Revert "Upload f_lite.model.py (#8)"

This reverts commit 908e1ddce50dfc02bbcc7eb88494ecd72d79ffad because it makes original inference way stop working

dit_model/f_lite.model.py

@@ -1,455 +0,0 @@
-# DiT with cross attention
-
-import math
-
-import torch
-import torch.nn.functional as F
-import torch.utils.checkpoint
-from diffusers.configuration_utils import ConfigMixin, register_to_config
-from diffusers.loaders import FromOriginalModelMixin, PeftAdapterMixin
-from diffusers.models.modeling_utils import ModelMixin
-from diffusers.utils.accelerate_utils import apply_forward_hook
-from einops import rearrange
-from peft import get_peft_model_state_dict, set_peft_model_state_dict
-from torch import nn
-
-
-def timestep_embedding(t, dim, max_period=10000):
-    half = dim // 2
-    freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half).to(
-        device=t.device
-    )
-    args = t[:, None].float() * freqs[None]
-    embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
-
-    return embedding
-
-
-class RMSNorm(nn.Module):
-    def __init__(self, dim, eps=1e-6, trainable=False):
-        super().__init__()
-        self.eps = eps
-        if trainable:
-            self.weight = nn.Parameter(torch.ones(dim))
-        else:
-            self.weight = None
-
-    def forward(self, x):
-        x_dtype = x.dtype
-        x = x.float()
-        norm = torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
-        if self.weight is not None:
-            return (x * norm * self.weight).to(dtype=x_dtype)
-        else:
-            return (x * norm).to(dtype=x_dtype)
-
-
-class QKNorm(nn.Module):
-    """Normalizing the query and the key independently, as Flux proposes"""
-
-    def __init__(self, dim, trainable=False):
-        super().__init__()
-        self.query_norm = RMSNorm(dim, trainable=trainable)
-        self.key_norm = RMSNorm(dim, trainable=trainable)
-
-    def forward(self, q, k):
-        q = self.query_norm(q)
-        k = self.key_norm(k)
-        return q, k
-
-
-class Attention(nn.Module):
-    def __init__(
-        self,
-        dim,
-        num_heads=8,
-        qkv_bias=False,
-        is_self_attn=True,
-        cross_attn_input_size=None,
-        residual_v=False,
-        dynamic_softmax_temperature=False,
-    ):
-        super().__init__()
-        assert dim % num_heads == 0
-        self.num_heads = num_heads
-        self.head_dim = dim // num_heads
-        self.scale = self.head_dim**-0.5
-        self.is_self_attn = is_self_attn
-        self.residual_v = residual_v
-        self.dynamic_softmax_temperature = dynamic_softmax_temperature
-
-        if is_self_attn:
-            self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
-        else:
-            self.q = nn.Linear(dim, dim, bias=qkv_bias)
-            self.context_kv = nn.Linear(cross_attn_input_size, dim * 2, bias=qkv_bias)
-
-        self.proj = nn.Linear(dim, dim, bias=False)
-
-        if residual_v:
-            self.lambda_param = nn.Parameter(torch.tensor(0.5).reshape(1))
-
-        self.qk_norm = QKNorm(self.head_dim)
-
-    def forward(self, x, context=None, v_0=None, rope=None):
-        if self.is_self_attn:
-            qkv = self.qkv(x)
-            qkv = rearrange(qkv, "b l (k h d) -> k b h l d", k=3, h=self.num_heads)
-            q, k, v = qkv.unbind(0)
-
-            if self.residual_v and v_0 is not None:
-                v = self.lambda_param * v + (1 - self.lambda_param) * v_0
-
-            if rope is not None:
-                # print(q.shape, rope[0].shape, rope[1].shape)
-                q = apply_rotary_emb(q, rope[0], rope[1])
-                k = apply_rotary_emb(k, rope[0], rope[1])
-
-                # https://arxiv.org/abs/2306.08645
-                # https://arxiv.org/abs/2410.01104
-                # ratioonale is that if tokens get larger, categorical distribution get more uniform
-                # so you want to enlargen entropy.
