clear gitignore cache

.gitignore

@@ -1,14 +0,0 @@
-model.safetensors
-
-# python and vs code stuff
-__pycache__/
-.vscode/
-.env
-.venv/
-.DS_Store
-.env.local
-.env.development.local
-.env.test.local
-.env.production.local
-# venv
-venv/

moondream2/config.json

@@ -1,13 +0,0 @@
-{
-  "architectures": [
-    "HfMoondream"
-  ],
-  "auto_map": {
-    "AutoConfig": "hf_moondream.HfConfig",
-    "AutoModelForCausalLM": "hf_moondream.HfMoondream"
-  },
-  "config": {},
-  "model_type": "moondream1",
-  "torch_dtype": "float16",
-  "transformers_version": "4.44.0"
-}

moondream2/config.py

@@ -1,86 +0,0 @@
-from dataclasses import dataclass, field
-from typing import Dict, List, Optional
-
-
-@dataclass(frozen=True)
-class TextConfig:
-    dim: int = 2048
-    ff_dim: int = 8192
-    n_layers: int = 24
-    vocab_size: int = 51200
-    max_context: int = 2048
-    n_heads: int = 32
-    n_kv_heads: int = 32
-    prefix_attn: int = 730
-
-
-@dataclass(frozen=True)
-class VisionConfig:
-    enc_dim: int = 1152
-    enc_patch_size: int = 14
-    enc_n_layers: int = 27
-    enc_ff_dim: int = 4304
-    enc_n_heads: int = 16
-    proj_out_dim: int = 2048
-    crop_size: int = 378
-    in_channels: int = 3
-    max_crops: int = 12
-    overlap_margin: int = 4
-    proj_inner_dim: int = 8192
-
-
-@dataclass(frozen=True)
-class RegionConfig:
-    dim: int = 2048
-    coord_feat_dim: int = 256
-    coord_out_dim: int = 1024
-    size_feat_dim: int = 512
-    size_out_dim: int = 2048
-    inner_dim: int = 8192
-
-
-@dataclass(frozen=True)
-class TokenizerConfig:
-    bos_id: int = 50256
-    eos_id: int = 50256
-    templates: Dict[str, Optional[Dict[str, List[int]]]] = field(
-        default_factory=lambda: {
-            "caption": {
-                "short": [198, 198, 16438, 8305, 25],
-                "normal": [198, 198, 24334, 1159, 25],
-                "long": [198, 198, 14617, 8305, 25],
-            },
-            "query": {"prefix": [198, 198, 24361, 25], "suffix": [198, 198, 33706, 25]},
-            "detect": {"prefix": [198, 198, 47504, 25], "suffix": [628]},
-            "point": {"prefix": [198, 198, 12727, 25], "suffix": [628]},
-        }
-    )
-
-
-@dataclass(frozen=True)
-class MoondreamConfig:
-    text: TextConfig = TextConfig()
-    vision: VisionConfig = VisionConfig()
-    region: RegionConfig = RegionConfig()
-    tokenizer: TokenizerConfig = TokenizerConfig()
-
-    @classmethod
-    def from_dict(cls, config_dict: dict):
-        text_config = TextConfig(**config_dict.get("text", {}))
-        vision_config = VisionConfig(**config_dict.get("vision", {}))
-        region_config = RegionConfig(**config_dict.get("region", {}))
-        tokenizer_config = TokenizerConfig(**config_dict.get("tokenizer", {}))
-        return cls(
-            text=text_config,
-            vision=vision_config,
-            region=region_config,
-            tokenizer=tokenizer_config,
-        )
-
-    def to_dict(self):
-        return {
-            "text": self.text.__dict__,
-            "vision": self.vision.__dict__,
-            "region": self.region.__dict__,
-            "tokenizer": self.tokenizer.__dict__,
-        }

moondream2/hf_moondream.py

@@ -1,132 +0,0 @@
-from transformers import PreTrainedModel, PretrainedConfig
-
-from .config import MoondreamConfig
-from .moondream import MoondreamModel
-
-# Files sometimes don't get loaded without these...
-from .image_crops import *
-from .vision import *
-from .text import *
-from .region import *
-from .utils import *
-
-
-def extract_question(text):
-    prefix = "<image>\n\nQuestion: "
-    suffix = "\n\nAnswer:"
-
-    if text.startswith(prefix) and text.endswith(suffix):
-        return text[len(prefix) : -len(suffix)]
-    else:
-        return None
-
-
-class HfConfig(PretrainedConfig):
-    _auto_class = "AutoConfig"
-    model_type = "moondream1"
-
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-        self.config = {}
-
-
-class HfMoondream(PreTrainedModel):
-    _supports_gradient_checkpointing = True
-    _auto_class = "AutoModelForCausalLM"
-    config_class = HfConfig
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.model = MoondreamModel(
-            MoondreamConfig.from_dict(config.config), setup_caches=False
-        )
-        self.model._setup_caches()
-
-    @property
-    def encode_image(self):
-        return self.model.encode_image
-
-    @property
-    def query(self):
-        return self.model.query
-
-    @property
-    def caption(self):
-        return self.model.caption
-
-    @property
-    def detect(self):
-        return self.model.detect
-
-    @property
-    def point(self):
-        return self.model.point
-
-    @property
-    def detect_gaze(self):
-        return self.model.detect_gaze
-
-    def answer_question(
-        self,
-        image_embeds,
-        question,
-        tokenizer=None,
-        chat_history="",
-        result_queue=None,
-        max_new_tokens=256,
-        **kwargs
-    ):
-        answer = self.query(image_embeds, question)["answer"].strip()
-
-        if result_queue is not None:
-            result_queue.put(answer)
-        return answer
-
-    def batch_answer(self, images, prompts, tokenizer=None, **kwargs):
-        answers = []
-        for image, prompt in zip(images, prompts):
-            answers.append(self.query(image, prompt)["answer"].strip())
-        return answers
-
-    def _unsupported_exception(self):
-        raise NotImplementedError(
-            "This method is not supported in the latest version of moondream. "
-            "Consider upgrading to the updated API spec, or alternately pin "
-            "to 'revision=2024-08-26'."
-        )
-
-    def generate(self, image_embeds, prompt, tokenizer, max_new_tokens=128, **kwargs):
-        """
-        Function definition remains unchanged for backwards compatibility.
-        Be aware that tokenizer, max_new_takens, and kwargs are ignored.
-        """
-        prompt_extracted = extract_question(prompt)
-        if prompt_extracted is not None:
-            answer = self.model.query(
-                image=image_embeds, question=prompt_extracted, stream=False
-            )["answer"]
-        else:
-            image_embeds = self.encode_image(image_embeds)
-            prompt_tokens = torch.tensor(
-                [self.model.tokenizer.encode(prompt).ids],
-                device=self.device,
-            )
-
-            def generator():
-                for token in self.model._generate_text(
-                    prompt_tokens,
-                    image_embeds.kv_cache,
-                    image_embeds.pos,
-                    max_new_tokens,
-                ):
-                    yield token
-
-            answer = "".join(list(generator()))
-
-        return [answer]
-
-    def get_input_embeddings(self):
-        return super().get_input_embeddings()
-
-    def input_embeds(self, *args, **kwargs):
-        self._unsupported_exception()

