updating reqs
This commit is contained in:
Victor Hall
parent
3150f7d299
commit
4fb64fed66
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@ -1,7 +1,7 @@
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###################
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# Builder Stage
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FROM nvidia/cuda:11.8.0-devel-ubuntu22.04 AS builder
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LABEL org.opencontainers.image.licenses="AGPL-1.0-only"
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FROM nvidia/cuda:12.1.0-devel-ubuntu22.04 AS builder
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LABEL org.opencontainers.image.licenses="AGPL-3.0-only"
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ARG DEBIAN_FRONTEND=noninteractive
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# Don't write .pyc bytecode
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@ -32,13 +32,13 @@ ENV PATH="$VIRTUAL_ENV/bin:$PATH"
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ADD requirements-build.txt /build
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RUN --mount=type=cache,target=/root/.cache/pip \
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python3 -m venv ${VIRTUAL_ENV} && \
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pip install -U -I torch==2.0.0+cu118 torchvision==0.15.1+cu118 --extra-index-url "https://download.pytorch.org/whl/cu118" && \
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pip install -U -I torch==2.1.0+cu121 torchvision==0.16.0+cu121 --extra-index-url "https://download.pytorch.org/whl/cu121" && \
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pip install -r requirements-build.txt && \
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pip install --no-deps xformers==0.0.18
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pip install --no-deps xformers==0.0.20.post7
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###################
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# Runtime Stage
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FROM nvidia/cuda:11.8.0-runtime-ubuntu22.04 as runtime
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FROM nvidia/cuda:12.1.0-runtime-ubuntu22.04 as runtime
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# Use bash shell
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SHELL ["/bin/bash", "-o", "pipefail", "-c"]
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@ -77,9 +77,9 @@ RUN echo "source ${VIRTUAL_ENV}/bin/activate" >> /root/.bashrc
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# Workaround for:
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# https://github.com/TimDettmers/bitsandbytes/issues/62
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# https://github.com/TimDettmers/bitsandbytes/issues/73
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ENV LD_LIBRARY_PATH="/usr/local/cuda-11.8/targets/x86_64-linux/lib/"
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RUN ln /usr/local/cuda/targets/x86_64-linux/lib/libcudart.so.11.8.89 /usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudart.so
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RUN ln /usr/local/cuda/targets/x86_64-linux/lib/libnvrtc.so.11.8.89 /usr/local/cuda-11.8/targets/x86_64-linux/lib/libnvrtc.so
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ENV LD_LIBRARY_PATH="/usr/local/cuda-12.1/targets/x86_64-linux/lib/"
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RUN ln /usr/local/cuda/targets/x86_64-linux/lib/libcudart.so.12.1.55 /usr/local/cuda-12.1/targets/x86_64-linux/lib/libcudart.so
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RUN ln /usr/local/cuda/targets/x86_64-linux/lib/libnvrtc.so.12.1.55 /usr/local/cuda-12.1/targets/x86_64-linux/lib/libnvrtc.so
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# Vast.ai SSH ignores ENV vars unless fully exported
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# Exporting anything with an _ should cover the bases
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@ -42,11 +42,13 @@ class EveryDreamOptimizer():
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text_encoder: text encoder model parameters
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unet: unet model parameters
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"""
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def __init__(self, args, optimizer_config, text_encoder, unet, epoch_len):
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def __init__(self, args, optimizer_config, text_encoder, unet, epoch_len, log_writer=None):
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del optimizer_config["doc"]
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print(f"\n raw optimizer_config:")
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pprint.pprint(optimizer_config)
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self.epoch_len = epoch_len
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self.unet = unet # needed for weight norm logging, unet.parameters() has to be called again, Diffusers quirk
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self.log_writer = log_writer
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self.te_config, self.base_config = self.get_final_optimizer_configs(args, optimizer_config)
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self.te_freeze_config = optimizer_config.get("text_encoder_freezing", {})
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print(f" Final unet optimizer config:")
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@ -57,10 +59,16 @@ class EveryDreamOptimizer():
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self.grad_accum = args.grad_accum
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self.clip_grad_norm = args.clip_grad_norm
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self.apply_grad_scaler_step_tweaks = optimizer_config.get("apply_grad_scaler_step_tweaks", True)
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self.log_grad_norm = optimizer_config.get("log_grad_norm", True)
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self.text_encoder_params = self._apply_text_encoder_freeze(text_encoder)
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self.unet_params = unet.parameters()
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with torch.no_grad():
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log_action = lambda n, label: logging.info(f"{Fore.LIGHTBLUE_EX} {label} weight normal: {n}{Style.RESET_ALL}")
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self._log_weight_normal(text_encoder.text_model.encoder.layers.parameters(), "text encoder", log_action)
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self._log_weight_normal(unet.parameters(), "unet", log_action)
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self.optimizers = []
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self.optimizer_te, self.optimizer_unet = self.create_optimizers(args,
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self.text_encoder_params,
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@ -71,7 +79,6 @@ class EveryDreamOptimizer():
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self.lr_schedulers = []
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schedulers = self.create_lr_schedulers(args, optimizer_config)
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self.lr_schedulers.extend(schedulers)
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#print(self.lr_schedulers)
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self.load(args.resume_ckpt)
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@ -85,6 +92,34 @@ class EveryDreamOptimizer():
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logging.info(f" Grad scaler enabled: {self.scaler.is_enabled()} (amp mode)")
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def _log_gradient_normal(self, parameters: Generator, label: str, log_action=None):
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total_norm = self._get_norm(parameters, lambda p: p.grad.data)
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log_action(total_norm, label)
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def _log_weight_normal(self, parameters: Generator, label: str, log_action=None):
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total_norm = self._get_norm(parameters, lambda p: p.data)
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log_action(total_norm, label)
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def _calculate_normal(param, param_type):
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if param_type(param) is not None:
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return param_type(param).norm(2).item() ** 2
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else:
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return 0.0
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def _get_norm(self, parameters: Generator, param_type):
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total_norm = 0
