Get rid of checkpointing

It isn't needed in inference.

tortoise/models/arch_util.py

@@ -342,7 +342,7 @@ class CheckpointedLayer(nn.Module):
         for k, v in kwargs.items():
             assert not (isinstance(v, torch.Tensor) and v.requires_grad)  # This would screw up checkpointing.
         partial = functools.partial(self.wrap, **kwargs)
-        return torch.utils.checkpoint.checkpoint(partial, x, *args)
+        return partial(x, *args)
 
 
 class CheckpointedXTransformerEncoder(nn.Module):

tortoise/models/classifier.py

@@ -1,6 +1,5 @@
 import torch
 import torch.nn as nn
-from torch.utils.checkpoint import checkpoint
 
 from tortoise.models.arch_util import Upsample, Downsample, normalization, zero_module, AttentionBlock
 
@@ -64,14 +63,6 @@ class ResBlock(nn.Module):
             self.skip_connection = nn.Conv1d(dims, channels, self.out_channels, 1)
 
     def forward(self, x):
-        if self.do_checkpoint:
-            return checkpoint(
-                self._forward, x
-            )
-        else:
-            return self._forward(x)
-
-    def _forward(self, x):
         if self.updown:
             in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
             h = in_rest(x)
@@ -125,7 +116,7 @@ class AudioMiniEncoder(nn.Module):
         h = self.res(h)
         h = self.final(h)
         for blk in self.attn:
-            h = checkpoint(blk, h)
+            h = blk(h)
         return h[:, :, 0]
 
 

tortoise/models/cvvp.py

@@ -2,7 +2,6 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 from torch import einsum
-from torch.utils.checkpoint import checkpoint
 
 from tortoise.models.arch_util import AttentionBlock
 from tortoise.models.xtransformers import ContinuousTransformerWrapper, Encoder
@@ -44,7 +43,7 @@ class CollapsingTransformer(nn.Module):
     def forward(self, x, **transformer_kwargs):
         h = self.transformer(x, **transformer_kwargs)
         h = h.permute(0, 2, 1)
-        h = checkpoint(self.pre_combiner, h).permute(0, 2, 1)
+        h = self.pre_combiner(h).permute(0, 2, 1)
         if self.training:
             mask = torch.rand_like(h.float()) > self.mask_percentage
         else:

tortoise/models/xtransformers.py

@@ -1,16 +1,12 @@
-import functools
 import math
-import torch
+from collections import namedtuple
-from torch import nn, einsum
-import torch.nn.functional as F
 from functools import partial
 from inspect import isfunction
-from collections import namedtuple
 
-from einops import rearrange, repeat, reduce
+import torch
-from einops.layers.torch import Rearrange
+import torch.nn.functional as F
-
+from einops import rearrange, repeat
-from torch.utils.checkpoint import checkpoint
+from torch import nn, einsum
 
 DEFAULT_DIM_HEAD = 64
 
@@ -969,16 +965,16 @@ class AttentionLayers(nn.Module):
                     layer_past = None
 
             if layer_type == 'a':
-                out, inter, k, v = checkpoint(block, x, None, mask, None, attn_mask, self.pia_pos_emb, rotary_pos_emb,
+                out, inter, k, v = block(x, None, mask, None, attn_mask, self.pia_pos_emb, rotary_pos_emb,
                                         prev_attn, layer_mem, layer_past)
             elif layer_type == 'c':
                 if exists(full_context):
-                    out, inter, k, v = checkpoint(block, x, full_context[cross_attn_count], mask, context_mask, None, None,
+                    out, inter, k, v = block(x, full_context[cross_attn_count], mask, context_mask, None, None,
                                             None, prev_attn, None, layer_past)
                 else:
-                    out, inter, k, v = checkpoint(block, x, context, mask, context_mask, None, None, None, prev_attn, None, layer_past)
+                    out, inter, k, v = block(x, context, mask, context_mask, None, None, None, prev_attn, None, layer_past)
             elif layer_type == 'f':
-                out = checkpoint(block, x)
+                out = block(x)
 
             if layer_type == 'a' or layer_type == 'c' and present_key_values is not None:
                 present_key_values.append((k.detach(), v.detach()))