moondream2/image_crops.py

@@ -1,208 +0,0 @@
-import math
-import numpy as np
-import torch
-import pyvips
-
-from typing import TypedDict
-
-
-def select_tiling(
-    height: int, width: int, crop_size: int, max_crops: int
-) -> tuple[int, int]:
-    """
-    Determine the optimal number of tiles to cover an image with overlapping crops.
-    """
-    if height <= crop_size or width <= crop_size:
-        return (1, 1)
-
-    # Minimum required tiles in each dimension
-    min_h = math.ceil(height / crop_size)
-    min_w = math.ceil(width / crop_size)
-
-    # If minimum required tiles exceed max_crops, return proportional distribution
-    if min_h * min_w > max_crops:
-        ratio = math.sqrt(max_crops / (min_h * min_w))
-        return (max(1, math.floor(min_h * ratio)), max(1, math.floor(min_w * ratio)))
-
-    # Perfect aspect-ratio tiles that satisfy max_crops
-    h_tiles = math.floor(math.sqrt(max_crops * height / width))
-    w_tiles = math.floor(math.sqrt(max_crops * width / height))
-
-    # Ensure we meet minimum tile requirements
-    h_tiles = max(h_tiles, min_h)
-    w_tiles = max(w_tiles, min_w)
-
-    # If we exceeded max_crops, scale down the larger dimension
-    if h_tiles * w_tiles > max_crops:
-        if w_tiles > h_tiles:
-            w_tiles = math.floor(max_crops / h_tiles)
-        else:
-            h_tiles = math.floor(max_crops / w_tiles)
-
-    return (max(1, h_tiles), max(1, w_tiles))
-
-
-class OverlapCropOutput(TypedDict):
-    crops: np.ndarray
-    tiling: tuple[int, int]
-
-
-def overlap_crop_image(
-    image: np.ndarray,
-    overlap_margin: int,
-    max_crops: int,
-    base_size: tuple[int, int] = (378, 378),
-    patch_size: int = 14,
-) -> OverlapCropOutput:
-    """
-    Process an image using an overlap-and-resize cropping strategy with margin handling.
-
-    This function takes an input image and creates multiple overlapping crops with
-    consistent margins. It produces:
-    1. A single global crop resized to base_size
-    2. Multiple overlapping local crops that maintain high resolution details
-    3. A patch ordering matrix that tracks correspondence between crops
-
-    The overlap strategy ensures:
-    - Smooth transitions between adjacent crops
-    - No loss of information at crop boundaries
-    - Proper handling of features that cross crop boundaries
-    - Consistent patch indexing across the full image
-
-    Args:
-        image (np.ndarray): Input image as numpy array with shape (H,W,C)
-        base_size (tuple[int,int]): Target size for crops, default (378,378)
-        patch_size (int): Size of patches in pixels, default 14
-        overlap_margin (int): Margin size in patch units, default 4
-        max_crops (int): Maximum number of crops allowed, default 12
-
-    Returns:
-        OverlapCropOutput: Dictionary containing:
-            - crops: A numpy array containing the global crop of the full image (index 0)
-                followed by the overlapping cropped regions (indices 1+)
-            - tiling: Tuple of (height,width) tile counts
-    """
-    original_h, original_w = image.shape[:2]
-
-    # Convert margin from patch units to pixels
-    margin_pixels = patch_size * overlap_margin
-    total_margin_pixels = margin_pixels * 2  # Both sides
-
-    # Calculate crop parameters
-    crop_patches = base_size[0] // patch_size  # patches per crop dimension
-    crop_window_patches = crop_patches - (2 * overlap_margin)  # usable patches
-    crop_window_size = crop_window_patches * patch_size  # usable size in pixels
-
-    # Determine tiling
-    tiling = select_tiling(
-        original_h - total_margin_pixels,
-        original_w - total_margin_pixels,
-        crop_window_size,
-        max_crops,
-    )
-
-    # Pre-allocate crops.
-    n_crops = tiling[0] * tiling[1] + 1  # 1 = global crop
-    crops = np.zeros(
-        (n_crops, base_size[0], base_size[1], image.shape[2]), dtype=np.uint8
-    )
-
-    # Resize image to fit tiling
-    target_size = (
-        tiling[0] * crop_window_size + total_margin_pixels,
-        tiling[1] * crop_window_size + total_margin_pixels,
-    )
-
-    # Convert to vips for resizing
-    vips_image = pyvips.Image.new_from_array(image)
-    scale_x = target_size[1] / image.shape[1]
-    scale_y = target_size[0] / image.shape[0]
-    resized = vips_image.resize(scale_x, vscale=scale_y)
-    image = resized.numpy()
-
-    # Create global crop
-    scale_x = base_size[1] / vips_image.width
-    scale_y = base_size[0] / vips_image.height
-    global_vips = vips_image.resize(scale_x, vscale=scale_y)
-    crops[0] = global_vips.numpy()
-
-    for i in range(tiling[0]):
-        for j in range(tiling[1]):
-            # Calculate crop coordinates
-            y0 = i * crop_window_size
-            x0 = j * crop_window_size
-
-            # Extract crop with padding if needed
-            y_end = min(y0 + base_size[0], image.shape[0])
-            x_end = min(x0 + base_size[1], image.shape[1])
-
-            crop_region = image[y0:y_end, x0:x_end]
-            crops[
-                1 + i * tiling[1] + j, : crop_region.shape[0], : crop_region.shape[1]
-            ] = crop_region
-
-    return {"crops": crops, "tiling": tiling}
-
-
-def reconstruct_from_crops(
-    crops: torch.Tensor,
-    tiling: tuple[int, int],
-    overlap_margin: int,
-    patch_size: int = 14,
-) -> torch.Tensor:
-    """
-    Reconstruct the original image from overlapping crops into a single seamless image.
-
-    Takes a list of overlapping image crops along with their positional metadata and
-    reconstructs them into a single coherent image by carefully stitching together
-    non-overlapping regions. Handles both numpy arrays and PyTorch tensors.
-
-    Args:
-        crops: List of image crops as numpy arrays or PyTorch tensors with shape
-            (H,W,C)
-        tiling: Tuple of (height,width) indicating crop grid layout
-        patch_size: Size in pixels of each patch, default 14
-        overlap_margin: Number of overlapping patches on each edge, default 4
-
-    Returns:
-        Reconstructed image as numpy array or PyTorch tensor matching input type,
-        with shape (H,W,C) where H,W are the original image dimensions
-    """
-    tiling_h, tiling_w = tiling
-    crop_height, crop_width = crops[0].shape[:2]
-    margin_pixels = overlap_margin * patch_size
-
-    # Calculate output size (only adding margins once)
-    output_h = (crop_height - 2 * margin_pixels) * tiling_h + 2 * margin_pixels
-    output_w = (crop_width - 2 * margin_pixels) * tiling_w + 2 * margin_pixels
-
-    reconstructed = torch.zeros(
-        (output_h, output_w, crops[0].shape[2]),
-        device=crops[0].device,
-        dtype=crops[0].dtype,
-    )
-
-    for i, crop in enumerate(crops):
-        tile_y = i // tiling_w
-        tile_x = i % tiling_w
-
-        # For each tile, determine which part to keep
-        # Keep left margin only for first column
-        x_start = 0 if tile_x == 0 else margin_pixels
-        # Keep right margin only for last column
-        x_end = crop_width if tile_x == tiling_w - 1 else crop_width - margin_pixels
-        # Keep top margin only for first row
-        y_start = 0 if tile_y == 0 else margin_pixels
-        # Keep bottom margin only for last row
-        y_end = crop_height if tile_y == tiling_h - 1 else crop_height - margin_pixels
-
-        # Calculate where this piece belongs in the output
-        out_x = tile_x * (crop_width - 2 * margin_pixels)
-        out_y = tile_y * (crop_height - 2 * margin_pixels)
-
-        # Place the piece
-        reconstructed[
-            out_y + y_start : out_y + y_end, out_x + x_start : out_x + x_end
-        ] = crop[y_start:y_end, x_start:x_end]
-
-    return reconstructed

moondream2/layers.py

@@ -1,63 +0,0 @@
-from dataclasses import dataclass
-from typing import Literal
-
-import torch
-from torch.nn import functional as F
-
-
-def gelu_approx(x):
-    return F.gelu(x, approximate="tanh")
-
-
-@dataclass
-class LinearWeights:
-    weight: torch.Tensor
-    bias: torch.Tensor
-
-
-def linear(x: torch.Tensor, w: LinearWeights) -> torch.Tensor:
-    return F.linear(x, w.weight, w.bias)
-
-
-@dataclass
-class LayerNormWeights:
-    weight: torch.Tensor
-    bias: torch.Tensor
-
-
-def layer_norm(x: torch.Tensor, w: LayerNormWeights) -> torch.Tensor:
-    return F.layer_norm(x, w.bias.shape, w.weight, w.bias)
-
-
-@dataclass
-class MLPWeights:
-    fc1: LinearWeights
-    fc2: LinearWeights
-    act: Literal["gelu_approx"] = "gelu_approx"
-
-
-def mlp(x: torch.Tensor, w: MLPWeights) -> torch.Tensor:
-    x = w.fc1(x)
-    x = gelu_approx(x)
-    x = w.fc2(x)
-    return x
-
-
-@dataclass
-class AttentionWeights:
-    qkv: LinearWeights
-    proj: LinearWeights
-
-
-def attn(x: torch.Tensor, w: AttentionWeights, n_heads: int) -> torch.Tensor:
-    bsz, q_len, d_model = x.shape
-    head_dim = d_model // n_heads
-
-    q, k, v = [
-        t.view(bsz, q_len, n_heads, head_dim).transpose(1, 2)
-        for t in linear(x, w.qkv).chunk(3, dim=-1)
-    ]
-    out = F.scaled_dot_product_attention(q, k, v)
-    out = out.transpose(1, 2).reshape(bsz, q_len, d_model)
-    out = linear(out, w.proj)
-    return out

moondream2/moondream.py

@@ -1,476 +0,0 @@
-import torch
-import torch.nn as nn
-import random
-
-from typing import Literal, Tuple, TypedDict, Union, Dict, Any, Optional, List
-from PIL import Image
-from dataclasses import dataclass
-from tokenizers import Tokenizer
-
-from .config import MoondreamConfig
-from .image_crops import reconstruct_from_crops
-from .vision import vision_encoder, vision_projection, prepare_crops, build_vision_model
-from .text import build_text_model, text_encoder, lm_head, text_decoder
-from .region import decode_coordinate, encode_coordinate, decode_size, encode_size
-from .utils import remove_outlier_points
-import os
-
-TextSamplingSettings = TypedDict(
-    "TextSamplingSettings",
-    {
-        "max_tokens": int,
-        "temperature": float,
-        "top_p": float,
-    },
-    total=False,
-)
-
-ObjectSamplingSettings = TypedDict(
-    "ObjectSamplingSettings",
-    {"max_objects": int},
-    total=False,
-)
-
-DEFAULT_MAX_TOKENS = 768
-DEFAULT_TEMPERATURE = 0.5
-DEFAULT_TOP_P = 0.3
-DEFAULT_MAX_OBJECTS = 50
-
-
-@dataclass(frozen=True)
-class EncodedImage:
-    pos: int
-    caches: List[Tuple[torch.Tensor, torch.Tensor]]
-
-
-class KVCache(nn.Module):
-
-    def __init__(self, n_heads, n_kv_heads, max_context, dim, device, dtype):
-        super().__init__()
-        cache_shape = (1, n_kv_heads, max_context, dim // n_heads)
-        self.register_buffer(
-            "k_cache", torch.zeros(*cache_shape, device=device, dtype=dtype)
-        )
-        self.register_buffer(
-            "v_cache", torch.zeros(*cache_shape, device=device, dtype=dtype)
-        )
-
-    def update(self, pos_ids, k, v):
-        kout, vout = self.k_cache, self.v_cache
-        kout[:, :, pos_ids, :] = k
-        vout[:, :, pos_ids, :] = v
-        return kout, vout
-
-
-class MoondreamModel(nn.Module):
-    def __init__(self, config: MoondreamConfig, dtype=torch.float16, setup_caches=True):
-        super().__init__()
-        self.config = config
-        current_dir = os.path.dirname(os.path.abspath(__file__))
-        self.tokenizer = Tokenizer.from_file(os.path.join(current_dir, "tokenizer.json"))
-        self.vision = build_vision_model(config.vision, dtype)
-        self.text = build_text_model(config.text, dtype)
-
-        # Region Model
-        self.region = nn.ModuleDict(
-            {
-                "coord_encoder": nn.Linear(
-                    config.region.coord_feat_dim, config.region.dim, dtype=dtype
-                ),
-                "coord_decoder": nn.ModuleDict(
-                    {
-                        "fc1": nn.Linear(
-                            config.region.dim, config.region.inner_dim, dtype=dtype
-                        ),
-                        "fc2": nn.Linear(
-                            config.region.inner_dim,
-                            config.region.coord_out_dim,
-                            dtype=dtype,
-                        ),
-                    }
-                ),
-                "size_encoder": nn.Linear(
-                    config.region.size_feat_dim, config.region.dim, dtype=dtype
-                ),
-                "size_decoder": nn.ModuleDict(
-                    {
-                        "fc1": nn.Linear(
-                            config.region.dim, config.region.inner_dim, dtype=dtype
-                        ),
-                        "fc2": nn.Linear(
-                            config.region.inner_dim,
-                            config.region.size_out_dim,
-                            dtype=dtype,
-                        ),
-                    }
-                ),
-            }
-        )
-        self.region.coord_features = nn.Parameter(
-            torch.empty(config.region.coord_feat_dim // 2, 1, dtype=dtype).T
-        )
-        self.region.size_features = nn.Parameter(
-            torch.empty(config.region.size_feat_dim // 2, 2, dtype=dtype).T
-        )
-
-        attn_mask = torch.tril(
-            torch.ones(
-                1, 1, config.text.max_context, config.text.max_context, dtype=torch.bool
-            )
-        )
-        patch_w = config.vision.crop_size // config.vision.enc_patch_size
-        prefix_attn_len = 1 + patch_w**2
-        attn_mask[..., :prefix_attn_len, :prefix_attn_len] = 1
-        self.register_buffer("attn_mask", attn_mask, persistent=False)
-
-        # Initialize KV caches.
-        if setup_caches:
-            self._setup_caches()
-
-    def _setup_caches(self):
-        c = self.config.text
-        for b in self.text.blocks:
-            b.kv_cache = KVCache(
-                c.n_heads,
-                c.n_kv_heads,
-                c.max_context,
-                c.dim,
-                device=self.device,
-                dtype=self.vision.pos_emb.dtype,
-            )
-    @property
-    def device(self):
-        return self.vision.pos_emb.device
-
-    def _vis_enc(self, x: torch.Tensor):
-        return vision_encoder(x, self.vision, self.config.vision)
-
-    def _vis_proj(self, g: torch.Tensor, r: torch.Tensor):
-        return vision_projection(g, r, self.vision, self.config.vision)
-
-    def _prefill(self, x: torch.Tensor, attn_mask: torch.Tensor, pos_ids: torch.Tensor):
-        return text_decoder(x, self.text, attn_mask, pos_ids, self.config.text)
-
-    def _decode_one_tok(
-        self, x: torch.Tensor, attn_mask: torch.Tensor, pos_ids: torch.Tensor
-    ):
-        hidden = text_decoder(x, self.text, attn_mask, pos_ids, self.config.text)
-        logits = lm_head(hidden, self.text)
-        return logits, hidden
-
-    def compile(self):
-        # TODO: vision_projection is not being compiled
-        self._vis_enc = torch.compile(self._vis_enc, fullgraph=True)
-        self._prefill = torch.compile(self._prefill, fullgraph=True)
-        self._decode_one_tok = torch.compile(
-            self._decode_one_tok, fullgraph=True, mode="reduce-overhead"
-        )
-
-    def _run_vision_encoder(self, image: Image.Image) -> torch.Tensor:
-        all_crops, tiling = prepare_crops(image, self.config.vision, device=self.device)
-        torch._dynamo.mark_dynamic(all_crops, 0)
-
-        outputs = self._vis_enc(all_crops)
-
-        global_features = outputs[0]
-        local_features = outputs[1:].view(
-            -1,
-            self.config.vision.enc_n_layers,
-            self.config.vision.enc_n_layers,
-            self.config.vision.enc_dim,
-        )
-
-        reconstructed = reconstruct_from_crops(
-            local_features,
-            tiling,
-            patch_size=1,
-            overlap_margin=self.config.vision.overlap_margin,
-        )
-
-        return self._vis_proj(global_features, reconstructed)
-
-    def encode_image(self, image: Union[Image.Image, EncodedImage]) -> EncodedImage:
-        if isinstance(image, EncodedImage):
-            return image
-        elif not isinstance(image, Image.Image):
-            raise ValueError("image must be a PIL Image or EncodedImage")
-
-        # Run through text model in addition to the vision encoder, to minimize
-        # re-computation if multiple queries are performed on this image.
-        with torch.inference_mode():
-            
-            bos = torch.tensor([[self.config.tokenizer.bos_id]], device=self.device)
-            
-            img_emb = self._run_vision_encoder(image)
-            bos_emb = text_encoder(
-                bos,
-                self.text,
-            )
-            inputs_embeds = torch.cat([bos_emb, img_emb[None]], dim=1)
-            mask = self.attn_mask[:, :, 0 : inputs_embeds.size(1), :]
-            pos_ids = torch.arange(inputs_embeds.size(1), dtype=torch.long, device=self.device)
-            self._prefill(inputs_embeds, mask, pos_ids)
-
-        return EncodedImage(
-            pos=inputs_embeds.size(1),
-            caches=[
-                (
-                    b.kv_cache.k_cache[:, :, : inputs_embeds.size(1), :].clone(),
-                    b.kv_cache.v_cache[:, :, : inputs_embeds.size(1), :].clone(),
-                )
-                for b in self.text.blocks
-            ],
-        )
-
-    def _apply_top_p(self, probs: torch.Tensor, top_p: float):
-        probs_sort, probs_idx = torch.sort(probs, dim=-1, descending=True)
-        probs_sum = torch.cumsum(probs_sort, dim=-1)
-        mask = probs_sum - probs_sort > top_p
-        probs_sort[mask] = 0.0
-        probs_sort.div_(probs_sort.sum(dim=-1, keepdim=True))
-        next_probs = torch.zeros_like(probs)
-        next_probs.scatter_(dim=-1, index=probs_idx, src=probs_sort)
-        return next_probs
-
-    def _prefill_prompt(
-        self, prompt_tokens: torch.Tensor, pos: int, temperature: float, top_p: float
-    ):
-        with torch.inference_mode():
-            prompt_emb = text_encoder(prompt_tokens, self.text)
-            torch._dynamo.mark_dynamic(prompt_emb, 1)
-            mask = self.attn_mask[:, :, pos : pos + prompt_emb.size(1), :]
-            pos_ids = torch.arange(pos, pos + prompt_emb.size(1), dtype=torch.long, device=self.device)
-            hidden = self._prefill(prompt_emb, mask, pos_ids)
-            logits = lm_head(hidden, self.text)
-
-            if temperature == 0:
-                next_token = torch.argmax(logits, dim=-1).unsqueeze(1)
-            else:
-                probs = torch.softmax(logits / temperature, dim=-1)
-                probs = self._apply_top_p(probs, top_p)
-                next_token = torch.multinomial(probs, num_samples=1)
-
-        pos = pos + prompt_emb.size(1)
-        return logits, hidden, next_token, pos
-
-    def _generate_text(
-        self,
-        prompt_tokens: torch.Tensor,
-        pos: int,
-        settings: Optional[TextSamplingSettings] = None,
-    ):
-        max_tokens = (
-            settings.get("max_tokens", DEFAULT_MAX_TOKENS)
-            if settings
-            else DEFAULT_MAX_TOKENS
-        )
-        temperature = (
-            settings.get("temperature", DEFAULT_TEMPERATURE)
-            if settings
-            else DEFAULT_TEMPERATURE
-        )
-        top_p = settings.get("top_p", DEFAULT_TOP_P) if settings else DEFAULT_TOP_P
-
-        _, _, next_token, pos = self._prefill_prompt(
-            prompt_tokens, pos, temperature, top_p
-        )
-
-        def generator(next_token, pos):
-            mask = torch.zeros(1, 1, 2048, device=self.device, dtype=torch.bool)
-            mask[:, :, :pos] = 1
-            pos_ids = torch.tensor([pos], device=self.device, dtype=torch.long)
-            generated_tokens = 0
-
-            # For properly handling token streaming with Unicode
-            token_cache = []
-            print_len = 0
-
-            while (
-                next_token_id := next_token.item()
-            ) != self.config.tokenizer.eos_id and generated_tokens < max_tokens:
-                # Add token to our cache
-                token_cache.append(next_token_id)
-
-                # Decode all tokens collected so far
-                text = self.tokenizer.decode(token_cache)
-
-                # After a newline, we flush the cache completely
-                if text.endswith("\n"):
-                    printable_text = text[print_len:]
-                    token_cache = []
-                    print_len = 0
-                    if printable_text:
-                        yield printable_text
-                # If the last token is a CJK character, we can safely print it
-                elif len(text) > 0 and _is_cjk_char(ord(text[-1])):
-                    printable_text = text[print_len:]
-                    print_len += len(printable_text)
-                    if printable_text:
-                        yield printable_text
-                # Otherwise, only print up to the last space to avoid cutting words
-                else:
-                    last_space_idx = text.rfind(" ", print_len)
-                    if last_space_idx >= print_len:
-                        printable_text = text[print_len : last_space_idx + 1]
-                        print_len += len(printable_text)
-                        if printable_text:
-                            yield printable_text
-
-                with torch.inference_mode():
-                    next_emb = text_encoder(next_token, self.text)
-                    mask[:, :, pos], pos_ids[0] = 1, pos
-                    logits, _ = self._decode_one_tok(next_emb, mask, pos_ids)
-                    pos += 1
-
-                    if temperature == 0:
-                        next_token = torch.argmax(logits, dim=-1).unsqueeze(1)  # (1, 1)
-                    else:
-                        probs = torch.softmax(logits / temperature, dim=-1)  # (1, V)
-                        probs = self._apply_top_p(probs, top_p)
-                        next_token = torch.multinomial(probs, num_samples=1)  # (1, 1)
-
-                    generated_tokens += 1
-
-            # Flush any remaining text in the cache
-            if token_cache:
-                text = self.tokenizer.decode(token_cache)
-                printable_text = text[print_len:]
-                if printable_text:
-                    yield printable_text
-
-        return generator(next_token, pos)
-
-    def load_encoded_image(self, encoded_image: EncodedImage):
-        for b, (k, v) in zip(self.text.blocks, encoded_image.caches):
-            b.kv_cache.k_cache[:, :, : k.size(2), :] = k
-            b.kv_cache.v_cache[:, :, : v.size(2), :] = v
-
-    def _generate_points(
-        self,
-        hidden: torch.Tensor,
-        next_token: torch.Tensor,
-        pos: int,
-        include_size: bool = True,
-        max_objects: int = DEFAULT_MAX_OBJECTS,
-    ):
-        out = []
-        mask = torch.zeros(1, 1, 2048, device=self.device, dtype=torch.bool)
-        mask[:, :, :pos] = 1
-        pos_ids = torch.tensor([pos], device=self.device, dtype=torch.long)
-
-        with torch.inference_mode():
-            while (
-                next_token.item() != self.config.tokenizer.eos_id
-                and len(out) < max_objects
-            ):
-                x_logits = decode_coordinate(hidden, self.region)
-                x_center = torch.argmax(x_logits, dim=-1) / x_logits.size(-1)
-                next_emb = encode_coordinate(
-                    x_center.to(dtype=x_logits.dtype), self.region
-                ).unsqueeze(0)
-
-                # Decode y-coordinate
-                mask[:, :, pos], pos_ids[0] = 1, pos
-                _, hidden = self._decode_one_tok(next_emb, mask, pos_ids)
-                pos += 1
-                y_logits = decode_coordinate(hidden, self.region)
-                y_center = torch.argmax(y_logits, dim=-1) / y_logits.size(-1)
-                next_emb = encode_coordinate(
-                    y_center.to(dtype=y_logits.dtype), self.region
-                ).unsqueeze(0)
-
-                # Decode size
-                if include_size:
-                    mask[:, :, pos], pos_ids[0] = 1, pos
-                    logits, hidden = self._decode_one_tok(next_emb, mask, pos_ids)
-                    pos += 1
-                    size_logits = decode_size(hidden, self.region)
-
-                    # Get bin indices from the logits
-                    w_bin = torch.argmax(size_logits[0], dim=-1)
-                    h_bin = torch.argmax(size_logits[1], dim=-1)
-
-                    # Convert from bin indices to actual size values using the inverse of the log-scale mapping
-                    # Formula: size = 2^((bin / 1023.0) * 10.0 - 10.0)
-                    w = torch.pow(2.0, (w_bin.float() / 1023.0) * 10.0 - 10.0)
-                    h = torch.pow(2.0, (h_bin.float() / 1023.0) * 10.0 - 10.0)
-
-                    next_emb = (
-                        encode_size(
-                            torch.tensor(
-                                [w, h], device=self.device, dtype=size_logits.dtype
-                            ),
-                            self.region,
-                        )
-                        .unsqueeze(0)
-                        .unsqueeze(0)
-                    )
-
-                    # Add object
-                    out.append(
-                        {
-                            "x_min": x_center.item() - w.item() / 2,
-                            "y_min": y_center.item() - h.item() / 2,
-                            "x_max": x_center.item() + w.item() / 2,
-                            "y_max": y_center.item() + h.item() / 2,
-                        }
-                    )
-                else:
-                    out.append({"x": x_center.item(), "y": y_center.item()})
-
-                # Decode next token (x-coordinate, or eos)
-                mask[:, :, pos], pos_ids[0] = 1, pos
-                logits, hidden = self._decode_one_tok(next_emb, mask, pos_ids)
-                pos += 1
-                next_token = torch.argmax(logits, dim=-1)
-
-        return out
-
-    def point(
-        self,
-        image: Union[Image.Image, EncodedImage],
-        object: str,
-        settings: Optional[ObjectSamplingSettings] = None,
-    ):
-        if self.config.tokenizer.templates["point"] is None:
-            raise NotImplementedError("Model does not support pointing.")
-
-        image = self.encode_image(image)
-        self.load_encoded_image(image)
-
-        prompt_tokens = torch.tensor(
-            [
-                self.config.tokenizer.templates["point"]["prefix"]
-                + self.tokenizer.encode(" " + object).ids
-                + self.config.tokenizer.templates["point"]["suffix"]
-            ],
-            device=self.device,
-        )
-
-        _, hidden, next_token, pos = self._prefill_prompt(
-            prompt_tokens, image.pos, temperature=0, top_p=0
-        )
-        hidden = hidden[:, -1:, :]
-
-        max_objects = (
-            settings.get("max_objects", DEFAULT_MAX_OBJECTS)
-            if settings
-            else DEFAULT_MAX_OBJECTS
-        )
-        objects = self._generate_points(
-            hidden, next_token, pos, include_size=False, max_objects=max_objects
-        )
-
-        return {"points": objects}
-
-def _is_cjk_char(cp):
-    """Checks whether CP is the codepoint of a CJK character."""
-    # This defines a "chinese character" as anything in the CJK Unicode block:
-    # https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
-    if (
-        (cp >= 0x4E00 and cp <= 0x9FFF)
-        or (cp >= 0x3400 and cp <= 0x4DBF)
-        or (cp >= 0x2F800 and cp <= 0x2FA1F)
-    ):
-        return True
-    return False

