Merge pull request #14 from airaria/v0.1.10dev

V0.1.10dev

README.md

@@ -28,6 +28,11 @@ Paper: [https://arxiv.org/abs/2002.12620](https://arxiv.org/abs/2002.12620)
 
 ## Update
 
+**Jul 14, 2020**
+* Updated to 0.1.10:
+    * Now supports mixed precision training with Apex! Just set `fp16` to `True` in `TrainingConfig`. See the documentation of `TrainingConfig` for detail.
+    * Added `data_parallel` option in `TrainingConfig` to enable data parallel training and mixed precision training work together.
+
 **Apr 26, 2020**
 
 * Added Chinese NER task (MSRA NER) results.
@@ -116,6 +121,7 @@ See [Full Documentation](https://textbrewer.readthedocs.io/) for detailed usages
   * NumPy
   * tqdm
   * Transformers >= 2.0 (optional, used by some examples)
+  * Apex == 0.1.0 (optional, mixed precision training)
 
 * Install from PyPI
 
@@ -392,7 +398,6 @@ We recommend that users use pre-trained student models whenever possible to full
 
 ## Known Issues
 
-* Compatibility with FP16 training has not been tested.
 * Multi-GPU training support is only available through `DataParallel` currently.
 
 ## Citation

README_ZH.md

@@ -27,6 +27,11 @@ Paper: [https://arxiv.org/abs/2002.12620](https://arxiv.org/abs/2002.12620)
 
 ## 更新
 
+**Jul 14, 2020**
+* **版本更新至0.1.10**:
+    * 支持apex混合精度训练功能：可通过在`TrainingConfig`中设置`fp16=True`启用。详细设置参见`TraningConfig`的说明。
+    * 在`TrainingConfig`中增加了`data_parallel`选项，使得数据并行与混合精度训练可同时启用。
+
 **Apr 26, 2020**
 
 * 增加了中文NER任务(MSRA NER)上的实验结果。
@@ -115,6 +120,7 @@ Paper: [https://arxiv.org/abs/2002.12620](https://arxiv.org/abs/2002.12620)
 * NumPy
 * tqdm
 * Transformers >= 2.0 (可选, Transformer相关示例需要用到)
+* Apex == 0.1.0 (可选，用于混合精度训练)
 
 ### 安装方式
 
@@ -381,7 +387,6 @@ Distiller负责执行实际的蒸馏过程。目前实现了以下的distillers:
 
 ## 已知问题
 
-* FP16精度训练的兼容性尚未测试。
 * 尚不支持DataParallel以外的多卡训练策略。
 
 ## 引用

setup.py

@@ -29,7 +29,7 @@
 
 setup(
     name="textbrewer",
-    version="0.1.9",
+    version="0.1.10",
     author="ziqingyang",
     author_email="[email protected]",
     description="PyTorch-based knowledge distillation toolkit for natural language processing",

src/textbrewer/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "0.1.9"
+__version__ = "0.1.10"
 
 from .distillers import BasicTrainer
 from .distillers import BasicDistiller

src/textbrewer/compatibility.py

@@ -3,4 +3,12 @@
 if torch.__version__ < '1.2':
     mask_dtype = torch.uint8
 else:
-    mask_dtype = torch.bool
+    mask_dtype = torch.bool
+
+def is_apex_available():
+    try:
+        from apex import amp 
+        _has_apex = True
+    except ImportError:
+        _has_apex = False
+    return _has_apex

src/textbrewer/configurations.py

@@ -43,18 +43,24 @@ class TrainingConfig(Config):
         ckpt_steps (int):  if *num_steps* is passes to ``distiller.train()``, saves the model every **ckpt_steps**, meanwhile ignore `ckpt_frequency` and `ckpt_epoch_frequency` .
         log_dir (str): directory to save the tensorboard log file. Set it to ``None`` to disable tensorboard.
         output_dir (str): directory to save model weights.
-        device (str or torch.device) : training on CPU or GPU.
-
+        device (str or torch.device): training on CPU or GPU.
+        fp16 (bool): if ``True``, enables mixed precision training using Apex.
+        fp16_opt_level(str): Pure or mixed precision optimization level. Accepted values are "O0", "O1", "O2", and "O3". See Apex documenation for details.
+        data_parallel (bool): If ``True``, wraps the models with ``torch.nn.DataParallel``.
+    Note:
+        * To perform data parallel training, you could either wrap the models with ``torch.nn.DataParallel`` outside TextBrewer by yourself, or leave the work for TextBrewer by setting **data_parallel** to ``True``.
+        * To enable both data parallel training and mixed precision training, you should set **data_parallel** to ``True``, and DO NOT wrap the models by yourself.
+        * In some experiments, we have observed the slowing down in the speed with ``torch.nn.DataParallel``. In the future we will move to DistributedDataParallel.
     Example::
 
