release v

pytorch_lightning/models/trainer.py

@@ -5,7 +5,13 @@ from pytorch_lightning.root_module.memory import get_gpu_memory_map
 import traceback
 from pytorch_lightning.root_module.model_saving import TrainerIO
 from torch.optim.lr_scheduler import MultiStepLR
+import pdb
 
+try:
+    from apex import amp
+    APEX_AVAILABLE = True
+except ModuleNotFoundError:
+    APEX_AVAILABLE = False
 
 class Trainer(TrainerIO):
 
@@ -26,6 +32,9 @@ class Trainer(TrainerIO):
                  train_percent_check=1.0, val_percent_check=1.0, test_percent_check=1.0, val_check_interval=0.95,
                  log_save_interval=1, add_log_row_interval=1,
                  lr_scheduler_milestones=None,
+                 use_amp=False,
+                 check_grad_nans=False,
+                 amp_level='O2',
                  nb_sanity_val_steps=5):
 
         # Transfer params
@@ -51,6 +60,8 @@ class Trainer(TrainerIO):
         self.nb_sanity_val_steps = nb_sanity_val_steps
         self.lr_scheduler_milestones = [] if lr_scheduler_milestones is None else [int(x.strip()) for x in lr_scheduler_milestones.split(',')]
         self.lr_schedulers = []
+        self.amp_level = amp_level
+        self.check_grad_nans = check_grad_nans
 
         # training state
         self.optimizers = None
@@ -73,6 +84,11 @@ class Trainer(TrainerIO):
         self.__determine_data_use_amount(train_percent_check, val_percent_check, test_percent_check, overfit_pct)
         print('gpu available: {}, used: {}'.format(torch.cuda.is_available(), self.on_gpu))
 
+        # apex test
+        self.use_amp = use_amp and APEX_AVAILABLE
+        if self.use_amp:
+            print('using 16bit precision')
+
     def __determine_data_use_amount(self, train_percent_check, val_percent_check, test_percent_check, overfit_pct):
         """
         Use less data for debugging purposes
@@ -110,21 +126,21 @@ class Trainer(TrainerIO):
         self.tqdm_metrics = {}
 
         # determine number of training batches
-        nb_tng_batches = self.model.nb_batches(self.tng_dataloader)
-        self.nb_tng_batches = int(nb_tng_batches * self.train_percent_check)
+        self.nb_tng_batches = self.model.nb_batches(self.tng_dataloader)
+        self.nb_tng_batches = int(self.nb_tng_batches * self.train_percent_check)
 
         # determine number of validation batches
-        nb_val_batches = self.model.nb_batches(self.val_dataloader)
-        nb_val_batches = int(nb_val_batches * self.val_percent_check)
-        nb_val_batches = max(1, nb_val_batches)
-        self.nb_val_batches = nb_val_batches
+        self.nb_val_batches = self.model.nb_batches(self.val_dataloader)
+        self.nb_val_batches = int(self.nb_val_batches * self.val_percent_check)
+        self.nb_val_batches = max(1, self.nb_val_batches)
+        self.nb_val_batches = self.nb_val_batches
 
         # determine number of test batches
-        nb_test_batches = self.model.nb_batches(self.test_dataloader)
-        self.nb_test_batches = int(nb_test_batches * self.test_percent_check)
+        self.nb_test_batches = self.model.nb_batches(self.test_dataloader)
+        self.nb_test_batches = int(self.nb_test_batches * self.test_percent_check)
 
         # determine when to check validation
-        self.val_check_batch = int(nb_tng_batches * self.val_check_interval)
+        self.val_check_batch = int(self.nb_tng_batches * self.val_check_interval)
 
     def __add_tqdm_metrics(self, metrics):
         for k, v in metrics.items():
@@ -151,19 +167,19 @@ class Trainer(TrainerIO):
         outputs = []
 
         # run training
-        for i, data_batch in enumerate(dataloader):
+        for batch_i, data_batch in enumerate(dataloader):
 
             if data_batch is None:
                 continue
 
             # stop short when on fast dev run
-            if max_batches is not None and i >= max_batches:
+            if max_batches is not None and batch_i >= max_batches:
                 break
 
             # -----------------
             # RUN VALIDATION STEP
             # -----------------
-            output = model.validation_step(data_batch)
+            output = model.validation_step(data_batch, batch_i)
             outputs.append(output)
 
             # batch done
@@ -207,6 +223,14 @@ class Trainer(TrainerIO):
         # filter out the weights that were done on gpu so we can load on good old cpus
         self.optimizers = model.configure_optimizers()
 
+        if self.use_amp:
+            # An example
+            self.model, optimizer = amp.initialize(
+                self.model, self.optimizers[0], opt_level=self.amp_level,
+            )
+            self.optimizers[0] = optimizer
+            model.trainer = self
+
         # add lr schedulers
         if self.lr_scheduler_milestones is not None:
             for optimizer in self.optimizers:
@@ -270,7 +294,7 @@ class Trainer(TrainerIO):
                 # ---------------
                 # RUN TRAIN STEP
                 # ---------------
-                batch_result = self.__run_tng_batch(data_batch)
+                batch_result = self.__run_tng_batch(data_batch, batch_nb)
                 early_stop_epoch = batch_result == -1
 
                 # ---------------
@@ -281,11 +305,11 @@ class Trainer(TrainerIO):
                     self.__run_validation()
 
                 # when batch should be saved
-                if (batch_nb + 1) % self.log_save_interval == 0:
+                if (batch_nb + 1) % self.log_save_interval == 0 or early_stop_epoch:
                     self.experiment.save()
 