moondream2/region.py

@@ -1,89 +0,0 @@
-import torch
-import torch.nn as nn
-import math
-
-from .layers import linear, mlp
-
-
-def fourier_features(x: torch.Tensor, w: torch.Tensor) -> torch.Tensor:
-    """
-    Applies Fourier feature mapping to input tensor x using frequency matrix w. This
-    projects inputs through sinusoidal functions to create higher dimensional features
-    that help mitigate spectral bias - the tendency of neural networks to learn
-    low-frequency functions more easily than high-frequency ones. By explicitly
-    mapping inputs to higher frequencies through sin/cos transformations, we enable
-    better learning of fine details and higher frequency patterns.
-
-    Args:
-        x: Input tensor to transform
-        w: Matrix of frequencies for the Fourier features transformation
-
-    Returns:
-        Concatenated cosine and sine transformed features as a tensor
-    """
-    f = 2 * math.pi * x @ w
-    return torch.cat([f.cos(), f.sin()], dim=-1)
-
-
-def encode_coordinate(coord: torch.Tensor, w: nn.Module) -> torch.Tensor:
-    """
-    Takes as input a tensor containing a single float coordinate value (x or y)
-    and encodes it into hidden states for input to the text model.
-
-    Args:
-        coord: Tensor with single float coordinate value
-
-    Returns:
-        Encoded hidden states tensor for input to text model
-    """
-    return linear(fourier_features(coord, w.coord_features), w.coord_encoder)
-
-
-def decode_coordinate(hidden_state: torch.Tensor, w: nn.Module) -> torch.Tensor:
-    """
-    Takes as input the last hidden state from the text model and outputs a single logit
-    representing either an x or y coordinate prediction.
-
-    Args:
-        hidden_state: The final hidden state tensor from the text model.
-
-    Returns:
-        A single logit representing the predicted coordinate value (x or y)
-    """
-    return mlp(hidden_state, w.coord_decoder)
-
-
-def encode_size(size: torch.Tensor, w: nn.Module) -> torch.Tensor:
-    """
-    Takes a tensor containing width and height values and encodes them into
-    hidden states for input to the text model.
-
-    Args:
-        size: Tensor with two floats for width and height
-
-    Returns:
-        Encoded hidden states tensor for input to text model
-    """
-    return linear(fourier_features(size, w.size_features), w.size_encoder)
-
-
-def decode_size(hidden_state: torch.Tensor, w: nn.Module) -> torch.Tensor:
-    """
-    Takes as input the last hidden state from the text model and outputs logits
-    for 1024 bins representing width and height in log-scale.
-
-    The bins are distributed according to the formula:
-    bin = (log2(size) + 10.0) / 10.0 * 1023.0
-    where size values are clamped to be at least 1/1024.
-
-    To convert from bin back to size:
-    size = 2^((bin / 1023.0) * 10.0 - 10.0)
-
-    Args:
-        hidden_state: The final hidden state tensor from the text model.
-
-    Returns:
-        A tensor containing logits for 1024 bins for width and height.
-        Shape is (2, 1024) where the first dimension corresponds to width and height.
-    """
-    return mlp(hidden_state, w.size_decoder).view(2, -1)

moondream2/rope.py

@@ -1,90 +0,0 @@
-# Ethically sourced from https://github.com/xjdr-alt/entropix
-
-import torch
-import time
-
-def precompute_freqs_cis(
-    dim: int,
-    end: int,
-    theta: float = 10000.0,
-    use_scaled: bool = False,
-    dtype: torch.dtype = torch.float32,
-) -> torch.Tensor:
-    freqs = 1.0 / (theta ** (torch.arange(0, dim, 2, dtype=dtype)[: (dim // 2)] / dim))
-    t = torch.arange(end, dtype=dtype).unsqueeze(1)
-    freqs = t * freqs.unsqueeze(0)
-    freqs = torch.exp(1j * freqs)
-    return torch.stack([freqs.real, freqs.imag], dim=-1)
-
-def func1(x):
-    #print(x)
-    pass
-
-def func2(x):
-    #print(x)
-    pass
-
-def func3(x):
-    #print(x)
-    pass
-
-def func4(x):
-    #print(x)
-    pass
-
-def func5(x):
-    #print(x)
-    pass
-
-def func6(x):
-    #print(x)
-    pass
-
-def func7(x):
-    #print(x)
-    pass
-
-def func8(x):
-    #print(x)
-    pass
-
-def func9(x):
-    #print(x)
-    pass
-
-def func10(x):
-    #print(x)
-    pass
-
-def func11(x):
-    #print(x)
-    pass
-
-def apply_rotary_emb(
-    x: torch.Tensor,
-    freqs_cis: torch.Tensor,
-    position_ids: torch.Tensor,
-    num_heads: int,
-    rot_dim: int = 32,
-    interleave: bool = False,
-) -> torch.Tensor:
-    assert rot_dim == freqs_cis.shape[-2] * 2
-    assert num_heads == x.shape[1]
-    x_rot, x_pass = x[..., :rot_dim], x[..., rot_dim:]
-
-    d_q = x_rot.shape[-1] // 2
-    xq_r, xq_i = x_rot[..., :d_q], x_rot[..., d_q:]
-
-    # Get the cosine component from freqs_cis
-    cos_component = freqs_cis[..., 0]
-    # Index with position_ids
-    cos_indexed = cos_component[position_ids, :]
-    # Add two dimensions at the beginning
-    freqs_cos = cos_indexed.unsqueeze(0).unsqueeze(0)
-    freqs_sin = freqs_cis[..., 1][position_ids, :].unsqueeze(0).unsqueeze(0)
-
-    # Complex multiplication: (a + bi) * (c + di) = (ac - bd) + (ad + bc)i
-    xq_out_r = xq_r * freqs_cos - xq_i * freqs_sin
-    xq_out_i = xq_r * freqs_sin + xq_i * freqs_cos
-    xq_out = torch.stack((xq_out_r, xq_out_i), dim=-1).flatten(-2)
-    return torch.cat([xq_out.to(x.dtype), x_pass], dim=-1)

moondream2/text.py

@@ -1,129 +0,0 @@
-import torch
-import torch.nn as nn
-
-from torch.nn import functional as F
-
-from .layers import layer_norm, mlp
-from .rope import apply_rotary_emb, precompute_freqs_cis
-from .config import TextConfig
-
-
-def text_encoder(input_ids: torch.Tensor, w: nn.Module):
-    return F.embedding(input_ids, w.wte)
-
-
-def attn(
-    x: torch.Tensor,
-    w: nn.Module,
-    freqs_cis: torch.Tensor,
-    kv_cache: nn.Module,
-    attn_mask: torch.Tensor,
-    n_heads: int,
-    position_ids: torch.Tensor,
-    do_apply_rotary_emb: bool = True,
-):
-    bsz, q_len, d_model = x.shape
-    head_dim = d_model // n_heads
-
-    qkv_out = w.qkv(x)  # shape: (bsz, q_len, (n_heads * 3)*head_dim)
-
-    qkv_reshaped = qkv_out.view(bsz, q_len, 3, n_heads, head_dim)
-
-    # 2. Permute to bring heads before sequence length and QKV to the front
-    # Current: (bsz, q_len, 3, n_heads, head_dim) -> (0, 1, 2, 3, 4)
-    # Target:  (3, bsz, n_heads, q_len, head_dim) -> (2, 0, 3, 1, 4)
-    qkv_permuted = qkv_reshaped.permute(2, 0, 3, 1, 4)
-
-    # 3. Unpack/Split along the first dimension (which now separates Q, K, V)
-    q, k, v = qkv_permuted[0], qkv_permuted[1], qkv_permuted[2]
-
-    q = apply_rotary_emb(q, freqs_cis, position_ids, n_heads)
-    k = apply_rotary_emb(k, freqs_cis, position_ids, n_heads)
-
-    if kv_cache is not None:
-        k, v = kv_cache.update(position_ids, k, v)
-
-    out = F.scaled_dot_product_attention(
-        q, k, v, attn_mask=attn_mask
-    )
-    out = out.transpose(1, 2).reshape(bsz, q_len, d_model)
-    out = w.proj(out)
-    return out
-
-
-def text_decoder(
-    x: torch.Tensor,
-    w: nn.Module,
-    attn_mask: torch.Tensor,
-    position_ids: torch.Tensor,
-    config: TextConfig,
-):
-    for i, block in enumerate(w.blocks):
-        l_in = layer_norm(x, block.ln)
-        l_attn = attn(
-            l_in,
-            block.attn,
-            freqs_cis=w.freqs_cis,
-            kv_cache=block.kv_cache,
-            attn_mask=attn_mask,
-            n_heads=config.n_heads,
-            position_ids=position_ids,
-        )
-        l_mlp = mlp(l_in, block.mlp)
-        x = x + l_attn + l_mlp
-
-    return x
-
-
-def lm_head(hidden_BTC: torch.Tensor, w: nn.Module):
-    hidden_BC = hidden_BTC[:, -1, :]
-    hidden_BC = layer_norm(hidden_BC, w.post_ln)
-    logits = w.lm_head(hidden_BC)
-    return logits
-
-
-def build_text_model(config: TextConfig, dtype: torch.dtype) -> nn.Module:
-    qkv_dim = int(config.dim * 3)
-
-    text = nn.ModuleDict(
-        {
-            "blocks": nn.ModuleList(
-                [
-                    nn.ModuleDict(
-                        {
-                            "ln": nn.LayerNorm(config.dim, dtype=dtype),
-                            "attn": nn.ModuleDict(
-                                {
-                                    "qkv": nn.Linear(config.dim, qkv_dim, dtype=dtype),
-                                    "proj": nn.Linear(
-                                        config.dim, config.dim, dtype=dtype
-                                    ),
-                                }
-                            ),
-                            "mlp": nn.ModuleDict(
-                                {
-                                    "fc1": nn.Linear(
-                                        config.dim, config.ff_dim, dtype=dtype
-                                    ),
-                                    "fc2": nn.Linear(
-                                        config.ff_dim, config.dim, dtype=dtype
-                                    ),
-                                }
-                            ),
-                        }
-                    )
-                    for _ in range(config.n_layers)
-                ]
-            ),
-            "post_ln": nn.LayerNorm(config.dim, dtype=dtype),
-            "lm_head": nn.Linear(config.dim, config.vocab_size, dtype=dtype),
-        }
-    )
-    text.wte = nn.Parameter(torch.empty(config.vocab_size, config.dim, dtype=dtype))
-    text.register_buffer(
-        "freqs_cis",
-        precompute_freqs_cis(config.dim // (2 * config.n_heads), config.max_context),
-        persistent=False,
-    )
-
-    return text

moondream2/utils.py

@@ -1,55 +0,0 @@
-import numpy as np
-
-import torch.cuda.nvtx
-from functools import wraps
-
-def nvtx_annotate(label=None, color='blue'):
-    def decorator(func):
-        @wraps(func)
-        def wrapper(*args, **kwargs):
-            range_name = label or func.__name__
-            torch.cuda.nvtx.range_push(range_name)
-            result = func(*args, **kwargs)
-            torch.cuda.nvtx.range_pop()
-            return result
-        return wrapper
-    return decorator
-
-def remove_outlier_points(points_tuples, k_nearest=2, threshold=2.0):
-    """
-    Robust outlier detection for list of (x,y) tuples.
-    Only requires numpy.
-
-    Args:
-        points_tuples: list of (x,y) tuples
-        k_nearest: number of neighbors to consider
-        threshold: multiplier for median distance
-
-    Returns:
-        list: filtered list of (x,y) tuples with outliers removed
-        list: list of booleans indicating which points were kept (True = kept)
-    """
-    points = np.array(points_tuples)
-    n_points = len(points)
-
-    # Calculate pairwise distances manually
-    dist_matrix = np.zeros((n_points, n_points))
-    for i in range(n_points):
-        for j in range(i + 1, n_points):
-            # Euclidean distance between points i and j
-            dist = np.sqrt(np.sum((points[i] - points[j]) ** 2))
-            dist_matrix[i, j] = dist
-            dist_matrix[j, i] = dist
-
-    # Get k nearest neighbors' distances
-    k = min(k_nearest, n_points - 1)
-    neighbor_distances = np.partition(dist_matrix, k, axis=1)[:, :k]
-    avg_neighbor_dist = np.mean(neighbor_distances, axis=1)
-
-    # Calculate mask using median distance
-    median_dist = np.median(avg_neighbor_dist)
-    mask = avg_neighbor_dist <= threshold * median_dist
-
-    # Return filtered tuples and mask
-    filtered_tuples = [t for t, m in zip(points_tuples, mask) if m]
-    return filtered_tuples

moondream2/vision.py

@@ -1,148 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import numpy as np
-
-from typing import Union, Tuple
-from PIL import Image
-
-from .layers import attn, layer_norm, linear, mlp
-from .image_crops import overlap_crop_image
-from .config import VisionConfig
-
-if torch.backends.mps.is_available():
-    # Non-divisible input sizes are not implemented on MPS device yet.
-    # https://github.com/pytorch/pytorch/issues/96056
-    def adaptive_avg_pool2d(input, output_size):
-        return F.adaptive_avg_pool2d(input.to("cpu"), output_size).to("mps")
-
-else:
-    adaptive_avg_pool2d = F.adaptive_avg_pool2d
-
-DeviceLike = Union[str, torch.device, int]
-
-def prepare_crops(
-    image: Image.Image, config: VisionConfig, device: DeviceLike
-) -> Tuple[torch.Tensor, Tuple[int, int]]:
-    np_image = np.array(image.convert("RGB"))
-    overlap_crops = overlap_crop_image(
-        np_image, max_crops=config.max_crops, overlap_margin=config.overlap_margin
-    )
-    all_crops = overlap_crops["crops"]
-    all_crops = np.transpose(all_crops, (0, 3, 1, 2))
-    all_crops = (
-        torch.from_numpy(all_crops)
-        .to(device=device, dtype=torch.float16)
-        .div_(255.0)
-        .sub_(0.5)
-        .div_(0.5)
-    )
-    return all_crops, overlap_crops["tiling"]
-
-
-def create_patches(x, patch_size):
-    # Original shape: [B, C, H, W]
-    B, C, H, W = x.shape
-    P1 = P2 = patch_size
-
-    # Step 1: Split H and W dimensions into patches
-    # [B, C, H/P1, P1, W/P2, P2]
-    x = x.reshape(B, C, H // P1, P1, W // P2, P2)
-
-    # Step 2: Rearrange dimensions to match target shape
-    # [B, H/P1, W/P2, C, P1, P2]
-    x = x.permute(0, 2, 4, 1, 3, 5)
-
-    # Step 3: Combine dimensions to get final shape
-    # [B, (H/P1)*(W/P2), C*P1*P2]
-    x = x.reshape(B, (H // P1) * (W // P2), C * P1 * P2)
-
-    return x
-
-
-def vision_encoder(input_BCHW: torch.Tensor, w: nn.Module, config: VisionConfig):
-    x = create_patches(input_BCHW, config.enc_patch_size)
-
-    x = linear(x, w.patch_emb)
-    x = x + w.pos_emb
-    for block in w.blocks:
-        x = x + attn(layer_norm(x, block.ln1), block.attn, n_heads=config.enc_n_heads)
-        x = x + mlp(layer_norm(x, block.ln2), block.mlp)
-    x = layer_norm(x, w.post_ln)
-
-    return x
-
-
-def vision_projection(
-    global_features: torch.Tensor,
-    reconstructed: torch.Tensor,
-    w: nn.Module,
-    config: VisionConfig,
-):
-    reconstructed = reconstructed.permute(2, 0, 1)
-    reconstructed = adaptive_avg_pool2d(
-        reconstructed, output_size=(config.enc_n_layers, config.enc_n_layers)
-    )
-    reconstructed = reconstructed.permute(1, 2, 0).view(729, config.enc_dim)
-    final_features = torch.cat([global_features, reconstructed], dim=-1)
-    return mlp(final_features, w.proj_mlp)
-
-
-def build_vision_model(config: VisionConfig, dtype: torch.dtype):
-    patch_dim = config.enc_patch_size * config.enc_patch_size * config.in_channels
-    grid_size = config.crop_size // config.enc_patch_size
-    num_patches = grid_size * grid_size
-
-    vision = nn.ModuleDict(
-        {
-            "patch_emb": nn.Linear(patch_dim, config.enc_dim, dtype=dtype),
-            "blocks": nn.ModuleList(
-                [
-                    nn.ModuleDict(
-                        {
-                            "ln1": nn.LayerNorm(config.enc_dim, dtype=dtype),
-                            "attn": nn.ModuleDict(
-                                {
-                                    "qkv": nn.Linear(
-                                        config.enc_dim, 3 * config.enc_dim, dtype=dtype
-                                    ),
-                                    "proj": nn.Linear(
-                                        config.enc_dim, config.enc_dim, dtype=dtype
-                                    ),
-                                }
-                            ),
-                            "ln2": nn.LayerNorm(config.enc_dim, dtype=dtype),
-                            "mlp": nn.ModuleDict(
-                                {
-                                    "fc1": nn.Linear(
-                                        config.enc_dim, config.enc_ff_dim, dtype=dtype
-                                    ),
-                                    "fc2": nn.Linear(
-                                        config.enc_ff_dim, config.enc_dim, dtype=dtype
-                                    ),
-                                }
-                            ),
-                        }
-                    )
-                    for _ in range(config.enc_n_layers)
-                ]
-            ),
-            "post_ln": nn.LayerNorm(config.enc_dim, dtype=dtype),
-            "proj_mlp": nn.ModuleDict(
-                {
-                    "fc1": nn.Linear(
-                        config.enc_dim * 2, config.proj_inner_dim, dtype=dtype
-                    ),
-                    "fc2": nn.Linear(
-                        config.proj_inner_dim, config.proj_out_dim, dtype=dtype
-                    ),
-                }
-            ),
-        }
-    )
-    vision.pos_emb = nn.Parameter(
-        torch.zeros(1, num_patches, config.enc_dim, dtype=dtype)
-    )
-    return vision
-
-

moondream2/weights.py

@@ -1,292 +0,0 @@
-import safetensors
-import torch
-import torch.nn as nn
-
-from contextlib import contextmanager
-from dataclasses import dataclass
-from typing import Callable, List
-
-from .layers import AttentionWeights, LayerNormWeights, LinearWeights, MLPWeights
-
-
-@dataclass
-class VisionBlock:
-    ln1: LayerNormWeights
-    attn: AttentionWeights
-    ln2: LayerNormWeights
-    mlp: MLPWeights
-
-
-@dataclass
-class VisionModel:
-    patch_emb: LinearWeights
-    pos_emb: torch.Tensor
-    blocks: List[VisionBlock]
-    post_ln: LayerNormWeights
-    proj_mlp: MLPWeights
-
-
-@dataclass
-class TextBlock:
-    ln: LayerNormWeights
-    attn: AttentionWeights
-    mlp: MLPWeights
-
-
-@dataclass
-class TextModel:
-    wte: torch.Tensor
-    blocks: List[TextBlock]
-    post_ln: LayerNormWeights
-    lm_head: LinearWeights
-
-
-@dataclass
-class RegionModel:
-    coord_features: torch.Tensor
-    coord_encoder: LinearWeights
-    coord_decoder: MLPWeights
-    size_features: torch.Tensor
-    size_encoder: LinearWeights
-    size_decoder: MLPWeights
-
-
-@dataclass
-class MoondreamModel:
-    vision: VisionModel
-    text: TextModel
-    region: RegionModel
-
-
-@contextmanager
-def safetensors_open(safetensors_file: str):
-    """
-    Simplify interfacing with safetensors files. Eliminates the need to ignore
-    type errors when using the `safe_open` function.
-    """
-    with safetensors.safe_open(
-        safetensors_file, framework="pt"
-    ) as st:  # pyright: ignore
-
-        def get_tensor(name: str) -> torch.Tensor:
-            return st.get_tensor(name)
-
-        def get_keys() -> List[str]:
-            return st.keys()
-
-        get_tensor.keys = get_keys
-
-        yield get_tensor
-
-
-def _load_weights(get_tensor: Callable[[str], torch.Tensor], model: nn.Module) -> None:
-    """Internal function to load weights using a tensor getter function."""
-    model = model.to(dtype=torch.float16)
-
-    # Vision Model
-    model.vision["patch_emb"].weight.data.copy_(
-        get_tensor("vision_encoder.encoder.model.visual.patch_embed.linear.weight")
-    )
-    model.vision["patch_emb"].bias.data.copy_(
-        get_tensor("vision_encoder.encoder.model.visual.patch_embed.linear.bias")
-    )
-    model.vision.pos_emb.data.copy_(
-        get_tensor("vision_encoder.encoder.model.visual.pos_embed")
-    )
-
-    for i in range(len(model.vision["blocks"])):
-        prefix = f"vision_encoder.encoder.model.visual.blocks.{i}"
-
-        # Layer norms
-        model.vision["blocks"][i]["ln1"].weight.data.copy_(
-            get_tensor(f"{prefix}.norm1.weight")
-        )
-        model.vision["blocks"][i]["ln1"].bias.data.copy_(
-            get_tensor(f"{prefix}.norm1.bias")
-        )
-        model.vision["blocks"][i]["ln2"].weight.data.copy_(
-            get_tensor(f"{prefix}.norm2.weight")
-        )
-        model.vision["blocks"][i]["ln2"].bias.data.copy_(
-            get_tensor(f"{prefix}.norm2.bias")
-        )
-
-        # Attention
-        model.vision["blocks"][i]["attn"]["qkv"].weight.data.copy_(
-            get_tensor(f"{prefix}.attn.qkv.weight")
-        )
-        model.vision["blocks"][i]["attn"]["qkv"].bias.data.copy_(
-            get_tensor(f"{prefix}.attn.qkv.bias")
-        )
-        model.vision["blocks"][i]["attn"]["proj"].weight.data.copy_(
-            get_tensor(f"{prefix}.attn.proj.weight")
-        )
-        model.vision["blocks"][i]["attn"]["proj"].bias.data.copy_(
-            get_tensor(f"{prefix}.attn.proj.bias")
-        )
-
-        # MLP
-        model.vision["blocks"][i]["mlp"]["fc1"].weight.data.copy_(
-            get_tensor(f"{prefix}.mlp.fc1.weight")
-        )
-        model.vision["blocks"][i]["mlp"]["fc1"].bias.data.copy_(
-            get_tensor(f"{prefix}.mlp.fc1.bias")
-        )
-        model.vision["blocks"][i]["mlp"]["fc2"].weight.data.copy_(
-            get_tensor(f"{prefix}.mlp.fc2.weight")
-        )
-        model.vision["blocks"][i]["mlp"]["fc2"].bias.data.copy_(
-            get_tensor(f"{prefix}.mlp.fc2.bias")
-        )
-
-    model.vision["post_ln"].weight.data.copy_(
-        get_tensor("vision_encoder.encoder.model.visual.norm.weight")
-    )
-    model.vision["post_ln"].bias.data.copy_(
-        get_tensor("vision_encoder.encoder.model.visual.norm.bias")
-    )
-
-    model.vision["proj_mlp"]["fc1"].weight.data.copy_(
-        get_tensor("vision_encoder.projection.mlp.fc1.weight")
-    )
-    model.vision["proj_mlp"]["fc1"].bias.data.copy_(
-        get_tensor("vision_encoder.projection.mlp.fc1.bias")
-    )
-    model.vision["proj_mlp"]["fc2"].weight.data.copy_(
-        get_tensor("vision_encoder.projection.mlp.fc2.weight")
-    )
-    model.vision["proj_mlp"]["fc2"].bias.data.copy_(
-        get_tensor("vision_encoder.projection.mlp.fc2.bias")
-    )
-
-    # Text Model
-    model.text.wte.data.copy_(get_tensor("text_model.transformer.embd.wte.weight"))
-
-    for i in range(len(model.text["blocks"])):
-        prefix = f"text_model.transformer.h.{i}"
-
-        # Layer norm
-        model.text["blocks"][i]["ln"].weight.data.copy_(
-            get_tensor(f"{prefix}.ln.weight")
-        )
-        model.text["blocks"][i]["ln"].bias.data.copy_(get_tensor(f"{prefix}.ln.bias"))
-
-        # Attention
-        model.text["blocks"][i]["attn"]["qkv"].weight.data.copy_(
-            get_tensor(f"{prefix}.mixer.Wqkv.weight")
-        )
-        model.text["blocks"][i]["attn"]["qkv"].bias.data.copy_(
-            get_tensor(f"{prefix}.mixer.Wqkv.bias")
-        )
-        model.text["blocks"][i]["attn"]["proj"].weight.data.copy_(
-            get_tensor(f"{prefix}.mixer.out_proj.weight")
-        )
-        model.text["blocks"][i]["attn"]["proj"].bias.data.copy_(
-            get_tensor(f"{prefix}.mixer.out_proj.bias")
-        )
-
-        # MLP
-        model.text["blocks"][i]["mlp"]["fc1"].weight.data.copy_(
-            get_tensor(f"{prefix}.mlp.fc1.weight")
-        )
-        model.text["blocks"][i]["mlp"]["fc1"].bias.data.copy_(
-            get_tensor(f"{prefix}.mlp.fc1.bias")
-        )
-        model.text["blocks"][i]["mlp"]["fc2"].weight.data.copy_(
-            get_tensor(f"{prefix}.mlp.fc2.weight")
-        )
-        model.text["blocks"][i]["mlp"]["fc2"].bias.data.copy_(
-            get_tensor(f"{prefix}.mlp.fc2.bias")
-        )
-
-    model.text["post_ln"].weight.data.copy_(get_tensor("text_model.lm_head.ln.weight"))
-    model.text["post_ln"].bias.data.copy_(get_tensor("text_model.lm_head.ln.bias"))
-
-    model.text["lm_head"].weight.data.copy_(
-        get_tensor("text_model.lm_head.linear.weight")
-    )
-    model.text["lm_head"].bias.data.copy_(get_tensor("text_model.lm_head.linear.bias"))
-
-    # Region Model
-    model.region.coord_features.data.copy_(
-        get_tensor("region_model.coordinate_features.weight").T
-    )
-    model.region["coord_encoder"].weight.data.copy_(
-        get_tensor("region_model.coordinate_encoder.weight")
-    )
-    model.region["coord_encoder"].bias.data.copy_(
-        get_tensor("region_model.coordinate_encoder.bias")
-    )
-
-    model.region["coord_decoder"]["fc1"].weight.data.copy_(
-        get_tensor("region_model.coordinate_decoder.fc1.weight")
-    )
-    model.region["coord_decoder"]["fc1"].bias.data.copy_(
-        get_tensor("region_model.coordinate_decoder.fc1.bias")
-    )
-    model.region["coord_decoder"]["fc2"].weight.data.copy_(
-        get_tensor("region_model.coordinate_decoder.fc2.weight")
-    )
-    model.region["coord_decoder"]["fc2"].bias.data.copy_(
-        get_tensor("region_model.coordinate_decoder.fc2.bias")
-    )
-
-    model.region.size_features.data.copy_(
-        get_tensor("region_model.size_features.weight").T
-    )
-    model.region["size_encoder"].weight.data.copy_(
-        get_tensor("region_model.size_encoder.weight")
-    )
-    model.region["size_encoder"].bias.data.copy_(
-        get_tensor("region_model.size_encoder.bias")
-    )
-
-    model.region["size_decoder"]["fc1"].weight.data.copy_(
-        get_tensor("region_model.size_decoder.fc1.weight")
-    )
-    model.region["size_decoder"]["fc1"].bias.data.copy_(
-        get_tensor("region_model.size_decoder.fc1.bias")
-    )
-    model.region["size_decoder"]["fc2"].weight.data.copy_(
-        get_tensor("region_model.size_decoder.fc2.weight")
-    )
-    model.region["size_decoder"]["fc2"].bias.data.copy_(
-        get_tensor("region_model.size_decoder.fc2.bias")
-    )
-
-
-def load_weights_from_safetensors(weights_file: str, model: nn.Module) -> None:
-    """Load weights from a safetensors file into a MoondreamModel instance."""
-    with safetensors_open(weights_file) as get_tensor:
-        # Wrap the get_tensor function to handle key normalization
-        name_map = {k.replace("._orig_mod", ""): k for k in get_tensor.keys()}
-        _load_weights(lambda x: get_tensor(name_map[x]).to(dtype=torch.float16), model)
-
-
-def load_weights_from_pt(weights_file: str, model: nn.Module) -> None:
-    """Load weights from a PyTorch file into a MoondreamModel instance."""
-    device = str(torch.empty(0).device)
-    tensors = torch.load(weights_file, map_location=device, weights_only=True)
-    tensors = {
-        k.replace("._orig_mod", ""): v.to(dtype=torch.float16)
-        for k, v in tensors.items()
-    }
-    _load_weights(lambda x: tensors[x], model)
-
-
-def load_weights_into_model(weights_file: str, model: nn.Module) -> None:
-    """
-    Load weights from either a safetensors or PyTorch file directly into a MoondreamModel instance.
-
-    Args:
-        weights_file: Path to weights file (either .safetensors or .pt)
-        model: MoondreamModel instance to load weights into
-    """
-    if weights_file.endswith(".safetensors"):
-        load_weights_from_safetensors(weights_file, model)
-    else:
-        load_weights_from_pt(weights_file, model)
-
-    # Make all parameters contiguous
-    for param in model.parameters():
-        param.data = param.data.contiguous()