-
-                token_length = q.shape[2]
-                if self.dynamic_softmax_temperature:
-                    ratio = math.sqrt(math.log(token_length) / math.log(1040.0))  # 1024 + 16
-                    k = k * ratio
-            q, k = self.qk_norm(q, k)
-
-        else:
-            q = rearrange(self.q(x), "b l (h d) -> b h l d", h=self.num_heads)
-            kv = rearrange(
-                self.context_kv(context),
-                "b l (k h d) -> k b h l d",
-                k=2,
-                h=self.num_heads,
-            )
-            k, v = kv.unbind(0)
-            q, k = self.qk_norm(q, k)
-
-        x = F.scaled_dot_product_attention(q, k, v)
-        x = rearrange(x, "b h l d -> b l (h d)")
-        x = self.proj(x)
-        return x, v if self.is_self_attn else None
-
-
-class DiTBlock(nn.Module):
-    def __init__(
-        self,
-        hidden_size,
-        cross_attn_input_size,
-        num_heads,
-        mlp_ratio=4.0,
-        qkv_bias=True,
-        residual_v=False,
-        dynamic_softmax_temperature=False,
-    ):
-        super().__init__()
-        self.hidden_size = hidden_size
-        self.norm1 = RMSNorm(hidden_size, trainable=qkv_bias)
-        self.self_attn = Attention(
-            hidden_size,
-            num_heads=num_heads,
-            qkv_bias=qkv_bias,
-            is_self_attn=True,
-            residual_v=residual_v,
-            dynamic_softmax_temperature=dynamic_softmax_temperature,
-        )
-
-        if cross_attn_input_size is not None:
-            self.norm2 = RMSNorm(hidden_size, trainable=qkv_bias)
-            self.cross_attn = Attention(
-                hidden_size,
-                num_heads=num_heads,
-                qkv_bias=qkv_bias,
-                is_self_attn=False,
-                cross_attn_input_size=cross_attn_input_size,
-                dynamic_softmax_temperature=dynamic_softmax_temperature,
-            )
-        else:
-            self.norm2 = None
-            self.cross_attn = None
-
-        self.norm3 = RMSNorm(hidden_size, trainable=qkv_bias)
-        mlp_hidden = int(hidden_size * mlp_ratio)
-        self.mlp = nn.Sequential(
-            nn.Linear(hidden_size, mlp_hidden),
-            nn.GELU(),
-            nn.Linear(mlp_hidden, hidden_size),
-        )
-
-        self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 9 * hidden_size, bias=True))
-
-        self.adaLN_modulation[-1].weight.data.zero_()
-        self.adaLN_modulation[-1].bias.data.zero_()
-
-    # @torch.compile(mode='reduce-overhead')
-    def forward(self, x, context, c, v_0=None, rope=None):
-        (
-            shift_sa,
-            scale_sa,
-            gate_sa,
-            shift_ca,
-            scale_ca,
-            gate_ca,
-            shift_mlp,
-            scale_mlp,
-            gate_mlp,
-        ) = self.adaLN_modulation(c).chunk(9, dim=1)
-
-        scale_sa = scale_sa[:, None, :]
-        scale_ca = scale_ca[:, None, :]
-        scale_mlp = scale_mlp[:, None, :]
-
-        shift_sa = shift_sa[:, None, :]
-        shift_ca = shift_ca[:, None, :]
-        shift_mlp = shift_mlp[:, None, :]
-
-        gate_sa = gate_sa[:, None, :]
-        gate_ca = gate_ca[:, None, :]
-        gate_mlp = gate_mlp[:, None, :]
-
-        norm_x = self.norm1(x.clone())
-        norm_x = norm_x * (1 + scale_sa) + shift_sa
-        attn_out, v = self.self_attn(norm_x, v_0=v_0, rope=rope)
-        x = x + attn_out * gate_sa
-
-        if self.norm2 is not None:
-            norm_x = self.norm2(x)
-            norm_x = norm_x * (1 + scale_ca) + shift_ca
-            x = x + self.cross_attn(norm_x, context)[0] * gate_ca
-
-        norm_x = self.norm3(x)
-        norm_x = norm_x * (1 + scale_mlp) + shift_mlp
-        x = x + self.mlp(norm_x) * gate_mlp
-
-        return x, v
-
-
-class PatchEmbed(nn.Module):
-    def __init__(self, patch_size=16, in_channels=3, embed_dim=768):
-        super().__init__()
-        self.patch_proj = nn.Conv2d(in_channels, embed_dim, kernel_size=patch_size, stride=patch_size)
-        self.patch_size = patch_size
-
-    def forward(self, x):
-        B, C, H, W = x.shape
-        x = self.patch_proj(x)
-        x = rearrange(x, "b c h w -> b (h w) c")
-        return x
-
-
-class TwoDimRotary(torch.nn.Module):
-    def __init__(self, dim, base=10000, h=256, w=256):
-        super().__init__()
-        self.inv_freq = torch.FloatTensor([1.0 / (base ** (i / dim)) for i in range(0, dim, 2)])
-        self.h = h
-        self.w = w
-
-        t_h = torch.arange(h, dtype=torch.float32)
-        t_w = torch.arange(w, dtype=torch.float32)
-
-        freqs_h = torch.outer(t_h, self.inv_freq).unsqueeze(1)  # h, 1, d / 2
-        freqs_w = torch.outer(t_w, self.inv_freq).unsqueeze(0)  # 1, w, d / 2
-        freqs_h = freqs_h.repeat(1, w, 1)  # h, w, d / 2
-        freqs_w = freqs_w.repeat(h, 1, 1)  # h, w, d / 2
-        freqs_hw = torch.cat([freqs_h, freqs_w], 2)  # h, w, d
-
-        self.register_buffer("freqs_hw_cos", freqs_hw.cos())
-        self.register_buffer("freqs_hw_sin", freqs_hw.sin())
-
-    def forward(self, x, height_width=None, extend_with_register_tokens=0):
-        if height_width is not None:
-            this_h, this_w = height_width
-        else:
-            this_hw = x.shape[1]
-            this_h, this_w = int(this_hw**0.5), int(this_hw**0.5)
-
-        cos = self.freqs_hw_cos[0 : this_h, 0 : this_w]
-        sin = self.freqs_hw_sin[0 : this_h, 0 : this_w]
-
-        cos = cos.clone().reshape(this_h * this_w, -1)
-        sin = sin.clone().reshape(this_h * this_w, -1)
-
-        # append N of zero-attn tokens
-        if extend_with_register_tokens > 0:
-            cos = torch.cat(
-                [
-                    torch.ones(extend_with_register_tokens, cos.shape[1]).to(cos.device),
-                    cos,
-                ],
-                0,
-            )
-            sin = torch.cat(
-                [
-                    torch.zeros(extend_with_register_tokens, sin.shape[1]).to(sin.device),
-                    sin,
-                ],
-                0,
-            )
-
-        return cos[None, None, :, :], sin[None, None, :, :]  # [1, 1, T + N, Attn-dim]
-
-
-def apply_rotary_emb(x, cos, sin):
-    orig_dtype = x.dtype
-    x = x.to(dtype=torch.float32)
-    assert x.ndim == 4  # multihead attention
-    d = x.shape[3] // 2
-    x1 = x[..., :d]
-    x2 = x[..., d:]
-    y1 = x1 * cos + x2 * sin
-    y2 = x1 * (-sin) + x2 * cos
-    return torch.cat([y1, y2], 3).to(dtype=orig_dtype)
-
-
-class DiT(ModelMixin, ConfigMixin, FromOriginalModelMixin, PeftAdapterMixin):  # type: ignore[misc]
-    @register_to_config
-    def __init__(
-        self,
-        in_channels=4,
-        patch_size=2,
-        hidden_size=1152,
-        depth=28,
-        num_heads=16,
-        mlp_ratio=4.0,
-        cross_attn_input_size=128,
-        residual_v=False,
-        train_bias_and_rms=True,
-        use_rope=True,
-        gradient_checkpoint=False,
-        dynamic_softmax_temperature=False,
-        rope_base=10000,
-    ):
-        super().__init__()
-
-        self.patch_embed = PatchEmbed(patch_size, in_channels, hidden_size)
-
-        if use_rope:
-            self.rope = TwoDimRotary(hidden_size // (2 * num_heads), base=rope_base, h=512, w=512)
-        else:
-            self.positional_embedding = nn.Parameter(torch.zeros(1, 2048, hidden_size))
-
-        self.register_tokens = nn.Parameter(torch.randn(1, 16, hidden_size))
-
-        self.time_embed = nn.Sequential(
-            nn.Linear(hidden_size, 4 * hidden_size),
-            nn.SiLU(),
-            nn.Linear(4 * hidden_size, hidden_size),
-        )
-
-        self.blocks = nn.ModuleList(
-            [
-                DiTBlock(
-                    hidden_size=hidden_size,