         # Usually just need to set log_dir and output_dir and leave others default
         train_config = TrainingConfig(log_dir=my_log_dir, output_dir=my_output_dir)
         
-        # Stores model at the end of each epoch
+        # Stores the model at the end of each epoch
         train_config = TrainingConfig(ckpt_frequency=1, ckpt_epoch_frequency=1)
-        # Stores model twice (at the middle and at the end) in each epoch
+        # Stores the model twice (at the middle and at the end) in each epoch
         train_config = TrainingConfig(ckpt_frequency=2, ckpt_epoch_frequency=1)
-        # Stores model once every two epochs
+        # Stores the model once every two epochs
         train_config = TrainingConfig(ckpt_frequency=1, ckpt_epoch_frequency=2)
 
     """
@@ -64,7 +70,10 @@ def __init__(self,gradient_accumulation_steps = 1,
                  ckpt_steps = None,
                  log_dir = None,
                  output_dir = './saved_models',
-                 device = 'cuda'
+                 device = 'cuda',
+                 fp16 = False,
+                 fp16_opt_level = 'O1',
+                 data_parallel = False
                  ):
         super(TrainingConfig, self).__init__()
 
@@ -75,6 +84,9 @@ def __init__(self,gradient_accumulation_steps = 1,
         self.log_dir = log_dir
         self.output_dir = output_dir
         self.device = device
+        self.fp16 = fp16
+        self.fp16_opt_level = fp16_opt_level
+        self.data_parallel = data_parallel
 
         if not os.path.exists(self.output_dir):
             os.makedirs(self.output_dir)

src/textbrewer/distillation.py

@@ -120,8 +120,7 @@ def __init__(self, train_config,
 
     def save_and_callback(self,global_step, step, epoch, callback):
         logger.info(f"Saving at global step {global_step}, epoch step {step + 1} epoch {epoch+1}")
-        coreModel = self.model_S.module if \
-            'DataParallel' in self.model_S.__class__.__name__ else self.model_S
+        coreModel = self.model.module if hasattr(self.model, "module") else self
         state_dict = coreModel.state_dict()
         torch.save(state_dict, os.path.join(self.t_config.output_dir, f"gs{global_step}.pkl"))
         if callback is not None:

src/textbrewer/distiller_basic.py

@@ -25,8 +25,7 @@ def __init__(self, train_config,
 
     def save_and_callback(self,global_step, step, epoch, callback):
         logger.info(f"Saving at global step {global_step}, epoch step {step + 1} epoch {epoch+1}")
-        coreModel = self.model_S.module if \
-            'DataParallel' in self.model_S.__class__.__name__ else self.model_S
+        coreModel = self.model_S.module if hasattr(self.model_S, "module") else self.model_S
         state_dict = coreModel.state_dict()
         torch.save(state_dict, os.path.join(self.t_config.output_dir, f"gs{global_step}.pkl"))
         if callback is not None:
@@ -77,6 +76,23 @@ def train(self, optimizer, dataloader, num_epochs, scheduler_class=None, schedul
             # overwrite scheduler
             scheduler = scheduler_class(**{'optimizer':optimizer},**scheduler_args)
 
+        if self.t_config.fp16:
+            if not has_apex:
+                raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
+            if isinstance(self.model_T,(list,tuple)):
+                models = [self.model_S] + list(self.model_T)
+                models, optimizer = amp.initialize(models, optimizer, opt_level=self.t_config.fp16_opt_level)
+                self.model_S = models[0]
+                self.model_T =models[1:]
+            else:
+                (self.model_S, self.model_T), optimizer = amp.initialize([self.model_S, self.model_T], optimizer, opt_level=self.t_config.fp16_opt_level)
+        if self.t_config.data_parallel:
+            self.model_S = torch.nn.DataParallel(self.model_S)
+            if isinstance(self.model_T,(list,tuple)):
+                self.model_T = [torch.nn.DataParallel(model_t) for model_t in self.model_T]
+            else:
+                self.model_T = torch.nn.DataParallel(self.model_T)
+
         if num_steps is not None:
             if self.d_config.is_caching_logits is True:
                 logger.warning("is_caching_logits is True, but num_steps is not None!")
@@ -96,14 +112,21 @@ def train(self, optimizer, dataloader, num_epochs, scheduler_class=None, schedul
                     batch = batch_postprocessor(batch)
                 total_loss = self.train_on_batch(batch,args)
                 total_loss /= self.t_config.gradient_accumulation_steps
-                total_loss.backward()
+                if self.t_config.fp16:
+                    with amp.scale_loss(total_loss,optimizer) as scaled_loss:
+                        scaled_loss.backward()
+                else:
+                    total_loss.backward()
 
