take care of gradient accumulation automatically for researchers, by passing in a max_batch_size on the decoder or diffusion prior trainer forward

README.md

@@ -732,8 +732,8 @@ clip = CLIP(
 
 # mock data
 
-text = torch.randint(0, 49408, (4, 256)).cuda()
-images = torch.randn(4, 3, 256, 256).cuda()
+text = torch.randint(0, 49408, (32, 256)).cuda()
+images = torch.randn(32, 3, 256, 256).cuda()
 
 # decoder (with unet)
 
@@ -774,7 +774,12 @@ decoder_trainer = DecoderTrainer(
 )
 
 for unet_number in (1, 2):
-    loss = decoder_trainer(images, text = text, unet_number = unet_number)  # use the decoder_trainer forward
+    loss = decoder_trainer(
+        images,
+        text = text,
+        unet_number = unet_number, # which unet to train on
+        max_batch_size = 4         # gradient accumulation - this sets the maximum batch size in which to do forward and backwards pass - for this example 32 / 4 == 8 times
+    )
 
     decoder_trainer.update(unet_number) # update the specific unet as well as its exponential moving average
 

dalle2_pytorch/train.py

@@ -1,6 +1,8 @@
 import time
 import copy
+from math import ceil
 from functools import partial
+from collections.abc import Iterable
 
 import torch
 from torch import nn
@@ -14,6 +16,9 @@ from dalle2_pytorch.optimizer import get_optimizer
 def exists(val):
     return val is not None
 
+def default(val, d):
+    return val if exists(val) else d
+
 def cast_tuple(val, length = 1):
     return val if isinstance(val, tuple) else ((val,) * length)
 
@@ -40,6 +45,46 @@ def groupby_prefix_and_trim(prefix, d):
     kwargs_without_prefix = dict(map(lambda x: (x[0][len(prefix):], x[1]), tuple(kwargs_with_prefix.items())))
     return kwargs_without_prefix, kwargs
 
+# gradient accumulation functions
+
+def split_iterable(it, split_size):
+    accum = []
+    for ind in range(ceil(len(it) / split_size)):
+        start_index = ind * split_size
+        accum.append(it[start_index: (start_index + split_size)])
+    return accum
+
+def split(t, split_size = None):
+    if not exists(split_size):
+        return t
+
+    if isinstance(t, torch.Tensor):
+        return t.split(split_size, dim = 0)
+
+    if isinstance(t, Iterable):
+        return split_iterable(t, split_size)
+
+    return TypeError
+
+def split_args_and_kwargs(x, *args, split_size = None, **kwargs):
+    batch_size = len(x)
+    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_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]
+    chunk_sizes = tuple(map(len, split_all_args[0]))
+
+    for (chunk_size, *chunked_all_args) in tuple(zip(chunk_sizes, *split_all_args)):
+        chunked_args, chunked_kwargs_values = chunked_all_args[:split_index], chunked_all_args[split_index:]
+        chunked_kwargs = dict(tuple(zip(dict_keys, chunked_kwargs_values)))
+        yield chunk_size, (chunked_args, chunked_kwargs)
+
 # print helpers
 
 def print_ribbon(s, symbol = '=', repeat = 40):
@@ -208,15 +253,25 @@ class DiffusionPriorTrainer(nn.Module):
 
     def forward(
         self,
+        x,
         *args,
-        divisor = 1,
+        max_batch_size = None,
         **kwargs
     ):
-        with autocast(enabled = self.amp):
-            loss = self.diffusion_prior(*args, **kwargs)
-            scaled_loss = self.scaler.scale(loss / divisor)
-            scaled_loss.backward()
-        return loss.item()
+        batch_size = x.shape[0]
+        total_samples = 0
+        total_loss = 0.
+
+        for chunk_size, (chunked_args, chunked_kwargs) in split_args_and_kwargs(x, *args, split_size = max_batch_size, **kwargs):
+            with autocast(enabled = self.amp):
+                loss = self.diffusion_prior(*chunked_args, **chunked_kwargs)
+
+                total_loss += loss.item() * chunk_size
+                total_samples += chunk_size
+
+                self.scaler.scale(loss * (chunk_size / batch_size)).backward()
+
+        return total_loss / total_samples
 
 # decoder trainer
 
@@ -327,11 +382,20 @@ class DecoderTrainer(nn.Module):
         x,
         *,
         unet_number,
-        divisor = 1,
+        max_batch_size = None,
         **kwargs
     ):
-        with autocast(enabled = self.amp):
-            loss = self.decoder(x, unet_number = unet_number, **kwargs)
-            scaled_loss = self.scale(loss / divisor, unet_number = unet_number)
-            scaled_loss.backward()
-        return loss.item()
+        batch_size = x.shape[0]
+        total_samples = 0
+        total_loss = 0.
+
+        for chunk_size, (chunked_args, chunked_kwargs) in split_args_and_kwargs(x, split_size = max_batch_size, **kwargs):
+            with autocast(enabled = self.amp):
+                loss = self.decoder(*chunked_args, unet_number = unet_number, **chunked_kwargs)
+
+                total_loss += loss.item() * chunk_size
+                total_samples += chunk_size
+
+                self.scale(loss * (chunk_size / batch_size), unet_number = unet_number).backward()
+
+        return total_loss / total_samples

setup.py

@@ -10,7 +10,7 @@ setup(
       'dream = dalle2_pytorch.cli:dream'
     ],
   },
-  version = '0.2.24',
+  version = '0.2.26',
   license='MIT',
   description = 'DALL-E 2',
   author = 'Phil Wang',