support for native amp

.drone.yml

@@ -31,7 +31,7 @@ steps:
     - pip install pip -U
     - pip --version
     - nvidia-smi
-    # - bash ./tests/install_AMP.sh
+    #- bash ./tests/install_AMP.sh
     - apt-get update && apt-get install -y cmake
     - pip install -r requirements.txt --user -q
     - pip install -r ./tests/requirements-devel.txt --user -q

pytorch_lightning/core/hooks.py

@@ -140,9 +140,15 @@ def backward(self, use_amp, loss, optimizer):
 
         """
         if trainer.precision == 16:
-
             # .backward is not special on 16-bit with TPUs
-            if not trainer.on_tpu:
+            if trainer.on_tpu:
+                return
+
+            if self.trainer.use_native_amp:
+                self.trainer.scaler.scale(loss).backward()
+
+            # TODO: remove in v0.8.0
+            else:
                 with amp.scale_loss(loss, optimizer) as scaled_loss:
                     scaled_loss.backward()
         else:

pytorch_lightning/core/lightning.py

@@ -1157,9 +1157,22 @@ def optimizer_step(self, current_epoch, batch_idx, optimizer,
         if self.trainer.use_tpu and XLA_AVAILABLE:
             xm.optimizer_step(optimizer)
         elif isinstance(optimizer, torch.optim.LBFGS):
+
+            # native amp + lbfgs is a no go right now
+            if self.use_amp and self.use_native_amp:
+                m = 'native PyTorch amp and lbfgs are not compatible. To request, please file' \
+                    'a Github issue in PyTorch and tag @mcarilli'
+                raise MisconfigurationException(m)
             optimizer.step(second_order_closure)
         else:
-            optimizer.step()
+            if self.use_amp and self.use_native_amp:
+                self.trainer.scaler.step(optimizer)
+            else:
+                optimizer.step()
+
+        # in native 16-bit we need to update scaler after optimizer step
+        if self.use_amp and self.use_native_amp:
+            self.trainer.scaler.update()
 
         # model hook
         self.on_before_zero_grad(optimizer)

pytorch_lightning/trainer/auto_mix_precision.py

@@ -1,6 +1,8 @@
 from abc import ABC
+import torch
 
 from pytorch_lightning import _logger as log
+from pytorch_lightning.utilities import rank_zero_warn
 
 try:
     from apex import amp
@@ -15,8 +17,28 @@ class TrainerAMPMixin(ABC):
     # this is just a summary on variables used in this abstract class,
     #  the proper values/initialisation should be done in child class
     precision: int
+    use_native_amp: bool
 
     def init_amp(self, use_amp):
+        # TODO: remove in v 0.8.0
+        if self.use_native_amp:
+            rank_zero_warn("`amp_level` has been deprecated since v0.7.4 "
+                           "(native amp does not require it)"
+                           " and this argument will be removed in v0.8.0", DeprecationWarning)
+
+        # Backward compatibility, TODO: remove in v0.9.0
+        if use_amp is not None:
+            rank_zero_warn("`use_amp` has been replaced by `precision` since v0.7.0"
+                           " and this argument will be removed in v0.9.0", DeprecationWarning)
+            self.precision = 16 if use_amp else 32
+
+        assert self.precision in (16, 32), 'only 32 or 16 bit precision supported'
+
+        if use_amp and self.use_native_amp:
+            log.info('Using 16bit precision.')
+            return
+
+        # TODO: remove all below for v0.8.0
         if use_amp and not APEX_AVAILABLE:  # pragma: no-cover
             raise ModuleNotFoundError("""
             You set `use_amp=True` but do not have apex installed.
@@ -31,4 +53,4 @@ def init_amp(self, use_amp):
 
     @property
     def use_amp(self) -> bool:
-        return self.precision == 16 and APEX_AVAILABLE
+        return self.precision == 16

pytorch_lightning/trainer/distrib_data_parallel.py

@@ -151,6 +151,7 @@ class TrainerDDPMixin(ABC):
     amp_level: str
     use_tpu: bool
     default_root_dir: str
+    use_native_amp: bool
 
     @property
     @abstractmethod
@@ -350,8 +351,8 @@ def ddp_train(self, process_idx, model):
 
