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add mt classifier by using deepspeed (#117)
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* add mt classifier by using deepspeed

* add parallel of infer

* fix bug for mt cls and cls

* fix bug

* restore model loader

* fix bug

---------

Co-authored-by: hermanyu <hermanyu@tencent.com>
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@yuzhangogogo
yuzhangogogo and hermanyu authored Jan 6, 2024
1 parent 1f28a18 commit c703d03
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232 changes: 155 additions & 77 deletions finetune/run_classifier_deepspeed.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,70 +14,83 @@
sys.path.append(tencentpretrain_dir)

from tencentpretrain.opts import deepspeed_opts
from tencentpretrain.model_loader import *
from finetune.run_classifier import *
from tencentpretrain.model_loader import _load_state_dict_into_model


def read_dataset(args, path, split):
dataset, columns = [], {}
if split:
for i in range(args.world_size):
dataset.append([])
index = 0
def read_dataset(args, path):
dataset, instances, columns = [], [], {}
for i in range(args.world_size):
dataset.append([])
with open(path, mode="r", encoding="utf-8") as f:
for line_id, line in enumerate(f):
if line_id == 0:
for i, column_name in enumerate(line.rstrip("\r\n").split("\t")):
columns[column_name] = i
continue
line = line.rstrip("\r\n").split("\t")
tgt = int(line[columns["label"]])
if args.soft_targets and "logits" in columns.keys():
soft_tgt = [float(value) for value in line[columns["logits"]].split(" ")]
if "text_b" not in columns: # Sentence classification.
text_a = line[columns["text_a"]]
src = args.tokenizer.convert_tokens_to_ids([CLS_TOKEN] + args.tokenizer.tokenize(text_a) + [SEP_TOKEN])
seg = [1] * len(src)
else: # Sentence-pair classification.
text_a, text_b = line[columns["text_a"]], line[columns["text_b"]]
src_a = args.tokenizer.convert_tokens_to_ids([CLS_TOKEN] + args.tokenizer.tokenize(text_a) + [SEP_TOKEN])
src_b = args.tokenizer.convert_tokens_to_ids(args.tokenizer.tokenize(text_b) + [SEP_TOKEN])
src = src_a + src_b
seg = [1] * len(src_a) + [2] * len(src_b)

if len(src) > args.seq_length:
src = src[: args.seq_length]
seg = seg[: args.seq_length]
PAD_ID = args.tokenizer.convert_tokens_to_ids([PAD_TOKEN])[0]
while len(src) < args.seq_length:
src.append(PAD_ID)
seg.append(0)
if split:
if args.soft_targets and "logits" in columns.keys():
dataset[index].append((src, tgt, seg, soft_tgt))
else:
dataset[index].append((src, tgt, seg))
index += 1
if index == args.world_size:
index = 0
else:
if args.soft_targets and "logits" in columns.keys():
dataset.append((src, tgt, seg, soft_tgt))
else:
dataset.append((src, tgt, seg))
if split:
max_data_num_rank_index = 0
max_data_num = len(dataset[0])
for i in range(args.world_size):
if len(dataset[i]) > max_data_num:
max_data_num_rank_index = i
max_data_num = len(dataset[i])
for i in range(args.world_size):
if len(dataset[i]) < max_data_num:
dataset[i].append(dataset[max_data_num_rank_index][-1])

if len(columns) != len(line):
continue
instances.append(line)
rank_num = math.ceil(1.0 * len(instances) / args.world_size)
index = 0
for line_id, line in enumerate(instances):
tgt = int(line[columns["label"]])
if args.soft_targets and "logits" in columns.keys():
soft_tgt = [float(value) for value in line[columns["logits"]].split(" ")]
if "text_b" not in columns: # Sentence classification.
text_a = line[columns["text_a"]]
src = args.tokenizer.convert_tokens_to_ids([CLS_TOKEN] + args.tokenizer.tokenize(text_a) + [SEP_TOKEN])
seg = [1] * len(src)
else: # Sentence-pair classification.
text_a, text_b = line[columns["text_a"]], line[columns["text_b"]]
src_a = args.tokenizer.convert_tokens_to_ids([CLS_TOKEN] + args.tokenizer.tokenize(text_a) + [SEP_TOKEN])
src_b = args.tokenizer.convert_tokens_to_ids(args.tokenizer.tokenize(text_b) + [SEP_TOKEN])
src = src_a + src_b
seg = [1] * len(src_a) + [2] * len(src_b)

