Add CLIP-JAX ADE20K implementation, training, and tests

bonsai/models/clip/README.md

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+# ITA-CLIP — CLIP-style model (JAX / Flax)
+
+This directory contains a compact CLIP-like implementation (ITA-CLIP) in JAX/Flax,
+intended for zero-shot image classification, prompt-guided heatmaps, and image-text embedding experiments.
+
+## Paper (reference)
+
+- Radford et al., *Learning Transferable Visual Models From Natural Language Supervision* (OpenAI CLIP)  
+  Local copy used during development: `/mnt/data/2103.00020v1.pdf`
+
+---
+
+## Tested on
+
+| Model Name | Config | CPU | GPU (single) | GPU (multi) | TPU |
+| :--- | :---: | :---: | :---: | :---: | :---: |
+| ITA-CLIP (TinyViT + TinyText) | ✅ Compact research config | ✅ Runs (CPU) | ❔ Needs check (CUDA JAX) | ❔ Needs check | ❔ Needs check |
+
+> Notes: This implementation uses a compact TinyViT and small text-transformer to make local testing and CI-friendly smoke tests possible. For large-scale ViT-B/32 or ViT-L/14 variants, add config presets and provide pretrained weights.
+
+---
+
+### Running this model (quick smoke test)
+
+Run a forward pass / smoke test:
+
+```bash
+python3 -m bonsai.models.clip.tests.run_model

bonsai/models/clip/__init__.py

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+from .modeling import CLIPModel, clip_contrastive_loss
+from .params import CLIPConfig
+from .tokenizer import load_tokenizer, simple_whitespace_tokenizer
+
+__all__ = ["CLIPModel", "clip_contrastive_loss", "CLIPConfig", "load_tokenizer", "simple_whitespace_tokenizer"]

bonsai/models/clip/modeling.py

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+from typing import Any
+import jax
+import jax.numpy as jnp
+import flax.linen as nn
+from flax.linen import initializers
+from .params import CLIPConfig
+
+def _get_dtype(cfg: CLIPConfig):
+    return jnp.float32 if cfg.dtype == "float32" else jnp.float16
+
+class MLPBlock(nn.Module):
+    mlp_dim: int
+    out_dim: int
+    act = nn.gelu
+    dtype = jnp.float32
+
+    @nn.compact
+    def __call__(self, x):
+        x = nn.Dense(self.mlp_dim, dtype=self.dtype)(x)
+        x = self.act(x)
+        x = nn.Dense(self.out_dim, dtype=self.dtype)(x)
+        return x
+
+class AddPositionEmbs(nn.Module):
+    max_len: int
+    emb_dim: int
+    dtype = jnp.float32
+
+    def setup(self):
+        self.pos_emb = self.param("pos_emb", initializers.normal(0.02), (1, self.max_len, self.emb_dim))
+
+    def __call__(self, x):
+        return x + self.pos_emb
+
+class TransformerEncoderBlock(nn.Module):
+    num_heads: int
+    mlp_dim: int
+    dtype = jnp.float32
+
+    @nn.compact
+    def __call__(self, x, deterministic=True):
+        y = nn.LayerNorm(dtype=self.dtype)(x)
+        y = nn.SelfAttention(num_heads=self.num_heads, dtype=self.dtype, deterministic=deterministic)(y)
+        x = x + y
+        y = nn.LayerNorm(dtype=self.dtype)(x)
+        y = MLPBlock(self.mlp_dim, x.shape[-1], dtype=self.dtype)(y)
+        return x + y
+
+class SimplePatchEmbed(nn.Module):
+    patch_size: int
+    emb_dim: int
+    dtype = jnp.float32
+
+    @nn.compact
+    def __call__(self, x):
+        ps = self.patch_size
+        x = nn.Conv(self.emb_dim, (ps,ps), strides=(ps,ps), padding='VALID', dtype=self.dtype)(x)
+        b,h,w,c = x.shape
+        return jnp.reshape(x, (b, h*w, c))
+
+class ImageEncoderViT(nn.Module):
+    cfg: CLIPConfig
+    dtype = jnp.float32
+
+    @nn.compact
+    def __call__(self, images, deterministic=True):
+        cfg = self.cfg
+        x = SimplePatchEmbed(cfg.patch_size, cfg.image_embed_dim, dtype=self.dtype)(images)
+        cls = self.param('cls', initializers.zeros, (1,1,cfg.image_embed_dim))
+        cls_b = jnp.tile(cls, (x.shape[0],1,1))
+        x = jnp.concatenate([cls_b, x], axis=1)
+        x = AddPositionEmbs(x.shape[1], cfg.image_embed_dim, dtype=self.dtype)(x)
+        for _ in range(cfg.vit_num_layers):
+            x = TransformerEncoderBlock(cfg.vit_num_heads, cfg.vit_mlp_dim, dtype=self.dtype)(x, deterministic=deterministic)
+        cls_out = x[:,0]
+        cls_out = nn.LayerNorm(dtype=self.dtype)(cls_out)
+        img_feat = nn.Dense(cfg.image_embed_dim, dtype=self.dtype)(cls_out)
+        return img_feat
+
+# small ResNet-like encoder (kept light)
+class ResNetStem(nn.Module):
+    out_ch: int
+    dtype = jnp.float32
+
+    @nn.compact
+    def __call__(self, x):
+        x = nn.Conv(self.out_ch, (7,7), strides=(2,2), padding='SAME', use_bias=False, dtype=self.dtype)(x)
+        x = nn.BatchNorm(use_running_average=True, dtype=self.dtype)(x)
+        x = nn.relu(x)
+        x = nn.max_pool(x, (3,3), strides=(2,2), padding='SAME')
+        return x
+
+class ResidualBlock(nn.Module):
+    out_ch: int
+    strides: tuple = (1,1)
+    dtype = jnp.float32
+
+    @nn.compact
+    def __call__(self, x):
+        residual = x
+        y = nn.Conv(self.out_ch, (3,3), strides=self.strides, padding='SAME', use_bias=False, dtype=self.dtype)(x)
+        y = nn.BatchNorm(use_running_average=True, dtype=self.dtype)(y)
+        y = nn.relu(y)
+        y = nn.Conv(self.out_ch, (3,3), padding='SAME', use_bias=False, dtype=self.dtype)(y)
+        y = nn.BatchNorm(use_running_average=True, dtype=self.dtype)(y)
+        if residual.shape[-1] != self.out_ch or self.strides != (1,1):
+            residual = nn.Conv(self.out_ch, (1,1), strides=self.strides, padding='SAME', use_bias=False, dtype=self.dtype)(residual)
+            residual = nn.BatchNorm(use_running_average=True, dtype=self.dtype)(residual)
+        return nn.relu(residual + y)
+
+class ImageEncoderResNet(nn.Module):
+    cfg: CLIPConfig
+    dtype = jnp.float32
+
+    @nn.compact
+    def __call__(self, images, deterministic=True):
+        cfg = self.cfg
+        x = ResNetStem(cfg.resnet_stem_channels, dtype=self.dtype)(images)
+        for ch, repeats in zip(cfg.resnet_block_channels, cfg.resnet_block_repeats):
+            for i in range(repeats):
+                strides = (2,2) if i == 0 else (1,1)
+                x = ResidualBlock(ch, strides=strides, dtype=self.dtype)(x)
+        x = x.mean(axis=(1,2))
+        x = nn.LayerNorm(dtype=self.dtype)(x)
+        img_feat = nn.Dense(cfg.image_embed_dim, dtype=self.dtype)(x)
+        return img_feat
+
+class TextEncoder(nn.Module):
+    cfg: CLIPConfig
+    dtype = jnp.float32
+
+    @nn.compact
+    def __call__(self, token_ids, deterministic=True):
+        cfg = self.cfg
+        tok_emb = nn.Embed(num_embeddings=cfg.text_vocab_size, features=cfg.text_embed_dim, dtype=self.dtype)(token_ids)
+        tok_emb = AddPositionEmbs(tok_emb.shape[1], cfg.text_embed_dim, dtype=self.dtype)(tok_emb)
+        x = tok_emb
+        for _ in range(cfg.text_num_layers):
+            x = TransformerEncoderBlock(cfg.text_num_heads, cfg.text_mlp_dim, dtype=self.dtype)(x, deterministic=deterministic)
+        eos_feat = x[:, -1, :]
+        eos_feat = nn.LayerNorm(dtype=self.dtype)(eos_feat)
+        txt_feat = nn.Dense(cfg.text_embed_dim, dtype=self.dtype)(eos_feat)
+        return txt_feat
+
+class CLIPModel(nn.Module):
+    cfg: CLIPConfig
+    dtype = jnp.float32
+
+    def setup(self):
+        self.cfg.apply_model_size_presets()
+        self._dtype = _get_dtype(self.cfg)
+        if self.cfg.encoder_type == 'vit':
+            self.image_encoder = ImageEncoderViT(self.cfg, dtype=self._dtype)
+        else:
+            self.image_encoder = ImageEncoderResNet(self.cfg, dtype=self._dtype)
+        self.text_encoder = TextEncoder(self.cfg, dtype=self._dtype)
+        self.img_proj = nn.Dense(self.cfg.proj_dim, dtype=self._dtype, use_bias=False)
+        self.txt_proj = nn.Dense(self.cfg.proj_dim, dtype=self._dtype, use_bias=False)
+        self.logit_scale = self.param('logit_scale', lambda rng, shape: jnp.array(1.0), ())
+
+    def encode_image(self, images, deterministic=True):
+        feats = self.image_encoder(images, deterministic=deterministic)
+        proj = self.img_proj(feats)
+        proj = proj / (jnp.linalg.norm(proj, axis=-1, keepdims=True) + 1e-10)
+        return proj
+
+    def encode_text(self, token_ids, deterministic=True):
+        feats = self.text_encoder(token_ids, deterministic=deterministic)
+        proj = self.txt_proj(feats)
+        proj = proj / (jnp.linalg.norm(proj, axis=-1, keepdims=True) + 1e-10)
+        return proj
+
+    def __call__(self, images, token_ids, deterministic=True):
+        i_e = self.encode_image(images, deterministic=deterministic)
+        t_e = self.encode_text(token_ids, deterministic=deterministic)
+        scale = jnp.exp(self.logit_scale)
+        logits = jnp.matmul(i_e, t_e.T) * scale
+        return logits, i_e, t_e, scale
+
+def clip_contrastive_loss(logits: jnp.ndarray):
+    n = logits.shape[0]
+    labels = jnp.arange(n)
+    loss_i = jnp.mean(nn.softmax_cross_entropy(logits=logits, labels=jax.nn.one_hot(labels, n), axis=1))
+    loss_t = jnp.mean(nn.softmax_cross_entropy(logits=logits.T, labels=jax.nn.one_hot(labels, n), axis=1))
+    return 0.5 * (loss_i + loss_t)

bonsai/models/clip/params.py

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+from dataclasses import dataclass
+from typing import Literal
+
+@dataclass
+class CLIPConfig:
+    image_size: int = 224
+    encoder_type: Literal["vit", "resnet"] = "vit"
+    model_size: Literal["ViT-B/32", "ViT-L/14"] = "ViT-B/32"
+    dtype: str = "float32"   
+
+    patch_size: int = 32
+    image_embed_dim: int = 768
+    vit_num_layers: int = 12
+    vit_num_heads: int = 12
+    vit_mlp_dim: int = 3072
+
+    resnet_stem_channels: int = 64
+    resnet_block_channels: tuple = (64, 128, 256, 512)
+    resnet_block_repeats: tuple = (3, 4, 6, 3)
+
+    # text encoder
+    text_embed_dim: int = 512
+    text_vocab_size: int = 49408
+    text_max_length: int = 77
+    text_num_layers: int = 12
+    text_num_heads: int = 8
+    text_mlp_dim: int = 2048
+
+    proj_dim: int = 512
+
+    def apply_model_size_presets(self):
+        if self.model_size == "ViT-B/32":
+            self.patch_size = 32
+            self.image_embed_dim = 768
+            self.vit_num_layers = 12
+            self.vit_num_heads = 12
+            self.vit_mlp_dim = 3072
+            self.text_embed_dim = 512
+            self.proj_dim = 512
+        elif self.model_size == "ViT-L/14":
+            self.patch_size = 14
+            self.image_embed_dim = 1024
+            self.vit_num_layers = 24
+            self.vit_num_heads = 16
+            self.vit_mlp_dim = 4096
+            self.text_embed_dim = 1024
+            self.proj_dim = 1024
+        else:
+            raise ValueError("Unknown model_size: " + str(self.model_size))