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a/checkpoints/relay_kitchen_fdk_fw0_step80k_20260917/comparison_20260918_cp_eval3/source_original_verified/cleandiffuser/__init__.py b/checkpoints/relay_kitchen_fdk_fw0_step80k_20260917/comparison_20260918_cp_eval3/source_original_verified/cleandiffuser/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..3dc1f76bc69e3f559bee6253b24fc93acee9e1f9 --- /dev/null +++ b/checkpoints/relay_kitchen_fdk_fw0_step80k_20260917/comparison_20260918_cp_eval3/source_original_verified/cleandiffuser/__init__.py @@ -0,0 +1 @@ +__version__ = "0.1.0" diff --git a/checkpoints/relay_kitchen_fdk_fw0_step80k_20260917/comparison_20260918_cp_eval3/source_original_verified/cleandiffuser/classifier/__init__.py b/checkpoints/relay_kitchen_fdk_fw0_step80k_20260917/comparison_20260918_cp_eval3/source_original_verified/cleandiffuser/classifier/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ded4a13729f250664955000b1bcd78c514be1fac --- /dev/null +++ b/checkpoints/relay_kitchen_fdk_fw0_step80k_20260917/comparison_20260918_cp_eval3/source_original_verified/cleandiffuser/classifier/__init__.py @@ -0,0 +1,4 @@ +from .base import BaseClassifier +from .mse_classifier import MSEClassifier +from .rew_classifiers import CumRewClassifier +from .qgpo_classifier import QGPOClassifier diff --git a/checkpoints/relay_kitchen_fdk_fw0_step80k_20260917/comparison_20260918_cp_eval3/source_original_verified/cleandiffuser/classifier/base.py b/checkpoints/relay_kitchen_fdk_fw0_step80k_20260917/comparison_20260918_cp_eval3/source_original_verified/cleandiffuser/classifier/base.py new file mode 100644 index 0000000000000000000000000000000000000000..586573b92824f390baf9a9cfeafa93d24616bbc5 --- /dev/null +++ b/checkpoints/relay_kitchen_fdk_fw0_step80k_20260917/comparison_20260918_cp_eval3/source_original_verified/cleandiffuser/classifier/base.py @@ -0,0 +1,91 @@ +from copy import deepcopy +from typing import Optional + +import torch + +from cleandiffuser.nn_classifier import BaseNNClassifier + + +class BaseClassifier: + """ + Basic classifier for classifier-guidance. + Generally, the classifier predicts the logp(c|x_t, noise), + and then uses the gradient with respect to x_t to guide the diffusion model in sampling the distribution p(x_0|c). + """ + + def __init__( + self, + nn_classifier: BaseNNClassifier, + ema_rate: float = 0.995, + grad_clip_norm: Optional[float] = None, + optim_params: Optional[dict] = None, + device: str = "cpu", + ): + if optim_params is None: + optim_params = {"lr": 2e-4, "weight_decay": 1e-4} + self.device = device + self.ema_rate, self.grad_clip_norm = ema_rate, grad_clip_norm + self.model = nn_classifier.to(device) + self.model_ema = deepcopy(self.model).eval() + self.optim = torch.optim.Adam(self.model.parameters(), **optim_params) + + def eval(self): + self.model.eval() + self.model_ema.eval() + + def train(self): + self.model.train() + + def ema_update(self): + with torch.no_grad(): + for p, p_ema in zip(self.model.parameters(), self.model_ema.parameters()): + p_ema.data.mul_(self.ema_rate).add_(p.data, alpha=1. - self.ema_rate) + + def loss(self, x: torch.Tensor, noise: torch.Tensor, y: torch.Tensor): + raise NotImplementedError + + def update(self, x: torch.Tensor, noise: torch.Tensor, y: torch.Tensor, update_ema: bool = True): + loss = self.loss(x, noise, y) + self.optim.zero_grad() + loss.backward() + if isinstance(self.grad_clip_norm, float): + grad_norm = torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.grad_clip_norm).item() + else: + grad_norm = None + self.optim.step() + if update_ema: + self.ema_update() + return {"loss": loss.item(), "grad_norm": grad_norm} + + def logp(self, x: torch.Tensor, noise: torch.Tensor, c: torch.Tensor): + """ + Calculate logp(c|x_t / scale, noise) for classifier-guidance. + + Input: + - x: (batch, *x_shape) + - noise: (batch, ) + - c: (batch, *c_shape) + + Output: + - logp(c|x, noise): (batch, 1) + """ + raise NotImplementedError + + def gradients(self, x: torch.Tensor, noise: torch.Tensor, c: torch.Tensor): + x.requires_grad_() + logp = self.logp(x, noise, c) + grad = torch.autograd.grad([logp.sum()], [x])[0] + x.detach() + return logp.detach(), grad.detach() + + def save(self, path): + torch.save({ + "model": self.model.state_dict(), + "model_ema": self.model_ema.state_dict() + }, path) + + def load(self, path): + checkpoint = torch.load(path, map_location=self.device) + self.model.load_state_dict(checkpoint["model"]) + self.model_ema.load_state_dict(checkpoint["model_ema"]) + diff --git a/checkpoints/relay_kitchen_fdk_fw0_step80k_20260917/comparison_20260918_cp_eval3/source_original_verified/cleandiffuser/classifier/mse_classifier.py b/checkpoints/relay_kitchen_fdk_fw0_step80k_20260917/comparison_20260918_cp_eval3/source_original_verified/cleandiffuser/classifier/mse_classifier.py new file mode 100644 index 0000000000000000000000000000000000000000..ed2d38bce42de866d32ca10a66d0bcecdee6e881 --- /dev/null +++ b/checkpoints/relay_kitchen_fdk_fw0_step80k_20260917/comparison_20260918_cp_eval3/source_original_verified/cleandiffuser/classifier/mse_classifier.py @@ -0,0 +1,29 @@ +from typing import Optional + +import torch +import torch.nn as nn + +from cleandiffuser.nn_classifier import BaseNNClassifier +from .base import BaseClassifier + + +class MSEClassifier(BaseClassifier): + """ + MSEClassifier defines logp(y | x, t) using negative MSE. + Assuming nn_classifier is a NN used to predict y through x and t, i.e, pred_y = nn_classifier(x, t), + logp is defined as - temperature * MSE(nn_classifier(x, t), y). + """ + def __init__( + self, nn_classifier: BaseNNClassifier, temperature: float = 1.0, + ema_rate: float = 0.995, grad_clip_norm: Optional[float] = None, + optim_params: Optional[dict] = None, device: str = "cpu"): + super().__init__(nn_classifier, ema_rate, grad_clip_norm, optim_params, device) + self.temperature = temperature + + def loss(self, x: torch.Tensor, noise: torch.Tensor, y: torch.Tensor): + pred_y = self.model(x, noise) + return nn.functional.mse_loss(pred_y, y) + + def logp(self, x: torch.Tensor, noise: torch.Tensor, c: torch.Tensor): + pred_y = self.model_ema(x, noise) + return -self.temperature * ((pred_y - c) ** 2).mean(-1, keepdim=True)