Add custom endpoint handler

handler.py

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+from typing import  Dict, List, Any
+import base64
+from io import BytesIO
+from pathlib import Path
+import torch
+from torch import autocast
+import open_clip
+from open_clip import tokenizer
+from rudalle import get_vae
+from einops import rearrange
+from PIL import Image
+
+from modules import DenoiseUNet
+
+# set device
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+batch_size = 1
+steps = 11
+scale = 5
+
+def to_pil(images):
+    images = images.permute(0, 2, 3, 1).cpu().numpy()
+    images = (images * 255).round().astype("uint8")
+    images = [Image.fromarray(image) for image in images]
+    return images
+
+def log(t, eps=1e-20):
+    return torch.log(t + eps)
+
+def gumbel_noise(t):
+    noise = torch.zeros_like(t).uniform_(0, 1)
+    return -log(-log(noise))
+
+def gumbel_sample(t, temperature=1., dim=-1):
+    return ((t / max(temperature, 1e-10)) + gumbel_noise(t)).argmax(dim=dim)
+
+def sample(model, c, x=None, mask=None, T=12, size=(32, 32), starting_t=0, temp_range=[1.0, 1.0], typical_filtering=True, typical_mass=0.2, typical_min_tokens=1, classifier_free_scale=-1, renoise_steps=11, renoise_mode='start'):
+    with torch.inference_mode():
+        r_range = torch.linspace(0, 1, T+1)[:-1][:, None].expand(-1, c.size(0)).to(c.device)
+        temperatures = torch.linspace(temp_range[0], temp_range[1], T)
+        preds = []
+        if x is None:
+            x = torch.randint(0, model.num_labels, size=(c.size(0), *size), device=c.device)
+        elif mask is not None:
+            noise = torch.randint(0, model.num_labels, size=(c.size(0), *size), device=c.device)
+            x = noise * mask + (1-mask) * x
+        init_x = x.clone()
+        for i in range(starting_t, T):
+            if renoise_mode == 'prev':
+                prev_x = x.clone()
+            r, temp = r_range[i], temperatures[i]
+            logits = model(x, c, r)
+            if classifier_free_scale >= 0:
+                logits_uncond = model(x, torch.zeros_like(c), r)
+                logits = torch.lerp(logits_uncond, logits, classifier_free_scale)
+            x = logits
+            x_flat = x.permute(0, 2, 3, 1).reshape(-1, x.size(1))
+            if typical_filtering:
+                x_flat_norm = torch.nn.functional.log_softmax(x_flat, dim=-1)
+                x_flat_norm_p = torch.exp(x_flat_norm)
+                entropy = -(x_flat_norm * x_flat_norm_p).nansum(-1, keepdim=True)
+
+                c_flat_shifted = torch.abs((-x_flat_norm) - entropy)
+                c_flat_sorted, x_flat_indices = torch.sort(c_flat_shifted, descending=False)
+                x_flat_cumsum = x_flat.gather(-1, x_flat_indices).softmax(dim=-1).cumsum(dim=-1)
+
+                last_ind = (x_flat_cumsum < typical_mass).sum(dim=-1)
+                sorted_indices_to_remove = c_flat_sorted > c_flat_sorted.gather(1, last_ind.view(-1, 1))
+                if typical_min_tokens > 1:
+                    sorted_indices_to_remove[..., :typical_min_tokens] = 0
+                indices_to_remove = sorted_indices_to_remove.scatter(1, x_flat_indices, sorted_indices_to_remove)
+                x_flat = x_flat.masked_fill(indices_to_remove, -float("Inf"))
+            # x_flat = torch.multinomial(x_flat.div(temp).softmax(-1), num_samples=1)[:, 0]
+            x_flat = gumbel_sample(x_flat, temperature=temp)
+            x = x_flat.view(x.size(0), *x.shape[2:])
+            if mask is not None:
+                x = x * mask + (1-mask) * init_x
+            if i < renoise_steps:
+                if renoise_mode == 'start':
+                    x, _ = model.add_noise(x, r_range[i+1], random_x=init_x)
+                elif renoise_mode == 'prev':
+                    x, _ = model.add_noise(x, r_range[i+1], random_x=prev_x)
+                else: # 'rand'
+                    x, _ = model.add_noise(x, r_range[i+1])
+            preds.append(x.detach())
+    return preds
+
+
+class EndpointHandler():
+    def __init__(self, path=""):
+        model_path = Path(path) / "model_600000.pt"
+        state_dict = torch.load(model_path, map_location=device)
+        model = DenoiseUNet(num_labels=8192).to(device)
+        model.load_state_dict(state_dict)
+        model.to(device).eval().requires_grad_()
+        self.model = model
+
+        vqmodel = get_vae().to(device)
+        vqmodel.eval().requires_grad_(False)
+        self.vqmodel = vqmodel
+
+        clip_model, _, _ = open_clip.create_model_and_transforms('ViT-g-14', pretrained='laion2b_s12b_b42k')
+        clip_model = clip_model.to(device).eval().requires_grad_(False)
+        self.clip_model = clip_model
+
+    def encode(self, x):
+        return self.vqmodel.model.encode((2 * x - 1))[-1][-1]
+        
+    def decode(self, img_seq, shape=(32,32)):
+        img_seq = img_seq.view(img_seq.shape[0], -1)
+        b, n = img_seq.shape
+        one_hot_indices = torch.nn.functional.one_hot(img_seq, num_classes=self.vqmodel.num_tokens).float()
+        z = (one_hot_indices @ self.vqmodel.model.quantize.embed.weight)
+        z = rearrange(z, 'b (h w) c -> b c h w', h=shape[0], w=shape[1])
+        img = self.vqmodel.model.decode(z)
+        img = (img.clamp(-1., 1.) + 1) * 0.5
+        return img
+
+    def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
+        """
+        Args:
+            data (:obj:):
+                includes the input data and the parameters for the inference.
+        Return:
+            A :obj:`dict`:. base64 encoded image
+        """
+        inputs = data.pop("inputs", data)
+
+        latent_shape = (32, 32)
+        tokenized_text = tokenizer.tokenize([inputs] * batch_size).to(device)
+        with autocast(device.type):
+            clip_embeddings = self.clip_model.encode_text(tokenized_text)
+            images = sample(
+                self.model, clip_embeddings, T=12, size=latent_shape, starting_t=0, temp_range=[1.0, 1.0],
+                typical_filtering=True, typical_mass=0.2, typical_min_tokens=1,
+                classifier_free_scale=scale, renoise_steps=steps, renoise_mode="start"
+            )
+        images = self.decode(images[-1], latent_shape)    
+        images = to_pil(images)
+
+        # encode image as base 64
+        buffered = BytesIO()
+        images[0].save(buffered, format="JPEG")
+        img_str = base64.b64encode(buffered.getvalue())
+
+        # postprocess the prediction
+        return {"image": img_str.decode()}