notes.ipynb

@@ -1,393 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "torch.cuda.is_available()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers.utils import is_flash_attn_2_available\n",
-    "is_flash_attn_2_available()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "from moondream2.config import MoondreamConfig\n",
-    "from moondream2.moondream import MoondreamModel\n",
-    "import torch.profiler\n",
-    "\n",
-    "config = MoondreamConfig()\n",
-    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
-    "model = MoondreamModel(config, setup_caches=False).to(device)\n",
-    "from safetensors.torch import load_file\n",
-    "weights_path = \"moondream2/model.safetensors\"  # Path to your local weights file\n",
-    "state_dict = load_file(weights_path, device=device)\n",
-    "new_state_dict = {}\n",
-    "for key, value in state_dict.items():\n",
-    "    # Remove 'model.' prefix if it exists\n",
-    "    if key.startswith('model.'):\n",
-    "        new_key = key[6:]  # Skip the first 6 characters ('model.')\n",
-    "    else:\n",
-    "        new_key = key\n",
-    "    new_state_dict[new_key] = value\n",
-    "state_dict = new_state_dict\n",
-    "missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=True)\n",
-    "model._setup_caches()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from PIL import Image\n",
-    "image = Image.open(\"example.png\")\n",
-    "query = \"home icon at the bottom of the screen is visible\"\n",
-    "points = model.point(image, query)[\"points\"]\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'x': 0.0849609375, 'y': 0.9453125}]"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\n",
-    "from PIL import Image\n",
-    "image = Image.open(\"example.png\")\n",
-    "query = \"home icon at the bottom\"\n",
-    "with torch.profiler.profile(\n",
-    "    activities=[\n",
-    "        torch.profiler.ProfilerActivity.CPU,\n",
-    "        torch.profiler.ProfilerActivity.CUDA],\n",
-    "    schedule=torch.profiler.schedule(\n",
-    "        wait=1, warmup=1, active=3),\n",
-    "    on_trace_ready=torch.profiler.tensorboard_trace_handler('log'),\n",
-    "    record_shapes=True,\n",
-    "    with_stack=True\n",
-    ") as prof:\n",
-    "    for i in range(3):\n",
-    "        points = model.point(image, query)[\"points\"]\n",
-    "        prof.step()\n",
-    "points"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "points = model.point(image, query)[\"points\"]\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "IndexError",
-     "evalue": "list index out of range",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
-      "\u001b[31mIndexError\u001b[39m                                Traceback (most recent call last)",
-      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[17]\u001b[39m\u001b[32m, line 29\u001b[39m\n\u001b[32m     27\u001b[39m     plt.axis(\u001b[33m'\u001b[39m\u001b[33moff\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m     28\u001b[39m     plt.show()\n\u001b[32m---> \u001b[39m\u001b[32m29\u001b[39m show_point_on_image(\u001b[43mpoints\u001b[49m\u001b[43m[\u001b[49m\u001b[32;43m0\u001b[39;49m\u001b[43m]\u001b[49m[\u001b[33m'\u001b[39m\u001b[33mx\u001b[39m\u001b[33m'\u001b[39m], points[\u001b[32m0\u001b[39m][\u001b[33m'\u001b[39m\u001b[33my\u001b[39m\u001b[33m'\u001b[39m])\n",
-      "\u001b[31mIndexError\u001b[39m: list index out of range"
-     ]
-    }
-   ],
-   "source": [
-    "from PIL import Image\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "def show_point_on_image(rel_x, rel_y, point_color='red', point_size=30):\n",
-    "    \"\"\"\n",
-    "    Display 'example.png' with a point marked at the given relative coordinates.\n",
-    "\n",
-    "    Parameters:\n",
-    "    - rel_x: float, relative x-coordinate (0 to 1).\n",
-    "    - rel_y: float, relative y-coordinate (0 to 1).\n",
-    "    - point_color: str, color of the point (default: 'red').\n",
-    "    - point_size: int, size of the point (default: 50).\n",
-    "    \"\"\"\n",
-    "    image_path = 'example.png'  # Hardcoded image path\n",
-    "\n",
-    "    # Load image\n",
-    "    img = Image.open(image_path)\n",
-    "    width, height = img.size\n",
-    "\n",
-    "    # Convert relative coordinates to absolute\n",
-    "    abs_x = rel_x * width\n",
-    "    abs_y = rel_y * height\n",
-    "\n",
-    "    # Plot\n",
-    "    plt.imshow(img)\n",
-    "    plt.scatter([abs_x], [abs_y], color=point_color, s=point_size, alpha=0.7)\n",
-    "    plt.axis('off')\n",
-    "    plt.show()\n",
-    "show_point_on_image(points[0]['x'], points[0]['y'])\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from PIL import Image\n",
-    "image = Image.open(\"example.png\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import time"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Time taken: 0.5298349857330322 seconds\n"
-     ]
-    }
-   ],
-   "source": [
-    "start_time = time.time()\n",
-    "\n",
-    "query = \"the login button\"\n",
-    "points = model.point(image, query)[\"points\"]\n",
-    "end_time = time.time()\n",
-    "print(f\"Time taken: {end_time - start_time} seconds\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers.utils import is_flash_attn_2_available\n",
-    "is_flash_attn_2_available()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
-    "\n",
-    "model = AutoModelForCausalLM.from_pretrained(\n",
-    "    \"vikhyatk/moondream2\",\n",
-    "    revision=\"2025-04-14\",\n",
-    "    trust_remote_code=True,\n",
-    "    cache_dir=\"moondream\",\n",
-    "    # Uncomment to run on GPU.\n",
-    "    device_map={\"\": \"cuda\"}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "HfMoondream(\n",
-       "  (model): MoondreamModel(\n",
-       "    (vision): ModuleDict(\n",
-       "      (patch_emb): Linear(in_features=588, out_features=1152, bias=True)\n",
-       "      (blocks): ModuleList(\n",
-       "        (0-26): 27 x ModuleDict(\n",
-       "          (ln1): LayerNorm((1152,), eps=1e-05, elementwise_affine=True)\n",
-       "          (attn): ModuleDict(\n",
-       "            (qkv): Linear(in_features=1152, out_features=3456, bias=True)\n",
-       "            (proj): Linear(in_features=1152, out_features=1152, bias=True)\n",
-       "          )\n",
-       "          (ln2): LayerNorm((1152,), eps=1e-05, elementwise_affine=True)\n",
-       "          (mlp): ModuleDict(\n",
-       "            (fc1): Linear(in_features=1152, out_features=4304, bias=True)\n",
-       "            (fc2): Linear(in_features=4304, out_features=1152, bias=True)\n",
-       "          )\n",
-       "        )\n",
-       "      )\n",
-       "      (post_ln): LayerNorm((1152,), eps=1e-05, elementwise_affine=True)\n",
-       "      (proj_mlp): ModuleDict(\n",
-       "        (fc1): Linear(in_features=2304, out_features=8192, bias=True)\n",
-       "        (fc2): Linear(in_features=8192, out_features=2048, bias=True)\n",
-       "      )\n",
-       "    )\n",
-       "    (text): ModuleDict(\n",
-       "      (blocks): ModuleList(\n",
-       "        (0-23): 24 x ModuleDict(\n",
-       "          (ln): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
-       "          (attn): ModuleDict(\n",
-       "            (qkv): Linear(in_features=2048, out_features=6144, bias=True)\n",
-       "            (proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
-       "          )\n",
-       "          (mlp): ModuleDict(\n",
-       "            (fc1): Linear(in_features=2048, out_features=8192, bias=True)\n",
-       "            (fc2): Linear(in_features=8192, out_features=2048, bias=True)\n",
-       "          )\n",
-       "        )\n",
-       "      )\n",
-       "      (post_ln): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
-       "      (lm_head): Linear(in_features=2048, out_features=51200, bias=True)\n",
-       "    )\n",
-       "    (region): ModuleDict(\n",
-       "      (coord_encoder): Linear(in_features=256, out_features=2048, bias=True)\n",
-       "      (coord_decoder): ModuleDict(\n",
-       "        (fc1): Linear(in_features=2048, out_features=8192, bias=True)\n",
-       "        (fc2): Linear(in_features=8192, out_features=1024, bias=True)\n",
-       "      )\n",
-       "      (size_encoder): Linear(in_features=512, out_features=2048, bias=True)\n",
-       "      (size_decoder): ModuleDict(\n",
-       "        (fc1): Linear(in_features=2048, out_features=8192, bias=True)\n",
-       "        (fc2): Linear(in_features=8192, out_features=2048, bias=True)\n",
-       "      )\n",
-       "    )\n",
-       "  )\n",
-       ")"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.point()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

