Update all files for BitDance-ImageNet-diffusers

BitDance_B_1x/transformer/model.py

@@ -0,0 +1,432 @@
+import argparse
+from functools import partial
+
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+from torch.utils.checkpoint import checkpoint
+
+from .diff_head import DiffHead
+from .layers import TransformerBlock, get_2d_pos, precompute_freqs_cis_2d
+from .qae import VQModel
+
+def get_model_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model", type=str, choices=list(BitDance_models.keys()), default="BitDance-L"
+    )
+    parser.add_argument("--image-size", type=int, choices=[256, 512], default=256)
+    parser.add_argument("--down-size", type=int, default=16, choices=[16])
+    parser.add_argument("--patch-size", type=int, default=1, choices=[1, 2, 4])
+    parser.add_argument("--num-classes", type=int, default=1000)
+    parser.add_argument("--cls-token-num", type=int, default=64)
+    parser.add_argument("--latent-dim", type=int, default=16)
+    parser.add_argument("--diff-batch-mul", type=int, default=4)
+    parser.add_argument("--grad-checkpointing", action="store_true")
+    parser.add_argument("--trained-vae", type=str, default="")
+    parser.add_argument("--drop-rate", type=float, default=0.0)
+    parser.add_argument("--perturb-schedule", type=str, default="constant")
+    parser.add_argument("--perturb-rate", type=float, default=0.0)
+    parser.add_argument("--perturb-rate-max", type=float, default=0.3)
+    parser.add_argument("--time-schedule", type=str, default='logit_normal')
+    parser.add_argument("--time-shift", type=float, default=1.)
+    parser.add_argument("--P-std", type=float, default=1.)
+    parser.add_argument("--P-mean", type=float, default=0.)
+    return parser
+
+
+def create_model(args, device):
+    model = BitDance_models[args.model](
+        resolution=args.image_size,
+        down_size=args.down_size,
+        patch_size=args.patch_size,
+        latent_dim=args.latent_dim,
+        diff_batch_mul=args.diff_batch_mul,
+        cls_token_num=args.cls_token_num,
+        num_classes=args.num_classes,
+        grad_checkpointing=args.grad_checkpointing,
+        trained_vae=args.trained_vae,
+        drop_rate=args.drop_rate,
+        perturb_schedule=args.perturb_schedule,
+        perturb_rate=args.perturb_rate,
+        perturb_rate_max=args.perturb_rate_max,
+        time_schedule=args.time_schedule,
+        time_shift=args.time_shift,
+        P_std=args.P_std,
+        P_mean=args.P_mean,
+    ).to(device, memory_format=torch.channels_last)
+    return model
+
+class MLPConnector(nn.Module):
+    def __init__(self, in_dim, dim, dropout_p=0.0):
+        super().__init__()
+        hidden_dim = int(dim * 1.5)
+        self.w1 = nn.Linear(in_dim, hidden_dim * 2, bias=True)
+        self.w2 = nn.Linear(hidden_dim, dim, bias=True)
+        self.ffn_dropout = nn.Dropout(dropout_p)
+
+    def forward(self, x):
+        h1, h2 = self.w1(x).chunk(2, dim=-1)
+        return self.ffn_dropout(self.w2(F.silu(h1) * h2))
+
+def flip_tensor_elements_uniform_prob(tensor: torch.Tensor, p_max: float) -> torch.Tensor:
+    if not 0.0 <= p_max <= 1.0:
+        raise ValueError(f"p_max must be in [0.0, 1.0] range, but got: {p_max}")
+    r1 = torch.rand_like(tensor)
+    r2 = torch.rand_like(tensor)
+    flip_mask = r1 < p_max * r2
+    multiplier = torch.where(flip_mask, -1.0, 1.0)
+    multiplier = multiplier.to(tensor.dtype)
+    flipped_tensor = tensor * multiplier
+    return flipped_tensor
+
+class BitDance(nn.Module):
+
+    def __init__(
+        self,
+        dim,
+        n_layer,
+        n_head,
+        diff_layers,
+        diff_dim,
+        diff_adanln_layers,
+        latent_dim,
