From d430beef767c2fbca4ed8447c120ddc7c26ff6d5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=B1=AA=E5=BF=97=E9=B9=8F?= Date: Mon, 5 Jan 2026 12:18:04 +0800 Subject: [PATCH] [Bagel]Add image edit (#588) Signed-off-by: princepride --- .../models/bagel/bagel_transformer.py | 340 +++++++++++++++++- .../diffusion/models/bagel/pipeline_bagel.py | 155 +++++++- 2 files changed, 477 insertions(+), 18 deletions(-) diff --git a/vllm_omni/diffusion/models/bagel/bagel_transformer.py b/vllm_omni/diffusion/models/bagel/bagel_transformer.py index 7670248c0..7950389ef 100644 --- a/vllm_omni/diffusion/models/bagel/bagel_transformer.py +++ b/vllm_omni/diffusion/models/bagel/bagel_transformer.py @@ -29,6 +29,43 @@ from vllm.vllm_flash_attn import flash_attn_varlen_func from vllm_omni.diffusion.layers.rope import RotaryEmbedding + +def patchify(imgs, p): + """ + imgs: (N, 3, H, W) or (3, H, W) + x: (N, L, patch_size**2 *3) or (L, patch_size**2 *3) + """ + is_batch = imgs.ndim == 4 + if not is_batch: + imgs = imgs.unsqueeze(0) + + # n: batch, c: channel, h: grid_h, p: patch_h, w: grid_w, q: patch_w + x = imgs.reshape(imgs.shape[0], 3, imgs.shape[2] // p, p, imgs.shape[3] // p, p) + # Permute to (n, grid_h, grid_w, c, patch_h, patch_w) to match Conv2d (c, h, w) flattening + x = torch.einsum("nchpwq->nhwcpq", x) + x = x.reshape(imgs.shape[0], -1, 3 * p**2) + + if not is_batch: + x = x.squeeze(0) + return x + + +class MLPconnector(nn.Module): + def __init__(self, input_dim, output_dim, activation="gelu_pytorch_tanh"): + super().__init__() + self.fc1 = nn.Linear(input_dim, output_dim) + if activation == "gelu": + self.act = nn.GELU() + elif activation == "gelu_pytorch_tanh": + self.act = nn.GELU(approximate="tanh") + else: + self.act = nn.ReLU() + self.fc2 = nn.Linear(output_dim, output_dim) + + def forward(self, x): + return self.fc2(self.act(self.fc1(x))) + + torch._dynamo.config.cache_size_limit = 512 torch._dynamo.config.accumulated_cache_size_limit = 4096 flex_attention = torch.compile(flex_attention) @@ -600,18 +637,30 @@ def get_flattened_position_ids_extrapolate(img_h, img_w, patch_size, max_num_pat class BagelConfig(PretrainedConfig): def __init__( self, + visual_gen=True, + visual_und=True, llm_config=None, + vit_config=None, vae_config=None, latent_patch_size=2, max_latent_size=32, + vit_max_num_patch_per_side=70, + connector_act="gelu_pytorch_tanh", + interpolate_pos=False, timestep_shift=1.0, **kwargs, ): super().__init__(**kwargs) + self.visual_gen = visual_gen + self.visual_und = visual_und self.llm_config = llm_config + self.vit_config = vit_config self.vae_config = vae_config self.latent_patch_size = latent_patch_size self.max_latent_size = max_latent_size + self.vit_max_num_patch_per_side = vit_max_num_patch_per_side + self.connector_act = connector_act + self.interpolate_pos = interpolate_pos self.timestep_shift = timestep_shift @@ -619,23 +668,32 @@ class Bagel(torch.nn.Module): config_class = BagelConfig base_model_prefix = "bagel" - def __init__(self, language_model, config: BagelConfig): + def __init__(self, language_model, vit_model, config: BagelConfig): super().