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Revert "(re) attempt diffusion checkpointing logic"

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This reverts commit b22eec8fe3.
This commit is contained in:
James Betker 2022-01-22 09:14:50 -07:00
parent b22eec8fe3
commit d18aec793a
1 changed files with 8 additions and 6 deletions
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14
codes/models/gpt_voice/unet_diffusion_tts_experimental.py
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@ -66,6 +66,11 @@ class ResBlock(TimestepBlock):
:param emb: an [N x emb_channels] Tensor of timestep embeddings. :param emb: an [N x emb_channels] Tensor of timestep embeddings.
:return: an [N x C x ...] Tensor of outputs. :return: an [N x C x ...] Tensor of outputs.
""" """
return checkpoint(
self._forward, x, emb
)
def _forward(self, x, emb):
h = self.in_layers(x) h = self.in_layers(x)
emb_out = self.emb_layers(emb).type(h.dtype) emb_out = self.emb_layers(emb).type(h.dtype)
while len(emb_out.shape) < len(h.shape): while len(emb_out.shape) < len(h.shape):
@ -313,12 +318,12 @@ class DiffusionTts(nn.Module):
h_tok = F.interpolate(module(tokens).permute(0,2,1), size=(h.shape[-1]), mode='nearest') h_tok = F.interpolate(module(tokens).permute(0,2,1), size=(h.shape[-1]), mode='nearest')
h = h + h_tok h = h + h_tok
else: else:
h = checkpoint(module, h, emb) h = module(h, emb)
hs.append(h) hs.append(h)
h = checkpoint(self.middle_block, h, emb) h = self.middle_block(h, emb)
for module in self.output_blocks: for module in self.output_blocks:
h = torch.cat([h, hs.pop()], dim=1) h = torch.cat([h, hs.pop()], dim=1)
h = checkpoint(module, h, emb) h = module(h, emb)
h = h.type(x.dtype) h = h.type(x.dtype)
out = self.out(h) out = self.out(h)
return out[:, :, :orig_x_shape] return out[:, :, :orig_x_shape]
@ -372,8 +377,6 @@ if __name__ == '__main__':
model = DiffusionTts(64, channel_mult=[1,1.5,2, 3, 4, 6, 8, 8, 8, 8], num_res_blocks=[2, 2, 2, 2, 2, 2, 2, 4, 4, 4], model = DiffusionTts(64, channel_mult=[1,1.5,2, 3, 4, 6, 8, 8, 8, 8], num_res_blocks=[2, 2, 2, 2, 2, 2, 2, 4, 4, 4],
token_conditioning_resolutions=[1,4,16,64], attention_resolutions=[256,512], num_heads=4, kernel_size=3, token_conditioning_resolutions=[1,4,16,64], attention_resolutions=[256,512], num_heads=4, kernel_size=3,
scale_factor=2, conditioning_inputs_provided=True, time_embed_dim_multiplier=4) scale_factor=2, conditioning_inputs_provided=True, time_embed_dim_multiplier=4)
model(clip, ts, tok, cond)
"""
p, r = model.benchmark(clip, ts, tok, cond) p, r = model.benchmark(clip, ts, tok, cond)
p = {k: v / 1000000000 for k, v in p.items()} p = {k: v / 1000000000 for k, v in p.items()}
p = sorted(p.items(), key=operator.itemgetter(1)) p = sorted(p.items(), key=operator.itemgetter(1))
@ -386,5 +389,4 @@ if __name__ == '__main__':
r = sorted(r.items(), key=operator.itemgetter(1)) r = sorted(r.items(), key=operator.itemgetter(1))
print(r) print(r)
print(sum([j[1] for j in r])) print(sum([j[1] for j in r]))
"""