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* train

* train

* train

* train

* train

* train

* train

* train
This commit is contained in:
zhifu gao 2024-03-27 23:20:53 +08:00 committed by GitHub
parent 87b62d6895
commit 2297d7515a
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7 changed files with 58 additions and 40 deletions
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4
docs/tutorial/README_zh.md
View File

@ -268,7 +268,7 @@ torchrun --nnodes 1 --nproc_per_node ${gpu_num} \
export CUDA_VISIBLE_DEVICES="0,1"
gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
torchrun --nnodes 2 --node_rank 0 --nproc_per_node ${gpu_num} --master_addr=192.168.1.1 --master_port=12345 \
torchrun --nnodes 2 --node_rank 0 --nproc_per_node ${gpu_num} --master_addr 192.168.1.1 --master_port 12345 \
../../../funasr/bin/train.py ${train_args}
```
在从节点上（假设IP为192.168.1.2），你需要确保MASTER_ADDR和MASTER_PORT环境变量与主节点设置的一致，并运行同样的命令：
@ -276,7 +276,7 @@ torchrun --nnodes 2 --node_rank 0 --nproc_per_node ${gpu_num} --master_addr=192.
export CUDA_VISIBLE_DEVICES="0,1"
gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
torchrun --nnodes 2 --node_rank 1 --nproc_per_node ${gpu_num} --master_addr=192.168.1.1 --master_port=12345 \
torchrun --nnodes 2 --node_rank 1 --nproc_per_node ${gpu_num} --master_addr 192.168.1.1 --master_port 12345 \
../../../funasr/bin/train.py ${train_args}
```

4
examples/README_zh.md
View File

@ -268,7 +268,7 @@ torchrun --nnodes 1 --nproc_per_node ${gpu_num} \
export CUDA_VISIBLE_DEVICES="0,1"
gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
torchrun --nnodes 2 --node_rank 0 --nproc_per_node ${gpu_num} --master_addr=192.168.1.1 --master_port=12345 \
torchrun --nnodes 2 --node_rank 0 --nproc_per_node ${gpu_num} --master_addr 192.168.1.1 --master_port 12345 \
../../../funasr/bin/train.py ${train_args}
```
在从节点上（假设IP为192.168.1.2），你需要确保MASTER_ADDR和MASTER_PORT环境变量与主节点设置的一致，并运行同样的命令：
@ -276,7 +276,7 @@ torchrun --nnodes 2 --node_rank 0 --nproc_per_node ${gpu_num} --master_addr=192.
export CUDA_VISIBLE_DEVICES="0,1"
gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
torchrun --nnodes 2 --node_rank 1 --nproc_per_node ${gpu_num} --master_addr=192.168.1.1 --master_port=12345 \
torchrun --nnodes 2 --node_rank 1 --nproc_per_node ${gpu_num} --master_addr 192.168.1.1 --master_port 12345 \
../../../funasr/bin/train.py ${train_args}
```

4
examples/industrial_data_pretraining/paraformer-zh-spk/README_zh.md
View File

@ -268,7 +268,7 @@ torchrun --nnodes 1 --nproc_per_node ${gpu_num} \
export CUDA_VISIBLE_DEVICES="0,1"
gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
torchrun --nnodes 2 --node_rank 0 --nproc_per_node ${gpu_num} --master_addr=192.168.1.1 --master_port=12345 \
torchrun --nnodes 2 --node_rank 0 --nproc_per_node ${gpu_num} --master_addr 192.168.1.1 --master_port 12345 \
../../../funasr/bin/train.py ${train_args}
```
在从节点上（假设IP为192.168.1.2），你需要确保MASTER_ADDR和MASTER_PORT环境变量与主节点设置的一致，并运行同样的命令：
@ -276,7 +276,7 @@ torchrun --nnodes 2 --node_rank 0 --nproc_per_node ${gpu_num} --master_addr=192.
export CUDA_VISIBLE_DEVICES="0,1"
gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
torchrun --nnodes 2 --node_rank 1 --nproc_per_node ${gpu_num} --master_addr=192.168.1.1 --master_port=12345 \
torchrun --nnodes 2 --node_rank 1 --nproc_per_node ${gpu_num} --master_addr 192.168.1.1 --master_port 12345 \
../../../funasr/bin/train.py ${train_args}
```

4
examples/industrial_data_pretraining/paraformer/README_zh.md
View File

@ -268,7 +268,7 @@ torchrun --nnodes 1 --nproc_per_node ${gpu_num} \
export CUDA_VISIBLE_DEVICES="0,1"
gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
torchrun --nnodes 2 --node_rank 0 --nproc_per_node ${gpu_num} --master_addr=192.168.1.1 --master_port=12345 \
torchrun --nnodes 2 --node_rank 0 --nproc_per_node ${gpu_num} --master_addr 192.168.1.1 --master_port 12345 \
../../../funasr/bin/train.py ${train_args}
```
在从节点上（假设IP为192.168.1.2），你需要确保MASTER_ADDR和MASTER_PORT环境变量与主节点设置的一致，并运行同样的命令：
@ -276,7 +276,7 @@ torchrun --nnodes 2 --node_rank 0 --nproc_per_node ${gpu_num} --master_addr=192.
export CUDA_VISIBLE_DEVICES="0,1"
gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
torchrun --nnodes 2 --node_rank 1 --nproc_per_node ${gpu_num} --master_addr=192.168.1.1 --master_port=12345 \
torchrun --nnodes 2 --node_rank 1 --nproc_per_node ${gpu_num} --master_addr 192.168.1.1 --master_port 12345 \
../../../funasr/bin/train.py ${train_args}
```