__init__() self.language_model = language_model self.hidden_size = config.llm_config.hidden_size self.use_moe = "Mo" in config.llm_config.layer_module self.num_heads = config.llm_config.num_attention_heads - self.latent_patch_size = config.latent_patch_size - self.timestep_shift = config.timestep_shift - self.latent_downsample = config.vae_config.downsample * config.latent_patch_size - self.max_latent_size = config.max_latent_size - self.latent_channel = config.vae_config.z_channels - self.patch_latent_dim = self.latent_patch_size**2 * self.latent_channel - self.time_embedder = TimestepEmbedder(self.hidden_size) - self.vae2llm = nn.Linear(self.patch_latent_dim, self.hidden_size) - self.llm2vae = nn.Linear(self.hidden_size, self.patch_latent_dim) - self.latent_pos_embed = PositionEmbedding(self.max_latent_size, self.hidden_size) + if config.visual_gen: + self.latent_patch_size = config.latent_patch_size + self.timestep_shift = config.timestep_shift + self.latent_downsample = config.vae_config.downsample * config.latent_patch_size + self.max_latent_size = config.max_latent_size + self.latent_channel = config.vae_config.z_channels + self.patch_latent_dim = self.latent_patch_size**2 * self.latent_channel + self.time_embedder = TimestepEmbedder(self.hidden_size) + self.vae2llm = nn.Linear(self.patch_latent_dim, self.hidden_size) + self.llm2vae = nn.Linear(self.hidden_size, self.patch_latent_dim) + self.latent_pos_embed = PositionEmbedding(self.max_latent_size, self.hidden_size) + + if config.visual_und: + self.vit_model = vit_model + self.vit_patch_size = config.vit_config.patch_size + self.vit_max_num_patch_per_side = config.vit_max_num_patch_per_side + self.vit_hidden_size = config.vit_config.hidden_size + self.connector = MLPconnector(self.vit_hidden_size, self.hidden_size, config.connector_act) + self.vit_pos_embed = PositionEmbedding(self.vit_max_num_patch_per_side, self.hidden_size) self.get_flattened_position_ids = get_flattened_position_ids_extrapolate @@ -643,8 +701,9 @@ class Bagel(torch.nn.Module): self._init_weights() def _init_weights(self): - nn.init.constant_(self.llm2vae.weight, 0) - nn.init.constant_(self.llm2vae.bias, 0) + if self.config.visual_gen: + nn.init.constant_(self.llm2vae.weight, 0) + nn.init.constant_(self.llm2vae.bias, 0) def prepare_prompts(self, curr_kvlens, curr_rope, prompts, tokenizer, new_token_ids): packed_text_ids = list() @@ -713,6 +772,261 @@ class Bagel(torch.nn.Module): return past_key_values + def prepare_vae_images(self, curr_kvlens, curr_rope, images, transforms, new_token_ids, timestep=0): + patchified_vae_latent_shapes, packed_vae_position_ids = list(), list() + packed_vae_token_indexes = list() + packed_text_ids, packed_text_indexes = list(), list() + packed_seqlens, packed_position_ids, packed_indexes = list(), list(), list() + packed_key_value_indexes = list() + + _curr = curr = 0 + vae_image_tensors = list() + newlens, new_rope = list(), list() + for image, curr_kvlen, curr_position_id in zip(images, curr_kvlens, curr_rope): + packed_key_value_indexes.extend(range(curr, curr + curr_kvlen)) + curr += curr_kvlen + + packed_text_ids.append(new_token_ids["start_of_image"]) + packed_text_indexes.append(_curr) + packed_indexes.append(curr) + curr += 1 + _curr += 1 + + image_tensor = transforms(image) + vae_image_tensors.append(image_tensor) + vae_position_ids = self.get_flattened_position_ids( + image_tensor.size(1), + image_tensor.size(2), + self.latent_downsample, + max_num_patches_per_side=self.max_latent_size, + ) + packed_vae_position_ids.append(vae_position_ids) + H, W = image_tensor.shape[1:] + h = H // self.latent_downsample + w = W // self.latent_downsample + patchified_vae_latent_shapes.append((h, w)) + + num_img_tokens = w * h + packed_vae_token_indexes.extend(range(_curr, _curr + num_img_tokens)) + packed_indexes.extend(range(curr, curr + num_img_tokens)) + curr += num_img_tokens + _curr += num_img_tokens + + packed_text_ids.append(new_token_ids["end_of_image"]) + packed_text_indexes.append(_curr) + packed_indexes.append(curr) + curr += 1 + _curr += 1 + + packed_position_ids.extend([curr_position_id] * (num_img_tokens + 2)) + packed_seqlens.append(num_img_tokens + 2) + newlens.append(curr_kvlen + num_img_tokens + 2) + new_rope.append(curr_position_id + 1) + + image_sizes = [item.shape for item in vae_image_tensors] + max_image_size = [max(item) for item in list(zip(*image_sizes))] + padded_images = torch.zeros(size=(len(vae_image_tensors), *max_image_size)) + for i, image_tensor in enumerate(vae_image_tensors): + padded_images[i, :, : image_tensor.shape[1], : image_tensor.shape[2]] = image_tensor + + generation_input = { + "padded_images": padded_images, + "patchified_vae_latent_shapes": patchified_vae_latent_shapes, + "packed_vae_position_ids": torch.cat(packed_vae_position_ids, dim=0), + "packed_timesteps": torch.tensor([timestep]), + "packed_vae_token_indexes": torch.tensor(packed_vae_token_indexes, dtype=torch.long), + "packed_text_ids": torch.tensor(packed_text_ids, dtype=torch.long), + "packed_text_indexes": torch.tensor(packed_text_indexes, dtype=torch.long), + "packed_position_ids": torch.tensor(packed_position_ids, dtype=torch.long), + "packed_seqlens": torch.tensor(packed_seqlens, dtype=torch.int), + "packed_indexes": torch.tensor(packed_indexes, dtype=torch.long), + "packed_key_value_indexes": torch.tensor(packed_key_value_indexes, dtype=torch.long), + "key_values_lens": torch.tensor(curr_kvlens, dtype=torch.int), + } + + return generation_input, newlens, new_rope + + @torch.no_grad + def forward_cache_update_vae( + self, + vae_model, + past_key_values: NaiveCache, + padded_images: torch.Tensor, + patchified_vae_latent_shapes: list, + packed_vae_position_ids: torch.LongTensor, + packed_timesteps: torch.Tensor, + packed_vae_token_indexes: torch.LongTensor, + packed_text_ids: torch.LongTensor, + packed_text_indexes: torch.LongTensor, + packed_position_ids: torch.LongTensor, + packed_seqlens: torch.IntTensor, + packed_indexes: torch.LongTensor, + key_values_lens: torch.IntTensor, + packed_key_value_indexes: torch.Tensor, + ): + packed_text_embedding = self.language_model.model.embed_tokens(packed_text_ids) + packed_sequence = packed_text_embedding.new_zeros((sum(packed_seqlens), self.hidden_size)) + packed_sequence[packed_text_indexes] = packed_text_embedding + + padded_latent = vae_model.encode(padded_images) + + p = self.latent_patch_size + packed_latent = list() + for latent, (h, w) in zip(padded_latent, patchified_vae_latent_shapes): + latent = latent[:, : h * p, : w * p].reshape(self.latent_channel, h, p, w, p) + latent = torch.einsum("chpwq->hwpqc", latent).reshape(-1, p * p * self.latent_channel) + packed_latent.append(latent) + packed_latent = torch.cat(packed_latent, dim=0) + packed_pos_embed = self.latent_pos_embed(packed_vae_position_ids) + packed_timestep_embeds = self.time_embedder(packed_timesteps) + packed_latent = self.vae2llm(packed_latent) + packed_timestep_embeds + packed_pos_embed + if packed_latent.dtype != packed_sequence.dtype: + packed_latent = packed_latent.to(packed_sequence.dtype) + packed_sequence[packed_vae_token_indexes] = packed_latent + + extra_inputs = {} + if self.use_moe: + extra_inputs = { + "mode": "gen", + "packed_vae_token_indexes": packed_vae_token_indexes, + "packed_text_indexes": packed_text_indexes, + } + + output = self.language_model.forward( + packed_query_sequence=packed_sequence, + query_lens=packed_seqlens, + packed_query_position_ids=packed_position_ids, + packed_query_indexes=packed_indexes, + past_key_values=past_key_values, + key_values_lens=key_values_lens, + packed_key_value_indexes=packed_key_value_indexes, + update_past_key_values=True, + is_causal=False, + **extra_inputs, + ) + past_key_values = output.past_key_values + + return past_key_values + + def prepare_vit_images(self, curr_kvlens, curr_rope, images, transforms, new_token_ids): + packed_vit_token_indexes = list() + vit_token_seqlens, packed_vit_tokens, packed_vit_position_ids = list(), list(), list() + packed_text_ids, packed_text_indexes = list(), list() + packed_seqlens, packed_position_ids, packed_indexes = list(), list(), list() + packed_key_value_indexes = list() + + _curr = curr = 0 + newlens, new_rope = list(), list() + for image, curr_kvlen, curr_position_id in zip(images, curr_kvlens, curr_rope): + packed_key_value_indexes.extend(range(curr, curr + curr_kvlen)) + curr += curr_kvlen + + packed_text_ids.append(new_token_ids["start_of_image"]) + packed_text_indexes.append(_curr) + packed_indexes.append(curr) + curr += 1 + _curr += 1 + + image_tensor = transforms(image) + vit_position_ids = self.get_flattened_position_ids( + image_tensor.size(1), + image_tensor.size(2), + self.vit_patch_size, + max_num_patches_per_side=self.vit_max_num_patch_per_side, + ) + vit_tokens = patchify(image_tensor, self.vit_patch_size) + packed_vit_tokens.append(vit_tokens) + num_img_tokens = vit_tokens.shape[0] + packed_vit_position_ids.append(vit_position_ids) + vit_token_seqlens.append(num_img_tokens) + packed_vit_token_indexes.extend(range(_curr, _curr + num_img_tokens)) + packed_indexes.extend(range(curr, curr + num_img_tokens)) + curr += num_img_tokens + _curr += num_img_tokens + + packed_text_ids.append(new_token_ids["end_of_image"]) + packed_text_indexes.append(_curr) + packed_indexes.append(curr) + curr += 1 + _curr += 1 + + packed_position_ids.extend([curr_position_id] * (num_img_tokens + 2)) + packed_seqlens.append(num_img_tokens + 2) + newlens.append(curr_kvlen + num_img_tokens + 2) + new_rope.append(curr_position_id + 1) + + generation_input = { + "packed_text_ids": torch.tensor(packed_text_ids, dtype=torch.long), + "packed_text_indexes": torch.tensor(packed_text_indexes, dtype=torch.long), + "vit_token_seqlens": torch.tensor(vit_token_seqlens, dtype=torch.int), + "packed_vit_tokens": torch.cat(packed_vit_tokens, dim=0), + "packed_vit_position_ids": torch.cat(packed_vit_position_ids, dim=0), + "packed_vit_token_indexes": torch.tensor(packed_vit_token_indexes, dtype=torch.long), + "packed_position_ids": torch.tensor(packed_position_ids, dtype=torch.long), + "packed_seqlens": torch.tensor(packed_seqlens, dtype=torch.int), + "packed_indexes": torch.tensor(packed_indexes, dtype=torch.long), + "packed_key_value_indexes": torch.tensor(packed_key_value_indexes, dtype=torch.long), + "key_values_lens": torch.tensor(curr_kvlens, dtype=torch.int), + } + + return generation_input, newlens, new_rope + + @torch.no_grad + def forward_cache_update_vit( + self, + past_key_values: NaiveCache, + packed_text_ids: torch.LongTensor, + packed_text_indexes: torch.LongTensor, + packed_vit_tokens: torch.Tensor, + packed_vit_token_indexes: torch.LongTensor, + packed_vit_position_ids: torch.LongTensor, + vit_token_seqlens: torch.IntTensor, + packed_position_ids: torch.LongTensor, + packed_seqlens: torch.IntTensor, + packed_indexes: torch.LongTensor, + packed_key_value_indexes: torch.LongTensor, + key_values_lens: torch.IntTensor, + ): + packed_text_embedding = self.language_model.model.embed_tokens(packed_text_ids) + packed_sequence = packed_text_embedding.new_zeros((sum(packed_seqlens), self.hidden_size)) + packed_sequence[packed_text_indexes] = packed_text_embedding + + cu_seqlens = torch.nn.functional.pad(torch.cumsum(vit_token_seqlens, dim=0), (1, 0)) + cu_seqlens = cu_seqlens.to(torch.int32) + max_seqlen = torch.max(vit_token_seqlens).item() + packed_vit_token_embed = self.vit_model( + packed_pixel_values=packed_vit_tokens, + packed_flattened_position_ids=packed_vit_position_ids, + cu_seqlens=cu_seqlens, + max_seqlen=max_seqlen, + ) + packed_vit_token_embed = self.connector(packed_vit_token_embed) + pos_emb = self.vit_pos_embed(packed_vit_position_ids) + packed_vit_token_embed = packed_vit_token_embed + pos_emb + if packed_vit_token_embed.dtype != packed_sequence.dtype: + packed_vit_token_embed = packed_vit_token_embed.to(packed_sequence.dtype) + packed_sequence[packed_vit_token_indexes] = packed_vit_token_embed + + extra_inputs = {} + if self.use_moe: + extra_inputs = {"mode": "und"} + + output = self.language_model.forward( + packed_query_sequence=packed_sequence, + query_lens=packed_seqlens, + packed_query_position_ids=packed_position_ids, + packed_query_indexes=packed_indexes, + past_key_values=past_key_values, + packed_key_value_indexes=packed_key_value_indexes, + key_values_lens=key_values_lens, + update_past_key_values=True, + is_causal=False, + **extra_inputs, + ) + past_key_values = output.past_key_values + + return past_key_values + def prepare_input(self, curr_kvlens, curr_rope, image_sizes, new_token_ids=None): packed_text_ids, packed_text_indexes = list(), list() packed_vae_position_ids, packed_vae_token_indexes, packed_init_noises = list(), list(), list() diff --git a/vllm_omni/diffusion/models/bagel/pipeline_bagel.py b/vllm_omni/diffusion/models/bagel/pipeline_bagel.py index 82e68f70b..4d47fac3b 100644 --- a/vllm_omni/diffusion/models/bagel/pipeline_bagel.py +++ b/vllm_omni/diffusion/models/bagel/pipeline_bagel.py @@ -12,10 +12,12 @@ from collections.abc import Iterable from dataclasses import dataclass from math import isqrt +import numpy as np import torch +import torch.nn.functional as F from PIL import Image from torch import nn -from transformers import AutoTokenizer +from transformers import AutoTokenizer, SiglipImageProcessor, SiglipVisionConfig, SiglipVisionModel from vllm.logger import init_logger from vllm.model_executor.models.utils import AutoWeightsLoader @@ -89,6 +91,12 @@ class _VaeCfg: downsample: int = 8 +@dataclass +class _VitCfg: + patch_size: int = 14 + hidden_size: int = 1152 + + def default_ae_params() -> AutoEncoderParams: return AutoEncoderParams( resolution=256, @@ -104,6 +112,37 @@ def default_ae_params() -> AutoEncoderParams: ) +class SiglipNaViTWrapper(nn.Module): + def __init__(self, vision_model): + super().__init__() + # If input is SiglipVisionModel, unwrap it to get SiglipVisionTransformer + if hasattr(vision_model, "vision_model"): + self.vision_model = vision_model.vision_model + else: + self.vision_model = vision_model + + # Configure weights for linear equivalent of patch embedding + self.patch_embed_weight = self.vision_model.embeddings.patch_embedding.weight + self.patch_embed_bias = self.vision_model.embeddings.patch_embedding.bias + + def forward(self, packed_pixel_values, packed_flattened_position_ids, cu_seqlens, max_seqlen): + w = self.patch_embed_weight.view(self.patch_embed_weight.shape[0], -1) + x = F.linear(packed_pixel_values, w, self.patch_embed_bias) + pos = self.vision_model.embeddings.position_embedding(packed_flattened_position_ids) + x = x + pos + hidden_states = x.unsqueeze(0) + seq_len = x.shape[0] + mask = torch.full((1, 1, seq_len, seq_len), torch.finfo(x.dtype).min, device=x.device, dtype=x.dtype) + cu_seqlens_list = cu_seqlens.tolist() + for i in range(len(cu_seqlens_list) - 1): + start = cu_seqlens_list[i] + end = cu_seqlens_list[i + 1] + mask[..., start:end, start:end] = 0.0 + + outputs = self.vision_model.encoder(inputs_embeds=hidden_states, attention_mask=mask) + return outputs.last_hidden_state.squeeze(0) + + class BagelPipeline(nn.Module): """Bagel generation pipeline (MoT) packaged for vllm-omni diffusion engine. @@ -150,12 +189,26 @@ class BagelPipeline(nn.Module): local_files_only=True, trust_remote_code=True, ) + + # Try finding vision_config or interpolate from top-level config + vit_cfg_dict = bagel_cfg.get("vit_config") or {} + vit_cfg = _VitCfg( + patch_size=int(vit_cfg_dict.get("patch_size", 14)), + hidden_size=int(vit_cfg_dict.get("hidden_size", 1152)), + ) + vit_config_path = os.path.join(model_path, "vit_config.json") + vit_conf = SiglipVisionConfig.from_json_file(vit_config_path) + self.vit_model = SiglipVisionModel(vit_conf) + self.image_processor = SiglipImageProcessor.from_pretrained(model_path, local_files_only=True) + + if self.vit_model: + self.vit_model = SiglipNaViTWrapper(self.vit_model) + vit_cfg.hidden_size = self.vit_model.vision_model.config.hidden_size + vit_cfg.patch_size = self.vit_model.vision_model.config.patch_size + self.tokenizer, self.new_token_ids, _ = add_special_tokens(self.tokenizer) - try: - tok_len = len(self.tokenizer) - except Exception: # pragma: no cover - very old tokenizers - tok_len = getattr(self.tokenizer, "vocab_size", llm_config.vocab_size) + tok_len = len(self.tokenizer) required_max_id = max(int(v) for v in self.new_token_ids.values()) llm_config.vocab_size = max( int(getattr(llm_config, "vocab_size", tok_len)), @@ -169,9 +222,14 @@ class BagelPipeline(nn.Module): self.bagel = Bagel( language_model=self.language_model, + vit_model=self.vit_model, config=BagelConfig( llm_config=llm_config, vae_config=vae_cfg, + vit_config=vit_cfg, + vit_max_num_patch_per_side=int(bagel_cfg.get("vit_max_num_patch_per_side", 70)), + connector_act=str(bagel_cfg.get("connector_act", "gelu_pytorch_tanh")), + interpolate_pos=bool(bagel_cfg.get("interpolate_pos", False)), latent_patch_size=int(bagel_cfg.get("latent_patch_size", 2)), max_latent_size=int(bagel_cfg.get("max_latent_size", 32)), timestep_shift=float(bagel_cfg.get("timestep_shift", 1.0)), @@ -248,6 +306,69 @@ class BagelPipeline(nn.Module): injected_kv = getattr(req, "past_key_values", None) injected_metadata = getattr(req, "kv_metadata", None) + # Image input handling + image_input = getattr(req, "pil_image", None) + if image_input and not isinstance(image_input, list): + image_input = [image_input] + + if image_input: + # If we have an image, we prefill with it + if self.image_processor and self.vae: + + def vit_transforms(img): + # SigLIP processor returns dict with pixel_values; we want the tensor + return self.image_processor(images=img, return_tensors="pt").pixel_values[0] + + def vae_transforms(img): + if img.mode != "RGB": + img = img.convert("RGB") + # Convert to [-1, 1] tensor (H, W, C) -> (C, H, W) + arr = torch.from_numpy(np.array(img)).float() / 127.5 - 1.0 + return arr.permute(2, 0, 1) + + # 1. Update VAE + gen_input_vae, newlens_vae, new_rope_vae = self.bagel.prepare_vae_images( + curr_kvlens=gen_context["kv_lens"], + curr_rope=gen_context["ropes"], + images=image_input, + transforms=vae_transforms, + new_token_ids=self.new_token_ids, + ) + + for k, v in gen_input_vae.items(): + if torch.is_tensor(v): + gen_input_vae[k] = v.to(self.device) + + # VAE needs bfloat16 to match model strings usually, specifically encode + with torch.autocast(device_type="cuda", enabled=self.device.type == "cuda", dtype=torch.bfloat16): + gen_context["past_key_values"] = self.bagel.forward_cache_update_vae( + self.vae, gen_context["past_key_values"], **gen_input_vae + ) + gen_context["kv_lens"] = newlens_vae + gen_context["ropes"] = new_rope_vae + + # 2. Update ViT + gen_input_img, newlens_img, new_rope_img = self.bagel.prepare_vit_images( + curr_kvlens=gen_context["kv_lens"], + curr_rope=gen_context["ropes"], + images=image_input, + transforms=vit_transforms, + new_token_ids=self.new_token_ids, + ) + + for k, v in gen_input_img.items(): + if torch.is_tensor(v): + gen_input_img[k] = v.to(self.device) + + with torch.autocast(device_type="cuda", enabled=self.device.type == "cuda", dtype=torch.bfloat16): + gen_context["past_key_values"] = self.bagel.forward_cache_update_vit( + gen_context["past_key_values"], **gen_input_img + ) + gen_context["kv_lens"] = newlens_img + gen_context["ropes"] = new_rope_img + else: + logger.warning("Image provided but no image processor available.") + if injected_kv is not None and injected_metadata is not None: logger.info("Using injected KV Cache from remote transfer") @@ -409,6 +530,19 @@ class BagelPipeline(nn.Module): yield "bagel." + n break + # Map connector and vit_pos_embed to `bagel.*` + for pfx in ("connector.", "vit_pos_embed."): + if n.startswith(pfx): + yield "bagel." + n + break + + if n.startswith("vit_model."): + yield "bagel." + n # matches self.bagel.vit_model + elif n.startswith("vision_model."): + yield "bagel.vit_model." + n + elif n.startswith("model.vision_model."): + yield "bagel.vit_model." + n[len("model.") :] + def _filtered_weights(): total = 0 kept = 0 @@ -439,6 +573,17 @@ class BagelPipeline(nn.Module): shapes[cand] = (npos, hdim) picked = cand break + # Handle flattened patch embedding for SigLIP + if cand.endswith("embeddings.patch_embedding.weight") and tensor.ndim == 2: + # Checkpoint has (Hidden, C*P*P), model expects (Hidden, C, P, P) + if shapes.get(cand) is not None: + target_shape = shapes[cand] + if tensor.numel() == torch.prod(torch.tensor(target_shape)): + # Reshape tensor to match target + tensor = tensor.view(target_shape) + picked = cand + break + shape_mismatch += 1 # Keep this quiet; shape mismatches are expected for ignored modules. if picked is not None: