters=1000)
# For batch_by_size = True, the batches should be returned in
# increasing order of size.
assert results["ordered_by_size"]
# Allow tolerance in distribution error of 2%.
assert results["max_distribution_diff"] < 0.02
if __name__ == "__main__":
unittest.main()
| 3,365
| 31.365385
| 77
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/test_roberta.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import functools
import unittest
from typing import Any, Dict, Sequence
import fairseq
import fairseq.options
import fairseq.tasks
import torch
from tests.utils import dummy_dictionary
VOCAB_SIZE = 100
@fairseq.tasks.register_task("fake_task")
class FakeTask(fairseq.tasks.LegacyFairseqTask):
def __init__(self, args):
super().__init__(args)
self.dictionary = dummy_dictionary(VOCAB_SIZE - 4)
assert len(self.dictionary) == VOCAB_SIZE
@property
def source_dictionary(self):
return self.dictionary
@property
def target_dictionary(self):
return self.dictionary
@functools.lru_cache()
def get_toy_model(
device: str,
architecture: str = "roberta_enc_dec",
**extra_args: Any,
):
assert device in ("gpu", "cpu")
kwargs = {
"arch": architecture,
# Use characteristics dimensions
"encoder_layers": 3,
"encoder_embed_dim": 12,
"encoder_ffn_embed_dim": 14,
"encoder_attention_heads": 4,
"decoder_layers": 3,
"decoder_embed_dim": 12,
"decoder_ffn_embed_dim": 14,
"decoder_attention_heads": 4,
# Disable dropout so we have comparable tests.
"dropout": 0,
"attention_dropout": 0,
"activation_dropout": 0,
"encoder_layerdrop": 0,
# required args
"tokens_per_sample": 256,
"data": "/tmp/test_roberta",
}
kwargs.update(extra_args)
fake_task = FakeTask(kwargs)
args = fairseq.options.get_args(
task="online_backtranslation",
mono_langs="en,ro",
valid_lang_pairs="en-ro",
**kwargs,
)
torch.manual_seed(0)
model = fake_task.build_model(args)
if device == "gpu":
model.cuda()
return fake_task, model
def mk_sample(
lang: str, device: str, tok: Sequence[int] = None, batch_size: int = 2
) -> Dict[str, Any]:
assert device in ("gpu", "cpu")
if not tok:
if lang == "en":
tok = [10, 11, 12, 13, 14, 15, 2]
else:
tok = [20, 21, 22, 23, 24, 25, 26, 27, 2]
batch = torch.stack([torch.tensor(tok, dtype=torch.long)] * batch_size)
if device == "gpu":
batch = batch.cuda()
sample = {
"net_input": {
"src_tokens": batch,
"prev_output_tokens": batch,
"src_lengths": torch.tensor(
[len(tok)] * batch_size, dtype=torch.long, device=batch.device
),
},
"target": batch[:, 1:],
}
return sample
def cpu_gpu(fn):
def helper(self):
fn(self, "cpu")
if torch.cuda.is_available():
fn(self, "gpu")
return helper
def architectures(fn):
def helper(self):
for arch in ["roberta_enc_dec", "transformer"]:
fn(self, arch)
return helper
class RobertaTest(unittest.TestCase):
def assertTensorEqual(self, t1, t2, delta: float = 1e-6):
self.assertEqual(t1.size(), t2.size(), "size mismatch")
if delta == 0.0:
self.assertEqual(t1.ne(t2).long().sum(), 0)
else:
self.assertEqual(((t2 - t1).abs() > delta).long().sum(), 0)
def assertSharing(self, model, link_groups: Sequence[Sequence[str]]):
ids = {}
for group in link_groups:
group_ids = {name: id(params(model, name)) for name in group}
shared_id = group_ids[group[0]]
self.assertEqual(group_ids, {name: shared_id for name in group})
self.assertNotIn(shared_id, ids)
ids[shared_id] = group
def test_roberta_shared_params(self):
_, roberta = get_toy_model("cpu", architecture="roberta")
self.assertSharing(
roberta,
[
[
"encoder.sentence_encoder.embed_tokens.weight",
"encoder.lm_head.weight",
]
],
)
_, roberta = get_toy_model(
"cpu", architecture="roberta", untie_weights_roberta=True
)
self.assertSharing(
roberta,
[
["encoder.sentence_encoder.embed_tokens.weight"],
["encoder.lm_head.weight"],
],
)
def test_roberta_enc_dec_shared_params(self):
# 3 distinct embeddings
_, enc_dec = get_toy_model("cpu", architecture="roberta_enc_dec")
self.assertSharing(
enc_dec,
[
["encoder.embed_tokens.weight"],
["decoder.embed_tokens.weight"],
["decoder.output_projection.weight"],
],
)
# 2 distinct embeddings, one for encoder, one for decoder
_, enc_dec = get_toy_model(
"cpu", architecture="roberta_enc_dec", share_decoder_input_output_embed=True
)
self.assertSharing(
enc_dec,
[
["encoder.embed_tokens.weight"],
[
"decoder.embed_tokens.weight",
"decoder.output_projection.weight",
],
],
)
# shared embeddings
_, enc_dec = get_toy_model(
"cpu", architecture="roberta_enc_dec", share_all_embeddings=True
)
self.assertSharing(
enc_dec,
[
[
"encoder.embed_tokens.weight",
"decoder.embed_tokens.weight",
"decoder.output_projection.weight",
]
],
)
def test_roberta_max_positions_is_correctly_set(self):
device = "cpu"
task, model = get_toy_model(device)
max_pos = model.max_decoder_positions()
self.assertEqual(max_pos, 256)
self.assertEqual(max_pos, model.decoder.max_positions())
self.assertEqual(max_pos, model.encoder.max_positions())
self.assertEqual(max_pos, model.encoder.embed_positions.max_positions)
sentence = [31 for _ in range(max_pos)]
sample = mk_sample("en", device, sentence, batch_size=1)
self.assertEqual(list(sample["net_input"]["src_lengths"]), [max_pos])
self.assertEqual(len(sample["net_input"]["src_tokens"][0]), max_pos)
x, _ = model.forward(**sample["net_input"])
self.assertEqual(x.shape, (1, max_pos, VOCAB_SIZE))
@cpu_gpu
def test_roberta_forward_backward(self, device: str):
_, model = get_toy_model(device)
sample = mk_sample("en", device)
en_tokens = sample["net_input"]["src_tokens"]
(bs, l) = en_tokens.shape
# Forward
logits, _ = model(**sample["net_input"])
self.assertEqual(logits.shape, (bs, l, VOCAB_SIZE))
# Backward
loss = logits.sum()
loss.backward()
@cpu_gpu
def test_roberta_forward_backward_bs1(self, device: str):
_, model = get_toy_model(device)
sample = mk_sample("en", device, batch_size=1)
o, _ = model.forward(**sample["net_input"])
loss = o.sum()
sample2 = mk_sample("ro", device, batch_size=1)
o, _ = model.forward(**sample2["net_input"])
loss += o.sum()
loss.backward()
@cpu_gpu
def test_roberta_batching(self, device: str):
"""
Checks that the batch of size 2 give twice the same results than the batch of size 1.
"""
_, model = get_toy_model(device)
sample = mk_sample("en", device, batch_size=1)
slen = sample["net_input"]["src_lengths"][0]
sample2 = mk_sample("en", device, batch_size=2)
with torch.no_grad():
z = model.encoder.forward(
sample["net_input"]["src_tokens"], sample["net_input"]["src_lengths"]
)
z = z["encoder_out"][-1]
logits, _ = model.forward(**sample["net_input"])
z2 = model.encoder.forward(
sample2["net_input"]["src_tokens"], sample["net_input"]["src_lengths"]
)
z2 = z2["encoder_out"][-1]
logits2, _ = model.forward(**sample2["net_input"])
self.assertEqual(z.shape, (slen, 1, 12))
self.assertEqual(z2.shape, (slen, 2, 12))
self.assertTensorEqual(logits2[0], logits2[1])
self.assertTensorEqual(logits[0], logits2[0])
@cpu_gpu
def test_roberta_incremental_decoder(self, device: str):
"""
Checks that incremental decoding yields the same result than non incremental one.
"""
task, model = get_toy_model(device)
en_sample = mk_sample("en", device)
en_tokens = en_sample["net_input"]["src_tokens"]
ro_sample = mk_sample("ro", device)
ro_tokens = ro_sample["net_input"]["src_tokens"]
en_enc = model.encoder.forward(
en_tokens, src_lengths=en_sample["net_input"]["src_lengths"]
)
(bs, tgt_len) = ro_tokens.shape
# Decode without incremental state
ro_dec, _ = model.decoder.forward(ro_tokens, encoder_out=en_enc)
self.assertEqual(ro_dec.shape, (bs, tgt_len, VOCAB_SIZE))
self.assertTensorEqual(ro_dec[0], ro_dec[1])
# Decode with incremental state
inc_state = {}
ro_dec_inc = []
for l in range(tgt_len):
ro, _ = model.decoder.forward(
ro_tokens[:, : l + 1], encoder_out=en_enc, incremental_state=inc_state
)
self.assertEqual(ro.shape, (bs, 1, VOCAB_SIZE))
ro_dec_inc.append(ro)
for l in range(tgt_len):
# Intra-batch
self.assertTensorEqual(ro_dec_inc[l][0], ro_dec_inc[l][1])
# Incremental vs non-incremental
self.assertTensorEqual(ro_dec_inc[l][:, 0], ro_dec[:, l])
def params(model, name):
if "." not in name:
return getattr(model, name)
prefix, name = name.split(".", 1)
return params(getattr(model, prefix), name)
| 10,095
| 31.050794
| 93
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/test_online_backtranslation.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import tempfile
import unittest
from pathlib import Path
from typing import Any, Dict, Sequence
import fairseq.data.indexed_dataset as indexed_dataset
import fairseq.options
import fairseq.tasks.online_backtranslation as obt
import torch
from tests import utils
def mk_sample(tokens: Sequence[int], batch_size: int = 2) -> Dict[str, Any]:
batch = torch.stack([torch.tensor(tokens, dtype=torch.long)] * batch_size)
sample = {
"net_input": {
"src_tokens": batch,
"prev_output_tokens": batch,
"src_lengths": torch.tensor([len(tokens)] * batch_size, dtype=torch.long),
},
"target": batch[:, 1:],
}
return sample
def mk_dataset(num_samples: int, max_len: int, output: Path):
output.parent.mkdir(exist_ok=True)
idx = indexed_dataset.IndexedDatasetBuilder(str(output))
data = torch.randint(5, 100, (num_samples, max_len))
lengths = torch.randint(3, max_len, (num_samples,))
for d, l in zip(data, lengths):
d[0] = 0
idx.add_item(d[:l])
idx.finalize(output.with_suffix(".idx"))
assert output.exists()
assert output.with_suffix(".idx").exists()
class OnlineBacktranslationTest(unittest.TestCase):
tmp_dir = Path(tempfile.mkdtemp(suffix="OnlineBacktranslationTest"))
@classmethod
def obt_task(
cls, languages: Sequence[str], data: Path = None, language_mapping: str = None
):
dict_path = cls.tmp_dir / "dict.txt"
if not dict_path.exists():
dictionary = utils.dummy_dictionary(100)
dictionary.save(str(dict_path))
if data is not None:
(data / "dict.txt").write_text(dict_path.read_text())
else:
data = cls.tmp_dir
assert len(languages) >= 2
kwargs = {
"arch": "transformer",
# --max-sentences=1 for better predictability of batches
"max_sentences": 1,
# Use characteristics dimensions
"encoder_layers": 3,
"encoder_embed_dim": 12,
"encoder_ffn_embed_dim": 14,
"encoder_attention_heads": 4,
"decoder_layers": 3,
"decoder_embed_dim": 12,
"decoder_output_dim": 12,
"decoder_ffn_embed_dim": 14,
"decoder_attention_heads": 4,
# Disable dropout so we have comparable tests.
"dropout": 0,
"attention_dropout": 0,
"activation_dropout": 0,
"encoder_layerdrop": 0,
}
args = fairseq.options.get_args(
data,
task="online_backtranslation",
mono_langs=",".join(languages),
valid_lang_pairs=f"{languages[0]}-{languages[1]}",
tokens_per_sample=256,
language_mapping=language_mapping,
**kwargs,
)
task = obt.OnlineBackTranslationTask.setup_task(args)
# we need to build the model to have the correct dictionary
model = task.build_model(task.args)
return task, model
def tmp_path(self, test_case: str) -> Path:
return Path(tempfile.mkdtemp(test_case, dir=self.tmp_dir))
def test_lang_tokens(self):
task, model = self.obt_task(["en", "ro", "zh"])
assert obt._lang_token("en") in task.dictionary
assert obt._lang_token("ro") in task.dictionary
assert obt._lang_token("zh") in task.dictionary
en_bos = obt._lang_token_index(task.common_dict, "en")
assert "en" == task.common_dict[en_bos].strip("_")
zh_bos = obt._lang_token_index(task.common_dict, "zh")
assert "zh" == task.common_dict[zh_bos].strip("_")
zh_sample = mk_sample([zh_bos, 16, 14, 12, 10])
# we expect to receive the bos token for translation
assert task.get_bos_token_from_sample(zh_sample) == en_bos
def test_backtranslate_sample(self):
task, model = self.obt_task(["en", "ro", "zh"])
en_bos = obt._lang_token_index(task.common_dict, "en")
zh_bos = obt._lang_token_index(task.common_dict, "zh")
sample = mk_sample([zh_bos, 16, 14, 12, 10])
task.backtranslate_sample(sample, "zh", "en")
target_zh = list(sample["target"][0])
assert target_zh == [16, 14, 12, 10] # original zh sentence
generated_en = sample["net_input"]["src_tokens"][0]
assert generated_en[0] == en_bos
def test_train_dataset(self):
data = self.tmp_path("test_train_dataset")
mk_dataset(20, 10, data / "en" / "train.bin")
mk_dataset(10, 10, data / "zh" / "train.bin")
task, model = self.obt_task(["en", "zh"], data)
task.load_dataset("train")
en_bos = obt._lang_token_index(task.common_dict, "en")
zh_bos = obt._lang_token_index(task.common_dict, "zh")
train = task.datasets["train"]
train.ordered_indices()
train.prefetch([0, 19])
sample_0 = train[0]
sample_19 = train[19]
self.assertEqual(
set(sample_0.keys()), {"en-BT", "en-DENOISE", "zh-BT", "zh-DENOISE"}
)
for sample in (sample_0, sample_19):
self.assertEqual(sample["en-BT"]["source"][0], en_bos)
# bt target isn't ready to look at.
self.assertEqual(sample["en-DENOISE"]["source"][0], en_bos)
# TODO What could we check on the target side ?
for i in range(10):
# Zh dataset is shorter, and is wrapped around En dataset.
train.prefetch([i, i + 10])
self.assertEqual(
list(train[i]["zh-DENOISE"]["source"]),
list(train[i + 10]["zh-DENOISE"]["source"]),
)
self.assertEqual(train[i]["zh-DENOISE"]["source"][0].item(), zh_bos)
# Sorted by increasing len
self.assertLess(
len(sample_0["en-BT"]["source"]), len(sample_19["en-BT"]["source"])
)
def test_valid_dataset(self):
data = self.tmp_path("test_valid_dataset")
mk_dataset(10, 21, data / "valid.en-zh.en.bin")
mk_dataset(10, 21, data / "valid.en-zh.zh.bin")
task, model = self.obt_task(["en", "zh"], data)
valid = task.load_dataset("valid")
en_bos = obt._lang_token_index(task.common_dict, "en")
assert valid is not None
valid.prefetch(range(10))
sample_0 = valid[0]
sample_9 = valid[9]
self.assertEqual(sample_0["id"], 0)
self.assertEqual(sample_9["id"], 9)
self.assertEqual(sample_0["source"][0], en_bos)
self.assertEqual(sample_9["source"][0], en_bos)
# TODO: could we test the target side ?
def assertFnMatch(self, fn, values):
for x, y in values.items():
fn_x = fn(x)
self.assertEqual(fn_x, y, f"Fn has wrong value: fn({x}) = {fn_x} != {y}")
def test_piecewise_linear_fn(self):
self.assertFnMatch(
obt.PiecewiseLinearFn.from_string("1.0"), {0: 1, 100: 1, 500: 1, 1000: 1}
)
self.assertFnMatch(
obt.PiecewiseLinearFn.from_string("0:1,1000:0"),
{0: 1, 500: 0.5, 1000: 0, 2000: 0},
)
self.assertFnMatch(
obt.PiecewiseLinearFn.from_string("0:0,1000:1"),
{0: 0, 500: 0.5, 1000: 1, 2000: 1},
)
self.assertFnMatch(
obt.PiecewiseLinearFn.from_string("0:0,1000:1,2000:0"),
{0: 0, 500: 0.5, 1000: 1, 1500: 0.5, 2000: 0, 3000: 0},
)
| 7,650
| 35.961353
| 86
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/test_backtranslation_dataset.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import tests.utils as test_utils
import torch
from fairseq.data import (
BacktranslationDataset,
LanguagePairDataset,
TransformEosDataset,
)
from fairseq.sequence_generator import SequenceGenerator
class TestBacktranslationDataset(unittest.TestCase):
def setUp(self):
(
self.tgt_dict,
self.w1,
self.w2,
self.src_tokens,
self.src_lengths,
self.model,
) = test_utils.sequence_generator_setup()
dummy_src_samples = self.src_tokens
self.tgt_dataset = test_utils.TestDataset(data=dummy_src_samples)
self.cuda = torch.cuda.is_available()
def _backtranslation_dataset_helper(
self,
remove_eos_from_input_src,
remove_eos_from_output_src,
):
tgt_dataset = LanguagePairDataset(
src=self.tgt_dataset,
src_sizes=self.tgt_dataset.sizes,
src_dict=self.tgt_dict,
tgt=None,
tgt_sizes=None,
tgt_dict=None,
)
generator = SequenceGenerator(
[self.model],
tgt_dict=self.tgt_dict,
max_len_a=0,
max_len_b=200,
beam_size=2,
unk_penalty=0,
)
backtranslation_dataset = BacktranslationDataset(
tgt_dataset=TransformEosDataset(
dataset=tgt_dataset,
eos=self.tgt_dict.eos(),
# remove eos from the input src
remove_eos_from_src=remove_eos_from_input_src,
),
src_dict=self.tgt_dict,
backtranslation_fn=(
lambda sample: generator.generate([self.model], sample)
),
output_collater=TransformEosDataset(
dataset=tgt_dataset,
eos=self.tgt_dict.eos(),
# if we remove eos from the input src, then we need to add it
# back to the output tgt
append_eos_to_tgt=remove_eos_from_input_src,
remove_eos_from_src=remove_eos_from_output_src,
).collater,
cuda=self.cuda,
)
dataloader = torch.utils.data.DataLoader(
backtranslation_dataset,
batch_size=2,
collate_fn=backtranslation_dataset.collater,
)
backtranslation_batch_result = next(iter(dataloader))
eos, pad, w1, w2 = self.tgt_dict.eos(), self.tgt_dict.pad(), self.w1, self.w2
# Note that we sort by src_lengths and add left padding, so actually
# ids will look like: [1, 0]
expected_src = torch.LongTensor([[w1, w2, w1, eos], [pad, pad, w1, eos]])
if remove_eos_from_output_src:
expected_src = expected_src[:, :-1]
expected_tgt = torch.LongTensor([[w1, w2, eos], [w1, w2, eos]])
generated_src = backtranslation_batch_result["net_input"]["src_tokens"]
tgt_tokens = backtranslation_batch_result["target"]
self.assertTensorEqual(expected_src, generated_src)
self.assertTensorEqual(expected_tgt, tgt_tokens)
def test_backtranslation_dataset_no_eos_in_output_src(self):
self._backtranslation_dataset_helper(
remove_eos_from_input_src=False,
remove_eos_from_output_src=True,
)
def test_backtranslation_dataset_with_eos_in_output_src(self):
self._backtranslation_dataset_helper(
remove_eos_from_input_src=False,
remove_eos_from_output_src=False,
)
def test_backtranslation_dataset_no_eos_in_input_src(self):
self._backtranslation_dataset_helper(
remove_eos_from_input_src=True,
remove_eos_from_output_src=False,
)
def assertTensorEqual(self, t1, t2):
self.assertEqual(t1.size(), t2.size(), "size mismatch")
self.assertEqual(t1.ne(t2).long().sum(), 0)
if __name__ == "__main__":
unittest.main()
| 4,140
| 32.395161
| 85
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/test_fp16_optimizer.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import copy
import logging
import unittest
import torch
from fairseq.optim.fp16_optimizer import FP16Optimizer, MemoryEfficientFP16Optimizer
from omegaconf import OmegaConf
@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU")
class TestGradientScaling(unittest.TestCase):
def setUp(self):
self.x = torch.tensor([2.0]).cuda().half()
weight = 3.0
bias = 5.0
self.error = 1.0
self.target = torch.tensor([self.x * weight + bias + self.error]).cuda().half()
self.loss_fn = torch.nn.L1Loss()
self.model = torch.nn.Linear(1, 1)
self.model.weight.data = torch.tensor([[weight]])
self.model.bias.data = torch.tensor([bias])
self.model.cuda().half()
self.params = list(self.model.parameters())
self.cfg_dls = OmegaConf.create(
{
"optimization": {
"lr": [0.1],
},
"optimizer": {
"_name": "adam",
"lr": [0.1],
"adam_betas": "(0.9, 0.999)",
"adam_eps": 1e-8,
"weight_decay": 0.0,
},
"common": {
"fp16_init_scale": 1,
"fp16_scale_window": 1,
"fp16_scale_tolerance": 1,
"threshold_loss_scale": 1,
"min_loss_scale": 1e-4,
"tpu": False,
},
}
)
logging.disable(logging.CRITICAL)
def tearDown(self):
logging.disable(logging.NOTSET)
def run_iter(self, model, params, optimizer):
optimizer.zero_grad()
y = model(self.x)
loss = self.loss_fn(y, self.target)
optimizer.backward(loss)
self.assertEqual(loss, torch.tensor(1.0, device="cuda:0", dtype=torch.float16))
grad_norm = optimizer.clip_grad_norm(0)
self.assertAlmostEqual(grad_norm.item(), 2.2361, 4)
optimizer.step()
self.assertEqual(
model.weight,
torch.tensor(
[[3.0996]], device="cuda:0", dtype=torch.float16, requires_grad=True
),
)
self.assertEqual(
model.bias,
torch.tensor(
[5.1016], device="cuda:0", dtype=torch.float16, requires_grad=True
),
)
self.assertEqual(optimizer.scaler.loss_scale, 2.0)
def test_mixed_precision(self):
model = copy.deepcopy(self.model)
params = list(model.parameters())
optimizer = FP16Optimizer.build_optimizer(self.cfg_dls, params)
self.run_iter(model, params, optimizer)
self.assertTrue(
all(
torch.all(
fp32_params.eq(
torch.tensor(
[3.1000, 5.1000], device="cuda:0", requires_grad=True
)
)
)
for fp32_params in optimizer.fp32_params.values()
)
)
def test_memory_efficient(self):
model = copy.deepcopy(self.model)
params = list(model.parameters())
optimizer = MemoryEfficientFP16Optimizer.build_optimizer(self.cfg_dls, params)
self.run_iter(model, params, optimizer)
if __name__ == "__main__":
unittest.main()
| 3,587
| 30.752212
| 87
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/__init__.py
| 0
| 0
| 0
|
py
|
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/test_metrics.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import uuid
from fairseq import metrics
class TestMetrics(unittest.TestCase):
def test_nesting(self):
with metrics.aggregate() as a:
metrics.log_scalar("loss", 1)
with metrics.aggregate() as b:
metrics.log_scalar("loss", 2)
self.assertEqual(a.get_smoothed_values()["loss"], 1.5)
self.assertEqual(b.get_smoothed_values()["loss"], 2)
def test_new_root(self):
with metrics.aggregate() as a:
metrics.log_scalar("loss", 1)
with metrics.aggregate(new_root=True) as b:
metrics.log_scalar("loss", 2)
self.assertEqual(a.get_smoothed_values()["loss"], 1)
self.assertEqual(b.get_smoothed_values()["loss"], 2)
def test_nested_new_root(self):
with metrics.aggregate() as layer1:
metrics.log_scalar("loss", 1)
with metrics.aggregate(new_root=True) as layer2:
metrics.log_scalar("loss", 2)
with metrics.aggregate() as layer3:
metrics.log_scalar("loss", 3)
with metrics.aggregate(new_root=True) as layer4:
metrics.log_scalar("loss", 4)
metrics.log_scalar("loss", 1.5)
self.assertEqual(layer4.get_smoothed_values()["loss"], 4)
self.assertEqual(layer3.get_smoothed_values()["loss"], 3)
self.assertEqual(layer2.get_smoothed_values()["loss"], 2.5)
self.assertEqual(layer1.get_smoothed_values()["loss"], 1.25)
def test_named(self):
name = str(uuid.uuid4())
metrics.reset_meters(name)
with metrics.aggregate(name):
metrics.log_scalar("loss", 1)
metrics.log_scalar("loss", 3)
with metrics.aggregate(name):
metrics.log_scalar("loss", 2)
self.assertEqual(metrics.get_smoothed_values(name)["loss"], 1.5)
def test_nested_duplicate_names(self):
name = str(uuid.uuid4())
metrics.reset_meters(name)
with metrics.aggregate(name):
metrics.log_scalar("loss", 1)
with metrics.aggregate() as other:
with metrics.aggregate(name):
metrics.log_scalar("loss", 2)
metrics.log_scalar("loss", 6)
self.assertEqual(metrics.get_smoothed_values(name)["loss"], 3)
self.assertEqual(other.get_smoothed_values()["loss"], 2)
if __name__ == "__main__":
unittest.main()
| 2,637
| 32.820513
| 72
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/test_huffman.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
import random
import string
import typing as tp
import unittest
from collections import Counter
from tempfile import NamedTemporaryFile, TemporaryDirectory
from fairseq.data import Dictionary, indexed_dataset
from fairseq.data.huffman import (
HuffmanCodeBuilder,
HuffmanCoder,
HuffmanMMapIndexedDataset,
HuffmanMMapIndexedDatasetBuilder,
)
POPULATION = string.ascii_letters + string.digits
def make_sentence() -> tp.List[str]:
length = random.randint(10, 50)
return random.choices(
population=POPULATION, k=length, weights=range(1, len(POPULATION) + 1)
)
def make_data(length=1000) -> tp.List[tp.List[str]]:
return (
[make_sentence() for _ in range(0, length)]
# add all the symbols at least once
+ [list(string.ascii_letters), list(string.digits)]
)
def make_counts(data: tp.List[tp.List[str]]) -> Counter:
return Counter([symbol for sentence in data for symbol in sentence])
def make_code_builder(data: tp.List[tp.List[str]]) -> HuffmanCodeBuilder:
builder = HuffmanCodeBuilder()
for sentence in data:
builder.add_symbols(*sentence)
return builder
class TestCodeBuilder(unittest.TestCase):
def test_code_builder_can_count(self):
data = make_data()
counts = make_counts(data)
builder = make_code_builder(data)
self.assertEqual(builder.symbols, counts)