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for p in parameters:
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param = param_type(p)
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total_norm += self._calculate_norm(param, p)
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total_norm = total_norm ** (1. / 2)
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return total_norm
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def _calculate_norm(self, param, p):
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if param is not None:
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return param.norm(2).item() ** 2
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else:
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return 0.0
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def step(self, loss, step, global_step):
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self.scaler.scale(loss).backward()
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@ -92,13 +127,31 @@ class EveryDreamOptimizer():
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if self.clip_grad_norm is not None:
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for optimizer in self.optimizers:
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self.scaler.unscale_(optimizer)
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torch.nn.utils.clip_grad_norm_(parameters=self.unet_params, max_norm=self.clip_grad_norm)
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torch.nn.utils.clip_grad_norm_(parameters=self.text_encoder_params, max_norm=self.clip_grad_norm)
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if self.log_grad_norm:
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pre_clip_norm = torch.nn.utils.clip_grad_norm_(parameters=self.unet.parameters(), max_norm=float('inf'))
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self.log_writer.add_scalar("optimizer/unet_pre_clip_norm", pre_clip_norm, global_step)
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pre_clip_norm = torch.nn.utils.clip_grad_norm_(parameters=self.text_encoder_params, max_norm=float('inf'))
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self.log_writer.add_scalar("optimizer/te_pre_clip_norm", pre_clip_norm, global_step)
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unet_grad_norm = torch.nn.utils.clip_grad_norm_(parameters=self.unet.parameters(), max_norm=self.clip_grad_norm)
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self.log_writer.add_scalar("optimizer/unet_grad_norm", unet_grad_norm, global_step)
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te_grad_norm = torch.nn.utils.clip_grad_norm_(parameters=self.text_encoder_params, max_norm=self.clip_grad_norm)
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self.log_writer.add_scalar("optimizer/te_grad_norm", te_grad_norm, global_step)
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for optimizer in self.optimizers:
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self.scaler.step(optimizer)
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self.scaler.update()
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if self.log_grad_norm and self.log_writer:
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log_info_unet_fn = lambda n, label: self.log_writer.add_scalar(label, n, global_step)
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log_info_te_fn = lambda n, label: self.log_writer.add_scalar(label, n, global_step)
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with torch.no_grad():
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self._log_gradient_normal(self.unet_params, "optimizer/unet_grad_norm", log_info_unet_fn)
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self._log_gradient_normal(self.text_encoder_params, "optimizer/te_grad_norm", log_info_te_fn)
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self._zero_grad(set_to_none=True)
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for scheduler in self.lr_schedulers:
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@ -278,6 +331,7 @@ class EveryDreamOptimizer():
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no_prox = False # ????, dadapt_adan
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use_bias_correction = True # suggest by prodigy github
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growth_rate=float("inf") # dadapt various, no idea what a sane default is
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safeguard_warmup = True # per recommendation from prodigy documentation
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if local_optimizer_config is not None:
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betas = local_optimizer_config.get("betas", betas)
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@ -290,6 +344,7 @@ class EveryDreamOptimizer():
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decouple = local_optimizer_config.get("decouple", decouple)
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momentum = local_optimizer_config.get("momentum", momentum)
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growth_rate = local_optimizer_config.get("growth_rate", growth_rate)
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safeguard_warmup = local_optimizer_config.get("safeguard_warmup", safeguard_warmup)
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if args.lr is not None:
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curr_lr = args.lr
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logging.info(f"Overriding LR from optimizer config with main config/cli LR setting: {curr_lr}")
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@ -322,7 +377,6 @@ class EveryDreamOptimizer():
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elif optimizer_name == "prodigy":
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from prodigyopt import Prodigy
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opt_class = Prodigy
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safeguard_warmup = True # per recommendation from prodigy documentation
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optimizer = opt_class(
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itertools.chain(parameters),
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lr=curr_lr,
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@ -449,6 +503,7 @@ class EveryDreamOptimizer():
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unfreeze_final_layer_norm = not self.te_freeze_config["freeze_final_layer_norm"]
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parameters = itertools.chain([])
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if unfreeze_embeddings:
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parameters = itertools.chain(parameters, text_encoder.text_model.embeddings.parameters())
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else:
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@ -20,7 +20,7 @@
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},
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"base": {
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"optimizer": "adamw8bit",
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"lr": 2e-6,
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"lr": 1e-6,
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"lr_scheduler": "constant",
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"lr_decay_steps": null,
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"lr_warmup_steps": null,
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@ -30,7 +30,7 @@
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},
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"text_encoder_overrides": {
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"optimizer": null,
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"lr": null,
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"lr": 3e-7,
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"lr_scheduler": null,
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"lr_decay_steps": null,
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"lr_warmup_steps": null,
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@ -1,7 +1,7 @@
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torch==2.0.1
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torchvision==0.15.2
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transformers==4.29.2
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diffusers[torch]==0.18.0
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torch==2.1.0
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torchvision==0.16.0
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transformers==4.35.0
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diffusers[torch]==0.21.4
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pynvml==11.4.1
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bitsandbytes==0.41.1
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ftfy==6.1.1
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