+        down_size,
+        patch_size,
+        resolution,
+        diff_batch_mul,
+        grad_checkpointing=False,
+        cls_token_num=16,
+        num_classes: int = 1000,
+        class_dropout_prob: float = 0.1,
+        trained_vae: str = "",
+        drop_rate: float = 0.0,
+        perturb_schedule: str = "constant",
+        perturb_rate: float = 0.0,
+        perturb_rate_max: float = 0.3,
+        time_schedule: str = 'logit_normal',
+        time_shift: float = 1.,
+        P_std: float = 1.,
+        P_mean: float = 0.,
+    ):
+        super().__init__()
+
+        self.n_layer = n_layer
+        self.resolution = resolution
+        self.down_size = down_size
+        self.patch_size = patch_size
+        self.num_classes = num_classes
+        self.cls_token_num = cls_token_num
+        self.class_dropout_prob = class_dropout_prob
+        self.latent_dim = latent_dim
+        self.trained_vae = trained_vae
+        self.perturb_schedule = perturb_schedule
+        self.perturb_rate = perturb_rate
+        self.perturb_rate_max = perturb_rate_max
+
+        # define the vae and mar model
+        ddconfig = {
+        "double_z": False,
+        "z_channels": latent_dim,
+        "in_channels": 3,
+        "out_ch": 3,
+        "ch": 256,
+        "ch_mult": [1,1,2,2,4],
+        "num_res_blocks": 4
+    }
+        num_codebooks = 4
+        # print(f"loading vae unexpected_keys: {unexpected_keys}")
+        self.vae = VQModel(ddconfig, num_codebooks)
+        self.grad_checkpointing = grad_checkpointing
+
+        self.cls_embedding = nn.Embedding(num_classes + 1, dim * self.cls_token_num)
+        self.proj_in = MLPConnector(latent_dim * self.patch_size * self.patch_size, dim, drop_rate)
+        self.emb_norm = nn.RMSNorm(dim, eps=1e-6, elementwise_affine=True)
+        self.h, self.w = resolution // (down_size * patch_size), resolution // (down_size * patch_size)
+        self.total_tokens = self.h * self.w + self.cls_token_num
+
+        self.layers = torch.nn.ModuleList()
+        for layer_id in range(n_layer):
+            self.layers.append(
+                TransformerBlock(
+                    dim,
+                    n_head,
+                    resid_dropout_p=drop_rate,
+                    causal=True,
+                )
+            )
+
+        self.norm = nn.RMSNorm(dim, eps=1e-6, elementwise_affine=True)
+        self.pos_for_diff = nn.Embedding(self.h * self.w, dim)
+        self.head = DiffHead(
+            ch_target=latent_dim * self.patch_size * self.patch_size,
+            ch_cond=dim,
+            ch_latent=diff_dim,
+            depth_latent=diff_layers,
+            depth_adanln=diff_adanln_layers,
+            grad_checkpointing=grad_checkpointing,
+            time_shift=time_shift,
+            time_schedule=time_schedule,
+            P_std=P_std,
+            P_mean=P_mean,
+        )
+        self.diff_batch_mul = diff_batch_mul
+
+        patch_2d_pos = get_2d_pos(resolution, int(down_size * patch_size))
+
+        self.register_buffer(
+            "freqs_cis",
+            precompute_freqs_cis_2d(
+                patch_2d_pos,
+                dim // n_head,
+                10000,
+                cls_token_num=self.cls_token_num,
+            )[:-1],
+            persistent=False,
+        )
+        self.freeze_vae()
+
+        self.initialize_weights()
+
+    def load_vae_weight(self):
+        state = torch.load(
+                    self.trained_vae, 
+                    map_location="cpu",
+                )
+        missing_keys, unexpected_keys = self.vae.load_state_dict(state["state_dict"], strict=False)
+        print(f"loading vae, missing_keys: {missing_keys}")