-                    num_heads=num_heads,
-                    mlp_ratio=mlp_ratio,
-                    cross_attn_input_size=cross_attn_input_size,
-                    residual_v=residual_v,
-                    qkv_bias=train_bias_and_rms,
-                    dynamic_softmax_temperature=dynamic_softmax_temperature,
-                )
-                for _ in range(depth)
-            ]
-        )
-
-        self.final_modulation = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 2 * hidden_size, bias=True))
-
-        self.final_norm = RMSNorm(hidden_size, trainable=train_bias_and_rms)
-        self.final_proj = nn.Linear(hidden_size, patch_size * patch_size * in_channels)
-        nn.init.zeros_(self.final_modulation[-1].weight)
-        nn.init.zeros_(self.final_modulation[-1].bias)
-        nn.init.zeros_(self.final_proj.weight)
-        nn.init.zeros_(self.final_proj.bias)
-        self.paramstatus = {}
-        for n, p in self.named_parameters():
-            self.paramstatus[n] = {
-                "shape": p.shape,
-                "requires_grad": p.requires_grad,
-            }
-
-    def save_lora_weights(self, save_directory):
-        """Save LoRA weights to a file"""
-        lora_state_dict = get_peft_model_state_dict(self)
-        torch.save(lora_state_dict, f"{save_directory}/lora_weights.pt")
-
-    def load_lora_weights(self, load_directory):
-        """Load LoRA weights from a file"""
-        lora_state_dict = torch.load(f"{load_directory}/lora_weights.pt")
-        set_peft_model_state_dict(self, lora_state_dict)
-
-    @apply_forward_hook
-    def forward(self, x, context, timesteps):
-        b, c, h, w = x.shape
-        x = self.patch_embed(x)  # b, T, d
-
-        x = torch.cat([self.register_tokens.repeat(b, 1, 1), x], 1)  # b, T + N, d
-
-        if self.config.use_rope:
-            cos, sin = self.rope(
-                x,
-                extend_with_register_tokens=16,
-                height_width=(h // self.config.patch_size, w // self.config.patch_size),
-            )
-        else:
-            x = x + self.positional_embedding.repeat(b, 1, 1)[:, : x.shape[1], :]
-            cos, sin = None, None
-
-        t_emb = timestep_embedding(timesteps * 1000, self.config.hidden_size).to(x.device, dtype=x.dtype)
-        t_emb = self.time_embed(t_emb)
-
-        v_0 = None
-
-        for _idx, block in enumerate(self.blocks):
-            if self.config.gradient_checkpoint:
-                x, v = torch.utils.checkpoint.checkpoint(
-                    block,
-                    x,
-                    context,
-                    t_emb,
-                    v_0,
-                    (cos, sin),
-                    use_reentrant=False,
-                )
-            else:
-                x, v = block(x, context, t_emb, v_0, (cos, sin))
-            if v_0 is None:
-                v_0 = v
-
-        x = x[:, 16:, :]
-        final_shift, final_scale = self.final_modulation(t_emb).chunk(2, dim=1)
-        x = self.final_norm(x)
-        x = x * (1 + final_scale[:, None, :]) + final_shift[:, None, :]
-        x = self.final_proj(x)
-
-        x = rearrange(
-            x,
-            "b (h w) (p1 p2 c) -> b c (h p1) (w p2)",
-            h=h // self.config.patch_size,
-            w=w // self.config.patch_size,
-            p1=self.config.patch_size,
-            p2=self.config.patch_size,
-        )
-        return x
-
-
-if __name__ == "__main__":
-    model = DiT(
-        in_channels=4,
-        patch_size=2,
-        hidden_size=1152,
-        depth=28,
-        num_heads=16,
-        mlp_ratio=4.0,
-        cross_attn_input_size=128,
-        residual_v=False,
-        train_bias_and_rms=True,
-        use_rope=True,
-    ).cuda()
-    print(
-        model(
-            torch.randn(1, 4, 64, 64).cuda(),
-            torch.randn(1, 37, 128).cuda(),
-            torch.tensor([1.0]).cuda(),
-        )
-    )