make sure gradient accumulation feature works even if all arguments passed in are keyword arguments

README.md

@@ -814,8 +814,8 @@ clip = CLIP(
 
 # mock data
 
-text = torch.randint(0, 49408, (4, 256)).cuda()
+text = torch.randint(0, 49408, (32, 256)).cuda()
-images = torch.randn(4, 3, 256, 256).cuda()
+images = torch.randn(32, 3, 256, 256).cuda()
 
 # prior networks (with transformer)
 
@@ -842,7 +842,7 @@ diffusion_prior_trainer = DiffusionPriorTrainer(
     ema_update_every = 10,
 )
 
-loss = diffusion_prior_trainer(text, images)
+loss = diffusion_prior_trainer(text, images, max_batch_size = 4)
 diffusion_prior_trainer.update()  # this will update the optimizer as well as the exponential moving averaged diffusion prior
 
 # after much of the above three lines in a loop

dalle2_pytorch/train.py

@@ -66,15 +66,24 @@ def split(t, split_size = None):
 
     return TypeError
 
-def split_args_and_kwargs(x, *args, split_size = None, **kwargs):
+def find_first(cond, arr):
-    batch_size = len(x)
+    for el in arr:
+        if cond(el):
+            return el
+    return None
+
+def split_args_and_kwargs(*args, split_size = None, **kwargs):
+    all_args = (*args, *kwargs.values())
+    len_all_args = len(all_args)
+    first_tensor = find_first(lambda t: isinstance(t, torch.Tensor), all_args)
+    assert exists(first_tensor)
+
+    batch_size = len(first_tensor)
     split_size = default(split_size, batch_size)
     chunk_size = ceil(batch_size / split_size)
 
     dict_len = len(kwargs)
     dict_keys = kwargs.keys()
-    all_args = (x, *args, *kwargs.values())
-    len_all_args = len(all_args)
     split_kwargs_index = len_all_args - dict_len
 
     split_all_args = [split(arg, split_size = split_size) if exists(arg) and isinstance(arg, (torch.Tensor, Iterable)) else ((arg,) * chunk_size) for arg in all_args]
@@ -258,14 +267,13 @@ class DiffusionPriorTrainer(nn.Module):
 
     def forward(
         self,
-        x,
         *args,
         max_batch_size = None,
         **kwargs
     ):
         total_loss = 0.
 
-        for chunk_size_frac, (chunked_args, chunked_kwargs) in split_args_and_kwargs(x, *args, split_size = max_batch_size, **kwargs):
+        for chunk_size_frac, (chunked_args, chunked_kwargs) in split_args_and_kwargs(*args, split_size = max_batch_size, **kwargs):
             with autocast(enabled = self.amp):
                 loss = self.diffusion_prior(*chunked_args, **chunked_kwargs)
                 loss = loss * chunk_size_frac
@@ -385,15 +393,14 @@ class DecoderTrainer(nn.Module):
 
     def forward(
         self,
-        x,
+        *args,
-        *,
         unet_number,
         max_batch_size = None,
         **kwargs
     ):
         total_loss = 0.
 
-        for chunk_size_frac, (chunked_args, chunked_kwargs) in split_args_and_kwargs(x, split_size = max_batch_size, **kwargs):
+        for chunk_size_frac, (chunked_args, chunked_kwargs) in split_args_and_kwargs(*args, split_size = max_batch_size, **kwargs):
             with autocast(enabled = self.amp):
                 loss = self.decoder(*chunked_args, unet_number = unet_number, **chunked_kwargs)
                 loss = loss * chunk_size_frac

setup.py

@@ -10,7 +10,7 @@ setup(
       'dream = dalle2_pytorch.cli:dream'
     ],
   },
-  version = '0.2.30',
+  version = '0.2.31',
   license='MIT',
   description = 'DALL-E 2',
   author = 'Phil Wang',