if len(src) > args.seq_length:
src = src[: args.seq_length]
seg = seg[: args.seq_length]
PAD_ID = args.tokenizer.convert_tokens_to_ids([PAD_TOKEN])[0]
while len(src) < args.seq_length:
src.append(PAD_ID)
seg.append(0)
if args.soft_targets and "logits" in columns.keys():
dataset[index].append((src, tgt, seg, 0, soft_tgt))
else:
dataset[index].append((src, tgt, seg, 0))
if (line_id+1) % rank_num == 0:
index += 1
for i in range(args.world_size):
while len(dataset[i]) < rank_num:
dataset[i].append(tuple([1 if j == 3 else dataset[0][-1][j] for j in range(len(dataset[0][-1]))]))
return dataset

def load_model(args, model, model_path):
if args.enable_zero3:
with deepspeed.zero.Init(config_dict_or_path=args.deepspeed_config):
if os.path.isdir(model_path):
index_filename = os.path.join(model_path, "tencentpretrain_model.bin.index.json")
with open(index_filename, "r") as f:
index = json.loads(f.read())
shard_filenames = sorted(set(index["weight_map"].values()))
shard_filenames = [os.path.join(model_path, f) for f in shard_filenames]
for shard_file in shard_filenames:
model = _load_state_dict_into_model(model, shard_file, "")
elif model_path is not None:
model = _load_state_dict_into_model(model, model_path, "")
else:
if os.path.isdir(model_path):
index_filename = os.path.join(model_path, "tencentpretrain_model.bin.index.json")
with open(index_filename, "r") as f:
index = json.loads(f.read())
shard_filenames = sorted(set(index["weight_map"].values()))
shard_filenames = [os.path.join(model_path, f) for f in shard_filenames]
for shard_file in shard_filenames:
model.load_state_dict(torch.load(shard_file, map_location="cpu"), strict=False)
elif model_path is not None:
model.load_state_dict(torch.load(model_path, map_location="cpu"), strict=False)
return model

def train_model(args, model, optimizer, scheduler, src_batch, tgt_batch, seg_batch, soft_tgt_batch=None):
model.zero_grad()
Expand All @@ -98,6 +111,75 @@ def train_model(args, model, optimizer, scheduler, src_batch, tgt_batch, seg_bat

return loss

def batch_loader(batch_size, src, tgt, seg, is_pad, soft_tgt=None):
instances_num = src.size()[0]
for i in range(instances_num // batch_size):
src_batch = src[i * batch_size : (i + 1) * batch_size, :]
tgt_batch = tgt[i * batch_size : (i + 1) * batch_size]
seg_batch = seg[i * batch_size : (i + 1) * batch_size, :]
is_pad_batch = is_pad[i * batch_size : (i + 1) * batch_size]
if soft_tgt is not None:
soft_tgt_batch = soft_tgt[i * batch_size : (i + 1) * batch_size, :]
yield src_batch, tgt_batch, seg_batch, is_pad_batch, soft_tgt_batch
else:
yield src_batch, tgt_batch, seg_batch, is_pad_batch, None
if instances_num > instances_num // batch_size * batch_size:
src_batch = src[instances_num // batch_size * batch_size :, :]
tgt_batch = tgt[instances_num // batch_size * batch_size :]
seg_batch = seg[instances_num // batch_size * batch_size :, :]
is_pad_batch = is_pad[instances_num // batch_size * batch_size :]
if soft_tgt is not None:
soft_tgt_batch = soft_tgt[instances_num // batch_size * batch_size :, :]
yield src_batch, tgt_batch, seg_batch, is_pad_batch, soft_tgt_batch
else:
yield src_batch, tgt_batch, seg_batch, is_pad_batch, None

def predict(args, dataset):
src = torch.LongTensor([sample[0] for sample in dataset])
tgt = torch.LongTensor([sample[1] for sample in dataset])
seg = torch.LongTensor([sample[2] for sample in dataset])
is_pad = torch.LongTensor([sample[3] for sample in dataset])

batch_size = args.batch_size

args.model.eval()

result = []
for _, (src_batch, tgt_batch, seg_batch, is_pad_batch, _) in enumerate(batch_loader(batch_size, src, tgt, seg, is_pad)):
src_batch = src_batch.to(args.device)
tgt_batch = tgt_batch.to(args.device)
seg_batch = seg_batch.to(args.device)
is_pad_batch = is_pad_batch.to(args.device)
with torch.no_grad():
_, logits = args.model(src_batch, tgt_batch, seg_batch)
pred = torch.argmax(nn.Softmax(dim=1)(logits), dim=1)
gold = tgt_batch
pad = is_pad_batch
for j in range(pred.size()[0]):
result.append([pred[j], gold[j], pad[j]])
return result