def test_code_builder_can_add(self):
data = make_data()
counts = make_counts(data)
builder = make_code_builder(data)
new_builder = builder + builder
self.assertEqual(new_builder.symbols, counts + counts)
def test_code_builder_can_io(self):
data = make_data()
builder = make_code_builder(data)
with NamedTemporaryFile() as tmp_fp:
builder.to_file(tmp_fp.name)
other_builder = HuffmanCodeBuilder.from_file(tmp_fp.name)
self.assertEqual(builder.symbols, other_builder.symbols)
class TestCoder(unittest.TestCase):
def test_coder_can_io(self):
data = make_data()
builder = make_code_builder(data)
coder = builder.build_code()
with NamedTemporaryFile() as tmp_fp:
coder.to_file(tmp_fp.name)
other_coder = HuffmanCoder.from_file(tmp_fp.name)
self.assertEqual(coder, other_coder)
def test_coder_can_encode_decode(self):
data = make_data()
builder = make_code_builder(data)
coder = builder.build_code()
encoded = [coder.encode(sentence) for sentence in data]
decoded = [[n.symbol for n in coder.decode(enc)] for enc in encoded]
self.assertEqual(decoded, data)
unseen_data = make_data()
unseen_encoded = [coder.encode(sentence) for sentence in unseen_data]
unseen_decoded = [
[n.symbol for n in coder.decode(enc)] for enc in unseen_encoded
]
self.assertEqual(unseen_decoded, unseen_data)
def build_dataset(prefix, data, coder):
with HuffmanMMapIndexedDatasetBuilder(prefix, coder) as builder:
for sentence in data:
builder.add_item(sentence)
def sizes(data):
return [len(sentence) for sentence in data]
class TestHuffmanDataset(unittest.TestCase):
def test_huffman_can_encode_decode(self):
data = make_data()
builder = make_code_builder(data)
coder = builder.build_code()
with TemporaryDirectory() as dirname:
prefix = os.path.join(dirname, "test1")
build_dataset(prefix, data, coder)
dataset = HuffmanMMapIndexedDataset(prefix)
self.assertEqual(len(dataset), len(data))
decoded = [list(dataset.get_symbols(i)) for i in range(0, len(dataset))]
self.assertEqual(decoded, data)
data_sizes = [i.item() for i in dataset.sizes]
self.assertEqual(data_sizes, sizes(data))
def test_huffman_compresses(self):
data = make_data()
builder = make_code_builder(data)
coder = builder.build_code()
with TemporaryDirectory() as dirname:
prefix = os.path.join(dirname, "huffman")
build_dataset(prefix, data, coder)
prefix_mmap = os.path.join(dirname, "mmap")
mmap_builder = indexed_dataset.make_builder(
indexed_dataset.data_file_path(prefix_mmap),
"mmap",
vocab_size=len(POPULATION),
)
dictionary = Dictionary()
for c in POPULATION:
dictionary.add_symbol(c)
dictionary.finalize()
for sentence in data:
mmap_builder.add_item(dictionary.encode_line(" ".join(sentence)))
mmap_builder.finalize(indexed_dataset.index_file_path(prefix_mmap))
huff_size = os.stat(indexed_dataset.data_file_path(prefix)).st_size
mmap_size = os.stat(indexed_dataset.data_file_path(prefix_mmap)).st_size
self.assertLess(huff_size, mmap_size)
def test_huffman_can_append(self):
data1 = make_data()
builder = make_code_builder(data1)
coder = builder.build_code()
with TemporaryDirectory() as dirname:
prefix1 = os.path.join(dirname, "test1")
build_dataset(prefix1, data1, coder)
data2 = make_data()
prefix2 = os.path.join(dirname, "test2")
build_dataset(prefix2, data2, coder)
prefix3 = os.path.join(dirname, "test3")
with HuffmanMMapIndexedDatasetBuilder(prefix3, coder) as builder:
builder.append(prefix1)
builder.append(prefix2)
dataset = HuffmanMMapIndexedDataset(prefix3)
self.assertEqual(len(dataset), len(data1) + len(data2))
decoded1 = [list(dataset.get_symbols(i)) for i in range(0, len(data1))]
self.assertEqual(decoded1, data1)
decoded2 = [
list(dataset.get_symbols(i)) for i in range(len(data1), len(dataset))
]
self.assertEqual(decoded2, data2)
data_sizes = [i.item() for i in dataset.sizes]
self.assertEqual(data_sizes[: len(data1)], sizes(data1))
self.assertEqual(data_sizes[len(data1) : len(dataset)], sizes(data2))
if __name__ == "__main__":
unittest.main()
| 6,549
| 31.425743
| 85
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/test_sequence_generator.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import tempfile
import unittest
import math
import numpy as np
import tests.utils as test_utils
import torch
from fairseq import search
from fairseq.data.dictionary import Dictionary
from fairseq.models.transformer import TransformerModel
from fairseq.sequence_generator import EnsembleModel, SequenceGenerator
from fairseq.ngram_repeat_block import NGramRepeatBlock
from fairseq.tasks.fairseq_task import LegacyFairseqTask
DEFAULT_TEST_VOCAB_SIZE = 100
class DummyTask(LegacyFairseqTask):
def __init__(self, args):
super().__init__(args)
self.dictionary = get_dummy_dictionary()
if getattr(self.args, "ctc", False):
self.dictionary.add_symbol("<ctc_blank>")
self.src_dict = self.dictionary
self.tgt_dict = self.dictionary
@property
def source_dictionary(self):
return self.src_dict
@property
def target_dictionary(self):
return self.dictionary
def get_dummy_dictionary(vocab_size=DEFAULT_TEST_VOCAB_SIZE):
dummy_dict = Dictionary()
# add dummy symbol to satisfy vocab size
for id, _ in enumerate(range(vocab_size)):
dummy_dict.add_symbol("{}".format(id), n=1000)
return dummy_dict
def get_dummy_task_and_parser():
"""
to build a fariseq model, we need some dummy parse and task. This function
is used to create dummy task and parser to faciliate model/criterion test
Note: we use FbSpeechRecognitionTask as the dummy task. You may want
to use other task by providing another function
"""
parser = argparse.ArgumentParser(
description="test_dummy_s2s_task", argument_default=argparse.SUPPRESS
)
DummyTask.add_args(parser)
args = parser.parse_args([])
task = DummyTask.setup_task(args)
return task, parser
class TestJitSequenceGeneratorBase(unittest.TestCase):
def setUp(self):
self.task, self.parser = get_dummy_task_and_parser()
eos = self.task.tgt_dict.eos()
src_tokens = torch.randint(3, 50, (2, 10)).long()
src_tokens = torch.cat((src_tokens, torch.LongTensor([[eos], [eos]])), -1)
src_lengths = torch.LongTensor([2, 10])
self.sample = {
"net_input": {"src_tokens": src_tokens, "src_lengths": src_lengths}
}
TransformerModel.add_args(self.parser)
args = self.parser.parse_args([])
args.encoder_layers = 2
args.decoder_layers = 1
self.transformer_model = TransformerModel.build_model(args, self.task)
def assertOutputEqual(self, hypo, pos_probs):
pos_scores = torch.FloatTensor(pos_probs).log()
self.assertTensorSizeEqual(hypo["positional_scores"], pos_scores)
self.assertTensorSizeEqual(pos_scores.numel(), hypo["tokens"].numel())
def assertTensorSizeEqual(self, t1, t2):
self.assertEqual(t1.size(), t2.size(), "size mismatch")
def assertAlmostEqual(self, t1, t2):
self.assertEqual(t1.size(), t2.size(), "size mismatch")
self.assertLess((t1 - t2).abs().max(), 1e-4)
def assertTensorEqual(self, t1, t2):
self.assertEqual(t1.size(), t2.size(), "size mismatch")
self.assertEqual(t1.ne(t2).long().sum(), 0)
def assertHypoEqual(self, h1, h2):
"Check two hypos are equal"
self.assertTensorEqual(h1["tokens"], h2["tokens"])
self.assertAlmostEqual(h1["positional_scores"], h2["positional_scores"])
self.assertLess(abs(h1["score"] - h2["score"]), 1e-6)
self.assertAlmostEqual(h1["attention"], h2["attention"])
def _test_save_and_load(self, scripted_module):
with tempfile.NamedTemporaryFile() as f:
scripted_module.save(f.name)
torch.jit.load(f.name)
JIT_MSG = "Targeting OSS scriptability for the 1.6 release"
@unittest.skipIf(torch.__version__ < "1.6.0", JIT_MSG)
class TestJitSequenceGenerator(TestJitSequenceGeneratorBase):
def test_export_transformer(self):
model = self.transformer_model
torch.jit.script(model)
def test_ensemble_sequence_generator(self):
model = self.transformer_model
generator = SequenceGenerator(
[model],
self.task.tgt_dict,
beam_size=2,
no_repeat_ngram_size=2,
max_len_b=10,
)
scripted_model = torch.jit.script(generator)
self._test_save_and_load(scripted_model)
def test_export_ensemble_model(self):
model = self.transformer_model
ensemble_models = EnsembleModel([model])
torch.jit.script(ensemble_models)
class TestExportSearch(unittest.TestCase):
def setUp(self):
task, _ = get_dummy_task_and_parser()
self.tgt_dict = task.tgt_dict
self.min_top1_prob = 0.4
def test_export_diverse_bs(self):
search_strategy = search.DiverseBeamSearch(
self.tgt_dict, num_groups=2, diversity_strength=0.0
)
torch.jit.script(search_strategy)
def test_export_sampling(self):
low_sampling_topp = self.min_top1_prob / 2.0
search_strategy = search.Sampling(
self.tgt_dict, sampling_topp=low_sampling_topp
)
torch.jit.script(search_strategy)
def test_export_diverse_siblings_search(self):
search_strategy = search.DiverseSiblingsSearch(
self.tgt_dict, diversity_rate=0.5
)
torch.jit.script(search_strategy)
class TestSequenceGeneratorBase(unittest.TestCase):
def assertHypoTokens(self, hypo, tokens):
self.assertTensorEqual(hypo["tokens"], torch.LongTensor(tokens))
def assertHypoScore(self, hypo, pos_probs, normalized=True, lenpen=1.0):
pos_scores = torch.FloatTensor(pos_probs).log()
self.assertAlmostEqual(hypo["positional_scores"], pos_scores)
self.assertEqual(pos_scores.numel(), hypo["tokens"].numel())
score = pos_scores.sum()
if normalized:
score /= pos_scores.numel() ** lenpen
self.assertLess(abs(score - hypo["score"]), 1e-6)
def assertAlmostEqual(self, t1, t2):
self.assertEqual(t1.size(), t2.size(), "size mismatch")
self.assertLess((t1 - t2).abs().max(), 1e-4)
def assertTensorEqual(self, t1, t2):
self.assertEqual(t1.size(), t2.size(), "size mismatch")
self.assertEqual(t1.ne(t2).long().sum(), 0)
class TestSequenceGenerator(TestSequenceGeneratorBase):
def setUp(self):
(
self.tgt_dict,
self.w1,
self.w2,
src_tokens,
src_lengths,
self.model,
) = test_utils.sequence_generator_setup()
self.sample = {
"net_input": {"src_tokens": src_tokens, "src_lengths": src_lengths}
}
def test_with_normalization(self):
generator = SequenceGenerator([self.model], self.tgt_dict, beam_size=2)
hypos = generator.forward(self.sample)
eos, w1, w2 = self.tgt_dict.eos(), self.w1, self.w2
# sentence 1, beam 1
self.assertHypoTokens(hypos[0][0], [w1, eos])
self.assertHypoScore(hypos[0][0], [0.9, 1.0])
# sentence 1, beam 2
self.assertHypoTokens(hypos[0][1], [w2, w1, w2, eos])
self.assertHypoScore(hypos[0][1], [0.1, 0.9, 0.9, 1.0])
# sentence 2, beam 1
self.assertHypoTokens(hypos[1][0], [w1, w2, w1, eos])
self.assertHypoScore(hypos[1][0], [0.7, 0.4, 0.4, 1.0])
# sentence 2, beam 2
self.assertHypoTokens(hypos[1][1], [w1, w2, eos])
self.assertHypoScore(hypos[1][1], [0.7, 0.4, 0.6])
def test_without_normalization(self):
# Sentence 1: unchanged from the normalized case
# Sentence 2: beams swap order
generator = SequenceGenerator(
[self.model], self.tgt_dict, beam_size=2, normalize_scores=False
)
hypos = generator.forward(self.sample)
eos, w1, w2 = self.tgt_dict.eos(), self.w1, self.w2
# sentence 1, beam 1
self.assertHypoTokens(hypos[0][0], [w1, eos])
self.assertHypoScore(hypos[0][0], [0.9, 1.0], normalized=False)
# sentence 1, beam 2
self.assertHypoTokens(hypos[0][1], [w2, w1, w2, eos])
self.assertHypoScore(hypos[0][1], [0.1, 0.9, 0.9, 1.0], normalized=False)
# sentence 2, beam 1
self.assertHypoTokens(hypos[1][0], [w1, w2, eos])
self.assertHypoScore(hypos[1][0], [0.7, 0.4, 0.6], normalized=False)
# sentence 2, beam 2
self.assertHypoTokens(hypos[1][1], [w1, w2, w1, eos])
self.assertHypoScore(hypos[1][1], [0.7, 0.4, 0.4, 1.0], normalized=False)
def test_with_lenpen_favoring_short_hypos(self):
lenpen = 0.6
generator = SequenceGenerator(
[self.model], self.tgt_dict, beam_size=2, len_penalty=lenpen
)
hypos = generator.forward(self.sample)
eos, w1, w2 = self.tgt_dict.eos(), self.w1, self.w2
# sentence 1, beam 1
self.assertHypoTokens(hypos[0][0], [w1, eos])
self.assertHypoScore(hypos[0][0], [0.9, 1.0], lenpen=lenpen)
# sentence 1, beam 2
self.assertHypoTokens(hypos[0][1], [w2, w1, w2, eos])
self.assertHypoScore(hypos[0][1], [0.1, 0.9, 0.9, 1.0], lenpen=lenpen)
# sentence 2, beam 1
self.assertHypoTokens(hypos[1][0], [w1, w2, eos])
self.assertHypoScore(hypos[1][0], [0.7, 0.4, 0.6], lenpen=lenpen)
# sentence 2, beam 2
self.assertHypoTokens(hypos[1][1], [w1, w2, w1, eos])
self.assertHypoScore(hypos[1][1], [0.7, 0.4, 0.4, 1.0], lenpen=lenpen)
def test_with_lenpen_favoring_long_hypos(self):
lenpen = 5.0
generator = SequenceGenerator(
[self.model], self.tgt_dict, beam_size=2, len_penalty=lenpen
)
hypos = generator.forward(self.sample)
eos, w1, w2 = self.tgt_dict.eos(), self.w1, self.w2
# sentence 1, beam 1
self.assertHypoTokens(hypos[0][0], [w2, w1, w2, eos])
self.assertHypoScore(hypos[0][0], [0.1, 0.9, 0.9, 1.0], lenpen=lenpen)
# sentence 1, beam 2
self.assertHypoTokens(hypos[0][1], [w1, eos])
self.assertHypoScore(hypos[0][1], [0.9, 1.0], lenpen=lenpen)
# sentence 2, beam 1
self.assertHypoTokens(hypos[1][0], [w1, w2, w1, eos])
self.assertHypoScore(hypos[1][0], [0.7, 0.4, 0.4, 1.0], lenpen=lenpen)
# sentence 2, beam 2
self.assertHypoTokens(hypos[1][1], [w1, w2, eos])
self.assertHypoScore(hypos[1][1], [0.7, 0.4, 0.6], lenpen=lenpen)
def test_maxlen(self):
generator = SequenceGenerator(
[self.model], self.tgt_dict, beam_size=2, max_len_b=2
)
hypos = generator.forward(self.sample)
eos, w1, w2 = self.tgt_dict.eos(), self.w1, self.w2
# sentence 1, beam 1
self.assertHypoTokens(hypos[0][0], [w1, eos])
self.assertHypoScore(hypos[0][0], [0.9, 1.0])
# sentence 1, beam 2
self.assertHypoTokens(hypos[0][1], [w2, w2, eos])
self.assertHypoScore(hypos[0][1], [0.1, 0.1, 0.6])
# sentence 2, beam 1
self.assertHypoTokens(hypos[1][0], [w1, w2, eos])
self.assertHypoScore(hypos[1][0], [0.7, 0.4, 0.6])
# sentence 2, beam 2
self.assertHypoTokens(hypos[1][1], [w2, w2, eos])
self.assertHypoScore(hypos[1][1], [0.3, 0.9, 0.01])
def test_encoder_with_different_output_len(self):
args = self.model.encoder.args
task = test_utils.TestTranslationTask.setup_task(
args, self.tgt_dict, self.tgt_dict
)
reshaping_model = test_utils.TestReshapingModel.build_model(args, task)
generator = SequenceGenerator(
[reshaping_model], self.tgt_dict, beam_size=2, max_len_b=2
)
hypos = generator.forward(self.sample)
for sent in [0, 1]:
for beam in [0, 1]:
assert hypos[sent][beam]["attention"] is not None
def test_generation_with_additional_input(self):
args = self.model.encoder.args
task = test_utils.TestTranslationTask.setup_task(
args, self.tgt_dict, self.tgt_dict
)
add_input_model = test_utils.TestAdditionalInputModel.build_model(args, task)
generator = SequenceGenerator([add_input_model], self.tgt_dict, beam_size=2)
sample = self.sample.copy()
sample["net_input"]["fancy_other_input"] = sample["net_input"]["src_tokens"]
hypos = generator.forward(self.sample)
eos, w1, w2 = self.tgt_dict.eos(), self.w1, self.w2
# sentence 1, beam 1
self.assertHypoTokens(hypos[0][0], [w1, eos])
self.assertHypoScore(hypos[0][0], [0.9, 1.0])
@unittest.skipUnless(torch.cuda.is_available(), "")
class TestRepeatNgramBlocking(TestSequenceGeneratorBase):
@classmethod
def setUpClass(cls):
(
cls.tgt_dict,
cls.w1,
cls.w2,
src_tokens,
src_lengths,
cls.model,
) = test_utils.sequence_generator_setup()
return cls
def test_finds_repetitive_tokens(self):
bsz, vocab_size, beam_size, step = 2, 4, 1, 3
generated_tok = torch.tensor(
[[2, 2, 2, 2], [3, 3, 3, 3]], dtype=torch.long, device="cuda"
)
lprobs = torch.zeros((beam_size * bsz, vocab_size), device="cuda")
desired_result = lprobs.new_tensor(
[[0.0, 0.0, -math.inf, 0.0], [0.0, 0.0, 0.0, -math.inf]]
)
cuda_ext_result, baseline_result = self._compare_cuda_ext_to_default_implem(
bsz, beam_size, generated_tok, lprobs, step, 2
)
self.assertTensorEqual(cuda_ext_result, desired_result)
self.assertTensorEqual(baseline_result, desired_result)
@unittest.skipIf(torch.__version__ < "1.6.0", JIT_MSG)
def test_jit_no_extension(self):
bsz, vocab_size, beam_size, step = 2, 4, 1, 3
generated_tok = torch.tensor(
[[2, 2, 2, 2], [3, 3, 3, 3]], dtype=torch.long, device="cuda"
)
lprobs = torch.zeros((beam_size * bsz, vocab_size), device="cuda")
blocker = NGramRepeatBlock(2, use_extension=False)
base_result = blocker(generated_tok, lprobs.clone(), bsz, beam_size, step)
scripted_blocker = torch.jit.script(blocker)
jit_result = scripted_blocker(
generated_tok, lprobs.clone(), bsz, beam_size, step
)
self.assertTensorEqual(base_result, jit_result)
def test_ngram_blocking_same_as_default_implem(self):
"""Test that cuda extension returns same things as default impl in many settings."""
vocab_size = 4
step = 6
for _ in range(2):
block_param = np.random.choice([1, 2, 3, 4])
batch_size = np.random.randint(1, 8)
beam_size = np.random.choice([1, 2, 4, 8])
lprobs = torch.zeros((beam_size * batch_size, vocab_size), device="cuda")
generated_tok = torch.tensor(
np.random.randint(
0, vocab_size, size=(batch_size * beam_size, step + 1)
),
device="cuda",
dtype=torch.long,
)
self._compare_cuda_ext_to_default_implem(
batch_size,
beam_size,
generated_tok,
lprobs,
step,
block_param,
)
def _compare_cuda_ext_to_default_implem(
self, bsz, beam_size, generated_tok, lprobs, step, block_param
):
"""Assert that cuda extension and default implem return the same thing."""
blocker = NGramRepeatBlock(block_param)
assert blocker.use_extension, "Extension not compiled"
cuda_ext_result = blocker(
generated_tok,
lprobs.clone(),
bsz,
beam_size,
step,
)
blocker.use_extension = False
baseline_result = blocker(
generated_tok,
lprobs.clone(),
bsz,
beam_size,
step,
)
self.assertTensorEqual(cuda_ext_result, baseline_result)
blocker.use_extension = True
return cuda_ext_result, baseline_result
class TestDiverseBeamSearch(TestSequenceGeneratorBase):
def setUp(self):
# construct dummy dictionary
d = test_utils.dummy_dictionary(vocab_size=2)
self.assertEqual(d.pad(), 1)
self.assertEqual(d.eos(), 2)
self.assertEqual(d.unk(), 3)
self.eos = d.eos()
self.w1 = 4
self.w2 = 5
# construct source data
self.src_tokens = torch.LongTensor(
[
[self.w1, self.w2, self.eos],
[self.w1, self.w2, self.eos],
]
)
self.src_lengths = torch.LongTensor([2, 2])
args = argparse.Namespace()
unk = 0.0
args.beam_probs = [
# step 0:
torch.FloatTensor(
[
# eos w1 w2
# sentence 1:
[0.0, unk, 0.9, 0.1], # beam 1
[0.0, unk, 0.9, 0.1], # beam 2
# sentence 2:
[0.0, unk, 0.7, 0.3],
[0.0, unk, 0.7, 0.3],
]
),
# step 1:
torch.FloatTensor(
[
# eos w1 w2
# sentence 1:
[0.0, unk, 0.6, 0.4],
[0.0, unk, 0.6, 0.4],
# sentence 2:
[0.25, unk, 0.35, 0.4],
[0.25, unk, 0.35, 0.4],
]
),
# step 2:
torch.FloatTensor(
[
# eos w1 w2
# sentence 1:
[1.0, unk, 0.0, 0.0],
[1.0, unk, 0.0, 0.0],
# sentence 2:
[0.9, unk, 0.1, 0.0],
[0.9, unk, 0.1, 0.0],
]
),
]
task = test_utils.TestTranslationTask.setup_task(args, d, d)
self.model = task.build_model(args)
self.tgt_dict = task.target_dictionary
def test_diverse_beam_search(self):
search_strategy = search.DiverseBeamSearch(
self.tgt_dict, num_groups=2, diversity_strength=0.0
)
generator = SequenceGenerator(
[self.model],
self.tgt_dict,
beam_size=2,
search_strategy=search_strategy,
)
sample = {
"net_input": {
"src_tokens": self.src_tokens,
"src_lengths": self.src_lengths,
}
}
hypos = generator.forward(sample)
eos, w1, w2 = self.eos, self.w1, self.w2
# sentence 1, beam 1
self.assertHypoTokens(hypos[0][0], [w1, w1, eos])
self.assertHypoScore(hypos[0][0], [0.9, 0.6, 1.0])
# sentence 1, beam 2
self.assertHypoTokens(hypos[0][1], [w1, w1, eos])
self.assertHypoScore(hypos[0][1], [0.9, 0.6, 1.0])
# sentence 2, beam 1
self.assertHypoTokens(hypos[1][0], [w1, w2, eos])
self.assertHypoScore(hypos[1][0], [0.7, 0.4, 0.9])
# sentence 2, beam 2
self.assertHypoTokens(hypos[1][1], [w1, w2, eos])
self.assertHypoScore(hypos[1][1], [0.7, 0.4, 0.9])
class TestDiverseSiblingsSearch(TestDiverseBeamSearch):
def assertHypoScore(
self, hypo, pos_probs, sibling_rank, diversity_rate, normalized=True, lenpen=1.0
):
pos_scores = torch.FloatTensor(pos_probs).log()
pos_scores.sub_(torch.Tensor(sibling_rank) * diversity_rate)
self.assertAlmostEqual(hypo["positional_scores"], pos_scores)
self.assertEqual(pos_scores.numel(), hypo["tokens"].numel())
score = pos_scores.sum()
if normalized:
score /= pos_scores.numel() ** lenpen
self.assertLess(abs(score - hypo["score"]), 1e-6)
def test_diverse_beam_search(self):
search_strategy = search.DiverseSiblingsSearch(
self.tgt_dict, diversity_rate=0.5
)
generator = SequenceGenerator(
[self.model], self.tgt_dict, beam_size=2, search_strategy=search_strategy
)
sample = {
"net_input": {
"src_tokens": self.src_tokens,
"src_lengths": self.src_lengths,
}
}
hypos = generator.forward(sample)
eos, w1, w2 = self.eos, self.w1, self.w2
# sentence 1, beam 1
self.assertHypoTokens(hypos[0][0], [w1, w1, eos])
self.assertHypoScore(hypos[0][0], [0.9, 0.6, 1.0], [0, 1, 1], 0.5)
# sentence 1, beam 2
self.assertHypoTokens(hypos[0][1], [w1, w2, eos])
self.assertHypoScore(hypos[0][1], [0.9, 0.4, 1.0], [0, 2, 1], 0.5)
# sentence 2, beam 1
self.assertHypoTokens(hypos[1][0], [w1, w2, eos])
self.assertHypoScore(hypos[1][0], [0.7, 0.4, 0.9], [0, 1, 1], 0.5)
# sentence 2, beam 2
self.assertHypoTokens(hypos[1][1], [w1, w1, eos])
self.assertHypoScore(hypos[1][1], [0.7, 0.35, 0.9], [0, 2, 1], 0.5)
class TestPrefixBeamSearch(TestSequenceGeneratorBase):
def setUp(self):
# construct dummy dictionary
vocab_size = 10
d = test_utils.dummy_dictionary(vocab_size=vocab_size)
self.assertEqual(d.pad(), 1)
self.assertEqual(d.eos(), 2)
self.assertEqual(d.unk(), 3)
self.eos = d.eos()
self.w1 = 4
self.w2 = 5
self.beam_size = 3
# construct prefix data
self.tokens = torch.LongTensor(
[
[self.w1, self.w2, self.eos],
]
)
self.token_lengths = torch.LongTensor([2])
args = argparse.Namespace()
unk = 0.0
args.beam_probs = [
# prefix step 0:
torch.FloatTensor(
[
# eos
[0.0, unk]
+ [1.0 / vocab_size] * vocab_size # beam 1
]
* self.beam_size
),
] * vocab_size
task = test_utils.TestTranslationTask.setup_task(args, d, d)
self.model = task.build_model(args)
self.tgt_dict = task.target_dictionary
def test_prefix_beam_search(self):
search_strategy = search.BeamSearch(self.tgt_dict)
generator = SequenceGenerator(
[self.model],
self.tgt_dict,
beam_size=self.beam_size,
search_strategy=search_strategy,
)
sample = {
"net_input": {
"src_tokens": self.tokens,
"src_lengths": self.token_lengths,
}
}
# make sure test sample doesn't break any assertion
generator.forward(sample, prefix_tokens=self.tokens[:, :-1])
class TestTopPSamplingSearch(TestSequenceGeneratorBase):
def setUp(self):
# construct dummy dictionary
d = test_utils.dummy_dictionary(vocab_size=2)
self.assertEqual(d.pad(), 1)
self.assertEqual(d.eos(), 2)
self.assertEqual(d.unk(), 3)
self.eos = d.eos()
self.w1 = 4
self.w2 = 5
# construct source data
self.src_tokens = torch.LongTensor(
[
[self.w1, self.w2, self.eos],
[self.w1, self.w2, self.eos],
]
)
self.src_lengths = torch.LongTensor([2, 2])
args = argparse.Namespace()
unk = 0.0
# The minimal probability of top 2 tokens.
self.min_top2_prob = 0.75
# The minimal probability of the top 1 token.
self.min_top1_prob = 0.4
w1_prob = self.min_top1_prob
w2_prob = self.min_top2_prob - self.min_top1_prob
eos_prob = 1 - self.min_top2_prob
args.beam_probs = [
# step 0:
torch.FloatTensor(
[
# eos w1 w2
[0.0, unk, 1.0, 0.0],
[0.0, unk, 1.0, 0.0],
[0.0, unk, 1.0, 0.0],
[0.0, unk, 1.0, 0.0],
]
),
# step 1:
torch.FloatTensor(
[
# eos w1 w2
[eos_prob, unk, w1_prob, w2_prob],
[eos_prob, unk, w1_prob, w2_prob],
[eos_prob, unk, w1_prob, w2_prob],
[eos_prob, unk, w1_prob, w2_prob],
]
),
# step 2:
torch.FloatTensor(
[
# eos w1 w2
[1.0, unk, 0.0, 0.0],
[1.0, unk, 0.0, 0.0],
[1.0, unk, 0.0, 0.0],
[1.0, unk, 0.0, 0.0],
]
),
]
task = test_utils.TestTranslationTask.setup_task(args, d, d)
self.model = task.build_model(args)
self.tgt_dict = task.target_dictionary
def test_topp_sampling_search_low_prob(self):
# Given a prob low enough to top-P sampling, we expect only the top
# 1 token to be sampled, which always results in the same output.
low_sampling_topp = self.min_top1_prob / 2.0
search_strategy = search.Sampling(
self.tgt_dict, sampling_topp=low_sampling_topp
)
generator = SequenceGenerator(
[self.model], self.tgt_dict, beam_size=2, search_strategy=search_strategy
)
sample = {
"net_input": {
"src_tokens": self.src_tokens,
"src_lengths": self.src_lengths,
}
}
hypos = generator.forward(sample)
eos, w1 = self.eos, self.w1
# sentence 1, beam 1
self.assertHypoTokens(hypos[0][0], [w1, w1, eos])
self.assertHypoScore(hypos[0][0], [1.0, 0.4, 1.0])
# sentence 1, beam 2
self.assertHypoTokens(hypos[0][1], [w1, w1, eos])
self.assertHypoScore(hypos[0][1], [1.0, 0.4, 1.0])
# sentence 2, beam 1
self.assertHypoTokens(hypos[1][0], [w1, w1, eos])
self.assertHypoScore(hypos[1][0], [1.0, 0.4, 1.0])
# sentence 2, beam 2
self.assertHypoTokens(hypos[1][1], [w1, w1, eos])
self.assertHypoScore(hypos[1][1], [1.0, 0.4, 1.0])
def test_topp_sampling_search_high_prob(self):
# Given a prob high enough to top-P sampling, any of the top 2
# tokens could be sampled. This can cause different outputs.
high_sampling_topp = (self.min_top1_prob + self.min_top2_prob) / 2.0
search_strategy = search.Sampling(
self.tgt_dict, sampling_topp=high_sampling_topp
)
generator = SequenceGenerator(
[self.model], self.tgt_dict, beam_size=2, search_strategy=search_strategy
)
sample = {
"net_input": {
"src_tokens": self.src_tokens,
"src_lengths": self.src_lengths,
}
}
hypos = generator.forward(sample)
eos, w1, w2 = self.eos, self.w1, self.w2
# sentence 1, beam 1
self.assertTrue(
self.hypoTokens(hypos[0][0], [w1, w1, eos])
or self.hypoTokens(hypos[0][0], [w1, w2, eos])
)
self.assertTrue(
self.hypoScore(hypos[0][0], [1.0, 0.4, 1.0])
or self.hypoScore(hypos[0][0], [1.0, 0.35, 1.0])
)
# sentence 1, beam 2
self.assertTrue(
self.hypoTokens(hypos[0][1], [w1, w1, eos])
or self.hypoTokens(hypos[0][1], [w1, w2, eos])
)
self.assertTrue(
self.hypoScore(hypos[0][1], [1.0, 0.4, 1.0])
or self.hypoScore(hypos[0][1], [1.0, 0.35, 1.0])
)
# sentence 2, beam 1
self.assertTrue(
self.hypoTokens(hypos[1][0], [w1, w1, eos])
or self.hypoTokens(hypos[1][0], [w1, w2, eos])
)
self.assertTrue(
self.hypoScore(hypos[1][0], [1.0, 0.4, 1.0])
or self.hypoScore(hypos[1][0], [1.0, 0.35, 1.0])
)
# sentence 2, beam 2
self.assertTrue(
self.hypoTokens(hypos[1][1], [w1, w1, eos])
or self.hypoTokens(hypos[1][1], [w1, w2, eos])
)
self.assertTrue(
self.hypoScore(hypos[1][1], [1.0, 0.4, 1.0])
or self.hypoScore(hypos[1][1], [1.0, 0.35, 1.0])
)
def hypoTokens(self, hypo, tokens):
return self.tensorEqual(hypo["tokens"], torch.LongTensor(tokens))
def hypoScore(self, hypo, pos_probs, normalized=True, lenpen=1.0):
pos_scores = torch.FloatTensor(pos_probs).log()
if not self.almostEqual(hypo["positional_scores"], pos_scores):
return False
if pos_scores.numel() != hypo["tokens"].numel():
return False
score = pos_scores.sum()
if normalized:
score /= pos_scores.numel() ** lenpen
return abs(score - hypo["score"]) < 1e-6
def almostEqual(self, t1, t2):
return t1.size() == t2.size() and (t1 - t2).abs().max() < 1e-4
def tensorEqual(self, t1, t2):
return t1.size() == t2.size() and t1.ne(t2).long().sum() == 0
if __name__ == "__main__":
unittest.main()
| 29,548
| 35.798257
| 92
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/test_label_smoothing.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import copy
import unittest
import tests.utils as test_utils
import torch
from fairseq.criterions.cross_entropy import CrossEntropyCriterion
from fairseq.criterions.label_smoothed_cross_entropy import (
LabelSmoothedCrossEntropyCriterion,
)
class TestLabelSmoothing(unittest.TestCase):
def setUp(self):
# build dictionary
self.d = test_utils.dummy_dictionary(3)
vocab = len(self.d)
self.assertEqual(vocab, 4 + 3) # 4 special + 3 tokens
self.assertEqual(self.d.pad(), 1)
self.assertEqual(self.d.eos(), 2)
self.assertEqual(self.d.unk(), 3)
pad, eos, unk, w1, w2, w3 = 1, 2, 3, 4, 5, 6 # noqa: F841
# build dataset
self.data = [
# the first batch item has padding
{
"source": torch.LongTensor([w1, eos]),
"target": torch.LongTensor([w1, eos]),
},
{
"source": torch.LongTensor([w1, eos]),
"target": torch.LongTensor([w1, w1, eos]),
},
]
self.sample = next(test_utils.dummy_dataloader(self.data))
# build model
self.args = argparse.Namespace()
self.args.sentence_avg = False
self.args.report_accuracy = False
self.args.probs = (
torch.FloatTensor(
[
# pad eos unk w1 w2 w3
[0.05, 0.05, 0.1, 0.05, 0.3, 0.4, 0.05],
[0.05, 0.10, 0.2, 0.05, 0.2, 0.3, 0.10],
[0.05, 0.15, 0.3, 0.05, 0.1, 0.2, 0.15],
]
)
.unsqueeze(0)
.expand(2, 3, 7)
) # add batch dimension
self.task = test_utils.TestTranslationTask.setup_task(self.args, self.d, self.d)
self.model = self.task.build_model(self.args)
def test_nll_loss(self):
self.args.label_smoothing = 0.1
nll_crit = CrossEntropyCriterion.build_criterion(self.args, self.task)
smooth_crit = LabelSmoothedCrossEntropyCriterion.build_criterion(
self.args, self.task
)
nll_loss, nll_sample_size, nll_logging_output = nll_crit(
self.model, self.sample
)
smooth_loss, smooth_sample_size, smooth_logging_output = smooth_crit(
self.model, self.sample
)
self.assertLess(abs(nll_loss - nll_logging_output["loss"]), 1e-6)
self.assertLess(abs(nll_loss - smooth_logging_output["nll_loss"]), 1e-6)
def test_padding(self):
self.args.label_smoothing = 0.1
crit = LabelSmoothedCrossEntropyCriterion.build_criterion(self.args, self.task)
loss, _, logging_output = crit(self.model, self.sample)
def get_one_no_padding(idx):
# create a new sample with just a single batch item so that there's
# no padding
sample1 = next(test_utils.dummy_dataloader([self.data[idx]]))
args1 = copy.copy(self.args)
args1.probs = args1.probs[idx, :, :].unsqueeze(0)
model1 = self.task.build_model(args1)
loss1, _, _ = crit(model1, sample1)
return loss1
loss1 = get_one_no_padding(0)
loss2 = get_one_no_padding(1)
self.assertAlmostEqual(loss, loss1 + loss2)
def test_reduction(self):
self.args.label_smoothing = 0.1
crit = LabelSmoothedCrossEntropyCriterion.build_criterion(self.args, self.task)
loss, _, logging_output = crit(self.model, self.sample, reduce=True)
unreduced_loss, _, _ = crit(self.model, self.sample, reduce=False)
self.assertAlmostEqual(loss, unreduced_loss.sum())
def test_zero_eps(self):
self.args.label_smoothing = 0.0
nll_crit = CrossEntropyCriterion.build_criterion(self.args, self.task)
smooth_crit = LabelSmoothedCrossEntropyCriterion.build_criterion(
self.args, self.task
)
nll_loss, nll_sample_size, nll_logging_output = nll_crit(
self.model, self.sample
)
smooth_loss, smooth_sample_size, smooth_logging_output = smooth_crit(
self.model, self.sample
)
self.assertAlmostEqual(nll_loss, smooth_loss)
def assertAlmostEqual(self, t1, t2):
self.assertEqual(t1.size(), t2.size(), "size mismatch")
self.assertLess((t1 - t2).abs().max(), 1e-6)
if __name__ == "__main__":
unittest.main()
| 4,629
| 36.33871
| 88
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/test_convtbc.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import torch
import torch.nn as nn
from fairseq.modules import ConvTBC
class TestConvTBC(unittest.TestCase):
def test_convtbc(self):
# ksz, in_channels, out_channels
conv_tbc = ConvTBC(4, 5, kernel_size=3, padding=1)
# out_channels, in_channels, ksz
conv1d = nn.Conv1d(4, 5, kernel_size=3, padding=1)
conv_tbc.weight.data.copy_(conv1d.weight.data.transpose(0, 2))
conv_tbc.bias.data.copy_(conv1d.bias.data)
input_tbc = torch.randn(7, 2, 4, requires_grad=True)
input1d = input_tbc.data.transpose(0, 1).transpose(1, 2)
input1d.requires_grad = True
output_tbc = conv_tbc(input_tbc)
output1d = conv1d(input1d)
self.assertAlmostEqual(
output_tbc.data.transpose(0, 1).transpose(1, 2), output1d.data
)
grad_tbc = torch.randn(output_tbc.size())
grad1d = grad_tbc.transpose(0, 1).transpose(1, 2).contiguous()
output_tbc.backward(grad_tbc)
output1d.backward(grad1d)
self.assertAlmostEqual(
conv_tbc.weight.grad.data.transpose(0, 2), conv1d.weight.grad.data
)
self.assertAlmostEqual(conv_tbc.bias.grad.data, conv1d.bias.grad.data)
self.assertAlmostEqual(
input_tbc.grad.data.transpose(0, 1).transpose(1, 2), input1d.grad.data
)
def assertAlmostEqual(self, t1, t2):
self.assertEqual(t1.size(), t2.size(), "size mismatch")
self.assertLess((t1 - t2).abs().max(), 1e-4)
if __name__ == "__main__":
unittest.main()
| 1,745
| 30.745455
| 82
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/test_lm_context_window.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import torch
from fairseq.data import MonolingualDataset
from fairseq.tasks.language_modeling import LanguageModelingTask, LanguageModelingConfig
from tests import utils as test_utils
class TestLMContextWindow(unittest.TestCase):
def test_eval_dataloader(self):
dictionary = test_utils.dummy_dictionary(10)
assert len(dictionary) == 14 # 4 extra special symbols
assert dictionary.pad() == 1
dataset = test_utils.TestDataset(
[
torch.tensor([4, 5, 6, 7], dtype=torch.long),
torch.tensor([8, 9, 10, 11], dtype=torch.long),
torch.tensor([12, 13], dtype=torch.long),
]
)
dataset = MonolingualDataset(dataset, sizes=[4, 4, 2], src_vocab=dictionary)
config = LanguageModelingConfig(tokens_per_sample=4)
task = LanguageModelingTask(config, dictionary)
eval_dataloader = task.eval_lm_dataloader(
dataset=dataset,
batch_size=1,
context_window=2,
)
batch = next(eval_dataloader)
assert batch["net_input"]["src_tokens"][0].tolist() == [4, 5, 6, 7, 1, 1]
assert batch["target"][0].tolist() == [4, 5, 6, 7, 1, 1]
batch = next(eval_dataloader)
assert batch["net_input"]["src_tokens"][0].tolist() == [6, 7, 8, 9, 10, 11]
assert batch["target"][0].tolist() == [1, 1, 8, 9, 10, 11]
batch = next(eval_dataloader)
assert batch["net_input"]["src_tokens"][0].tolist() == [10, 11, 12, 13]
assert batch["target"][0].tolist() == [1, 1, 12, 13]
if __name__ == "__main__":
unittest.main()
| 1,837
| 33.679245
| 88
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/test_amp_optimizer.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import copy
import unittest
import torch
from torch.cuda.amp import autocast, GradScaler
from fairseq.optim import build_optimizer
@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU")
class TestGradientScalingAMP(unittest.TestCase):
def setUp(self):
self.x = torch.tensor([2.0]).cuda().half()
weight = 3.0
bias = 5.0
self.error = 1.0
self.target = torch.tensor([self.x * weight + bias + self.error]).cuda()
self.loss_fn = torch.nn.L1Loss()
self.model = torch.nn.Linear(1, 1)
self.model.weight.data = torch.tensor([[weight]])
self.model.bias.data = torch.tensor([bias])
self.model.cuda()
self.params = list(self.model.parameters())
self.namespace_dls = argparse.Namespace(
optimizer="adam",
lr=[0.1],
adam_betas="(0.9, 0.999)",
adam_eps=1e-8,
weight_decay=0.0,
threshold_loss_scale=1,
min_loss_scale=1e-4,
)
self.scaler = GradScaler(
init_scale=1,
growth_interval=1,
)
def run_iter(self, model, params, optimizer):
optimizer.zero_grad()
with autocast():
y = model(self.x)
loss = self.loss_fn(y, self.target)
self.scaler.scale(loss).backward()
self.assertEqual(loss, torch.tensor(1.0, device="cuda:0", dtype=torch.float16))
self.scaler.unscale_(optimizer)
grad_norm = optimizer.clip_grad_norm(0)
self.assertAlmostEqual(grad_norm.item(), 2.2361, 4)
self.scaler.step(optimizer)
self.scaler.update()
self.assertEqual(
model.weight,
torch.tensor([[3.1]], device="cuda:0", requires_grad=True),
)
self.assertEqual(
model.bias,
torch.tensor([5.1], device="cuda:0", requires_grad=True),
)
self.assertEqual(self.scaler.get_scale(), 2.0)
def test_automatic_mixed_precision(self):
model = copy.deepcopy(self.model)
params = list(model.parameters())
optimizer = build_optimizer(self.namespace_dls, params)
self.run_iter(model, params, optimizer)
| 2,410
| 31.146667
| 87
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/test_token_block_dataset.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import tests.utils as test_utils
import torch
from fairseq.data import TokenBlockDataset
class TestTokenBlockDataset(unittest.TestCase):
def _build_dataset(self, data, **kwargs):
sizes = [len(x) for x in data]
underlying_ds = test_utils.TestDataset(data)
return TokenBlockDataset(underlying_ds, sizes, **kwargs)
def test_eos_break_mode(self):
data = [
torch.tensor([5, 4, 3, 2, 1], dtype=torch.long),
torch.tensor([1], dtype=torch.long),
torch.tensor([8, 7, 6, 1], dtype=torch.long),
]
ds = self._build_dataset(data, block_size=None, pad=0, eos=1, break_mode="eos")
self.assertEqual(ds[0].tolist(), [5, 4, 3, 2, 1])
self.assertEqual(ds[1].tolist(), [1])
self.assertEqual(ds[2].tolist(), [8, 7, 6, 1])
data = [
torch.tensor([5, 4, 3, 2, 1], dtype=torch.long),
torch.tensor([8, 7, 6, 1], dtype=torch.long),
torch.tensor([1], dtype=torch.long),
]
ds = self._build_dataset(data, block_size=None, pad=0, eos=1, break_mode="eos")
self.assertEqual(ds[0].tolist(), [5, 4, 3, 2, 1])
self.assertEqual(ds[1].tolist(), [8, 7, 6, 1])
self.assertEqual(ds[2].tolist(), [1])
def test_block_break_mode(self):
data = [
torch.tensor([5, 4, 3, 2, 1], dtype=torch.long),
torch.tensor([8, 7, 6, 1], dtype=torch.long),
torch.tensor([9, 1], dtype=torch.long),
]
ds = self._build_dataset(data, block_size=3, pad=0, eos=1, break_mode="none")
self.assertEqual(ds[0].tolist(), [5, 4, 3])
self.assertEqual(ds[1].tolist(), [2, 1, 8])
self.assertEqual(ds[2].tolist(), [7, 6, 1])
self.assertEqual(ds[3].tolist(), [9, 1])
def test_complete_break_mode(self):
data = [
torch.tensor([5, 4, 3, 2, 1], dtype=torch.long),
torch.tensor([8, 7, 6, 1], dtype=torch.long),
torch.tensor([9, 1], dtype=torch.long),
]
ds = self._build_dataset(
data, block_size=6, pad=0, eos=1, break_mode="complete"
)
self.assertEqual(ds[0].tolist(), [5, 4, 3, 2, 1])
self.assertEqual(ds[1].tolist(), [8, 7, 6, 1, 9, 1])
data = [
torch.tensor([4, 3, 2, 1], dtype=torch.long),
torch.tensor([5, 1], dtype=torch.long),
torch.tensor([1], dtype=torch.long),
torch.tensor([6, 1], dtype=torch.long),
]
ds = self._build_dataset(
data, block_size=3, pad=0, eos=1, break_mode="complete"
)
self.assertEqual(ds[0].tolist(), [4, 3, 2, 1])
self.assertEqual(ds[1].tolist(), [5, 1, 1])
self.assertEqual(ds[2].tolist(), [6, 1])
def test_4billion_tokens(self):
"""Regression test for numpy type promotion issue https://github.com/numpy/numpy/issues/5745"""
data = [torch.tensor(list(range(10000)), dtype=torch.long)] * 430000
ds = self._build_dataset(
data, block_size=6, pad=0, eos=1, break_mode="complete"
)
ds[-1] # __getitem__ works
start, end = ds.slice_indices[-1]
assert end > 4294967295 # data must be sufficiently large to overflow uint32
assert not isinstance(
end + 1, float
) # this would also raise, since np.uint64(1) + 1 => 2.0
if __name__ == "__main__":
unittest.main()
| 3,629
| 38.032258
| 103
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/test_valid_subset_checks.py
|
import os
import shutil
import tempfile
import unittest
from fairseq import options
from fairseq.dataclass.utils import convert_namespace_to_omegaconf
from fairseq.data.data_utils import raise_if_valid_subsets_unintentionally_ignored
from .utils import create_dummy_data, preprocess_lm_data, train_language_model
def make_lm_config(
data_dir=None,
extra_flags=None,
task="language_modeling",
arch="transformer_lm_gpt2_tiny",
):
task_args = [task]
if data_dir is not None:
task_args += [data_dir]
train_parser = options.get_training_parser()
train_args = options.parse_args_and_arch(
train_parser,
[
"--task",
*task_args,
"--arch",
arch,
"--optimizer",
"adam",
"--lr",
"0.0001",
"--max-tokens",
"500",
"--tokens-per-sample",
"500",
"--save-dir",
data_dir,
"--max-epoch",
"1",
]
+ (extra_flags or []),
)
cfg = convert_namespace_to_omegaconf(train_args)
return cfg
def write_empty_file(path):
with open(path, "w"):
pass
assert os.path.exists(path)
class TestValidSubsetsErrors(unittest.TestCase):
"""Test various filesystem, clarg combinations and ensure that error raising happens as expected"""
def _test_case(self, paths, extra_flags):
with tempfile.TemporaryDirectory() as data_dir:
[
write_empty_file(os.path.join(data_dir, f"{p}.bin"))
for p in paths + ["train"]
]
cfg = make_lm_config(data_dir, extra_flags=extra_flags)
raise_if_valid_subsets_unintentionally_ignored(cfg)
def test_default_raises(self):
with self.assertRaises(ValueError):
self._test_case(["valid", "valid1"], [])
with self.assertRaises(ValueError):
self._test_case(
["valid", "valid1", "valid2"], ["--valid-subset", "valid,valid1"]
)
def partially_specified_valid_subsets(self):
with self.assertRaises(ValueError):
self._test_case(
["valid", "valid1", "valid2"], ["--valid-subset", "valid,valid1"]
)
# Fix with ignore unused
self._test_case(
["valid", "valid1", "valid2"],
["--valid-subset", "valid,valid1", "--ignore-unused-valid-subsets"],
)
def test_legal_configs(self):
self._test_case(["valid"], [])
self._test_case(["valid", "valid1"], ["--ignore-unused-valid-subsets"])
self._test_case(["valid", "valid1"], ["--combine-val"])
self._test_case(["valid", "valid1"], ["--valid-subset", "valid,valid1"])
self._test_case(["valid", "valid1"], ["--valid-subset", "valid1"])
self._test_case(
["valid", "valid1"], ["--combine-val", "--ignore-unused-valid-subsets"]
)
self._test_case(
["valid1"], ["--valid-subset", "valid1"]
) # valid.bin doesn't need to be ignored.
def test_disable_validation(self):
self._test_case([], ["--disable-validation"])
self._test_case(["valid", "valid1"], ["--disable-validation"])
def test_dummy_task(self):
cfg = make_lm_config(task="dummy_lm")
raise_if_valid_subsets_unintentionally_ignored(cfg)
def test_masked_dummy_task(self):
cfg = make_lm_config(task="dummy_masked_lm")
raise_if_valid_subsets_unintentionally_ignored(cfg)
class TestCombineValidSubsets(unittest.TestCase):
def _train(self, extra_flags):
with self.assertLogs() as logs:
with tempfile.TemporaryDirectory("test_transformer_lm") as data_dir:
create_dummy_data(data_dir, num_examples=20)
preprocess_lm_data(data_dir)
shutil.copyfile(f"{data_dir}/valid.bin", f"{data_dir}/valid1.bin")
shutil.copyfile(f"{data_dir}/valid.idx", f"{data_dir}/valid1.idx")
train_language_model(
data_dir,
"transformer_lm",
["--max-update", "0", "--log-format", "json"] + extra_flags,
run_validation=False,
)
return [x.message for x in logs.records]
def test_combined(self):
flags = ["--combine-valid-subsets"]
logs = self._train(flags)
assert any(["valid1" in x for x in logs]) # loaded 100 examples from valid1
assert not any(["valid1_ppl" in x for x in logs]) # metrics are combined
def test_subsets(self):
flags = ["--valid-subset", "valid,valid1"]
logs = self._train(flags)
assert any(["valid_ppl" in x for x in logs]) # loaded 100 examples from valid1
assert any(["valid1_ppl" in x for x in logs]) # metrics are combined
| 4,894
| 34.215827
| 103
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/test_transformer.py
|
import argparse
import unittest
from typing import Any, Dict, Sequence
import torch
from fairseq.models import transformer
from tests.test_roberta import FakeTask
def mk_sample(tok: Sequence[int] = None, batch_size: int = 2) -> Dict[str, Any]:
if not tok:
tok = [10, 11, 12, 13, 14, 15, 2]
batch = torch.stack([torch.tensor(tok, dtype=torch.long)] * batch_size)
sample = {
"net_input": {
"src_tokens": batch,
"prev_output_tokens": batch,
"src_lengths": torch.tensor(
[len(tok)] * batch_size, dtype=torch.long, device=batch.device
),
},
"target": batch[:, 1:],
}
return sample
def mk_transformer(**extra_args: Any):