4
examples/industrial_data_pretraining/paraformer_streaming/README_zh.md
View File

@ -268,7 +268,7 @@ torchrun --nnodes 1 --nproc_per_node ${gpu_num} \
export CUDA_VISIBLE_DEVICES="0,1"
gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
torchrun --nnodes 2 --node_rank 0 --nproc_per_node ${gpu_num} --master_addr=192.168.1.1 --master_port=12345 \
torchrun --nnodes 2 --node_rank 0 --nproc_per_node ${gpu_num} --master_addr 192.168.1.1 --master_port 12345 \
../../../funasr/bin/train.py ${train_args}
```
在从节点上（假设IP为192.168.1.2），你需要确保MASTER_ADDR和MASTER_PORT环境变量与主节点设置的一致，并运行同样的命令：
@ -276,7 +276,7 @@ torchrun --nnodes 2 --node_rank 0 --nproc_per_node ${gpu_num} --master_addr=192.
export CUDA_VISIBLE_DEVICES="0,1"
gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
torchrun --nnodes 2 --node_rank 1 --nproc_per_node ${gpu_num} --master_addr=192.168.1.1 --master_port=12345 \
torchrun --nnodes 2 --node_rank 1 --nproc_per_node ${gpu_num} --master_addr 192.168.1.1 --master_port 12345 \
../../../funasr/bin/train.py ${train_args}
```

70
funasr/datasets/audio_datasets/samplers.py
View File

@ -23,11 +23,11 @@ def CustomDistributedBatchSampler_fn(dataset, **kwargs):
batch_sampler = CustomDistributedBatchSampler(dataset, **kwargs)
else:
# if kwargs.get("sort_size", -1) > 0:
# batch_sampler = CustomDistributedBufferDynamicBatchSampler(dataset, **kwargs)
# else:
# batch_sampler = CustomDistributedDynamicBatchSampler(dataset, **kwargs)
batch_sampler = CustomDistributedDynamicBatchSampler(dataset, **kwargs)
if kwargs.get("sort_size", -1) > 0:
batch_sampler = CustomDistributedBufferDynamicBatchSampler(dataset, **kwargs)
else:
batch_sampler = CustomDistributedDynamicBatchSampler(dataset, **kwargs)
# batch_sampler = CustomDistributedDynamicBatchSampler(dataset, **kwargs)
dataloader_args["batch_sampler"] = batch_sampler
dataloader_args["num_workers"] = kwargs.get("num_workers", 4)
@ -244,6 +244,8 @@ class CustomDistributedDynamicBatchSampler(DistributedSampler):
self.total_size = len(self.dataset)
# self.num_samples = int(math.ceil(self.total_size / self.num_replicas))
self.epoch = 0
self.max_token_length = kwargs.get("max_token_length", 2048)
self.length_scale_source = kwargs.get("length_scale_source", 1.0)
def __iter__(self):
if self.shuffle:
@ -262,6 +264,8 @@ class CustomDistributedDynamicBatchSampler(DistributedSampler):
for idx in indices:
sample_length = self.dataset.get_source_len(idx)
if sample_length > self.max_token_length:
continue
potential_batch_length = (max_len_in_batch if sample_length < max_len_in_batch else sample_length) * (
len(batch) + 1)
@ -269,12 +273,12 @@ class CustomDistributedDynamicBatchSampler(DistributedSampler):
batch.append(idx)
if sample_length > max_len_in_batch:
max_len_in_batch = sample_length
current_batch_length = max_len_in_batch * len(batch)
# current_batch_length = max_len_in_batch * len(batch)
else:
batches.append(batch)
batch = [idx]
max_len_in_batch = sample_length
current_batch_length = max_len_in_batch
# current_batch_length = max_len_in_batch
# Add the last batch if it's not empty and we're not dropping it
if batch and (not self.drop_last or len(batch) * max_len_in_batch == self.batch_size):
@ -293,6 +297,7 @@ class CustomDistributedDynamicBatchSampler(DistributedSampler):
class CustomDistributedBufferDynamicBatchSampler(DistributedSampler):
def __init__(self, dataset,
batch_size,
batch_type="token",
num_replicas=None,
rank=None,
shuffle=True,
@ -312,6 +317,7 @@ class CustomDistributedBufferDynamicBatchSampler(DistributedSampler):
self.num_replicas = num_replicas
self.dataset = dataset
self.batch_size = batch_size
self.batch_type = batch_type
self.is_training = is_training
self.shuffle = shuffle and is_training
self.drop_last = drop_last
@ -319,42 +325,54 @@ class CustomDistributedBufferDynamicBatchSampler(DistributedSampler):
self.total_size = len(self.dataset)
# self.num_samples = int(math.ceil(self.total_size / self.num_replicas))
self.epoch = 0
self.sort_size = sort_size
self.sort_size = sort_size * num_replicas
self.max_token_length = kwargs.get("max_token_length", 2048)
self.length_scale_source = kwargs.get("length_scale_source", 1.0)
def __iter__(self):
if self.shuffle:
g = torch.Generator()
g.manual_seed(self.epoch)
indices = torch.randperm(self.total_size, generator=g).tolist()
indices = torch.randperm(len(self.dataset), generator=g).tolist()
else:
indices = list(range(self.total_size))
# Distribute indices among replicas
indices = indices[self.rank:self.total_size:self.num_replicas]
indices = list(range(len(self.dataset)))
# Sort indices into buffers
sorted_buffers = [sorted(indices[i:i + self.sort_size], key=lambda idx: self.dataset.get_source_len(idx)) for i in range(0, len(indices), self.sort_size)]
batches = []
for buffer in sorted_buffers:
# Create sorted buffers and form batches
buffer_batches = []
for i in range(0, len(indices), self.sort_size):
buffer = sorted(indices[i:i + self.sort_size], key=lambda idx: self.dataset.get_source_len(idx))
batch = []
max_len_in_batch = 0
for idx in buffer:
sample_length = self.dataset.get_source_len(idx)
original_sample_length = self.dataset.get_source_len(idx)
if original_sample_length > self.max_sample_length:
continue
sample_length = 1 if self.batch_type == "example" else original_sample_length
potential_batch_length = max(max_len_in_batch, sample_length) * (len(batch) + 1)
if potential_batch_length <= self.batch_size:
batch.append(idx)
max_len_in_batch = max(max_len_in_batch, sample_length)
else:
batches.append(batch)
buffer_batches.append(batch)
batch = [idx]
max_len_in_batch = sample_length
# Add the last batch if it's not empty and we're not dropping it
if batch and (not self.drop_last or len(batch) * max_len_in_batch == self.batch_size):
batches.append(batch)
if batch:
buffer_batches.append(batch)
return iter(batches)
# Ensure each rank gets the same number of batches, duplicate data if needed
batches_per_rank = math.ceil(len(buffer_batches) / self.num_replicas)
total_batches_needed = batches_per_rank * self.num_replicas
buffer_batches.extend(buffer_batches[:total_batches_needed - len(buffer_batches)])
# Evenly distribute batches from buffer_batches to each rank
rank_batches = [[] for _ in range(self.num_replicas)]
for i, batch in enumerate(buffer_batches):
rank_batches[i % self.num_replicas].append(batch)
# Assign all batches for the current rank directly
final_batches = rank_batches[self.rank]
return iter(final_batches)
def __len__(self):

8
funasr/train_utils/trainer.py
View File

@ -161,17 +161,17 @@ class Trainer:
self.best_step_or_epoch = ckpt_name
best_ckpt = Path(os.path.join(self.output_dir, f'model.pt.best'))
torch.save(state, best_ckpt)
logging.info(f"Update best acc: {self.val_acc_step_or_eoch[self.best_step_or_epoch]}, {best_ckpt}")
logging.info(f"Update best acc: {self.val_acc_step_or_eoch[self.best_step_or_epoch]:.4f}, {best_ckpt}")
else:
logging.info(f"No improvement in acc: {self.val_acc_step_or_eoch[ckpt_name]} < {self.val_acc_step_or_eoch[self.best_step_or_epoch]}")
logging.info(f"No improvement in acc: {self.val_acc_step_or_eoch[ckpt_name]:.4f} < {self.val_acc_step_or_eoch[self.best_step_or_epoch]:.4f}")
elif self.avg_keep_nbest_models_type == "loss":
if self.val_loss_step_or_eoch[ckpt_name] <= self.val_loss_step_or_eoch[self.best_step_or_epoch]:
self.best_step_or_epoch = ckpt_name
best_ckpt = Path(os.path.join(self.output_dir, f'model.pt.best'))
torch.save(state, best_ckpt)
logging.info(f"Update best loss: {self.val_loss_step_or_eoch[self.best_step_or_epoch]}, {best_ckpt}")
logging.info(f"Update best loss: {self.val_loss_step_or_eoch[self.best_step_or_epoch]:.4f}, {best_ckpt}")
else:
logging.info(f"No improvement in loss: {self.val_loss_step_or_eoch[ckpt_name]} > {self.val_loss_step_or_eoch[self.best_step_or_epoch]}")
logging.info(f"No improvement in loss: {self.val_loss_step_or_eoch[ckpt_name]:.4f} > {self.val_loss_step_or_eoch[self.best_step_or_epoch]:.4f}")
else:
print("Undo")
self.saved_ckpts[ckpt_name] = getattr(self, f"val_{self.avg_keep_nbest_models_type}_step_or_eoch")[ckpt_name]