attempting to fix issue with deepspeed fp16 seeing overflowing gradient

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/version.py

@@ -1 +1 @@
-__version__ = '0.16.9'
+__version__ = '0.16.10'