                 # when metrics should be logged
-                if batch_nb % self.add_log_row_interval == 0:
+                if batch_nb % self.add_log_row_interval == 0 or early_stop_epoch:
                     # count items in memory
                     # nb_params, nb_tensors = count_mem_items()
 
@@ -328,7 +352,7 @@ class Trainer(TrainerIO):
                     return
 
 
-    def __run_tng_batch(self, data_batch):
+    def __run_tng_batch(self, data_batch, batch_nb):
         if data_batch is None:
             return 0
 
@@ -343,11 +367,21 @@ class Trainer(TrainerIO):
 
         # forward pass
         # return a scalar value and a dic with tqdm metrics
-        loss, model_specific_tqdm_metrics_dic = self.model.training_step(data_batch)
+        loss, model_specific_tqdm_metrics_dic = self.model.training_step(data_batch, batch_nb)
         self.__add_tqdm_metrics(model_specific_tqdm_metrics_dic)
 
         # backward pass
-        loss.backward()
+        if self.use_amp:
+            for optimizer in self.optimizers:
+                with amp.scale_loss(loss, optimizer) as scaled_loss:
+                    scaled_loss.backward()
+        else:
+            loss.backward()
+
+        if self.check_grad_nans:
+            for param in self.model.parameters():
+                print(param.grad.float().sum())
+
         self.batch_loss_value += loss.item()
 
         # gradient update with accumulated gradients

pytorch_lightning/root_module/model_saving.py

@@ -88,6 +88,7 @@ class TrainerIO(object):
         self.early_stop_callback.wait = checkpoint['early_stop_callback_wait']
         self.early_stop_callback.patience = checkpoint['early_stop_callback_patience']
         self.global_step = checkpoint['global_step']
+        self.current_epoch = checkpoint['epoch']
 
         # restore the optimizers
         optimizer_states = checkpoint['optimizer_states']

pytorch_lightning/root_module/root_module.py

@@ -40,8 +40,7 @@ class RootModule(GradInformation, ModelIO, OptimizerConfig, ModelHooks):
 
         if self.on_gpu:
             print('running on gpu...')
-            self.dtype = torch.cuda.FloatTensor
-            torch.set_default_tensor_type('torch.cuda.FloatTensor')
+            torch.set_default_tensor_type(hparams.default_tensor_type)
 
     def forward(self, *args, **kwargs):
         """
@@ -52,7 +51,7 @@ class RootModule(GradInformation, ModelIO, OptimizerConfig, ModelHooks):
         """
         raise NotImplementedError
 
-    def validation_step(self, data_batch):
+    def validation_step(self, data_batch, batch_nb):
         """
         return whatever outputs will need to be aggregated in validation_end
         :param data_batch:
@@ -68,7 +67,7 @@ class RootModule(GradInformation, ModelIO, OptimizerConfig, ModelHooks):
         """
         raise NotImplementedError
 
-    def training_step(self, data_batch):
+    def training_step(self, data_batch, batch_nb):
         """
         return loss, dict with metrics for tqdm
         :param data_batch:
@@ -151,19 +150,23 @@ class RootModule(GradInformation, ModelIO, OptimizerConfig, ModelHooks):
             return 0, 0
 
     @classmethod
-    def load_from_metrics(cls, weights_path, tags_csv, on_gpu):
+    def load_from_metrics(cls, weights_path, tags_csv, on_gpu, map_location=None):
         """
         Primary way of loading model from csv weights path
         :param weights_path:
         :param tags_csv:
         :param on_gpu:
+        :param map_location: dic for mapping storage {'cuda:1':'cuda:0'}
         :return:
         """
         hparams = load_hparams_from_tags_csv(tags_csv)
         hparams.__setattr__('on_gpu', on_gpu)
 
         if on_gpu:
-            checkpoint = torch.load(weights_path)
+            if map_location is not None:
+                checkpoint = torch.load(weights_path, map_location=map_location)
+            else:
+                checkpoint = torch.load(weights_path)
         else:
             checkpoint = torch.load(weights_path, map_location=lambda storage, loc: storage)
 

pytorch_lightning/utils/arg_parse.py

@@ -49,6 +49,11 @@ def add_default_args(parser, root_dir, rand_seed=None, possible_model_names=None
     parser.add_argument('--gpus', default='0', type=str)
     parser.add_argument('--single_run_gpu', dest='single_run_gpu', action='store_true')
     parser.add_argument('--disable_cuda', dest='disable_cuda', action='store_true')
+    parser.add_argument('--default_tensor_type', default='torch.cuda.FloatTensor', type=str)
+    parser.add_argument('--use_amp', dest='use_amp', action='store_true')
+    parser.add_argument('--check_grad_nans', dest='check_grad_nans', action='store_true')
+    parser.add_argument('--amp_level', default='O2',type=str)
+
 
     # run on hpc
     parser.add_argument('--on_cluster', dest='on_cluster', action='store_true')

setup.py

@@ -7,7 +7,7 @@ from setuptools import setup, find_packages
 # http://blog.ionelmc.ro/2014/05/25/python-packaging/
 setup(
     name="pytorch-lightning",
-    version='0.1.dev15',
+    version='0.1.dev1832',
     description="The Keras for ML researchers using PyTorch",
     author="William Falcon",
     author_email="waf2107@columbia.edu",
@@ -23,6 +23,7 @@ setup(
     ],
     packages=find_packages(),
     long_description=open("README.md", encoding="utf-8").read(),
+    long_description_content_type='text/markdown',
     include_package_data=True,
     zip_safe=False,
 )