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# ------------------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License (MIT). See LICENSE in the repo root for license information.
# ------------------------------------------------------------------------------------------
# python -m torch.distributed.launch --nproc_per_node=1 src/gpt2_beam.py \
# --data ./data/e2e/test.jsonl \
# --batch_size 1 \
# --seq_len 512 \
# --eval_len 64 \
# --model_card gpt2.md \
# --platform local \
# --beam 10 \
# --length_penalty 0.8 \
# --no_repeat_ngram_size 4 \
# --repetition_penalty 1.0 \
# --eos_token_id 628 \
# --lora_dim 4 \
# --lora_alpha 32 \
# --work_dir ./trained_models/GPT2_M/e2e \
# --output_file predict.26290.jsonl \
# --init_checkpoint ./trained_models/GPT2_M/e2e/model.26290.pt
import argparse
import time
import math
import os, sys
import json
import itertools
from typing import Callable, Dict, Iterable, List, Optional, Tuple
import torch
from torch import Tensor, device, dtype, nn
from torch.nn import CrossEntropyLoss
from torch.nn import functional as F
from torch.utils.data import DataLoader
import torch.nn.functional as F
torch.set_printoptions(threshold=100000)
import numpy as np
from gpu import (
add_gpu_params,
parse_gpu,
distributed_opt,
distributed_gather,
distributed_sync,
cleanup
)
from exp_utils import create_exp_dir
from data_utils import FT_Dataset
from model import GPT2Config, GPT2LMModel
parser = argparse.ArgumentParser(description='PyTorch GPT2 beam decoding')
add_gpu_params(parser)
parser.add_argument('--data', type=str, default='../data/wikitext-103',
help='location of the data corpus')
parser.add_argument('--batch_size', type=int, default=10,
help='batch size')
parser.add_argument('--seq_len', type=int, default=512,
help='number of tokens to predict')
parser.add_argument('--eval_len', type=int, default=256,
help='evaluation length')
parser.add_argument('--min_length', type=int, default=0,
help='minimum generation length')
parser.add_argument('--model_card', default='gpt2.sm', choices=['gpt2.sm', 'gpt2.md', 'gpt2.lg'],
help='model names')
parser.add_argument('--init_checkpoint', default=None, type=str, help='initial checkpoint')
parser.add_argument('--lora_dim', type=int, default=0, help='lora attn dimension')
parser.add_argument('--lora_alpha', type=int, default=128, help='lora attn alpha')
parser.add_argument('--work_dir', type=str, default=os.getenv('PT_OUTPUT_DIR', 'gpt2_model'),
help='working folder')
parser.add_argument('--beam', type=int, default=1, help='beam search size')
parser.add_argument('--length_penalty', type=float, default=1.0, help='length penalty')
parser.add_argument('--no_repeat_ngram_size', type=int, default=4, help='no_repeat_ngram_size')
parser.add_argument('--repetition_penalty', type=float, default=1.0, help='repetition_penalty')
parser.add_argument('--eos_token_id', action='append', type=int, default=[50256],
help='eos token id')
parser.add_argument('--output_file', type=str, default='beam_prediction.jsonl',
help='output file name')
def print_args(args):
if args.rank == 0:
print('=' * 100)
for k, v in args.__dict__.items():
print(' - {} : {}'.format(k, v))
print('=' * 100)
def _reorder_cache(past: Tuple, beam_idx: Tensor) -> Tuple[Tensor]:
return tuple(layer_past.index_select(1, beam_idx).contiguous().detach() for layer_past in past)
def _calc_banned_ngram_tokens(
prev_input_ids: Tensor,
num_hypos: int,
no_repeat_ngram_size: int,
cur_len: int
) -> None:
"""Copied from fairseq for no_repeat_ngram in beam_search"""
if cur_len + 1 < no_repeat_ngram_size:
# return no banned tokens if we haven't generated no_repeat_ngram_size tokens yet
return [[] for _ in range(num_hypos)]
generated_ngrams = [{} for _ in range(num_hypos)]
for idx in range(num_hypos):
gen_tokens = prev_input_ids[idx].tolist()
generated_ngram = generated_ngrams[idx]
for ngram in zip(*[gen_tokens[i:] for i in range(no_repeat_ngram_size)]):
prev_ngram_tuple = tuple(ngram[:-1])
generated_ngram[prev_ngram_tuple] = generated_ngram.get(prev_ngram_tuple, []) + [ngram[-1]]
def _get_generated_ngrams(hypo_idx):
# Before decoding the next token, prevent decoding of ngrams that have already appeared
start_idx = cur_len + 1 - no_repeat_ngram_size
ngram_idx = tuple(prev_input_ids[hypo_idx, start_idx:cur_len].tolist())
return generated_ngrams[hypo_idx].get(ngram_idx, [])
banned_tokens = [_get_generated_ngrams(hypo_idx) for hypo_idx in range(num_hypos)]
return banned_tokens
def _enforce_repetition_penalty_(
lprobs,
batch_size,
num_beams,
prev_output_tokens,
repetition_penalty
):
"""repetition penalty (from CTRL paper https://arxiv.org/abs/1909.05858). """
for i in range(batch_size * num_beams):
print('prev_output_tokens.shape', prev_output_tokens.shape)
print('prev_output_tokens[i].shape', prev_output_tokens[i].shape)
for previous_token in set(prev_output_tokens[i].tolist()):
# if score < 0 then repetition penalty has to multiplied to reduce the previous token probability
if lprobs[i, previous_token] < 0:
lprobs[i, previous_token] *= repetition_penalty
else:
lprobs[i, previous_token] /= repetition_penalty
def _postprocess_next_token_scores(
scores,
history,
cur_len,
batch_size,
num_beams,
repetition_penalty=1.0,
no_repeat_ngram_size=4,
bad_words_ids=None,
min_length=0,
max_length=100,
eos_token_id=None,
):
# repetition penalty (from CTRL paper https://arxiv.org/abs/1909.05858)
if repetition_penalty != 1.0 and history is not None:
_enforce_repetition_penalty_(scores, batch_size, num_beams, history, repetition_penalty)
# score: batch_size * beam, vocab
# set eos token prob to zero if min_length is not reached
if eos_token_id is not None and cur_len < min_length:
for eos in eos_token_id:
scores[:, eos] = -float("inf")
if no_repeat_ngram_size > 0 and history is not None:
# calculate a list of banned tokens to prevent repetitively generating the same ngrams
num_batch_hypotheses = batch_size * num_beams
# from fairseq: https://github.com/pytorch/fairseq/blob/a07cb6f40480928c9e0548b737aadd36ee66ac76/fairseq/sequence_generator.py#L345
banned_batch_tokens = _calc_banned_ngram_tokens(
history, num_batch_hypotheses, no_repeat_ngram_size, cur_len
)
for i, banned_tokens in enumerate(banned_batch_tokens):
scores[i, banned_tokens] = -float("inf")
return scores
def _add_beam_candidate(
best_score,
best_sequence,
batch_size,
num_beams,
beam_scores,
history,
eos_token_id=None
):
last_tokens = history[:, -1]
for _i in range(batch_size * num_beams):
if eos_token_id is None or last_tokens[_i] in eos_token_id:
cur_len = history.shape[-1]
_score = beam_scores.view(-1)[_i] / cur_len ** args.length_penalty
batch_id = _i // num_beams
if not batch_id in best_score or best_score[batch_id] < _score:
best_score[batch_id] = _score
best_sequence[batch_id][:cur_len] = history[_i]
beam_scores.view(-1)[_i] = -float("inf")
def beam(model, data_iter, args):
model.eval()
total_loss = 0.
start_time = time.time()
all_predictions = {}
with torch.no_grad():
for idx, data in enumerate(data_iter):
data = {key: value for key, value in data.items()}
_id = data['id'].to(args.device)
_query = data['query'].to(args.device)
_query_len = data['query_len'].to(args.device)
## local adaptation start.
## local adaptation end.
output = None
score = None
batch_size = _id.size(0)
num_beams = args.beam
length_penalty = args.length_penalty
_batch = torch.arange(0, _id.size(0), device=args.device, dtype=torch.long)
past = None
len_past = None
_query = _query.repeat(1, num_beams).view(batch_size * num_beams, -1)
_query_len = _query_len.unsqueeze(-1).repeat(1, num_beams).view(-1)
_bbatch = _batch.unsqueeze(-1).repeat(1, num_beams).view(-1)
# scores for each sentence in the beam
beam_scores = torch.zeros(
(batch_size, num_beams), dtype=torch.float, device=_query.device
)
best_sequence = torch.zeros(
(batch_size, args.eval_len), dtype=torch.long, device=_query.device
)
best_score = {}
history = None
with torch.no_grad():
for i in range(0, args.eval_len):
if i == 0:
logits, past = model(_query)
logits = logits[_bbatch, (_query_len-1).long(), :] # batch_size * beam, vocab
else:
#print('token_id.shape', token_id.shape, token_id)
#print('past.shape', past[0].shape)
#print('len_past.shape', len_past.shape, len_past)
logits, past = model(token_id, past=past, len_past=len_past)
logits = logits[:, -1, :] # batch_size * beam, vocab
logits = _postprocess_next_token_scores(
logits,
history,
i,
batch_size,
num_beams,
repetition_penalty=args.repetition_penalty,
no_repeat_ngram_size=args.no_repeat_ngram_size,
min_length=args.min_length,
eos_token_id=args.eos_token_id,
)
softmax_probs = F.softmax(logits, dim=-1)
##_prob, _w_idx = torch.topk(softmax_probs, num_beams) # batch_size, beam
vocab_size = softmax_probs.shape[-1]
_logprob = torch.log(softmax_probs) # batch_size * beam, vocab
if i == 0:
next_scores = _logprob.view(batch_size, num_beams, -1)[:, 0, :] # batch_size, vocab
else:
next_scores = beam_scores.unsqueeze(-1) + _logprob.view(batch_size, num_beams, -1)
next_scores = next_scores.view(batch_size, -1) # batch_size, beam * vocab
next_scores, next_tokens = torch.topk(
next_scores, num_beams, dim=1, largest=True, sorted=True
) # batch_size, num_beams
beam_id = (next_tokens // vocab_size).view(-1) # batch_size * num_beams
token_id = (next_tokens % vocab_size).view(-1).unsqueeze(-1) # batch_size, num_beams
beam_idx = beam_id.view(batch_size, num_beams) + (_batch * num_beams).unsqueeze(-1)
past = _reorder_cache(past, beam_idx.view(-1))
beam_scores = next_scores # batch_size, num_beams
len_past = (_query_len + i).long()
if history is None:
history = token_id.detach()
else:
history = torch.cat((history[beam_idx.view(-1)], token_id.detach()), dim=1).detach()
_add_beam_candidate(
best_score, best_sequence, batch_size, num_beams, beam_scores, history,
eos_token_id=args.eos_token_id
)
_add_beam_candidate(
best_score, best_sequence, batch_size, num_beams, beam_scores, history
)
with torch.no_grad():
_id = distributed_gather(args, _id)
output = distributed_gather(args, best_sequence)
#score = distributed_gather(args, score)
distributed_sync(args)
if args.rank == 0:
_id = _id.view(-1).cpu()
output = output.view(-1, output.shape[-1]).cpu()
#score = score.view(-1, score.shape[-1]).cpu()
for _b in range(0, _id.shape[-1]):
_i = int(_id[_b].item())
all_predictions[_i] = {}
all_predictions[_i]['id'] = _i
all_predictions[_i]['predict'] = output[_b].tolist()
#all_predictions[_i]['score'] = score[_b].tolist()
if idx % 10 == 0:
print('inference samples', idx)
# pred_file = os.path.join(args.work_dir, args.output_file)
# print('saving prediction file', pred_file)
# with open(pred_file, 'w') as writer:
# for _i in all_predictions:
# writer.write(json.dumps(all_predictions[_i]) + '\n')
if args.rank == 0:
pred_file = os.path.join(args.work_dir, args.output_file)
print('saving prediction file', pred_file)
with open(pred_file, 'w') as writer:
for _i in all_predictions:
writer.write(json.dumps(all_predictions[_i]) + '\n')
if __name__ == '__main__':
args = parser.parse_args()
parse_gpu(args)
print_args(args)
if args.rank == 0:
args.logging = create_exp_dir(args.work_dir)
valid_data = FT_Dataset(
args.data, args.batch_size, args.seq_len, args.eval_len,
)
valid_data = valid_data.get_item_list(0, 1000)
valid_sampler = torch.utils.data.distributed.DistributedSampler(valid_data)
valid_loader = DataLoader(
valid_data, batch_size=args.batch_size, num_workers=0, shuffle=False,
pin_memory=False, drop_last=False, sampler=valid_sampler
)
if args.model_card == 'gpt2.sm':
config = GPT2Config(
n_embd=768, n_layer=12, n_head=12,
lora_attn_dim=args.lora_dim, lora_attn_alpha=args.lora_alpha,
)
elif args.model_card == 'gpt2.md':
config = GPT2Config(
n_embd=1024, n_layer=24, n_head=16,
lora_attn_dim=args.lora_dim, lora_attn_alpha=args.lora_alpha,
)
elif args.model_card == 'gpt2.lg':
config = GPT2Config(
n_embd=1280, n_layer=36, n_head=20,
lora_attn_dim=args.lora_dim, lora_attn_alpha=args.lora_alpha,
)
lm_net = GPT2LMModel(config)
if args.init_checkpoint is not None:
print('loading model pretrained weight.')
cp = torch.load(args.init_checkpoint, map_location=torch.device('cpu'))
lm_net.load_weight(cp)
lm_net = lm_net.cuda()
print(lm_net.transformer.h[0].mlp)
print('model sampling ...')
beam(lm_net, valid_loader, args)
distributed_sync(args)
print('cleanup dist ...')
cleanup(args)
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