models/CSDI/tiffusion.py

import math
import torch
import torch.nn.functional as F

from torch import nn
from einops import reduce
from tqdm.auto import tqdm
from functools import partial
from ..model_utils import default, identity, extract
from .control import *
from .diff_csdi import diff_CSDI
from .csdi import CSDI_base
import numpy as np


def linear_beta_schedule(timesteps):
    scale = 1000 / timesteps
    beta_start = scale * 0.0001
    beta_end = scale * 0.02
    return torch.linspace(beta_start, beta_end, timesteps, dtype=torch.float64)


def cosine_beta_schedule(timesteps, s=0.008):
    """

    cosine schedule

    as proposed in https://openreview.net/forum?id=-NEXDKk8gZ

    """
    steps = timesteps + 1
    x = torch.linspace(0, timesteps, steps, dtype=torch.float64)
    alphas_cumprod = torch.cos(((x / timesteps) + s) / (1 + s) * math.pi * 0.5) ** 2
    alphas_cumprod = alphas_cumprod / alphas_cumprod[0]
    betas = 1 - (alphas_cumprod[1:] / alphas_cumprod[:-1])
    return torch.clip(betas, 0, 0.999)


class Tiffusion(nn.Module):
    def __init__(

        self,

        seq_length,

        feature_size,

        n_layer_enc=3,

        n_layer_dec=6,

        d_model=None,

        timesteps=1000,

        sampling_timesteps=None,

        loss_type="l1",

        beta_schedule="cosine",

        n_heads=4,

        mlp_hidden_times=4,

        eta=0.0,

        attn_pd=0.0,

        resid_pd=0.0,

        kernel_size=None,

        padding_size=None,

        use_ff=True,

        reg_weight=None,

        control_signal={},

        moving_average=False,

        is_unconditional=False,

        target_strategy="mix",

        **kwargs,

    ):
        super(Tiffusion, self).__init__()

        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

        self.eta, self.use_ff = eta, use_ff
        self.seq_length = seq_length
        self.feature_size = feature_size
        self.ff_weight = default(reg_weight, math.sqrt(self.seq_length) / 5)
        self.sum_weight = default(reg_weight, math.sqrt(self.seq_length // 10) / 50)
        self.training_control_signal = control_signal # training control signal
        self.moving_average = moving_average
        self.is_unconditional = is_unconditional
        self.target_strategy = target_strategy
        
        self.target_strategy = "random"
        config = {
            "model": {
                "timeemb": 128,
                "featureemb": 16,
                "is_unconditional": False,
                "target_strategy": "mix",
            },
            "diffusion": {
                "layers": 3,
                "channels": 64,
                "nheads": 8,
                "diffusion_embedding_dim": 128,
                "is_linear": False,
                "beta_start": 0.0001,
                "beta_end": 0.5,
                "schedule": "quad",
                "num_steps": 50,
            }
        }

        self.emb_time_dim = config["model"]["timeemb"]
        self.emb_feature_dim = config["model"]["featureemb"]
        self.is_unconditional = config["model"]["is_unconditional"]
        self.target_strategy = config["model"]["target_strategy"]
        # parameters for diffusion models
        config_diff = config["diffusion"]
        self.num_steps = config_diff["num_steps"]
        if config_diff["schedule"] == "quad":
            self.beta = np.linspace(
                config_diff["beta_start"] ** 0.5, config_diff["beta_end"] ** 0.5, self.num_steps
            ) ** 2
        elif config_diff["schedule"] == "linear":
            self.beta = np.linspace(
                config_diff["beta_start"], config_diff["beta_end"], self.num_steps
            )

        self.alpha_hat = 1 - self.beta
        self.alpha = np.cumprod(self.alpha_hat)
        self.alpha_torch = torch.tensor(self.alpha).float().to(self.device).unsqueeze(1).unsqueeze(1)
        
        self.emb_total_dim = self.emb_time_dim + self.emb_feature_dim
        if self.is_unconditional == False:
            self.emb_total_dim += 1  # for conditional mask
        self.target_dim = feature_size
        print(feature_size)
        self.embed_layer = nn.Embedding(
            num_embeddings=self.target_dim
            , embedding_dim=self.emb_feature_dim
        )
                
