default the device to the device that the diffusion prior parameters are on, if the trainer was never given the accelerator nor device

dalle2_pytorch/dalle2_pytorch.py

@@ -335,6 +335,10 @@ def approx_standard_normal_cdf(x):
 def discretized_gaussian_log_likelihood(x, *, means, log_scales, thres = 0.999):
     assert x.shape == means.shape == log_scales.shape
 
+    # attempting to correct nan gradients when learned variance is turned on
+    # in the setting of deepspeed fp16
+    eps = 1e-12 if x.dtype == torch.float32 else 1e-5
+
     centered_x = x - means
     inv_stdv = torch.exp(-log_scales)
     plus_in = inv_stdv * (centered_x + 1. / 255.)
@@ -349,7 +353,7 @@ def discretized_gaussian_log_likelihood(x, *, means, log_scales, thres = 0.999):
         log_cdf_plus,
         torch.where(x > thres,
             log_one_minus_cdf_min,
-            log(cdf_delta)))
+            log(cdf_delta, eps = eps)))
 
     return log_probs
 

dalle2_pytorch/trainer.py

@@ -173,14 +173,26 @@ class DiffusionPriorTrainer(nn.Module):
         super().__init__()
         assert isinstance(diffusion_prior, DiffusionPrior)
         assert not exists(accelerator) or isinstance(accelerator, Accelerator)
-        assert exists(accelerator) or exists(device), "You must supply some method of obtaining a device."
         ema_kwargs, kwargs = groupby_prefix_and_trim('ema_', kwargs)
 
+        # verbosity
+
+        self.verbose = verbose
+
         # assign some helpful member vars
+
         self.accelerator = accelerator
-        self.device = accelerator.device if exists(accelerator) else device
         self.text_conditioned = diffusion_prior.condition_on_text_encodings
 
+        # setting the device
+
+        if not exists(accelerator) and not exists(device):
+            diffusion_prior_device = next(diffusion_prior.parameters()).device
+            self.print(f'accelerator not given, and device not specified: defaulting to device of diffusion prior parameters - {diffusion_prior_device}')
+            self.device = diffusion_prior_device
+        else:
+            self.device = accelerator.device if exists(accelerator) else device
+
         # save model
 
         self.diffusion_prior = diffusion_prior
@@ -214,13 +226,9 @@ class DiffusionPriorTrainer(nn.Module):
 
         self.max_grad_norm = max_grad_norm
 
-        # verbosity
-
-        self.verbose = verbose
-
         # track steps internally
 
-        self.register_buffer('step', torch.tensor([0]))
+        self.register_buffer('step', torch.tensor([0], device = self.device))
 
     # accelerator wrappers
 

dalle2_pytorch/version.py

@@ -1 +1 @@
-__version__ = '0.16.9'
+__version__ = '0.16.12'