modules.py

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+import math
+import numpy as np
+import torch
+import torch.nn as nn
+
+
+class ModulatedLayerNorm(nn.Module):
+    def __init__(self, num_features, eps=1e-6, channels_first=True):
+        super().__init__()
+        self.ln = nn.LayerNorm(num_features, eps=eps)
+        self.gamma = nn.Parameter(torch.randn(1, 1, 1))
+        self.beta = nn.Parameter(torch.randn(1, 1, 1))
+        self.channels_first = channels_first
+
+    def forward(self, x, w=None):
+        x = x.permute(0, 2, 3, 1) if self.channels_first else x
+        if w is None:
+            x = self.ln(x)
+        else:
+            x = self.gamma * w * self.ln(x) + self.beta * w
+        x = x.permute(0, 3, 1, 2) if self.channels_first else x
+        return x
+
+
+class ResBlock(nn.Module):
+    def __init__(self, c, c_hidden, c_cond=0, c_skip=0, scaler=None, layer_scale_init_value=1e-6):
+        super().__init__()
+        self.depthwise = nn.Sequential(
+            nn.ReflectionPad2d(1),
+            nn.Conv2d(c, c, kernel_size=3, groups=c)
+        )
+        self.ln = ModulatedLayerNorm(c, channels_first=False)
+        self.channelwise = nn.Sequential(
+            nn.Linear(c + c_skip, c_hidden),
+            nn.GELU(),
+            nn.Linear(c_hidden, c),
+        )
+        self.gamma = nn.Parameter(layer_scale_init_value * torch.ones(c), requires_grad=True) if layer_scale_init_value > 0 else None
+        self.scaler = scaler
+        if c_cond > 0:
+            self.cond_mapper = nn.Linear(c_cond, c)
+
+    def forward(self, x, s=None, skip=None):
+        res = x
+        x = self.depthwise(x)
+        if s is not None:
+            if s.size(2) == s.size(3) == 1:
+                s = s.expand(-1, -1, x.size(2), x.size(3))
+            elif s.size(2) != x.size(2) or s.size(3) != x.size(3):
+                s = nn.functional.interpolate(s, size=x.shape[-2:], mode='bilinear')
+            s = self.cond_mapper(s.permute(0, 2, 3, 1))
+            # s = self.cond_mapper(s.permute(0, 2, 3, 1))
+            # if s.size(1) == s.size(2) == 1:
+            #     s = s.expand(-1, x.size(2), x.size(3), -1)
+        x = self.ln(x.permute(0, 2, 3, 1), s)
+        if skip is not None:
+            x = torch.cat([x, skip.permute(0, 2, 3, 1)], dim=-1)
+        x = self.channelwise(x)
+        x = self.gamma * x if self.gamma is not None else x
+        x = res + x.permute(0, 3, 1, 2)
+        if self.scaler is not None:
+            x = self.scaler(x)
+        return x
+
+
+class DenoiseUNet(nn.Module):
+    def __init__(self, num_labels, c_hidden=1280, c_clip=1024, c_r=64, down_levels=[4, 8, 16], up_levels=[16, 8, 4]):
+        super().__init__()
+        self.num_labels = num_labels
+        self.c_r = c_r
+        self.down_levels = down_levels
+        self.up_levels = up_levels
+        c_levels = [c_hidden // (2 ** i) for i in reversed(range(len(down_levels)))]
+        self.embedding = nn.Embedding(num_labels, c_levels[0])
+
+        # DOWN BLOCKS
+        self.down_blocks = nn.ModuleList()
+        for i, num_blocks in enumerate(down_levels):
+            blocks = []
+            if i > 0:
+                blocks.append(nn.Conv2d(c_levels[i - 1], c_levels[i], kernel_size=4, stride=2, padding=1))
+            for _ in range(num_blocks):
+                block = ResBlock(c_levels[i], c_levels[i] * 4, c_clip + c_r)
+                block.channelwise[-1].weight.data *= np.sqrt(1 / sum(down_levels))
+                blocks.append(block)
+            self.down_blocks.append(nn.ModuleList(blocks))
+
+        # UP BLOCKS
+        self.up_blocks = nn.ModuleList()
+        for i, num_blocks in enumerate(up_levels):
