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bagel_inference.py

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+import os
+from copy import deepcopy
+from typing import (
+    Any,
+    AsyncIterable,
+    Callable,
+    Dict,
+    Generator,
+    List,
+    NamedTuple,
+    Optional,
+    Tuple,
+    Union,
+)
+import requests
+from io import BytesIO
+
+from PIL import Image
+import torch
+from accelerate import infer_auto_device_map, load_checkpoint_and_dispatch, init_empty_weights
+
+from data.transforms import ImageTransform
+from data.data_utils import pil_img2rgb, add_special_tokens
+from modeling.bagel import (
+    BagelConfig, Bagel, Qwen2Config, Qwen2ForCausalLM, SiglipVisionConfig, SiglipVisionModel
+)
+from modeling.qwen2 import Qwen2Tokenizer
+from modeling.bagel.qwen2_navit import NaiveCache
+from modeling.autoencoder import load_ae
+from safetensors.torch import load_file
+
+
+
+
+
+model_path = "/mnt/beegfs/Workspace/Models/BAGEL-7B-MoT"  # Download from https://huggingface.co/ByteDance-Seed/BAGEL-7B-MoT
+
+# LLM config preparing
+llm_config = Qwen2Config.from_json_file(os.path.join(model_path, "llm_config.json"))
+llm_config.qk_norm = True
+llm_config.tie_word_embeddings = False
+llm_config.layer_module = "Qwen2MoTDecoderLayer"
+
+# ViT config preparing
+vit_config = SiglipVisionConfig.from_json_file(os.path.join(model_path, "vit_config.json"))
+vit_config.rope = False
+vit_config.num_hidden_layers = vit_config.num_hidden_layers - 1
+
+# VAE loading
+vae_model, vae_config = load_ae(local_path=os.path.join(model_path, "ae.safetensors"))
+
+# Bagel config preparing
+config = BagelConfig(
+    visual_gen=True,
+    visual_und=True,
+    llm_config=llm_config, 
+    vit_config=vit_config,
+    vae_config=vae_config,
+    vit_max_num_patch_per_side=70,
+    connector_act='gelu_pytorch_tanh',
+    latent_patch_size=2,
+    max_latent_size=64,
+)
+
+with init_empty_weights():
+    language_model = Qwen2ForCausalLM(llm_config)
+    vit_model      = SiglipVisionModel(vit_config)
+    model          = Bagel(language_model, vit_model, config)
+    model.vit_model.vision_model.embeddings.convert_conv2d_to_linear(vit_config, meta=True)
+
+# Tokenizer Preparing
+tokenizer = Qwen2Tokenizer.from_pretrained(model_path)
+tokenizer, new_token_ids, _ = add_special_tokens(tokenizer)
+
+# Image Transform Preparing
+vae_transform = ImageTransform(1024, 512, 16)
+vit_transform = ImageTransform(980, 224, 14)
+
+
+
+
+
+# ========= Step 1: 设备规划（仅 GPU，禁止 CPU/offload） =========
+import os, torch
+from accelerate import infer_auto_device_map, load_checkpoint_and_dispatch
+from safetensors.torch import load_file as safe_load
+
+# 单/多卡最大显存设置（按需调整；确保足够避免 CPU 回退）
+max_mem_per_gpu = "40GiB"  # A100 80GiB 可设更高，例如 "78GiB"
+
+def build_cuda_only_device_map(model, same_device_modules):
+    assert torch.cuda.device_count() >= 1, "需要至少 1 张 CUDA GPU。"
+    cuda_count = torch.cuda.device_count()
+    max_memory = {i: max_mem_per_gpu for i in range(cuda_count)}
+
+    device_map = infer_auto_device_map(
+        model,
+        max_memory=max_memory,
+        no_split_module_classes=["Bagel", "Qwen2MoTDecoderLayer"],
+    )
+
+    # 禁止任何 'cpu' 或 'disk' 的落点
+    bad_devices = {v for v in device_map.values() if isinstance(v, str) and v not in [f"cuda:{i}" for i in range(cuda_count)]}
+    if bad_devices:
+        raise RuntimeError(f"发现非 CUDA 设备分配 {bad_devices}，请增大 max_mem_per_gpu 或减少模型容量以避免 CPU/offload。")
+
+    # 将若干关键子模块强制放在同一张 GPU 上
+    if cuda_count == 1:
+        first_device = next(iter(device_map.values()))
+        first_device = first_device if isinstance(first_device, str) else f"cuda:{first_device['cuda_device']}"
+        for k in same_device_modules:
+            device_map[k] = first_device
+    else:
+        # 取 embed_tokens 的设备作为锚点
+        anchor = device_map.get(same_device_modules[0])
+        if anchor is None:
+            # 回退到 cuda:0
+            anchor = "cuda:0"
+        for k in same_device_modules:
+            device_map[k] = anchor
+
+    return device_map
+
+same_device_modules = [
+    'language_model.model.embed_tokens',
+    'time_embedder',
+    'latent_pos_embed',
+    'vae2llm',
+    'llm2vae',