         # AMP
         # run through amp wrapper before going to distributed DP
-        if self.use_amp:
-            # An example
+        # TODO: remove in v0.8.0
+        if self.use_amp and not self.use_native_amp:
             model, optimizers = model.configure_apex(amp, model, self.optimizers, self.amp_level)
             self.optimizers = optimizers
 

pytorch_lightning/trainer/distrib_parts.py

@@ -394,6 +394,7 @@ class TrainerDPMixin(ABC):
     tpu_local_core_rank: int
     tpu_global_core_rank: int
     use_tpu: bool
+    use_native_amp: bool
     data_parallel_device_ids: ...
     logger: Union[LightningLoggerBase, bool]
 
@@ -481,7 +482,8 @@ def single_gpu_train(self, model):
         # allow for lr schedulers as well
         self.optimizers, self.lr_schedulers, self.optimizer_frequencies = self.init_optimizers(model)
 
-        if self.use_amp:
+        # TODO: update for 0.8.0
+        if self.use_amp and not self.use_native_amp:
             # An example
             model, optimizers = model.configure_apex(amp, model, self.optimizers, self.amp_level)
             self.optimizers = optimizers
@@ -528,9 +530,16 @@ def dp_train(self, model):
 
         model.cuda(self.root_gpu)
 
+        # hack forward to do autocast for the user
+        model_autocast_original_forward = model.forward
+        if self.use_amp and self.use_native_amp:
+            # wrap the user's forward in autocast and give it back at the end
+            model.forward = torch.cuda.amp.autocast()(model.forward)
+
+        # TODO: remove in v0.8.0
         # check for this bug (amp + dp + !01 doesn't work)
         # https://github.com/NVIDIA/apex/issues/227
-        if self.use_dp and self.use_amp:
+        if self.use_dp and self.use_amp and not self.use_native_amp:
             if self.amp_level == 'O2':
                 raise MisconfigurationException(
                     f'Amp level {self.amp_level} with DataParallel is not supported.'
@@ -551,6 +560,8 @@ def dp_train(self, model):
 
         self.run_pretrain_routine(model)
 
+        model.forward = model_autocast_original_forward
+
     def horovod_train(self, model):
         # Horovod: initialize library
         hvd.init()

pytorch_lightning/trainer/evaluation_loop.py

@@ -268,7 +268,11 @@ def _evaluate(self, model: LightningModule, dataloaders, max_batches: int, test_
                 # -----------------
                 # RUN EVALUATION STEP
                 # -----------------
-                output = self.evaluation_forward(model, batch, batch_idx, dataloader_idx, test_mode)
+                if self.use_amp and self.use_native_amp:
+                    with torch.cuda.amp.autocast():
+                        output = self.evaluation_forward(model, batch, batch_idx, dataloader_idx, test_mode)
+                else:
+                    output = self.evaluation_forward(model, batch, batch_idx, dataloader_idx, test_mode)
 
                 # on dp / ddp2 might still want to do something with the batch parts
                 if test_mode:

pytorch_lightning/trainer/trainer.py

@@ -115,7 +115,6 @@ def __init__(
             print_nan_grads: bool = False,  # backward compatible, todo: remove in v0.9.0
             weights_summary: Optional[str] = 'full',
             weights_save_path: Optional[str] = None,
-            amp_level: str = 'O1',
             num_sanity_val_steps: int = 5,
             truncated_bptt_steps: Optional[int] = None,
             resume_from_checkpoint: Optional[str] = None,
@@ -124,6 +123,7 @@ def __init__(
             reload_dataloaders_every_epoch: bool = False,
             auto_lr_find: Union[bool, str] = False,
             replace_sampler_ddp: bool = True,
+            amp_level: str = 'O1',  # backward compatible, todo: remove in v0.8.0
             default_save_path=None,  # backward compatible, todo: remove in v0.8.0
             gradient_clip=None,  # backward compatible, todo: remove in v0.8.0
             nb_gpu_nodes=None,  # backward compatible, todo: remove in v0.8.0
@@ -487,20 +487,18 @@ def __init__(
         self.determine_data_use_amount(train_percent_check, val_percent_check,
                                        test_percent_check, overfit_pct)
 