        self.diffmodel = diff_CSDI(
            {
                "layers": 3,
                "channels": 64,
                "nheads": 8,
                "diffusion_embedding_dim": 128,
                "is_linear": False,
                "beta_start": 0.0001,
                "beta_end": 0.5,
                "schedule": "quad",
                "num_steps": 50,
                "side_dim": self.emb_total_dim
            },
            (1 if self.is_unconditional == True else 2)
        )

        if beta_schedule == "linear":
            betas = linear_beta_schedule(timesteps)
        elif beta_schedule == "cosine":
            betas = cosine_beta_schedule(timesteps)
        else:
            raise ValueError(f"unknown beta schedule {beta_schedule}")

        alphas = 1.0 - betas
        alphas_cumprod = torch.cumprod(alphas, dim=0)
        alphas_cumprod_prev = F.pad(alphas_cumprod[:-1], (1, 0), value=1.0)

        (timesteps,) = betas.shape
        self.num_timesteps = int(timesteps)
        self.loss_type = loss_type

        # sampling related parameters
        self.sampling_timesteps = default(
            sampling_timesteps, timesteps
        )  # default num sampling timesteps to number of timesteps at training

        assert self.sampling_timesteps <= timesteps
        self.fast_sampling = self.sampling_timesteps < timesteps

        # helper function to register buffer from float64 to float32

        register_buffer = lambda name, val: self.register_buffer(
            name, val.to(torch.float32)
        )

        register_buffer("betas", betas)
        register_buffer("alphas_cumprod", alphas_cumprod)
        register_buffer("alphas_cumprod_prev", alphas_cumprod_prev)

        # calculations for diffusion q(x_t | x_{t-1}) and others

        register_buffer("sqrt_alphas_cumprod", torch.sqrt(alphas_cumprod))
        register_buffer(
            "sqrt_one_minus_alphas_cumprod", torch.sqrt(1.0 - alphas_cumprod)
        )
        register_buffer("log_one_minus_alphas_cumprod", torch.log(1.0 - alphas_cumprod))
        register_buffer("sqrt_recip_alphas_cumprod", torch.sqrt(1.0 / alphas_cumprod))
        register_buffer(
            "sqrt_recipm1_alphas_cumprod", torch.sqrt(1.0 / alphas_cumprod - 1)
        )

        # calculations for posterior q(x_{t-1} | x_t, x_0)

        posterior_variance = (
            betas * (1.0 - alphas_cumprod_prev) / (1.0 - alphas_cumprod)
        )

        # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)

        register_buffer("posterior_variance", posterior_variance)

        # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain

        register_buffer(
            "posterior_log_variance_clipped",
            torch.log(posterior_variance.clamp(min=1e-20)),
        )
        register_buffer(
            "posterior_mean_coef1",
            betas * torch.sqrt(alphas_cumprod_prev) / (1.0 - alphas_cumprod),
        )
        register_buffer(
            "posterior_mean_coef2",
            (1.0 - alphas_cumprod_prev) * torch.sqrt(alphas) / (1.0 - alphas_cumprod),
        )

        # calculate reweighting

        register_buffer(
            "loss_weight",
            torch.sqrt(alphas) * torch.sqrt(1.0 - alphas_cumprod) / betas / 100,
        )

    def predict_noise_from_start(self, x_t, t, x0):
        return (
            extract(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - x0
        ) / extract(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)

    def predict_start_from_noise(self, x_t, t, noise):
        return (
            extract(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t
            - extract(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
        )

    def q_posterior(self, x_start, x_t, t):
        posterior_mean = (
            extract(self.posterior_mean_coef1, t, x_t.shape) * x_start
            + extract(self.posterior_mean_coef2, t, x_t.shape) * x_t
        )
        posterior_variance = extract(self.posterior_variance, t, x_t.shape)
        posterior_log_variance_clipped = extract(
            self.posterior_log_variance_clipped, t, x_t.shape
        )
        return posterior_mean, posterior_variance, posterior_log_variance_clipped

    def output(self, x, t, padding_masks=None, control_signal=None):
        """Modified output function to work with CSDI"""
        if isinstance(t, int):
            t = torch.tensor([t]).to(x.device)
            