+        del state
+
+    def non_decay_keys(self):
+        return ["proj_in", "cls_embedding"]
+
+    def freeze_module(self, module: nn.Module):
+        for param in module.parameters():
+            param.requires_grad = False
+
+    def freeze_vae(self):
+        self.freeze_module(self.vae)
+        self.vae.eval()
+
+    def initialize_weights(self):
+        # Initialize nn.Linear and nn.Embedding
+        self.apply(self.__init_weights)
+        self.head.initialize_weights()
+        # self.vae.initialize_weights()
+
+    def __init_weights(self, module):
+        std = 0.02
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+
+    def drop_label(self, class_id):
+        if self.class_dropout_prob > 0.0 and self.training:
+            is_drop = (
+                torch.rand(class_id.shape, device=class_id.device)
+                < self.class_dropout_prob
+            )
+            class_id = torch.where(is_drop, self.num_classes, class_id)
+        return class_id
+
+    def patchify(self, x):
+        bsz, c, h, w = x.shape
+        p = self.patch_size
+        h_, w_ = h // p, w // p
+
+        x = x.reshape(bsz, c, h_, p, w_, p)
+        x = torch.einsum('nchpwq->nhwcpq', x)
+        x = x.reshape(bsz, h_ * w_, c * p ** 2)
+        return x  # [n, l, d]
+
+    def unpatchify(self, x):
+        bsz = x.shape[0]
+        p = self.patch_size
+        c = self.latent_dim
+        h_, w_ = self.h, self.w
+
+        x = x.reshape(bsz, h_, w_, c, p, p)
+        x = torch.einsum('nhwcpq->nchpwq', x)
+        x = x.reshape(bsz, c, h_ * p, w_ * p)
+        return x  # [n, c, h, w]
+
+    def forward(
+        self,
+        images,
+        class_id,
+        cached=False
+    ):
+        if cached:
+            vae_latent = images
+        else:
+            vae_latent, _, _, _ = self.vae.encode(images) # b c h w
+
+        vae_latent = self.patchify(vae_latent)
+        x = vae_latent.clone().detach()
+        if self.training:
+            if self.perturb_schedule =="constant":
+                x = flip_tensor_elements_uniform_prob(x, self.perturb_rate)
+            else:
+                raise NotImplementedError(f"unknown perturb_schedule {self.perturb_schedule}")
+        x = self.proj_in(x[:, :-1, :])
+        class_id = self.drop_label(class_id)
+        bsz = x.shape[0]
+        c = self.cls_embedding(class_id).view(bsz, self.cls_token_num, -1)
+        x = torch.cat([c, x], dim=1)
+        x = self.emb_norm(x)
+
+        if self.grad_checkpointing and self.training:
+            for layer in self.layers:
+                block = partial(layer.forward, freqs_cis=self.freqs_cis)
+                x = checkpoint(block, x, use_reentrant=False)
+        else:
+            for layer in self.layers:
+                x = layer(x, self.freqs_cis)
+
+        x = x[:, -self.h * self.w :, :]
+        x = self.norm(x)
+        x = x + self.pos_for_diff.weight
+
+        target = vae_latent.clone().detach()
+        x = x.view(-1, x.shape[-1])
+        target = target.view(-1, target.shape[-1])
+
+        x = x.repeat(self.diff_batch_mul, 1)
+        target = target.repeat(self.diff_batch_mul, 1)
+        loss = self.head(target, x)
+
+        return loss
+
+    def enable_kv_cache(self, bsz):
+        for layer in self.layers:
+            layer.attention.enable_kv_cache(bsz, self.total_tokens)
+
+    @torch.compile()
+    def forward_model(self, x, start_pos, end_pos):
+        x = self.emb_norm(x)
+        for layer in self.layers:
+            x = layer.forward_onestep(
+                x, self.freqs_cis[start_pos:end_pos,], start_pos, end_pos
+            )
+        x = self.norm(x)
+        return x
+    
