mirror of
https://github.com/facebookresearch/fairseq.git
synced 2024-09-11 17:25:31 +03:00
995c204337
Summary: Preliminaries for data2vec release, include some minor improvements and bug fixes Most important change is that we now default to raising an exception when fields in config do not have a corresponding field in the model dataclass Pull Request resolved: https://github.com/fairinternal/fairseq-py/pull/2929 Reviewed By: wnhsu Differential Revision: D33649708 Pulled By: alexeib fbshipit-source-id: 629bdb4c361550740b451c570c2005bb956c6fcb
789 lines
23 KiB
Python
789 lines
23 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates.
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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import argparse
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import json
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import os
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import random
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import shutil
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import string
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import sys
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import typing as tp
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from io import StringIO
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import torch
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import torch.nn.functional as F
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import fairseq.distributed.utils as distributed_utils
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from fairseq import options, utils
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from fairseq.data import Dictionary
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from fairseq.data.language_pair_dataset import collate
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from fairseq.dataclass.utils import convert_namespace_to_omegaconf
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from fairseq.models import (
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FairseqEncoder,
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FairseqEncoderDecoderModel,
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FairseqIncrementalDecoder,
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)
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from fairseq.models.fairseq_encoder import EncoderOut
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from fairseq.tasks import LegacyFairseqTask
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from fairseq_cli import generate, interactive, preprocess, train, validate
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def dummy_dictionary(vocab_size, prefix="token_"):
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d = Dictionary()
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for i in range(vocab_size):
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token = prefix + str(i)
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d.add_symbol(token)
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d.finalize(padding_factor=1) # don't add extra padding symbols
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return d
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def dummy_dataloader(
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samples,
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padding_idx=1,
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eos_idx=2,
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batch_size=None,
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):
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if batch_size is None:
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batch_size = len(samples)
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# add any missing data to samples
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for i, sample in enumerate(samples):
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if "id" not in sample:
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sample["id"] = i
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# create dataloader
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dataset = TestDataset(samples)
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dataloader = torch.utils.data.DataLoader(
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dataset,
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batch_size=batch_size,
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collate_fn=(lambda samples: collate(samples, padding_idx, eos_idx)),
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)
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return iter(dataloader)
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def sequence_generator_setup():
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# construct dummy dictionary
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d = dummy_dictionary(vocab_size=2)
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eos = d.eos()
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w1 = 4
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w2 = 5
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# construct source data
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src_tokens = torch.LongTensor([[w1, w2, eos], [w1, w2, eos]])
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src_lengths = torch.LongTensor([2, 2])
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args = argparse.Namespace()
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unk = 0.0
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args.beam_probs = [
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# step 0:
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torch.FloatTensor(
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[
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# eos w1 w2
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# sentence 1:
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[0.0, unk, 0.9, 0.1], # beam 1
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[0.0, unk, 0.9, 0.1], # beam 2
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# sentence 2:
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[0.0, unk, 0.7, 0.3],
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[0.0, unk, 0.7, 0.3],
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]
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),
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# step 1:
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torch.FloatTensor(
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[
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# eos w1 w2 prefix
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# sentence 1:
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[1.0, unk, 0.0, 0.0], # w1: 0.9 (emit: w1 <eos>: 0.9*1.0)
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[0.0, unk, 0.9, 0.1], # w2: 0.1
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# sentence 2:
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[0.25, unk, 0.35, 0.4], # w1: 0.7 (don't emit: w1 <eos>: 0.7*0.25)
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[0.00, unk, 0.10, 0.9], # w2: 0.3
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]
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),
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# step 2:
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torch.FloatTensor(
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[
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# eos w1 w2 prefix
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# sentence 1:
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[0.0, unk, 0.1, 0.9], # w2 w1: 0.1*0.9
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[
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0.6,
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unk,
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0.2,
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0.2,
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], # w2 w2: 0.1*0.1 (emit: w2 w2 <eos>: 0.1*0.1*0.6)
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# sentence 2:
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[
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0.60,
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unk,
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0.4,
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0.00,
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], # w1 w2: 0.7*0.4 (emit: w1 w2 <eos>: 0.7*0.4*0.6)
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[0.01, unk, 0.0, 0.99], # w2 w2: 0.3*0.9
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]
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),
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# step 3:
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torch.FloatTensor(
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[
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# eos w1 w2 prefix
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# sentence 1:
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[
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1.0,
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unk,
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0.0,
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0.0,
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], # w2 w1 w2: 0.1*0.9*0.9 (emit: w2 w1 w2 <eos>: 0.1*0.9*0.9*1.0)
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[
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1.0,
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unk,
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0.0,
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0.0,
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], # w2 w1 w1: 0.1*0.9*0.1 (emit: w2 w1 w1 <eos>: 0.1*0.9*0.1*1.0)
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# sentence 2:
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[
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0.1,
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unk,
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0.5,
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0.4,
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], # w2 w2 w2: 0.3*0.9*0.99 (emit: w2 w2 w2 <eos>: 0.3*0.9*0.99*0.1)
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[
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1.0,
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unk,
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0.0,
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0.0,
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], # w1 w2 w1: 0.7*0.4*0.4 (emit: w1 w2 w1 <eos>: 0.7*0.4*0.4*1.0)
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]
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),
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]
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task = TestTranslationTask.setup_task(args, d, d)
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model = task.build_model(args)
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tgt_dict = task.target_dictionary
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return tgt_dict, w1, w2, src_tokens, src_lengths, model
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def create_dummy_data(
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data_dir, num_examples=100, maxlen=20, alignment=False, languages=None
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):
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def _create_dummy_data(dir, filename):
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data = torch.rand(num_examples * maxlen)
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data = 97 + torch.floor(26 * data).int()
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with open(os.path.join(dir, filename), "w") as h:
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offset = 0
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for _ in range(num_examples):
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ex_len = random.randint(1, maxlen)
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ex_str = " ".join(map(chr, data[offset : offset + ex_len]))
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print(ex_str, file=h)
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offset += ex_len
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def _create_dummy_alignment_data(filename_src, filename_tgt, filename):
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with open(os.path.join(data_dir, filename_src), "r") as src_f, open(
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os.path.join(data_dir, filename_tgt), "r"
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) as tgt_f, open(os.path.join(data_dir, filename), "w") as h:
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for src, tgt in zip(src_f, tgt_f):
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src_len = len(src.split())
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tgt_len = len(tgt.split())
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avg_len = (src_len + tgt_len) // 2
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num_alignments = random.randint(avg_len // 2, 2 * avg_len)
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src_indices = torch.floor(torch.rand(num_alignments) * src_len).int()
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tgt_indices = torch.floor(torch.rand(num_alignments) * tgt_len).int()
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ex_str = " ".join(
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[
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"{}-{}".format(src, tgt)
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for src, tgt in zip(src_indices, tgt_indices)
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]
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)
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print(ex_str, file=h)
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files_to_write = [
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"train.in",
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"train.out",
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"valid.in",
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"valid.out",
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"test.in",
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"test.out",
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]
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if languages is None: # En only dummy dataset
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for f in files_to_write:
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_create_dummy_data(data_dir, f)
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else:
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for lang in languages:
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lang_dir = os.path.join(data_dir, lang)
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os.makedirs(lang_dir, exist_ok=True)
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for f in files_to_write:
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_create_dummy_data(lang_dir, f)
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if alignment:
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_create_dummy_alignment_data("train.in", "train.out", "train.align")
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_create_dummy_alignment_data("valid.in", "valid.out", "valid.align")
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_create_dummy_alignment_data("test.in", "test.out", "test.align")
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def preprocess_lm_data(data_dir, languages=None):
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preprocess_parser = options.get_preprocessing_parser()
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if languages is None:
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preprocess_args = preprocess_parser.parse_args(
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[
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"--only-source",
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"--trainpref",
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os.path.join(data_dir, "train.out"),
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"--validpref",
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os.path.join(data_dir, "valid.out"),
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"--testpref",
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os.path.join(data_dir, "test.out"),
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"--destdir",
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data_dir,
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]
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)
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preprocess.main(preprocess_args)
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else:
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for lang in languages:
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lang_dir = os.path.join(data_dir, lang)
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assert os.path.exists(lang_dir)
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preprocess_args = preprocess_parser.parse_args(
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[
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"--only-source",
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"--trainpref",
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os.path.join(lang_dir, "train.out"),
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"--validpref",
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os.path.join(lang_dir, "valid.out"),
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"--testpref",
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os.path.join(lang_dir, "test.out"),
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"--destdir",
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lang_dir,
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]
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)
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preprocess.main(preprocess_args)
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shutil.copyfile(
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os.path.join(data_dir, languages[0], "dict.txt"),
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os.path.join(data_dir, "dict.txt"),
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)
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def preprocess_translation_data(data_dir, extra_flags=None):
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preprocess_parser = options.get_preprocessing_parser()
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preprocess_args = preprocess_parser.parse_args(
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[
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"--source-lang",
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"in",
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"--target-lang",
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"out",
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"--trainpref",
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os.path.join(data_dir, "train"),
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"--validpref",
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os.path.join(data_dir, "valid"),
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"--testpref",
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os.path.join(data_dir, "test"),
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"--thresholdtgt",
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"0",
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"--thresholdsrc",
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"0",
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"--destdir",
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data_dir,
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]
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+ (extra_flags or []),
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)
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preprocess.main(preprocess_args)
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def preprocess_summarization_data(data_dir, extra_flags=None):
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preprocess_parser = options.get_preprocessing_parser()
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preprocess_args = preprocess_parser.parse_args(
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[
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"--source-lang",
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"in",
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"--target-lang",
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"out",
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"--trainpref",
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os.path.join(data_dir, "train"),
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"--validpref",
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os.path.join(data_dir, "valid"),
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"--testpref",
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os.path.join(data_dir, "test"),
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"--thresholdtgt",
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"0",
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"--thresholdsrc",
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"0",
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"--joined-dictionary",
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"--destdir",
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data_dir,
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]
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+ (extra_flags or []),
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)
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preprocess.main(preprocess_args)
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def create_laser_data_and_config_json(data_dir):
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src_langs = ["de", "fr", "ru", "tr", "zh"]
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tgt_langs = ["en", "es"]
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config_json = {}
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config_train_json = []
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src_vocab = None
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tgt_vocab = None
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for src_lang in src_langs:
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for tgt_lang in tgt_langs:
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langpair_folder = f"{src_lang}-{tgt_lang}"
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langpair_path = os.path.join(data_dir, langpair_folder)
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os.mkdir(langpair_path)
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create_dummy_data(langpair_path)
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preprocess_translation_data(langpair_path, ["--dataset-impl", "cached"])
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src_vocab = os.path.join(langpair_path, "dict.in.txt")
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tgt_vocab = os.path.join(langpair_path, "dict.out.txt")
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config_train_json.append(
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{
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"id": 0 if tgt_lang == "en" else 1,
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"src": os.path.join(langpair_path, "train.in-out.in"),
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"tgt": os.path.join(langpair_path, "train.in-out.out"),
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}
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)
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config_json["src_vocab"] = src_vocab
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config_json["tgt_vocab"] = tgt_vocab
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config_json["train"] = config_train_json
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with open(os.path.join(data_dir, "laserconfig.json"), "w") as config_file:
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json.dump(config_json, config_file)
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return config_file
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def train_translation_model(
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data_dir,
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arch,
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extra_flags=None,
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task="translation",
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run_validation=False,
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lang_flags=None,
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extra_valid_flags=None,
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world_size=1,
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):
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if lang_flags is None:
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lang_flags = [
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"--source-lang",
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"in",
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"--target-lang",
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"out",
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]
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train_parser = options.get_training_parser()
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train_args = options.parse_args_and_arch(
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train_parser,
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[
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"--task",
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task,
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data_dir,
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"--save-dir",
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data_dir,
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"--arch",
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arch,
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"--optimizer",
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"nag",
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"--lr",
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"0.05",
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"--max-tokens",
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"500",
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"--max-epoch",
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"1",
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"--no-progress-bar",