        # Prepare side info
        observed_tp = torch.arange(x.shape[1], device=x.device).float()
        observed_tp = observed_tp.unsqueeze(0).expand(x.shape[0], -1)
        side_info = self.get_side_info(observed_tp, padding_masks)
        
        # Get model prediction
        predicted, _ = self.diffmodel(x, side_info, t)
        return predicted

    def generate_mts(self, batch_size=16):
        feature_size, seq_length = self.feature_size, self.seq_length
        sample_fn = self.fast_sample if self.fast_sampling else self.sample
        return sample_fn((batch_size, seq_length, feature_size))

    def generate_mts_infill(self, target, partial_mask=None, clip_denoised=True, model_kwargs=None):
        """Improved method for conditional generation"""
        with torch.no_grad():
            # Setup inputs
            observed_tp = torch.arange(target.shape[1], device=target.device).float()
            observed_tp = observed_tp.unsqueeze(0).expand(target.shape[0], -1)
            
            # Generate side info
            side_info = self.get_side_info(observed_tp, partial_mask)
            
            # Sample using CSDI imputation
            samples = self.impute(
                observed_data=target,
                cond_mask=partial_mask,
                side_info=side_info,
                n_samples=1
            )
            
            return samples.squeeze(1)

    # def fast_sample_infill_float_mask(
    #     self,
    #     shape,
    #     target: torch.Tensor,  # target time series # [B, L, C]
    #     sampling_timesteps,
    #     partial_mask: torch.Tensor = None,  # float mask between 0 and 1 # [B, L, C]
    #     clip_denoised=True,
    #     model_kwargs=None,
    # ):
    #     batch, device, total_timesteps, eta = (
    #         shape[0],
    #         self.betas.device,
    #         self.num_timesteps,
    #         self.eta,
    #     )
    #     # [-1, 0, 1, 2, ..., T-1] when sampling_timesteps == total_timesteps
    #     times = torch.linspace(-1, total_timesteps - 1, steps=sampling_timesteps + 1)

    #     times = list(reversed(times.int().tolist()))
    #     time_pairs = list(
    #         zip(times[:-1], times[1:])
    #     )  # [(T-1, T-2), (T-2, T-3), ..., (1, 0), (0, -1)]

    #     # Initialize with noise
    #     img = torch.randn(shape, device=device) # [B, L, C]

    #     for time, time_next in tqdm(
    #         time_pairs, desc="conditional sampling loop time step"
    #     ):
    #         time_cond = torch.full((batch,), time, device=device, dtype=torch.long)
    #         # pred_noise, x_start, *_ = self.model_predictions(
    #         #     img,
    #         #     time_cond,
    #         #     clip_x_start=clip_denoised,
    #         #     control_signal=model_kwargs.get("model_control_signal", {}),
    #         # )
    #         # x, t, clip_x_start=False, padding_masks=None, control_signal=None
    #         # if padding_masks is None:
    #         padding_masks = torch.ones(
    #             img.shape[0], self.seq_length, dtype=bool, device=img.device
    #         )
    #         maybe_clip = (
    #             partial(torch.clamp, min=-1.0, max=1.0) if clip_denoised else identity
    #         )
    #         # def output(self, x, t, padding_masks=None, control_signal=None):
    #         #     """Modified output function to work with CSDI"""
    #         #     if isinstance(t, int):
    #         #         t = torch.tensor([t]).to(x.device)
                    
    #         #     # Prepare side info
    #         #     observed_tp = torch.arange(x.shape[1], device=x.device).float()
    #         #     observed_tp = observed_tp.unsqueeze(0).expand(x.shape[0], -1)
    #         #     side_info = self.get_side_info(observed_tp, padding_masks)
                
