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Add torch2.0 compile support #54

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Add torch2.0 compile support #54

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15 changes: 11 additions & 4 deletions training/train_muse.py
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Original file line number Diff line number Diff line change
Expand Up @@ -363,6 +363,13 @@ def main():
text_encoder.to(device=accelerator.device, dtype=weight_dtype)
vq_model.to(device=accelerator.device)

if config.training.get("torch_compile_vqgan", False):
vq_encode = torch.compile(vq_model.encode, mode="reduce-overhead")
vq_decode = torch.compile(vq_model.decode_code, mode="reduce-overhead")
else:
vq_encode = vq_model.encode
vq_decode = vq_model.decode_code

if config.training.overfit_one_batch:
train_dataloader = [next(iter(train_dataloader))]

Expand Down Expand Up @@ -423,7 +430,7 @@ def prepare_inputs_and_labels(
pixel_values, temp=config.training.soft_code_temp, stochastic=config.training.use_stochastic_code
)
else:
image_tokens = vq_model.encode(pixel_values)[1]
image_tokens = vq_encode(pixel_values)[1]
soft_targets = None

encoder_hidden_states = text_encoder(input_ids)[0]
Expand Down Expand Up @@ -554,7 +561,7 @@ def prepare_inputs_and_labels(

# Generate images
if (global_step + 1) % config.experiment.generate_every == 0 and accelerator.is_main_process:
generate_images(model, vq_model, text_encoder, tokenizer, accelerator, config, global_step + 1)
generate_images(model, vq_decode, text_encoder, tokenizer, accelerator, config, global_step + 1)

global_step += 1
# TODO: Add generation
Expand Down Expand Up @@ -601,7 +608,7 @@ def validate_model(model, eval_dataloader, accelerator, global_step, prepare_inp


@torch.no_grad()
def generate_images(model, vq_model, text_encoder, tokenizer, accelerator, config, global_step):
def generate_images(model, vq_decode, text_encoder, tokenizer, accelerator, config, global_step):
logger.info("Generating images...")
model.eval()
# fmt: off
Expand Down Expand Up @@ -634,7 +641,7 @@ def generate_images(model, vq_model, text_encoder, tokenizer, accelerator, confi
# In the beginning of training, the model is not fully trained and the generated token ids can be out of range
# so we clamp them to the correct range.
gen_token_ids = torch.clamp(gen_token_ids, max=accelerator.unwrap_model(model).config.codebook_size - 1)
images = vq_model.decode_code(gen_token_ids)
images = vq_decode(gen_token_ids)
model.train()

# Convert to PIL images
Expand Down