overrides = {
# Use characteristics dimensions
"encoder_embed_dim": 12,
"encoder_ffn_embed_dim": 14,
"decoder_embed_dim": 12,
"decoder_ffn_embed_dim": 14,
# Disable dropout so we have comparable tests.
"dropout": 0,
"attention_dropout": 0,
"activation_dropout": 0,
"encoder_layerdrop": 0,
}
overrides.update(extra_args)
# Overrides the defaults from the parser
args = argparse.Namespace(**overrides)
transformer.tiny_architecture(args)
torch.manual_seed(0)
task = FakeTask(args)
return transformer.TransformerModel.build_model(args, task)
class TransformerTestCase(unittest.TestCase):
def test_forward_backward(self):
model = mk_transformer(encoder_embed_dim=12, decoder_embed_dim=12)
sample = mk_sample()
o, _ = model.forward(**sample["net_input"])
loss = o.sum()
loss.backward()
def test_different_encoder_decoder_embed_dim(self):
model = mk_transformer(encoder_embed_dim=12, decoder_embed_dim=16)
sample = mk_sample()
o, _ = model.forward(**sample["net_input"])
loss = o.sum()
loss.backward()
| 1,942
| 28.439394
| 80
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/test_multi_corpus_sampled_dataset.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
from collections import OrderedDict
import numpy as np
import torch
from fairseq.data import LanguagePairDataset, TokenBlockDataset
from fairseq.data.multi_corpus_sampled_dataset import MultiCorpusSampledDataset
from tests.test_train import mock_dict
class TestMultiCorpusSampledDataset(unittest.TestCase):
def setUp(self):
d = mock_dict()
tokens_1 = torch.LongTensor([1]).view(1, -1)
tokens_ds1 = TokenBlockDataset(
tokens_1,
sizes=[tokens_1.size(-1)],
block_size=1,
pad=0,
eos=1,
include_targets=False,
)
self.dataset_1 = LanguagePairDataset(
tokens_ds1, tokens_ds1.sizes, d, shuffle=False
)
tokens_2 = torch.LongTensor([2]).view(1, -1)
tokens_ds2 = TokenBlockDataset(
tokens_2,
sizes=[tokens_2.size(-1)],
block_size=1,
pad=0,
eos=1,
include_targets=False,
)
self.dataset_2 = LanguagePairDataset(
tokens_ds2, tokens_ds2.sizes, d, shuffle=False
)
def _test_sample_helper(
self,
expected_sample_from_first_ds_percentage,
num_samples=1000,
sampling_func=None,
):
# To make sure test is not flaky
np.random.seed(0)
if sampling_func is None:
m = MultiCorpusSampledDataset(
OrderedDict({0: self.dataset_1, 1: self.dataset_2}),
)
else:
m = MultiCorpusSampledDataset(
OrderedDict({0: self.dataset_1, 1: self.dataset_2}),
sampling_func=sampling_func,
)
m.ordered_indices()
count_sample_from_first_dataset = 0
for _ in range(num_samples):
if m.collater([m[0], m[1]])["net_input"]["src_tokens"][0] == 1:
count_sample_from_first_dataset += 1
sample_from_first_ds_percentage = (
1.0 * count_sample_from_first_dataset / num_samples
)
self.assertLess(
abs(
sample_from_first_ds_percentage
- expected_sample_from_first_ds_percentage
),
0.01,
)
def test_multi_corpus_sampled_dataset_uniform_sample(self):
self._test_sample_helper(expected_sample_from_first_ds_percentage=0.5)
def test_multi_corpus_sampled_dataset_weighted_sample(self):
def naive_weighted_sample(weights):
def f(l):
v = np.random.random()
agg = 0
for i, weight in enumerate(weights):
agg += weight
if agg > v:
return i
return f
self._test_sample_helper(
expected_sample_from_first_ds_percentage=0.9,
sampling_func=naive_weighted_sample(weights=[0.9, 0.1]),
)
| 3,105
| 31.354167
| 79
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/test_plasma_utils.py
|
import contextlib
import unittest
import tempfile
from io import StringIO
import numpy as np
from tests.utils import create_dummy_data, preprocess_lm_data, train_language_model
try:
from pyarrow import plasma
from fairseq.data.plasma_utils import PlasmaView, PlasmaStore
PYARROW_AVAILABLE = True
except ImportError:
PYARROW_AVAILABLE = False
dummy_path = "dummy"
@unittest.skipUnless(PYARROW_AVAILABLE, "")
class TestPlasmaView(unittest.TestCase):
def setUp(self) -> None:
self.tmp_file = tempfile.NamedTemporaryFile() # noqa: P201
self.path = self.tmp_file.name
self.server = PlasmaStore.start(path=self.path, nbytes=10000)
self.client = plasma.connect(self.path, num_retries=10)
def tearDown(self) -> None:
self.client.disconnect()
self.tmp_file.close()
self.server.kill()
def test_two_servers_do_not_share_object_id_space(self):
data_server_1 = np.array([0, 1])
data_server_2 = np.array([2, 3])
server_2_path = self.path
with tempfile.NamedTemporaryFile() as server_1_path:
server = PlasmaStore.start(path=server_1_path.name, nbytes=10000)
arr1 = PlasmaView(
data_server_1, dummy_path, 1, plasma_path=server_1_path.name
)
assert len(arr1.client.list()) == 1
assert (arr1.array == data_server_1).all()
arr2 = PlasmaView(data_server_2, dummy_path, 1, plasma_path=server_2_path)
assert (arr2.array == data_server_2).all()
assert (arr1.array == data_server_1).all()
server.kill()
def test_hash_collision(self):
data_server_1 = np.array([0, 1])
data_server_2 = np.array([2, 3])
arr1 = PlasmaView(data_server_1, dummy_path, 1, plasma_path=self.path)
assert len(arr1.client.list()) == 1
arr2 = PlasmaView(data_server_2, dummy_path, 1, plasma_path=self.path)
assert len(arr1.client.list()) == 1
assert len(arr2.client.list()) == 1
assert (arr2.array == data_server_1).all()
# New hash key based on tuples
arr3 = PlasmaView(
data_server_2, dummy_path, (1, 12312312312, None), plasma_path=self.path
)
assert (
len(arr2.client.list()) == 2
), "No new object was created by using a novel hash key"
assert (
arr3.object_id in arr2.client.list()
), "No new object was created by using a novel hash key"
assert (
arr3.object_id in arr3.client.list()
), "No new object was created by using a novel hash key"
del arr3, arr2, arr1
@staticmethod
def _assert_view_equal(pv1, pv2):
np.testing.assert_array_equal(pv1.array, pv2.array)
def test_putting_same_array_twice(self):
data = np.array([4, 4, 4])
arr1 = PlasmaView(data, dummy_path, 1, plasma_path=self.path)
assert len(self.client.list()) == 1
arr1b = PlasmaView(
data, dummy_path, 1, plasma_path=self.path
) # should not change contents of store
arr1c = PlasmaView(
None, dummy_path, 1, plasma_path=self.path
) # should not change contents of store
assert len(self.client.list()) == 1
self._assert_view_equal(arr1, arr1b)
self._assert_view_equal(arr1, arr1c)
PlasmaView(
data, dummy_path, 2, plasma_path=self.path
) # new object id, adds new entry
assert len(self.client.list()) == 2
new_client = plasma.connect(self.path)
assert len(new_client.list()) == 2 # new client can access same objects
assert isinstance(arr1.object_id, plasma.ObjectID)
del arr1b
del arr1c
def test_plasma_store_full_raises(self):
with tempfile.NamedTemporaryFile() as new_path:
server = PlasmaStore.start(path=new_path.name, nbytes=10000)
with self.assertRaises(plasma.PlasmaStoreFull):
# 2000 floats is more than 2000 bytes
PlasmaView(
np.random.rand(10000, 1), dummy_path, 1, plasma_path=new_path.name
)
server.kill()
def test_object_id_overflow(self):
PlasmaView.get_object_id("", 2**21)
def test_training_lm_plasma(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_transformer_lm") as data_dir:
create_dummy_data(data_dir)
preprocess_lm_data(data_dir)
train_language_model(
data_dir,
"transformer_lm",
["--use-plasma-view", "--plasma-path", self.path],
run_validation=True,
)
| 4,791
| 36.732283
| 86
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/test_dictionary.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import io
import os
import string
import tempfile
import unittest
import torch
from fairseq import tokenizer
from fairseq.data import Dictionary
class TestDictionary(unittest.TestCase):
def test_finalize(self):
txt = [
"A B C D",
"B C D",
"C D",
"D",
]
ref_ids1 = list(
map(
torch.IntTensor,
[
[4, 5, 6, 7, 2],
[5, 6, 7, 2],
[6, 7, 2],
[7, 2],
],
)
)
ref_ids2 = list(
map(
torch.IntTensor,
[
[7, 6, 5, 4, 2],
[6, 5, 4, 2],
[5, 4, 2],
[4, 2],
],
)
)
# build dictionary
d = Dictionary()
for line in txt:
d.encode_line(line, add_if_not_exist=True)
def get_ids(dictionary):
ids = []
for line in txt:
ids.append(dictionary.encode_line(line, add_if_not_exist=False))
return ids
def assertMatch(ids, ref_ids):
for toks, ref_toks in zip(ids, ref_ids):
self.assertEqual(toks.size(), ref_toks.size())
self.assertEqual(0, (toks != ref_toks).sum().item())
ids = get_ids(d)
assertMatch(ids, ref_ids1)
# check finalized dictionary
d.finalize()
finalized_ids = get_ids(d)
assertMatch(finalized_ids, ref_ids2)
# write to disk and reload
with tempfile.NamedTemporaryFile(mode="w") as tmp_dict:
d.save(tmp_dict.name)
d = Dictionary.load(tmp_dict.name)
reload_ids = get_ids(d)
assertMatch(reload_ids, ref_ids2)
assertMatch(finalized_ids, reload_ids)
def test_overwrite(self):
# for example, Camembert overwrites <unk>, <s> and </s>
dict_file = io.StringIO(
"<unk> 999 #fairseq:overwrite\n"
"<s> 999 #fairseq:overwrite\n"
"</s> 999 #fairseq:overwrite\n"
", 999\n"
"▁de 999\n"
)
d = Dictionary()
d.add_from_file(dict_file)
self.assertEqual(d.index("<pad>"), 1)
self.assertEqual(d.index("foo"), 3)
self.assertEqual(d.index("<unk>"), 4)
self.assertEqual(d.index("<s>"), 5)
self.assertEqual(d.index("</s>"), 6)
self.assertEqual(d.index(","), 7)
self.assertEqual(d.index("▁de"), 8)
def test_no_overwrite(self):
# for example, Camembert overwrites <unk>, <s> and </s>
dict_file = io.StringIO(
"<unk> 999\n" "<s> 999\n" "</s> 999\n" ", 999\n" "▁de 999\n"
)
d = Dictionary()
with self.assertRaisesRegex(RuntimeError, "Duplicate"):
d.add_from_file(dict_file)
def test_space(self):
# for example, character models treat space as a symbol
dict_file = io.StringIO(" 999\n" "a 999\n" "b 999\n")
d = Dictionary()
d.add_from_file(dict_file)
self.assertEqual(d.index(" "), 4)
self.assertEqual(d.index("a"), 5)
self.assertEqual(d.index("b"), 6)
def test_add_file_to_dict(self):
counts = {}
num_lines = 100
per_line = 10
with tempfile.TemporaryDirectory("test_sampling") as data_dir:
filename = os.path.join(data_dir, "dummy.txt")
with open(filename, "w", encoding="utf-8") as data:
for c in string.ascii_letters:
line = f"{c} " * per_line
for _ in range(num_lines):
data.write(f"{line}\n")
counts[c] = per_line * num_lines
per_line += 5
dict = Dictionary()
Dictionary.add_file_to_dictionary(
filename, dict, tokenizer.tokenize_line, 10
)
dict.finalize(threshold=0, nwords=-1, padding_factor=8)
for c in string.ascii_letters:
count = dict.get_count(dict.index(c))
self.assertEqual(
counts[c], count, f"{c} count is {count} but should be {counts[c]}"
)
if __name__ == "__main__":
unittest.main()
| 4,545
| 30.136986
| 87
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/test_utils.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import torch
from fairseq import utils
class TestUtils(unittest.TestCase):
def test_convert_padding_direction(self):
pad = 1
left_pad = torch.LongTensor(
[
[2, 3, 4, 5, 6],
[1, 7, 8, 9, 10],
[1, 1, 1, 11, 12],
]
)
right_pad = torch.LongTensor(
[
[2, 3, 4, 5, 6],
[7, 8, 9, 10, 1],
[11, 12, 1, 1, 1],
]
)
self.assertAlmostEqual(
right_pad,
utils.convert_padding_direction(
left_pad,
pad,
left_to_right=True,
),
)
self.assertAlmostEqual(
left_pad,
utils.convert_padding_direction(
right_pad,
pad,
right_to_left=True,
),
)
def test_make_positions(self):
pad = 1
left_pad_input = torch.LongTensor(
[
[9, 9, 9, 9, 9],
[1, 9, 9, 9, 9],
[1, 1, 1, 9, 9],
]
)
left_pad_output = torch.LongTensor(
[
[2, 3, 4, 5, 6],
[1, 2, 3, 4, 5],
[1, 1, 1, 2, 3],
]
)
right_pad_input = torch.LongTensor(
[
[9, 9, 9, 9, 9],
[9, 9, 9, 9, 1],
[9, 9, 1, 1, 1],
]
)
right_pad_output = torch.LongTensor(
[
[2, 3, 4, 5, 6],
[2, 3, 4, 5, 1],
[2, 3, 1, 1, 1],
]
)
self.assertAlmostEqual(
left_pad_output,
utils.make_positions(left_pad_input, pad),
)
self.assertAlmostEqual(
right_pad_output,
utils.make_positions(right_pad_input, pad),
)
def test_clip_grad_norm_(self):
params = torch.nn.Parameter(torch.zeros(5)).requires_grad_(False)
grad_norm = utils.clip_grad_norm_(params, 1.0)
self.assertTrue(torch.is_tensor(grad_norm))
self.assertEqual(grad_norm, 0.0)
params = [torch.nn.Parameter(torch.zeros(5)) for i in range(3)]
for p in params:
p.grad = torch.full((5,), fill_value=2.0)
grad_norm = utils.clip_grad_norm_(params, 1.0)
exp_grad_norm = torch.full((15,), fill_value=2.0).norm()
self.assertTrue(torch.is_tensor(grad_norm))
self.assertEqual(grad_norm, exp_grad_norm)
grad_norm = utils.clip_grad_norm_(params, 1.0)
self.assertAlmostEqual(grad_norm, torch.tensor(1.0))
def test_resolve_max_positions_with_tuple(self):
resolved = utils.resolve_max_positions(None, (2000, 100, 2000), 12000)
self.assertEqual(resolved, (2000, 100, 2000))
def assertAlmostEqual(self, t1, t2):
self.assertEqual(t1.size(), t2.size(), "size mismatch")
self.assertLess(utils.item((t1 - t2).abs().max()), 1e-4)
if __name__ == "__main__":
unittest.main()
| 3,295
| 27.66087
| 78
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/test_character_token_embedder.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import torch
from fairseq.data import Dictionary
from fairseq.modules import CharacterTokenEmbedder
class TestCharacterTokenEmbedder(unittest.TestCase):
def test_character_token_embedder(self):
vocab = Dictionary()
vocab.add_symbol("hello")
vocab.add_symbol("there")
embedder = CharacterTokenEmbedder(
vocab, [(2, 16), (4, 32), (8, 64), (16, 2)], 64, 5, 2
)
test_sents = [["hello", "unk", "there"], ["there"], ["hello", "there"]]
max_len = max(len(s) for s in test_sents)
input = torch.LongTensor(len(test_sents), max_len + 2).fill_(vocab.pad())
for i in range(len(test_sents)):
input[i][0] = vocab.eos()
for j in range(len(test_sents[i])):
input[i][j + 1] = vocab.index(test_sents[i][j])
input[i][j + 2] = vocab.eos()
embs = embedder(input)
assert embs.size() == (len(test_sents), max_len + 2, 5)
self.assertAlmostEqual(embs[0][0], embs[1][0])
self.assertAlmostEqual(embs[0][0], embs[0][-1])
self.assertAlmostEqual(embs[0][1], embs[2][1])
self.assertAlmostEqual(embs[0][3], embs[1][1])
embs.sum().backward()
assert embedder.char_embeddings.weight.grad is not None
def assertAlmostEqual(self, t1, t2):
self.assertEqual(t1.size(), t2.size(), "size mismatch")
self.assertLess((t1 - t2).abs().max(), 1e-6)
if __name__ == "__main__":
unittest.main()
| 1,678
| 33.265306
| 81
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/gpu/test_ema_gpu.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
from copy import deepcopy
from dataclasses import dataclass
from typing import Optional
import torch
from fairseq.models.ema import EMA
class DummyModule(torch.nn.Module):
def __init__(self) -> None:
"""LightningModule for testing purposes
Args:
epoch_min_loss_override (int, optional): Pass in an epoch that will be set to the minimum
validation loss for testing purposes (zero based). If None this is ignored. Defaults to None.
"""
super().__init__()
self.layer = torch.nn.Linear(in_features=32, out_features=2)
self.another_layer = torch.nn.Linear(in_features=2, out_features=2)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.layer(x)
return self.another_layer(x)
@dataclass
class EMAConfig(object):
ema_decay: float = 0.99
ema_start_update: int = 0
ema_fp32: bool = False
ema_seed_model: Optional[str] = None
@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU")
class TestEMAGPU(unittest.TestCase):
def assertTorchAllClose(self, x, y, atol=1e-8, rtol=1e-5, msg=None):
diff = x.float() - y.float()
diff_norm = torch.norm(diff)
other_norm = torch.norm(y.float())
if msg is None:
msg = "|input - other| > {} + {} * |other|".format(atol, rtol)
self.assertLessEqual(
diff_norm,
atol + rtol * other_norm,
msg=msg,
)
def test_ema(self):
model = DummyModule().cuda()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
state = deepcopy(model.state_dict())
config = EMAConfig()
ema = EMA(model, config)
# set decay
ema._set_decay(config.ema_decay)
self.assertEqual(ema.get_decay(), config.ema_decay)
# get model
self.assertEqual(ema.get_model(), ema.model)
# Since fp32 params is not used, it should be of size 0
self.assertEqual(len(ema.fp32_params), 0)
# EMA step
x = torch.randn(32).cuda()
y = model(x)
loss = y.sum()
loss.backward()
optimizer.step()
ema.step(model)
ema_state_dict = ema.get_model().state_dict()
for key, param in model.state_dict().items():
prev_param = state[key]
ema_param = ema_state_dict[key]
if "version" in key:
# Do not decay a model.version pytorch param
continue
self.assertTorchAllClose(
ema_param,
config.ema_decay * prev_param + (1 - config.ema_decay) * param,
)
# Since fp32 params is not used, it should be of size 0
self.assertEqual(len(ema.fp32_params), 0)
# Load EMA into model
model2 = DummyModule().cuda()
ema.reverse(model2)
for key, param in model2.state_dict().items():
ema_param = ema_state_dict[key]
self.assertTrue(torch.allclose(ema_param, param))
def test_ema_fp32(self):
model = DummyModule().cuda().half()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
state = deepcopy(model.state_dict())
config = EMAConfig(ema_fp32=True)
ema = EMA(model, config)
x = torch.randn(32).cuda()
y = model(x.half())
loss = y.sum()
loss.backward()
optimizer.step()
ema.step(model)
for key, param in model.state_dict().items():
prev_param = state[key]
ema_param = ema.get_model().state_dict()[key]
if "version" in key:
# Do not decay a model.version pytorch param
continue
self.assertIn(key, ema.fp32_params)
# EMA update is done in fp32, and hence the EMA param must be
# closer to the EMA update done in fp32 than in fp16.
self.assertLessEqual(
torch.norm(
ema_param.float()
- (
config.ema_decay * prev_param.float()
+ (1 - config.ema_decay) * param.float()
)
.half()
.float()
),
torch.norm(
ema_param.float()
- (
config.ema_decay * prev_param + (1 - config.ema_decay) * param
).float()
),
)
self.assertTorchAllClose(
ema_param,
(
config.ema_decay * prev_param.float()
+ (1 - config.ema_decay) * param.float()
).half(),
)
def test_ema_fp16(self):
model = DummyModule().cuda().half()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
state = deepcopy(model.state_dict())
config = EMAConfig(ema_fp32=False)
ema = EMA(model, config)
# Since fp32 params is not used, it should be of size 0
self.assertEqual(len(ema.fp32_params), 0)
x = torch.randn(32).cuda()
y = model(x.half())
loss = y.sum()
loss.backward()
optimizer.step()
ema.step(model)
for key, param in model.state_dict().items():
prev_param = state[key]
ema_param = ema.get_model().state_dict()[key]
if "version" in key:
# Do not decay a model.version pytorch param
continue
# EMA update is done in fp16, and hence the EMA param must be
# closer to the EMA update done in fp16 than in fp32.
self.assertLessEqual(
torch.norm(
ema_param.float()
- (
config.ema_decay * prev_param + (1 - config.ema_decay) * param
).float()
),
torch.norm(
ema_param.float()
- (
config.ema_decay * prev_param.float()
+ (1 - config.ema_decay) * param.float()
)
.half()
.float()
),
)
self.assertTorchAllClose(
ema_param,
config.ema_decay * prev_param + (1 - config.ema_decay) * param,
)
# Since fp32 params is not used, it should be of size 0
self.assertEqual(len(ema.fp32_params), 0)
if __name__ == "__main__":
unittest.main()
| 6,795
| 30.757009
| 109
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/gpu/__init__.py
| 0
| 0
| 0
|
py
|
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/gpu/test_binaries_gpu.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import contextlib
import logging
import json
import os
import tempfile
import unittest
from io import StringIO
import torch
from fairseq import options
from fairseq_cli import train
from tests.utils import (
create_dummy_data,
generate_main,
preprocess_lm_data,
preprocess_translation_data,
train_translation_model,
)
@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU")
class TestTranslationGPU(unittest.TestCase):
def setUp(self):
logging.disable(logging.CRITICAL)
def tearDown(self):
logging.disable(logging.NOTSET)
def test_fp16_multigpu(self):
self._test_multigpu("test_fp16", ["--fp16"])
def test_slowmo_multigpu(self):
self._test_multigpu(
"test_slowmo", ["--ddp-backend", "slowmo", "--nprocs-per-node", "1"]
)
def test_slowmo_single_node_multigpu(self):
self._test_multigpu(
"test_slowmo_single_node",
["--ddp-backend", "slowmo", "--nprocs-per-node", "2"],
)
def _test_multigpu(self, test_name, test_args):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory(test_name) as data_dir:
log = os.path.join(data_dir, "train.log")
create_dummy_data(data_dir)
preprocess_translation_data(data_dir)
train_translation_model(
data_dir,
"fconv_iwslt_de_en",
test_args + ["--log-file", log],
world_size=min(torch.cuda.device_count(), 2),
)
generate_main(data_dir)
assert os.path.exists(log)
@staticmethod
def parse_logs(logfile):
logs = []
for ln in open(logfile, "r").readlines():
try:
logs.append(json.loads(ln))
except json.JSONDecodeError:
continue
return logs
def test_resume_training_fsdp(self):
self._test_resume_training(["--ddp-backend", "fully_sharded"])
def test_resume_training_fsdp_sharded_state(self):
self._test_resume_training(
["--ddp-backend", "fully_sharded", "--use-sharded-state"]
)
def test_resume_training_noc10d(self):
self._test_resume_training([])
def _test_resume_training(self, extra_clargs, arch="fconv_iwslt_de_en"):
flags = [
"--fp16",
"--log-format",
"json",
"--max-update",
"10",
"--save-interval-updates",
"2",
"--log-interval",
"1",
] + extra_clargs
world_size = min(torch.cuda.device_count(), 2)
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_fp16") as data_dir:
log = os.path.join(data_dir, "train.log")
create_dummy_data(data_dir)
preprocess_translation_data(data_dir)
train_translation_model(
data_dir,
arch,
flags + ["--log-file", log],
world_size=world_size,
)
log2 = os.path.join(data_dir, "resume.log")
restore_file = os.path.join(data_dir, "checkpoint_1_2.pt")
train_translation_model(
data_dir,
arch,
flags + ["--log-file", log2, "--restore-file", restore_file],
world_size=world_size,
)
l1 = self.parse_logs(log)
l2 = self.parse_logs(log2)
assert int(l2[0]["num_updates"]) == 3, f"{l1}\n\n {l2}"
for k in [
"train_loss",
"train_num_updates",
"train_ppl",
"train_gnorm",
]:
from_scratch, resumed = l1[-1][k], l2[-1][k]
assert (
from_scratch == resumed
), f"difference at {k} {from_scratch} != {resumed}"
def test_memory_efficient_fp16(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_memory_efficient_fp16") as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir)
train_translation_model(
data_dir, "fconv_iwslt_de_en", ["--memory-efficient-fp16"]
)
generate_main(data_dir)
def test_transformer_fp16(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_transformer") as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir)
train_translation_model(
data_dir,
"transformer_iwslt_de_en",
[
"--encoder-layers",
"2",
"--decoder-layers",
"2",
"--encoder-embed-dim",
"64",
"--decoder-embed-dim",
"64",
"--fp16",
],
run_validation=True,
)
generate_main(data_dir)
@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU")
def test_amp(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_amp") as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir)
train_translation_model(data_dir, "fconv_iwslt_de_en", ["--amp"])
generate_main(data_dir)
@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU")
def test_transformer_amp(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_transformer") as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir)
train_translation_model(
data_dir,
"transformer_iwslt_de_en",
[
"--encoder-layers",
"2",
"--decoder-layers",
"2",
"--encoder-embed-dim",
"64",
"--decoder-embed-dim",
"64",
"--amp",
],
run_validation=True,
)
generate_main(data_dir)
@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU")
def test_levenshtein_transformer(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory(
"test_levenshtein_transformer"
) as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir, ["--joined-dictionary"])
train_translation_model(
data_dir,
"levenshtein_transformer",
[
"--apply-bert-init",
"--early-exit",
"6,6,6",
"--criterion",
"nat_loss",
],
task="translation_lev",
)
gen_config = [
"--task",
"translation_lev",
"--iter-decode-max-iter",
"9",
"--iter-decode-eos-penalty",
"0",
"--print-step",
]
# non-ensemble generation
generate_main(data_dir, gen_config)
# ensemble generation
generate_main(
data_dir,
gen_config,
path=os.pathsep.join(
[
os.path.join(data_dir, "checkpoint_last.pt"),
os.path.join(data_dir, "checkpoint_last.pt"),
]
),
)
def test_fsdp_checkpoint_generate(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_fsdp_sharded") as data_dir:
log = os.path.join(data_dir, "train.log")
create_dummy_data(data_dir)
preprocess_translation_data(data_dir)
world_size = min(torch.cuda.device_count(), 2)
train_translation_model(
data_dir,
"fconv_iwslt_de_en",
["--log-file", log, "--ddp-backend", "fully_sharded"],
world_size=world_size,
)
generate_main(data_dir)
assert os.path.exists(log)
def test_fsdp_sharded_checkpoint_generate(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_fsdp_sharded") as data_dir:
log = os.path.join(data_dir, "train.log")
create_dummy_data(data_dir)
preprocess_translation_data(data_dir)
world_size = min(torch.cuda.device_count(), 2)
train_translation_model(
data_dir,
"fconv_iwslt_de_en",
[
"--log-file",
log,
"--ddp-backend",
"fully_sharded",
"--use-sharded-state",
],
world_size=world_size,
)
generate_main(data_dir, ["--checkpoint-shard-count", str(world_size)])
assert os.path.exists(log)
def _quantize_language_model(data_dir, arch, extra_flags=None, run_validation=False):
train_parser = options.get_training_parser()
train_args = options.parse_args_and_arch(
train_parser,
[
"--task",
"language_modeling",
data_dir,
"--arch",
arch,
"--optimizer",
"adam",
"--lr",
"0.0001",
"--criterion",
"adaptive_loss",
"--adaptive-softmax-cutoff",
"5,10,15",
"--max-tokens",
"500",
"--tokens-per-sample",
"500",
"--save-dir",
data_dir,
"--max-epoch",
"1",
"--no-progress-bar",
"--distributed-world-size",
"1",
"--ddp-backend",
"no_c10d",
"--num-workers",
"0",
]
+ (extra_flags or []),
)
train.main(train_args)
# try scalar quantization
scalar_quant_train_parser = options.get_training_parser()
scalar_quant_train_args = options.parse_args_and_arch(
scalar_quant_train_parser,
[
"--task",
"language_modeling",
data_dir,
"--arch",
arch,
"--optimizer",
"adam",
"--lr",
"0.0001",
"--criterion",
"adaptive_loss",
"--adaptive-softmax-cutoff",
"5,10,15",
"--max-tokens",
"500",
"--tokens-per-sample",
"500",
"--save-dir",
data_dir,
"--max-update",
"3",
"--no-progress-bar",
"--distributed-world-size",
"1",
"--ddp-backend",
"no_c10d",
"--num-workers",
"0",
"--quant-noise-scalar",
"0.5",
]
+ (extra_flags or []),
)
train.main(scalar_quant_train_args)
# try iterative PQ quantization
quantize_parser = options.get_training_parser()
quantize_args = options.parse_args_and_arch(
quantize_parser,
[
"--task",
"language_modeling",
data_dir,
"--arch",
arch,
"--optimizer",
"adam",
"--lr",
"0.0001",
"--criterion",
"adaptive_loss",
"--adaptive-softmax-cutoff",
"5,10,15",
"--max-tokens",
"50",
"--tokens-per-sample",
"50",
"--max-update",
"6",
"--no-progress-bar",
"--distributed-world-size",
"1",
"--ddp-backend",
"no_c10d",
"--num-workers",
"0",
"--restore-file",
os.path.join(data_dir, "checkpoint_last.pt"),
"--reset-optimizer",
"--quantization-config-path",
os.path.join(
os.path.dirname(__file__), "transformer_quantization_config.yaml"
),
]
+ (extra_flags or []),
)
train.main(quantize_args)
@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU")
class TestQuantization(unittest.TestCase):
def setUp(self):
logging.disable(logging.CRITICAL)
def tearDown(self):
logging.disable(logging.NOTSET)
def test_quantization(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_quantization") as data_dir:
create_dummy_data(data_dir)
preprocess_lm_data(data_dir)
# tests both scalar and iterative PQ quantization
_quantize_language_model(data_dir, "transformer_lm")
@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU")
class TestOptimizersGPU(unittest.TestCase):
def setUp(self):
logging.disable(logging.CRITICAL)
def tearDown(self):
logging.disable(logging.NOTSET)
def test_flat_grads(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory("test_flat_grads") as data_dir:
# Use just a bit of data and tiny model to keep this test runtime reasonable
create_dummy_data(data_dir, num_examples=10, maxlen=5)
preprocess_translation_data(data_dir)
with self.assertRaises(RuntimeError):
# adafactor isn't compatible with flat grads, which
# are used by default with --fp16
train_translation_model(
data_dir,
"lstm",
[
"--required-batch-size-multiple",
"1",
"--encoder-layers",
"1",
"--encoder-hidden-size",
"32",
"--decoder-layers",
"1",
"--optimizer",
"adafactor",
"--fp16",
],
)
# but it should pass once we set --fp16-no-flatten-grads
train_translation_model(
data_dir,
"lstm",
[
"--required-batch-size-multiple",
"1",
"--encoder-layers",
"1",
"--encoder-hidden-size",
"32",
"--decoder-layers",
"1",
"--optimizer",
"adafactor",
"--fp16",
"--fp16-no-flatten-grads",
],
)
if __name__ == "__main__":
unittest.main()
| 16,419
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|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/distributed/test_distributed_timeout_wrapper.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
import signal
import time
import unittest
import torch
from torch import nn
from fairseq.distributed import DistributedTimeoutWrapper
class ModuleWithDelay(nn.Module):
def __init__(self, delay):
super().__init__()
self.delay = delay
def forward(self, x):
time.sleep(self.delay)
return x
class TestDistributedTimeoutWrapper(unittest.TestCase):
def setUp(self):
logging.disable(logging.CRITICAL)
def tearDown(self):
logging.disable(logging.NOTSET)
def test_no_timeout(self):
module = DistributedTimeoutWrapper(ModuleWithDelay(1), 0, signal.SIGINT)
module(torch.rand(5))
module.stop_timeout()
def test_timeout_safe(self):
module = DistributedTimeoutWrapper(ModuleWithDelay(1), 10, signal.SIGINT)
module(torch.rand(5))
module.stop_timeout()
def test_timeout_killed(self):
with self.assertRaises(KeyboardInterrupt):
module = DistributedTimeoutWrapper(ModuleWithDelay(5), 1, signal.SIGINT)
module(torch.rand(5))
module.stop_timeout()
if __name__ == "__main__":
unittest.main()
| 1,349
| 24.471698
| 84
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/distributed/test_module_proxy_wrapper.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import torch
from torch import nn
from fairseq.distributed import ModuleProxyWrapper
from .utils import objects_are_equal
class MockDDPWrapper(nn.Module):
"""A simple wrapper with an interface similar to DistributedDataParallel."""