def evaluate(args, output_list):
# Confusion matrix.
correct, total = 0, 0
confusion = torch.zeros(args.labels_num, args.labels_num, dtype=torch.long)
for result in output_list:
for pred, gold, is_pad in result.tolist():
if is_pad == 1: continue
confusion[pred, gold] += 1
correct += pred == gold
total += 1
args.logger.info("Confusion matrix:")
args.logger.info(confusion)
args.logger.info("Report precision, recall, and f1:")
eps = 1e-9
for i in range(confusion.size()[0]):
p = confusion[i, i].item() / (confusion[i, :].sum().item() + eps)
r = confusion[i, i].item() / (confusion[:, i].sum().item() + eps)
f1 = 2 * p * r / (p + r + eps)
args.logger.info("Label {}: {:.3f}, {:.3f}, {:.3f}".format(i, p, r, f1))
args.logger.info("Acc. (Correct/Total): {:.4f} ({}/{}) ".format(correct / total, correct, total))
return correct / total, confusion

def main():
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
Expand Down Expand Up @@ -129,17 +211,15 @@ def main():
# Build tokenizer.
args.tokenizer = str2tokenizer[args.tokenizer](args)

# Build classification model.
if args.enable_zero3:
with deepspeed.zero.Init(config_dict_or_path=args.deepspeed_config):
model = Classifier(args)
if args.pretrained_model_path:
model = _load_state_dict_into_model(model, args.load_model_path)
# Build multi-task classification model.
model = Classifier(args)
if args.pretrained_model_path:
load_model(args, model, args.pretrained_model_path)
else:
model = Classifier(args)

# Load or initialize parameters.
load_or_initialize_parameters(args, model)
# Initialize with normal distribution.
for n, p in list(model.named_parameters()):
if "gamma" not in n and "beta" not in n:
p.data.normal_(0, 0.02)

# Get logger.
args.logger = init_logger(args)
Expand All @@ -155,7 +235,7 @@ def main():
rank = dist.get_rank()
args.rank = rank

trainset = read_dataset(args, args.train_path, split=True)[args.rank]
trainset = read_dataset(args, args.train_path)[args.rank]
random.shuffle(trainset)
instances_num = len(trainset)
batch_size = args.batch_size
Expand All @@ -172,11 +252,12 @@ def main():
mpu=None,
dist_init_required=False)

src = torch.LongTensor([example[0] for example in trainset])
tgt = torch.LongTensor([example[1] for example in trainset])
seg = torch.LongTensor([example[2] for example in trainset])
src = torch.LongTensor([sample[0] for sample in trainset])
tgt = torch.LongTensor([sample[1] for sample in trainset])
seg = torch.LongTensor([sample[2] for sample in trainset])
is_pad = torch.LongTensor([sample[3] for sample in trainset])
if args.soft_targets:
soft_tgt = torch.FloatTensor([example[3] for example in trainset])
soft_tgt = torch.FloatTensor([sample[4] for sample in trainset])
else:
soft_tgt = None

Expand All @@ -193,27 +274,24 @@ def main():

for epoch in range(1, args.epochs_num + 1):
model.train()
for i, (src_batch, tgt_batch, seg_batch, soft_tgt_batch) in enumerate(batch_loader(batch_size, src, tgt, seg, soft_tgt)):
for i, (src_batch, tgt_batch, seg_batch, _, soft_tgt_batch) in enumerate(batch_loader(batch_size, src, tgt, seg, is_pad, soft_tgt)):
loss = train_model(args, model, optimizer, scheduler, src_batch, tgt_batch, seg_batch, soft_tgt_batch)
total_loss += loss.item()
if (i + 1) % args.report_steps == 0 and args.rank == 0:
args.logger.info("Epoch id: {}, Training steps: {}, Avg loss: {:.3f}".format(epoch, i + 1, total_loss / args.report_steps))
total_loss = 0.0
output = predict(args, read_dataset(args, args.dev_path)[args.rank])
output = torch.as_tensor(output).to(args.device)
output_list = [torch.zeros_like(output).to(args.device) for _ in range(args.world_size)]
dist.all_gather(output_list, output)
if args.rank == 0:
result = evaluate(args, read_dataset(args, args.dev_path, split=False))
result = evaluate(args, output_list)
result_tensor = torch.tensor(result[0]).to(args.device)
dist.broadcast(result_tensor, 0, async_op=False)
if result_tensor.float() >= best_result:
best_result = result_tensor.float().item()
best_epoch = epoch
model.save_checkpoint(args.output_model_path, str(epoch))

# Evaluation phase.
if args.test_path is not None and args.rank == 0:
args.logger.info("Test set evaluation.")
model.load_checkpoint(args.output_model_path, str(best_epoch))
evaluate(args, read_dataset(args, args.test_path, split=False))


if __name__ == "__main__":
main()
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