+    'connector',
+    'vit_pos_embed'
+]
+
+device_map = build_cuda_only_device_map(model, same_device_modules)
+print("Device map (CUDA-only) built:", device_map)
+
+# ========= Step 2: 装载原始权重（不启用 offload） =========
+# 说明：为了“杜绝 CPU/GPU 混用”，这里将 offload 相关选项全部关闭
+# 注意：这要求你的显存设置足以容纳模型；否则请调大 max_mem_per_gpu 或减少 batch/分辨率
+model = load_checkpoint_and_dispatch(
+    model,
+    checkpoint=os.path.join(model_path, "ema.safetensors"),
+    device_map=device_map,
+    dtype=torch.bfloat16,
+    offload_buffers=False,    # 禁止 offload
+    force_hooks=True,
+)
+model = model.eval()
+print("[Stage-1] 原始权重已加载到 CUDA。")
+
+# 若你在“构图阶段”添加了新特殊 token，通常需要在这里同步词表尺寸（如已做可忽略）
+# try:
+#     model.language_model.resize_token_embeddings(len(tokenizer))
+# except Exception as e:
+#     print("resize_token_embeddings 跳过：", e)
+
+# ========= Step 3: 加载你训练后的权重，进行“就地覆盖” =========
+finetuned_ckpt = "/mnt/beegfs/Workspace/ICLR_2026/Bagel-GUI/results/checkpoints/0064000/ema_bf16.safetensors"
+
+def load_and_override(model, ckpt_path):
+    """
+    将 ckpt_path 中与当前模型 state_dict 同名且形状一致的权重，按位复制到现有参数上。
+    复制前将张量 to(param.device, dtype=param.dtype)，以杜绝 CPU/GPU 混放或 dtype 不一致。
+    """
+    print(f"[Stage-2] 读取训练后权重：{ckpt_path}")
+    ft_state = safe_load(ckpt_path)  # 全在 CPU 上的原始张量容器，不会改变模型设备分配
+    model_state = model.state_dict()
+
+    matched, skipped_shape, skipped_missing = 0, 0, 0
+    with torch.no_grad():
+        for k, v in ft_state.items():
+            if k in model_state:
+                tgt = model_state[k]
+                if tgt.shape == v.shape:
+                    # 保持与目标参数一致的 device & dtype
+                    dev = tgt.device
+                    dt  = tgt.dtype
+                    tgt.copy_(v.to(dev, dtype=dt))
+                    matched += 1
+                else:
+                    skipped_shape += 1
+            else:
+                skipped_missing += 1
+
+    print(f"[Stage-2] 覆盖完成：匹配 {matched} 个；形状不符跳过 {skipped_shape} 个；缺失键跳过 {skipped_missing} 个。")
+    return matched
+
+_ = load_and_override(model, finetuned_ckpt)
+
+# ========= Step 4: 终检 - 确保没有参数/缓冲区落在 CPU =========
+def assert_all_cuda(module):
+    bad = []
+    for n, p in module.named_parameters(recurse=True):
+        if p.device.type != "cuda":
+            bad.append(("param", n, str(p.device)))
+    for n, b in module.named_buffers(recurse=True):
+        if b.device.type != "cuda":
+            bad.append(("buffer", n, str(b.device)))
+    if bad:
+        lines = "\n".join([f" - {t}\t{name}\t@{dev}" for (t, name, dev) in bad[:20]])
+        raise RuntimeError(f"发现非 CUDA 张量（共{len(bad)}个，列出前20个）：\n{lines}")
+    print("[Check] 所有参数与缓冲区均在 CUDA。")
+
+assert_all_cuda(model)
+print("Model ready ✓")
+
+
+
+
+
+
+from inferencer import InterleaveInferencer
+
+inferencer = InterleaveInferencer(
+    model=model, 
+    vae_model=vae_model, 
+    tokenizer=tokenizer, 
+    vae_transform=vae_transform, 
+    vit_transform=vit_transform, 
+    new_token_ids=new_token_ids
+)
+
+import random
+import numpy as np
+
+seed = 42
+random.seed(seed)
+np.random.seed(seed)
+torch.manual_seed(seed)
+if torch.cuda.is_available():
+    torch.cuda.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+torch.backends.cudnn.deterministic = True
+torch.backends.cudnn.benchmark = False
+
+
+
+inference_hyper=dict(
+    cfg_text_scale=4.0,
+    cfg_img_scale=2.0,
+    cfg_interval=[0.0, 1.0],
+    timestep_shift=3.0,
+    num_timesteps=50,
+    cfg_renorm_min=0.0,
+    cfg_renorm_type="text_channel",
+)
+
+
+
+image = Image.open('train_verify/5813_0.png')
+# prompt = 'Click on the First image of "saffola classic masala oats,Click on the First image of "saffola classic masala oats'
+prompt = ' Swipe up on the screen. '
+
+
+# 保存输入图
+image.save("input_image.png")
+
+print(prompt)
+print('-'*10)
+
+# 推理
+output_dict = inferencer(image=image, text=prompt, **inference_hyper)
+
+# 保存输出图
+out_img = output_dict['image']
+if isinstance(out_img, Image.Image):
+    out_img.save("output_image.png")
+elif isinstance(out_img, torch.Tensor):
+    from torchvision.transforms.functional import to_pil_image
+    to_pil_image(out_img[0].cpu()).save("output_image.png")
+
+print("保存完成：input_image.png, output_image.png")