def __init__(self, module):
super().__init__()
self.module = module
def forward(self, x):
return self.module(x)
class Model(nn.Module):
def __init__(self):
super().__init__()
self.linear = nn.Linear(5, 10)
self.xyz = "hello"
def forward(self, x):
return self.linear(x)
def get_xyz(self):
return self.xyz
class TestModuleProxyWrapper(unittest.TestCase):
def _get_module(self):
module = Model()
wrapped_module = MockDDPWrapper(module)
wrapped_module = ModuleProxyWrapper(wrapped_module)
return wrapped_module, module
def test_getattr_forwarding(self):
wrapped_module, module = self._get_module()
assert module.xyz == "hello"
assert module.get_xyz() == "hello"
assert wrapped_module.xyz == "hello"
wrapped_module.xyz = "world"
assert wrapped_module.xyz == "world"
assert module.get_xyz() == "hello"
def test_state_dict(self):
wrapped_module, module = self._get_module()
assert objects_are_equal(wrapped_module.state_dict(), module.state_dict())
def test_load_state_dict(self):
wrapped_module, module = self._get_module()
wrapped_module.load_state_dict(module.state_dict())
input = torch.rand(4, 5)
torch.testing.assert_allclose(wrapped_module(input), module(input))
def test_forward(self):
wrapped_module, module = self._get_module()
input = torch.rand(4, 5)
torch.testing.assert_allclose(wrapped_module(input), module(input))
if __name__ == "__main__":
unittest.main()
| 2,085
| 26.813333
| 82
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/distributed/utils.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import functools
import tempfile
import torch
def spawn_and_init(fn, world_size, args=None):
if args is None:
args = ()
with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
torch.multiprocessing.spawn(
fn=functools.partial(init_and_run, fn, args),
args=(
world_size,
tmp_file.name,
),
nprocs=world_size,
join=True,
)
def distributed_init(rank, world_size, tmp_file):
torch.distributed.init_process_group(
backend="nccl",
init_method="file://{}".format(tmp_file),
world_size=world_size,
rank=rank,
)
torch.cuda.set_device(rank)
def init_and_run(fn, args, rank, world_size, tmp_file):
distributed_init(rank, world_size, tmp_file)
group = torch.distributed.new_group()
fn(rank, group, *args)
def objects_are_equal(a, b) -> bool:
if type(a) is not type(b):
return False
if isinstance(a, dict):
if set(a.keys()) != set(b.keys()):
return False
for k in a.keys():
if not objects_are_equal(a[k], b[k]):
return False
return True
elif isinstance(a, (list, tuple, set)):
if len(a) != len(b):
return False
return all(objects_are_equal(x, y) for x, y in zip(a, b))
elif torch.is_tensor(a):
return (
a.size() == b.size()
and a.dtype == b.dtype
and a.device == b.device
and torch.all(a == b)
)
else:
return a == b
| 1,765
| 25.757576
| 65
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/distributed/__init__.py
| 0
| 0
| 0
|
py
|
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/distributed/test_bmuf.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import functools
import random
import unittest
from multiprocessing import Manager
import torch
import torch.nn as nn
from fairseq import optim
from fairseq.distributed import utils as distributed_utils
from omegaconf import OmegaConf
class Model(nn.Module):
def __init__(self, input_size, output_size):
super(Model, self).__init__()
self.fc = nn.Linear(input_size, output_size)
def forward(self, input):
output = self.fc(input)
return output
def setup_model_loss_criterion(cfg, args, rank, is_cuda):
"""
setup model, criterion and optimizer based on input args
"""
args.distributed_rank = rank
cfg.distributed_training.distributed_rank = args.distributed_rank
if cfg.distributed_training.distributed_world_size > 1:
distributed_utils.distributed_init(cfg)
torch.manual_seed(1)
model = Model(args.input_size, args.nb_classes)
loss_fn = nn.CrossEntropyLoss()
if is_cuda:
model = model.cuda()
loss_fn = loss_fn.cuda()
optimizer = optim.sgd.SGD(args, model.parameters())
optimizer = optim.FairseqBMUF(cfg=cfg.bmuf, optimizer=optimizer)
return model, loss_fn, optimizer
def train_step(input, target, model, loss_fn, optimizer, **unused):
"""Do forward, backward and parameter update."""
model.train()
output = model(input)
loss = loss_fn(output, target)
optimizer.backward(loss)
optimizer.step()
def single_gpu_training(cfg, args, rank, iterations, shared_results):
is_cuda = torch.cuda.is_available()
if is_cuda:
torch.cuda.set_device(rank)
model, loss_fn, optimizer = setup_model_loss_criterion(cfg, args, rank, is_cuda)
for _ in range(iterations):
input = torch.randn(1, args.input_size)
target = torch.empty(args.batch_size, dtype=torch.long).random_(args.nb_classes)
if is_cuda:
input = input.cuda()
target = target.cuda()
train_step(input, target, model, loss_fn, optimizer)
results = []
for param in model.parameters():
if len(results) == 0:
results = param.flatten().cpu().data
else:
results = torch.cat((results, param.flatten().cpu().data), 0)
shared_results[rank] = results
def setup_args():
args = argparse.Namespace()
args.global_sync_iter = 20
args.block_momentum = 0.875
args.block_lr = 0.5
args.input_size = 5
args.nb_classes = 2
args.batch_size = 1
args.lr = [1e-3]
args.momentum = 0
args.weight_decay = 0
args.warmup_iterations = 0
args.use_nbm = True
args.average_sync = True
args.global_sync_iter = 1
args.model_parallel_size = 1
args.distributed_backend = "gloo"
args.distributed_world_size = 2
port = random.randint(10000, 20000)
args.distributed_init_method = "tcp://localhost:{port}".format(port=port)
args.distributed_init_host = "localhost"
args.distributed_port = port + 1
args.local_world_size = args.distributed_world_size
cfg = OmegaConf.create()
cfg.optimization = OmegaConf.create()
cfg.common = OmegaConf.create()
cfg.distributed_training = OmegaConf.create()
cfg.dataset = OmegaConf.create()
cfg.bmuf = OmegaConf.create()
cfg.optimizer = OmegaConf.create()
cfg.bmuf.global_sync_iter = args.global_sync_iter
cfg.bmuf.block_momentum = args.block_momentum
cfg.bmuf.block_lr = args.block_lr
cfg.dataset.batch_size = args.batch_size
cfg.optimization.lr = args.lr
cfg.optimizer.momentum = args.momentum
cfg.optimizer.weight_decay = args.weight_decay
cfg.bmuf.warmup_iterations = args.warmup_iterations
cfg.bmuf.use_nbm = args.use_nbm
cfg.bmuf.average_sync = args.average_sync
cfg.common.model_parallel_size = args.model_parallel_size
cfg.distributed_training.distributed_backend = args.distributed_backend
cfg.distributed_training.distributed_world_size = args.distributed_world_size
cfg.bmuf.distributed_world_size = args.distributed_world_size
cfg.distributed_training.distributed_init_method = args.distributed_init_method
cfg.distributed_training.distributed_port = args.distributed_port
return cfg, args
@unittest.skipIf(torch.cuda.device_count() < 2, "test requires 2 GPUs")
class TestBMUF(unittest.TestCase):
def bmuf_process(self, cfg, args, iterations):
processes = []
results = Manager().dict()
torch.multiprocessing.spawn(
fn=functools.partial(single_gpu_training, cfg, args),
args=(iterations, results),
nprocs=args.distributed_world_size,
join=True,
)
return results
def test_bmuf_sync(self):
# Train model for 1 iteration and do bmuf sync without doing warmup
cfg, args = setup_args()
iterations = 1
results = self.bmuf_process(cfg, args, iterations)
# Make sure params in both machines are same
assert len(results) == 2
self.assertAlmostEqual(results[0], results[1])
def test_warmup_sync(self):
# Train model for 20 iteration and do warmup sync without doing bmuf sync
cfg, args = setup_args()
args.warmup_iterations = 20
cfg.bmuf.warmup_iterations = args.warmup_iterations
iterations = 20
results = self.bmuf_process(cfg, args, iterations)
# Make sure params in both machines are same
assert len(results) == 2
self.assertAlmostEqual(results[0], results[1])
def test_warmup_sync_bmuf_sync(self):
# Train model for 25 iteration and do warmup sync after 20 iteration
# and bmuf sync after 25 iteration
cfg, args = setup_args()
args.warmup_iterations = 20
args.global_sync_iter = 5
cfg.bmuf.warmup_iterations = args.warmup_iterations
cfg.bmuf.global_sync_iter = args.global_sync_iter
iterations = 25
results = self.bmuf_process(cfg, args, iterations)
# Make sure params in both machines are same
assert len(results) == 2
self.assertAlmostEqual(results[0], results[1])
def test_single_gpu_bmuf(self):
# Train model for 5 iterations and use GPU 1
cfg, args = setup_args()
args.distributed_world_size = 1
args.warmup_iterations = 5
cfg.distributed_training.distributed_world_size = args.distributed_world_size
cfg.bmuf.distributed_world_size = args.distributed_world_size
cfg.bmuf.warmup_iterations = args.warmup_iterations
iterations = 20
results = self.bmuf_process(cfg, args, iterations)
assert len(results) == 1
def assertAlmostEqual(self, t1, t2):
self.assertEqual(t1.size(), t2.size(), "size mismatch")
self.assertLess((t1 - t2).abs().max(), 1e-4)
if __name__ == "__main__":
unittest.main()
| 7,049
| 33.390244
| 88
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/distributed/test_utils.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import functools
import sys
import unittest
import torch
from fairseq.distributed import utils as dist_utils
from .utils import objects_are_equal, spawn_and_init
class DistributedTest(unittest.TestCase):
def setUp(self):
if not torch.cuda.is_available():
raise unittest.SkipTest("CUDA not available, skipping test")
if sys.platform == "win32":
raise unittest.SkipTest("NCCL doesn't support Windows, skipping test")
if torch.cuda.device_count() < 2:
raise unittest.SkipTest("distributed tests require 2+ GPUs, skipping")
class TestBroadcastObject(DistributedTest):
def test_str(self):
spawn_and_init(
functools.partial(
TestBroadcastObject._test_broadcast_object, "hello world"
),
world_size=2,
)
def test_tensor(self):
spawn_and_init(
functools.partial(
TestBroadcastObject._test_broadcast_object,
torch.rand(5),
),
world_size=2,
)
def test_complex(self):
spawn_and_init(
functools.partial(
TestBroadcastObject._test_broadcast_object,
{
"a": "1",
"b": [2, torch.rand(2, 3), 3],
"c": (torch.rand(2, 3), 4),
"d": {5, torch.rand(5)},
"e": torch.rand(5),
"f": torch.rand(5).int().cuda(),
},
),
world_size=2,
)
@staticmethod
def _test_broadcast_object(ref_obj, rank, group):
obj = dist_utils.broadcast_object(
ref_obj if rank == 0 else None, src_rank=0, group=group
)
assert objects_are_equal(ref_obj, obj)
class TestAllGatherList(DistributedTest):
def test_str_equality(self):
spawn_and_init(
functools.partial(
TestAllGatherList._test_all_gather_list_equality,
"hello world",
),
world_size=2,
)
def test_tensor_equality(self):
spawn_and_init(
functools.partial(
TestAllGatherList._test_all_gather_list_equality,
torch.rand(5),
),
world_size=2,
)
def test_complex_equality(self):
spawn_and_init(
functools.partial(
TestAllGatherList._test_all_gather_list_equality,
{
"a": "1",
"b": [2, torch.rand(2, 3), 3],
"c": (torch.rand(2, 3), 4),
"d": {5, torch.rand(5)},
"e": torch.rand(5),
"f": torch.rand(5).int(),
},
),
world_size=2,
)
@staticmethod
def _test_all_gather_list_equality(ref_obj, rank, group):
objs = dist_utils.all_gather_list(ref_obj, group)
for obj in objs:
assert objects_are_equal(ref_obj, obj)
def test_rank_tensor(self):
spawn_and_init(
TestAllGatherList._test_all_gather_list_rank_tensor, world_size=2
)
@staticmethod
def _test_all_gather_list_rank_tensor(rank, group):
obj = torch.tensor([rank])
objs = dist_utils.all_gather_list(obj, group)
for i, obj in enumerate(objs):
assert obj.item() == i
if __name__ == "__main__":
unittest.main()
| 3,656
| 28.256
| 82
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/speech_recognition/test_cross_entropy.py
|
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from examples.speech_recognition.criterions.cross_entropy_acc import (
CrossEntropyWithAccCriterion,
)
from .asr_test_base import CrossEntropyCriterionTestBase
class CrossEntropyWithAccCriterionTest(CrossEntropyCriterionTestBase):
def setUp(self):
self.criterion_cls = CrossEntropyWithAccCriterion
super().setUp()
def test_cross_entropy_all_correct(self):
sample = self.get_test_sample(correct=True, soft_target=False, aggregate=False)
loss, sample_size, logging_output = self.criterion(
self.model, sample, "sum", log_probs=True
)
assert logging_output["correct"] == 20
assert logging_output["total"] == 20
assert logging_output["sample_size"] == 20
assert logging_output["ntokens"] == 20
def test_cross_entropy_all_wrong(self):
sample = self.get_test_sample(correct=False, soft_target=False, aggregate=False)
loss, sample_size, logging_output = self.criterion(
self.model, sample, "sum", log_probs=True
)
assert logging_output["correct"] == 0
assert logging_output["total"] == 20
assert logging_output["sample_size"] == 20
assert logging_output["ntokens"] == 20
| 1,438
| 36.868421
| 88
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/speech_recognition/test_data_utils.py
|
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import torch
from examples.speech_recognition.data import data_utils
class DataUtilsTest(unittest.TestCase):
def test_normalization(self):
sample_len1 = torch.tensor(
[
[
-0.7661,
-1.3889,
-2.0972,
-0.9134,
-0.7071,
-0.9765,
-0.8700,
-0.8283,
0.7512,
1.3211,
2.1532,
2.1174,
1.2800,
1.2633,
1.6147,
1.6322,
2.0723,
3.1522,
3.2852,
2.2309,
2.5569,
2.2183,
2.2862,
1.5886,
0.8773,
0.8725,
1.2662,
0.9899,
1.1069,
1.3926,
1.2795,
1.1199,
1.1477,
1.2687,
1.3843,
1.1903,
0.8355,
1.1367,
1.2639,
1.4707,
]
]
)
out = data_utils.apply_mv_norm(sample_len1)
assert not torch.isnan(out).any()
assert (out == sample_len1).all()
| 1,736
| 26.571429
| 65
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/speech_recognition/asr_test_base.py
|
#!/usr/bin/env python3
import argparse
import os
import unittest
from inspect import currentframe, getframeinfo
import numpy as np
import torch
from examples.speech_recognition.data.data_utils import lengths_to_encoder_padding_mask
from fairseq.data import data_utils as fairseq_data_utils
from fairseq.data.dictionary import Dictionary
from fairseq.models import (
BaseFairseqModel,
FairseqDecoder,
FairseqEncoder,
FairseqEncoderDecoderModel,
FairseqEncoderModel,
FairseqModel,
)
from fairseq.tasks.fairseq_task import LegacyFairseqTask
DEFAULT_TEST_VOCAB_SIZE = 100
# ///////////////////////////////////////////////////////////////////////////
# utility function to setup dummy dict/task/input
# ///////////////////////////////////////////////////////////////////////////
def get_dummy_dictionary(vocab_size=DEFAULT_TEST_VOCAB_SIZE):
dummy_dict = Dictionary()
# add dummy symbol to satisfy vocab size
for id, _ in enumerate(range(vocab_size)):
dummy_dict.add_symbol("{}".format(id), 1000)
return dummy_dict
class DummyTask(LegacyFairseqTask):
def __init__(self, args):
super().__init__(args)
self.dictionary = get_dummy_dictionary()
if getattr(self.args, "ctc", False):
self.dictionary.add_symbol("<ctc_blank>")
self.tgt_dict = self.dictionary
@property
def target_dictionary(self):
return self.dictionary
def get_dummy_task_and_parser():
"""
to build a fariseq model, we need some dummy parse and task. This function
is used to create dummy task and parser to faciliate model/criterion test
Note: we use FbSpeechRecognitionTask as the dummy task. You may want
to use other task by providing another function
"""
parser = argparse.ArgumentParser(
description="test_dummy_s2s_task", argument_default=argparse.SUPPRESS
)
DummyTask.add_args(parser)
args = parser.parse_args([])
task = DummyTask.setup_task(args)
return task, parser
def get_dummy_input(T=100, D=80, B=5, K=100):
forward_input = {}
# T max sequence length
# D feature vector dimension
# B batch size
# K target dimension size
feature = torch.randn(B, T, D)
# this (B, T, D) layout is just a convention, you can override it by
# write your own _prepare_forward_input function
src_lengths = torch.from_numpy(
np.random.randint(low=1, high=T, size=B, dtype=np.int64)
)
src_lengths[0] = T # make sure the maximum length matches
prev_output_tokens = []
for b in range(B):
token_length = np.random.randint(low=1, high=src_lengths[b].item() + 1)
tokens = np.random.randint(low=0, high=K, size=token_length, dtype=np.int64)
prev_output_tokens.append(torch.from_numpy(tokens))
prev_output_tokens = fairseq_data_utils.collate_tokens(
prev_output_tokens,
pad_idx=1,
eos_idx=2,
left_pad=False,
move_eos_to_beginning=False,
)
src_lengths, sorted_order = src_lengths.sort(descending=True)
forward_input["src_tokens"] = feature.index_select(0, sorted_order)
forward_input["src_lengths"] = src_lengths
forward_input["prev_output_tokens"] = prev_output_tokens
return forward_input
def get_dummy_encoder_output(encoder_out_shape=(100, 80, 5)):
"""
This only provides an example to generate dummy encoder output
"""
(T, B, D) = encoder_out_shape
encoder_out = {}
encoder_out["encoder_out"] = torch.from_numpy(
np.random.randn(*encoder_out_shape).astype(np.float32)
)
seq_lengths = torch.from_numpy(np.random.randint(low=1, high=T, size=B))
# some dummy mask
encoder_out["encoder_padding_mask"] = torch.arange(T).view(1, T).expand(
B, -1
) >= seq_lengths.view(B, 1).expand(-1, T)
encoder_out["encoder_padding_mask"].t_()
# encoer_padding_mask is (T, B) tensor, with (t, b)-th element indicate
# whether encoder_out[t, b] is valid (=0) or not (=1)
return encoder_out
def _current_postion_info():
cf = currentframe()
frameinfo = " (at {}:{})".format(
os.path.basename(getframeinfo(cf).filename), cf.f_back.f_lineno
)
return frameinfo
def check_encoder_output(encoder_output, batch_size=None):
"""we expect encoder_output to be a dict with the following
key/value pairs:
- encoder_out: a Torch.Tensor
- encoder_padding_mask: a binary Torch.Tensor
"""
if not isinstance(encoder_output, dict):
msg = (
"FairseqEncoderModel.forward(...) must be a dict" + _current_postion_info()
)
return False, msg
if "encoder_out" not in encoder_output:
msg = (
"FairseqEncoderModel.forward(...) must contain encoder_out"
+ _current_postion_info()
)
return False, msg
if "encoder_padding_mask" not in encoder_output:
msg = (
"FairseqEncoderModel.forward(...) must contain encoder_padding_mask"
+ _current_postion_info()
)
return False, msg
if not isinstance(encoder_output["encoder_out"], torch.Tensor):
msg = "encoder_out must be a torch.Tensor" + _current_postion_info()
return False, msg
if encoder_output["encoder_out"].dtype != torch.float32:
msg = "encoder_out must have float32 dtype" + _current_postion_info()
return False, msg
mask = encoder_output["encoder_padding_mask"]
if mask is not None:
if not isinstance(mask, torch.Tensor):
msg = (
"encoder_padding_mask must be a torch.Tensor" + _current_postion_info()
)
return False, msg
if mask.dtype != torch.uint8 and (
not hasattr(torch, "bool") or mask.dtype != torch.bool
):
msg = (
"encoder_padding_mask must have dtype of uint8"
+ _current_postion_info()
)
return False, msg
if mask.dim() != 2:
msg = (
"we expect encoder_padding_mask to be a 2-d tensor, in shape (T, B)"
+ _current_postion_info()
)
return False, msg
if batch_size is not None and mask.size(1) != batch_size:
msg = (
"we expect encoder_padding_mask to be a 2-d tensor, with size(1)"
+ " being the batch size"
+ _current_postion_info()
)
return False, msg
return True, None
def check_decoder_output(decoder_output):
"""we expect output from a decoder is a tuple with the following constraint:
- the first element is a torch.Tensor
- the second element can be anything (reserved for future use)
"""
if not isinstance(decoder_output, tuple):
msg = "FariseqDecoder output must be a tuple" + _current_postion_info()
return False, msg
if len(decoder_output) != 2:
msg = "FairseqDecoder output must be 2-elem tuple" + _current_postion_info()
return False, msg
if not isinstance(decoder_output[0], torch.Tensor):
msg = (
"FariseqDecoder output[0] must be a torch.Tensor" + _current_postion_info()
)
return False, msg
return True, None
# ///////////////////////////////////////////////////////////////////////////
# Base Test class
# ///////////////////////////////////////////////////////////////////////////
class TestBaseFairseqModelBase(unittest.TestCase):
"""
This class is used to facilitate writing unittest for any class derived from
`BaseFairseqModel`.
"""
@classmethod
def setUpClass(cls):
if cls is TestBaseFairseqModelBase:
raise unittest.SkipTest("Skipping test case in base")
super().setUpClass()
def setUpModel(self, model):
self.assertTrue(isinstance(model, BaseFairseqModel))
self.model = model
def setupInput(self):
pass
def setUp(self):
self.model = None
self.forward_input = None
pass
class TestFairseqEncoderDecoderModelBase(TestBaseFairseqModelBase):
"""
base code to test FairseqEncoderDecoderModel (formally known as
`FairseqModel`) must be derived from this base class
"""
@classmethod
def setUpClass(cls):
if cls is TestFairseqEncoderDecoderModelBase:
raise unittest.SkipTest("Skipping test case in base")
super().setUpClass()
def setUpModel(self, model_cls, extra_args_setters=None):
self.assertTrue(
issubclass(model_cls, (FairseqEncoderDecoderModel, FairseqModel)),
msg="This class only tests for FairseqModel subclasses",
)
task, parser = get_dummy_task_and_parser()
model_cls.add_args(parser)
args = parser.parse_args([])
if extra_args_setters is not None:
for args_setter in extra_args_setters:
args_setter(args)
model = model_cls.build_model(args, task)
self.model = model
def setUpInput(self, input=None):
self.forward_input = get_dummy_input() if input is None else input
def setUp(self):
super().setUp()
def test_forward(self):
if self.model and self.forward_input:
forward_output = self.model.forward(**self.forward_input)
# for FairseqEncoderDecoderModel, forward returns a tuple of two
# elements, the first one is a Torch.Tensor
succ, msg = check_decoder_output(forward_output)
if not succ:
self.assertTrue(succ, msg=msg)
self.forward_output = forward_output
def test_get_normalized_probs(self):
if self.model and self.forward_input:
forward_output = self.model.forward(**self.forward_input)
logprob = self.model.get_normalized_probs(forward_output, log_probs=True)
prob = self.model.get_normalized_probs(forward_output, log_probs=False)
# in order for different models/criterion to play with each other
# we need to know whether the logprob or prob output is batch_first
# or not. We assume an additional attribute will be attached to logprob
# or prob. If you find your code failed here, simply override
# FairseqModel.get_normalized_probs, see example at
# https://fburl.com/batch_first_example
self.assertTrue(hasattr(logprob, "batch_first"))
self.assertTrue(hasattr(prob, "batch_first"))
self.assertTrue(torch.is_tensor(logprob))
self.assertTrue(torch.is_tensor(prob))
class TestFairseqEncoderModelBase(TestBaseFairseqModelBase):
"""
base class to test FairseqEncoderModel
"""
@classmethod
def setUpClass(cls):
if cls is TestFairseqEncoderModelBase:
raise unittest.SkipTest("Skipping test case in base")
super().setUpClass()
def setUpModel(self, model_cls, extra_args_setters=None):
self.assertTrue(
issubclass(model_cls, FairseqEncoderModel),
msg="This class is only used for testing FairseqEncoderModel",
)
task, parser = get_dummy_task_and_parser()
model_cls.add_args(parser)
args = parser.parse_args([])
if extra_args_setters is not None:
for args_setter in extra_args_setters:
args_setter(args)
model = model_cls.build_model(args, task)
self.model = model
def setUpInput(self, input=None):
self.forward_input = get_dummy_input() if input is None else input
# get_dummy_input() is originally for s2s, here we delete extra dict
# items, so it can be used for EncoderModel / Encoder as well
self.forward_input.pop("prev_output_tokens", None)
def setUp(self):
super().setUp()
def test_forward(self):
if self.forward_input and self.model:
bsz = self.forward_input["src_tokens"].size(0)
forward_output = self.model.forward(**self.forward_input)
# we expect forward_output to be a dict with the following
# key/value pairs:
# - encoder_out: a Torch.Tensor
# - encoder_padding_mask: a binary Torch.Tensor
succ, msg = check_encoder_output(forward_output, batch_size=bsz)
if not succ:
self.assertTrue(succ, msg=msg)
self.forward_output = forward_output
def test_get_normalized_probs(self):
if self.model and self.forward_input:
forward_output = self.model.forward(**self.forward_input)
logprob = self.model.get_normalized_probs(forward_output, log_probs=True)
prob = self.model.get_normalized_probs(forward_output, log_probs=False)
# in order for different models/criterion to play with each other
# we need to know whether the logprob or prob output is batch_first
# or not. We assume an additional attribute will be attached to logprob
# or prob. If you find your code failed here, simply override
# FairseqModel.get_normalized_probs, see example at
# https://fburl.com/batch_first_example
self.assertTrue(hasattr(logprob, "batch_first"))
self.assertTrue(hasattr(prob, "batch_first"))
self.assertTrue(torch.is_tensor(logprob))
self.assertTrue(torch.is_tensor(prob))
class TestFairseqEncoderBase(unittest.TestCase):
"""
base class to test FairseqEncoder
"""
@classmethod
def setUpClass(cls):
if cls is TestFairseqEncoderBase:
raise unittest.SkipTest("Skipping test case in base")
super().setUpClass()
def setUpEncoder(self, encoder):
self.assertTrue(
isinstance(encoder, FairseqEncoder),
msg="This class is only used for test FairseqEncoder",
)
self.encoder = encoder
def setUpInput(self, input=None):
self.forward_input = get_dummy_input() if input is None else input
# get_dummy_input() is originally for s2s, here we delete extra dict
# items, so it can be used for EncoderModel / Encoder as well
self.forward_input.pop("prev_output_tokens", None)
def setUp(self):
self.encoder = None
self.forward_input = None
def test_forward(self):
if self.encoder and self.forward_input:
bsz = self.forward_input["src_tokens"].size(0)
forward_output = self.encoder.forward(**self.forward_input)
succ, msg = check_encoder_output(forward_output, batch_size=bsz)
if not succ:
self.assertTrue(succ, msg=msg)
self.forward_output = forward_output
class TestFairseqDecoderBase(unittest.TestCase):
"""
base class to test FairseqDecoder
"""
@classmethod
def setUpClass(cls):
if cls is TestFairseqDecoderBase:
raise unittest.SkipTest("Skipping test case in base")
super().setUpClass()
def setUpDecoder(self, decoder):
self.assertTrue(
isinstance(decoder, FairseqDecoder),
msg="This class is only used for test FairseqDecoder",
)
self.decoder = decoder
def setUpInput(self, input=None):
self.forward_input = get_dummy_encoder_output() if input is None else input
def setUpPrevOutputTokens(self, tokens=None):
if tokens is None:
self.encoder_input = get_dummy_input()
self.prev_output_tokens = self.encoder_input["prev_output_tokens"]
else:
self.prev_output_tokens = tokens
def setUp(self):
self.decoder = None
self.forward_input = None
self.prev_output_tokens = None
def test_forward(self):
if (
self.decoder is not None
and self.forward_input is not None
and self.prev_output_tokens is not None
):
forward_output = self.decoder.forward(
prev_output_tokens=self.prev_output_tokens,
encoder_out=self.forward_input,
)
succ, msg = check_decoder_output(forward_output)
if not succ:
self.assertTrue(succ, msg=msg)
self.forward_input = forward_output
class DummyEncoderModel(FairseqEncoderModel):
def __init__(self, encoder):
super().__init__(encoder)
@classmethod
def build_model(cls, args, task):
return cls(DummyEncoder())
def get_logits(self, net_output):
# Inverse of sigmoid to use with BinaryCrossEntropyWithLogitsCriterion as
# F.binary_cross_entropy_with_logits combines sigmoid and CE
return torch.log(
torch.div(net_output["encoder_out"], 1 - net_output["encoder_out"])
)
def get_normalized_probs(self, net_output, log_probs, sample=None):
lprobs = super().get_normalized_probs(net_output, log_probs, sample=sample)
lprobs.batch_first = True
return lprobs
class DummyEncoder(FairseqEncoder):
def __init__(self):
super().__init__(None)
def forward(self, src_tokens, src_lengths):
mask, max_len = lengths_to_encoder_padding_mask(src_lengths)
return {"encoder_out": src_tokens, "encoder_padding_mask": mask}
class CrossEntropyCriterionTestBase(unittest.TestCase):
@classmethod
def setUpClass(cls):
if cls is CrossEntropyCriterionTestBase:
raise unittest.SkipTest("Skipping base class test case")
super().setUpClass()
def setUpArgs(self):
args = argparse.Namespace()
args.sentence_avg = False
args.threshold = 0.1 # to use with BinaryCrossEntropyWithLogitsCriterion
return args
def setUp(self):
args = self.setUpArgs()
self.model = DummyEncoderModel(encoder=DummyEncoder())
self.criterion = self.criterion_cls.build_criterion(args, task=DummyTask(args))
def get_src_tokens(self, correct_prediction, aggregate):
"""
correct_prediction: True if the net_output (src_tokens) should
predict the correct target
aggregate: True if the criterion expects net_output (src_tokens)
aggregated across time axis
"""
predicted_idx = 0 if correct_prediction else 1
if aggregate:
src_tokens = torch.zeros((2, 2), dtype=torch.float)
for b in range(2):
src_tokens[b][predicted_idx] = 1.0
else:
src_tokens = torch.zeros((2, 10, 2), dtype=torch.float)
for b in range(2):
for t in range(10):
src_tokens[b][t][predicted_idx] = 1.0
return src_tokens
def get_target(self, soft_target):
if soft_target:
target = torch.zeros((2, 2), dtype=torch.float)
for b in range(2):
target[b][0] = 1.0
else:
target = torch.zeros((2, 10), dtype=torch.long)
return target
def get_test_sample(self, correct, soft_target, aggregate):
src_tokens = self.get_src_tokens(correct, aggregate)
target = self.get_target(soft_target)
L = src_tokens.size(1)
return {
"net_input": {"src_tokens": src_tokens, "src_lengths": torch.tensor([L])},
"target": target,
"ntokens": src_tokens.size(0) * src_tokens.size(1),
}
| 19,469
| 33.892473
| 87
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/speech_recognition/test_vggtransformer.py
|
#!/usr/bin/env python3
# import models/encoder/decoder to be tested
from examples.speech_recognition.models.vggtransformer import (
TransformerDecoder,
VGGTransformerEncoder,
VGGTransformerModel,
vggtransformer_1,
vggtransformer_2,
vggtransformer_base,
)
# import base test class
from .asr_test_base import (
DEFAULT_TEST_VOCAB_SIZE,
TestFairseqDecoderBase,
TestFairseqEncoderBase,
TestFairseqEncoderDecoderModelBase,
get_dummy_dictionary,
get_dummy_encoder_output,
get_dummy_input,
)
class VGGTransformerModelTest_mid(TestFairseqEncoderDecoderModelBase):
def setUp(self):
def override_config(args):
"""
vggtrasformer_1 use 14 layers of transformer,
for testing purpose, it is too expensive. For fast turn-around
test, reduce the number of layers to 3.
