set ability to do warmup steps for each unet during training

dalle2_pytorch/dalle2_pytorch.py

@@ -335,10 +335,6 @@ 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.)
@@ -353,7 +349,7 @@ 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, eps = eps)))
+            log(cdf_delta)))
 
     return log_probs
 
@@ -708,7 +704,7 @@ class Attention(nn.Module):
         sim = sim - sim.amax(dim = -1, keepdim = True).detach()
         sim = sim * self.pb_relax_alpha
 
-        attn = sim.softmax(dim = -1)
+        attn = sim.softmax(dim = -1, dtype = torch.float32)
         attn = self.dropout(attn)
 
         # aggregate values
@@ -1131,12 +1127,11 @@ class SinusoidalPosEmb(nn.Module):
         self.dim = dim
 
     def forward(self, x):
-        dtype, device = x.dtype, x.device
         half_dim = self.dim // 2
         emb = math.log(10000) / (half_dim - 1)
-        emb = torch.exp(torch.arange(half_dim, device = device, dtype = dtype) * -emb)
+        emb = torch.exp(torch.arange(half_dim, device = x.device) * -emb)
         emb = rearrange(x, 'i -> i 1') * rearrange(emb, 'j -> 1 j')
-        return torch.cat((emb.sin(), emb.cos()), dim = -1).type(dtype)
+        return torch.cat((emb.sin(), emb.cos()), dim = -1)
 
 class Block(nn.Module):
     def __init__(
@@ -1277,7 +1272,7 @@ class CrossAttention(nn.Module):
         sim = sim - sim.amax(dim = -1, keepdim = True).detach()
         sim = sim * self.pb_relax_alpha
 
-        attn = sim.softmax(dim = -1)
+        attn = sim.softmax(dim = -1, dtype = torch.float32)
 
         out = einsum('b h i j, b h j d -> b h i d', attn, v)
         out = rearrange(out, 'b h n d -> b n (h d)')
@@ -1631,7 +1626,6 @@ class Unet(nn.Module):
 
         # time conditioning
 
-        time = time.type_as(x)
         time_hiddens = self.to_time_hiddens(time)
 
         time_tokens = self.to_time_tokens(time_hiddens)

dalle2_pytorch/trainer.py

@@ -550,7 +550,7 @@ class DecoderTrainer(nn.Module):
         if only_model:
             return loaded_obj
 
-        for ind, last_step in zip(range(0, self.num_unets), self.steps.tolist()):
+        for ind, last_step in zip(range(0, self.num_unets), self.steps.cpu().unbind()):
 
             optimizer_key = f'optim{ind}'
             optimizer = getattr(self, optimizer_key)

dalle2_pytorch/version.py

@@ -1 +1 @@
-__version__ = '0.16.10'
+__version__ = '0.16.5'