ollama.ipynb

@@ -1,195 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoConfig\n",
-    "\n",
-    "config = AutoConfig.from_pretrained(\"vikhyatk/moondream2\", trust_remote_code=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'return_dict': True,\n",
-       " 'output_hidden_states': False,\n",
-       " 'output_attentions': False,\n",
-       " 'torchscript': False,\n",
-       " 'torch_dtype': 'float16',\n",
-       " 'use_bfloat16': False,\n",
-       " 'tf_legacy_loss': False,\n",
-       " 'pruned_heads': {},\n",
-       " 'tie_word_embeddings': True,\n",
-       " 'chunk_size_feed_forward': 0,\n",
-       " 'is_encoder_decoder': False,\n",
-       " 'is_decoder': False,\n",
-       " 'cross_attention_hidden_size': None,\n",
-       " 'add_cross_attention': False,\n",
-       " 'tie_encoder_decoder': False,\n",
-       " 'max_length': 20,\n",
-       " 'min_length': 0,\n",
-       " 'do_sample': False,\n",
-       " 'early_stopping': False,\n",
-       " 'num_beams': 1,\n",
-       " 'num_beam_groups': 1,\n",
-       " 'diversity_penalty': 0.0,\n",
-       " 'temperature': 1.0,\n",
-       " 'top_k': 50,\n",
-       " 'top_p': 1.0,\n",
-       " 'typical_p': 1.0,\n",
-       " 'repetition_penalty': 1.0,\n",
-       " 'length_penalty': 1.0,\n",
-       " 'no_repeat_ngram_size': 0,\n",
-       " 'encoder_no_repeat_ngram_size': 0,\n",
-       " 'bad_words_ids': None,\n",
-       " 'num_return_sequences': 1,\n",
-       " 'output_scores': False,\n",
-       " 'return_dict_in_generate': False,\n",
-       " 'forced_bos_token_id': None,\n",
-       " 'forced_eos_token_id': None,\n",
-       " 'remove_invalid_values': False,\n",
-       " 'exponential_decay_length_penalty': None,\n",
-       " 'suppress_tokens': None,\n",
-       " 'begin_suppress_tokens': None,\n",
-       " 'architectures': ['HfMoondream'],\n",
-       " 'finetuning_task': None,\n",
-       " 'id2label': {0: 'LABEL_0', 1: 'LABEL_1'},\n",
-       " 'label2id': {'LABEL_0': 0, 'LABEL_1': 1},\n",
-       " 'tokenizer_class': None,\n",
-       " 'prefix': None,\n",
-       " 'bos_token_id': None,\n",
-       " 'pad_token_id': None,\n",
-       " 'eos_token_id': None,\n",
-       " 'sep_token_id': None,\n",
-       " 'decoder_start_token_id': None,\n",
-       " 'task_specific_params': None,\n",
-       " 'problem_type': None,\n",
-       " '_name_or_path': 'vikhyatk/moondream2',\n",
-       " '_attn_implementation_autoset': False,\n",
-       " 'transformers_version': '4.49.0',\n",
-       " 'auto_map': {'AutoConfig': 'vikhyatk/moondream2--hf_moondream.HfConfig',\n",
-       "  'AutoModelForCausalLM': 'vikhyatk/moondream2--hf_moondream.HfMoondream'},\n",
-       " 'config': {},\n",
-       " 'model_type': 'moondream1'}"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "config.to_dict()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from moondream2.hf_moondream import HfConfig\n",
-    "# serialize config.json to data variable\n",
-    "import json\n",
-    "with open(\"moondream2/config.json\", \"r\") as f:\n",
-    "    data = json.load(f)\n",
-    "configo = HfConfig(**data)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "_name_or_path : vikhyatk/moondream2 != \n",
-      "auto_map : {'AutoConfig': 'vikhyatk/moondream2--hf_moondream.HfConfig', 'AutoModelForCausalLM': 'vikhyatk/moondream2--hf_moondream.HfMoondream'} != {'AutoConfig': 'hf_moondream.HfConfig', 'AutoModelForCausalLM': 'hf_moondream.HfMoondream'}\n"
-     ]
-    }
-   ],
-   "source": [
-    "# compare dicts of config.to_dict() and configo.to_dict()    \n",
-    "\n",
-    "# make it check values of the dicts\n",
-    "\n",
-    "for key, value in config.to_dict().items():\n",
-    "    if key not in configo.to_dict():\n",
-    "        print(key + \" : \" + str(value) + \" not in hf_config\")\n",
-    "    elif value != configo.to_dict()[key]:\n",
-    "        print(key+ \" : \"+str(value)+\" != \"+str(configo.to_dict()[key]))\n",
-    "\n",
-    "for key, value in configo.to_dict().items():\n",
-    "    if key not in config.to_dict():\n",
-    "        print(key + \" : \" + str(value) + \" not in from_pretrained\")\n",
-    "\n",
-    "hparams = config.to_dict()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "text_config = hparams.get(\"text_config\", {})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "text_config.get(\"architectures\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hparams.get(\"vision_config\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

test.py

@@ -1,41 +0,0 @@
-print("app Started")
-import torch
-from moondream2.config import MoondreamConfig
-from moondream2.moondream import MoondreamModel
-import torch.profiler
-
-config = MoondreamConfig()
-device = "cuda"
-model = MoondreamModel(config, setup_caches=False).to(device)
-from safetensors.torch import load_file
-weights_path = "moondream2/model.safetensors"  # Path to your local weights file
-state_dict = load_file(weights_path, device=device)
-new_state_dict = {}
-for key, value in state_dict.items():
-    # Remove 'model.' prefix if it exists
-    if key.startswith('model.'):
-        new_key = key[6:]  # Skip the first 6 characters ('model.')
-    else:
-        new_key = key
-    new_state_dict[new_key] = value
-state_dict = new_state_dict
-missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=True)
-model._setup_caches()
-
-
-
-from PIL import Image
-image = Image.open("example.png")
-query = "home icon at the bottom"
-warmup_iters = 2
-
-
-for i in range(3):
-    if i == warmup_iters: torch.cuda.cudart().cudaProfilerStart()
-    if i >= warmup_iters: torch.cuda.nvtx.range_push("iteration{}".format(i))
-    if i >= warmup_iters: torch.cuda.nvtx.range_push("forward")
-    points = model.point(image, query)["points"]
-    if i >= warmup_iters: torch.cuda.nvtx.range_pop()
-    if i >= warmup_iters: torch.cuda.nvtx.range_pop()
-
-torch.cuda.cudart().cudaProfilerStop()