                 self.write_loss(total_loss, writer_step)
                 writer_step += 1
 
                 if (step+1)%self.t_config.gradient_accumulation_steps == 0:
                     if max_grad_norm > 0:
-                        torch.nn.utils.clip_grad_norm_(self.model_S.parameters(), max_grad_norm)
+                        if self.t_config.fp16:
+                            torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), max_grad_norm)
+                        else:
+                            torch.nn.utils.clip_grad_norm_(self.model_S.parameters(), max_grad_norm)
                     optimizer.step()
                     if scheduler is not None:
                         scheduler.step()
@@ -153,14 +176,21 @@ def train(self, optimizer, dataloader, num_epochs, scheduler_class=None, schedul
                         batch = batch_postprocessor(batch)
                 total_loss = self.train_on_batch(batch,args)
                 total_loss /= self.t_config.gradient_accumulation_steps
-                total_loss.backward()
+                if self.t_config.fp16:
+                    with amp.scale_loss(total_loss,optimizer) as scaled_loss:
+                        scaled_loss.backward()
+                else:
+                    total_loss.backward()
 
                 self.write_loss(total_loss, writer_step)
                 writer_step += 1
 
                 if (step+1)%self.t_config.gradient_accumulation_steps == 0:
                     if max_grad_norm > 0:
-                        torch.nn.utils.clip_grad_norm_(self.model_S.parameters(), max_grad_norm)
+                        if self.t_config.fp16:
+                            torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), max_grad_norm)
+                        else:
+                            torch.nn.utils.clip_grad_norm_(self.model_S.parameters(), max_grad_norm) 
                     optimizer.step()
                     if scheduler is not None:
                         scheduler.step()

src/textbrewer/distiller_multitask.py

@@ -56,6 +56,18 @@ def train(self, optimizer, dataloaders, num_steps, scheduler_class=None, schedul
             # overwrite scheduler
             scheduler = scheduler_class(**{'optimizer':optimizer},**scheduler_args)
 
+        if self.t_config.fp16:
+            if not has_apex:
+                raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
+            tasknames, model_Ts = zip(*self.model_T.items())
+            models = [self.model_S] + list(model_Ts)
+            models, optimizer = amp.initialize(models, optimizer, opt_level=self.t_config.fp16_opt_level)
+            self.model_S = models[0]
+            self.model_T = dict(zip(tasknames,models[1:]))
+        if self.t_config.data_parallel:
+            self.model_S = torch.nn.DataParallel(self.model_S)
+            self.model_T = {k:torch.nn.DataParallel(v) for k,v in self.model_T.items()}
+
         total_global_steps = num_steps
         ckpt_steps =self.t_config.ckpt_steps
         print_every = ckpt_steps // self.print_freq
@@ -93,13 +105,19 @@ def train(self, optimizer, dataloaders, num_steps, scheduler_class=None, schedul
                 batch_taskname = (batch, taskname)
                 total_loss = self.train_on_batch(batch_taskname, args)
                 total_loss /= self.t_config.gradient_accumulation_steps
-                total_loss.backward()
-
+                if self.t_config.fp16:
+                    with amp.scale_loss(total_loss,optimizer) as scaled_loss:
+                        scaled_loss.backward()
+                else:
+                    total_loss.backward()
                 scalar_total_loss = total_loss.cpu().item() * self.t_config.gradient_accumulation_steps
                 self.tb_writer.add_scalar('scalar/total_loss', scalar_total_loss, writer_step)
                 writer_step += 1
             if max_grad_norm > 0:
-                torch.nn.utils.clip_grad_norm_(self.model_S.parameters(), max_grad_norm) 
+                if self.t_config.fp16:
+                    torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), max_grad_norm)
+                else:
+                    torch.nn.utils.clip_grad_norm_(self.model_S.parameters(), max_grad_norm)
             optimizer.step()
             if scheduler is not None:
                 scheduler.step()

src/textbrewer/distiller_train.py

@@ -41,6 +41,15 @@ def train(self, optimizer, dataloader, num_epochs, scheduler_class=None, schedul
             # overwrite scheduler
             scheduler = scheduler_class(**{'optimizer':optimizer},**scheduler_args)
 