+    def head_sample(self, x, diff_pos, sample_steps, cfg_scale, cfg_schedule="linear"):
+        x = x + self.pos_for_diff.weight[diff_pos : diff_pos + 1, :]
+        x = x.view(-1, x.shape[-1])
+        seq_len = self.h * self.w
+        if cfg_scale > 1.0:
+            if cfg_schedule == "constant":
+                cfg_iter = cfg_scale
+            elif cfg_schedule == "linear":
+                start = 1.0
+                cfg_iter = start + (cfg_scale - start) * diff_pos / seq_len
+            else:
+                raise NotImplementedError(f"unknown cfg_schedule {cfg_schedule}")
+        else:
+            cfg_iter = 1.0
+        pred = self.head.sample(x, num_sampling_steps=sample_steps, cfg=cfg_iter)
+        pred = pred.view(-1, 1, pred.shape[-1])
+        # Important: LFQ here, sign the prediction
+        pred = torch.sign(pred)
+        return pred
+
+    @torch.no_grad()
+    def sample(self, cond, sample_steps, cfg_scale=1.0, cfg_schedule="linear", chunk_size=0):
+        self.eval()
+        if cfg_scale > 1.0:
+            cond_null = torch.ones_like(cond) * self.num_classes
+            cond_combined = torch.cat([cond, cond_null])
+        else:
+            cond_combined = cond
+        bsz = cond_combined.shape[0]
+        act_bsz = bsz // 2 if cfg_scale > 1.0 else bsz
+        self.enable_kv_cache(bsz)
+
+        c = self.cls_embedding(cond_combined).view(bsz, self.cls_token_num, -1)
+        last_pred = None
+        all_preds = []
+        for i in range(self.h * self.w):
+            if i == 0:
+                x = self.forward_model(c, 0, self.cls_token_num)
+            else:
+                x = self.proj_in(last_pred)
+                x = self.forward_model(
+                    x, i + self.cls_token_num - 1, i + self.cls_token_num
+                )
+            last_pred = self.head_sample(
+                x[:, -1:, :],
+                i,
+                sample_steps,
+                cfg_scale,
+                cfg_schedule,
+            )
+            all_preds.append(last_pred)
+
+        x = torch.cat(all_preds, dim=-2)[:act_bsz]
+        if x.dim() == 3: #b n c -> b c h w
+            x = self.unpatchify(x)
+        if chunk_size > 0:
+            recon = self.decode_in_chunks(x, chunk_size)
+        else:
+            recon = self.vae.decode(x)
+        return recon
+
+    def decode_in_chunks(self, latent_tensor, chunk_size=64):
+        total_bsz = latent_tensor.shape[0]
+        recon_chunks_on_cpu = []
+        with torch.no_grad():
+            for i in range(0, total_bsz, chunk_size):
+                end_idx = min(i + chunk_size, total_bsz)
+                latent_chunk = latent_tensor[i:end_idx]
+                recon_chunk = self.vae.decode(latent_chunk)
+                recon_chunks_on_cpu.append(recon_chunk.cpu())
+        return torch.cat(recon_chunks_on_cpu, dim=0)
+
+    def get_fsdp_wrap_module_list(self):
+        return list(self.layers)
+
+def BitDance_H(**kwargs):
+    return BitDance(
+        n_layer=40,
+        n_head=20,
+        dim=1280,
+        diff_layers=12,
+        diff_dim=1280,
+        diff_adanln_layers=3,
+        **kwargs,
+    )
+
+
+def BitDance_L(**kwargs):
+    return BitDance(
+        n_layer=32,
+        n_head=16,
+        dim=1024,
+        diff_layers=8,
+        diff_dim=1024,
+        diff_adanln_layers=2,
+        **kwargs,
+    )
+
+
+def BitDance_B(**kwargs):
+    return BitDance(
+        n_layer=24,
+        n_head=12,
+        dim=768,
+        diff_layers=6,
+        diff_dim=768,
+        diff_adanln_layers=2,
+        **kwargs,
+    )
+
+
+BitDance_models = {
+    "BitDance-B": BitDance_B,
+    "BitDance-L": BitDance_L,
+    "BitDance-H": BitDance_H,
+}