    #         #     # Get model prediction
    #         #     predicted, _ = self.diffmodel(x, side_info, t)
    #         #     return predicted
    #         predicted, _ = self.diffmodel(img, time_cond)
    #         coeff1 = 1 / self.alpha_hat[time] ** 0.5
    #         coeff2 = (1 - self.alpha_hat[time]) / (1 - self.alpha[time]) ** 0.5
    #         x_start = coeff1 * (img - coeff2 * predicted)
    #         # x_start = self.output(img, time_cond, padding_masks)
    #         x_start = maybe_clip(x_start)
    #         pred_noise = self.predict_noise_from_start(img, time_cond, x_start)
    #         # return pred_noise, x_start

    #         if time_next < 0:
    #             img = x_start
    #             continue

    #         # Compute the predicted mean
    #         alpha = self.alphas_cumprod[time]
    #         alpha_next = self.alphas_cumprod[time_next]
    #         sigma = (
    #             eta * ((1 - alpha / alpha_next) * (1 - alpha_next) / (1 - alpha)).sqrt()
    #         )
    #         c = (1 - alpha_next - sigma**2).sqrt()

    #         noise = torch.randn_like(img)
    #         pred_mean = x_start * alpha_next.sqrt() + c * pred_noise
    #         img = pred_mean + sigma * noise

    #         # # Langevin Dynamics part for additional gradient updates
    #         # img = self.langevin_fn(
    #         #     sample=img,
    #         #     mean=pred_mean,
    #         #     sigma=sigma,
    #         #     t=time_cond,
    #         #     tgt_embs=target,
    #         #     partial_mask=partial_mask,
    #         #     enable_float_mask=True,
    #         #     **model_kwargs,
    #         # )
    #         img = img * (1 - partial_mask) + target * partial_mask

    #     img = img * (1 - partial_mask) + target * partial_mask
    #     return img


    def langevin_fn(

        self,

        coef,

        partial_mask,

        tgt_embs,

        learning_rate,

        sample,

        mean,

        sigma,

        t,

        coef_=0.0,

        gradient_control_signal={},

        model_control_signal={},

        side_info=None,

        **kwargs,

    ):
        # we thus run more gradient updates at large diffusion step t to guide the generation then
        # reduce the number of gradient steps in stages to accelerate sampling.
        if t[0].item() < self.num_timesteps * 0.02 :
            K = 0
        elif t[0].item() > self.num_timesteps * 0.9:
            K = 3
        elif t[0].item() > self.num_timesteps * 0.75:
            K = 2
            learning_rate = learning_rate * 0.5
        else:
            K = 1
            learning_rate = learning_rate * 0.25

        input_embs_param = torch.nn.Parameter(sample)

        # 获取时间相关的权重调整因子
        time_weight = get_time_dependent_weights(t[0], self.num_timesteps)

        with torch.enable_grad():
            for iteration in range(K):
                # x_i+1 = x_i + noise * grad(logp(x_i)) + sqrt(2*noise) * z_i
                optimizer = torch.optim.Adagrad([input_embs_param], lr=learning_rate)
                optimizer.zero_grad()

                # x_start = self.output(
                #     x=input_embs_param,
                #     t=t,
                #     control_signal=model_control_signal,
                # )

                # Prepare model input
                # if self.is_unconditional:
                #     diff_input = cond_mask * observed_data + (1.0 - cond_mask) * current_sample
                #     diff_input = diff_input.unsqueeze(1)
                # else:
                #     cond_obs = (cond_mask * observed_data).unsqueeze(1)
                #     noisy_target = ((1 - cond_mask) * current_sample).unsqueeze(1)
                #     diff_input = torch.cat([cond_obs, noisy_target], dim=1)
                if self.is_unconditional:
                    diff_input = input_embs_param.unsqueeze(1)
                else:
                    cond_obs = (partial_mask * tgt_embs).unsqueeze(1) 
                    noisy_target = ((1 - partial_mask) * input_embs_param).unsqueeze(1)
                    diff_input = torch.cat([cond_obs, noisy_target], dim=1)

                x_start, _ = self.diffmodel(diff_input, side_info, t)


                if sigma.mean() == 0:
                    logp_term = (
                        coef * ((mean - input_embs_param) ** 2 / 1.0).mean(dim=0).sum()
                    )
                    # determine the partical_mask is float
                    if kwargs.get("enable_float_mask", False):
                        infill_loss = (x_start * (partial_mask) - tgt_embs * (partial_mask)) ** 2
                    else:
                        infill_loss = (x_start[partial_mask] - tgt_embs[partial_mask]) ** 2
                    infill_loss = infill_loss.mean(dim=0).sum()
                else:
                    logp_term = (
                        coef
                        * ((mean - input_embs_param) ** 2 / sigma).mean(dim=0).sum()
                    )
                    if kwargs.get("enable_float_mask", False):
                        infill_loss = (x_start * (partial_mask) - tgt_embs * (partial_mask)) ** 2
                    else:
                        infill_loss = (x_start[partial_mask] - tgt_embs[partial_mask]) ** 2
                    infill_loss = (infill_loss / sigma.mean()).mean(dim=0).sum()
                gradient_scale = gradient_control_signal.get("gradient_scale", 1.0)  # 全局梯度缩放因子
                control_loss = 0

                auc_sum, peak_points, bar_regions, target_freq = \
                    gradient_control_signal.get("auc"), gradient_control_signal.get("peak_points"), gradient_control_signal.get("bar_regions"), gradient_control_signal.get("target_freq")

                # 1. 原有的sum控制
                if auc_sum is not None:
                    sum_weight = gradient_control_signal.get("auc_weight", 1.0) * time_weight
                    auc_loss = - sum_weight * sum_guidance(
                        x=input_embs_param,
                        t=t,
                        target_sum=auc_sum,
                        gradient_scale=gradient_scale,
                        segments=gradient_control_signal.get("segments", ())
                    )
                    control_loss += auc_loss

                # 峰值引导
                if peak_points is not None:
                    peak_weight = gradient_control_signal.get("peak_weight", 1.0) * time_weight
                    peak_loss = - peak_weight * peak_guidance(
                        x=input_embs_param,
                        t=t,
                        peak_points=peak_points,
                        window_size=gradient_control_signal.get("peak_window_size", 5),
                        alpha_1=gradient_control_signal.get("peak_alpha_1", 1.2),
                        gradient_scale=gradient_scale
                    )
                    control_loss += peak_loss

                # 区间引导
                if bar_regions is not None:
                    bar_weight = gradient_control_signal.get("bar_weight", 1.0) * time_weight
                    bar_loss = -bar_weight * bar_guidance(
                        x=input_embs_param,
                        t=t,
                        bar_regions=bar_regions,
                        gradient_scale=gradient_scale
                    )
                    control_loss += bar_loss

                # 频率引导
                if target_freq is not None:
                    freq_weight = gradient_control_signal.get("freq_weight", 1.0) * time_weight
                    freq_loss = -freq_weight * frequency_guidance(
                        x=input_embs_param,
                        t=t,
                        target_freq=target_freq,
                        freq_weight=freq_weight,
                        gradient_scale=gradient_scale
                    )
                    control_loss += freq_loss


                loss = logp_term + infill_loss + control_loss
                loss.backward()
                optimizer.step()
                torch.nn.utils.clip_grad_norm_([input_embs_param], gradient_control_signal.get("max_grad_norm", 1.0))

                epsilon = torch.randn_like(input_embs_param.data)
                noise_scale = coef_ * sigma.mean().item()
                input_embs_param = torch.nn.Parameter(
                    (
                        input_embs_param.data + noise_scale * epsilon
                    ).detach()
                )

        if kwargs.get("enable_float_mask", False):
            sample = sample * partial_mask + input_embs_param.data * (1 - partial_mask)
        else:
            sample[~partial_mask] = input_embs_param.data[~partial_mask]
        return sample

    def predict_weighted_points(

        self,

        observed_points: torch.Tensor,

        observed_mask: torch.Tensor,

        coef=1e-1,

        stepsize=1e-1,

        sampling_steps=50,

        **kargs,

    ):
        model_kwargs = {}
        model_kwargs["coef"] = coef
        model_kwargs["learning_rate"] = stepsize
        model_kwargs = {**model_kwargs, **kargs}
        assert len(observed_points.shape) == 2, "observed_points should be 2D, batch size = 1"
        x = observed_points.unsqueeze(0)
        float_mask = observed_mask.unsqueeze(0) # x != 0, 1 for observed, 0 for missing, bool tensor
        binary_mask = float_mask.clone()
        binary_mask[binary_mask > 0] = 1

        x = x * 2 - 1 # normalize
        self.device = x.device
        x, float_mask, binary_mask = x.to(self.device), float_mask.to(self.device), binary_mask.to(self.device)
        if sampling_steps == self.num_timesteps:
            print("normal sampling")
            raise NotImplementedError
            sample = self.ema.ema_model.sample_infill_float_mask(
                shape=x.shape,
                target=x * binary_mask, # x * t_m, 1 for observed, 0 for missing
                partial_mask=float_mask,
                model_kwargs=model_kwargs,
            )
            # x: partially noise : (batch_size, seq_length, feature_dim)
        else:
            print("fast sampling")
            sample = self.fast_sample_infill_float_mask(
                shape=x.shape,
                target=x * binary_mask, # x * t_m, 1 for observed, 0 for missing
                partial_mask=float_mask,
                model_kwargs=model_kwargs,
                sampling_timesteps=sampling_steps,
            )

        # unnormalize
        sample = (sample + 1) / 2
        return sample.squeeze(0).detach().cpu().numpy()

    def forward(self, x, **kwargs):
        """Modified forward pass for CSDI training"""
        # Convert input from [B, C, L] to [B, L, C]
        observed_data = x.permute(0, 2, 1)
        observed_mask = kwargs.get("observed_mask", torch.ones_like(observed_data))
        observed_tp = torch.arange(observed_data.shape[1], device=x.device).float()
        observed_tp = observed_tp.unsqueeze(0).expand(x.shape[0], -1)
        
        # Generate masks
        is_train = kwargs.get("is_train", 1)
        if is_train:
            cond_mask = self.get_randmask(observed_mask)
        else:
            gt_mask = kwargs.get("gt_mask", observed_mask.clone())
            if "pred_length" in kwargs:
                gt_mask[:,:,-kwargs["pred_length"]:] = 0
            cond_mask = gt_mask
            
        # Get side info and calculate loss
        side_info = self.get_side_info(observed_tp, cond_mask)
        loss_func = self.calc_loss if is_train else self.calc_loss_valid
        return loss_func(observed_data, cond_mask, observed_mask, side_info, is_train)

    def time_embedding(self, pos, d_model=128):
        pe = torch.zeros(pos.shape[0], pos.shape[1], d_model).to(pos.device)
        position = pos.unsqueeze(2)
        div_term = 1 / torch.pow(
            10000.0, torch.arange(0, d_model, 2).to(pos.device) / d_model
        )
        pe[:, :, 0::2] = torch.sin(position * div_term)
        pe[:, :, 1::2] = torch.cos(position * div_term)
        return pe

    def get_randmask(self, observed_mask):
        rand_for_mask = torch.rand_like(observed_mask) * observed_mask
        rand_for_mask = rand_for_mask.reshape(len(rand_for_mask), -1)
        for i in range(len(observed_mask)):
            sample_ratio = np.random.rand()  # missing ratio
            num_observed = observed_mask[i].sum().item()
            num_masked = round(num_observed * sample_ratio)
            rand_for_mask[i][rand_for_mask[i].topk(num_masked).indices] = -1
        cond_mask = (rand_for_mask > 0).reshape(observed_mask.shape).float()
        return cond_mask

    def get_hist_mask(self, observed_mask, for_pattern_mask=None):
        if for_pattern_mask is None:
            for_pattern_mask = observed_mask
        if self.target_strategy == "mix":
            rand_mask = self.get_randmask(observed_mask)