-        # 16 bit mixed precision training using apex
+        # AMP init
+        # These are the only lines needed after v0.8.0
+        # we wrap the user's forward with autocast and give it back at the end of fit
+        self.autocast_original_forward = None
+        self.use_native_amp = hasattr(torch.cuda, "amp") and hasattr(torch.cuda.amp, "autocast")
+        if self.use_native_amp and self.precision == 16:
+            self.scaler = torch.cuda.amp.GradScaler()
+            self.precision = precision
+
+        # TODO: remove for v0.8.0
         self.amp_level = amp_level
         self.precision = precision
-
-        # Backward compatibility, TODO: remove in v0.9.0
-        if use_amp is not None:
-            rank_zero_warn("`use_amp` has been replaced by `precision` since v0.7.0"
-                           " and this argument will be removed in v0.9.0", DeprecationWarning)
-            self.precision = 16 if use_amp else 32
-
-        assert self.precision in (16, 32), 'only 32 or 16 bit precision supported'
-
-        if self.precision == 16 and self.num_tpu_cores is None:
-            use_amp = True
         self.init_amp(use_amp)
 
         # Callback system

pytorch_lightning/trainer/training_io.py

@@ -281,6 +281,10 @@ def restore(self, checkpoint_path: str, on_gpu: bool):
         if on_gpu:
             model.cuda(self.root_gpu)
 
+        # restore amp scaling
+        if self.use_amp and self.use_native_amp and 'native_amp_scaling_state' in checkpoint:
+            self.scaler.load_state_dict(checkpoint['native_amp_scaling_state'])
+
         # load training state (affects trainer only)
         self.restore_training_state(checkpoint)
 
@@ -316,6 +320,10 @@ def dump_checkpoint(self):
 
         checkpoint['state_dict'] = model.state_dict()
 
+        # restore native amp scaling
+        if self.use_amp and self.use_native_amp and 'native_amp_scaling_state' in checkpoint:
+            checkpoint['native_amp_scaling_state'] = self.scaler.state_dict()
+
         if hasattr(model, "hparams"):
             is_namespace = isinstance(model.hparams, Namespace)
             checkpoint['hparams'] = vars(model.hparams) if is_namespace else model.hparams
@@ -441,6 +449,10 @@ def hpc_load(self, folderpath, on_gpu):
         # load the state_dict on the model automatically
         model.load_state_dict(checkpoint['state_dict'])
 
+        # restore amp scaling
+        if self.use_amp and self.use_native_amp and 'native_amp_scaling_state' in checkpoint:
+            self.scaler.load_state_dict(checkpoint['native_amp_scaling_state'])
+
         if self.root_gpu is not None:
             model.cuda(self.root_gpu)
 

pytorch_lightning/trainer/training_loop.py

@@ -148,6 +148,7 @@ def training_step(self, batch, batch_idx):
 
 import numpy as np
 from torch.utils.data import DataLoader
+import torch
 
 from pytorch_lightning import _logger as log
 from pytorch_lightning.callbacks.base import Callback
@@ -588,8 +589,12 @@ def run_training_batch(self, batch, batch_idx):
                 def optimizer_closure():
                     # forward pass
                     with self.profiler.profile('model_forward'):
-                        output_dict = self.training_forward(
-                            split_batch, batch_idx, opt_idx, self.hiddens)
+                        if self.use_amp and self.use_native_amp:
+                            with torch.cuda.amp.autocast():
+                                output_dict = self.training_forward(split_batch, batch_idx,
+                                                                    opt_idx, self.hiddens)
+                        else:
+                            output_dict = self.training_forward(split_batch, batch_idx, opt_idx, self.hiddens)
 
                         # format and reduce outputs accordingly
                         processed_output = self.process_output(output_dict, train=True)
@@ -645,6 +650,8 @@ def optimizer_closure():
                                 self.track_grad_norm)
 
                     # clip gradients
+                    if self.use_amp and self.use_native_amp:
+                        self.scaler.unscale_(optimizer)
                     self.clip_gradients()
 
                     # calls .step(), .zero_grad()

pytorch_lightning/trainer/training_tricks.py

@@ -24,6 +24,7 @@ def get_model(self):
         """Warning: this is just empty shell for code implemented in other class."""
 
     def clip_gradients(self):
+
         # this code is a modification of torch.nn.utils.clip_grad_norm_
         # with TPU support based on https://github.com/pytorch/xla/blob/master/TROUBLESHOOTING.md
         if self.gradient_clip_val > 0: