trainer classes now takes care of auto-casting numpy to torch tensors, and setting correct device based on model parameter devices

README.md

@@ -1013,6 +1013,7 @@ Once built, images will be saved to the same directory the command is invoked
 - [ ] decoder needs one day worth of refactor for tech debt
 - [ ] allow for unet to be able to condition non-cross attention style as well
 - [ ] for all model classes with hyperparameters that changes the network architecture, make it requirement that they must expose a config property, and write a simple function that asserts that it restores the object correctly
+- [ ] for both diffusion prior and decoder, all exponential moving averaged models needs to be saved and restored as well (as well as the step number)
 
 ## Citations
 

dalle2_pytorch/train.py

@@ -1,7 +1,7 @@
 import time
 import copy
 from math import ceil
-from functools import partial
+from functools import partial, wraps
 from collections.abc import Iterable
 
 import torch
@@ -11,6 +11,8 @@ from torch.cuda.amp import autocast, GradScaler
 from dalle2_pytorch.dalle2_pytorch import Decoder, DiffusionPrior
 from dalle2_pytorch.optimizer import get_optimizer
 
+import numpy as np
+
 # helper functions
 
 def exists(val):
@@ -45,6 +47,29 @@ 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
 
+# decorators
+
+def cast_torch_tensor(fn):
+    @wraps(fn)
+    def inner(model, *args, **kwargs):
+        device = kwargs.pop('_device', next(model.parameters()).device)
+        cast_device = kwargs.pop('_cast_device', True)
+
+        kwargs_keys = kwargs.keys()
+        all_args = (*args, *kwargs.values())
+        split_kwargs_index = len(all_args) - len(kwargs_keys)
+        all_args = tuple(map(lambda t: torch.from_numpy(t) if exists(t) and isinstance(t, np.ndarray) else t, all_args))
+
+        if cast_device:
+            all_args = tuple(map(lambda t: t.to(device) if exists(t) and isinstance(t, torch.Tensor) else t, all_args))
+
+        args, kwargs_values = all_args[:split_kwargs_index], all_args[split_kwargs_index:]
+        kwargs = dict(tuple(zip(kwargs_keys, kwargs_values)))
+
+        out = fn(model, *args, **kwargs)
+        return out
+    return inner
+
 # gradient accumulation functions
 
 def split_iterable(it, split_size):
@@ -254,10 +279,12 @@ class DiffusionPriorTrainer(nn.Module):
         self.step += 1
 
     @torch.inference_mode()
+    @cast_torch_tensor
     def p_sample_loop(self, *args, **kwargs):
         return self.ema_diffusion_prior.ema_model.p_sample_loop(*args, **kwargs)
 
     @torch.inference_mode()
+    @cast_torch_tensor
     def sample(self, *args, **kwargs):
         return self.ema_diffusion_prior.ema_model.sample(*args, **kwargs)
 
@@ -265,6 +292,7 @@ class DiffusionPriorTrainer(nn.Module):
     def sample_batch_size(self, *args, **kwargs):
         return self.ema_diffusion_prior.ema_model.sample_batch_size(*args, **kwargs)
 
+    @cast_torch_tensor
     def forward(
         self,
         *args,
@@ -377,6 +405,7 @@ class DecoderTrainer(nn.Module):
         self.step += 1
 
     @torch.no_grad()
+    @cast_torch_tensor
     def sample(self, *args, **kwargs):
         if self.use_ema:
             trainable_unets = self.decoder.unets
@@ -393,6 +422,7 @@ class DecoderTrainer(nn.Module):
 
         return output
 
+    @cast_torch_tensor
     def forward(
         self,
         *args,

setup.py

@@ -10,7 +10,7 @@ setup(
       'dream = dalle2_pytorch.cli:dream'
     ],
   },
-  version = '0.2.32',
+  version = '0.2.34',
   license='MIT',
   description = 'DALL-E 2',
   author = 'Phil Wang',
@@ -30,6 +30,7 @@ setup(
     'einops-exts>=0.0.3',
     'embedding-reader',
     'kornia>=0.5.4',
+    'numpy',
     'pillow',
     'resize-right>=0.0.2',
     'rotary-embedding-torch',