        cond_mask = observed_mask.clone()
        for i in range(len(cond_mask)):
            mask_choice = np.random.rand()
            if self.target_strategy == "mix" and mask_choice > 0.5:
                cond_mask[i] = rand_mask[i]
            else:  # draw another sample for histmask (i-1 corresponds to another sample)
                cond_mask[i] = cond_mask[i] * for_pattern_mask[i - 1] 
        return cond_mask

    def get_test_pattern_mask(self, observed_mask, test_pattern_mask):
        return observed_mask * test_pattern_mask


    def get_side_info(self, observed_tp, cond_mask):
        B, K, L = cond_mask.shape

        time_embed = self.time_embedding(observed_tp, self.emb_time_dim)  # (B,L,emb) torch.Size([64, 24, 128])
        # print(time_embed.shape)
        time_embed = time_embed.unsqueeze(2).expand(-1, -1, K, -1)
        feature_embed = self.embed_layer(
            torch.arange(self.target_dim).to(observed_tp.device)
        )  # (K, emb)
        # print("feature_embed",feature_embed.shape)
        feature_embed = feature_embed.unsqueeze(0).unsqueeze(0).expand(B, L, -1, -1)
        
        # torch.Size([64, 24, 24, 128])[64, 28, 28, 16])    
        # print(time_embed.shape, feature_embed.shape)
        side_info = torch.cat([time_embed, feature_embed], dim=-1)  # (B,L,K,*)
        side_info = side_info.permute(0, 3, 2, 1)  # (B,*,K,L)

        if self.is_unconditional == False:
            side_mask = cond_mask.unsqueeze(1)  # (B,1,K,L)
            side_info = torch.cat([side_info, side_mask], dim=1)

        return side_info

    def calc_loss_valid(

        self, observed_data, cond_mask, observed_mask, side_info, is_train

    ):
        loss_sum = 0
        for t in range(self.num_steps):  # calculate loss for all t
            loss = self.calc_loss(
                observed_data, cond_mask, observed_mask, side_info, is_train, set_t=t
            )
            loss_sum += loss.detach()
        return loss_sum / self.num_steps

    def calc_loss(

        self, observed_data, cond_mask, observed_mask, side_info, is_train, set_t=-1

    ):
        B, K, L = observed_data.shape
        if is_train != 1:  # for validation
            t = (torch.ones(B) * set_t).long().to(self.device)
        else:
            t = torch.randint(0, self.num_steps, [B]).to(self.device)
        current_alpha = self.alpha_torch[t]  # (B,1,1)
        noise = torch.randn_like(observed_data)
        noisy_data = (current_alpha ** 0.5) * observed_data + (1.0 - current_alpha) ** 0.5 * noise

        total_input = self.set_input_to_diffmodel(noisy_data, observed_data, cond_mask)

        predicted, _ = self.diffmodel(total_input, side_info, t)  # (B,K,L)

        target_mask = observed_mask - cond_mask
        residual = (noise - predicted) * target_mask
        num_eval = target_mask.sum()
        loss = (residual ** 2).sum() / (num_eval if num_eval > 0 else 1)
        return loss

    def evaluate(self, batch, n_samples):
        (
            observed_data, # [B, L, K]
            observed_mask, # 1 for observed, 0 for missing
            observed_tp, # [0, 1, 2, ..., L-1]
            gt_mask,
            _,
            cut_length,
        ) = self.process_data(batch)

        with torch.no_grad():
            cond_mask = gt_mask
            target_mask = observed_mask - cond_mask # 1 for missing, 0 for observed

            side_info = self.get_side_info(observed_tp, cond_mask)

            samples = self.impute(observed_data, cond_mask, side_info, n_samples)

            for i in range(len(cut_length)):  # to avoid double evaluation
                target_mask[i, ..., 0 : cut_length[i].item()] = 0
        return samples, observed_data, target_mask, observed_mask, observed_tp

    def impute(self, observed_data, cond_mask, side_info, n_samples):
        """Modified impute function with Langevin dynamics and control signals"""
        B, K, L = observed_data.shape
        imputed_samples = torch.zeros(B, n_samples, K, L).to(self.device)
        