"""
args.transformer_enc_config = (
"((1024, 16, 4096, True, 0.15, 0.15, 0.15),) * 3"
)
super().setUp()
extra_args_setter = [vggtransformer_1, override_config]
self.setUpModel(VGGTransformerModel, extra_args_setter)
self.setUpInput(get_dummy_input(T=50, D=80, B=5, K=DEFAULT_TEST_VOCAB_SIZE))
class VGGTransformerModelTest_big(TestFairseqEncoderDecoderModelBase):
def setUp(self):
def override_config(args):
"""
vggtrasformer_2 use 16 layers of transformer,
for testing purpose, it is too expensive. For fast turn-around
test, reduce the number of layers to 3.
"""
args.transformer_enc_config = (
"((1024, 16, 4096, True, 0.15, 0.15, 0.15),) * 3"
)
super().setUp()
extra_args_setter = [vggtransformer_2, override_config]
self.setUpModel(VGGTransformerModel, extra_args_setter)
self.setUpInput(get_dummy_input(T=50, D=80, B=5, K=DEFAULT_TEST_VOCAB_SIZE))
class VGGTransformerModelTest_base(TestFairseqEncoderDecoderModelBase):
def setUp(self):
def override_config(args):
"""
vggtrasformer_base use 12 layers of transformer,
for testing purpose, it is too expensive. For fast turn-around
test, reduce the number of layers to 3.
"""
args.transformer_enc_config = (
"((512, 8, 2048, True, 0.15, 0.15, 0.15),) * 3"
)
super().setUp()
extra_args_setter = [vggtransformer_base, override_config]
self.setUpModel(VGGTransformerModel, extra_args_setter)
self.setUpInput(get_dummy_input(T=50, D=80, B=5, K=DEFAULT_TEST_VOCAB_SIZE))
class VGGTransformerEncoderTest(TestFairseqEncoderBase):
def setUp(self):
super().setUp()
self.setUpInput(get_dummy_input(T=50, D=80, B=5))
def test_forward(self):
print("1. test standard vggtransformer")
self.setUpEncoder(VGGTransformerEncoder(input_feat_per_channel=80))
super().test_forward()
print("2. test vggtransformer with limited right context")
self.setUpEncoder(
VGGTransformerEncoder(
input_feat_per_channel=80, transformer_context=(-1, 5)
)
)
super().test_forward()
print("3. test vggtransformer with limited left context")
self.setUpEncoder(
VGGTransformerEncoder(
input_feat_per_channel=80, transformer_context=(5, -1)
)
)
super().test_forward()
print("4. test vggtransformer with limited right context and sampling")
self.setUpEncoder(
VGGTransformerEncoder(
input_feat_per_channel=80,
transformer_context=(-1, 12),
transformer_sampling=(2, 2),
)
)
super().test_forward()
print("5. test vggtransformer with windowed context and sampling")
self.setUpEncoder(
VGGTransformerEncoder(
input_feat_per_channel=80,
transformer_context=(12, 12),
transformer_sampling=(2, 2),
)
)
class TransformerDecoderTest(TestFairseqDecoderBase):
def setUp(self):
super().setUp()
dict = get_dummy_dictionary(vocab_size=DEFAULT_TEST_VOCAB_SIZE)
decoder = TransformerDecoder(dict)
dummy_encoder_output = get_dummy_encoder_output(encoder_out_shape=(50, 5, 256))
self.setUpDecoder(decoder)
self.setUpInput(dummy_encoder_output)
self.setUpPrevOutputTokens()
| 4,578
| 32.669118
| 87
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/speech_recognition/__init__.py
| 0
| 0
| 0
|
py
|
|
CIF-HieraDist
|
CIF-HieraDist-main/tests/speech_recognition/test_collaters.py
|
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import numpy as np
import torch
from examples.speech_recognition.data.collaters import Seq2SeqCollater
class TestSeq2SeqCollator(unittest.TestCase):
def test_collate(self):
eos_idx = 1
pad_idx = 0
collater = Seq2SeqCollater(
feature_index=0, label_index=1, pad_index=pad_idx, eos_index=eos_idx
)
# 2 frames in the first sample and 3 frames in the second one
frames1 = np.array([[7, 8], [9, 10]])
frames2 = np.array([[1, 2], [3, 4], [5, 6]])
target1 = np.array([4, 2, 3, eos_idx])
target2 = np.array([3, 2, eos_idx])
sample1 = {"id": 0, "data": [frames1, target1]}
sample2 = {"id": 1, "data": [frames2, target2]}
batch = collater.collate([sample1, sample2])
# collate sort inputs by frame's length before creating the batch
self.assertTensorEqual(batch["id"], torch.tensor([1, 0]))
self.assertEqual(batch["ntokens"], 7)
self.assertTensorEqual(
batch["net_input"]["src_tokens"],
torch.tensor(
[[[1, 2], [3, 4], [5, 6]], [[7, 8], [9, 10], [pad_idx, pad_idx]]]
),
)
self.assertTensorEqual(
batch["net_input"]["prev_output_tokens"],
torch.tensor([[eos_idx, 3, 2, pad_idx], [eos_idx, 4, 2, 3]]),
)
self.assertTensorEqual(batch["net_input"]["src_lengths"], torch.tensor([3, 2]))
self.assertTensorEqual(
batch["target"],
torch.tensor([[3, 2, eos_idx, pad_idx], [4, 2, 3, eos_idx]]),
)
self.assertEqual(batch["nsentences"], 2)
def assertTensorEqual(self, t1, t2):
self.assertEqual(t1.size(), t2.size(), "size mismatch")
self.assertEqual(t1.ne(t2).long().sum(), 0)
if __name__ == "__main__":
unittest.main()
| 2,048
| 33.728814
| 87
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/fairseq/file_chunker_utils.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
import typing as tp
def _safe_readline(fd) -> str:
pos = fd.tell()
while True:
try:
return fd.readline()
except UnicodeDecodeError:
pos -= 1
fd.seek(pos) # search where this character begins
def find_offsets(filename: str, num_chunks: int) -> tp.List[int]:
"""
given a file and a number of chuncks, find the offsets in the file
to be able to chunk around full lines.
"""
with open(filename, "r", encoding="utf-8") as f:
size = os.fstat(f.fileno()).st_size
chunk_size = size // num_chunks
offsets = [0 for _ in range(num_chunks + 1)]
for i in range(1, num_chunks):
f.seek(chunk_size * i)
_safe_readline(f)
offsets[i] = f.tell()
offsets[-1] = size
return offsets
class ChunkLineIterator:
"""
Iterator to properly iterate over lines of a file chunck.
"""
def __init__(self, fd, start_offset: int, end_offset: int):
self._fd = fd
self._start_offset = start_offset
self._end_offset = end_offset
def __iter__(self) -> tp.Iterable[str]:
self._fd.seek(self._start_offset)
# next(f) breaks f.tell(), hence readline() must be used
line = _safe_readline(self._fd)
while line:
pos = self._fd.tell()
# f.tell() does not always give the byte position in the file
# sometimes it skips to a very large number
# it is unlikely that through a normal read we go from
# end bytes to end + 2**32 bytes (4 GB) and this makes it unlikely
# that the procedure breaks by the undeterministic behavior of
# f.tell()
if (
self._end_offset > 0
and pos > self._end_offset
and pos < self._end_offset + 2**32
):
break
yield line
line = self._fd.readline()
class Chunker:
"""
contextmanager to read a chunck of a file line by line.
"""
def __init__(self, path: str, start_offset: int, end_offset: int):
self.path = path
self.start_offset = start_offset
self.end_offset = end_offset
def __enter__(self) -> ChunkLineIterator:
self.fd = open(self.path, "r", encoding="utf-8")
return ChunkLineIterator(self.fd, self.start_offset, self.end_offset)
def __exit__(self, exc_type, exc_val, exc_tb) -> None:
self.fd.close()
| 2,691
| 30.670588
| 78
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/fairseq/registry.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from argparse import Namespace
from typing import Union
from fairseq.dataclass import FairseqDataclass
from fairseq.dataclass.utils import merge_with_parent
from hydra.core.config_store import ConfigStore
from omegaconf import DictConfig
REGISTRIES = {}
def setup_registry(registry_name: str, base_class=None, default=None, required=False):
assert registry_name.startswith("--")
registry_name = registry_name[2:].replace("-", "_")
REGISTRY = {}
REGISTRY_CLASS_NAMES = set()
DATACLASS_REGISTRY = {}
# maintain a registry of all registries
if registry_name in REGISTRIES:
return # registry already exists
REGISTRIES[registry_name] = {
"registry": REGISTRY,
"default": default,
"dataclass_registry": DATACLASS_REGISTRY,
}
def build_x(cfg: Union[DictConfig, str, Namespace], *extra_args, **extra_kwargs):
if isinstance(cfg, DictConfig):
choice = cfg._name
if choice and choice in DATACLASS_REGISTRY:
dc = DATACLASS_REGISTRY[choice]
cfg = merge_with_parent(dc(), cfg)
elif isinstance(cfg, str):
choice = cfg
if choice in DATACLASS_REGISTRY:
cfg = DATACLASS_REGISTRY[choice]()
else:
choice = getattr(cfg, registry_name, None)
if choice in DATACLASS_REGISTRY:
cfg = DATACLASS_REGISTRY[choice].from_namespace(cfg)
if choice is None:
if required:
raise ValueError("{} is required!".format(registry_name))
return None
cls = REGISTRY[choice]
if hasattr(cls, "build_" + registry_name):
builder = getattr(cls, "build_" + registry_name)
else:
builder = cls
return builder(cfg, *extra_args, **extra_kwargs)
def register_x(name, dataclass=None):
def register_x_cls(cls):
if name in REGISTRY:
raise ValueError(
"Cannot register duplicate {} ({})".format(registry_name, name)
)
if cls.__name__ in REGISTRY_CLASS_NAMES:
raise ValueError(
"Cannot register {} with duplicate class name ({})".format(
registry_name, cls.__name__
)
)
if base_class is not None and not issubclass(cls, base_class):
raise ValueError(
"{} must extend {}".format(cls.__name__, base_class.__name__)
)
if dataclass is not None and not issubclass(dataclass, FairseqDataclass):
raise ValueError(
"Dataclass {} must extend FairseqDataclass".format(dataclass)
)
cls.__dataclass = dataclass
if cls.__dataclass is not None:
DATACLASS_REGISTRY[name] = cls.__dataclass
cs = ConfigStore.instance()
node = dataclass()
node._name = name
cs.store(name=name, group=registry_name, node=node, provider="fairseq")
REGISTRY[name] = cls
return cls
return register_x_cls
return build_x, register_x, REGISTRY, DATACLASS_REGISTRY
| 3,449
| 33.158416
| 87
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/fairseq/checkpoint_utils.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import sys
import ast
import collections
import contextlib
import logging
import numpy as np
import os
import re
import time
import traceback
from collections import OrderedDict
from typing import Any, Dict, Optional, Union
import argparse
import torch
from fairseq.data import data_utils
from fairseq.dataclass.configs import CheckpointConfig
from fairseq.dataclass.utils import (
convert_namespace_to_omegaconf,
overwrite_args_by_name,
)
from fairseq.distributed.fully_sharded_data_parallel import FSDP, has_FSDP
from fairseq.file_io import PathManager
from fairseq.models import FairseqDecoder, FairseqEncoder
from omegaconf import DictConfig, open_dict, OmegaConf
logger = logging.getLogger(__name__)
def save_checkpoint(cfg: CheckpointConfig, trainer, epoch_itr, val_loss):
from fairseq import meters
# only one worker should attempt to create the required dir
if trainer.data_parallel_rank == 0:
os.makedirs(cfg.save_dir, exist_ok=True)
prev_best = getattr(save_checkpoint, "best", val_loss)
if val_loss is not None:
best_function = max if cfg.maximize_best_checkpoint_metric else min
save_checkpoint.best = best_function(val_loss, prev_best)
if cfg.no_save:
return
trainer.consolidate_optimizer() # TODO(SS): do we need this if no_save_optimizer_state
if not trainer.should_save_checkpoint_on_current_rank:
if trainer.always_call_state_dict_during_save_checkpoint:
trainer.state_dict()
return
write_timer = meters.StopwatchMeter()
write_timer.start()
epoch = epoch_itr.epoch
end_of_epoch = epoch_itr.end_of_epoch()
updates = trainer.get_num_updates()
logger.info(f"Preparing to save checkpoint for epoch {epoch} @ {updates} updates")
def is_better(a, b):
return a >= b if cfg.maximize_best_checkpoint_metric else a <= b
suffix = trainer.checkpoint_suffix
checkpoint_conds = collections.OrderedDict()
checkpoint_conds["checkpoint{}{}.pt".format(epoch, suffix)] = (
end_of_epoch and not cfg.no_epoch_checkpoints and epoch % cfg.save_interval == 0
)
checkpoint_conds["checkpoint_{}_{}{}.pt".format(epoch, updates, suffix)] = (
not end_of_epoch
and cfg.save_interval_updates > 0
and updates % cfg.save_interval_updates == 0
)
checkpoint_conds["checkpoint_best{}.pt".format(suffix)] = val_loss is not None and (
not hasattr(save_checkpoint, "best")
or is_better(val_loss, save_checkpoint.best)
)
if val_loss is not None and cfg.keep_best_checkpoints > 0:
worst_best = getattr(save_checkpoint, "best", None)
chkpts = checkpoint_paths(
cfg.save_dir,
pattern=r"checkpoint\.best_{}_(\d+\.?\d*){}\.pt".format(
cfg.best_checkpoint_metric, suffix
),
)
if len(chkpts) > 0:
p = chkpts[-1] if cfg.maximize_best_checkpoint_metric else chkpts[0]
worst_best = float(p.rsplit("_")[-1].replace("{}.pt".format(suffix), ""))
# add random digits to resolve ties
with data_utils.numpy_seed(epoch, updates, val_loss):
rand_sfx = np.random.randint(0, cfg.keep_best_checkpoints)
checkpoint_conds[
"checkpoint.best_{}_{:.3f}{}{}.pt".format(
cfg.best_checkpoint_metric, val_loss, rand_sfx, suffix
)
] = worst_best is None or is_better(val_loss, worst_best)
checkpoint_conds[
"checkpoint_last{}.pt".format(suffix)
] = not cfg.no_last_checkpoints
extra_state = {"train_iterator": epoch_itr.state_dict(), "val_loss": val_loss}
if hasattr(save_checkpoint, "best"):
extra_state.update({"best": save_checkpoint.best})
checkpoints = [
os.path.join(cfg.save_dir, fn) for fn, cond in checkpoint_conds.items() if cond
]
if len(checkpoints) > 0:
trainer.save_checkpoint(checkpoints[0], extra_state)
for cp in checkpoints[1:]:
if cfg.write_checkpoints_asynchronously:
# TODO[ioPath]: Need to implement a delayed asynchronous
# file copying/moving feature.
logger.warning(
f"ioPath is not copying {checkpoints[0]} to {cp} "
"since async write mode is on."
)
else:
assert PathManager.copy(
checkpoints[0], cp, overwrite=True
), f"Failed to copy {checkpoints[0]} to {cp}"
write_timer.stop()
logger.info(
"Saved checkpoint {} (epoch {} @ {} updates, score {}) (writing took {} seconds)".format(
checkpoints[0], epoch, updates, val_loss, write_timer.sum
)
)
if not end_of_epoch and cfg.keep_interval_updates > 0:
# remove old checkpoints; checkpoints are sorted in descending order
if cfg.keep_interval_updates_pattern == -1:
checkpoints = checkpoint_paths(
cfg.save_dir, pattern=r"checkpoint_\d+_(\d+){}\.pt".format(suffix)
)
else:
checkpoints = checkpoint_paths(
cfg.save_dir,
pattern=r"checkpoint_\d+_(\d+){}\.pt".format(suffix),
keep_match=True,
)
checkpoints = [
x[0]
for x in checkpoints
if x[1] % cfg.keep_interval_updates_pattern != 0
]
for old_chk in checkpoints[cfg.keep_interval_updates :]:
if os.path.lexists(old_chk):
os.remove(old_chk)
elif PathManager.exists(old_chk):
PathManager.rm(old_chk)
if cfg.keep_last_epochs > 0:
# remove old epoch checkpoints; checkpoints are sorted in descending order
checkpoints = checkpoint_paths(
cfg.save_dir, pattern=r"checkpoint(\d+){}\.pt".format(suffix)
)
for old_chk in checkpoints[cfg.keep_last_epochs :]:
if os.path.lexists(old_chk):
os.remove(old_chk)
elif PathManager.exists(old_chk):
PathManager.rm(old_chk)
if cfg.keep_best_checkpoints > 0:
# only keep the best N checkpoints according to validation metric
checkpoints = checkpoint_paths(
cfg.save_dir,
pattern=r"checkpoint\.best_{}_(\d+\.?\d*){}\.pt".format(
cfg.best_checkpoint_metric, suffix
),
)
if not cfg.maximize_best_checkpoint_metric:
checkpoints = checkpoints[::-1]
for old_chk in checkpoints[cfg.keep_best_checkpoints :]:
if os.path.lexists(old_chk):
os.remove(old_chk)
elif PathManager.exists(old_chk):
PathManager.rm(old_chk)
def load_checkpoint(cfg: CheckpointConfig, trainer, **passthrough_args):
"""
Load a checkpoint and restore the training iterator.
*passthrough_args* will be passed through to
``trainer.get_train_iterator``.
"""
reset_optimizer = cfg.reset_optimizer
reset_lr_scheduler = cfg.reset_lr_scheduler
optimizer_overrides = ast.literal_eval(cfg.optimizer_overrides)
reset_meters = cfg.reset_meters
reset_dataloader = cfg.reset_dataloader
if cfg.finetune_from_model is not None and (
reset_optimizer or reset_lr_scheduler or reset_meters or reset_dataloader
):
raise ValueError(
"--finetune-from-model can not be set together with either --reset-optimizer"
" or reset_lr_scheduler or reset_meters or reset_dataloader"
)
suffix = trainer.checkpoint_suffix
if (
cfg.restore_file == "checkpoint_last.pt"
): # default value of restore_file is 'checkpoint_last.pt'
checkpoint_path = os.path.join(
cfg.save_dir, "checkpoint_last{}.pt".format(suffix)
)
first_launch = not PathManager.exists(checkpoint_path)
if cfg.finetune_from_model is not None and first_launch:
# if there is no last checkpoint to restore, start the finetune from pretrained model
# else just use usual logic to load checkpoint, e.g. restart from last checkpoint and etc.
if PathManager.exists(cfg.finetune_from_model):
checkpoint_path = cfg.finetune_from_model
reset_optimizer = True
reset_lr_scheduler = True
reset_meters = True
reset_dataloader = True
logger.info(
f"loading pretrained model from {checkpoint_path}: "
"optimizer, lr scheduler, meters, dataloader will be reset"
)
else:
raise ValueError(
f"--funetune-from-model {cfg.finetune_from_model} does not exist"
)
elif suffix is not None:
checkpoint_path = cfg.restore_file.replace(".pt", suffix + ".pt")
else:
checkpoint_path = cfg.restore_file
if cfg.restore_file != "checkpoint_last.pt" and cfg.finetune_from_model:
raise ValueError(
"--finetune-from-model and --restore-file (non-default value) "
"can not be specified together: " + str(cfg)
)
extra_state = trainer.load_checkpoint(
checkpoint_path,
reset_optimizer,
reset_lr_scheduler,
optimizer_overrides,
reset_meters=reset_meters,
)
if (
extra_state is not None
and "best" in extra_state
and not reset_optimizer
and not reset_meters
):
save_checkpoint.best = extra_state["best"]
if extra_state is not None and not reset_dataloader:
# restore iterator from checkpoint
itr_state = extra_state["train_iterator"]
epoch_itr = trainer.get_train_iterator(
epoch=itr_state["epoch"], load_dataset=True, **passthrough_args
)
epoch_itr.load_state_dict(itr_state)
else:
epoch_itr = trainer.get_train_iterator(
epoch=1, load_dataset=True, **passthrough_args
)
trainer.lr_step(epoch_itr.epoch)
return extra_state, epoch_itr
def load_checkpoint_to_cpu(path, arg_overrides=None, load_on_all_ranks=False):
"""Loads a checkpoint to CPU (with upgrading for backward compatibility).
If doing single-GPU training or if the checkpoint is only being loaded by at
most one process on each node (current default behavior is for only rank 0
to read the checkpoint from disk), load_on_all_ranks should be False to
avoid errors from torch.distributed not having been initialized or
torch.distributed.barrier() hanging.
If all processes on each node may be loading the checkpoint
simultaneously, load_on_all_ranks should be set to True to avoid I/O
conflicts.
There's currently no support for > 1 but < all processes loading the
checkpoint on each node.
"""
local_path = PathManager.get_local_path(path)
# The locally cached file returned by get_local_path() may be stale for
# remote files that are periodically updated/overwritten (ex:
# checkpoint_last.pt) - so we remove the local copy, sync across processes
# (if needed), and then download a fresh copy.
if local_path != path and PathManager.path_requires_pathmanager(path):
try:
os.remove(local_path)
except FileNotFoundError:
# With potentially multiple processes removing the same file, the
# file being missing is benign (missing_ok isn't available until
# Python 3.8).
pass
if load_on_all_ranks:
torch.distributed.barrier()
local_path = PathManager.get_local_path(path)
with open(local_path, "rb") as f:
state = torch.load(f, map_location=torch.device("cpu"))
if "args" in state and state["args"] is not None and arg_overrides is not None:
args = state["args"]
for arg_name, arg_val in arg_overrides.items():
setattr(args, arg_name, arg_val)
if "cfg" in state and state["cfg"] is not None:
# hack to be able to set Namespace in dict config. this should be removed when we update to newer
# omegaconf version that supports object flags, or when we migrate all existing models
from omegaconf import _utils
old_primitive = _utils.is_primitive_type
_utils.is_primitive_type = lambda _: True
state["cfg"] = OmegaConf.create(state["cfg"])
_utils.is_primitive_type = old_primitive
OmegaConf.set_struct(state["cfg"], True)
if arg_overrides is not None:
overwrite_args_by_name(state["cfg"], arg_overrides)
state = _upgrade_state_dict(state)
return state
def load_model_ensemble(
filenames,
arg_overrides: Optional[Dict[str, Any]] = None,
task=None,
strict=True,
suffix="",
num_shards=1,
state=None,
):
"""Loads an ensemble of models.
Args:
filenames (List[str]): checkpoint files to load
arg_overrides (Dict[str,Any], optional): override model args that
were used during model training
task (fairseq.tasks.FairseqTask, optional): task to use for loading
"""
assert not (
strict and num_shards > 1
), "Cannot load state dict with strict=True and checkpoint shards > 1"
ensemble, args, _task = load_model_ensemble_and_task(
filenames,
arg_overrides,
task,
strict,
suffix,
num_shards,
state,
)
return ensemble, args
def get_maybe_sharded_checkpoint_filename(
filename: str, suffix: str, shard_idx: int, num_shards: int
) -> str:
orig_filename = filename
filename = filename.replace(".pt", suffix + ".pt")
fsdp_filename = filename[:-3] + f"-shard{shard_idx}.pt"
model_parallel_filename = orig_filename[:-3] + f"_part{shard_idx}.pt"
if PathManager.exists(fsdp_filename):
return fsdp_filename
elif num_shards > 1:
return model_parallel_filename
else:
return filename
def load_model_ensemble_and_task(
filenames,
arg_overrides: Optional[Dict[str, Any]] = None,
task=None,
strict=True,
suffix="",
num_shards=1,
state=None,
):
assert state is None or len(filenames) == 1
from fairseq import tasks
assert not (
strict and num_shards > 1
), "Cannot load state dict with strict=True and checkpoint shards > 1"
ensemble = []
cfg = None
for filename in filenames:
orig_filename = filename
model_shard_state = {"shard_weights": [], "shard_metadata": []}
assert num_shards > 0
st = time.time()
for shard_idx in range(num_shards):
filename = get_maybe_sharded_checkpoint_filename(
orig_filename, suffix, shard_idx, num_shards
)
if not PathManager.exists(filename):
raise IOError("Model file not found: {}".format(filename))
if state is None:
state = load_checkpoint_to_cpu(filename, arg_overrides)
if "args" in state and state["args"] is not None:
cfg = convert_namespace_to_omegaconf(state["args"])
elif "cfg" in state and state["cfg"] is not None:
cfg = state["cfg"]
else:
raise RuntimeError(
f"Neither args nor cfg exist in state keys = {state.keys()}"
)
if task is None:
task = tasks.setup_task(cfg.task)
if "task_state" in state:
task.load_state_dict(state["task_state"])
if "fsdp_metadata" in state and num_shards > 1:
model_shard_state["shard_weights"].append(state["model"])
model_shard_state["shard_metadata"].append(state["fsdp_metadata"])
# check FSDP import before the code goes too far
if not has_FSDP:
raise ImportError(
"Cannot find FullyShardedDataParallel. "
"Please install fairscale with: pip install fairscale"
)
if shard_idx == num_shards - 1:
consolidated_model_state = FSDP.consolidate_shard_weights(
shard_weights=model_shard_state["shard_weights"],
shard_metadata=model_shard_state["shard_metadata"],
)
model = task.build_model(cfg.model)
if (
"optimizer_history" in state
and len(state["optimizer_history"]) > 0
and "num_updates" in state["optimizer_history"][-1]
):
model.set_num_updates(
state["optimizer_history"][-1]["num_updates"]
)
model.load_state_dict(
consolidated_model_state, strict=strict, model_cfg=cfg.model
)
else:
# model parallel checkpoint or unsharded checkpoint
model = task.build_model(cfg.model)
if (
"optimizer_history" in state
and len(state["optimizer_history"]) > 0
and "num_updates" in state["optimizer_history"][-1]
):
model.set_num_updates(state["optimizer_history"][-1]["num_updates"])
model.load_state_dict(
state["model"], strict=strict, model_cfg=cfg.model
)
# reset state so it gets loaded for the next model in ensemble
state = None
if shard_idx % 10 == 0 and shard_idx > 0:
elapsed = time.time() - st
logger.info(
f"Loaded {shard_idx} shards in {elapsed:.2f}s, {elapsed / (shard_idx+1):.2f}s/shard"
)
# build model for ensemble
ensemble.append(model)
return ensemble, cfg, task
# TODO: Revised by Minglun Han, for the convience of speech generation with speech chain
def load_speech_chain_to_tts(
filename,
datapath,
arg_overrides: Optional[Dict[str, Any]] = None,
task=None,
suffix="",
num_shards=1,
):
from fairseq import tasks
cfg = None
model = None
orig_filename = filename
for shard_idx in range(num_shards):
filename = get_maybe_sharded_checkpoint_filename(
orig_filename, suffix, shard_idx, num_shards
)
# Load model to cpu
state = load_checkpoint_to_cpu(filename, arg_overrides)
# TODO: Change the model-related hyper-parameters
state_sc = load_checkpoint_to_cpu(
path=arg_overrides["path_to_speech_chain_ckpt"]
)
sc_model_cfg_dict = vars(state_sc["cfg"].model)
sc_task_cfg_dict = vars(state_sc["cfg"].task)
model_cfg_dict = vars(state["cfg"].model)
task_cfg_dict = vars(state["cfg"].task)
for key, value in sc_task_cfg_dict.items():
if "speaker" in key:
task_cfg_dict[key] = value
task_cfg_dict["data"] = datapath
for key, value in sc_model_cfg_dict.items():
if key.startswith("tts"):
model_cfg_dict[key.replace("tts_", "")] = value
task_cfg = argparse.Namespace(**task_cfg_dict)
model_cfg = argparse.Namespace(**model_cfg_dict)
task = tasks.setup_task(task_cfg)
cfg = state["cfg"]
model = task.build_model(model_cfg)
return model, cfg, task
def checkpoint_paths(path, pattern=r"checkpoint(\d+)\.pt", keep_match=False):
"""Retrieves all checkpoints found in `path` directory.