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"--distributed-world-size",
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str(world_size),
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"--num-workers",
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"0",
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]
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+ lang_flags
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+ (extra_flags or []),
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)
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cfg = convert_namespace_to_omegaconf(train_args)
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distributed_utils.call_main(cfg, train.main)
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if run_validation:
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# test validation
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validate_parser = options.get_validation_parser()
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validate_args = options.parse_args_and_arch(
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validate_parser,
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[
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"--task",
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task,
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data_dir,
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"--path",
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os.path.join(data_dir, "checkpoint_last.pt"),
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"--valid-subset",
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"valid",
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"--max-tokens",
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"500",
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"--no-progress-bar",
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"--num-workers",
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"0",
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]
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+ lang_flags
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+ (extra_valid_flags or []),
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)
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validate.main(validate_args)
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def generate_main(data_dir, extra_flags=None, path=None):
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if extra_flags is None:
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extra_flags = [
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"--print-alignment",
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]
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if path is None:
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path = os.path.join(data_dir, "checkpoint_last.pt")
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generate_parser = options.get_generation_parser()
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generate_args = options.parse_args_and_arch(
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generate_parser,
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[
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data_dir,
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"--path",
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path,
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"--beam",
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"3",
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"--batch-size",
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"64",
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"--max-len-b",
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"5",
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"--gen-subset",
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"valid",
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"--no-progress-bar",
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"--num-workers",
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"0",
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]
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+ (extra_flags or []),
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)
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# evaluate model in batch mode
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generate.main(generate_args)
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# evaluate model interactively
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generate_args.buffer_size = 0
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generate_args.input = "-"
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generate_args.batch_size = None
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orig_stdin = sys.stdin
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sys.stdin = StringIO("h e l l o\n")
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interactive.main(generate_args)
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sys.stdin = orig_stdin
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|
|
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class TestDataset(torch.utils.data.Dataset):
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def __init__(self, data):
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super().__init__()
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self.data = data
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self.sizes = None
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def __getitem__(self, index):
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return self.data[index]
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def __len__(self):
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return len(self.data)
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|
|
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class TestTranslationTask(LegacyFairseqTask):
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def __init__(self, args, src_dict, tgt_dict, model):
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super().__init__(args)
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self.src_dict = src_dict
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self.tgt_dict = tgt_dict
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self.model = model
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@classmethod
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def setup_task(cls, args, src_dict=None, tgt_dict=None, model=None):
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return cls(args, src_dict, tgt_dict, model)
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def build_model(self, args, from_checkpoint=False):
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return TestModel.build_model(args, self)
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@property
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def source_dictionary(self):
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return self.src_dict
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@property
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def target_dictionary(self):
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return self.tgt_dict
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|
|
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class TestModel(FairseqEncoderDecoderModel):
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def __init__(self, encoder, decoder):
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super().__init__(encoder, decoder)
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@classmethod
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def build_model(cls, args, task):
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encoder = TestEncoder(args, task.source_dictionary)
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decoder = TestIncrementalDecoder(args, task.target_dictionary)
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return cls(encoder, decoder)
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|
|
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class TestEncoder(FairseqEncoder):
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def __init__(self, args, dictionary):
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super().__init__(dictionary)
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self.args = args
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|
|
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)
|
|
|
|
|
|
def sizes(data):
|
|
return [len(sentence) for sentence in data]
|
|
|
|
|
|
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, out_file=None) -> tp.List[tp.List[str]]:
|
|
data = (
|
|
[make_sentence() for _ in range(0, length)]
|
|
# add all the symbols at least once
|
|
+ [list(string.ascii_letters), list(string.digits)]
|
|
)
|
|
if out_file is not None:
|
|
with open(out_file, "w", encoding="utf-8") as out:
|
|
for s in data:
|
|
print(" ".join(s), file=out)
|
|
|
|
return data
|