+            blocks = []
+            for j in range(num_blocks):
+                block = ResBlock(c_levels[len(c_levels) - 1 - i], c_levels[len(c_levels) - 1 - i] * 4, c_clip + c_r,
+                                 c_levels[len(c_levels) - 1 - i] if (j == 0 and i > 0) else 0)
+                block.channelwise[-1].weight.data *= np.sqrt(1 / sum(up_levels))
+                blocks.append(block)
+            if i < len(up_levels) - 1:
+                blocks.append(
+                    nn.ConvTranspose2d(c_levels[len(c_levels) - 1 - i], c_levels[len(c_levels) - 2 - i], kernel_size=4, stride=2, padding=1))
+            self.up_blocks.append(nn.ModuleList(blocks))
+
+        self.clf = nn.Conv2d(c_levels[0], num_labels, kernel_size=1)
+
+    def gamma(self, r):
+        return (r * torch.pi / 2).cos()
+
+    def add_noise(self, x, r, random_x=None):
+        r = self.gamma(r)[:, None, None]
+        mask = torch.bernoulli(r * torch.ones_like(x), )
+        mask = mask.round().long()
+        if random_x is None:
+            random_x = torch.randint_like(x, 0, self.num_labels)
+        x = x * (1 - mask) + random_x * mask
+        return x, mask
+
+    def gen_r_embedding(self, r, max_positions=10000):
+        dtype = r.dtype
+        r = self.gamma(r) * max_positions
+        half_dim = self.c_r // 2
+        emb = math.log(max_positions) / (half_dim - 1)
+        emb = torch.arange(half_dim, device=r.device).float().mul(-emb).exp()
+        emb = r[:, None] * emb[None, :]
+        emb = torch.cat([emb.sin(), emb.cos()], dim=1)
+        if self.c_r % 2 == 1:  # zero pad
+            emb = nn.functional.pad(emb, (0, 1), mode='constant')
+        return emb.to(dtype)
+
+    def _down_encode_(self, x, s):
+        level_outputs = []
+        for i, blocks in enumerate(self.down_blocks):
+            for block in blocks:
+                if isinstance(block, ResBlock):
+                    # s_level = s[:, 0]
+                    # s = s[:, 1:]
+                    x = block(x, s)
+                else:
+                    x = block(x)
+            level_outputs.insert(0, x)
+        return level_outputs
+
+    def _up_decode(self, level_outputs, s):
+        x = level_outputs[0]
+        for i, blocks in enumerate(self.up_blocks):
+            for j, block in enumerate(blocks):
+                if isinstance(block, ResBlock):
+                    # s_level = s[:, 0]
+                    # s = s[:, 1:]
+                    if i > 0 and j == 0:
+                        x = block(x, s, level_outputs[i])
+                    else:
+                        x = block(x, s)
+                else:
+                    x = block(x)
+        return x
+
+    def forward(self, x, c, r):  # r is a uniform value between 0 and 1
+        r_embed = self.gen_r_embedding(r)
+        x = self.embedding(x).permute(0, 3, 1, 2)
+        if len(c.shape) == 2:
+            s = torch.cat([c, r_embed], dim=-1)[:, :, None, None]
+        else:
+            r_embed = r_embed[:, :, None, None].expand(-1, -1, c.size(2), c.size(3))
+            s = torch.cat([c, r_embed], dim=1)
+        level_outputs = self._down_encode_(x, s)
+        x = self._up_decode(level_outputs, s)
+        x = self.clf(x)
+        return x
+
+
+if __name__ == '__main__':
+    device = "cuda"
+    model = DenoiseUNet(1024).to(device)
+    print(sum([p.numel() for p in model.parameters()]))
+    x = torch.randint(0, 1024, (1, 32, 32)).long().to(device)
+    c = torch.randn((1, 1024)).to(device)
+    r = torch.rand(1).to(device)
+    model(x, c, r)
+

requirements.txt

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+-f https://download.pytorch.org/whl/cu116
+torch
+rudalle
+open_clip_torch
+einops