        # Setup sampling parameters
        # times = torch.linspace(-1, self.num_steps - 1, steps=self.sampling_timesteps + 1)
        # times = list(reversed(times.int().tolist()))
        # time_pairs = list(zip(times[:-1], times[1:]))
        for i in range(n_samples):
            # Initialize with noise
            current_sample = torch.randn_like(observed_data)
            
            # for t, time_next in tqdm(time_pairs, desc="Imputation sampling"):
            for t in range(self.num_steps - 1, -1, -1):
                # Prepare time condition
                # time_cond = torch.full((B,), time, device=self.device, dtype=torch.long)
                time_cond = torch.tensor([t]).to(self.device)

                # Prepare model input
                if self.is_unconditional:
                    diff_input = cond_mask * observed_data + (1.0 - cond_mask) * current_sample
                    diff_input = diff_input.unsqueeze(1)
                else:
                    cond_obs = (cond_mask * observed_data).unsqueeze(1)
                    noisy_target = ((1 - cond_mask) * current_sample).unsqueeze(1)
                    diff_input = torch.cat([cond_obs, noisy_target], dim=1)
                    
                predicted, _ = self.diffmodel(diff_input, side_info, torch.tensor([t]).to(self.device))

                coeff1 = 1 / self.alpha_hat[t] ** 0.5
                coeff2 = (1 - self.alpha_hat[t]) / (1 - self.alpha[t]) ** 0.5
                current_sample = coeff1 * (current_sample - coeff2 * predicted)

                if t > 0:
                    noise = torch.randn_like(current_sample)
                    sigma = (
                        (1.0 - self.alpha[t - 1]) / (1.0 - self.alpha[t]) * self.beta[t]
                    ) ** 0.5
                    current_sample += sigma * noise

                # # Get prediction
                # predicted = self.diffmodel(diff_input, side_info, time_cond)[0]

                # if time_next < 0:
                #     current_sample = predicted
                #     continue

                # # Update sample with noise
                # alpha = self.alpha[time]
                # alpha_next = self.alpha[time_next]

                # # Compute transition parameters
                # sigma = self.eta * ((1 - alpha_next) / (1 - alpha) * (1 - alpha / alpha_next)).sqrt()
                # c = (1 - alpha_next - sigma**2).sqrt()
                
                # # Update sample
                # noise = torch.randn_like(current_sample)
                # pred_mean = predicted * alpha_next.sqrt() + c * current_sample
                # current_sample = pred_mean + sigma * noise

                # # # Apply Langevin dynamics and control signals
                # # if model_kwargs is not None:
                # #     current_sample = self.langevin_fn(
                # #         sample=current_sample,
                # #         mean=pred_mean,
                # #         sigma=sigma,
                # #         t=time_cond,
                # #         tgt_embs=observed_data,
                # #         partial_mask=cond_mask,
                # #         enable_float_mask=True,
                # #         side_info=side_info,
                # #         **model_kwargs
                # #     )
                
                # # Apply conditioning
                # current_sample = current_sample * (1 - cond_mask) + observed_data * cond_mask
                    
            imputed_samples[:, i] = current_sample
            
        return imputed_samples

    def fast_sample_infill_float_mask(

        self,

        shape,

        target: torch.Tensor,

        sampling_timesteps,

        partial_mask: torch.Tensor = None,

        clip_denoised=True,

        model_kwargs=None,

    ):
        """Simplified fast sampling that uses improved impute function"""
        batch = shape[0]
        device = self.device
        
        target = target.permute(0, 2, 1)
        partial_mask = partial_mask.permute(0, 2, 1)

        # Generate timepoints
        observed_tp = torch.arange(shape[1], device=device).float()
        observed_tp = observed_tp.unsqueeze(0).expand(batch, -1)

        # Get side info
        side_info = self.get_side_info(observed_tp, partial_mask)

        # Use modified impute function with control signals
        samples = self.impute(
            observed_data=target,
            cond_mask=partial_mask,
            side_info=side_info,
            n_samples=1,
        )

        return samples.squeeze(1).permute(0, 2, 1)