Checkpoints are identified by matching filename to the specified pattern. If
the pattern contains groups, the result will be sorted by the first group in
descending order.
"""
pt_regexp = re.compile(pattern)
files = PathManager.ls(path)
entries = []
for i, f in enumerate(files):
m = pt_regexp.fullmatch(f)
if m is not None:
idx = float(m.group(1)) if len(m.groups()) > 0 else i
entries.append((idx, m.group(0)))
if keep_match:
return [(os.path.join(path, x[1]), x[0]) for x in sorted(entries, reverse=True)]
else:
return [os.path.join(path, x[1]) for x in sorted(entries, reverse=True)]
def torch_persistent_save(obj, filename, async_write: bool = False):
if async_write:
with PathManager.opena(filename, "wb") as f:
_torch_persistent_save(obj, f)
else:
if PathManager.supports_rename(filename):
# do atomic save
with PathManager.open(filename + ".tmp", "wb") as f:
_torch_persistent_save(obj, f)
PathManager.rename(filename + ".tmp", filename)
else:
# fallback to non-atomic save
with PathManager.open(filename, "wb") as f:
_torch_persistent_save(obj, f)
def _torch_persistent_save(obj, f):
if isinstance(f, str):
with PathManager.open(f, "wb") as h:
torch_persistent_save(obj, h)
return
for i in range(3):
try:
return torch.save(obj, f)
except Exception:
if i == 2:
logger.error(traceback.format_exc())
raise
def _upgrade_state_dict(state):
"""Helper for upgrading old model checkpoints."""
# add optimizer_history
if "optimizer_history" not in state:
state["optimizer_history"] = [
{"criterion_name": "CrossEntropyCriterion", "best_loss": state["best_loss"]}
]
state["last_optimizer_state"] = state["optimizer"]
del state["optimizer"]
del state["best_loss"]
# move extra_state into sub-dictionary
if "epoch" in state and "extra_state" not in state:
state["extra_state"] = {
"epoch": state["epoch"],
"batch_offset": state["batch_offset"],
"val_loss": state["val_loss"],
}
del state["epoch"]
del state["batch_offset"]
del state["val_loss"]
# reduce optimizer history's memory usage (only keep the last state)
if "optimizer" in state["optimizer_history"][-1]:
state["last_optimizer_state"] = state["optimizer_history"][-1]["optimizer"]
for optim_hist in state["optimizer_history"]:
del optim_hist["optimizer"]
# record the optimizer class name
if "optimizer_name" not in state["optimizer_history"][-1]:
state["optimizer_history"][-1]["optimizer_name"] = "FairseqNAG"
# move best_loss into lr_scheduler_state
if "lr_scheduler_state" not in state["optimizer_history"][-1]:
state["optimizer_history"][-1]["lr_scheduler_state"] = {
"best": state["optimizer_history"][-1]["best_loss"]
}
del state["optimizer_history"][-1]["best_loss"]
# keep track of number of updates
if "num_updates" not in state["optimizer_history"][-1]:
state["optimizer_history"][-1]["num_updates"] = 0
# old model checkpoints may not have separate source/target positions
if (
"args" in state
and hasattr(state["args"], "max_positions")
and not hasattr(state["args"], "max_source_positions")
):
state["args"].max_source_positions = state["args"].max_positions
state["args"].max_target_positions = state["args"].max_positions
# use stateful training data iterator
if "train_iterator" not in state["extra_state"]:
state["extra_state"]["train_iterator"] = {
"epoch": state["extra_state"]["epoch"],
"iterations_in_epoch": state["extra_state"].get("batch_offset", 0),
}
# backward compatibility, cfg updates
if "args" in state and state["args"] is not None:
# default to translation task
if not hasattr(state["args"], "task"):
state["args"].task = "translation"
# --raw-text and --lazy-load are deprecated
if getattr(state["args"], "raw_text", False):
state["args"].dataset_impl = "raw"
elif getattr(state["args"], "lazy_load", False):
state["args"].dataset_impl = "lazy"
# epochs start at 1
if state["extra_state"]["train_iterator"] is not None:
state["extra_state"]["train_iterator"]["epoch"] = max(
state["extra_state"]["train_iterator"].get("epoch", 1), 1
)
# --remove-bpe ==> --postprocess
if hasattr(state["args"], "remove_bpe"):
state["args"].post_process = state["args"].remove_bpe
# --min-lr ==> --stop-min-lr
if hasattr(state["args"], "min_lr"):
state["args"].stop_min_lr = state["args"].min_lr
del state["args"].min_lr
# binary_cross_entropy / kd_binary_cross_entropy => wav2vec criterion
if hasattr(state["args"], "criterion") and state["args"].criterion in [
"binary_cross_entropy",
"kd_binary_cross_entropy",
]:
state["args"].criterion = "wav2vec"
# remove log_keys if it's None (criteria will supply a default value of [])
if hasattr(state["args"], "log_keys") and state["args"].log_keys is None:
delattr(state["args"], "log_keys")
# speech_pretraining => audio pretraining
if (
hasattr(state["args"], "task")
and state["args"].task == "speech_pretraining"
):
state["args"].task = "audio_pretraining"
# audio_cpc => wav2vec
if hasattr(state["args"], "arch") and state["args"].arch == "audio_cpc":
state["args"].arch = "wav2vec"
# convert legacy float learning rate to List[float]
if hasattr(state["args"], "lr") and isinstance(state["args"].lr, float):
state["args"].lr = [state["args"].lr]
# convert task data arg to a string instead of List[string]
if (
hasattr(state["args"], "data")
and isinstance(state["args"].data, list)
and len(state["args"].data) > 0
):
state["args"].data = state["args"].data[0]
# remove keys in state["args"] related to teacher-student learning
for key in [
"static_teachers",
"static_teacher_weights",
"dynamic_teachers",
"dynamic_teacher_weights",
]:
if key in state["args"]:
delattr(state["args"], key)
state["cfg"] = convert_namespace_to_omegaconf(state["args"])
if "cfg" in state and state["cfg"] is not None:
cfg = state["cfg"]
with open_dict(cfg):
# any upgrades for Hydra-based configs
if (
"task" in cfg
and "eval_wer_config" in cfg.task
and isinstance(cfg.task.eval_wer_config.print_alignment, bool)
):
cfg.task.eval_wer_config.print_alignment = "hard"
if "generation" in cfg and isinstance(cfg.generation.print_alignment, bool):
cfg.generation.print_alignment = (
"hard" if cfg.generation.print_alignment else None
)
if (
"model" in cfg
and "w2v_args" in cfg.model
and cfg.model.w2v_args is not None
and (
hasattr(cfg.model.w2v_args, "task") or "task" in cfg.model.w2v_args
)
and hasattr(cfg.model.w2v_args.task, "eval_wer_config")
and cfg.model.w2v_args.task.eval_wer_config is not None
and isinstance(
cfg.model.w2v_args.task.eval_wer_config.print_alignment, bool
)
):
cfg.model.w2v_args.task.eval_wer_config.print_alignment = "hard"
return state
def prune_state_dict(state_dict, model_cfg: Optional[DictConfig]):
"""Prune the given state_dict if desired for LayerDrop
(https://arxiv.org/abs/1909.11556).
Training with LayerDrop allows models to be robust to pruning at inference
time. This function prunes state_dict to allow smaller models to be loaded
from a larger model and re-maps the existing state_dict for this to occur.
It's called by functions that load models from checkpoints and does not
need to be called directly.
"""
arch = None
if model_cfg is not None:
arch = (
model_cfg._name
if isinstance(model_cfg, DictConfig)
else getattr(model_cfg, "arch", None)
)
if not model_cfg or arch is None or arch == "ptt_transformer":
# args should not be none, but don't crash if it is.
return state_dict
encoder_layers_to_keep = getattr(model_cfg, "encoder_layers_to_keep", None)
decoder_layers_to_keep = getattr(model_cfg, "decoder_layers_to_keep", None)
if not encoder_layers_to_keep and not decoder_layers_to_keep:
return state_dict
# apply pruning
logger.info(
"Pruning model to specified layer configuration - this works best if the model was trained with LayerDrop"
)
def create_pruning_pass(layers_to_keep, layer_name):
keep_layers = sorted(
int(layer_string) for layer_string in layers_to_keep.split(",")
)
mapping_dict = {}
for i in range(len(keep_layers)):
mapping_dict[str(keep_layers[i])] = str(i)
regex = re.compile(r"^{layer}.*\.layers\.(\d+)".format(layer=layer_name))
return {"substitution_regex": regex, "mapping_dict": mapping_dict}
pruning_passes = []
if encoder_layers_to_keep:
pruning_passes.append(create_pruning_pass(encoder_layers_to_keep, "encoder"))
if decoder_layers_to_keep:
pruning_passes.append(create_pruning_pass(decoder_layers_to_keep, "decoder"))
new_state_dict = {}
for layer_name in state_dict.keys():
match = re.search(r"\.layers\.(\d+)\.", layer_name)
# if layer has no number in it, it is a supporting layer, such as an
# embedding
if not match:
new_state_dict[layer_name] = state_dict[layer_name]
continue
# otherwise, layer should be pruned.
original_layer_number = match.group(1)
# figure out which mapping dict to replace from
for pruning_pass in pruning_passes:
if original_layer_number in pruning_pass["mapping_dict"] and pruning_pass[
"substitution_regex"
].search(layer_name):
new_layer_number = pruning_pass["mapping_dict"][original_layer_number]
substitution_match = pruning_pass["substitution_regex"].search(
layer_name
)
new_state_key = (
layer_name[: substitution_match.start(1)]
+ new_layer_number
+ layer_name[substitution_match.end(1) :]
)
new_state_dict[new_state_key] = state_dict[layer_name]
# Since layers are now pruned, *_layers_to_keep are no longer needed.
# This is more of "It would make it work fix" rather than a proper fix.
if isinstance(model_cfg, DictConfig):
context = open_dict(model_cfg)
else:
context = contextlib.ExitStack()
with context:
if hasattr(model_cfg, "encoder_layers_to_keep"):
model_cfg.encoder_layers_to_keep = None
if hasattr(model_cfg, "decoder_layers_to_keep"):
model_cfg.decoder_layers_to_keep = None
return new_state_dict
def load_pretrained_component_from_model(
component: Union[FairseqEncoder, FairseqDecoder], checkpoint: str
):
"""
Load a pretrained FairseqEncoder or FairseqDecoder from checkpoint into the
provided `component` object. If state_dict fails to load, there may be a
mismatch in the architecture of the corresponding `component` found in the
`checkpoint` file.
"""
if not PathManager.exists(checkpoint):
raise IOError("Model file not found: {}".format(checkpoint))
state = load_checkpoint_to_cpu(checkpoint)
if isinstance(component, FairseqEncoder):
component_type = "encoder"
elif isinstance(component, FairseqDecoder):
component_type = "decoder"
else:
raise ValueError(
"component to load must be either a FairseqEncoder or "
"FairseqDecoder. Loading other component types are not supported."
)
component_state_dict = OrderedDict()
for key in state["model"].keys():
if key.startswith(component_type):
# encoder.input_layers.0.0.weight --> input_layers.0.0.weight
component_subkey = key[len(component_type) + 1 :]
component_state_dict[component_subkey] = state["model"][key]
component.load_state_dict(component_state_dict, strict=True)
return component
def verify_checkpoint_directory(save_dir: str) -> None:
if not os.path.exists(save_dir):
os.makedirs(save_dir, exist_ok=True)
temp_file_path = os.path.join(save_dir, "dummy")
try:
with open(temp_file_path, "w"):
pass
except OSError as e:
logger.warning(
"Unable to access checkpoint save directory: {}".format(save_dir)
)
raise e
else:
os.remove(temp_file_path)
def load_ema_from_checkpoint(fpath):
"""Loads exponential moving averaged (EMA) checkpoint from input and
returns a model with ema weights.
Args:
fpath: A string path of checkpoint to load from.
Returns:
A dict of string keys mapping to various values. The 'model' key
from the returned dict should correspond to an OrderedDict mapping
string parameter names to torch Tensors.
"""
params_dict = collections.OrderedDict()
new_state = None
with PathManager.open(fpath, "rb") as f:
new_state = torch.load(
f,
map_location=(
lambda s, _: torch.serialization.default_restore_location(s, "cpu")
),
)
# EMA model is stored in a separate "extra state"
model_params = new_state["extra_state"]["ema"]
for key in list(model_params.keys()):
p = model_params[key]
if isinstance(p, torch.HalfTensor):
p = p.float()
if key not in params_dict:
params_dict[key] = p.clone()
# NOTE: clone() is needed in case of p is a shared parameter
else:
raise ValueError("Key {} is repeated in EMA model params.".format(key))
if len(params_dict) == 0:
raise ValueError(
f"Input checkpoint path '{fpath}' does not contain "
"ema model weights, is this model trained with EMA?"
)
new_state["model"] = params_dict
return new_state
| 35,232
| 37.296739
| 114
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/fairseq/utils.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import contextlib
import copy
import importlib
import logging
import os
import sys
import warnings
from itertools import accumulate
from typing import Callable, Dict, List, Optional, TYPE_CHECKING
import torch
import torch.nn.functional as F
from torch import Tensor
import collections
if TYPE_CHECKING:
from fairseq.modules.multihead_attention import MultiheadAttention
try:
from amp_C import multi_tensor_l2norm
multi_tensor_l2norm_available = True
except ImportError:
multi_tensor_l2norm_available = False
try:
import torch_xla.core.xla_model as xm
except ImportError:
xm = None
logger = logging.getLogger(__name__)
MANIFOLD_PATH_SEP = "|"
class FileContentsAction(argparse.Action):
def __init__(self, option_strings, dest, nargs=None, **kwargs):
if nargs is not None:
raise ValueError("nargs not allowed")
super(FileContentsAction, self).__init__(option_strings, dest, **kwargs)
def __call__(self, parser, namespace, values, option_string=None):
from fairseq.file_io import PathManager
if PathManager.isfile(values):
with PathManager.open(values) as f:
argument = f.read().strip()
else:
argument = values
setattr(namespace, self.dest, argument)
def split_paths(paths: str, separator=os.pathsep) -> List[str]:
return (
paths.split(separator) if "://" not in paths else paths.split(MANIFOLD_PATH_SEP)
)
def load_ensemble_for_inference(filenames, task, model_arg_overrides=None):
from fairseq import checkpoint_utils
deprecation_warning(
"utils.load_ensemble_for_inference is deprecated. "
"Please use checkpoint_utils.load_model_ensemble instead."
)
return checkpoint_utils.load_model_ensemble(
filenames, arg_overrides=model_arg_overrides, task=task
)
def apply_to_sample(f, sample):
if hasattr(sample, "__len__") and len(sample) == 0:
return {}
def _apply(x):
if torch.is_tensor(x):
return f(x)
elif isinstance(x, collections.OrderedDict):
# OrderedDict has attributes that needs to be preserved
od = collections.OrderedDict(
(key, _apply(value)) for key, value in x.items()
)
od.__dict__ = x.__dict__
return od
elif isinstance(x, dict):
return {key: _apply(value) for key, value in x.items()}
elif isinstance(x, list):
return [_apply(x) for x in x]
elif isinstance(x, tuple):
return tuple(_apply(x) for x in x)
elif isinstance(x, set):
return {_apply(x) for x in x}
else:
return x
return _apply(sample)
def move_to_cuda(sample, device=None):
device = device or torch.cuda.current_device()
def _move_to_cuda(tensor):
# non_blocking is ignored if tensor is not pinned, so we can always set
# to True (see github.com/PyTorchLightning/pytorch-lightning/issues/620)
return tensor.to(device=device, non_blocking=True)
return apply_to_sample(_move_to_cuda, sample)
def move_to_cpu(sample):
def _move_to_cpu(tensor):
# PyTorch has poor support for half tensors (float16) on CPU.
# Move any such tensors to float32.
if tensor.dtype in {torch.bfloat16, torch.float16}:
tensor = tensor.to(dtype=torch.float32)
return tensor.cpu()
return apply_to_sample(_move_to_cpu, sample)
def move_to_tpu(sample):
import torch_xla.core.xla_model as xm
device = xm.xla_device()
def _move_to_tpu(tensor):
return tensor.to(device)
return apply_to_sample(_move_to_tpu, sample)
def get_incremental_state(
module: "MultiheadAttention",
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]],
key: str,
) -> Optional[Dict[str, Optional[Tensor]]]:
"""Helper for getting incremental state for an nn.Module."""
return module.get_incremental_state(incremental_state, key)
def set_incremental_state(
module: "MultiheadAttention",
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]],
key: str,
value: Dict[str, Optional[Tensor]],
) -> Optional[Dict[str, Dict[str, Optional[Tensor]]]]:
"""Helper for setting incremental state for an nn.Module."""
if incremental_state is not None:
result = module.set_incremental_state(incremental_state, key, value)
if result is not None:
incremental_state = result
return incremental_state
def load_align_dict(replace_unk):
if replace_unk is None:
align_dict = None
elif isinstance(replace_unk, str) and len(replace_unk) > 0:
# Load alignment dictionary for unknown word replacement if it was passed as an argument.
align_dict = {}
with open(replace_unk, "r") as f:
for line in f:
cols = line.split()
align_dict[cols[0]] = cols[1]
else:
# No alignment dictionary provided but we still want to perform unknown word replacement by copying the
# original source word.
align_dict = {}
return align_dict
def print_embed_overlap(embed_dict, vocab_dict):
embed_keys = set(embed_dict.keys())
vocab_keys = set(vocab_dict.symbols)
overlap = len(embed_keys & vocab_keys)
logger.info("found {}/{} types in embedding file".format(overlap, len(vocab_dict)))
def parse_embedding(embed_path):
"""Parse embedding text file into a dictionary of word and embedding tensors.
The first line can have vocabulary size and dimension. The following lines
should contain word and embedding separated by spaces.
Example:
2 5
the -0.0230 -0.0264 0.0287 0.0171 0.1403
at -0.0395 -0.1286 0.0275 0.0254 -0.0932
"""
embed_dict = {}
with open(embed_path) as f_embed:
next(f_embed) # skip header
for line in f_embed:
pieces = line.rstrip().split(" ")
embed_dict[pieces[0]] = torch.Tensor(
[float(weight) for weight in pieces[1:]]
)
return embed_dict
def load_embedding(embed_dict, vocab, embedding):
for idx in range(len(vocab)):
token = vocab[idx]
if token in embed_dict:
embedding.weight.data[idx] = embed_dict[token]
return embedding
def replace_unk(hypo_str, src_str, alignment, align_dict, unk):
from fairseq import tokenizer
# Tokens are strings here
hypo_tokens = tokenizer.tokenize_line(hypo_str)
# TODO: Very rare cases where the replacement is '<eos>' should be handled gracefully
src_tokens = tokenizer.tokenize_line(src_str) + ["<eos>"]
for i, ht in enumerate(hypo_tokens):
if ht == unk:
src_token = src_tokens[alignment[i]]
# Either take the corresponding value in the aligned dictionary or just copy the original value.
hypo_tokens[i] = align_dict.get(src_token, src_token)
return " ".join(hypo_tokens)
def post_process_prediction(
hypo_tokens,
src_str,
alignment,
align_dict,
tgt_dict,
remove_bpe=None,
extra_symbols_to_ignore=None,
):
hypo_str = tgt_dict.string(
hypo_tokens, remove_bpe, extra_symbols_to_ignore=extra_symbols_to_ignore
)
if align_dict is not None:
hypo_str = replace_unk(
hypo_str, src_str, alignment, align_dict, tgt_dict.unk_string()
)
if align_dict is not None or remove_bpe is not None:
# Convert back to tokens for evaluating with unk replacement or without BPE
# Note that the dictionary can be modified inside the method.
hypo_tokens = tgt_dict.encode_line(hypo_str, add_if_not_exist=True)
return hypo_tokens, hypo_str, alignment
def make_positions(tensor, padding_idx: int, onnx_trace: bool = False):
"""Replace non-padding symbols with their position numbers.
Position numbers begin at padding_idx+1. Padding symbols are ignored.
"""
# The series of casts and type-conversions here are carefully
# balanced to both work with ONNX export and XLA. In particular XLA
# prefers ints, cumsum defaults to output longs, and ONNX doesn't know
# how to handle the dtype kwarg in cumsum.
mask = tensor.ne(padding_idx).int()
return (torch.cumsum(mask, dim=1).type_as(mask) * mask).long() + padding_idx
def strip_pad(tensor, pad):
return tensor[tensor.ne(pad)]
def buffered_arange(max):
if not hasattr(buffered_arange, "buf"):
buffered_arange.buf = torch.LongTensor()
if max > buffered_arange.buf.numel():
buffered_arange.buf.resize_(max)
torch.arange(max, out=buffered_arange.buf)
return buffered_arange.buf[:max]
def convert_padding_direction(
src_tokens, padding_idx, right_to_left: bool = False, left_to_right: bool = False
):
assert right_to_left ^ left_to_right
pad_mask = src_tokens.eq(padding_idx)
if not pad_mask.any():
# no padding, return early
return src_tokens
if left_to_right and not pad_mask[:, 0].any():
# already right padded
return src_tokens
if right_to_left and not pad_mask[:, -1].any():
# already left padded
return src_tokens
max_len = src_tokens.size(1)
buffered = torch.empty(0).long()
if max_len > 0:
torch.arange(max_len, out=buffered)
range = buffered.type_as(src_tokens).expand_as(src_tokens)
num_pads = pad_mask.long().sum(dim=1, keepdim=True)
if right_to_left:
index = torch.remainder(range - num_pads, max_len)
else:
index = torch.remainder(range + num_pads, max_len)
return src_tokens.gather(1, index)
def item(tensor):
# tpu-comment: making this a no-op for xla devices.
if torch.is_tensor(tensor) and tensor.device.type == "xla":
return tensor.detach()
if hasattr(tensor, "item"):
return tensor.item()
if hasattr(tensor, "__getitem__"):
return tensor[0]
return tensor
def multi_tensor_total_norm(grads, chunk_size=2048 * 32) -> torch.Tensor:
per_device_grads = {}
norms = []
for grad in grads:
device = grad.device
cur_device_grads = per_device_grads.get(device)
if cur_device_grads is None:
cur_device_grads = []
per_device_grads[device] = cur_device_grads
cur_device_grads.append(grad)
for device in per_device_grads.keys():
cur_device_grads = per_device_grads[device]
if device.type == "cuda":
# TODO(msb) return has_inf
has_inf = torch.zeros((1, 1), dtype=torch.int, device=device)
with torch.cuda.device(device):
norm = multi_tensor_l2norm(
chunk_size, has_inf, [cur_device_grads], False
)
norms.append(norm[0].to(torch.cuda.current_device()))
else:
norms += [torch.norm(g, p=2, dtype=torch.float32) for g in cur_device_grads]
total_norm = torch.norm(torch.stack(norms))
return total_norm
@torch.no_grad()
def clip_grad_norm_(params, max_norm, aggregate_norm_fn=None) -> torch.Tensor:
def grad_exists(p):
return p is not None and getattr(p, "grad", None) is not None
if isinstance(params, torch.Tensor):
params = [params]
params = list(params)
grads = [
p.grad.detach() for p in params if grad_exists(p) and not hasattr(p, "expert")
]
expert_grads = [
p.grad.detach() for p in params if grad_exists(p) and hasattr(p, "expert")
]
if len(grads) == 0:
if len(params) > 0:
return params[0].new_tensor(0.0)
else:
return torch.tensor(0.0)
if len(grads) == 1:
total_norm = torch.norm(grads[0], p=2, dtype=torch.float32)
else:
if multi_tensor_l2norm_available:
total_norm = multi_tensor_total_norm(grads)
else:
if torch.cuda.is_available():
warnings.warn(
"amp_C fused kernels unavailable, disabling multi_tensor_l2norm; "
"you may get better performance by installing NVIDIA's apex library"
)
device = torch.cuda.current_device()
elif grads[0].device.type == "xla":
device = grads[0].device
else:
device = torch.device("cpu")
total_norm = torch.norm(
torch.stack(
[torch.norm(g, p=2, dtype=torch.float32).to(device) for g in grads]
)
)
if aggregate_norm_fn is not None:
total_norm = aggregate_norm_fn(total_norm)
if max_norm > 0:
max_norm = float(max_norm)
clip_coef = (max_norm / (total_norm + 1e-6)).clamp_(max=1)
for g in grads + expert_grads:
g.mul_(clip_coef)
return total_norm
def fill_with_neg_inf(t):
"""FP16-compatible function that fills a tensor with -inf."""
return t.float().fill_(float("-inf")).type_as(t)
def _match_types(arg1, arg2):
"""Convert the numerical argument to the same type as the other argument"""
def upgrade(arg_number, arg_structure):
if isinstance(arg_structure, tuple):
return tuple([arg_number] * len(arg_structure))
elif isinstance(arg_structure, dict):
arg = copy.deepcopy(arg_structure)
for k in arg:
arg[k] = upgrade(arg_number, arg_structure[k])
return arg
else:
return arg_number
if isinstance(arg1, float) or isinstance(arg1, int):
return upgrade(arg1, arg2), arg2
elif isinstance(arg2, float) or isinstance(arg2, int):
return arg1, upgrade(arg2, arg1)
return arg1, arg2
def resolve_max_positions(*args):
"""Resolve max position constraints from multiple sources."""
def map_value_update(d1, d2):
updated_value = copy.deepcopy(d1)
for key in d2:
if key not in updated_value:
updated_value[key] = d2[key]
else:
updated_value[key] = min(d1[key], d2[key])
return updated_value
def nullsafe_min(l):
minim = None
for item in l:
if minim is None:
minim = item
elif item is not None and item < minim:
minim = item
return minim
max_positions = None
for arg in args:
if max_positions is None:
max_positions = arg
elif arg is not None:
max_positions, arg = _match_types(max_positions, arg)
if isinstance(arg, float) or isinstance(arg, int):
max_positions = min(max_positions, arg)
elif isinstance(arg, dict):
max_positions = map_value_update(max_positions, arg)
else:
max_positions = tuple(map(nullsafe_min, zip(max_positions, arg)))
return max_positions
def import_user_module(args):
module_path = getattr(args, "user_dir", None)
if module_path is not None:
module_path = os.path.abspath(args.user_dir)
if not os.path.exists(module_path) and not os.path.isfile(
os.path.dirname(module_path)
):
fairseq_rel_path = os.path.join(os.path.dirname(__file__), args.user_dir)
if os.path.exists(fairseq_rel_path):
module_path = fairseq_rel_path
else:
fairseq_rel_path = os.path.join(
os.path.dirname(__file__), "..", args.user_dir
)
if os.path.exists(fairseq_rel_path):
module_path = fairseq_rel_path
else:
raise FileNotFoundError(module_path)
# ensure that user modules are only imported once
import_user_module.memo = getattr(import_user_module, "memo", set())
if module_path not in import_user_module.memo:
import_user_module.memo.add(module_path)
module_parent, module_name = os.path.split(module_path)
if module_name not in sys.modules:
sys.path.insert(0, module_parent)
importlib.import_module(module_name)
tasks_path = os.path.join(module_path, "tasks")
if os.path.exists(tasks_path):
from fairseq.tasks import import_tasks
import_tasks(tasks_path, f"{module_name}.tasks")
models_path = os.path.join(module_path, "models")
if os.path.exists(models_path):
from fairseq.models import import_models
import_models(models_path, f"{module_name}.models")
else:
raise ImportError(
"Failed to import --user-dir={} because the corresponding module name "
"({}) is not globally unique. Please rename the directory to "
"something unique and try again.".format(module_path, module_name)
)
def softmax(x, dim: int, onnx_trace: bool = False):
if onnx_trace:
return F.softmax(x.float(), dim=dim)
else:
return F.softmax(x, dim=dim, dtype=torch.float32)
def log_softmax(x, dim: int, onnx_trace: bool = False):
if onnx_trace:
return F.log_softmax(x.float(), dim=dim)
else:
return F.log_softmax(x, dim=dim, dtype=torch.float32)
def get_perplexity(loss, round=2, base=2):
from fairseq.logging.meters import safe_round
if loss is None:
return 0.0
try:
return safe_round(base**loss, round)
except OverflowError:
return float("inf")
def deprecation_warning(message, stacklevel=3):
# don't use DeprecationWarning, since it's ignored by default
warnings.warn(message, stacklevel=stacklevel)
def relu_squared(x: torch.Tensor):
return F.relu(x).pow(2)
def get_activation_fn(activation: str) -> Callable:
"""Returns the activation function corresponding to `activation`"""
from fairseq.modules import gelu, gelu_accurate
if activation == "relu":
return F.relu
elif activation == "relu_squared":
return relu_squared
elif activation == "gelu":
return gelu
elif activation == "gelu_fast":
deprecation_warning(
"--activation-fn=gelu_fast has been renamed to gelu_accurate"
)
return gelu_accurate
elif activation == "gelu_accurate":
return gelu_accurate
elif activation == "tanh":
return torch.tanh
elif activation == "linear":
return lambda x: x
elif activation == "swish":
# from fairseq.modules.swish import Swish
# return Swish
return torch.nn.SiLU
else:
raise RuntimeError("--activation-fn {} not supported".format(activation))
def get_available_activation_fns() -> List:
return [
"relu",
"gelu",
"gelu_fast", # deprecated
"gelu_accurate",
"tanh",
"linear",
]
@contextlib.contextmanager
def model_eval(model):
is_training = model.training
model.eval()
yield
model.train(is_training)
def has_parameters(module):
try:
next(module.parameters())
return True
except StopIteration:
return False
def get_rng_state():
state = {"torch_rng_state": torch.get_rng_state()}
if xm is not None:
state["xla_rng_state"] = xm.get_rng_state()
if torch.cuda.is_available():
state["cuda_rng_state"] = torch.cuda.get_rng_state()
return state
def set_rng_state(state):
torch.set_rng_state(state["torch_rng_state"])
if xm is not None:
xm.set_rng_state(state["xla_rng_state"])
if torch.cuda.is_available():
torch.cuda.set_rng_state(state["cuda_rng_state"])
class set_torch_seed(object):
def __init__(self, seed):
assert isinstance(seed, int)
self.rng_state = get_rng_state()
torch.manual_seed(seed)
if xm is not None:
xm.set_rng_state(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(seed)
def __enter__(self):
return self
def __exit__(self, *exc):
set_rng_state(self.rng_state)
def parse_alignment(line):
"""
Parses a single line from the alingment file.