+        if self.t_config.fp16:
+            if not has_apex:
+                raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
+            self.model, optimizer = amp.initialize(self.model, optimizer, opt_level=self.t_config.fp16_opt_level)
+
+        #dataparallel multi-gpu training
+        if self.t_config.data_parallel:
+            self.model = torch.nn.DataParallel(self.model)
+
         if num_steps is not None:
             total_global_steps = num_steps
             ckpt_steps =self.t_config.ckpt_steps
@@ -58,15 +67,22 @@ def train(self, optimizer, dataloader, num_epochs, scheduler_class=None, schedul
                     batch = batch_postprocessor(batch)
                 total_loss = self.train_on_batch(batch,args)
                 total_loss /= self.t_config.gradient_accumulation_steps
-                total_loss.backward()
+                if self.t_config.fp16:
+                    with amp.scale_loss(total_loss,optimizer) as scaled_loss:
+                        scaled_loss.backward()
+                else:
+                    total_loss.backward()
 
                 scalar_total_loss = total_loss.cpu().item() * self.t_config.gradient_accumulation_steps
                 self.tb_writer.add_scalar('scalar/total_loss', scalar_total_loss, writer_step)
                 writer_step += 1
 
                 if (step+1)%self.t_config.gradient_accumulation_steps == 0:
                     if max_grad_norm > 0:
-                        torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_grad_norm) 
+                        if self.t_config.fp16:
+                            torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), max_grad_norm)
+                        else:
+                            torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_grad_norm)
                     optimizer.step()
                     if scheduler is not None:
                         scheduler.step()
@@ -76,8 +92,7 @@ def train(self, optimizer, dataloader, num_epochs, scheduler_class=None, schedul
                         logger.info(f"Global step: {global_step}, epoch step:{step+1}")
                     if (global_step%ckpt_steps==0) or global_step==total_global_steps:
                         logger.info(f"Saving at global step {global_step}")
-                        coreModel = self.model.module if \
-                            'DataParallel' in self.model.__class__.__name__ else self.model
+                        coreModel = self.model.module if hasattr(self.model, "module") else self.model
                         state_dict = coreModel.state_dict()
                         torch.save(state_dict, os.path.join(self.t_config.output_dir,f"gs{global_step}.pkl"))
                         if callback is not None:
@@ -106,15 +121,22 @@ def train(self, optimizer, dataloader, num_epochs, scheduler_class=None, schedul
                     batch = batch_postprocessor(batch)
                 total_loss = self.train_on_batch(batch,args)
                 total_loss /= self.t_config.gradient_accumulation_steps
-                total_loss.backward()
+                if self.t_config.fp16:
+                    with amp.scale_loss(total_loss,optimizer) as scaled_loss:
+                        scaled_loss.backward()
+                else:
+                    total_loss.backward()
 
                 scalar_total_loss = total_loss.cpu().item() * self.t_config.gradient_accumulation_steps
                 self.tb_writer.add_scalar('scalar/total_loss', scalar_total_loss, writer_step)
                 writer_step += 1
 
                 if (step+1)%self.t_config.gradient_accumulation_steps == 0:
                     if max_grad_norm > 0:
-                        torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_grad_norm)
+                        if self.t_config.fp16:
+                            torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), max_grad_norm)
+                        else:
+                            torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_grad_norm) 
                     optimizer.step()
                     if scheduler is not None:
                         scheduler.step()
@@ -125,8 +147,7 @@ def train(self, optimizer, dataloader, num_epochs, scheduler_class=None, schedul
                     if (global_step%train_steps_per_epoch in checkpoints) \
                             and ((current_epoch+1)%self.t_config.ckpt_epoch_frequency==0 or current_epoch+1==num_epochs):
                         logger.info(f"Saving at global step {global_step}, epoch step {step+1} epoch {current_epoch+1}")
-                        coreModel = self.model.module if \
-                            'DataParallel' in self.model.__class__.__name__ else self.model
+                        coreModel = self.model.module if hasattr(self.model, "module") else self.model
                         state_dict = coreModel.state_dict()
                         torch.save(state_dict, os.path.join(self.t_config.output_dir,f"gs{global_step}.pkl"))
                         if callback is not None:

src/textbrewer/distiller_utils.py

@@ -13,7 +13,12 @@
 from .presets import *
 from .configurations import TrainingConfig, DistillationConfig
 import random
-from .compatibility import mask_dtype
+from .compatibility import mask_dtype, is_apex_available
+
+has_apex = is_apex_available()
+if has_apex:
+    from apex import amp
+
 
 logger = logging.getLogger("Distillation")
 #logger.setLevel(logging.INFO)