From 2297d7515afbf7a081132f11cfc9e225d1c784e3 Mon Sep 17 00:00:00 2001
From: zhifu gao <zhifu.gzf@alibaba-inc.com>
Date: 星期三, 27 三月 2024 23:20:53 +0800
Subject: [PATCH] Dev gzf new (#1553)
---
examples/industrial_data_pretraining/paraformer-zh-spk/README_zh.md | 4 +-
funasr/train_utils/trainer.py | 8 ++--
docs/tutorial/README_zh.md | 4 +-
examples/industrial_data_pretraining/paraformer/README_zh.md | 4 +-
examples/industrial_data_pretraining/paraformer_streaming/README_zh.md | 4 +-
examples/README_zh.md | 4 +-
funasr/datasets/audio_datasets/samplers.py | 70 ++++++++++++++++++++++-------------
7 files changed, 58 insertions(+), 40 deletions(-)
diff --git a/docs/tutorial/README_zh.md b/docs/tutorial/README_zh.md
index 78acb58..4d5e310 100644
--- a/docs/tutorial/README_zh.md
+++ b/docs/tutorial/README_zh.md
@@ -268,7 +268,7 @@
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 ${train_args}
```
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@@ -276,7 +276,7 @@
export CUDA_VISIBLE_DEVICES="0,1"
gpu_num=$(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 ${train_args}
```
diff --git a/examples/README_zh.md b/examples/README_zh.md
index 78acb58..4d5e310 100644
--- a/examples/README_zh.md
+++ b/examples/README_zh.md
@@ -268,7 +268,7 @@
export CUDA_VISIBLE_DEVICES="0,1"
gpu_num=$(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 ${train_args}
```
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@@ -276,7 +276,7 @@
export CUDA_VISIBLE_DEVICES="0,1"
gpu_num=$(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 ${train_args}
```
diff --git a/examples/industrial_data_pretraining/paraformer-zh-spk/README_zh.md b/examples/industrial_data_pretraining/paraformer-zh-spk/README_zh.md
index 78acb58..4d5e310 100644
--- a/examples/industrial_data_pretraining/paraformer-zh-spk/README_zh.md
+++ b/examples/industrial_data_pretraining/paraformer-zh-spk/README_zh.md
@@ -268,7 +268,7 @@
export CUDA_VISIBLE_DEVICES="0,1"
gpu_num=$(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 ${train_args}
```
鍦ㄤ粠鑺傜偣涓婏紙鍋囪IP涓�192.168.1.2锛夛紝浣犻渶瑕佺‘淇滿ASTER_ADDR鍜孧ASTER_PORT鐜鍙橀噺涓庝富鑺傜偣璁剧疆鐨勪竴鑷达紝骞惰繍琛屽悓鏍风殑鍛戒护锛�
@@ -276,7 +276,7 @@
export CUDA_VISIBLE_DEVICES="0,1"
gpu_num=$(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 ${train_args}
```
diff --git a/examples/industrial_data_pretraining/paraformer/README_zh.md b/examples/industrial_data_pretraining/paraformer/README_zh.md
index 78acb58..4d5e310 100644
--- a/examples/industrial_data_pretraining/paraformer/README_zh.md
+++ b/examples/industrial_data_pretraining/paraformer/README_zh.md
@@ -268,7 +268,7 @@
export CUDA_VISIBLE_DEVICES="0,1"
gpu_num=$(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 ${train_args}
```
鍦ㄤ粠鑺傜偣涓婏紙鍋囪IP涓�192.168.1.2锛夛紝浣犻渶瑕佺‘淇滿ASTER_ADDR鍜孧ASTER_PORT鐜鍙橀噺涓庝富鑺傜偣璁剧疆鐨勪竴鑷达紝骞惰繍琛屽悓鏍风殑鍛戒护锛�
@@ -276,7 +276,7 @@
export CUDA_VISIBLE_DEVICES="0,1"
gpu_num=$(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 ${train_args}
```
diff --git a/examples/industrial_data_pretraining/paraformer_streaming/README_zh.md b/examples/industrial_data_pretraining/paraformer_streaming/README_zh.md
index 78acb58..4d5e310 100644
--- a/examples/industrial_data_pretraining/paraformer_streaming/README_zh.md
+++ b/examples/industrial_data_pretraining/paraformer_streaming/README_zh.md
@@ -268,7 +268,7 @@
export CUDA_VISIBLE_DEVICES="0,1"
gpu_num=$(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 ${train_args}
```
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@@ -276,7 +276,7 @@
export CUDA_VISIBLE_DEVICES="0,1"
gpu_num=$(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}
```
diff --git a/funasr/datasets/audio_datasets/samplers.py b/funasr/datasets/audio_datasets/samplers.py
index a56a980..01f5e6a 100644
--- a/funasr/datasets/audio_datasets/samplers.py
+++ b/funasr/datasets/audio_datasets/samplers.py
@@ -23,11 +23,11 @@
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 @@
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 @@
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 @@
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 CustomDistributedBufferDynamicBatchSampler(DistributedSampler):
def __init__(self, dataset,
batch_size,
+ batch_type="token",
num_replicas=None,
rank=None,
shuffle=True,
@@ -312,6 +317,7 @@
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 @@
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):
diff --git a/funasr/train_utils/trainer.py b/funasr/train_utils/trainer.py
index 55dce99..35a266f 100644
--- a/funasr/train_utils/trainer.py
+++ b/funasr/train_utils/trainer.py
@@ -161,17 +161,17 @@
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]
--
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