Args:
line (str): String containing the alignment of the format:
<src_idx_1>-<tgt_idx_1> <src_idx_2>-<tgt_idx_2> ..
<src_idx_m>-<tgt_idx_m>. All indices are 0 indexed.
Returns:
torch.IntTensor: packed alignments of shape (2 * m).
"""
alignments = line.strip().split()
parsed_alignment = torch.IntTensor(2 * len(alignments))
for idx, alignment in enumerate(alignments):
src_idx, tgt_idx = alignment.split("-")
parsed_alignment[2 * idx] = int(src_idx)
parsed_alignment[2 * idx + 1] = int(tgt_idx)
return parsed_alignment
def get_token_to_word_mapping(tokens, exclude_list):
n = len(tokens)
word_start = [int(token not in exclude_list) for token in tokens]
word_idx = list(accumulate(word_start))
token_to_word = {i: word_idx[i] for i in range(n)}
return token_to_word
def extract_hard_alignment(attn, src_sent, tgt_sent, pad, eos):
tgt_valid = (
((tgt_sent != pad) & (tgt_sent != eos)).nonzero(as_tuple=False).squeeze(dim=-1)
)
src_invalid = (
((src_sent == pad) | (src_sent == eos)).nonzero(as_tuple=False).squeeze(dim=-1)
)
src_token_to_word = get_token_to_word_mapping(src_sent, [eos, pad])
tgt_token_to_word = get_token_to_word_mapping(tgt_sent, [eos, pad])
alignment = []
if len(tgt_valid) != 0 and len(src_invalid) < len(src_sent):
attn_valid = attn[tgt_valid]
attn_valid[:, src_invalid] = float("-inf")
_, src_indices = attn_valid.max(dim=1)
for tgt_idx, src_idx in zip(tgt_valid, src_indices):
alignment.append(
(
src_token_to_word[src_idx.item()] - 1,
tgt_token_to_word[tgt_idx.item()] - 1,
)
)
return alignment
def extract_soft_alignment(attn, src_sent, tgt_sent, pad, eos):
tgt_valid = ((tgt_sent != pad)).nonzero(as_tuple=False)
src_valid = ((src_sent != pad)).nonzero(as_tuple=False).squeeze(dim=-1)
alignment = []
if len(tgt_valid) != 0 and len(src_valid) != 0:
attn_valid = attn[tgt_valid, src_valid]
alignment = [
["{:.6f}".format(p) for p in src_probs.tolist()] for src_probs in attn_valid
]
return alignment
def new_arange(x, *size):
"""
Return a Tensor of `size` filled with a range function on the device of x.
If size is empty, using the size of the variable x.
"""
if len(size) == 0:
size = x.size()
return torch.arange(size[-1], device=x.device).expand(*size).contiguous()
def get_tpu_device():
return xm.xla_device()
def tpu_data_loader(itr):
import torch_xla.core.xla_model as xm
import torch_xla.distributed.parallel_loader as pl
from fairseq.data import iterators
xm.rendezvous("tpu_data_loader") # wait for all workers
xm.mark_step()
device = xm.xla_device()
return iterators.CountingIterator(
pl.ParallelLoader(itr, [device]).per_device_loader(device),
start=getattr(itr, "n", 0),
total=len(itr),
)
def is_xla_tensor(tensor):
return torch.is_tensor(tensor) and tensor.device.type == "xla"
def index_put(tensor, indices, value):
if is_xla_tensor(tensor):
for _ in range(indices.dim(), tensor.dim()):
indices = indices.unsqueeze(-1)
if indices.size(-1) < tensor.size(-1):
indices = indices.expand_as(tensor)
tensor = torch.mul(tensor, ~indices) + torch.mul(value, indices)
else:
tensor[indices] = value
return tensor
def xla_device_to_cpu(dat):
import torch_xla.core.xla_model as xm
return xm._maybe_convert_to_cpu(dat)
class CudaEnvironment(object):
def __init__(self):
cur_device = torch.cuda.current_device()
prop = torch.cuda.get_device_properties("cuda:{}".format(cur_device))
self.name = prop.name
self.major = prop.major
self.minor = prop.minor
self.total_memory_in_GB = prop.total_memory / 1024 / 1024 / 1024
@staticmethod
def pretty_print_cuda_env_list(cuda_env_list):
"""
Given a list of CudaEnviorments, pretty print them
"""
num_workers = len(cuda_env_list)
center = "CUDA enviroments for all {} workers".format(num_workers)
banner_len = 40 - len(center) // 2
first_line = "*" * banner_len + center + "*" * banner_len
logger.info(first_line)
for r, env in enumerate(cuda_env_list):
logger.info(
"rank {:3d}: ".format(r)
+ "capabilities = {:2d}.{:<2d} ; ".format(env.major, env.minor)
+ "total memory = {:.3f} GB ; ".format(env.total_memory_in_GB)
+ "name = {:40s}".format(env.name)
)
logger.info(first_line)
def csv_str_list(x):
return x.split(",")
def eval_str_list(x, type=float):
if x is None:
return None
if isinstance(x, str):
x = eval(x)
try:
return list(map(type, x))
except TypeError:
return [type(x)]
def eval_str_dict(x, type=dict):
if x is None:
return None
if isinstance(x, str):
x = eval(x)
return x
def eval_bool(x, default=False):
if x is None:
return default
try:
return bool(eval(x))
except TypeError:
return default
def reset_logging():
root = logging.getLogger()
for handler in root.handlers:
root.removeHandler(handler)
root.setLevel(os.environ.get("LOGLEVEL", "INFO").upper())
handler = logging.StreamHandler(sys.stdout)
handler.setFormatter(
logging.Formatter(
fmt="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
)
)
root.addHandler(handler)
def safe_getattr(obj, k, default=None):
"""Returns obj[k] if it exists and is not None, otherwise returns default."""
from omegaconf import OmegaConf
if OmegaConf.is_config(obj):
return obj[k] if k in obj and obj[k] is not None else default
return getattr(obj, k, default)
def safe_hasattr(obj, k):
"""Returns True if the given key exists and is not None."""
return getattr(obj, k, None) is not None
| 26,794
| 30.82304
| 111
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/fairseq/hub_utils.py
|
#!/usr/bin/env python3 -u
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import copy
import logging
import os
from typing import Any, Dict, Iterator, List
import torch
from fairseq import utils
from fairseq.data import encoders
from omegaconf import open_dict
from torch import nn
logger = logging.getLogger(__name__)
def from_pretrained(
model_name_or_path,
checkpoint_file="model.pt",
data_name_or_path=".",
archive_map=None,
**kwargs
):
from fairseq import checkpoint_utils, file_utils
if archive_map is not None:
if model_name_or_path in archive_map:
model_name_or_path = archive_map[model_name_or_path]
if data_name_or_path is not None and data_name_or_path in archive_map:
data_name_or_path = archive_map[data_name_or_path]
# allow archive_map to set default arg_overrides (e.g., tokenizer, bpe)
# for each model
if isinstance(model_name_or_path, dict):
for k, v in model_name_or_path.items():
if k == "checkpoint_file":
checkpoint_file = v
elif (
k != "path"
# only set kwargs that don't already have overrides
and k not in kwargs
):
kwargs[k] = v
model_name_or_path = model_name_or_path["path"]
model_path = file_utils.load_archive_file(model_name_or_path)
# convenience hack for loading data and BPE codes from model archive
if data_name_or_path.startswith("."):
kwargs["data"] = os.path.abspath(os.path.join(model_path, data_name_or_path))
else:
kwargs["data"] = file_utils.load_archive_file(data_name_or_path)
for file, arg in {
"code": "bpe_codes",
"bpecodes": "bpe_codes",
"sentencepiece.bpe.model": "sentencepiece_model",
"merges.txt": "bpe_merges",
"vocab.json": "bpe_vocab",
}.items():
path = os.path.join(model_path, file)
if os.path.exists(path):
kwargs[arg] = path
if "user_dir" in kwargs:
utils.import_user_module(argparse.Namespace(user_dir=kwargs["user_dir"]))
models, args, task = checkpoint_utils.load_model_ensemble_and_task(
[os.path.join(model_path, cpt) for cpt in checkpoint_file.split(os.pathsep)],
arg_overrides=kwargs,
)
return {
"args": args,
"task": task,
"models": models,
}
class GeneratorHubInterface(nn.Module):
"""
PyTorch Hub interface for generating sequences from a pre-trained
translation or language model.
"""
def __init__(self, cfg, task, models):
super().__init__()
self.cfg = cfg
self.task = task
self.models = nn.ModuleList(models)
self.src_dict = task.source_dictionary
self.tgt_dict = task.target_dictionary
# optimize model for generation
for model in self.models:
model.prepare_for_inference_(cfg)
# Load alignment dictionary for unknown word replacement
# (None if no unknown word replacement, empty if no path to align dictionary)
self.align_dict = utils.load_align_dict(cfg.generation.replace_unk)
self.tokenizer = encoders.build_tokenizer(cfg.tokenizer)
self.bpe = encoders.build_bpe(cfg.bpe)
self.max_positions = utils.resolve_max_positions(
self.task.max_positions(), *[model.max_positions() for model in models]
)
# this is useful for determining the device
self.register_buffer("_float_tensor", torch.tensor([0], dtype=torch.float))
@property
def device(self):
return self._float_tensor.device
def translate(
self, sentences: List[str], beam: int = 5, verbose: bool = False, **kwargs
) -> List[str]:
return self.sample(sentences, beam, verbose, **kwargs)
def sample(
self, sentences: List[str], beam: int = 1, verbose: bool = False, **kwargs
) -> List[str]:
if isinstance(sentences, str):
return self.sample([sentences], beam=beam, verbose=verbose, **kwargs)[0]
tokenized_sentences = [self.encode(sentence) for sentence in sentences]
batched_hypos = self.generate(tokenized_sentences, beam, verbose, **kwargs)
return [self.decode(hypos[0]["tokens"]) for hypos in batched_hypos]
def score(self, sentences: List[str], **kwargs):
if isinstance(sentences, str):
return self.score([sentences], **kwargs)[0]
# NOTE: this doesn't support translation tasks currently
tokenized_sentences = [self.encode(sentence) for sentence in sentences]
return [
hypos[0]
for hypos in self.generate(
tokenized_sentences, score_reference=True, **kwargs
)
]
def generate(
self,
tokenized_sentences: List[torch.LongTensor],
beam: int = 5,
verbose: bool = False,
skip_invalid_size_inputs=False,
inference_step_args=None,
prefix_allowed_tokens_fn=None,
**kwargs
) -> List[List[Dict[str, torch.Tensor]]]:
if torch.is_tensor(tokenized_sentences) and tokenized_sentences.dim() == 1:
return self.generate(
tokenized_sentences.unsqueeze(0), beam=beam, verbose=verbose, **kwargs
)[0]
# build generator using current args as well as any kwargs
gen_args = copy.deepcopy(self.cfg.generation)
with open_dict(gen_args):
gen_args.beam = beam
for k, v in kwargs.items():
setattr(gen_args, k, v)
generator = self.task.build_generator(
self.models,
gen_args,
prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
)
inference_step_args = inference_step_args or {}
results = []
for batch in self._build_batches(tokenized_sentences, skip_invalid_size_inputs):
batch = utils.apply_to_sample(lambda t: t.to(self.device), batch)
translations = self.task.inference_step(
generator, self.models, batch, **inference_step_args
)
for id, hypos in zip(batch["id"].tolist(), translations):
results.append((id, hypos))
# sort output to match input order
outputs = [hypos for _, hypos in sorted(results, key=lambda x: x[0])]
if verbose:
def getarg(name, default):
return getattr(gen_args, name, getattr(self.cfg, name, default))
for source_tokens, target_hypotheses in zip(tokenized_sentences, outputs):
src_str_with_unk = self.string(source_tokens)
logger.info("S\t{}".format(src_str_with_unk))
for hypo in target_hypotheses:
hypo_str = self.decode(hypo["tokens"])
logger.info("H\t{}\t{}".format(hypo["score"], hypo_str))
logger.info(
"P\t{}".format(
" ".join(
map(
lambda x: "{:.4f}".format(x),
hypo["positional_scores"].tolist(),
)
)
)
)
if hypo["alignment"] is not None and getarg(
"print_alignment", False
):
logger.info(
"A\t{}".format(
" ".join(
[
"{}-{}".format(src_idx, tgt_idx)
for src_idx, tgt_idx in hypo["alignment"]
]
)
)
)
return outputs
def encode(self, sentence: str) -> torch.LongTensor:
sentence = self.tokenize(sentence)
sentence = self.apply_bpe(sentence)
return self.binarize(sentence)
def decode(self, tokens: torch.LongTensor) -> str:
sentence = self.string(tokens)
sentence = self.remove_bpe(sentence)
return self.detokenize(sentence)
def tokenize(self, sentence: str) -> str:
if self.tokenizer is not None:
sentence = self.tokenizer.encode(sentence)
return sentence
def detokenize(self, sentence: str) -> str:
if self.tokenizer is not None:
sentence = self.tokenizer.decode(sentence)
return sentence
def apply_bpe(self, sentence: str) -> str:
if self.bpe is not None:
sentence = self.bpe.encode(sentence)
return sentence
def remove_bpe(self, sentence: str) -> str:
if self.bpe is not None:
sentence = self.bpe.decode(sentence)
return sentence
def binarize(self, sentence: str) -> torch.LongTensor:
return self.src_dict.encode_line(sentence, add_if_not_exist=False).long()
def string(self, tokens: torch.LongTensor) -> str:
return self.tgt_dict.string(tokens)
def _build_batches(
self, tokens: List[List[int]], skip_invalid_size_inputs: bool
) -> Iterator[Dict[str, Any]]:
lengths = torch.LongTensor([t.numel() for t in tokens])
batch_iterator = self.task.get_batch_iterator(
dataset=self.task.build_dataset_for_inference(tokens, lengths),
max_tokens=self.cfg.dataset.max_tokens,
max_sentences=self.cfg.dataset.batch_size,
max_positions=self.max_positions,
ignore_invalid_inputs=skip_invalid_size_inputs,
disable_iterator_cache=True,
).next_epoch_itr(shuffle=False)
return batch_iterator
class BPEHubInterface(object):
"""PyTorch Hub interface for Byte-Pair Encoding (BPE)."""
def __init__(self, bpe, **kwargs):
super().__init__()
args = argparse.Namespace(bpe=bpe, **kwargs)
self.bpe = encoders.build_bpe(args)
assert self.bpe is not None
def encode(self, sentence: str) -> str:
return self.bpe.encode(sentence)
def decode(self, sentence: str) -> str:
return self.bpe.decode(sentence)
class TokenizerHubInterface(object):
"""PyTorch Hub interface for tokenization."""
def __init__(self, tokenizer, **kwargs):
super().__init__()
args = argparse.Namespace(tokenizer=tokenizer, **kwargs)
self.tokenizer = encoders.build_tokenizer(args)
assert self.tokenizer is not None
def encode(self, sentence: str) -> str:
return self.tokenizer.encode(sentence)
def decode(self, sentence: str) -> str:
return self.tokenizer.decode(sentence)
| 10,996
| 35.174342
| 88
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/fairseq/sequence_scorer.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import sys
import os
import numpy as np
import torch
from fairseq import utils
class SequenceScorer(object):
"""Scores the target for a given source sentence."""
def __init__(
self,
tgt_dict,
softmax_batch=None,
compute_alignment=False,
eos=None,
symbols_to_strip_from_output=None,
):
self.pad = tgt_dict.pad()
self.eos = tgt_dict.eos() if eos is None else eos
self.softmax_batch = softmax_batch or sys.maxsize
assert self.softmax_batch > 0
self.compute_alignment = compute_alignment
self.symbols_to_strip_from_output = (
symbols_to_strip_from_output.union({self.eos})
if symbols_to_strip_from_output is not None
else {self.eos}
)
@torch.no_grad()
def generate(self, models, sample, **kwargs):
"""Score a batch of translations."""
net_input = sample["net_input"]
def batch_for_softmax(dec_out, target):
# assumes decoder_out[0] is the only thing needed (may not be correct for future models!)
first, rest = dec_out[0], dec_out[1:]
bsz, tsz, dim = first.shape
if bsz * tsz < self.softmax_batch:
yield dec_out, target, True
else:
flat = first.contiguous().view(1, -1, dim)
flat_tgt = target.contiguous().view(flat.shape[:-1])
s = 0
while s < flat.size(1):
e = s + self.softmax_batch
yield (flat[:, s:e],) + rest, flat_tgt[:, s:e], False
s = e
def gather_target_probs(probs, target):
probs = probs.gather(
dim=2,
index=target.unsqueeze(-1),
)
return probs
orig_target = sample["target"]
# compute scores for each model in the ensemble
avg_probs = None
avg_attn = None
for model in models:
model.eval()
decoder_out = model(**net_input)
attn = decoder_out[1] if len(decoder_out) > 1 else None
if type(attn) is dict:
attn = attn.get("attn", None)
batched = batch_for_softmax(decoder_out, orig_target)
probs, idx = None, 0
for bd, tgt, is_single in batched:
sample["target"] = tgt
curr_prob = model.get_normalized_probs(
bd, log_probs=len(models) == 1, sample=sample
).data
if is_single:
probs = gather_target_probs(curr_prob, orig_target)
else:
if probs is None:
probs = curr_prob.new(orig_target.numel())
step = curr_prob.size(0) * curr_prob.size(1)
end = step + idx
tgt_probs = gather_target_probs(
curr_prob.view(tgt.shape + (curr_prob.size(-1),)), tgt
)
probs[idx:end] = tgt_probs.view(-1)
idx = end
sample["target"] = orig_target
probs = probs.view(sample["target"].shape)
if avg_probs is None:
avg_probs = probs
else:
avg_probs.add_(probs)
if attn is not None:
if torch.is_tensor(attn):
attn = attn.data
else:
attn = attn[0]
if avg_attn is None:
avg_attn = attn
else:
avg_attn.add_(attn)
if len(models) > 1:
avg_probs.div_(len(models))
avg_probs.log_()
if avg_attn is not None:
avg_attn.div_(len(models))
bsz = avg_probs.size(0)
hypos = []
start_idxs = sample["start_indices"] if "start_indices" in sample else [0] * bsz
for i in range(bsz):
# remove padding from ref
ref = (
utils.strip_pad(sample["target"][i, start_idxs[i] :], self.pad)
if sample["target"] is not None
else None
)
tgt_len = ref.numel()
avg_probs_i = avg_probs[i][start_idxs[i] : start_idxs[i] + tgt_len]
score_i = avg_probs_i.sum() / tgt_len
if avg_attn is not None:
avg_attn_i = avg_attn[i]
if self.compute_alignment:
alignment = utils.extract_hard_alignment(
avg_attn_i,
sample["net_input"]["src_tokens"][i],
sample["target"][i],
self.pad,
self.eos,
)
else:
alignment = None
else:
avg_attn_i = alignment = None
hypos.append(
[
{
"tokens": ref,
"score": score_i,
"attention": avg_attn_i,
"alignment": alignment,
"positional_scores": avg_probs_i,
}
]
)
return hypos
| 5,479
| 34.128205
| 101
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/fairseq/binarizer.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from collections import Counter
from typing import Dict
import torch
from fairseq.file_chunker_utils import Chunker
from fairseq.file_io import PathManager
from fairseq.tokenizer import tokenize_line
class Binarizer:
@staticmethod
def binarize(
filename,
dict,
consumer,
tokenize=tokenize_line,
append_eos=True,
reverse_order=False,
offset=0,
end=-1,
already_numberized=False,
) -> Dict[str, int]:
nseq, ntok = 0, 0
replaced = Counter()
def replaced_consumer(word, idx):
if idx == dict.unk_index and word != dict.unk_word:
replaced.update([word])
with Chunker(
PathManager.get_local_path(filename), offset, end
) as line_iterator:
for line in line_iterator:
if already_numberized:
id_strings = line.strip().split()
id_list = [int(id_string) for id_string in id_strings]
if reverse_order:
id_list.reverse()
if append_eos:
id_list.append(dict.eos())
ids = torch.IntTensor(id_list)
else:
ids = dict.encode_line(
line=line,
line_tokenizer=tokenize,
add_if_not_exist=False,
consumer=replaced_consumer,
append_eos=append_eos,
reverse_order=reverse_order,
)
nseq += 1
ntok += len(ids)
consumer(ids)
return {
"nseq": nseq,
"nunk": sum(replaced.values()),
"ntok": ntok,
"replaced": replaced,
}
@staticmethod
def binarize_alignments(
filename, alignment_parser, consumer, offset=0, end=-1
) -> Dict[str, int]:
nseq = 0
with Chunker(
PathManager.get_local_path(filename), offset, end
) as line_iterator:
for line in line_iterator:
ids = alignment_parser(line)
nseq += 1
consumer(ids)
return {"nseq": nseq}
| 2,457
| 29.345679
| 74
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/fairseq/version.py
|
__version__ = "1.0.0a0"
| 24
| 11.5
| 23
|
py
|
CIF-HieraDist
|
CIF-HieraDist-main/fairseq/file_io.py
|
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
import os
import shutil
from typing import List, Optional
logger = logging.getLogger(__file__)
try:
from iopath.common.file_io import g_pathmgr as IOPathManager
try:
# [FB only - for now] AWS PathHandler for PathManager
from .fb_pathhandlers import S3PathHandler
IOPathManager.register_handler(S3PathHandler())
except KeyError:
logging.warning("S3PathHandler already registered.")
except ImportError:
logging.debug(
"S3PathHandler couldn't be imported. Either missing fb-only files, or boto3 module."
)
except ImportError:
IOPathManager = None
class PathManager:
"""
Wrapper for insulating OSS I/O (using Python builtin operations) from
iopath's PathManager abstraction (for transparently handling various
internal backends).
"""
@staticmethod
def open(
path: str,
mode: str = "r",
buffering: int = -1,
encoding: Optional[str] = None,
errors: Optional[str] = None,
newline: Optional[str] = None,
):
if IOPathManager:
return IOPathManager.open(
path=path,
mode=mode,
buffering=buffering,
encoding=encoding,
errors=errors,
newline=newline,
)
return open(
path,
mode=mode,
buffering=buffering,
encoding=encoding,
errors=errors,
newline=newline,
)
@staticmethod
def copy(src_path: str, dst_path: str, overwrite: bool = False) -> bool:
if IOPathManager:
return IOPathManager.copy(
src_path=src_path, dst_path=dst_path, overwrite=overwrite
)
return shutil.copyfile(src_path, dst_path)
@staticmethod
def get_local_path(path: str, **kwargs) -> str:
if IOPathManager:
return IOPathManager.get_local_path(path, **kwargs)
return path
@staticmethod
def exists(path: str) -> bool:
if IOPathManager:
return IOPathManager.exists(path)
return os.path.exists(path)
@staticmethod
def isfile(path: str) -> bool:
if IOPathManager:
return IOPathManager.isfile(path)
return os.path.isfile(path)
@staticmethod
def ls(path: str) -> List[str]:
if IOPathManager:
return IOPathManager.ls(path)
return os.listdir(path)
@staticmethod
def mkdirs(path: str) -> None:
if IOPathManager:
return IOPathManager.mkdirs(path)
os.makedirs(path, exist_ok=True)
@staticmethod
def rm(path: str) -> None:
if IOPathManager:
return IOPathManager.rm(path)
os.remove(path)
@staticmethod
def chmod(path: str, mode: int) -> None:
if not PathManager.path_requires_pathmanager(path):
os.chmod(path, mode)
@staticmethod
def register_handler(handler) -> None:
if IOPathManager:
return IOPathManager.register_handler(handler=handler)
@staticmethod
def copy_from_local(
local_path: str, dst_path: str, overwrite: bool = False, **kwargs
) -> None:
if IOPathManager:
return IOPathManager.copy_from_local(
local_path=local_path, dst_path=dst_path, overwrite=overwrite, **kwargs
)
return shutil.copyfile(local_path, dst_path)
@staticmethod
def path_requires_pathmanager(path: str) -> bool:
"""Do we require PathManager to access given path?"""
if IOPathManager:
for p in IOPathManager._path_handlers.keys():
if path.startswith(p):
return True
return False
@staticmethod
def supports_rename(path: str) -> bool:
# PathManager doesn't yet support renames
return not PathManager.path_requires_pathmanager(path)
@staticmethod
def rename(src: str, dst: str):
os.rename(src, dst)
"""
ioPath async PathManager methods:
"""
@staticmethod
def opena(
path: str,
mode: str = "r",
buffering: int = -1,
encoding: Optional[str] = None,
errors: Optional[str] = None,
newline: Optional[str] = None,
):
"""
Return file descriptor with asynchronous write operations.
"""
global IOPathManager
if not IOPathManager:
logging.info("ioPath is initializing PathManager.")
try:
from iopath.common.file_io import PathManager
IOPathManager = PathManager()
except Exception:
logging.exception("Failed to initialize ioPath PathManager object.")
return IOPathManager.opena(
path=path,
mode=mode,
buffering=buffering,
encoding=encoding,
errors=errors,
newline=newline,
)
@staticmethod
def async_close() -> bool:
"""
Wait for files to be written and clean up asynchronous PathManager.
NOTE: `PathManager.async_close()` must be called at the end of any
script that uses `PathManager.opena(...)`.
"""
global IOPathManager
if IOPathManager:
return IOPathManager.async_close()
return False
| 5,614
| 27.502538
| 96
|
py
|
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